trajs = null;\\n Iterable<IIntArray> cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_Iterable_int()\\n {\\n Iterable<IIntArray> trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateC_Iterable_int()\\n {\\n Iterable<IIntArray> trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateT()\\n {\\n \\n IDoubleArray counts = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateT(counts);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCstepping_Iterable_int()\\n {\\n Iterable<IIntArray> trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCstepping(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testMCMCWithoutAllelicPoN() {\\n final double meanBiasSimulated = 1.2;\\n final double biasVarianceSimulated = 0.04;\\n testMCMC(meanBiasSimulated, biasVarianceSimulated, meanBiasSimulated, biasVarianceSimulated, AllelicPanelOfNormals.EMPTY_PON);\\n }\",\n \"@Test\\n public void testMetastableMemberships()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.metastableMemberships(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test public void calcROItest() {\\n DirectMC h = new DirectMC(\\\"AdMailing\\\", 10000.00, 3.00, 2000);\\n Assert.assertEquals(.4286, h.calcROI(), 1);\\n }\",\n \"public void kolmogorovSmirnovTest(double cutoff, MWBMatchingAlgorithm mwbm) {\\n double p = 0;\\n\\n KolmogorovSmirnovTest t = new KolmogorovSmirnovTest();\\n for (int i = 0; i < this.train.numAttributes(); i++) {\\n for (int j = 0; j < this.test.numAttributes(); j++) {\\n // negative infinity counts as not present, we do this so we don't have to map\\n // between attribute indexs in weka\\n // and the result of the mwbm computation\\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\\n\\n // class attributes are not relevant\\n if (this.test.classIndex() == j) {\\n continue;\\n }\\n if (this.train.classIndex() == i) {\\n continue;\\n }\\n\\n // this may invoke exactP on small sample sizes which will not terminate in all\\n // cases\\n // p = t.kolmogorovSmirnovTest(this.train_values.get(i),\\n // this.test_values.get(j), false);\\n\\n // this uses approximateP everytime\\n p = t.approximateP(\\n t.kolmogorovSmirnovStatistic(this.train_values.get(i),\\n this.test_values.get(j)),\\n this.train_values.get(i).length,\\n this.test_values.get(j).length);\\n if (p > cutoff) {\\n mwbm.setWeight(i, j, p);\\n }\\n }\\n }\\n }\",\n \"@Test\\n public void testCorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable1 = null;\\n IDoubleArray observable2 = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"%\\\");\\n FileSystemHandling.createFolder((EvoSuiteFile) null);\\n discretize0.findNumBinsTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"%\\\");\\n discretize0.m_DefaultCols = \\\"%\\\";\\n double[][] doubleArray0 = new double[0][7];\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints(1);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 1\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test03() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setAttributeIndices(\\\"\\\");\\n Filter.makeCopy(discretize0);\\n discretize0.setDesiredWeightOfInstancesPerInterval(2.0);\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 0;\\n discretize0.setUseBinNumbers(true);\\n discretize0.listOptions();\\n discretize0.makeBinaryTipText();\\n discretize0.getInvertSelection();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-458));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testForwardCommittor()\\n {\\n IDoubleArray M = null;\\n IIntArray A = null;\\n IIntArray B = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.forwardCommittor(M, A, B);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testMMI() {\\n System.out.println(\\\"MMI\\\");\\n int a = 0;\\n int m = 0;\\n int expResult = 0;\\n int result = utilsHill.MMI(a, m);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void shouldCalculateProperMoveProbabilities() throws Exception {\\n\\n int actualCity = 0;\\n boolean[] visited = new boolean[] {true, false, false, false};\\n int[][] firstCriterium = new int[][] {{0, 2, 1, 2},\\n {2, 2, 1, 1},\\n {1, 1, 0, 1},\\n {2, 1, 1, 0}};\\n int[][] secondCriterium = new int[][] {{0, 3, 2, 1},\\n {3, 0, 0, 0},\\n {2, 0, 0, 0},\\n {1, 0, 0, 0}};\\n// final AttractivenessCalculator attractivenessCalculator = new AttractivenessCalculator(1.0, 0.0, 0.0, 0.0, 0.0);\\n// double[][] array = new double[0][0];\\n// final double[] probabilities = attractivenessCalculator.movesProbability(actualCity, visited, array, firstCriterium, secondCriterium, null, 0.0, 0.0);\\n\\n// assertTrue(probabilities[0] == 0.0);\\n// assertTrue(probabilities[1] == 0.0);\\n// assertTrue(probabilities[2] == 0.5);\\n// assertTrue(probabilities[3] == 0.5);\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n String[] stringArray0 = new String[2];\\n discretize0.m_ClassIndex = 906;\\n discretize0.m_IgnoreClass = true;\\n stringArray0[0] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n stringArray0[1] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n discretize0.setOptions(stringArray0);\\n discretize0.setAttributeIndices(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getFindNumBins());\\n }\",\n \"private double GetMCRTestSet() \\n\\t{\\n\\t\\tint numErrors = 0;\\n\\t\\t\\n\\t\\tfor(int i = ITrain; i < ITrain+ITest; i++)\\n\\t\\t{\\n\\t\\t\\tPreCompute(i);\\n\\t\\t\\t\\n\\t\\t\\tdouble max_Y_hat_ic = Double.MIN_VALUE;\\n\\t\\t\\tint label_i = -1; \\n\\t\\t\\t\\n\\t\\t\\tfor(int c = 0; c < C; c++)\\n\\t\\t\\t{\\n\\t\\t\\t\\tdouble Y_hat_ic = Predict(i, c);\\n\\t\\t\\t\\t\\n\\n\\t\\t\\t\\tif(lossType == LossTypes.LOGISTIC)\\n\\t\\t\\t\\t\\tY_hat_ic = Sigmoid.Calculate(Y_hat_ic);\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif(Y_hat_ic > max_Y_hat_ic)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tmax_Y_hat_ic = Y_hat_ic; \\n\\t\\t\\t\\t\\tlabel_i = (int)Math.ceil(c);\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\t\\n\\t\\t\\tif( nominalLabels.indexOf(Y.get(i)) != label_i ) \\n\\t\\t\\t\\tnumErrors++;\\n\\t\\t}\\n\\t\\t\\n\\t\\treturn (double)numErrors/(double)ITest; \\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.invertSelectionTipText();\\n boolean boolean0 = discretize0.m_UseBinNumbers;\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testJuvenilesSpreadFromCellsWithCorrespondingMatureLctFromInsideGrid() {\\n params = new CompletelySpatiallyRandomParams(3, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params);\\n coloniser.updateJuvenilePresenceLayers();\\n\\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 6);\\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 9);\\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 3);\\n }\",\n \"@Test\\n public void testEstimateC_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"l*{e5SNh\\\");\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 177;\\n discretize0.setUseEqualFrequency(true);\\n double[][] doubleArray0 = new double[5][9];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[0] = (double) 177;\\n doubleArray1[1] = (double) 177;\\n doubleArray1[2] = (double) 177;\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[2];\\n doubleArray2[0] = (double) 177;\\n doubleArray2[1] = (double) 177;\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[9];\\n doubleArray3[0] = (double) 177;\\n doubleArray3[1] = (double) 177;\\n doubleArray3[2] = (double) 177;\\n doubleArray3[3] = (double) 177;\\n doubleArray3[4] = (double) 177;\\n doubleArray3[5] = (double) 177;\\n doubleArray3[6] = (double) 177;\\n doubleArray3[7] = (double) 177;\\n doubleArray3[8] = (double) 177;\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[8];\\n doubleArray4[0] = (double) 177;\\n doubleArray4[1] = (double) 177;\\n doubleArray4[2] = (double) 177;\\n doubleArray4[3] = (double) 177;\\n doubleArray4[4] = (double) 177;\\n doubleArray4[5] = (double) 177;\\n doubleArray4[6] = (double) 177;\\n doubleArray4[7] = (double) 177;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[0];\\n doubleArray0[4] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.listOptions();\\n discretize0.setIgnoreClass(true);\\n String[] stringArray0 = new String[0];\\n Discretize.main(stringArray0);\\n discretize0.findNumBinsTipText();\\n discretize0.setInvertSelection(true);\\n // Undeclared exception!\\n try { \\n discretize0.getBinRangesString(511);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 511\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints((-2276));\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Test(timeout = 4000)\\n public void test07() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setInvertSelection(true);\\n discretize0.useEqualFrequencyTipText();\\n int int0 = (-501);\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-501));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test08() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"\\\");\\n discretize0.binsTipText();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning(25);\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testGetImportanceStats() {\\n System.out.println(\\\"getImportanceStats\\\");\\n MDA instance = new MDA();\\n\\n // make the circles close to force tree to do lots of splits / make it harder\\n ClassificationDataSet train = getHarderC(10000, RandomUtil.getRandom());\\n int good_featres = 2;\\n\\n DecisionTree tree = new DecisionTree();\\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\\n tree.train(train);\\n\\n double[] importances = instance.getImportanceStats(tree, train);\\n\\n // make sure the first 2 features were infered as more important than the\\n // others!\\n for (int i = good_featres; i < importances.length; i++) {\\n for (int j = 0; j < good_featres; j++)\\n assertTrue(importances[j] > importances[i]);\\n }\\n\\n // categorical features, make space wider b/c we lose resolution\\n train = getHarderC(10000, RandomUtil.getRandom());\\n\\n train.applyTransform(new NumericalToHistogram(train, 7));\\n tree = new DecisionTree();\\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\\n tree.train(train);\\n\\n importances = instance.getImportanceStats(tree, train);\\n\\n // make sure the first 2 features were infered as more important than the\\n // others!\\n for (int i = good_featres; i < importances.length; i++) {\\n for (int j = 0; j < good_featres; j++)\\n assertTrue(importances[j] > importances[i]);\\n }\\n\\n }\",\n \"private void findAbdomenCM() {\\r\\n \\r\\n if (foundCenterOfMass) return;\\r\\n int xcm = 0, ycm = 0, pixCount = 0;\\r\\n for (int idx= 0, y = 0; y < yDim; y++) {\\r\\n for (int x = 0; x < xDim; x++, idx++) {\\r\\n if (sliceBuffer[idx] == abdomenTissueLabel) {\\r\\n xcm += x;\\r\\n ycm += y;\\r\\n pixCount++;\\r\\n }\\r\\n } // end for (int x = 0; ...)\\r\\n } // end for (int idx = 0, ...)\\r\\n \\r\\n if (pixCount == 0) {\\r\\n// System.err.println(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n MipavUtil.displayError(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n return;\\r\\n }\\r\\n \\r\\n centerOfMass[0] = (short)(xcm / pixCount);\\r\\n centerOfMass[1] = (short)(ycm / pixCount);\\r\\n foundCenterOfMass = true;\\r\\n }\",\n \"final CM computeCM(int[/**/][/**/] votes, Chunk[] chks, boolean local, boolean balance) {\\n CM cm = new CM();\\n int rows = votes.length;\\n int validation_rows = 0;\\n int cmin = (int) _data.vecs()[_classcol].min();\\n\\n // Assemble the votes-per-class into predictions & score each row\\n\\n // Make an empty confusion matrix for this chunk\\n cm._matrix = new long[_N][_N];\\n float preds[] = new float[_N+1];\\n\\n float num_trees = _errorsPerTree.length;\\n\\n // Loop over the rows\\n for( int row = 0; row < rows; row++ ) {\\n\\n // Skip rows with missing response values\\n if (chks[_classcol].isNA0(row)) continue;\\n\\n // The class votes for the i-th row\\n int[] vi = votes[row];\\n\\n // Fill the predictions with the vote counts, keeping the 0th index unchanged\\n for( int v=0; v<_N; v++ ) preds[v+1] = vi[v];\\n\\n float s = doSum(vi);\\n if (s == 0) {\\n cm._skippedRows++;\\n continue;\\n }\\n\\n int result;\\n if (balance) {\\n float[] scored = toProbs(preds.clone(), doSum(vi));\\n double probsum=0;\\n for( int c=1; c<scored.length; c++ ) {\\n final double original_fraction = _model.priordist()[c-1];\\n assert(original_fraction > 0) : \\\"original fraction should be > 0, but is \\\" + original_fraction + \\\": not using enough training data?\\\";\\n final double oversampled_fraction = _model.modeldist()[c-1];\\n assert(oversampled_fraction > 0) : \\\"oversampled fraction should be > 0, but is \\\" + oversampled_fraction + \\\": not using enough training data?\\\";\\n assert(!Double.isNaN(scored[c]));\\n scored[c] *= original_fraction / oversampled_fraction;\\n probsum += scored[c];\\n }\\n for (int i=1;i<scored.length;++i) scored[i] /= probsum;\\n result = ModelUtils.getPrediction(scored, row);\\n } else {\\n // `result` is the class with the most votes, accounting for ties in the shared logic in ModelUtils\\n result = ModelUtils.getPrediction(preds, row);\\n }\\n\\n // Get the class value from the response column for the current row\\n int cclass = alignDataIdx((int) chks[_classcol].at80(row) - cmin);\\n assert 0 <= cclass && cclass < _N : (\\\"cclass \\\" + cclass + \\\" < \\\" + _N);\\n\\n // Ignore rows with zero votes, but still update the sum of squared errors\\n if( vi[result]==0 ) {\\n cm._skippedRows++;\\n if (!local) _sum += doSSECalc(vi, preds, cclass);\\n continue;\\n }\\n\\n // Update the confusion matrix\\n cm._matrix[cclass][result]++;\\n if( result != cclass ) cm._errors++;\\n validation_rows++;\\n\\n // Update the sum of squared errors\\n if (!local) _sum += doSSECalc(vi, preds, cclass);\\n float sum = doSum(vi);\\n\\n // Binomial classification -> compute AUC, draw ROC\\n if(_N == 2 && !local) {\\n float snd = preds[2] / sum;\\n for(int i = 0; i < ModelUtils.DEFAULT_THRESHOLDS.length; i++) {\\n int p = snd >= ModelUtils.DEFAULT_THRESHOLDS[i] ? 1 : 0;\\n _cms[i][cclass][p]++; // Increase matrix\\n }\\n }\\n }\\n\\n // End of loop over rows, return confusion matrix\\n cm._rows=validation_rows;\\n return cm;\\n }\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, true, false);\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints(3594);\\n Discretize discretize1 = new Discretize();\\n discretize1.attributeIndicesTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"\\\");\\n Discretize discretize2 = new Discretize();\\n // Undeclared exception!\\n try { \\n discretize2.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Before\\n\\tpublic void iniciaDisciplinaConvencional() {\\n\\t\\tList<String> pos = new ArrayList<String>();\\n\\t\\tList<String> pos1 = new ArrayList<String>();\\n\\t\\tpos1.add(\\\"Disciplina 3\\\");\\n\\t\\tpos1.add(\\\"Disciplina 4\\\");\\n\\t\\tpos1.add(\\\"Disciplina 5\\\");\\n\\n\\t\\tminitElimBai_1 = new MinitestesEliminandoBaixa(\\\"Disciplina 1\\\", 5, pos,\\n\\t\\t\\t\\t1);\\n\\t\\tminitElimBai_2 = new MinitestesEliminandoBaixa(\\\"Disciplina 2\\\", 10,\\n\\t\\t\\t\\tpos1, 2);\\n\\t}\",\n \"@Test\\n public void testGetMoveSpeedMultiplier() {\\n assertEquals(1, proj.getMoveSpeedMultiplier(), 0.001);\\n }\",\n \"@Test\\n public void test13() {\\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 1));\\n }\",\n \"private void computeMultiClassMCC(ConfusionMatrixElement[] elements) {\\n\\t\\tdouble Sq = matrixTotal * matrixTotal;\\n\\t\\tdouble CS = Arrays.stream(elements).map(n -> n.tp).reduce(0.0d, (n, m) -> n + m) * matrixTotal;\\n\\t\\tdouble t = 0.0d, p = 0.0d, tt = 0.0d, pp = 0.0d, tp = 0.0d;\\n\\t\\t\\n\\t\\tfor (ConfusionMatrixElement e : elements) {\\n\\t\\t\\tt = (e.tp + e.fp); \\n\\t\\t\\tp = (e.tp + e.fn);\\n\\t\\t\\t\\n\\t\\t\\ttt += (t * t);\\n\\t\\t\\tpp += (p * p);\\n\\t\\t\\ttp += (t * p);\\n\\t\\t}\\n\\t\\tmatrixMCC = (CS - tp) / (Math.sqrt(Sq - pp) * Math.sqrt(Sq - tt));\\n\\t}\",\n \"@Test\\n public void testLargeValues() {\\n double x = 123;\\n double dof = 6.4;\\n double nonCent = 100.34;\\n NonCentralChiSquaredDistribution dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.7930769).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n x = 455.038;\\n dof = 12;\\n nonCent = 444.44;\\n\\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.4961805).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n x = 999400;\\n dof = 500;\\n nonCent = 1000000;\\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.2913029).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n Locale.getISOLanguages();\\n double[][] doubleArray0 = new double[9][8];\\n double[] doubleArray1 = new double[3];\\n double[] doubleArray2 = new double[9];\\n doubleArray2[5] = (-163.0);\\n doubleArray2[8] = 1421.4058829;\\n doubleArray0[3] = doubleArray0[2];\\n doubleArray0[4] = doubleArray0[1];\\n discretize0.setOutputFormat();\\n double[] doubleArray3 = new double[2];\\n doubleArray3[0] = 1421.4058829;\\n doubleArray3[1] = (-163.0);\\n doubleArray0[6] = doubleArray2;\\n double[] doubleArray4 = new double[0];\\n doubleArray0[7] = doubleArray4;\\n double[] doubleArray5 = new double[6];\\n doubleArray5[2] = (-163.0);\\n doubleArray0[8] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test29() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[3][6];\\n discretize0.setIgnoreClass(false);\\n discretize0.m_CutPoints = doubleArray0;\\n double[] doubleArray1 = new double[4];\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints((-831));\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // -831\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test01() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"c9^n/\\\");\\n discretize0.getCapabilities();\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n }\",\n \"@Test\\n\\tpublic void testCollapseByEditDistance() {\\n\\t\\tString cellBC = \\\"ATCAGGGACAGA\\\";\\n\\t\\tString molBC = \\\"CGGGGCTC\\\";\\n\\t\\tint snpPos = 76227022;\\n\\t\\tInterval snpInterval = new Interval(\\\"HUMAN_1\\\", snpPos, snpPos, true, \\\"test\\\");\\n\\t\\tSampleGenotypeProbabilities result = new SampleGenotypeProbabilities(snpInterval, cellBC);\\n\\t\\tSNPUMIBasePileup p = getPileUpFromFile(cellBC, molBC);\\n\\t\\tresult.add(p);\\n\\n\\t\\t// let's add another pileup in that's within ed=1 and incorporates an error.\\n\\t\\t// result has only a single base because two UMIs are merged.\\n\\t\\tString molBC2 = \\\"AGGGGCTC\\\";\\n\\t\\tSNPUMIBasePileup p2 = getPileUpFromFile(cellBC, molBC2);\\n\\t\\tchar[] b2 = { 'G', 'G', 'T' };\\n\\t\\tbyte[] bases2 = new byte[b2.length];\\n\\t\\tStringUtil.charsToBytes(b2, 0, b2.length, bases2, 0);\\n\\t\\tbyte[] quals2 = { 37, 37, 10 };\\n\\t\\tp2.setBasesAndQualities(bases2, quals2);\\n\\t\\tresult.add(p2);\\n\\t\\tresult.collapseUMIs(1);\\n\\t\\tList<Byte> qualResult2 = result.getQualities();\\n\\t\\tAssert.assertEquals(qualResult2.size(), 1);\\n\\t\\tAssert.assertEquals(new Byte((byte) 8), qualResult2.get(0));\\n\\t}\",\n \"@Test\\n public void testLogLikelihoodCorrelationMatrix()\\n {\\n IDoubleArray corr = null;\\n IDoubleArray C = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n double expResult = 0.0;\\n double result = instance.logLikelihoodCorrelationMatrix(corr, C);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public void percentiles(double cutoff, MWBMatchingAlgorithm mwbm) {\\n for (int i = 0; i < this.train.numAttributes(); i++) {\\n for (int j = 0; j < this.test.numAttributes(); j++) {\\n // negative infinity counts as not present, we do this so we don't have to map\\n // between attribute indexes in weka\\n // and the result of the mwbm computation\\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\\n\\n // class attributes are not relevant\\n if (this.test.classIndex() == j) {\\n continue;\\n }\\n if (this.train.classIndex() == i) {\\n continue;\\n }\\n\\n // get percentiles\\n double trainvals[] = this.train_values.get(i);\\n double testvals[] = this.test_values.get(j);\\n\\n Arrays.sort(trainvals);\\n Arrays.sort(testvals);\\n\\n // percentiles\\n double train_p;\\n double test_p;\\n double score = 0.0;\\n for (int p = 1; p <= 9; p++) {\\n train_p = trainvals[(int) Math.ceil(trainvals.length * (p / 100))];\\n test_p = testvals[(int) Math.ceil(testvals.length * (p / 100))];\\n\\n if (train_p > test_p) {\\n score += test_p / train_p;\\n }\\n else {\\n score += train_p / test_p;\\n }\\n }\\n\\n if (score > cutoff) {\\n mwbm.setWeight(i, j, score);\\n }\\n }\\n }\\n }\",\n \"public double[] monteCarloPercentiles(){\\n if(!this.monteCarloDone)this.monteCarlo();\\n return this.randomEigenValuesPercentiles;\\n }\",\n \"@Test\\n public void test10() {\\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 3));\\n }\",\n \"@Test\\n public void testMesurerDistanceCoordonnees() {\\n System.out.println(\\\"MesurerDistanceCoordonnees\\\");\\n double lat1 = 0.0;\\n double lon1 = 0.0;\\n double lat2 = 0.0;\\n double lon2 = 0.0;\\n double expResult = 0.0;\\n double result = Utils.MesurerDistanceCoordonnees(lat1, lon1, lat2, lon2);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test13() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"\\\\tInvert matching sense of column indexes.\\\");\\n discretize0.setOutputFormat();\\n discretize0.listOptions();\\n Discretize discretize1 = new Discretize();\\n discretize1.setIgnoreClass(false);\\n String[] stringArray0 = new String[2];\\n stringArray0[0] = \\\"\\\\tInvert matching sense of column indexes.\\\";\\n stringArray0[1] = \\\"\\\";\\n Discretize.main(stringArray0);\\n String string0 = discretize1.findNumBinsTipText();\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", string0);\\n \\n discretize1.setInvertSelection(false);\\n discretize0.getBinRangesString(511);\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n \\n discretize1.getFindNumBins();\\n assertEquals(10, discretize1.getBins());\\n }\",\n \"@Test\\n public void testMetastableStates()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IIntArray expResult = null;\\n IIntArray result = instance.metastableStates(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCstepping_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCstepping(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test096() throws Throwable {\\n CostMatrix costMatrix0 = Evaluation.handleCostOption(\\\"\\\", 1281);\\n assertNull(costMatrix0);\\n }\",\n \"@Test\\n\\tpublic void evalCrispDistanceWithCrisp1(){\\n\\t\\tFuzzySet res = (CrispSet)cs1.distance(cs2);\\n\\t\\tassertTrue(res instanceof CrispSet);\\n\\t\\tCrispSet resCrisp = (CrispSet)res;\\n\\t\\t//Puntos limite\\t\\t\\n\\t\\tassertTrue(resCrisp.getLeftBoundary()==3);\\n\\t\\tassertTrue(resCrisp.getRightBoundary()==6);\\n\\t\\t//Valores extremos\\n\\t\\tassertTrue(resCrisp.getMembershipValue(0)==0);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(3)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(4)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(4.5)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(5)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(6)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(6.01)==0);\\n\\t}\",\n \"@Test\\n public void testJuvenilesSpreadFromOutsideInsideGrid() {\\n params = new CompletelySpatiallyRandomParams(0.5, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params);\\n coloniser.updateJuvenilePresenceLayers();\\n\\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 5);\\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 5);\\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 5);\\n }\",\n \"@Test\\n public void pruebaCalculCamiseta() {\\n System.out.println(\\\"prueba Calcul Camiseta\\\");\\n Camiseta camisetaTest = new Camiseta(1);\\n assertEquals(190, (camisetaTest.calculCamiseta(1)),0) ;\\n \\n Camiseta camisetaTestDos = new Camiseta(2);\\n assertEquals(1481.2, (camisetaTestDos.calculCamiseta(7)),0) ;\\n \\n Camiseta camisetaTestTres = new Camiseta(3);\\n assertEquals(1178, (camisetaTestTres.calculCamiseta(4)),0) ;\\n \\n \\n }\",\n \"public int getCostPerMile() {\\n return costPerMile;\\n }\",\n \"@Test()\\n public void testTrainWarmCFastSMO() {\\n ClassificationDataSet train = FixedProblems.getHalfCircles(250, RandomUtil.getRandom(), 0.1, 0.2);\\n\\n PlattSMO warmModel = new PlattSMO(new LinearKernel(1));\\n warmModel.setC(1);\\n warmModel.train(train);\\n\\n PlattSMO warm = new PlattSMO(new LinearKernel(1));\\n warm.setC(1e4);// too large to train efficently like noraml\\n\\n long start, end;\\n\\n start = System.currentTimeMillis();\\n warm.train(train, warmModel);\\n end = System.currentTimeMillis();\\n long warmTime = (end - start);\\n\\n PlattSMO notWarm = new PlattSMO(new LinearKernel(1));\\n notWarm.setC(1e4);// too large to train efficently like noraml\\n\\n start = System.currentTimeMillis();\\n notWarm.train(train);\\n end = System.currentTimeMillis();\\n long normTime = (end - start);\\n\\n assertTrue(warmTime < normTime * 0.75);\\n\\n }\",\n \"@Test\\n public void testConnectedComponents()\\n {\\n Collection<IIntArray> cc1 = MarkovModel.util.connectedComponents(P1);\\n assertTrue(cc1.containsAll(C1));\\n assertEquals(cc1.size(), C1.size());\\n\\n Collection<IIntArray> cc2 = MarkovModel.util.connectedComponents(P2);\\n assertTrue(cc2.containsAll(C2));\\n assertEquals(cc2.size(), C2.size());\\n\\n Collection<IIntArray> cc3 = MarkovModel.util.connectedComponents(P3);\\n assertTrue(cc3.containsAll(C3));\\n assertEquals(cc3.size(), C3.size()); \\n }\",\n \"@Test(timeout = 4000)\\n public void test26() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.m_FindNumBins = true;\\n int[] intArray0 = new int[6];\\n intArray0[0] = (-388);\\n intArray0[1] = (-2121610348);\\n intArray0[2] = 11;\\n intArray0[3] = (-1);\\n intArray0[4] = 1062;\\n intArray0[5] = 70;\\n discretize0.setAttributeIndicesArray(intArray0);\\n discretize0.setDesiredWeightOfInstancesPerInterval((-1149.15832593));\\n discretize0.getOptions();\\n assertEquals((-1149.15832593), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n }\",\n \"@Test\\r\\n\\tpublic void testAllMarkupsElectronics() throws BasePriceMustBePositiveException, \\r\\n\\t NegativePersonCountException, ProductCategoryNotSpecifiedException {\\r\\n\\t\\tNuPackEstimator estimator = new NuPackEstimator(new BigDecimal(\\\"100\\\"), 5, \\\"electronics\\\");\\r\\n\\t assertEquals(estimator.estimateFinalCost(), new BigDecimal(\\\"113.40\\\"));\\r\\n\\t}\",\n \"@Test\\n public void testGetProjectileSpeedMultiplier() {\\n assertEquals(1.3, proj.getProjectileSpeedMultiplier(), 0.001);\\n }\",\n \"public void testConcurrency() throws InterruptedException {\\n initCache();\\n\\n final ArrayList<WMSTile> wcos = new ArrayList<WMSTile>();\\n final ArrayList<Integer> test = new ArrayList<Integer>();\\n final LinkedBlockingQueue<Integer> idxs = new LinkedBlockingQueue<Integer>();\\n\\n Collection<Callable<Integer>> tasks = new ArrayList<Callable<Integer>>();\\n\\n for (int i = 0; i < 30000; i++) {\\n wcos.add(getDefaultWMSCacheObject(\\\"q\\\" + i, 50, true));\\n test.add(-1);\\n idxs.put(i);\\n\\n //put and get task\\n tasks.add(new Callable<Integer>() {\\n\\n public Integer call() throws Exception {\\n //put\\n int i = idxs.take();\\n boolean ok = wmsCache.put(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1, wcos.get(i));\\n\\n //get\\n if (ok) {\\n WMSTile wco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\\n if (wco.getCached()) {\\n if (compareWMSObjects(wco, wcos.get(i))) {\\n test.set(i, 1);\\n } else {\\n test.set(i, 0);\\n }\\n }\\n }\\n return i;\\n }\\n });\\n }\\n ExecutorService executorService = Executors.newFixedThreadPool(100);\\n executorService.invokeAll(tasks);\\n\\n //test cache cleaner operated correctly\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n\\n //test for presence of invalid test comparisons\\n int invalid = 0;\\n int valid = 0;\\n for (int i = 0; i < test.size(); i++) {\\n if (test.get(i) == 0) {\\n invalid++;\\n } else if (test.get(i) == 1) {\\n valid++;\\n }\\n }\\n assertTrue(valid > 0);\\n assertTrue(invalid == 0);\\n }\",\n \"@Test\\n public void testGetCenter() {\\n int expResult = 3;\\n int result = this.dummyDigitSize.getCenter();\\n assertEquals(expResult, result);\\n }\",\n \"@Before\\r\\n\\tpublic void setup() {\\r\\n\\t\\tMockitoAnnotations.initMocks(this);\\r\\n\\t\\tcompute(\\\"12-10-19\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"10-09-20\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"11-08-20\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"03-05-17\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"09-03-19\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"01-02-17\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t}\",\n \"@Test\\n public void testAutocorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.autocorrelation(M, observable, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testIsRateMatrix()\\n {\\n IDoubleArray K = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n boolean expResult = false;\\n boolean result = instance.isRateMatrix(K);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test28() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n doubleArray2[5] = (-236.4995664359);\\n doubleArray2[7] = 1421.4058829;\\n doubleArray0[3] = doubleArray2;\\n double[] doubleArray3 = new double[3];\\n doubleArray3[0] = 0.0;\\n doubleArray3[1] = (-236.4995664359);\\n doubleArray0[4] = doubleArray3;\\n double[] doubleArray4 = new double[21];\\n doubleArray4[0] = 1421.4058829;\\n doubleArray3[2] = (-236.4995664359);\\n doubleArray4[4] = 1.7976931348623157E308;\\n doubleArray0[7] = doubleArray4;\\n discretize0.m_NumBins = 162;\\n doubleArray0[1] = doubleArray4;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.getBinRangesString(1);\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"public void testSizeManagementWMS() {\\n initCache();\\n \\n ArrayList<WMSTile> wcos = new ArrayList<WMSTile>();\\n long putSize = 0;\\n long maxSize = 0;\\n int cacheSizeDropCount = 0;\\n for (int i = 0; i < 1000; i++) {\\n WMSTile wco = getDefaultWMSCacheObject(\\\"q\\\" + i, 50, true);\\n putSize += wco.getSize();\\n\\n wcos.add(wco);\\n long size = wmsCache.getSize();\\n\\n if (size < maxSize) {\\n maxSize = size;\\n cacheSizeDropCount++;\\n } else {\\n maxSize = size;\\n }\\n\\n boolean result = wmsCache.put(wco.getQuery(), wco.getColourmode(), PointType.POINT_1, wco);\\n\\n //test if cache is full the put was unsuccessful\\n //allow for cachecleaner to have reduced the size between put and test\\n boolean test = (wmsCache.getSize() + wco.getSize() > wmsCache.getMaxCacheSize()) == !result;\\n if (test == false) {\\n assertTrue((size + wco.getSize() > wmsCache.getMaxCacheSize()) == !result);\\n }\\n\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n }\\n\\n //test size calcuations are operating\\n assertTrue(putSize > 10000);\\n\\n //test cache cleaner was run more than once\\n assertTrue(cacheSizeDropCount > 1);\\n\\n //test cache size is under max\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n\\n //test gets. Anything that is a placeholder will be null.\\n int cachedCount = 0;\\n for (int i = 0; i < wcos.size(); i++) {\\n WMSTile getwco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\\n if (getwco.getCached()) {\\n assertTrue(compareWMSObjects(wcos.get(i), getwco));\\n cachedCount++;\\n }\\n }\\n\\n //test if at least 2 objects were cached at the end\\n assertTrue(cachedCount > 1);\\n }\",\n \"@Test\\r\\n public void testCalculateNDM() {\\r\\n System.out.println(\\\"calculateNDM\\\");\\r\\n NDM instance = null;\\r\\n Double expResult = null;\\r\\n //Double result = instance.calculateNDM();\\r\\n //assertEquals(expResult, result);\\r\\n // TODO review the generated test code and remove the default call to fail.\\r\\n //fail(\\\"The test case is a prototype.\\\");\\r\\n }\",\n \"@Test(timeout = 4000)\\n public void test098() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV(0, 0);\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[] intArray0 = new int[0];\\n resultMatrixCSV0.setRowOrder(intArray0);\\n assertArrayEquals(new int[] {}, intArray0);\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(0, intArray0.length);\\n \\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML();\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertNotNull(resultMatrixHTML0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n resultMatrixHTML0.setMeanPrec((-2615));\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n ResultMatrixHTML resultMatrixHTML1 = null;\\n try {\\n resultMatrixHTML1 = new ResultMatrixHTML(2312, (-2214));\\n fail(\\\"Expecting exception: NegativeArraySizeException\\\");\\n \\n } catch(NegativeArraySizeException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.experiment.ResultMatrix\\\", e);\\n }\\n }\",\n \"@Test\\n public void testNumCellsColonisedFromOutsideGrid() {\\n CompletelySpatiallyRandomParams params1 = new CompletelySpatiallyRandomParams(0.1, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params1);\\n assertEquals(1, coloniser.getNumCellsColonisedFromOutsideGrid());\\n\\n // if baseRate = 0.5 then 9 * 0.5 = 4.5 ~ 5 cells colonised from outside grid\\n CompletelySpatiallyRandomParams params2 = new CompletelySpatiallyRandomParams(0.5, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params2);\\n assertEquals(5, coloniser.getNumCellsColonisedFromOutsideGrid());\\n }\",\n \"@Test(timeout = 4000)\\n public void test117() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[] intArray0 = new int[1];\\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n String[] stringArray0 = new String[4];\\n stringArray0[0] = \\\"v\\\";\\n stringArray0[1] = \\\"[\\\";\\n stringArray0[2] = \\\"*\\\";\\n double[] doubleArray0 = new double[7];\\n doubleArray0[0] = (double) 0;\\n doubleArray0[1] = (double) 2;\\n doubleArray0[2] = (double) 0;\\n doubleArray0[3] = (double) 1;\\n doubleArray0[4] = (double) 2;\\n doubleArray0[5] = (double) 0;\\n ResultMatrixPlainText resultMatrixPlainText0 = new ResultMatrixPlainText();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertNotNull(resultMatrixPlainText0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n ResultMatrixLatex resultMatrixLatex0 = new ResultMatrixLatex(resultMatrixPlainText0);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertNotNull(resultMatrixLatex0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n String string0 = resultMatrixPlainText0.getSummaryTitle(216);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertNotNull(string0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"i\\\", string0);\\n \\n int[][] intArray1 = new int[8][0];\\n intArray1[0] = intArray0;\\n intArray1[1] = intArray0;\\n intArray1[2] = intArray0;\\n intArray1[3] = intArray0;\\n intArray1[4] = intArray0;\\n intArray1[5] = intArray0;\\n intArray1[6] = intArray0;\\n intArray1[7] = intArray0;\\n resultMatrixLatex0.setSummary(intArray1, intArray1);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(8, intArray1.length);\\n \\n ResultMatrixCSV resultMatrixCSV1 = new ResultMatrixCSV();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNotNull(resultMatrixCSV1);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n \\n int[] intArray2 = resultMatrixCSV1.getColOrder();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNull(intArray2);\\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n \\n boolean boolean0 = resultMatrixLatex0.getDefaultPrintColNames();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertFalse(boolean0);\\n \\n ResultMatrixSignificance resultMatrixSignificance1 = new ResultMatrixSignificance(resultMatrixSignificance0);\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getSignificanceWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance1.showStdDevTipText());\\n assertEquals(1, resultMatrixSignificance1.getRowCount());\\n assertTrue(resultMatrixSignificance1.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixSignificance1.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance1.meanWidthTipText());\\n assertFalse(resultMatrixSignificance1.getPrintColNames());\\n assertFalse(resultMatrixSignificance1.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance1.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance1.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance1.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance1.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance1.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixSignificance1.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixSignificance1.getVisibleColCount());\\n assertTrue(resultMatrixSignificance1.getPrintRowNames());\\n assertEquals(40, resultMatrixSignificance1.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance1.getCountWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance1.countWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance1.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance1.getRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance1.getRowNameWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance1.getDefaultRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultMeanWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance1.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultColNameWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance1.rowNameWidthTipText());\\n assertEquals(1, resultMatrixSignificance1.getColCount());\\n assertEquals(0, resultMatrixSignificance1.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateRowNamesTipText());\\n assertTrue(resultMatrixSignificance1.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance1.getMeanWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowAverage());\\n assertEquals(1, resultMatrixSignificance1.getVisibleRowCount());\\n assertTrue(resultMatrixSignificance1.getDefaultEnumerateColNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance1.colNameWidthTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance1.getShowAverage());\\n assertFalse(resultMatrixSignificance1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance1.meanPrecTipText());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance1.globalInfo());\\n assertEquals(2, resultMatrixSignificance1.getDefaultStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance1.getDisplayName());\\n assertEquals(0, resultMatrixSignificance1.getStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance1.showAverageTipText());\\n assertNotNull(resultMatrixSignificance1);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertFalse(resultMatrixSignificance1.equals((Object)resultMatrixSignificance0));\\n \\n String string1 = resultMatrixCSV1.toStringSummary();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNotNull(string1);\\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"-summary data not set-\\\", string1);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n assertFalse(string1.equals((Object)string0));\\n \\n boolean boolean1 = resultMatrixPlainText0.isRowName(1);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(boolean1);\\n assertTrue(boolean1 == boolean0);\\n \\n int int0 = resultMatrixLatex0.getDefaultColNameWidth();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, int0);\\n }\",\n \"int getN_estimators();\",\n \"@Test(timeout = 4000)\\n public void test048() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML(resultMatrixCSV0);\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertNotNull(resultMatrixHTML0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[][] intArray0 = new int[0][9];\\n // Undeclared exception!\\n try { \\n resultMatrixCSV0.setSummary(intArray0, intArray0);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 0\\n //\\n verifyException(\\\"weka.experiment.ResultMatrix\\\", e);\\n }\\n }\",\n \"@Test\\n public void testSmallDist() throws Exception {\\n assertTrue(\\n new ImageSimilarity(getFile(\\\"imageSimilarity/with.png\\\"))\\n .calcDistance(ImageIO.read(getFile(\\\"imageSimilarity/without.png\\\")))\\n > 0);\\n }\",\n \"double[] compute_t_Cm (double[][] t_mmnt, double thickness, double camber) {\\n double[] result = new double[15];\\n if (camber >= 0)\\n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \\n result[i] = ci15_from_javafoil_data(t_mmnt, aoa, thickness, camber);\\n else \\n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \\n result[i] = -ci15_from_javafoil_data(t_mmnt, -aoa, thickness, camber);\\n return result;\\n }\",\n \"public double[] jacobi_iter(double[] x, double tolerance, int M) {\\n double[][] sContent = {{1.0, 0, 0}, {0, 1.0, 0}, {0, 0, 1.0}};\\n Matrix s = new Matrix(sContent);\\n\\n double[][] tContent = {{0, -0.5, -0.33333333}, {-0.5, 0, -0.25}, {-0.3333333333, -0.25, 0}};\\n Matrix t = new Matrix(tContent);\\n\\n //keep track of the number of iterations\\n\\n int counter = 0;\\n double tol = 10000; //initial\\n double[] tempXVector;\\n Matrix firstFirst;\\n double[] firstPart; //s^-1(-T)(Xn)\\n double[] secondPart; //s^-1(b)\\n String output = \\\"\\\";\\n\\n double tempXVectorNorm;\\n double xVectorNorm;\\n\\n for (double elem : x) {\\n output += elem + \\\", \\\";\\n }\\n System.out.println(\\\"x(0): \\\" + output);\\n while (tol > tolerance && counter < M) {\\n //perform the jacobi iterations\\n firstFirst = s.multiply(t);\\n firstPart = Matrix.matrixVectorMultiply(firstFirst, x);\\n secondPart = Matrix.matrixVectorMultiply(s, bVector);\\n tempXVector = Matrix.plus(firstPart, secondPart);\\n\\n //find norm of vectors and then subtract the\\n tempXVectorNorm = Matrix.norm(tempXVector);\\n xVectorNorm = Matrix.norm(x);\\n tol = tempXVectorNorm - xVectorNorm;\\n tol = Math.abs(tol);\\n counter++;\\n numOfIterations++;\\n x = tempXVector;\\n }\\n\\n output = \\\"\\\";\\n if (counter == M) {\\n System.out.println(\\\"Jacobi Output for x(N): null\\\");\\n System.out.println(\\\"Too many iterations without reaching a accurate answer.\\\");\\n return null;\\n }\\n for (double elem : x) {\\n output += elem + \\\", \\\";\\n }\\n System.out.println(\\\"Jacobi Output for x(N): \\\" + output);\\n System.out.println(\\\"Number of Iterations using Jacobi Algorithm: \\\" + counter + \\\"\\\\n\\\");\\n return x;\\n }\",\n \"@Before\\n public void setup() {\\n threeByTwo = new Matrix(new int[][] {{1, 2, 3}, {2, 5, 6}});\\n compareTwoByThree = new Matrix(new int[][] {{1, 42, 32}, {2, 15, 65}});\\n oneByOne = new Matrix(new int[][] {{4}});\\n rightOneByOne = new Matrix(new int[][] {{8}});\\n twoByFour = new Matrix(new int[][] {{1, 2, 3, 4}, {2, 5, 6, 8}});\\n twoByThree = new Matrix(new int[][] {{4, 5}, {3, 2}, {1, 1}});\\n threeByThree = new Matrix(new int[][] {{4, 5, 6}, {3, 2, 0}, {1, 1, 1}});\\n rightThreeByThree = new Matrix(new int[][] {{1, 2, 3}, {5, 2, 0}, {1, 1, 1}});\\n emptyMatrix = new Matrix(new int[0][0]);\\n // this is the known correct result of multiplying M1 by M2\\n twoByTwoResult = new Matrix(new int[][] {{13, 12}, {29, 26}});\\n threeByThreeResult = new Matrix(new int[][] {{35, 24, 18}, {13, 10, 9}, {7, 5, 4}});\\n oneByOneSum = new Matrix(new int[][] {{12}});\\n oneByOneProduct = new Matrix(new int[][] {{32}});\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[7][2];\\n double[] doubleArray1 = new double[7];\\n doubleArray1[0] = 0.0;\\n doubleArray1[1] = (-587.14627);\\n doubleArray1[2] = (-2542.40202289275);\\n doubleArray1[3] = (-167.679515);\\n doubleArray1[4] = (-1.0);\\n doubleArray1[5] = 1.0;\\n doubleArray1[6] = (-1.0);\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[5];\\n doubleArray2[0] = (-167.679515);\\n doubleArray2[1] = (-2692.352);\\n doubleArray2[2] = (-167.679515);\\n doubleArray2[3] = (-2542.40202289275);\\n doubleArray2[4] = (-587.14627);\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[4];\\n doubleArray3[0] = (-1.0);\\n doubleArray3[1] = (-1.0);\\n doubleArray3[2] = 1.0;\\n doubleArray3[3] = (-2542.40202289275);\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[7];\\n doubleArray4[0] = (-2692.352);\\n doubleArray4[1] = (-1.0);\\n doubleArray4[2] = (-1.0);\\n doubleArray4[3] = 0.0;\\n doubleArray4[4] = 1.0;\\n doubleArray4[5] = 0.0;\\n doubleArray4[6] = 1.0;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[1];\\n doubleArray5[0] = (-167.679515);\\n doubleArray0[4] = doubleArray5;\\n double[] doubleArray6 = new double[0];\\n doubleArray0[5] = doubleArray6;\\n double[] doubleArray7 = new double[1];\\n doubleArray7[0] = (-587.14627);\\n doubleArray0[6] = doubleArray7;\\n discretize0.m_CutPoints = doubleArray0;\\n String string0 = discretize0.invertSelectionTipText();\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.\\\", string0);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Test\\r\\n\\tpublic void testGetReducedCell() {\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"a\\\", new double[] { 10., 6., 4. }, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(0), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"alen\\\", 12.328828005937952, lattice1.getCMLVector3(\\r\\n\\t\\t\\t\\t0).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"b\\\", new double[] { 7., 11., 5. }, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(1), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"blen\\\", 13.96424004376894, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(1).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"c\\\", new double[] { 6.8, -4., -9. },\\r\\n\\t\\t\\t\\tlattice1.getCMLVector3(2), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"clen\\\", 11.968291440301744, lattice1.getCMLVector3(\\r\\n\\t\\t\\t\\t2).getLength(), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"vol\\\", -619.2, lattice1.getVolume(), EPS);\\r\\n\\t\\tdouble[] params = lattice1.getCellParameters();\\r\\n\\t\\tDoubleTestBase.assertEquals(\\\"params\\\", new double[] {\\r\\n\\t\\t\\t\\t12.328828005937952, 13.96424004376894, 11.968291440301744,\\r\\n\\t\\t\\t\\t104.34229064539636, 86.89206686722031, 25.025508299339933 },\\r\\n\\t\\t\\t\\tparams, EPS);\\r\\n\\r\\n\\t\\tCMLLattice reducedLattice = lattice1.getReducedCell();\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"a\\\", new double[] { 3.0, -5.0, -1.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(0), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"alen\\\", 5.916079783099616, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(0).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"b\\\", new double[] { 3.8, 1.0, -8.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(1), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"blen\\\", 8.912911982062877, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(1).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"c\\\", new double[] { 10.0, 6.0, 4.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(2), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"clen\\\", 12.328828005937952, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(2).getLength(), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"vol\\\", 619.2, reducedLattice.getVolume(), EPS);\\r\\n\\t\\tparams = reducedLattice.getCellParameters();\\r\\n\\t\\tDoubleTestBase.assertEquals(\\\"params\\\", new double[] { 5.916079783099616,\\r\\n\\t\\t\\t\\t8.912911982062877, 12.328828005937952, 83.73054962879554,\\r\\n\\t\\t\\t\\t93.14372638090103, 74.15166267765416 }, params, EPS);\\r\\n\\r\\n\\t}\",\n \"@Test\\n\\tpublic void testOptimization() throws Exception {\\n\\t\\tfor(int i = 0; i < 20000000; i++) {\\n\\t\\t\\tint _300s = (int) (Math.random() * 100);\\n\\t\\t\\tint _100s = (int) (Math.random() * 100);\\n\\t\\t\\tint _50s = (int) (Math.random() * 100);\\n\\t\\t\\tint misses = (int) (Math.random() * 100);\\n\\t\\t\\t\\n\\t\\t\\tdouble acc = OsuApiScore.getAccuracy(_300s, _100s, _50s, misses);\\n\\t\\t\\t\\n\\t\\t\\tAccuracyDistribution accuracyDistribution = AccuracyDistribution.closest(\\n\\t\\t\\t\\t\\t_300s + _100s + _50s + misses, misses,\\n\\t\\t\\t\\t\\tacc);\\n\\t\\t\\t\\n\\t\\t\\tdouble rec = OsuApiScore.getAccuracy(accuracyDistribution.getX300(),\\n\\t\\t\\t\\t\\taccuracyDistribution.getX100(), accuracyDistribution.getX50(),\\n\\t\\t\\t\\t\\taccuracyDistribution.getMiss());\\n\\t\\t\\t\\n\\t\\t\\tassertEquals(acc, rec, 0d);\\n\\t\\t}\\n\\t}\",\n \"@Test\\n\\tpublic void testaGetMensagem() {\\n\\t\\tAssert.assertEquals(\\n\\t\\t\\t\\t\\\"Erro no getMensagem()\\\",\\n\\t\\t\\t\\t\\\"Disciplina 1 eh avaliada com 5 minitestes, eliminando 1 dos mais baixos.\\\",\\n\\t\\t\\t\\tminitElimBai_1.getMensagem());\\n\\t\\tAssert.assertEquals(\\n\\t\\t\\t\\t\\\"Erro no getMensagem()\\\",\\n\\t\\t\\t\\t\\\"Disciplina 2 eh avaliada com 10 minitestes, eliminando 2 dos mais baixos.\\\",\\n\\t\\t\\t\\tminitElimBai_2.getMensagem());\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test35() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n assertFalse(discretize0.getMakeBinary());\\n \\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n Attribute attribute0 = new Attribute(\\\"invalid CVS revision - not dots!\\\");\\n attribute0.setWeight(0.0);\\n Attribute.typeToString(attribute0);\\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\\n arrayList0.add(attribute0);\\n Instances instances0 = new Instances(\\\"UR<dj;O\\\", arrayList0, 3);\\n Instances instances1 = new Instances(instances0);\\n boolean boolean0 = discretize0.setInputFormat(instances1);\\n assertFalse(boolean0);\\n \\n String[] stringArray0 = new String[3];\\n stringArray0[0] = \\\".bsi\\\";\\n stringArray0[1] = \\\"invalid CVS revision - not dots!\\\";\\n stringArray0[2] = \\\"@end\\\";\\n discretize0.setOptions(stringArray0);\\n discretize0.setOptions(stringArray0);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n }\",\n \"@Test\\n\\tpublic void testNoCollapseByEditDistance() {\\n\\t\\tString cellBC = \\\"ATCAGGGACAGA\\\";\\n\\t\\tString molBC = \\\"CGGGGCTC\\\";\\n\\t\\tint snpPos = 76227022;\\n\\t\\tInterval snpInterval = new Interval(\\\"HUMAN_1\\\", snpPos, snpPos, true, \\\"test\\\");\\n\\t\\tSampleGenotypeProbabilities result = new SampleGenotypeProbabilities(snpInterval, cellBC);\\n\\t\\tSNPUMIBasePileup p = getPileUpFromFile(cellBC, molBC);\\n\\t\\tresult.add(p);\\n\\n\\t\\t// let's add another pileup in nowhere near the ED\\n\\t\\t// result has 2 different bases for the two pileups.\\n\\t\\tString molBC2 = \\\"ZZZZZZZZ\\\";\\n\\t\\tSNPUMIBasePileup p2 = getPileUpFromFile(cellBC, molBC2);\\n\\t\\tchar[] b2 = { 'G', 'G', 'T' };\\n\\t\\tbyte[] bases2 = new byte[b2.length];\\n\\t\\tStringUtil.charsToBytes(b2, 0, b2.length, bases2, 0);\\n\\t\\tbyte[] quals2 = { 37, 37, 10 };\\n\\t\\tp2.setBasesAndQualities(bases2, quals2);\\n\\t\\tresult.add(p2);\\n\\t\\tresult.collapseUMIs(1);\\n\\n\\t\\tList<Byte> qualResult2 = result.getQualities();\\n\\t\\tAssert.assertEquals(qualResult2.size(), 2);\\n\\t\\tAssert.assertEquals(new Byte((byte) 31), qualResult2.get(0));\\n\\t\\tAssert.assertEquals(new Byte((byte) 5), qualResult2.get(1));\\n\\t}\",\n \"@Test\\n public void testQuantile() {\\n System.out.println(\\\"quantile\\\");\\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\\n instance.rand();\\n assertEquals(0, instance.quantile(0), 1E-7);\\n assertEquals(0, instance.quantile(0.001), 1E-7);\\n assertEquals(0, instance.quantile(0.01), 1E-7);\\n assertEquals(2, instance.quantile(0.1), 1E-7);\\n assertEquals(3, instance.quantile(0.2), 1E-7);\\n assertEquals(13, instance.quantile(0.9), 1E-7);\\n assertEquals(22, instance.quantile(0.99), 1E-7);\\n assertEquals(30, instance.quantile(0.999), 1E-7);\\n }\",\n \"@Test(timeout = 4000)\\n public void test050() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate(\\\"\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.areaUnderPRC((-1));\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"public void testGetConformingStimulusLargeScale() throws Exception {\\n long start = System.currentTimeMillis();\\n Stimulus stimulus = new MockStimulus();\\n message.setConformingStimulus(stimulus);\\n for (int i = 0; i < TOTAL_ITERATION; i++) {\\n message.getConformingStimulus();\\n }\\n System.out.println(\\\"Calling \\\" + TOTAL_ITERATION + \\\"times getConformingStimulus method\\\"\\n + \\\" costs:\\\" + (System.currentTimeMillis() - start) + \\\" ms\\\");\\n }\",\n \"@Test\\n public void testLogLikelihood()\\n {\\n IDoubleArray T = null;\\n IDoubleArray C = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n double expResult = 0.0;\\n double result = instance.logLikelihood(T, C);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap<Integer,Integer> words)\\r\\n{\\r\\n int t=0;\\r\\n double LearningRates[] = {1, 0.1, 0.01};\\r\\n double Margins[] = {1, 0.1, 0.01};\\r\\n\\r\\n\\tint minInt =0; // Set To Your Desired Min Value\\r\\n int maxInt =2;\\r\\n\\tRandom rand = new Random();\\r\\n\\t\\r\\n \\tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\\r\\n\\r\\n \\tdouble LearningRate = LearningRates[randomNum];\\r\\n \\t\\r\\nfor(int m=0;m<LearningRates.length;m++)\\r\\n{\\r\\n for(int i=0;i<LearningRates.length;i++)\\r\\n {\\r\\n\\t LearningRate = LearningRates[i];\\r\\n\\t\\t double devAccuracyTotal = (double)0;\\r\\n\\t\\t int noOfEpochs= 20;\\r\\n\\t\\t int decreaseRate = 0;\\r\\n\\t\\t \\r\\n \\t\\t Double w[] = new Double[74500];\\r\\n\\t\\t Double min =-0.01; // Set To Your Desired Min Value\\r\\n\\t Double max = 0.01;\\r\\n\\t \\t\\tdouble smallRandNumber = min + new Random().nextDouble() * (max - min); \\r\\n\\t\\t\\tArrays.fill(w, smallRandNumber);\\r\\n\\t\\t\\t\\r\\n\\t\\t for(int epoch=0;epoch<noOfEpochs;epoch++)\\r\\n\\t\\t {\\r\\n\\t\\t\\t \\tw = learnMarginPerceptron(data,index,LearningRate,decreaseRate,w,Margins[m]);\\r\\n\\t\\t\\t \\tdecreaseRate = decreaseRate + index;\\r\\n\\t\\t\\r\\n\\t\\t }\\r\\n\\t\\t//CVSplit test\\r\\n \\tdouble cvAccuracy = testTestFile(w,testFileName,words);\\r\\n \\t\\r\\n// \\tSystem.out.println(\\\"Epoch\\\" + epoch +\\\" Dev Accuracy for Decaying Learning Rate \\\" + LearningRate + \\\" is \\\" + cvAccuracy);\\r\\n \\r\\n \\r\\n \\tSystem.out.println(\\\" ** Cv Accuracy for Margin Rate\\\" + Margins[m] + \\\" Learning Rate \\\" + LearningRate + \\\" is \\\" + cvAccuracy);\\r\\n \\tSystem.out.println(\\\" \\\");\\r\\n }\\r\\n}\\r\\n//double testAccuracy = testTestFile(w,testFileName);\\r\\n\\t//System.out.println(\\\"test Accuracy\\\" + testAccuracy);\\r\\n}\",\n \"@Test\\n void checkChangeImage() {\\n ImageViewModel imageModel = new ImageViewModel();\\n int countUp = 10;\\n int totalTime = 70;\\n\\n int i = imageModel.checkChangeImage(countUp, totalTime);\\n\\n assertEquals(1, i);\\n }\",\n \"@Test(timeout = 4000)\\n public void test04() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"9/&=Kq&\\\");\\n discretize0.getBinRangesString((-1679));\\n int int0 = (-98);\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-98));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test23() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setIgnoreClass(true);\\n discretize0.getOptions();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualFrequencyBinning((-821));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.core.Instances\\\", e);\\n }\\n }\",\n \"private void findAbdomenCM(int sliceNum) {\\r\\n \\r\\n // load up the sliceBuffer with the appropriate slice from the abdomen label image\\r\\n try {\\r\\n abdomenImage.exportData(sliceNum * sliceSize, sliceSize, sliceBuffer);\\r\\n } catch (IOException ex) {\\r\\n System.err.println(\\\"findAbdomenCM(...) Error exporting data on slice: \\\" +sliceNum);\\r\\n return;\\r\\n }\\r\\n\\r\\n int xcm = 0, ycm = 0, pixCount = 0;\\r\\n for (int idx= 0, y = 0; y < yDim; y++) {\\r\\n for (int x = 0; x < xDim; x++, idx++) {\\r\\n if (sliceBuffer[idx] == abdomenTissueLabel) {\\r\\n xcm += x;\\r\\n ycm += y;\\r\\n pixCount++;\\r\\n }\\r\\n } // end for (int x = 0; ...)\\r\\n } // end for (int idx = 0, ...)\\r\\n \\r\\n if (pixCount == 0) {\\r\\n// System.err.println(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n MipavUtil.displayError(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n // set the centerOfMass to an generate an obvious error \\r\\n centerOfMass[0] = (short)-1;\\r\\n centerOfMass[1] = (short)-1;\\r\\n return;\\r\\n }\\r\\n \\r\\n centerOfMass[0] = (short)(xcm / pixCount);\\r\\n centerOfMass[1] = (short)(ycm / pixCount);\\r\\n }\",\n \"@Test(timeout = 4000)\\n public void test13() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n String string0 = discretize0.makeBinaryTipText();\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(\\\"Make resulting attributes binary.\\\", string0);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(10, discretize0.getBins());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getMakeBinary());\\n }\",\n \"@Test(timeout = 4000)\\n public void test102() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate(\\\"\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double[][] doubleArray0 = evaluation0.confusionMatrix();\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(2, doubleArray0.length);\\n }\",\n \"@Test(timeout = 4000)\\n public void test028() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n String[] stringArray0 = new String[4];\\n stringArray0[0] = \\\"v\\\";\\n stringArray0[1] = \\\"[\\\";\\n stringArray0[2] = \\\"*\\\";\\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML(0, 16);\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(16, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertNotNull(resultMatrixHTML0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int int0 = 2298;\\n resultMatrixHTML0.setRowNameWidth(2298);\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(2298, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(16, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n resultMatrixHTML0.setRowNameWidth(1);\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(1, resultMatrixHTML0.getRowNameWidth());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(16, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n boolean boolean0 = resultMatrixHTML0.getShowAverage();\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(1, resultMatrixHTML0.getRowNameWidth());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(16, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(boolean0);\\n \\n ResultMatrixPlainText resultMatrixPlainText0 = new ResultMatrixPlainText();\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertNotNull(resultMatrixPlainText0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n boolean boolean1 = resultMatrixPlainText0.isSignificance(16);\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertFalse(boolean1);\\n assertTrue(boolean1 == boolean0);\\n \\n int int1 = resultMatrixHTML0.getColCount();\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(1, resultMatrixHTML0.getRowNameWidth());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(16, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(0, int1);\\n assertFalse(int1 == int0);\\n \\n ResultMatrixHTML resultMatrixHTML1 = null;\\n try {\\n resultMatrixHTML1 = new ResultMatrixHTML((-1157), 5);\\n fail(\\\"Expecting exception: NegativeArraySizeException\\\");\\n \\n } catch(NegativeArraySizeException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.experiment.ResultMatrix\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test18() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"%\\\");\\n FileSystemHandling.createFolder((EvoSuiteFile) null);\\n discretize0.findNumBinsTipText();\\n double[][] doubleArray0 = new double[0][7];\\n discretize0.m_CutPoints = doubleArray0;\\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, false, true);\\n discretize0.setInvertSelection(false);\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void test_IsOxideCorr() {\\n System.out.println(\\\"Testing MeasuredRatioModel's isOxideCorr()\\\");\\n MeasuredRatioModel instance = new MeasuredRatioModel();\\n boolean expResult = false;\\n boolean result = instance.isOxideCorr();\\n assertEquals(expResult, result);\\n instance = new MeasuredRatioModel(\\\"Specific\\\",new BigDecimal(\\\"213\\\"),\\\"ABS\\\",new BigDecimal(\\\"0.4324\\\"),true,true);\\n result = instance.isOxideCorr();\\n expResult=true;\\n assertEquals(expResult, result);\\n }\",\n \"@Test\\n\\tpublic final void testProcGridCoord() {\\n\\t\\tfor (int d=0; d<DIMENSIONALITY; d++) {\\n\\t\\t\\tassertTrue(0 <= block.procGridCoord(d)\\n\\t\\t\\t\\t\\t&& block.procGridCoord(d)<block.procGridSize(d));\\n\\t\\t}\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test061() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[][] intArray0 = new int[3][0];\\n int[] intArray1 = new int[1];\\n intArray1[0] = 1;\\n intArray0[0] = intArray1;\\n int[] intArray2 = new int[9];\\n assertFalse(intArray2.equals((Object)intArray1));\\n \\n intArray2[0] = 1;\\n intArray2[1] = 2;\\n intArray2[2] = 2;\\n intArray2[3] = 0;\\n intArray2[4] = 3;\\n intArray2[6] = 2;\\n intArray2[7] = 1;\\n intArray2[8] = 2;\\n intArray0[1] = intArray2;\\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n String string0 = resultMatrixSignificance0.getRevision();\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertNotNull(string0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(\\\"8034\\\", string0);\\n \\n resultMatrixSignificance0.setColHidden(1003, false);\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n ResultMatrixSignificance resultMatrixSignificance1 = new ResultMatrixSignificance();\\n assertEquals(0, resultMatrixSignificance1.getCountWidth());\\n assertEquals(1, resultMatrixSignificance1.getVisibleColCount());\\n assertFalse(resultMatrixSignificance1.getDefaultPrintColNames());\\n assertEquals(1, resultMatrixSignificance1.getColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance1.colNameWidthTipText());\\n assertEquals(2, resultMatrixSignificance1.getDefaultMeanPrec());\\n assertTrue(resultMatrixSignificance1.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance1.showAverageTipText());\\n assertEquals(40, resultMatrixSignificance1.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance1.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance1.getColNameWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance1.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance1.stdDevPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance1.printRowNamesTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance1.printColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance1.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance1.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance1.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance1.getVisibleRowCount());\\n assertTrue(resultMatrixSignificance1.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance1.getStdDevWidth());\\n assertEquals(0, resultMatrixSignificance1.getMeanWidth());\\n assertEquals(2, resultMatrixSignificance1.getDefaultStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance1.getDisplayName());\\n assertFalse(resultMatrixSignificance1.getShowAverage());\\n assertTrue(resultMatrixSignificance1.getEnumerateColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance1.globalInfo());\\n assertFalse(resultMatrixSignificance1.getPrintColNames());\\n assertFalse(resultMatrixSignificance1.getEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance1.getDefaultCountWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance1.showStdDevTipText());\\n assertEquals(1, resultMatrixSignificance1.getRowCount());\\n assertFalse(resultMatrixSignificance1.getShowStdDev());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance1.stdDevWidthTipText());\\n assertTrue(resultMatrixSignificance1.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixSignificance1.getSignificanceWidth());\\n assertEquals(2, resultMatrixSignificance1.getMeanPrec());\\n assertFalse(resultMatrixSignificance1.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance1.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance1.meanWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateRowNamesTipText());\\n assertEquals(40, resultMatrixSignificance1.getRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance1.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixSignificance1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance1.getDefaultStdDevWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance1.countWidthTipText());\\n assertNotNull(resultMatrixSignificance1);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(resultMatrixSignificance1.equals((Object)resultMatrixSignificance0));\\n \\n ResultMatrixSignificance resultMatrixSignificance2 = new ResultMatrixSignificance();\\n assertFalse(resultMatrixSignificance2.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance2.getMeanPrec());\\n assertFalse(resultMatrixSignificance2.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance2.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance2.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance2.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance2.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance2.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance2.getRowNameWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance2.meanWidthTipText());\\n assertTrue(resultMatrixSignificance2.getDefaultPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance2.meanPrecTipText());\\n assertEquals(2, resultMatrixSignificance2.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance2.getRowCount());\\n assertEquals(40, resultMatrixSignificance2.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance2.stdDevWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance2.printRowNamesTipText());\\n assertTrue(resultMatrixSignificance2.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance2.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance2.getDefaultCountWidth());\\n assertTrue(resultMatrixSignificance2.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance2.getShowAverage());\\n assertEquals(2, resultMatrixSignificance2.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance2.showAverageTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance2.colNameWidthTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance2.getDisplayName());\\n assertEquals(0, resultMatrixSignificance2.getStdDevWidth());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance2.globalInfo());\\n assertEquals(0, resultMatrixSignificance2.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance2.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance2.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance2.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance2.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance2.printColNamesTipText());\\n assertEquals(1, resultMatrixSignificance2.getColCount());\\n assertFalse(resultMatrixSignificance2.getRemoveFilterName());\\n assertFalse(resultMatrixSignificance2.getShowStdDev());\\n assertFalse(resultMatrixSignificance2.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance2.stdDevPrecTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance2.countWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance2.showStdDevTipText());\\n assertEquals(0, resultMatrixSignificance2.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixSignificance2.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance2.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance2.getSignificanceWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance2.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance2.getCountWidth());\\n assertEquals(1, resultMatrixSignificance2.getVisibleColCount());\\n assertTrue(resultMatrixSignificance2.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance2.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixSignificance2.getDefaultMeanPrec());\\n assertNotNull(resultMatrixSignificance2);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance0));\\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance1));\\n \\n int int0 = resultMatrixSignificance2.getVisibleColCount();\\n assertFalse(resultMatrixSignificance2.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance2.getMeanPrec());\\n assertFalse(resultMatrixSignificance2.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance2.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance2.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance2.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance2.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance2.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance2.getRowNameWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance2.meanWidthTipText());\\n assertTrue(resultMatrixSignificance2.getDefaultPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance2.meanPrecTipText());\\n assertEquals(2, resultMatrixSignificance2.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance2.getRowCount());\\n assertEquals(40, resultMatrixSignificance2.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance2.stdDevWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance2.printRowNamesTipText());\\n assertTrue(resultMatrixSignificance2.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance2.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance2.getDefaultCountWidth());\\n assertTrue(resultMatrixSignificance2.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance2.getShowAverage());\\n assertEquals(2, resultMatrixSignificance2.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance2.showAverageTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance2.colNameWidthTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance2.getDisplayName());\\n assertEquals(0, resultMatrixSignificance2.getStdDevWidth());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance2.globalInfo());\\n assertEquals(0, resultMatrixSignificance2.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance2.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance2.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance2.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance2.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance2.printColNamesTipText());\\n assertEquals(1, resultMatrixSignificance2.getColCount());\\n assertFalse(resultMatrixSignificance2.getRemoveFilterName());\\n assertFalse(resultMatrixSignificance2.getShowStdDev());\\n assertFalse(resultMatrixSignificance2.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance2.stdDevPrecTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance2.countWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance2.showStdDevTipText());\\n assertEquals(0, resultMatrixSignificance2.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixSignificance2.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance2.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance2.getSignificanceWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance2.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance2.getCountWidth());\\n assertEquals(1, resultMatrixSignificance2.getVisibleColCount());\\n assertTrue(resultMatrixSignificance2.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance2.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixSignificance2.getDefaultMeanPrec());\\n assertNotSame(resultMatrixSignificance2, resultMatrixSignificance0);\\n assertNotSame(resultMatrixSignificance2, resultMatrixSignificance1);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(1, int0);\\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance0));\\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance1));\\n \\n ResultMatrixLatex resultMatrixLatex0 = new ResultMatrixLatex();\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getCountWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getRowNameWidth());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertFalse(resultMatrixLatex0.getPrintColNames());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertNotNull(resultMatrixLatex0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n boolean boolean0 = resultMatrixLatex0.isSignificance((-1));\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getCountWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getRowNameWidth());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertFalse(resultMatrixLatex0.getPrintColNames());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(boolean0);\\n }\",\n \"@Test(timeout = 4000)\\n public void test126() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n String string0 = resultMatrixCSV0.toString();\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertNotNull(string0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"Dataset,[1]\\\\nrow0,''\\\\n'[v/ /*]',''\\\\n\\\", string0);\\n \\n String[] stringArray0 = new String[4];\\n ResultMatrixGnuPlot resultMatrixGnuPlot0 = new ResultMatrixGnuPlot();\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultCountWidth());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixGnuPlot0.countWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getSignificanceWidth());\\n assertTrue(resultMatrixGnuPlot0.getPrintRowNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixGnuPlot0.getCountWidth());\\n assertEquals(50, resultMatrixGnuPlot0.getRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixGnuPlot0.significanceWidthTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultRowNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getMeanPrec());\\n assertFalse(resultMatrixGnuPlot0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixGnuPlot0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixGnuPlot0.stdDevPrecTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getColNameWidth());\\n assertEquals(0, resultMatrixGnuPlot0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixGnuPlot0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixGnuPlot0.printColNamesTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultMeanWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixGnuPlot0.colNameWidthTipText());\\n assertEquals(1, resultMatrixGnuPlot0.getVisibleColCount());\\n assertEquals(0, resultMatrixGnuPlot0.getStdDevWidth());\\n assertFalse(resultMatrixGnuPlot0.getShowAverage());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixGnuPlot0.showStdDevTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixGnuPlot0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixGnuPlot0.meanWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixGnuPlot0.showAverageTipText());\\n assertTrue(resultMatrixGnuPlot0.getPrintColNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixGnuPlot0.meanPrecTipText());\\n assertEquals(1, resultMatrixGnuPlot0.getRowCount());\\n assertEquals(\\\"GNUPlot\\\", resultMatrixGnuPlot0.getDisplayName());\\n assertFalse(resultMatrixGnuPlot0.getRemoveFilterName());\\n assertEquals(1, resultMatrixGnuPlot0.getColCount());\\n assertEquals(1, resultMatrixGnuPlot0.getVisibleRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixGnuPlot0.stdDevWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateRowNamesTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowAverage());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintRowNames());\\n assertEquals(\\\"Generates output for a data and script file for GnuPlot.\\\", resultMatrixGnuPlot0.globalInfo());\\n assertNotNull(resultMatrixGnuPlot0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n resultMatrixGnuPlot0.setSize(0, 0);\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixGnuPlot0.getRowCount());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultCountWidth());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixGnuPlot0.countWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getSignificanceWidth());\\n assertTrue(resultMatrixGnuPlot0.getPrintRowNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixGnuPlot0.getCountWidth());\\n assertEquals(50, resultMatrixGnuPlot0.getRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixGnuPlot0.significanceWidthTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getVisibleColCount());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultRowNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getMeanPrec());\\n assertFalse(resultMatrixGnuPlot0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixGnuPlot0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixGnuPlot0.stdDevPrecTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getColNameWidth());\\n assertEquals(0, resultMatrixGnuPlot0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixGnuPlot0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixGnuPlot0.printColNamesTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultMeanWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixGnuPlot0.colNameWidthTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getVisibleRowCount());\\n assertEquals(0, resultMatrixGnuPlot0.getStdDevWidth());\\n assertFalse(resultMatrixGnuPlot0.getShowAverage());\\n assertEquals(0, resultMatrixGnuPlot0.getColCount());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixGnuPlot0.showStdDevTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixGnuPlot0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixGnuPlot0.meanWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixGnuPlot0.showAverageTipText());\\n assertTrue(resultMatrixGnuPlot0.getPrintColNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixGnuPlot0.meanPrecTipText());\\n assertEquals(\\\"GNUPlot\\\", resultMatrixGnuPlot0.getDisplayName());\\n assertFalse(resultMatrixGnuPlot0.getRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixGnuPlot0.stdDevWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateRowNamesTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowAverage());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintRowNames());\\n assertEquals(\\\"Generates output for a data and script file for GnuPlot.\\\", resultMatrixGnuPlot0.globalInfo());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n int int0 = resultMatrixGnuPlot0.getDefaultColNameWidth();\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixGnuPlot0.getRowCount());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultCountWidth());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixGnuPlot0.countWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getSignificanceWidth());\\n assertTrue(resultMatrixGnuPlot0.getPrintRowNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixGnuPlot0.getCountWidth());\\n assertEquals(50, resultMatrixGnuPlot0.getRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixGnuPlot0.significanceWidthTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getVisibleColCount());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultRowNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getMeanPrec());\\n assertFalse(resultMatrixGnuPlot0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixGnuPlot0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixGnuPlot0.stdDevPrecTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getColNameWidth());\\n assertEquals(0, resultMatrixGnuPlot0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixGnuPlot0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixGnuPlot0.printColNamesTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultMeanWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixGnuPlot0.colNameWidthTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getVisibleRowCount());\\n assertEquals(0, resultMatrixGnuPlot0.getStdDevWidth());\\n assertFalse(resultMatrixGnuPlot0.getShowAverage());\\n assertEquals(0, resultMatrixGnuPlot0.getColCount());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixGnuPlot0.showStdDevTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixGnuPlot0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixGnuPlot0.meanWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixGnuPlot0.showAverageTipText());\\n assertTrue(resultMatrixGnuPlot0.getPrintColNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixGnuPlot0.meanPrecTipText());\\n assertEquals(\\\"GNUPlot\\\", resultMatrixGnuPlot0.getDisplayName());\\n assertFalse(resultMatrixGnuPlot0.getRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixGnuPlot0.stdDevWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateRowNamesTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowAverage());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintRowNames());\\n assertEquals(\\\"Generates output for a data and script file for GnuPlot.\\\", resultMatrixGnuPlot0.globalInfo());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(50, int0);\\n }\",\n \"@Test\\n public void testMedian() {\\n System.out.println(\\\"median\\\");\\n ImageFilter instance = null;\\n BufferedImage expResult = null;\\n BufferedImage result = instance.median();\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testCompute() {\\n System.out.println(\\\"compute\\\");\\n ParallelMergeSort instance = null;\\n instance.compute();\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testPerturbationExpectation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray pi0 = null;\\n IDoubleArray observable = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.perturbationExpectation(M, pi0, observable, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test public void estimatedMonthlyCostTest() throws NegativeValueException {\\n PetBunny pb1 = new PetBunny(\\\"Floppy\\\", \\\"Holland Lop\\\", 3.5);\\n \\n Assert.assertEquals(6.475, pb1.estimatedMonthlyCost(), 0.00001);\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7719048","0.7314452","0.6584927","0.5736506","0.5605106","0.5367599","0.53661937","0.53501797","0.5276743","0.5212073","0.5192482","0.51843244","0.5181949","0.5180885","0.5106315","0.5093115","0.50918853","0.5088555","0.5084552","0.50662374","0.50125676","0.4966125","0.4925259","0.4916035","0.4904818","0.4897373","0.4895549","0.4877143","0.48735413","0.4864071","0.48632434","0.48575664","0.48549956","0.48405433","0.48392025","0.48380393","0.48249203","0.48199818","0.4816632","0.48130402","0.4805838","0.4802146","0.48009422","0.47922707","0.47898033","0.47840303","0.47821093","0.47802436","0.47629127","0.47511753","0.47333142","0.47305655","0.4725433","0.47171962","0.47146106","0.47067836","0.46845716","0.46632043","0.4658659","0.46537662","0.46536765","0.46477914","0.46441466","0.46402258","0.46346408","0.4631873","0.46044913","0.4601081","0.46004325","0.46003738","0.45952582","0.45915255","0.4590748","0.45904052","0.4590059","0.4588771","0.45861593","0.45860898","0.45849136","0.45820263","0.45817718","0.45815688","0.45786047","0.4573722","0.45692286","0.4568142","0.45679897","0.45642865","0.45628566","0.45587856","0.45554203","0.45536768","0.45530653","0.4550122","0.4548589","0.45407516","0.4540433","0.4530641","0.45272774","0.4513924"],"string":"[\n \"0.7719048\",\n \"0.7314452\",\n \"0.6584927\",\n \"0.5736506\",\n \"0.5605106\",\n \"0.5367599\",\n \"0.53661937\",\n \"0.53501797\",\n \"0.5276743\",\n \"0.5212073\",\n \"0.5192482\",\n \"0.51843244\",\n \"0.5181949\",\n \"0.5180885\",\n \"0.5106315\",\n \"0.5093115\",\n \"0.50918853\",\n \"0.5088555\",\n \"0.5084552\",\n \"0.50662374\",\n \"0.50125676\",\n \"0.4966125\",\n \"0.4925259\",\n \"0.4916035\",\n \"0.4904818\",\n \"0.4897373\",\n \"0.4895549\",\n \"0.4877143\",\n \"0.48735413\",\n \"0.4864071\",\n \"0.48632434\",\n \"0.48575664\",\n \"0.48549956\",\n \"0.48405433\",\n \"0.48392025\",\n \"0.48380393\",\n \"0.48249203\",\n \"0.48199818\",\n \"0.4816632\",\n \"0.48130402\",\n \"0.4805838\",\n \"0.4802146\",\n \"0.48009422\",\n \"0.47922707\",\n \"0.47898033\",\n \"0.47840303\",\n \"0.47821093\",\n \"0.47802436\",\n \"0.47629127\",\n \"0.47511753\",\n \"0.47333142\",\n \"0.47305655\",\n \"0.4725433\",\n \"0.47171962\",\n \"0.47146106\",\n \"0.47067836\",\n \"0.46845716\",\n \"0.46632043\",\n \"0.4658659\",\n \"0.46537662\",\n \"0.46536765\",\n \"0.46477914\",\n \"0.46441466\",\n \"0.46402258\",\n \"0.46346408\",\n \"0.4631873\",\n \"0.46044913\",\n \"0.4601081\",\n \"0.46004325\",\n \"0.46003738\",\n \"0.45952582\",\n \"0.45915255\",\n \"0.4590748\",\n \"0.45904052\",\n \"0.4590059\",\n \"0.4588771\",\n \"0.45861593\",\n \"0.45860898\",\n \"0.45849136\",\n \"0.45820263\",\n \"0.45817718\",\n \"0.45815688\",\n \"0.45786047\",\n \"0.4573722\",\n \"0.45692286\",\n \"0.4568142\",\n \"0.45679897\",\n \"0.45642865\",\n \"0.45628566\",\n \"0.45587856\",\n \"0.45554203\",\n \"0.45536768\",\n \"0.45530653\",\n \"0.4550122\",\n \"0.4548589\",\n \"0.45407516\",\n \"0.4540433\",\n \"0.4530641\",\n \"0.45272774\",\n \"0.4513924\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76356745"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":135796,"cells":{"query":{"kind":"string","value":"Test of estimateCmilestoning method, of class MarkovModelUtilities."},"document":{"kind":"string","value":"@Test\n public void testEstimateCmilestoning_IIntArray_int()\n {\n IIntArray traj = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(traj, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["@Test\n public void testEstimateCmilestoning_3args_1()\n {\n Iterable<IIntArray> trajs = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCmilestoning_3args_2()\n {\n IIntArray traj = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCmilestoning_Iterable_int()\n {\n Iterable<IIntArray> trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateC_Iterable_int()\n {\n Iterable<IIntArray> trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateC(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateT()\n {\n \n IDoubleArray counts = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateT(counts);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCstepping_Iterable_int()\n {\n Iterable<IIntArray> trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCstepping(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testMCMCWithoutAllelicPoN() {\n final double meanBiasSimulated = 1.2;\n final double biasVarianceSimulated = 0.04;\n testMCMC(meanBiasSimulated, biasVarianceSimulated, meanBiasSimulated, biasVarianceSimulated, AllelicPanelOfNormals.EMPTY_PON);\n }","@Test\n public void testMetastableMemberships()\n {\n IDoubleArray M = null;\n int nstates = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.metastableMemberships(M, nstates);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test public void calcROItest() {\n DirectMC h = new DirectMC(\"AdMailing\", 10000.00, 3.00, 2000);\n Assert.assertEquals(.4286, h.calcROI(), 1);\n }","public void kolmogorovSmirnovTest(double cutoff, MWBMatchingAlgorithm mwbm) {\n double p = 0;\n\n KolmogorovSmirnovTest t = new KolmogorovSmirnovTest();\n for (int i = 0; i < this.train.numAttributes(); i++) {\n for (int j = 0; j < this.test.numAttributes(); j++) {\n // negative infinity counts as not present, we do this so we don't have to map\n // between attribute indexs in weka\n // and the result of the mwbm computation\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\n\n // class attributes are not relevant\n if (this.test.classIndex() == j) {\n continue;\n }\n if (this.train.classIndex() == i) {\n continue;\n }\n\n // this may invoke exactP on small sample sizes which will not terminate in all\n // cases\n // p = t.kolmogorovSmirnovTest(this.train_values.get(i),\n // this.test_values.get(j), false);\n\n // this uses approximateP everytime\n p = t.approximateP(\n t.kolmogorovSmirnovStatistic(this.train_values.get(i),\n this.test_values.get(j)),\n this.train_values.get(i).length,\n this.test_values.get(j).length);\n if (p > cutoff) {\n mwbm.setWeight(i, j, p);\n }\n }\n }\n }","@Test\n public void testCorrelation()\n {\n IDoubleArray M = null;\n IDoubleArray observable1 = null;\n IDoubleArray observable2 = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test19() throws Throwable {\n Discretize discretize0 = new Discretize(\"%\");\n FileSystemHandling.createFolder((EvoSuiteFile) null);\n discretize0.findNumBinsTipText();\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \"%\");\n discretize0.m_DefaultCols = \"%\";\n double[][] doubleArray0 = new double[0][7];\n discretize0.m_CutPoints = doubleArray0;\n // Undeclared exception!\n try { \n discretize0.getCutPoints(1);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 1\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test03() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.setAttributeIndices(\"\");\n Filter.makeCopy(discretize0);\n discretize0.setDesiredWeightOfInstancesPerInterval(2.0);\n boolean boolean0 = true;\n discretize0.m_ClassIndex = 0;\n discretize0.setUseBinNumbers(true);\n discretize0.listOptions();\n discretize0.makeBinaryTipText();\n discretize0.getInvertSelection();\n // Undeclared exception!\n try { \n discretize0.calculateCutPointsByEqualWidthBinning((-458));\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void testForwardCommittor()\n {\n IDoubleArray M = null;\n IIntArray A = null;\n IIntArray B = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.forwardCommittor(M, A, B);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testMMI() {\n System.out.println(\"MMI\");\n int a = 0;\n int m = 0;\n int expResult = 0;\n int result = utilsHill.MMI(a, m);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void shouldCalculateProperMoveProbabilities() throws Exception {\n\n int actualCity = 0;\n boolean[] visited = new boolean[] {true, false, false, false};\n int[][] firstCriterium = new int[][] {{0, 2, 1, 2},\n {2, 2, 1, 1},\n {1, 1, 0, 1},\n {2, 1, 1, 0}};\n int[][] secondCriterium = new int[][] {{0, 3, 2, 1},\n {3, 0, 0, 0},\n {2, 0, 0, 0},\n {1, 0, 0, 0}};\n// final AttractivenessCalculator attractivenessCalculator = new AttractivenessCalculator(1.0, 0.0, 0.0, 0.0, 0.0);\n// double[][] array = new double[0][0];\n// final double[] probabilities = attractivenessCalculator.movesProbability(actualCity, visited, array, firstCriterium, secondCriterium, null, 0.0, 0.0);\n\n// assertTrue(probabilities[0] == 0.0);\n// assertTrue(probabilities[1] == 0.0);\n// assertTrue(probabilities[2] == 0.5);\n// assertTrue(probabilities[3] == 0.5);\n }","@Test(timeout = 4000)\n public void test05() throws Throwable {\n Discretize discretize0 = new Discretize(\"^yH*RVC#\\\"|tm,k2XpGN\");\n String[] stringArray0 = new String[2];\n discretize0.m_ClassIndex = 906;\n discretize0.m_IgnoreClass = true;\n stringArray0[0] = \"^yH*RVC#\\\"|tm,k2XpGN\";\n stringArray0[1] = \"^yH*RVC#\\\"|tm,k2XpGN\";\n discretize0.setOptions(stringArray0);\n discretize0.setAttributeIndices(\"^yH*RVC#\\\"|tm,k2XpGN\");\n assertFalse(discretize0.getMakeBinary());\n assertFalse(discretize0.getUseEqualFrequency());\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getFindNumBins());\n }","private double GetMCRTestSet() \n\t{\n\t\tint numErrors = 0;\n\t\t\n\t\tfor(int i = ITrain; i < ITrain+ITest; i++)\n\t\t{\n\t\t\tPreCompute(i);\n\t\t\t\n\t\t\tdouble max_Y_hat_ic = Double.MIN_VALUE;\n\t\t\tint label_i = -1; \n\t\t\t\n\t\t\tfor(int c = 0; c < C; c++)\n\t\t\t{\n\t\t\t\tdouble Y_hat_ic = Predict(i, c);\n\t\t\t\t\n\n\t\t\t\tif(lossType == LossTypes.LOGISTIC)\n\t\t\t\t\tY_hat_ic = Sigmoid.Calculate(Y_hat_ic);\n\t\t\t\t\n\t\t\t\tif(Y_hat_ic > max_Y_hat_ic)\n\t\t\t\t{\n\t\t\t\t\tmax_Y_hat_ic = Y_hat_ic; \n\t\t\t\t\tlabel_i = (int)Math.ceil(c);\n\t\t\t\t}\n\t\t\t}\n\t\t\t\n\t\t\tif( nominalLabels.indexOf(Y.get(i)) != label_i ) \n\t\t\t\tnumErrors++;\n\t\t}\n\t\t\n\t\treturn (double)numErrors/(double)ITest; \n\t}","@Test(timeout = 4000)\n public void test11() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.invertSelectionTipText();\n boolean boolean0 = discretize0.m_UseBinNumbers;\n // Undeclared exception!\n try { \n discretize0.calculateCutPoints();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void testJuvenilesSpreadFromCellsWithCorrespondingMatureLctFromInsideGrid() {\n params = new CompletelySpatiallyRandomParams(3, 0);\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\n juvenileDeciduous, params);\n coloniser.updateJuvenilePresenceLayers();\n\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 6);\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 9);\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 3);\n }","@Test\n public void testEstimateC_IIntArray_int()\n {\n IIntArray traj = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateC(traj, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test02() throws Throwable {\n Discretize discretize0 = new Discretize(\"l*{e5SNh\");\n boolean boolean0 = true;\n discretize0.m_ClassIndex = 177;\n discretize0.setUseEqualFrequency(true);\n double[][] doubleArray0 = new double[5][9];\n double[] doubleArray1 = new double[3];\n doubleArray1[0] = (double) 177;\n doubleArray1[1] = (double) 177;\n doubleArray1[2] = (double) 177;\n doubleArray0[0] = doubleArray1;\n double[] doubleArray2 = new double[2];\n doubleArray2[0] = (double) 177;\n doubleArray2[1] = (double) 177;\n doubleArray0[1] = doubleArray2;\n double[] doubleArray3 = new double[9];\n doubleArray3[0] = (double) 177;\n doubleArray3[1] = (double) 177;\n doubleArray3[2] = (double) 177;\n doubleArray3[3] = (double) 177;\n doubleArray3[4] = (double) 177;\n doubleArray3[5] = (double) 177;\n doubleArray3[6] = (double) 177;\n doubleArray3[7] = (double) 177;\n doubleArray3[8] = (double) 177;\n doubleArray0[2] = doubleArray3;\n double[] doubleArray4 = new double[8];\n doubleArray4[0] = (double) 177;\n doubleArray4[1] = (double) 177;\n doubleArray4[2] = (double) 177;\n doubleArray4[3] = (double) 177;\n doubleArray4[4] = (double) 177;\n doubleArray4[5] = (double) 177;\n doubleArray4[6] = (double) 177;\n doubleArray4[7] = (double) 177;\n doubleArray0[3] = doubleArray4;\n double[] doubleArray5 = new double[0];\n doubleArray0[4] = doubleArray5;\n discretize0.m_CutPoints = doubleArray0;\n discretize0.listOptions();\n discretize0.setIgnoreClass(true);\n String[] stringArray0 = new String[0];\n Discretize.main(stringArray0);\n discretize0.findNumBinsTipText();\n discretize0.setInvertSelection(true);\n // Undeclared exception!\n try { \n discretize0.getBinRangesString(511);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 511\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test19() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.getCutPoints((-2276));\n assertFalse(discretize0.getFindNumBins());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertFalse(discretize0.getUseEqualFrequency());\n }","@Test(timeout = 4000)\n public void test07() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.setInvertSelection(true);\n discretize0.useEqualFrequencyTipText();\n int int0 = (-501);\n // Undeclared exception!\n try { \n discretize0.calculateCutPointsByEqualWidthBinning((-501));\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test08() throws Throwable {\n Discretize discretize0 = new Discretize(\"\");\n discretize0.binsTipText();\n // Undeclared exception!\n try { \n discretize0.calculateCutPointsByEqualWidthBinning(25);\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void testGetImportanceStats() {\n System.out.println(\"getImportanceStats\");\n MDA instance = new MDA();\n\n // make the circles close to force tree to do lots of splits / make it harder\n ClassificationDataSet train = getHarderC(10000, RandomUtil.getRandom());\n int good_featres = 2;\n\n DecisionTree tree = new DecisionTree();\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\n tree.train(train);\n\n double[] importances = instance.getImportanceStats(tree, train);\n\n // make sure the first 2 features were infered as more important than the\n // others!\n for (int i = good_featres; i < importances.length; i++) {\n for (int j = 0; j < good_featres; j++)\n assertTrue(importances[j] > importances[i]);\n }\n\n // categorical features, make space wider b/c we lose resolution\n train = getHarderC(10000, RandomUtil.getRandom());\n\n train.applyTransform(new NumericalToHistogram(train, 7));\n tree = new DecisionTree();\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\n tree.train(train);\n\n importances = instance.getImportanceStats(tree, train);\n\n // make sure the first 2 features were infered as more important than the\n // others!\n for (int i = good_featres; i < importances.length; i++) {\n for (int j = 0; j < good_featres; j++)\n assertTrue(importances[j] > importances[i]);\n }\n\n }","private void findAbdomenCM() {\r\n \r\n if (foundCenterOfMass) return;\r\n int xcm = 0, ycm = 0, pixCount = 0;\r\n for (int idx= 0, y = 0; y < yDim; y++) {\r\n for (int x = 0; x < xDim; x++, idx++) {\r\n if (sliceBuffer[idx] == abdomenTissueLabel) {\r\n xcm += x;\r\n ycm += y;\r\n pixCount++;\r\n }\r\n } // end for (int x = 0; ...)\r\n } // end for (int idx = 0, ...)\r\n \r\n if (pixCount == 0) {\r\n// System.err.println(\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\");\r\n MipavUtil.displayError(\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\");\r\n return;\r\n }\r\n \r\n centerOfMass[0] = (short)(xcm / pixCount);\r\n centerOfMass[1] = (short)(ycm / pixCount);\r\n foundCenterOfMass = true;\r\n }","final CM computeCM(int[/**/][/**/] votes, Chunk[] chks, boolean local, boolean balance) {\n CM cm = new CM();\n int rows = votes.length;\n int validation_rows = 0;\n int cmin = (int) _data.vecs()[_classcol].min();\n\n // Assemble the votes-per-class into predictions & score each row\n\n // Make an empty confusion matrix for this chunk\n cm._matrix = new long[_N][_N];\n float preds[] = new float[_N+1];\n\n float num_trees = _errorsPerTree.length;\n\n // Loop over the rows\n for( int row = 0; row < rows; row++ ) {\n\n // Skip rows with missing response values\n if (chks[_classcol].isNA0(row)) continue;\n\n // The class votes for the i-th row\n int[] vi = votes[row];\n\n // Fill the predictions with the vote counts, keeping the 0th index unchanged\n for( int v=0; v<_N; v++ ) preds[v+1] = vi[v];\n\n float s = doSum(vi);\n if (s == 0) {\n cm._skippedRows++;\n continue;\n }\n\n int result;\n if (balance) {\n float[] scored = toProbs(preds.clone(), doSum(vi));\n double probsum=0;\n for( int c=1; c<scored.length; c++ ) {\n final double original_fraction = _model.priordist()[c-1];\n assert(original_fraction > 0) : \"original fraction should be > 0, but is \" + original_fraction + \": not using enough training data?\";\n final double oversampled_fraction = _model.modeldist()[c-1];\n assert(oversampled_fraction > 0) : \"oversampled fraction should be > 0, but is \" + oversampled_fraction + \": not using enough training data?\";\n assert(!Double.isNaN(scored[c]));\n scored[c] *= original_fraction / oversampled_fraction;\n probsum += scored[c];\n }\n for (int i=1;i<scored.length;++i) scored[i] /= probsum;\n result = ModelUtils.getPrediction(scored, row);\n } else {\n // `result` is the class with the most votes, accounting for ties in the shared logic in ModelUtils\n result = ModelUtils.getPrediction(preds, row);\n }\n\n // Get the class value from the response column for the current row\n int cclass = alignDataIdx((int) chks[_classcol].at80(row) - cmin);\n assert 0 <= cclass && cclass < _N : (\"cclass \" + cclass + \" < \" + _N);\n\n // Ignore rows with zero votes, but still update the sum of squared errors\n if( vi[result]==0 ) {\n cm._skippedRows++;\n if (!local) _sum += doSSECalc(vi, preds, cclass);\n continue;\n }\n\n // Update the confusion matrix\n cm._matrix[cclass][result]++;\n if( result != cclass ) cm._errors++;\n validation_rows++;\n\n // Update the sum of squared errors\n if (!local) _sum += doSSECalc(vi, preds, cclass);\n float sum = doSum(vi);\n\n // Binomial classification -> compute AUC, draw ROC\n if(_N == 2 && !local) {\n float snd = preds[2] / sum;\n for(int i = 0; i < ModelUtils.DEFAULT_THRESHOLDS.length; i++) {\n int p = snd >= ModelUtils.DEFAULT_THRESHOLDS[i] ? 1 : 0;\n _cms[i][cclass][p]++; // Increase matrix\n }\n }\n }\n\n // End of loop over rows, return confusion matrix\n cm._rows=validation_rows;\n return cm;\n }","@Test(timeout = 4000)\n public void test11() throws Throwable {\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, true, false);\n Discretize discretize0 = new Discretize();\n discretize0.getCutPoints(3594);\n Discretize discretize1 = new Discretize();\n discretize1.attributeIndicesTipText();\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \"\");\n Discretize discretize2 = new Discretize();\n // Undeclared exception!\n try { \n discretize2.calculateCutPoints();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void testGetMoveSpeedMultiplier() {\n assertEquals(1, proj.getMoveSpeedMultiplier(), 0.001);\n }","@Before\n\tpublic void iniciaDisciplinaConvencional() {\n\t\tList<String> pos = new ArrayList<String>();\n\t\tList<String> pos1 = new ArrayList<String>();\n\t\tpos1.add(\"Disciplina 3\");\n\t\tpos1.add(\"Disciplina 4\");\n\t\tpos1.add(\"Disciplina 5\");\n\n\t\tminitElimBai_1 = new MinitestesEliminandoBaixa(\"Disciplina 1\", 5, pos,\n\t\t\t\t1);\n\t\tminitElimBai_2 = new MinitestesEliminandoBaixa(\"Disciplina 2\", 10,\n\t\t\t\tpos1, 2);\n\t}","@Test\n public void test13() {\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 1));\n }","private void computeMultiClassMCC(ConfusionMatrixElement[] elements) {\n\t\tdouble Sq = matrixTotal * matrixTotal;\n\t\tdouble CS = Arrays.stream(elements).map(n -> n.tp).reduce(0.0d, (n, m) -> n + m) * matrixTotal;\n\t\tdouble t = 0.0d, p = 0.0d, tt = 0.0d, pp = 0.0d, tp = 0.0d;\n\t\t\n\t\tfor (ConfusionMatrixElement e : elements) {\n\t\t\tt = (e.tp + e.fp); \n\t\t\tp = (e.tp + e.fn);\n\t\t\t\n\t\t\ttt += (t * t);\n\t\t\tpp += (p * p);\n\t\t\ttp += (t * p);\n\t\t}\n\t\tmatrixMCC = (CS - tp) / (Math.sqrt(Sq - pp) * Math.sqrt(Sq - tt));\n\t}","@Test\n public void testLargeValues() {\n double x = 123;\n double dof = 6.4;\n double nonCent = 100.34;\n NonCentralChiSquaredDistribution dist = new NonCentralChiSquaredDistribution(dof, nonCent);\n assertThat(0.7930769).isCloseTo(dist.getCDF(x), offset(1e-6));\n\n x = 455.038;\n dof = 12;\n nonCent = 444.44;\n\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\n assertThat(0.4961805).isCloseTo(dist.getCDF(x), offset(1e-6));\n\n x = 999400;\n dof = 500;\n nonCent = 1000000;\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\n assertThat(0.2913029).isCloseTo(dist.getCDF(x), offset(1e-6));\n\n }","@Test(timeout = 4000)\n public void test02() throws Throwable {\n Discretize discretize0 = new Discretize();\n Locale.getISOLanguages();\n double[][] doubleArray0 = new double[9][8];\n double[] doubleArray1 = new double[3];\n double[] doubleArray2 = new double[9];\n doubleArray2[5] = (-163.0);\n doubleArray2[8] = 1421.4058829;\n doubleArray0[3] = doubleArray0[2];\n doubleArray0[4] = doubleArray0[1];\n discretize0.setOutputFormat();\n double[] doubleArray3 = new double[2];\n doubleArray3[0] = 1421.4058829;\n doubleArray3[1] = (-163.0);\n doubleArray0[6] = doubleArray2;\n double[] doubleArray4 = new double[0];\n doubleArray0[7] = doubleArray4;\n double[] doubleArray5 = new double[6];\n doubleArray5[2] = (-163.0);\n doubleArray0[8] = doubleArray5;\n discretize0.m_CutPoints = doubleArray0;\n // Undeclared exception!\n try { \n discretize0.setOutputFormat();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test29() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[3][6];\n discretize0.setIgnoreClass(false);\n discretize0.m_CutPoints = doubleArray0;\n double[] doubleArray1 = new double[4];\n // Undeclared exception!\n try { \n discretize0.getCutPoints((-831));\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // -831\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test01() throws Throwable {\n Discretize discretize0 = new Discretize(\"c9^n/\");\n discretize0.getCapabilities();\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseBinNumbers());\n assertFalse(discretize0.getUseEqualFrequency());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n }","@Test\n\tpublic void testCollapseByEditDistance() {\n\t\tString cellBC = \"ATCAGGGACAGA\";\n\t\tString molBC = \"CGGGGCTC\";\n\t\tint snpPos = 76227022;\n\t\tInterval snpInterval = new Interval(\"HUMAN_1\", snpPos, snpPos, true, \"test\");\n\t\tSampleGenotypeProbabilities result = new SampleGenotypeProbabilities(snpInterval, cellBC);\n\t\tSNPUMIBasePileup p = getPileUpFromFile(cellBC, molBC);\n\t\tresult.add(p);\n\n\t\t// let's add another pileup in that's within ed=1 and incorporates an error.\n\t\t// result has only a single base because two UMIs are merged.\n\t\tString molBC2 = \"AGGGGCTC\";\n\t\tSNPUMIBasePileup p2 = getPileUpFromFile(cellBC, molBC2);\n\t\tchar[] b2 = { 'G', 'G', 'T' };\n\t\tbyte[] bases2 = new byte[b2.length];\n\t\tStringUtil.charsToBytes(b2, 0, b2.length, bases2, 0);\n\t\tbyte[] quals2 = { 37, 37, 10 };\n\t\tp2.setBasesAndQualities(bases2, quals2);\n\t\tresult.add(p2);\n\t\tresult.collapseUMIs(1);\n\t\tList<Byte> qualResult2 = result.getQualities();\n\t\tAssert.assertEquals(qualResult2.size(), 1);\n\t\tAssert.assertEquals(new Byte((byte) 8), qualResult2.get(0));\n\t}","@Test\n public void testLogLikelihoodCorrelationMatrix()\n {\n IDoubleArray corr = null;\n IDoubleArray C = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n double expResult = 0.0;\n double result = instance.logLikelihoodCorrelationMatrix(corr, C);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","public void percentiles(double cutoff, MWBMatchingAlgorithm mwbm) {\n for (int i = 0; i < this.train.numAttributes(); i++) {\n for (int j = 0; j < this.test.numAttributes(); j++) {\n // negative infinity counts as not present, we do this so we don't have to map\n // between attribute indexes in weka\n // and the result of the mwbm computation\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\n\n // class attributes are not relevant\n if (this.test.classIndex() == j) {\n continue;\n }\n if (this.train.classIndex() == i) {\n continue;\n }\n\n // get percentiles\n double trainvals[] = this.train_values.get(i);\n double testvals[] = this.test_values.get(j);\n\n Arrays.sort(trainvals);\n Arrays.sort(testvals);\n\n // percentiles\n double train_p;\n double test_p;\n double score = 0.0;\n for (int p = 1; p <= 9; p++) {\n train_p = trainvals[(int) Math.ceil(trainvals.length * (p / 100))];\n test_p = testvals[(int) Math.ceil(testvals.length * (p / 100))];\n\n if (train_p > test_p) {\n score += test_p / train_p;\n }\n else {\n score += train_p / test_p;\n }\n }\n\n if (score > cutoff) {\n mwbm.setWeight(i, j, score);\n }\n }\n }\n }","public double[] monteCarloPercentiles(){\n if(!this.monteCarloDone)this.monteCarlo();\n return this.randomEigenValuesPercentiles;\n }","@Test\n public void test10() {\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 3));\n }","@Test\n public void testMesurerDistanceCoordonnees() {\n System.out.println(\"MesurerDistanceCoordonnees\");\n double lat1 = 0.0;\n double lon1 = 0.0;\n double lat2 = 0.0;\n double lon2 = 0.0;\n double expResult = 0.0;\n double result = Utils.MesurerDistanceCoordonnees(lat1, lon1, lat2, lon2);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test13() throws Throwable {\n Discretize discretize0 = new Discretize(\"\\tInvert matching sense of column indexes.\");\n discretize0.setOutputFormat();\n discretize0.listOptions();\n Discretize discretize1 = new Discretize();\n discretize1.setIgnoreClass(false);\n String[] stringArray0 = new String[2];\n stringArray0[0] = \"\\tInvert matching sense of column indexes.\";\n stringArray0[1] = \"\";\n Discretize.main(stringArray0);\n String string0 = discretize1.findNumBinsTipText();\n assertEquals(\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\", string0);\n \n discretize1.setInvertSelection(false);\n discretize0.getBinRangesString(511);\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseEqualFrequency());\n \n discretize1.getFindNumBins();\n assertEquals(10, discretize1.getBins());\n }","@Test\n public void testMetastableStates()\n {\n IDoubleArray M = null;\n int nstates = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IIntArray expResult = null;\n IIntArray result = instance.metastableStates(M, nstates);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCstepping_IIntArray_int()\n {\n IIntArray traj = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCstepping(traj, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test096() throws Throwable {\n CostMatrix costMatrix0 = Evaluation.handleCostOption(\"\", 1281);\n assertNull(costMatrix0);\n }","@Test\n\tpublic void evalCrispDistanceWithCrisp1(){\n\t\tFuzzySet res = (CrispSet)cs1.distance(cs2);\n\t\tassertTrue(res instanceof CrispSet);\n\t\tCrispSet resCrisp = (CrispSet)res;\n\t\t//Puntos limite\t\t\n\t\tassertTrue(resCrisp.getLeftBoundary()==3);\n\t\tassertTrue(resCrisp.getRightBoundary()==6);\n\t\t//Valores extremos\n\t\tassertTrue(resCrisp.getMembershipValue(0)==0);\n\t\tassertTrue(resCrisp.getMembershipValue(3)==1);\n\t\tassertTrue(resCrisp.getMembershipValue(4)==1);\n\t\tassertTrue(resCrisp.getMembershipValue(4.5)==1);\n\t\tassertTrue(resCrisp.getMembershipValue(5)==1);\n\t\tassertTrue(resCrisp.getMembershipValue(6)==1);\n\t\tassertTrue(resCrisp.getMembershipValue(6.01)==0);\n\t}","@Test\n public void testJuvenilesSpreadFromOutsideInsideGrid() {\n params = new CompletelySpatiallyRandomParams(0.5, 0);\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\n juvenileDeciduous, params);\n coloniser.updateJuvenilePresenceLayers();\n\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 5);\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 5);\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 5);\n }","@Test\n public void pruebaCalculCamiseta() {\n System.out.println(\"prueba Calcul Camiseta\");\n Camiseta camisetaTest = new Camiseta(1);\n assertEquals(190, (camisetaTest.calculCamiseta(1)),0) ;\n \n Camiseta camisetaTestDos = new Camiseta(2);\n assertEquals(1481.2, (camisetaTestDos.calculCamiseta(7)),0) ;\n \n Camiseta camisetaTestTres = new Camiseta(3);\n assertEquals(1178, (camisetaTestTres.calculCamiseta(4)),0) ;\n \n \n }","public int getCostPerMile() {\n return costPerMile;\n }","@Test()\n public void testTrainWarmCFastSMO() {\n ClassificationDataSet train = FixedProblems.getHalfCircles(250, RandomUtil.getRandom(), 0.1, 0.2);\n\n PlattSMO warmModel = new PlattSMO(new LinearKernel(1));\n warmModel.setC(1);\n warmModel.train(train);\n\n PlattSMO warm = new PlattSMO(new LinearKernel(1));\n warm.setC(1e4);// too large to train efficently like noraml\n\n long start, end;\n\n start = System.currentTimeMillis();\n warm.train(train, warmModel);\n end = System.currentTimeMillis();\n long warmTime = (end - start);\n\n PlattSMO notWarm = new PlattSMO(new LinearKernel(1));\n notWarm.setC(1e4);// too large to train efficently like noraml\n\n start = System.currentTimeMillis();\n notWarm.train(train);\n end = System.currentTimeMillis();\n long normTime = (end - start);\n\n assertTrue(warmTime < normTime * 0.75);\n\n }","@Test\n public void testConnectedComponents()\n {\n Collection<IIntArray> cc1 = MarkovModel.util.connectedComponents(P1);\n assertTrue(cc1.containsAll(C1));\n assertEquals(cc1.size(), C1.size());\n\n Collection<IIntArray> cc2 = MarkovModel.util.connectedComponents(P2);\n assertTrue(cc2.containsAll(C2));\n assertEquals(cc2.size(), C2.size());\n\n Collection<IIntArray> cc3 = MarkovModel.util.connectedComponents(P3);\n assertTrue(cc3.containsAll(C3));\n assertEquals(cc3.size(), C3.size()); \n }","@Test(timeout = 4000)\n public void test26() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.m_FindNumBins = true;\n int[] intArray0 = new int[6];\n intArray0[0] = (-388);\n intArray0[1] = (-2121610348);\n intArray0[2] = 11;\n intArray0[3] = (-1);\n intArray0[4] = 1062;\n intArray0[5] = 70;\n discretize0.setAttributeIndicesArray(intArray0);\n discretize0.setDesiredWeightOfInstancesPerInterval((-1149.15832593));\n discretize0.getOptions();\n assertEquals((-1149.15832593), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n }","@Test\r\n\tpublic void testAllMarkupsElectronics() throws BasePriceMustBePositiveException, \r\n\t NegativePersonCountException, ProductCategoryNotSpecifiedException {\r\n\t\tNuPackEstimator estimator = new NuPackEstimator(new BigDecimal(\"100\"), 5, \"electronics\");\r\n\t assertEquals(estimator.estimateFinalCost(), new BigDecimal(\"113.40\"));\r\n\t}","@Test\n public void testGetProjectileSpeedMultiplier() {\n assertEquals(1.3, proj.getProjectileSpeedMultiplier(), 0.001);\n }","public void testConcurrency() throws InterruptedException {\n initCache();\n\n final ArrayList<WMSTile> wcos = new ArrayList<WMSTile>();\n final ArrayList<Integer> test = new ArrayList<Integer>();\n final LinkedBlockingQueue<Integer> idxs = new LinkedBlockingQueue<Integer>();\n\n Collection<Callable<Integer>> tasks = new ArrayList<Callable<Integer>>();\n\n for (int i = 0; i < 30000; i++) {\n wcos.add(getDefaultWMSCacheObject(\"q\" + i, 50, true));\n test.add(-1);\n idxs.put(i);\n\n //put and get task\n tasks.add(new Callable<Integer>() {\n\n public Integer call() throws Exception {\n //put\n int i = idxs.take();\n boolean ok = wmsCache.put(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1, wcos.get(i));\n\n //get\n if (ok) {\n WMSTile wco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\n if (wco.getCached()) {\n if (compareWMSObjects(wco, wcos.get(i))) {\n test.set(i, 1);\n } else {\n test.set(i, 0);\n }\n }\n }\n return i;\n }\n });\n }\n ExecutorService executorService = Executors.newFixedThreadPool(100);\n executorService.invokeAll(tasks);\n\n //test cache cleaner operated correctly\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\n\n //test for presence of invalid test comparisons\n int invalid = 0;\n int valid = 0;\n for (int i = 0; i < test.size(); i++) {\n if (test.get(i) == 0) {\n invalid++;\n } else if (test.get(i) == 1) {\n valid++;\n }\n }\n assertTrue(valid > 0);\n assertTrue(invalid == 0);\n }","@Test\n public void testGetCenter() {\n int expResult = 3;\n int result = this.dummyDigitSize.getCenter();\n assertEquals(expResult, result);\n }","@Before\r\n\tpublic void setup() {\r\n\t\tMockitoAnnotations.initMocks(this);\r\n\t\tcompute(\"12-10-19\",\"a@gmail.com\", null, 22,20);\r\n\t\tcompute(\"10-09-20\",\"a@gmail.com\", null, 22,20);\r\n\t\tcompute(\"11-08-20\",\"a@gmail.com\", null, 22,20);\r\n\t\tcompute(\"03-05-17\",\"a@gmail.com\", null, 22,20);\r\n\t\tcompute(\"09-03-19\",\"a@gmail.com\", null, 22,20);\r\n\t\tcompute(\"01-02-17\",\"a@gmail.com\", null, 22,20);\r\n\t}","@Test\n public void testAutocorrelation()\n {\n IDoubleArray M = null;\n IDoubleArray observable = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.autocorrelation(M, observable, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testIsRateMatrix()\n {\n IDoubleArray K = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n boolean expResult = false;\n boolean result = instance.isRateMatrix(K);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test28() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[8][8];\n double[] doubleArray1 = new double[3];\n doubleArray1[2] = 1421.4058829;\n doubleArray0[1] = doubleArray1;\n double[] doubleArray2 = new double[9];\n doubleArray2[1] = 1.7976931348623157E308;\n doubleArray2[2] = 0.0;\n doubleArray2[5] = (-236.4995664359);\n doubleArray2[7] = 1421.4058829;\n doubleArray0[3] = doubleArray2;\n double[] doubleArray3 = new double[3];\n doubleArray3[0] = 0.0;\n doubleArray3[1] = (-236.4995664359);\n doubleArray0[4] = doubleArray3;\n double[] doubleArray4 = new double[21];\n doubleArray4[0] = 1421.4058829;\n doubleArray3[2] = (-236.4995664359);\n doubleArray4[4] = 1.7976931348623157E308;\n doubleArray0[7] = doubleArray4;\n discretize0.m_NumBins = 162;\n doubleArray0[1] = doubleArray4;\n discretize0.m_CutPoints = doubleArray0;\n discretize0.getBinRangesString(1);\n // Undeclared exception!\n try { \n discretize0.setOutputFormat();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","public void testSizeManagementWMS() {\n initCache();\n \n ArrayList<WMSTile> wcos = new ArrayList<WMSTile>();\n long putSize = 0;\n long maxSize = 0;\n int cacheSizeDropCount = 0;\n for (int i = 0; i < 1000; i++) {\n WMSTile wco = getDefaultWMSCacheObject(\"q\" + i, 50, true);\n putSize += wco.getSize();\n\n wcos.add(wco);\n long size = wmsCache.getSize();\n\n if (size < maxSize) {\n maxSize = size;\n cacheSizeDropCount++;\n } else {\n maxSize = size;\n }\n\n boolean result = wmsCache.put(wco.getQuery(), wco.getColourmode(), PointType.POINT_1, wco);\n\n //test if cache is full the put was unsuccessful\n //allow for cachecleaner to have reduced the size between put and test\n boolean test = (wmsCache.getSize() + wco.getSize() > wmsCache.getMaxCacheSize()) == !result;\n if (test == false) {\n assertTrue((size + wco.getSize() > wmsCache.getMaxCacheSize()) == !result);\n }\n\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\n }\n\n //test size calcuations are operating\n assertTrue(putSize > 10000);\n\n //test cache cleaner was run more than once\n assertTrue(cacheSizeDropCount > 1);\n\n //test cache size is under max\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\n\n //test gets. Anything that is a placeholder will be null.\n int cachedCount = 0;\n for (int i = 0; i < wcos.size(); i++) {\n WMSTile getwco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\n if (getwco.getCached()) {\n assertTrue(compareWMSObjects(wcos.get(i), getwco));\n cachedCount++;\n }\n }\n\n //test if at least 2 objects were cached at the end\n assertTrue(cachedCount > 1);\n }","@Test\r\n public void testCalculateNDM() {\r\n System.out.println(\"calculateNDM\");\r\n NDM instance = null;\r\n Double expResult = null;\r\n //Double result = instance.calculateNDM();\r\n //assertEquals(expResult, result);\r\n // TODO review the generated test code and remove the default call to fail.\r\n //fail(\"The test case is a prototype.\");\r\n }","@Test(timeout = 4000)\n public void test098() throws Throwable {\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV(0, 0);\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertEquals(0, resultMatrixCSV0.getColCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertEquals(0, resultMatrixCSV0.getRowCount());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertNotNull(resultMatrixCSV0);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n int[] intArray0 = new int[0];\n resultMatrixCSV0.setRowOrder(intArray0);\n assertArrayEquals(new int[] {}, intArray0);\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertEquals(0, resultMatrixCSV0.getColCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertEquals(0, resultMatrixCSV0.getRowCount());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(0, intArray0.length);\n \n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML();\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(1, resultMatrixHTML0.getColCount());\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(1, resultMatrixHTML0.getRowCount());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertNotNull(resultMatrixHTML0);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n resultMatrixHTML0.setMeanPrec((-2615));\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(1, resultMatrixHTML0.getColCount());\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(1, resultMatrixHTML0.getRowCount());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n ResultMatrixHTML resultMatrixHTML1 = null;\n try {\n resultMatrixHTML1 = new ResultMatrixHTML(2312, (-2214));\n fail(\"Expecting exception: NegativeArraySizeException\");\n \n } catch(NegativeArraySizeException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.experiment.ResultMatrix\", e);\n }\n }","@Test\n public void testNumCellsColonisedFromOutsideGrid() {\n CompletelySpatiallyRandomParams params1 = new CompletelySpatiallyRandomParams(0.1, 0);\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\n juvenileDeciduous, params1);\n assertEquals(1, coloniser.getNumCellsColonisedFromOutsideGrid());\n\n // if baseRate = 0.5 then 9 * 0.5 = 4.5 ~ 5 cells colonised from outside grid\n CompletelySpatiallyRandomParams params2 = new CompletelySpatiallyRandomParams(0.5, 0);\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\n juvenileDeciduous, params2);\n assertEquals(5, coloniser.getNumCellsColonisedFromOutsideGrid());\n }","@Test(timeout = 4000)\n public void test117() throws Throwable {\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertEquals(1, resultMatrixCSV0.getRowCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(1, resultMatrixCSV0.getColCount());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertNotNull(resultMatrixCSV0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n int[] intArray0 = new int[1];\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertNotNull(resultMatrixSignificance0);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n String[] stringArray0 = new String[4];\n stringArray0[0] = \"v\";\n stringArray0[1] = \"[\";\n stringArray0[2] = \"*\";\n double[] doubleArray0 = new double[7];\n doubleArray0[0] = (double) 0;\n doubleArray0[1] = (double) 2;\n doubleArray0[2] = (double) 0;\n doubleArray0[3] = (double) 1;\n doubleArray0[4] = (double) 2;\n doubleArray0[5] = (double) 0;\n ResultMatrixPlainText resultMatrixPlainText0 = new ResultMatrixPlainText();\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertNotNull(resultMatrixPlainText0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n ResultMatrixLatex resultMatrixLatex0 = new ResultMatrixLatex(resultMatrixPlainText0);\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\n assertEquals(5, resultMatrixLatex0.getCountWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixLatex0.rowNameWidthTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixLatex0.countWidthTipText());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateRowNamesTipText());\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixLatex0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixLatex0.removeFilterNameTipText());\n assertEquals(\"Generates the matrix output in LaTeX-syntax.\", resultMatrixLatex0.globalInfo());\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\n assertTrue(resultMatrixLatex0.getPrintRowNames());\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixLatex0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixLatex0.significanceWidthTipText());\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixLatex0.stdDevWidthTipText());\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\n assertFalse(resultMatrixLatex0.getShowAverage());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixLatex0.meanWidthTipText());\n assertFalse(resultMatrixLatex0.getShowStdDev());\n assertEquals(\"LaTeX\", resultMatrixLatex0.getDisplayName());\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixLatex0.showStdDevTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixLatex0.colNameWidthTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\n assertEquals(1, resultMatrixLatex0.getColCount());\n assertTrue(resultMatrixLatex0.getPrintColNames());\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixLatex0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixLatex0.showAverageTipText());\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixLatex0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixLatex0.meanPrecTipText());\n assertNotNull(resultMatrixLatex0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n \n String string0 = resultMatrixPlainText0.getSummaryTitle(216);\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertNotNull(string0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(\"i\", string0);\n \n int[][] intArray1 = new int[8][0];\n intArray1[0] = intArray0;\n intArray1[1] = intArray0;\n intArray1[2] = intArray0;\n intArray1[3] = intArray0;\n intArray1[4] = intArray0;\n intArray1[5] = intArray0;\n intArray1[6] = intArray0;\n intArray1[7] = intArray0;\n resultMatrixLatex0.setSummary(intArray1, intArray1);\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\n assertEquals(5, resultMatrixLatex0.getCountWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixLatex0.rowNameWidthTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixLatex0.countWidthTipText());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateRowNamesTipText());\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixLatex0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixLatex0.removeFilterNameTipText());\n assertEquals(\"Generates the matrix output in LaTeX-syntax.\", resultMatrixLatex0.globalInfo());\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\n assertTrue(resultMatrixLatex0.getPrintRowNames());\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixLatex0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixLatex0.significanceWidthTipText());\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixLatex0.stdDevWidthTipText());\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\n assertFalse(resultMatrixLatex0.getShowAverage());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixLatex0.meanWidthTipText());\n assertFalse(resultMatrixLatex0.getShowStdDev());\n assertEquals(\"LaTeX\", resultMatrixLatex0.getDisplayName());\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixLatex0.showStdDevTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixLatex0.colNameWidthTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\n assertEquals(1, resultMatrixLatex0.getColCount());\n assertTrue(resultMatrixLatex0.getPrintColNames());\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixLatex0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixLatex0.showAverageTipText());\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixLatex0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixLatex0.meanPrecTipText());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(8, intArray1.length);\n \n ResultMatrixCSV resultMatrixCSV1 = new ResultMatrixCSV();\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV1.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\n assertEquals(1, resultMatrixCSV1.getColCount());\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\n assertEquals(\"CSV\", resultMatrixCSV1.getDisplayName());\n assertTrue(resultMatrixCSV1.getPrintRowNames());\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV1.showAverageTipText());\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV1.globalInfo());\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV1.countWidthTipText());\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV1.stdDevWidthTipText());\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\n assertFalse(resultMatrixCSV1.getShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV1.meanPrecTipText());\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV1.printRowNamesTipText());\n assertEquals(1, resultMatrixCSV1.getRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateRowNamesTipText());\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV1.meanWidthTipText());\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV1.showStdDevTipText());\n assertFalse(resultMatrixCSV1.getShowStdDev());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV1.significanceWidthTipText());\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV1.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV1.rowNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV1.printColNamesTipText());\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\n assertFalse(resultMatrixCSV1.getPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV1.removeFilterNameTipText());\n assertNotNull(resultMatrixCSV1);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\n \n int[] intArray2 = resultMatrixCSV1.getColOrder();\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV1.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\n assertEquals(1, resultMatrixCSV1.getColCount());\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\n assertEquals(\"CSV\", resultMatrixCSV1.getDisplayName());\n assertTrue(resultMatrixCSV1.getPrintRowNames());\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV1.showAverageTipText());\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV1.globalInfo());\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV1.countWidthTipText());\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV1.stdDevWidthTipText());\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\n assertFalse(resultMatrixCSV1.getShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV1.meanPrecTipText());\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV1.printRowNamesTipText());\n assertEquals(1, resultMatrixCSV1.getRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateRowNamesTipText());\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV1.meanWidthTipText());\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV1.showStdDevTipText());\n assertFalse(resultMatrixCSV1.getShowStdDev());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV1.significanceWidthTipText());\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV1.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV1.rowNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV1.printColNamesTipText());\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\n assertFalse(resultMatrixCSV1.getPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV1.removeFilterNameTipText());\n assertNull(intArray2);\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\n \n boolean boolean0 = resultMatrixLatex0.getDefaultPrintColNames();\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\n assertEquals(5, resultMatrixLatex0.getCountWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixLatex0.rowNameWidthTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixLatex0.countWidthTipText());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateRowNamesTipText());\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixLatex0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixLatex0.removeFilterNameTipText());\n assertEquals(\"Generates the matrix output in LaTeX-syntax.\", resultMatrixLatex0.globalInfo());\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\n assertTrue(resultMatrixLatex0.getPrintRowNames());\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixLatex0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixLatex0.significanceWidthTipText());\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixLatex0.stdDevWidthTipText());\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\n assertFalse(resultMatrixLatex0.getShowAverage());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixLatex0.meanWidthTipText());\n assertFalse(resultMatrixLatex0.getShowStdDev());\n assertEquals(\"LaTeX\", resultMatrixLatex0.getDisplayName());\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixLatex0.showStdDevTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixLatex0.colNameWidthTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\n assertEquals(1, resultMatrixLatex0.getColCount());\n assertTrue(resultMatrixLatex0.getPrintColNames());\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixLatex0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixLatex0.showAverageTipText());\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixLatex0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixLatex0.meanPrecTipText());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertFalse(boolean0);\n \n ResultMatrixSignificance resultMatrixSignificance1 = new ResultMatrixSignificance(resultMatrixSignificance0);\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertEquals(0, resultMatrixSignificance1.getSignificanceWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance1.showStdDevTipText());\n assertEquals(1, resultMatrixSignificance1.getRowCount());\n assertTrue(resultMatrixSignificance1.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixSignificance1.getStdDevPrec());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance1.meanWidthTipText());\n assertFalse(resultMatrixSignificance1.getPrintColNames());\n assertFalse(resultMatrixSignificance1.getEnumerateRowNames());\n assertFalse(resultMatrixSignificance1.getShowStdDev());\n assertEquals(2, resultMatrixSignificance1.getMeanPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance1.removeFilterNameTipText());\n assertFalse(resultMatrixSignificance1.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance1.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance1.significanceWidthTipText());\n assertEquals(0, resultMatrixSignificance1.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixSignificance1.getDefaultMeanPrec());\n assertEquals(1, resultMatrixSignificance1.getVisibleColCount());\n assertTrue(resultMatrixSignificance1.getPrintRowNames());\n assertEquals(40, resultMatrixSignificance1.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance1.getDefaultSignificanceWidth());\n assertEquals(0, resultMatrixSignificance1.getCountWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance1.countWidthTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance1.printRowNamesTipText());\n assertFalse(resultMatrixSignificance1.getRemoveFilterName());\n assertEquals(40, resultMatrixSignificance1.getRowNameWidth());\n assertFalse(resultMatrixSignificance1.getDefaultShowStdDev());\n assertFalse(resultMatrixSignificance1.getDefaultRemoveFilterName());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance1.enumerateColNamesTipText());\n assertEquals(0, resultMatrixSignificance1.getDefaultMeanWidth());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance1.stdDevPrecTipText());\n assertEquals(0, resultMatrixSignificance1.getDefaultColNameWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance1.rowNameWidthTipText());\n assertEquals(1, resultMatrixSignificance1.getColCount());\n assertEquals(0, resultMatrixSignificance1.getColNameWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance1.enumerateRowNamesTipText());\n assertTrue(resultMatrixSignificance1.getEnumerateColNames());\n assertEquals(0, resultMatrixSignificance1.getMeanWidth());\n assertFalse(resultMatrixSignificance1.getDefaultShowAverage());\n assertEquals(1, resultMatrixSignificance1.getVisibleRowCount());\n assertTrue(resultMatrixSignificance1.getDefaultEnumerateColNames());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance1.colNameWidthTipText());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance1.stdDevWidthTipText());\n assertEquals(0, resultMatrixSignificance1.getDefaultCountWidth());\n assertFalse(resultMatrixSignificance1.getShowAverage());\n assertFalse(resultMatrixSignificance1.getDefaultPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance1.meanPrecTipText());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance1.globalInfo());\n assertEquals(2, resultMatrixSignificance1.getDefaultStdDevPrec());\n assertEquals(\"Significance only\", resultMatrixSignificance1.getDisplayName());\n assertEquals(0, resultMatrixSignificance1.getStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance1.showAverageTipText());\n assertNotNull(resultMatrixSignificance1);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertFalse(resultMatrixSignificance1.equals((Object)resultMatrixSignificance0));\n \n String string1 = resultMatrixCSV1.toStringSummary();\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV1.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\n assertEquals(1, resultMatrixCSV1.getColCount());\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\n assertEquals(\"CSV\", resultMatrixCSV1.getDisplayName());\n assertTrue(resultMatrixCSV1.getPrintRowNames());\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV1.showAverageTipText());\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV1.globalInfo());\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV1.countWidthTipText());\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV1.stdDevWidthTipText());\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\n assertFalse(resultMatrixCSV1.getShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV1.meanPrecTipText());\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV1.printRowNamesTipText());\n assertEquals(1, resultMatrixCSV1.getRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateRowNamesTipText());\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV1.meanWidthTipText());\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV1.showStdDevTipText());\n assertFalse(resultMatrixCSV1.getShowStdDev());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV1.significanceWidthTipText());\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV1.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV1.rowNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV1.printColNamesTipText());\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\n assertFalse(resultMatrixCSV1.getPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV1.removeFilterNameTipText());\n assertNotNull(string1);\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(\"-summary data not set-\", string1);\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\n assertFalse(string1.equals((Object)string0));\n \n boolean boolean1 = resultMatrixPlainText0.isRowName(1);\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertFalse(boolean1);\n assertTrue(boolean1 == boolean0);\n \n int int0 = resultMatrixLatex0.getDefaultColNameWidth();\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\n assertEquals(5, resultMatrixLatex0.getCountWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixLatex0.rowNameWidthTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixLatex0.countWidthTipText());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateRowNamesTipText());\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixLatex0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixLatex0.removeFilterNameTipText());\n assertEquals(\"Generates the matrix output in LaTeX-syntax.\", resultMatrixLatex0.globalInfo());\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\n assertTrue(resultMatrixLatex0.getPrintRowNames());\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixLatex0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixLatex0.significanceWidthTipText());\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixLatex0.stdDevWidthTipText());\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\n assertFalse(resultMatrixLatex0.getShowAverage());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixLatex0.meanWidthTipText());\n assertFalse(resultMatrixLatex0.getShowStdDev());\n assertEquals(\"LaTeX\", resultMatrixLatex0.getDisplayName());\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixLatex0.showStdDevTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixLatex0.colNameWidthTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\n assertEquals(1, resultMatrixLatex0.getColCount());\n assertTrue(resultMatrixLatex0.getPrintColNames());\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixLatex0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixLatex0.showAverageTipText());\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixLatex0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixLatex0.meanPrecTipText());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, int0);\n }","int getN_estimators();","@Test(timeout = 4000)\n public void test048() throws Throwable {\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertEquals(1, resultMatrixCSV0.getRowCount());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(1, resultMatrixCSV0.getColCount());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertNotNull(resultMatrixCSV0);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML(resultMatrixCSV0);\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertEquals(1, resultMatrixCSV0.getRowCount());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(1, resultMatrixCSV0.getColCount());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertEquals(1, resultMatrixHTML0.getRowCount());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertEquals(1, resultMatrixHTML0.getColCount());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertNotNull(resultMatrixHTML0);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n int[][] intArray0 = new int[0][9];\n // Undeclared exception!\n try { \n resultMatrixCSV0.setSummary(intArray0, intArray0);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 0\n //\n verifyException(\"weka.experiment.ResultMatrix\", e);\n }\n }","@Test\n public void testSmallDist() throws Exception {\n assertTrue(\n new ImageSimilarity(getFile(\"imageSimilarity/with.png\"))\n .calcDistance(ImageIO.read(getFile(\"imageSimilarity/without.png\")))\n > 0);\n }","double[] compute_t_Cm (double[][] t_mmnt, double thickness, double camber) {\n double[] result = new double[15];\n if (camber >= 0)\n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \n result[i] = ci15_from_javafoil_data(t_mmnt, aoa, thickness, camber);\n else \n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \n result[i] = -ci15_from_javafoil_data(t_mmnt, -aoa, thickness, camber);\n return result;\n }","@Test\r\n\tpublic void testGetReducedCell() {\r\n\t\tCMLVector3Test.assertEquals(\"a\", new double[] { 10., 6., 4. }, lattice1\r\n\t\t\t\t.getCMLVector3(0), EPS);\r\n\t\tAssert.assertEquals(\"alen\", 12.328828005937952, lattice1.getCMLVector3(\r\n\t\t\t\t0).getLength(), EPS);\r\n\t\tCMLVector3Test.assertEquals(\"b\", new double[] { 7., 11., 5. }, lattice1\r\n\t\t\t\t.getCMLVector3(1), EPS);\r\n\t\tAssert.assertEquals(\"blen\", 13.96424004376894, lattice1\r\n\t\t\t\t.getCMLVector3(1).getLength(), EPS);\r\n\t\tCMLVector3Test.assertEquals(\"c\", new double[] { 6.8, -4., -9. },\r\n\t\t\t\tlattice1.getCMLVector3(2), EPS);\r\n\t\tAssert.assertEquals(\"clen\", 11.968291440301744, lattice1.getCMLVector3(\r\n\t\t\t\t2).getLength(), EPS);\r\n\t\tAssert.assertEquals(\"vol\", -619.2, lattice1.getVolume(), EPS);\r\n\t\tdouble[] params = lattice1.getCellParameters();\r\n\t\tDoubleTestBase.assertEquals(\"params\", new double[] {\r\n\t\t\t\t12.328828005937952, 13.96424004376894, 11.968291440301744,\r\n\t\t\t\t104.34229064539636, 86.89206686722031, 25.025508299339933 },\r\n\t\t\t\tparams, EPS);\r\n\r\n\t\tCMLLattice reducedLattice = lattice1.getReducedCell();\r\n\t\tCMLVector3Test.assertEquals(\"a\", new double[] { 3.0, -5.0, -1.0 },\r\n\t\t\t\treducedLattice.getCMLVector3(0), EPS);\r\n\t\tAssert.assertEquals(\"alen\", 5.916079783099616, reducedLattice\r\n\t\t\t\t.getCMLVector3(0).getLength(), EPS);\r\n\t\tCMLVector3Test.assertEquals(\"b\", new double[] { 3.8, 1.0, -8.0 },\r\n\t\t\t\treducedLattice.getCMLVector3(1), EPS);\r\n\t\tAssert.assertEquals(\"blen\", 8.912911982062877, reducedLattice\r\n\t\t\t\t.getCMLVector3(1).getLength(), EPS);\r\n\t\tCMLVector3Test.assertEquals(\"c\", new double[] { 10.0, 6.0, 4.0 },\r\n\t\t\t\treducedLattice.getCMLVector3(2), EPS);\r\n\t\tAssert.assertEquals(\"clen\", 12.328828005937952, reducedLattice\r\n\t\t\t\t.getCMLVector3(2).getLength(), EPS);\r\n\t\tAssert.assertEquals(\"vol\", 619.2, reducedLattice.getVolume(), EPS);\r\n\t\tparams = reducedLattice.getCellParameters();\r\n\t\tDoubleTestBase.assertEquals(\"params\", new double[] { 5.916079783099616,\r\n\t\t\t\t8.912911982062877, 12.328828005937952, 83.73054962879554,\r\n\t\t\t\t93.14372638090103, 74.15166267765416 }, params, EPS);\r\n\r\n\t}","public double[] jacobi_iter(double[] x, double tolerance, int M) {\n double[][] sContent = {{1.0, 0, 0}, {0, 1.0, 0}, {0, 0, 1.0}};\n Matrix s = new Matrix(sContent);\n\n double[][] tContent = {{0, -0.5, -0.33333333}, {-0.5, 0, -0.25}, {-0.3333333333, -0.25, 0}};\n Matrix t = new Matrix(tContent);\n\n //keep track of the number of iterations\n\n int counter = 0;\n double tol = 10000; //initial\n double[] tempXVector;\n Matrix firstFirst;\n double[] firstPart; //s^-1(-T)(Xn)\n double[] secondPart; //s^-1(b)\n String output = \"\";\n\n double tempXVectorNorm;\n double xVectorNorm;\n\n for (double elem : x) {\n output += elem + \", \";\n }\n System.out.println(\"x(0): \" + output);\n while (tol > tolerance && counter < M) {\n //perform the jacobi iterations\n firstFirst = s.multiply(t);\n firstPart = Matrix.matrixVectorMultiply(firstFirst, x);\n secondPart = Matrix.matrixVectorMultiply(s, bVector);\n tempXVector = Matrix.plus(firstPart, secondPart);\n\n //find norm of vectors and then subtract the\n tempXVectorNorm = Matrix.norm(tempXVector);\n xVectorNorm = Matrix.norm(x);\n tol = tempXVectorNorm - xVectorNorm;\n tol = Math.abs(tol);\n counter++;\n numOfIterations++;\n x = tempXVector;\n }\n\n output = \"\";\n if (counter == M) {\n System.out.println(\"Jacobi Output for x(N): null\");\n System.out.println(\"Too many iterations without reaching a accurate answer.\");\n return null;\n }\n for (double elem : x) {\n output += elem + \", \";\n }\n System.out.println(\"Jacobi Output for x(N): \" + output);\n System.out.println(\"Number of Iterations using Jacobi Algorithm: \" + counter + \"\\n\");\n return x;\n }","@Test(timeout = 4000)\n public void test05() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[7][2];\n double[] doubleArray1 = new double[7];\n doubleArray1[0] = 0.0;\n doubleArray1[1] = (-587.14627);\n doubleArray1[2] = (-2542.40202289275);\n doubleArray1[3] = (-167.679515);\n doubleArray1[4] = (-1.0);\n doubleArray1[5] = 1.0;\n doubleArray1[6] = (-1.0);\n doubleArray0[0] = doubleArray1;\n double[] doubleArray2 = new double[5];\n doubleArray2[0] = (-167.679515);\n doubleArray2[1] = (-2692.352);\n doubleArray2[2] = (-167.679515);\n doubleArray2[3] = (-2542.40202289275);\n doubleArray2[4] = (-587.14627);\n doubleArray0[1] = doubleArray2;\n double[] doubleArray3 = new double[4];\n doubleArray3[0] = (-1.0);\n doubleArray3[1] = (-1.0);\n doubleArray3[2] = 1.0;\n doubleArray3[3] = (-2542.40202289275);\n doubleArray0[2] = doubleArray3;\n double[] doubleArray4 = new double[7];\n doubleArray4[0] = (-2692.352);\n doubleArray4[1] = (-1.0);\n doubleArray4[2] = (-1.0);\n doubleArray4[3] = 0.0;\n doubleArray4[4] = 1.0;\n doubleArray4[5] = 0.0;\n doubleArray4[6] = 1.0;\n doubleArray0[3] = doubleArray4;\n double[] doubleArray5 = new double[1];\n doubleArray5[0] = (-167.679515);\n doubleArray0[4] = doubleArray5;\n double[] doubleArray6 = new double[0];\n doubleArray0[5] = doubleArray6;\n double[] doubleArray7 = new double[1];\n doubleArray7[0] = (-587.14627);\n doubleArray0[6] = doubleArray7;\n discretize0.m_CutPoints = doubleArray0;\n String string0 = discretize0.invertSelectionTipText();\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertEquals(\"Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.\", string0);\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseEqualFrequency());\n }","@Before\n public void setup() {\n threeByTwo = new Matrix(new int[][] {{1, 2, 3}, {2, 5, 6}});\n compareTwoByThree = new Matrix(new int[][] {{1, 42, 32}, {2, 15, 65}});\n oneByOne = new Matrix(new int[][] {{4}});\n rightOneByOne = new Matrix(new int[][] {{8}});\n twoByFour = new Matrix(new int[][] {{1, 2, 3, 4}, {2, 5, 6, 8}});\n twoByThree = new Matrix(new int[][] {{4, 5}, {3, 2}, {1, 1}});\n threeByThree = new Matrix(new int[][] {{4, 5, 6}, {3, 2, 0}, {1, 1, 1}});\n rightThreeByThree = new Matrix(new int[][] {{1, 2, 3}, {5, 2, 0}, {1, 1, 1}});\n emptyMatrix = new Matrix(new int[0][0]);\n // this is the known correct result of multiplying M1 by M2\n twoByTwoResult = new Matrix(new int[][] {{13, 12}, {29, 26}});\n threeByThreeResult = new Matrix(new int[][] {{35, 24, 18}, {13, 10, 9}, {7, 5, 4}});\n oneByOneSum = new Matrix(new int[][] {{12}});\n oneByOneProduct = new Matrix(new int[][] {{32}});\n }","@Test\n\tpublic void testOptimization() throws Exception {\n\t\tfor(int i = 0; i < 20000000; i++) {\n\t\t\tint _300s = (int) (Math.random() * 100);\n\t\t\tint _100s = (int) (Math.random() * 100);\n\t\t\tint _50s = (int) (Math.random() * 100);\n\t\t\tint misses = (int) (Math.random() * 100);\n\t\t\t\n\t\t\tdouble acc = OsuApiScore.getAccuracy(_300s, _100s, _50s, misses);\n\t\t\t\n\t\t\tAccuracyDistribution accuracyDistribution = AccuracyDistribution.closest(\n\t\t\t\t\t_300s + _100s + _50s + misses, misses,\n\t\t\t\t\tacc);\n\t\t\t\n\t\t\tdouble rec = OsuApiScore.getAccuracy(accuracyDistribution.getX300(),\n\t\t\t\t\taccuracyDistribution.getX100(), accuracyDistribution.getX50(),\n\t\t\t\t\taccuracyDistribution.getMiss());\n\t\t\t\n\t\t\tassertEquals(acc, rec, 0d);\n\t\t}\n\t}","@Test(timeout = 4000)\n public void test35() throws Throwable {\n Discretize discretize0 = new Discretize();\n assertFalse(discretize0.getMakeBinary());\n \n double[][] doubleArray0 = new double[8][8];\n double[] doubleArray1 = new double[3];\n doubleArray1[2] = 1421.4058829;\n doubleArray0[1] = doubleArray1;\n double[] doubleArray2 = new double[9];\n doubleArray2[1] = 1.7976931348623157E308;\n doubleArray2[2] = 0.0;\n Attribute attribute0 = new Attribute(\"invalid CVS revision - not dots!\");\n attribute0.setWeight(0.0);\n Attribute.typeToString(attribute0);\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\n arrayList0.add(attribute0);\n Instances instances0 = new Instances(\"UR<dj;O\", arrayList0, 3);\n Instances instances1 = new Instances(instances0);\n boolean boolean0 = discretize0.setInputFormat(instances1);\n assertFalse(boolean0);\n \n String[] stringArray0 = new String[3];\n stringArray0[0] = \".bsi\";\n stringArray0[1] = \"invalid CVS revision - not dots!\";\n stringArray0[2] = \"@end\";\n discretize0.setOptions(stringArray0);\n discretize0.setOptions(stringArray0);\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseEqualFrequency());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n }","@Test\n\tpublic void testNoCollapseByEditDistance() {\n\t\tString cellBC = \"ATCAGGGACAGA\";\n\t\tString molBC = \"CGGGGCTC\";\n\t\tint snpPos = 76227022;\n\t\tInterval snpInterval = new Interval(\"HUMAN_1\", snpPos, snpPos, true, \"test\");\n\t\tSampleGenotypeProbabilities result = new SampleGenotypeProbabilities(snpInterval, cellBC);\n\t\tSNPUMIBasePileup p = getPileUpFromFile(cellBC, molBC);\n\t\tresult.add(p);\n\n\t\t// let's add another pileup in nowhere near the ED\n\t\t// result has 2 different bases for the two pileups.\n\t\tString molBC2 = \"ZZZZZZZZ\";\n\t\tSNPUMIBasePileup p2 = getPileUpFromFile(cellBC, molBC2);\n\t\tchar[] b2 = { 'G', 'G', 'T' };\n\t\tbyte[] bases2 = new byte[b2.length];\n\t\tStringUtil.charsToBytes(b2, 0, b2.length, bases2, 0);\n\t\tbyte[] quals2 = { 37, 37, 10 };\n\t\tp2.setBasesAndQualities(bases2, quals2);\n\t\tresult.add(p2);\n\t\tresult.collapseUMIs(1);\n\n\t\tList<Byte> qualResult2 = result.getQualities();\n\t\tAssert.assertEquals(qualResult2.size(), 2);\n\t\tAssert.assertEquals(new Byte((byte) 31), qualResult2.get(0));\n\t\tAssert.assertEquals(new Byte((byte) 5), qualResult2.get(1));\n\t}","@Test\n\tpublic void testaGetMensagem() {\n\t\tAssert.assertEquals(\n\t\t\t\t\"Erro no getMensagem()\",\n\t\t\t\t\"Disciplina 1 eh avaliada com 5 minitestes, eliminando 1 dos mais baixos.\",\n\t\t\t\tminitElimBai_1.getMensagem());\n\t\tAssert.assertEquals(\n\t\t\t\t\"Erro no getMensagem()\",\n\t\t\t\t\"Disciplina 2 eh avaliada com 10 minitestes, eliminando 2 dos mais baixos.\",\n\t\t\t\tminitElimBai_2.getMensagem());\n\t}","@Test(timeout = 4000)\n public void test050() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate(\"\");\n Evaluation evaluation0 = new Evaluation(instances0);\n evaluation0.areaUnderPRC((-1));\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","@Test\n public void testQuantile() {\n System.out.println(\"quantile\");\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\n instance.rand();\n assertEquals(0, instance.quantile(0), 1E-7);\n assertEquals(0, instance.quantile(0.001), 1E-7);\n assertEquals(0, instance.quantile(0.01), 1E-7);\n assertEquals(2, instance.quantile(0.1), 1E-7);\n assertEquals(3, instance.quantile(0.2), 1E-7);\n assertEquals(13, instance.quantile(0.9), 1E-7);\n assertEquals(22, instance.quantile(0.99), 1E-7);\n assertEquals(30, instance.quantile(0.999), 1E-7);\n }","public void testGetConformingStimulusLargeScale() throws Exception {\n long start = System.currentTimeMillis();\n Stimulus stimulus = new MockStimulus();\n message.setConformingStimulus(stimulus);\n for (int i = 0; i < TOTAL_ITERATION; i++) {\n message.getConformingStimulus();\n }\n System.out.println(\"Calling \" + TOTAL_ITERATION + \"times getConformingStimulus method\"\n + \" costs:\" + (System.currentTimeMillis() - start) + \" ms\");\n }","@Test\n public void testLogLikelihood()\n {\n IDoubleArray T = null;\n IDoubleArray C = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n double expResult = 0.0;\n double result = instance.logLikelihood(T, C);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap<Integer,Integer> words)\r\n{\r\n int t=0;\r\n double LearningRates[] = {1, 0.1, 0.01};\r\n double Margins[] = {1, 0.1, 0.01};\r\n\r\n\tint minInt =0; // Set To Your Desired Min Value\r\n int maxInt =2;\r\n\tRandom rand = new Random();\r\n\t\r\n \tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\r\n\r\n \tdouble LearningRate = LearningRates[randomNum];\r\n \t\r\nfor(int m=0;m<LearningRates.length;m++)\r\n{\r\n for(int i=0;i<LearningRates.length;i++)\r\n {\r\n\t LearningRate = LearningRates[i];\r\n\t\t double devAccuracyTotal = (double)0;\r\n\t\t int noOfEpochs= 20;\r\n\t\t int decreaseRate = 0;\r\n\t\t \r\n \t\t Double w[] = new Double[74500];\r\n\t\t Double min =-0.01; // Set To Your Desired Min Value\r\n\t Double max = 0.01;\r\n\t \t\tdouble smallRandNumber = min + new Random().nextDouble() * (max - min); \r\n\t\t\tArrays.fill(w, smallRandNumber);\r\n\t\t\t\r\n\t\t for(int epoch=0;epoch<noOfEpochs;epoch++)\r\n\t\t {\r\n\t\t\t \tw = learnMarginPerceptron(data,index,LearningRate,decreaseRate,w,Margins[m]);\r\n\t\t\t \tdecreaseRate = decreaseRate + index;\r\n\t\t\r\n\t\t }\r\n\t\t//CVSplit test\r\n \tdouble cvAccuracy = testTestFile(w,testFileName,words);\r\n \t\r\n// \tSystem.out.println(\"Epoch\" + epoch +\" Dev Accuracy for Decaying Learning Rate \" + LearningRate + \" is \" + cvAccuracy);\r\n \r\n \r\n \tSystem.out.println(\" ** Cv Accuracy for Margin Rate\" + Margins[m] + \" Learning Rate \" + LearningRate + \" is \" + cvAccuracy);\r\n \tSystem.out.println(\" \");\r\n }\r\n}\r\n//double testAccuracy = testTestFile(w,testFileName);\r\n\t//System.out.println(\"test Accuracy\" + testAccuracy);\r\n}","@Test\n void checkChangeImage() {\n ImageViewModel imageModel = new ImageViewModel();\n int countUp = 10;\n int totalTime = 70;\n\n int i = imageModel.checkChangeImage(countUp, totalTime);\n\n assertEquals(1, i);\n }","@Test(timeout = 4000)\n public void test04() throws Throwable {\n Discretize discretize0 = new Discretize(\"9/&=Kq&\");\n discretize0.getBinRangesString((-1679));\n int int0 = (-98);\n // Undeclared exception!\n try { \n discretize0.calculateCutPointsByEqualWidthBinning((-98));\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test23() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.setIgnoreClass(true);\n discretize0.getOptions();\n // Undeclared exception!\n try { \n discretize0.calculateCutPointsByEqualFrequencyBinning((-821));\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.core.Instances\", e);\n }\n }","private void findAbdomenCM(int sliceNum) {\r\n \r\n // load up the sliceBuffer with the appropriate slice from the abdomen label image\r\n try {\r\n abdomenImage.exportData(sliceNum * sliceSize, sliceSize, sliceBuffer);\r\n } catch (IOException ex) {\r\n System.err.println(\"findAbdomenCM(...) Error exporting data on slice: \" +sliceNum);\r\n return;\r\n }\r\n\r\n int xcm = 0, ycm = 0, pixCount = 0;\r\n for (int idx= 0, y = 0; y < yDim; y++) {\r\n for (int x = 0; x < xDim; x++, idx++) {\r\n if (sliceBuffer[idx] == abdomenTissueLabel) {\r\n xcm += x;\r\n ycm += y;\r\n pixCount++;\r\n }\r\n } // end for (int x = 0; ...)\r\n } // end for (int idx = 0, ...)\r\n \r\n if (pixCount == 0) {\r\n// System.err.println(\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\");\r\n MipavUtil.displayError(\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\");\r\n // set the centerOfMass to an generate an obvious error \r\n centerOfMass[0] = (short)-1;\r\n centerOfMass[1] = (short)-1;\r\n return;\r\n }\r\n \r\n centerOfMass[0] = (short)(xcm / pixCount);\r\n centerOfMass[1] = (short)(ycm / pixCount);\r\n }","@Test(timeout = 4000)\n public void test13() throws Throwable {\n Discretize discretize0 = new Discretize();\n String string0 = discretize0.makeBinaryTipText();\n assertFalse(discretize0.getFindNumBins());\n assertEquals(\"Make resulting attributes binary.\", string0);\n assertFalse(discretize0.getUseBinNumbers());\n assertFalse(discretize0.getUseEqualFrequency());\n assertEquals(10, discretize0.getBins());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getMakeBinary());\n }","@Test(timeout = 4000)\n public void test102() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate(\"\");\n Evaluation evaluation0 = new Evaluation(instances0);\n double[][] doubleArray0 = evaluation0.confusionMatrix();\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n assertEquals(2, doubleArray0.length);\n }","@Test(timeout = 4000)\n public void test028() throws Throwable {\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertEquals(1, resultMatrixCSV0.getColCount());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixCSV0.getRowCount());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertNotNull(resultMatrixCSV0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n \n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertNotNull(resultMatrixSignificance0);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n String[] stringArray0 = new String[4];\n stringArray0[0] = \"v\";\n stringArray0[1] = \"[\";\n stringArray0[2] = \"*\";\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML(0, 16);\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertEquals(16, resultMatrixHTML0.getRowCount());\n assertEquals(0, resultMatrixHTML0.getColCount());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertNotNull(resultMatrixHTML0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n int int0 = 2298;\n resultMatrixHTML0.setRowNameWidth(2298);\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(2298, resultMatrixHTML0.getRowNameWidth());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertEquals(16, resultMatrixHTML0.getRowCount());\n assertEquals(0, resultMatrixHTML0.getColCount());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n resultMatrixHTML0.setRowNameWidth(1);\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(1, resultMatrixHTML0.getRowNameWidth());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertEquals(16, resultMatrixHTML0.getRowCount());\n assertEquals(0, resultMatrixHTML0.getColCount());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n boolean boolean0 = resultMatrixHTML0.getShowAverage();\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(1, resultMatrixHTML0.getRowNameWidth());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertEquals(16, resultMatrixHTML0.getRowCount());\n assertEquals(0, resultMatrixHTML0.getColCount());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertFalse(boolean0);\n \n ResultMatrixPlainText resultMatrixPlainText0 = new ResultMatrixPlainText();\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertNotNull(resultMatrixPlainText0);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n boolean boolean1 = resultMatrixPlainText0.isSignificance(16);\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertFalse(boolean1);\n assertTrue(boolean1 == boolean0);\n \n int int1 = resultMatrixHTML0.getColCount();\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(1, resultMatrixHTML0.getRowNameWidth());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertEquals(16, resultMatrixHTML0.getRowCount());\n assertEquals(0, resultMatrixHTML0.getColCount());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(0, int1);\n assertFalse(int1 == int0);\n \n ResultMatrixHTML resultMatrixHTML1 = null;\n try {\n resultMatrixHTML1 = new ResultMatrixHTML((-1157), 5);\n fail(\"Expecting exception: NegativeArraySizeException\");\n \n } catch(NegativeArraySizeException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.experiment.ResultMatrix\", e);\n }\n }","@Test(timeout = 4000)\n public void test18() throws Throwable {\n Discretize discretize0 = new Discretize(\"%\");\n FileSystemHandling.createFolder((EvoSuiteFile) null);\n discretize0.findNumBinsTipText();\n double[][] doubleArray0 = new double[0][7];\n discretize0.m_CutPoints = doubleArray0;\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, false, true);\n discretize0.setInvertSelection(false);\n // Undeclared exception!\n try { \n discretize0.setOutputFormat();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void test_IsOxideCorr() {\n System.out.println(\"Testing MeasuredRatioModel's isOxideCorr()\");\n MeasuredRatioModel instance = new MeasuredRatioModel();\n boolean expResult = false;\n boolean result = instance.isOxideCorr();\n assertEquals(expResult, result);\n instance = new MeasuredRatioModel(\"Specific\",new BigDecimal(\"213\"),\"ABS\",new BigDecimal(\"0.4324\"),true,true);\n result = instance.isOxideCorr();\n expResult=true;\n assertEquals(expResult, result);\n }","@Test\n\tpublic final void testProcGridCoord() {\n\t\tfor (int d=0; d<DIMENSIONALITY; d++) {\n\t\t\tassertTrue(0 <= block.procGridCoord(d)\n\t\t\t\t\t&& block.procGridCoord(d)<block.procGridSize(d));\n\t\t}\n\t}","@Test(timeout = 4000)\n public void test061() throws Throwable {\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(1, resultMatrixCSV0.getColCount());\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(1, resultMatrixCSV0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertNotNull(resultMatrixCSV0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n int[][] intArray0 = new int[3][0];\n int[] intArray1 = new int[1];\n intArray1[0] = 1;\n intArray0[0] = intArray1;\n int[] intArray2 = new int[9];\n assertFalse(intArray2.equals((Object)intArray1));\n \n intArray2[0] = 1;\n intArray2[1] = 2;\n intArray2[2] = 2;\n intArray2[3] = 0;\n intArray2[4] = 3;\n intArray2[6] = 2;\n intArray2[7] = 1;\n intArray2[8] = 2;\n intArray0[1] = intArray2;\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertNotNull(resultMatrixSignificance0);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n String string0 = resultMatrixSignificance0.getRevision();\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertNotNull(string0);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(\"8034\", string0);\n \n resultMatrixSignificance0.setColHidden(1003, false);\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n ResultMatrixSignificance resultMatrixSignificance1 = new ResultMatrixSignificance();\n assertEquals(0, resultMatrixSignificance1.getCountWidth());\n assertEquals(1, resultMatrixSignificance1.getVisibleColCount());\n assertFalse(resultMatrixSignificance1.getDefaultPrintColNames());\n assertEquals(1, resultMatrixSignificance1.getColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance1.colNameWidthTipText());\n assertEquals(2, resultMatrixSignificance1.getDefaultMeanPrec());\n assertTrue(resultMatrixSignificance1.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance1.showAverageTipText());\n assertEquals(40, resultMatrixSignificance1.getDefaultRowNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance1.meanPrecTipText());\n assertEquals(0, resultMatrixSignificance1.getColNameWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance1.rowNameWidthTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance1.stdDevPrecTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance1.printRowNamesTipText());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance1.printColNamesTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance1.removeFilterNameTipText());\n assertFalse(resultMatrixSignificance1.getDefaultShowStdDev());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance1.enumerateColNamesTipText());\n assertFalse(resultMatrixSignificance1.getRemoveFilterName());\n assertEquals(1, resultMatrixSignificance1.getVisibleRowCount());\n assertTrue(resultMatrixSignificance1.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixSignificance1.getStdDevWidth());\n assertEquals(0, resultMatrixSignificance1.getMeanWidth());\n assertEquals(2, resultMatrixSignificance1.getDefaultStdDevPrec());\n assertEquals(\"Significance only\", resultMatrixSignificance1.getDisplayName());\n assertFalse(resultMatrixSignificance1.getShowAverage());\n assertTrue(resultMatrixSignificance1.getEnumerateColNames());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance1.globalInfo());\n assertFalse(resultMatrixSignificance1.getPrintColNames());\n assertFalse(resultMatrixSignificance1.getEnumerateRowNames());\n assertEquals(0, resultMatrixSignificance1.getDefaultCountWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance1.showStdDevTipText());\n assertEquals(1, resultMatrixSignificance1.getRowCount());\n assertFalse(resultMatrixSignificance1.getShowStdDev());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance1.stdDevWidthTipText());\n assertTrue(resultMatrixSignificance1.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixSignificance1.getSignificanceWidth());\n assertEquals(2, resultMatrixSignificance1.getMeanPrec());\n assertFalse(resultMatrixSignificance1.getDefaultShowAverage());\n assertEquals(2, resultMatrixSignificance1.getStdDevPrec());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance1.meanWidthTipText());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance1.enumerateRowNamesTipText());\n assertEquals(40, resultMatrixSignificance1.getRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance1.significanceWidthTipText());\n assertEquals(0, resultMatrixSignificance1.getDefaultMeanWidth());\n assertEquals(0, resultMatrixSignificance1.getDefaultColNameWidth());\n assertEquals(0, resultMatrixSignificance1.getDefaultSignificanceWidth());\n assertFalse(resultMatrixSignificance1.getDefaultEnumerateRowNames());\n assertFalse(resultMatrixSignificance1.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixSignificance1.getDefaultStdDevWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance1.countWidthTipText());\n assertNotNull(resultMatrixSignificance1);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertFalse(resultMatrixSignificance1.equals((Object)resultMatrixSignificance0));\n \n ResultMatrixSignificance resultMatrixSignificance2 = new ResultMatrixSignificance();\n assertFalse(resultMatrixSignificance2.getDefaultEnumerateRowNames());\n assertEquals(2, resultMatrixSignificance2.getMeanPrec());\n assertFalse(resultMatrixSignificance2.getDefaultShowAverage());\n assertFalse(resultMatrixSignificance2.getEnumerateRowNames());\n assertEquals(1, resultMatrixSignificance2.getVisibleRowCount());\n assertFalse(resultMatrixSignificance2.getPrintColNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance2.enumerateRowNamesTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance2.rowNameWidthTipText());\n assertEquals(40, resultMatrixSignificance2.getRowNameWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance2.meanWidthTipText());\n assertTrue(resultMatrixSignificance2.getDefaultPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance2.meanPrecTipText());\n assertEquals(2, resultMatrixSignificance2.getStdDevPrec());\n assertEquals(1, resultMatrixSignificance2.getRowCount());\n assertEquals(40, resultMatrixSignificance2.getDefaultRowNameWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance2.stdDevWidthTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance2.printRowNamesTipText());\n assertTrue(resultMatrixSignificance2.getDefaultEnumerateColNames());\n assertFalse(resultMatrixSignificance2.getDefaultPrintColNames());\n assertEquals(0, resultMatrixSignificance2.getDefaultCountWidth());\n assertTrue(resultMatrixSignificance2.getEnumerateColNames());\n assertFalse(resultMatrixSignificance2.getShowAverage());\n assertEquals(2, resultMatrixSignificance2.getDefaultStdDevPrec());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance2.showAverageTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance2.colNameWidthTipText());\n assertEquals(\"Significance only\", resultMatrixSignificance2.getDisplayName());\n assertEquals(0, resultMatrixSignificance2.getStdDevWidth());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance2.globalInfo());\n assertEquals(0, resultMatrixSignificance2.getMeanWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance2.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance2.getColNameWidth());\n assertEquals(0, resultMatrixSignificance2.getDefaultMeanWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance2.enumerateColNamesTipText());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance2.printColNamesTipText());\n assertEquals(1, resultMatrixSignificance2.getColCount());\n assertFalse(resultMatrixSignificance2.getRemoveFilterName());\n assertFalse(resultMatrixSignificance2.getShowStdDev());\n assertFalse(resultMatrixSignificance2.getDefaultShowStdDev());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance2.stdDevPrecTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance2.countWidthTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance2.showStdDevTipText());\n assertEquals(0, resultMatrixSignificance2.getDefaultColNameWidth());\n assertEquals(0, resultMatrixSignificance2.getDefaultSignificanceWidth());\n assertFalse(resultMatrixSignificance2.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixSignificance2.getSignificanceWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance2.significanceWidthTipText());\n assertEquals(0, resultMatrixSignificance2.getCountWidth());\n assertEquals(1, resultMatrixSignificance2.getVisibleColCount());\n assertTrue(resultMatrixSignificance2.getPrintRowNames());\n assertEquals(0, resultMatrixSignificance2.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixSignificance2.getDefaultMeanPrec());\n assertNotNull(resultMatrixSignificance2);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance0));\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance1));\n \n int int0 = resultMatrixSignificance2.getVisibleColCount();\n assertFalse(resultMatrixSignificance2.getDefaultEnumerateRowNames());\n assertEquals(2, resultMatrixSignificance2.getMeanPrec());\n assertFalse(resultMatrixSignificance2.getDefaultShowAverage());\n assertFalse(resultMatrixSignificance2.getEnumerateRowNames());\n assertEquals(1, resultMatrixSignificance2.getVisibleRowCount());\n assertFalse(resultMatrixSignificance2.getPrintColNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance2.enumerateRowNamesTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance2.rowNameWidthTipText());\n assertEquals(40, resultMatrixSignificance2.getRowNameWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance2.meanWidthTipText());\n assertTrue(resultMatrixSignificance2.getDefaultPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance2.meanPrecTipText());\n assertEquals(2, resultMatrixSignificance2.getStdDevPrec());\n assertEquals(1, resultMatrixSignificance2.getRowCount());\n assertEquals(40, resultMatrixSignificance2.getDefaultRowNameWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance2.stdDevWidthTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance2.printRowNamesTipText());\n assertTrue(resultMatrixSignificance2.getDefaultEnumerateColNames());\n assertFalse(resultMatrixSignificance2.getDefaultPrintColNames());\n assertEquals(0, resultMatrixSignificance2.getDefaultCountWidth());\n assertTrue(resultMatrixSignificance2.getEnumerateColNames());\n assertFalse(resultMatrixSignificance2.getShowAverage());\n assertEquals(2, resultMatrixSignificance2.getDefaultStdDevPrec());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance2.showAverageTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance2.colNameWidthTipText());\n assertEquals(\"Significance only\", resultMatrixSignificance2.getDisplayName());\n assertEquals(0, resultMatrixSignificance2.getStdDevWidth());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance2.globalInfo());\n assertEquals(0, resultMatrixSignificance2.getMeanWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance2.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance2.getColNameWidth());\n assertEquals(0, resultMatrixSignificance2.getDefaultMeanWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance2.enumerateColNamesTipText());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance2.printColNamesTipText());\n assertEquals(1, resultMatrixSignificance2.getColCount());\n assertFalse(resultMatrixSignificance2.getRemoveFilterName());\n assertFalse(resultMatrixSignificance2.getShowStdDev());\n assertFalse(resultMatrixSignificance2.getDefaultShowStdDev());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance2.stdDevPrecTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance2.countWidthTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance2.showStdDevTipText());\n assertEquals(0, resultMatrixSignificance2.getDefaultColNameWidth());\n assertEquals(0, resultMatrixSignificance2.getDefaultSignificanceWidth());\n assertFalse(resultMatrixSignificance2.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixSignificance2.getSignificanceWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance2.significanceWidthTipText());\n assertEquals(0, resultMatrixSignificance2.getCountWidth());\n assertEquals(1, resultMatrixSignificance2.getVisibleColCount());\n assertTrue(resultMatrixSignificance2.getPrintRowNames());\n assertEquals(0, resultMatrixSignificance2.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixSignificance2.getDefaultMeanPrec());\n assertNotSame(resultMatrixSignificance2, resultMatrixSignificance0);\n assertNotSame(resultMatrixSignificance2, resultMatrixSignificance1);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(1, int0);\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance0));\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance1));\n \n ResultMatrixLatex resultMatrixLatex0 = new ResultMatrixLatex();\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixLatex0.printColNamesTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixLatex0.colNameWidthTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\n assertEquals(0, resultMatrixLatex0.getCountWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixLatex0.removeFilterNameTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixLatex0.printRowNamesTipText());\n assertEquals(1, resultMatrixLatex0.getColCount());\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixLatex0.stdDevPrecTipText());\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixLatex0.rowNameWidthTipText());\n assertFalse(resultMatrixLatex0.getShowStdDev());\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\n assertFalse(resultMatrixLatex0.getShowAverage());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixLatex0.stdDevWidthTipText());\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixLatex0.meanPrecTipText());\n assertEquals(1, resultMatrixLatex0.getRowCount());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixLatex0.showAverageTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getRowNameWidth());\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\n assertFalse(resultMatrixLatex0.getPrintColNames());\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixLatex0.meanWidthTipText());\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\n assertEquals(\"LaTeX\", resultMatrixLatex0.getDisplayName());\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixLatex0.significanceWidthTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixLatex0.showStdDevTipText());\n assertTrue(resultMatrixLatex0.getPrintRowNames());\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixLatex0.countWidthTipText());\n assertEquals(\"Generates the matrix output in LaTeX-syntax.\", resultMatrixLatex0.globalInfo());\n assertNotNull(resultMatrixLatex0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n boolean boolean0 = resultMatrixLatex0.isSignificance((-1));\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixLatex0.printColNamesTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixLatex0.colNameWidthTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\n assertEquals(0, resultMatrixLatex0.getCountWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixLatex0.removeFilterNameTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixLatex0.printRowNamesTipText());\n assertEquals(1, resultMatrixLatex0.getColCount());\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixLatex0.stdDevPrecTipText());\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixLatex0.rowNameWidthTipText());\n assertFalse(resultMatrixLatex0.getShowStdDev());\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\n assertFalse(resultMatrixLatex0.getShowAverage());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixLatex0.stdDevWidthTipText());\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixLatex0.meanPrecTipText());\n assertEquals(1, resultMatrixLatex0.getRowCount());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixLatex0.showAverageTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getRowNameWidth());\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\n assertFalse(resultMatrixLatex0.getPrintColNames());\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixLatex0.meanWidthTipText());\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\n assertEquals(\"LaTeX\", resultMatrixLatex0.getDisplayName());\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixLatex0.significanceWidthTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixLatex0.showStdDevTipText());\n assertTrue(resultMatrixLatex0.getPrintRowNames());\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixLatex0.countWidthTipText());\n assertEquals(\"Generates the matrix output in LaTeX-syntax.\", resultMatrixLatex0.globalInfo());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertFalse(boolean0);\n }","@Test(timeout = 4000)\n public void test126() throws Throwable {\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(1, resultMatrixCSV0.getColCount());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(1, resultMatrixCSV0.getRowCount());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertNotNull(resultMatrixCSV0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n String string0 = resultMatrixCSV0.toString();\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(1, resultMatrixCSV0.getColCount());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(1, resultMatrixCSV0.getRowCount());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertNotNull(string0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(\"Dataset,[1]\\nrow0,''\\n'[v/ /*]',''\\n\", string0);\n \n String[] stringArray0 = new String[4];\n ResultMatrixGnuPlot resultMatrixGnuPlot0 = new ResultMatrixGnuPlot();\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultCountWidth());\n assertFalse(resultMatrixGnuPlot0.getEnumerateColNames());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixGnuPlot0.countWidthTipText());\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixGnuPlot0.getSignificanceWidth());\n assertTrue(resultMatrixGnuPlot0.getPrintRowNames());\n assertEquals(2, resultMatrixGnuPlot0.getDefaultMeanPrec());\n assertEquals(0, resultMatrixGnuPlot0.getCountWidth());\n assertEquals(50, resultMatrixGnuPlot0.getRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixGnuPlot0.significanceWidthTipText());\n assertEquals(50, resultMatrixGnuPlot0.getDefaultRowNameWidth());\n assertEquals(2, resultMatrixGnuPlot0.getMeanPrec());\n assertFalse(resultMatrixGnuPlot0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixGnuPlot0.rowNameWidthTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixGnuPlot0.enumerateColNamesTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixGnuPlot0.stdDevPrecTipText());\n assertEquals(50, resultMatrixGnuPlot0.getColNameWidth());\n assertEquals(0, resultMatrixGnuPlot0.getMeanWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixGnuPlot0.removeFilterNameTipText());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixGnuPlot0.printColNamesTipText());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultMeanWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixGnuPlot0.colNameWidthTipText());\n assertEquals(1, resultMatrixGnuPlot0.getVisibleColCount());\n assertEquals(0, resultMatrixGnuPlot0.getStdDevWidth());\n assertFalse(resultMatrixGnuPlot0.getShowAverage());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultStdDevWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixGnuPlot0.showStdDevTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixGnuPlot0.printRowNamesTipText());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixGnuPlot0.meanWidthTipText());\n assertFalse(resultMatrixGnuPlot0.getDefaultShowStdDev());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixGnuPlot0.showAverageTipText());\n assertTrue(resultMatrixGnuPlot0.getPrintColNames());\n assertEquals(2, resultMatrixGnuPlot0.getDefaultStdDevPrec());\n assertFalse(resultMatrixGnuPlot0.getDefaultRemoveFilterName());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixGnuPlot0.meanPrecTipText());\n assertEquals(1, resultMatrixGnuPlot0.getRowCount());\n assertEquals(\"GNUPlot\", resultMatrixGnuPlot0.getDisplayName());\n assertFalse(resultMatrixGnuPlot0.getRemoveFilterName());\n assertEquals(1, resultMatrixGnuPlot0.getColCount());\n assertEquals(1, resultMatrixGnuPlot0.getVisibleRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixGnuPlot0.stdDevWidthTipText());\n assertFalse(resultMatrixGnuPlot0.getEnumerateRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixGnuPlot0.enumerateRowNamesTipText());\n assertEquals(50, resultMatrixGnuPlot0.getDefaultColNameWidth());\n assertEquals(2, resultMatrixGnuPlot0.getStdDevPrec());\n assertFalse(resultMatrixGnuPlot0.getDefaultShowAverage());\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintRowNames());\n assertEquals(\"Generates output for a data and script file for GnuPlot.\", resultMatrixGnuPlot0.globalInfo());\n assertNotNull(resultMatrixGnuPlot0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n \n resultMatrixGnuPlot0.setSize(0, 0);\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixGnuPlot0.getRowCount());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultCountWidth());\n assertFalse(resultMatrixGnuPlot0.getEnumerateColNames());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixGnuPlot0.countWidthTipText());\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixGnuPlot0.getSignificanceWidth());\n assertTrue(resultMatrixGnuPlot0.getPrintRowNames());\n assertEquals(2, resultMatrixGnuPlot0.getDefaultMeanPrec());\n assertEquals(0, resultMatrixGnuPlot0.getCountWidth());\n assertEquals(50, resultMatrixGnuPlot0.getRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixGnuPlot0.significanceWidthTipText());\n assertEquals(0, resultMatrixGnuPlot0.getVisibleColCount());\n assertEquals(50, resultMatrixGnuPlot0.getDefaultRowNameWidth());\n assertEquals(2, resultMatrixGnuPlot0.getMeanPrec());\n assertFalse(resultMatrixGnuPlot0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixGnuPlot0.rowNameWidthTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixGnuPlot0.enumerateColNamesTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixGnuPlot0.stdDevPrecTipText());\n assertEquals(50, resultMatrixGnuPlot0.getColNameWidth());\n assertEquals(0, resultMatrixGnuPlot0.getMeanWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixGnuPlot0.removeFilterNameTipText());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixGnuPlot0.printColNamesTipText());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultMeanWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixGnuPlot0.colNameWidthTipText());\n assertEquals(0, resultMatrixGnuPlot0.getVisibleRowCount());\n assertEquals(0, resultMatrixGnuPlot0.getStdDevWidth());\n assertFalse(resultMatrixGnuPlot0.getShowAverage());\n assertEquals(0, resultMatrixGnuPlot0.getColCount());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultStdDevWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixGnuPlot0.showStdDevTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixGnuPlot0.printRowNamesTipText());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixGnuPlot0.meanWidthTipText());\n assertFalse(resultMatrixGnuPlot0.getDefaultShowStdDev());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixGnuPlot0.showAverageTipText());\n assertTrue(resultMatrixGnuPlot0.getPrintColNames());\n assertEquals(2, resultMatrixGnuPlot0.getDefaultStdDevPrec());\n assertFalse(resultMatrixGnuPlot0.getDefaultRemoveFilterName());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixGnuPlot0.meanPrecTipText());\n assertEquals(\"GNUPlot\", resultMatrixGnuPlot0.getDisplayName());\n assertFalse(resultMatrixGnuPlot0.getRemoveFilterName());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixGnuPlot0.stdDevWidthTipText());\n assertFalse(resultMatrixGnuPlot0.getEnumerateRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixGnuPlot0.enumerateRowNamesTipText());\n assertEquals(50, resultMatrixGnuPlot0.getDefaultColNameWidth());\n assertEquals(2, resultMatrixGnuPlot0.getStdDevPrec());\n assertFalse(resultMatrixGnuPlot0.getDefaultShowAverage());\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintRowNames());\n assertEquals(\"Generates output for a data and script file for GnuPlot.\", resultMatrixGnuPlot0.globalInfo());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n \n int int0 = resultMatrixGnuPlot0.getDefaultColNameWidth();\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixGnuPlot0.getRowCount());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultCountWidth());\n assertFalse(resultMatrixGnuPlot0.getEnumerateColNames());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixGnuPlot0.countWidthTipText());\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixGnuPlot0.getSignificanceWidth());\n assertTrue(resultMatrixGnuPlot0.getPrintRowNames());\n assertEquals(2, resultMatrixGnuPlot0.getDefaultMeanPrec());\n assertEquals(0, resultMatrixGnuPlot0.getCountWidth());\n assertEquals(50, resultMatrixGnuPlot0.getRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixGnuPlot0.significanceWidthTipText());\n assertEquals(0, resultMatrixGnuPlot0.getVisibleColCount());\n assertEquals(50, resultMatrixGnuPlot0.getDefaultRowNameWidth());\n assertEquals(2, resultMatrixGnuPlot0.getMeanPrec());\n assertFalse(resultMatrixGnuPlot0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixGnuPlot0.rowNameWidthTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixGnuPlot0.enumerateColNamesTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixGnuPlot0.stdDevPrecTipText());\n assertEquals(50, resultMatrixGnuPlot0.getColNameWidth());\n assertEquals(0, resultMatrixGnuPlot0.getMeanWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixGnuPlot0.removeFilterNameTipText());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixGnuPlot0.printColNamesTipText());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultMeanWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixGnuPlot0.colNameWidthTipText());\n assertEquals(0, resultMatrixGnuPlot0.getVisibleRowCount());\n assertEquals(0, resultMatrixGnuPlot0.getStdDevWidth());\n assertFalse(resultMatrixGnuPlot0.getShowAverage());\n assertEquals(0, resultMatrixGnuPlot0.getColCount());\n assertEquals(0, resultMatrixGnuPlot0.getDefaultStdDevWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixGnuPlot0.showStdDevTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixGnuPlot0.printRowNamesTipText());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixGnuPlot0.meanWidthTipText());\n assertFalse(resultMatrixGnuPlot0.getDefaultShowStdDev());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixGnuPlot0.showAverageTipText());\n assertTrue(resultMatrixGnuPlot0.getPrintColNames());\n assertEquals(2, resultMatrixGnuPlot0.getDefaultStdDevPrec());\n assertFalse(resultMatrixGnuPlot0.getDefaultRemoveFilterName());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixGnuPlot0.meanPrecTipText());\n assertEquals(\"GNUPlot\", resultMatrixGnuPlot0.getDisplayName());\n assertFalse(resultMatrixGnuPlot0.getRemoveFilterName());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixGnuPlot0.stdDevWidthTipText());\n assertFalse(resultMatrixGnuPlot0.getEnumerateRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixGnuPlot0.enumerateRowNamesTipText());\n assertEquals(50, resultMatrixGnuPlot0.getDefaultColNameWidth());\n assertEquals(2, resultMatrixGnuPlot0.getStdDevPrec());\n assertFalse(resultMatrixGnuPlot0.getDefaultShowAverage());\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintRowNames());\n assertEquals(\"Generates output for a data and script file for GnuPlot.\", resultMatrixGnuPlot0.globalInfo());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(50, int0);\n }","@Test\n public void testMedian() {\n System.out.println(\"median\");\n ImageFilter instance = null;\n BufferedImage expResult = null;\n BufferedImage result = instance.median();\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testCompute() {\n System.out.println(\"compute\");\n ParallelMergeSort instance = null;\n instance.compute();\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testPerturbationExpectation()\n {\n IDoubleArray M = null;\n IDoubleArray pi0 = null;\n IDoubleArray observable = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.perturbationExpectation(M, pi0, observable, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test public void estimatedMonthlyCostTest() throws NegativeValueException {\n PetBunny pb1 = new PetBunny(\"Floppy\", \"Holland Lop\", 3.5);\n \n Assert.assertEquals(6.475, pb1.estimatedMonthlyCost(), 0.00001);\n }"],"string":"[\n \"@Test\\n public void testEstimateCmilestoning_3args_1()\\n {\\n Iterable<IIntArray> trajs = null;\\n Iterable<IIntArray> cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_3args_2()\\n {\\n IIntArray traj = null;\\n Iterable<IIntArray> cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_Iterable_int()\\n {\\n Iterable<IIntArray> trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateC_Iterable_int()\\n {\\n Iterable<IIntArray> trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateT()\\n {\\n \\n IDoubleArray counts = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateT(counts);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCstepping_Iterable_int()\\n {\\n Iterable<IIntArray> trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCstepping(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testMCMCWithoutAllelicPoN() {\\n final double meanBiasSimulated = 1.2;\\n final double biasVarianceSimulated = 0.04;\\n testMCMC(meanBiasSimulated, biasVarianceSimulated, meanBiasSimulated, biasVarianceSimulated, AllelicPanelOfNormals.EMPTY_PON);\\n }\",\n \"@Test\\n public void testMetastableMemberships()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.metastableMemberships(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test public void calcROItest() {\\n DirectMC h = new DirectMC(\\\"AdMailing\\\", 10000.00, 3.00, 2000);\\n Assert.assertEquals(.4286, h.calcROI(), 1);\\n }\",\n \"public void kolmogorovSmirnovTest(double cutoff, MWBMatchingAlgorithm mwbm) {\\n double p = 0;\\n\\n KolmogorovSmirnovTest t = new KolmogorovSmirnovTest();\\n for (int i = 0; i < this.train.numAttributes(); i++) {\\n for (int j = 0; j < this.test.numAttributes(); j++) {\\n // negative infinity counts as not present, we do this so we don't have to map\\n // between attribute indexs in weka\\n // and the result of the mwbm computation\\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\\n\\n // class attributes are not relevant\\n if (this.test.classIndex() == j) {\\n continue;\\n }\\n if (this.train.classIndex() == i) {\\n continue;\\n }\\n\\n // this may invoke exactP on small sample sizes which will not terminate in all\\n // cases\\n // p = t.kolmogorovSmirnovTest(this.train_values.get(i),\\n // this.test_values.get(j), false);\\n\\n // this uses approximateP everytime\\n p = t.approximateP(\\n t.kolmogorovSmirnovStatistic(this.train_values.get(i),\\n this.test_values.get(j)),\\n this.train_values.get(i).length,\\n this.test_values.get(j).length);\\n if (p > cutoff) {\\n mwbm.setWeight(i, j, p);\\n }\\n }\\n }\\n }\",\n \"@Test\\n public void testCorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable1 = null;\\n IDoubleArray observable2 = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"%\\\");\\n FileSystemHandling.createFolder((EvoSuiteFile) null);\\n discretize0.findNumBinsTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"%\\\");\\n discretize0.m_DefaultCols = \\\"%\\\";\\n double[][] doubleArray0 = new double[0][7];\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints(1);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 1\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test03() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setAttributeIndices(\\\"\\\");\\n Filter.makeCopy(discretize0);\\n discretize0.setDesiredWeightOfInstancesPerInterval(2.0);\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 0;\\n discretize0.setUseBinNumbers(true);\\n discretize0.listOptions();\\n discretize0.makeBinaryTipText();\\n discretize0.getInvertSelection();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-458));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testForwardCommittor()\\n {\\n IDoubleArray M = null;\\n IIntArray A = null;\\n IIntArray B = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.forwardCommittor(M, A, B);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testMMI() {\\n System.out.println(\\\"MMI\\\");\\n int a = 0;\\n int m = 0;\\n int expResult = 0;\\n int result = utilsHill.MMI(a, m);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void shouldCalculateProperMoveProbabilities() throws Exception {\\n\\n int actualCity = 0;\\n boolean[] visited = new boolean[] {true, false, false, false};\\n int[][] firstCriterium = new int[][] {{0, 2, 1, 2},\\n {2, 2, 1, 1},\\n {1, 1, 0, 1},\\n {2, 1, 1, 0}};\\n int[][] secondCriterium = new int[][] {{0, 3, 2, 1},\\n {3, 0, 0, 0},\\n {2, 0, 0, 0},\\n {1, 0, 0, 0}};\\n// final AttractivenessCalculator attractivenessCalculator = new AttractivenessCalculator(1.0, 0.0, 0.0, 0.0, 0.0);\\n// double[][] array = new double[0][0];\\n// final double[] probabilities = attractivenessCalculator.movesProbability(actualCity, visited, array, firstCriterium, secondCriterium, null, 0.0, 0.0);\\n\\n// assertTrue(probabilities[0] == 0.0);\\n// assertTrue(probabilities[1] == 0.0);\\n// assertTrue(probabilities[2] == 0.5);\\n// assertTrue(probabilities[3] == 0.5);\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n String[] stringArray0 = new String[2];\\n discretize0.m_ClassIndex = 906;\\n discretize0.m_IgnoreClass = true;\\n stringArray0[0] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n stringArray0[1] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n discretize0.setOptions(stringArray0);\\n discretize0.setAttributeIndices(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getFindNumBins());\\n }\",\n \"private double GetMCRTestSet() \\n\\t{\\n\\t\\tint numErrors = 0;\\n\\t\\t\\n\\t\\tfor(int i = ITrain; i < ITrain+ITest; i++)\\n\\t\\t{\\n\\t\\t\\tPreCompute(i);\\n\\t\\t\\t\\n\\t\\t\\tdouble max_Y_hat_ic = Double.MIN_VALUE;\\n\\t\\t\\tint label_i = -1; \\n\\t\\t\\t\\n\\t\\t\\tfor(int c = 0; c < C; c++)\\n\\t\\t\\t{\\n\\t\\t\\t\\tdouble Y_hat_ic = Predict(i, c);\\n\\t\\t\\t\\t\\n\\n\\t\\t\\t\\tif(lossType == LossTypes.LOGISTIC)\\n\\t\\t\\t\\t\\tY_hat_ic = Sigmoid.Calculate(Y_hat_ic);\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif(Y_hat_ic > max_Y_hat_ic)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tmax_Y_hat_ic = Y_hat_ic; \\n\\t\\t\\t\\t\\tlabel_i = (int)Math.ceil(c);\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\t\\n\\t\\t\\tif( nominalLabels.indexOf(Y.get(i)) != label_i ) \\n\\t\\t\\t\\tnumErrors++;\\n\\t\\t}\\n\\t\\t\\n\\t\\treturn (double)numErrors/(double)ITest; \\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.invertSelectionTipText();\\n boolean boolean0 = discretize0.m_UseBinNumbers;\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testJuvenilesSpreadFromCellsWithCorrespondingMatureLctFromInsideGrid() {\\n params = new CompletelySpatiallyRandomParams(3, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params);\\n coloniser.updateJuvenilePresenceLayers();\\n\\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 6);\\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 9);\\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 3);\\n }\",\n \"@Test\\n public void testEstimateC_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"l*{e5SNh\\\");\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 177;\\n discretize0.setUseEqualFrequency(true);\\n double[][] doubleArray0 = new double[5][9];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[0] = (double) 177;\\n doubleArray1[1] = (double) 177;\\n doubleArray1[2] = (double) 177;\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[2];\\n doubleArray2[0] = (double) 177;\\n doubleArray2[1] = (double) 177;\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[9];\\n doubleArray3[0] = (double) 177;\\n doubleArray3[1] = (double) 177;\\n doubleArray3[2] = (double) 177;\\n doubleArray3[3] = (double) 177;\\n doubleArray3[4] = (double) 177;\\n doubleArray3[5] = (double) 177;\\n doubleArray3[6] = (double) 177;\\n doubleArray3[7] = (double) 177;\\n doubleArray3[8] = (double) 177;\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[8];\\n doubleArray4[0] = (double) 177;\\n doubleArray4[1] = (double) 177;\\n doubleArray4[2] = (double) 177;\\n doubleArray4[3] = (double) 177;\\n doubleArray4[4] = (double) 177;\\n doubleArray4[5] = (double) 177;\\n doubleArray4[6] = (double) 177;\\n doubleArray4[7] = (double) 177;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[0];\\n doubleArray0[4] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.listOptions();\\n discretize0.setIgnoreClass(true);\\n String[] stringArray0 = new String[0];\\n Discretize.main(stringArray0);\\n discretize0.findNumBinsTipText();\\n discretize0.setInvertSelection(true);\\n // Undeclared exception!\\n try { \\n discretize0.getBinRangesString(511);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 511\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints((-2276));\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Test(timeout = 4000)\\n public void test07() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setInvertSelection(true);\\n discretize0.useEqualFrequencyTipText();\\n int int0 = (-501);\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-501));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test08() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"\\\");\\n discretize0.binsTipText();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning(25);\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testGetImportanceStats() {\\n System.out.println(\\\"getImportanceStats\\\");\\n MDA instance = new MDA();\\n\\n // make the circles close to force tree to do lots of splits / make it harder\\n ClassificationDataSet train = getHarderC(10000, RandomUtil.getRandom());\\n int good_featres = 2;\\n\\n DecisionTree tree = new DecisionTree();\\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\\n tree.train(train);\\n\\n double[] importances = instance.getImportanceStats(tree, train);\\n\\n // make sure the first 2 features were infered as more important than the\\n // others!\\n for (int i = good_featres; i < importances.length; i++) {\\n for (int j = 0; j < good_featres; j++)\\n assertTrue(importances[j] > importances[i]);\\n }\\n\\n // categorical features, make space wider b/c we lose resolution\\n train = getHarderC(10000, RandomUtil.getRandom());\\n\\n train.applyTransform(new NumericalToHistogram(train, 7));\\n tree = new DecisionTree();\\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\\n tree.train(train);\\n\\n importances = instance.getImportanceStats(tree, train);\\n\\n // make sure the first 2 features were infered as more important than the\\n // others!\\n for (int i = good_featres; i < importances.length; i++) {\\n for (int j = 0; j < good_featres; j++)\\n assertTrue(importances[j] > importances[i]);\\n }\\n\\n }\",\n \"private void findAbdomenCM() {\\r\\n \\r\\n if (foundCenterOfMass) return;\\r\\n int xcm = 0, ycm = 0, pixCount = 0;\\r\\n for (int idx= 0, y = 0; y < yDim; y++) {\\r\\n for (int x = 0; x < xDim; x++, idx++) {\\r\\n if (sliceBuffer[idx] == abdomenTissueLabel) {\\r\\n xcm += x;\\r\\n ycm += y;\\r\\n pixCount++;\\r\\n }\\r\\n } // end for (int x = 0; ...)\\r\\n } // end for (int idx = 0, ...)\\r\\n \\r\\n if (pixCount == 0) {\\r\\n// System.err.println(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n MipavUtil.displayError(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n return;\\r\\n }\\r\\n \\r\\n centerOfMass[0] = (short)(xcm / pixCount);\\r\\n centerOfMass[1] = (short)(ycm / pixCount);\\r\\n foundCenterOfMass = true;\\r\\n }\",\n \"final CM computeCM(int[/**/][/**/] votes, Chunk[] chks, boolean local, boolean balance) {\\n CM cm = new CM();\\n int rows = votes.length;\\n int validation_rows = 0;\\n int cmin = (int) _data.vecs()[_classcol].min();\\n\\n // Assemble the votes-per-class into predictions & score each row\\n\\n // Make an empty confusion matrix for this chunk\\n cm._matrix = new long[_N][_N];\\n float preds[] = new float[_N+1];\\n\\n float num_trees = _errorsPerTree.length;\\n\\n // Loop over the rows\\n for( int row = 0; row < rows; row++ ) {\\n\\n // Skip rows with missing response values\\n if (chks[_classcol].isNA0(row)) continue;\\n\\n // The class votes for the i-th row\\n int[] vi = votes[row];\\n\\n // Fill the predictions with the vote counts, keeping the 0th index unchanged\\n for( int v=0; v<_N; v++ ) preds[v+1] = vi[v];\\n\\n float s = doSum(vi);\\n if (s == 0) {\\n cm._skippedRows++;\\n continue;\\n }\\n\\n int result;\\n if (balance) {\\n float[] scored = toProbs(preds.clone(), doSum(vi));\\n double probsum=0;\\n for( int c=1; c<scored.length; c++ ) {\\n final double original_fraction = _model.priordist()[c-1];\\n assert(original_fraction > 0) : \\\"original fraction should be > 0, but is \\\" + original_fraction + \\\": not using enough training data?\\\";\\n final double oversampled_fraction = _model.modeldist()[c-1];\\n assert(oversampled_fraction > 0) : \\\"oversampled fraction should be > 0, but is \\\" + oversampled_fraction + \\\": not using enough training data?\\\";\\n assert(!Double.isNaN(scored[c]));\\n scored[c] *= original_fraction / oversampled_fraction;\\n probsum += scored[c];\\n }\\n for (int i=1;i<scored.length;++i) scored[i] /= probsum;\\n result = ModelUtils.getPrediction(scored, row);\\n } else {\\n // `result` is the class with the most votes, accounting for ties in the shared logic in ModelUtils\\n result = ModelUtils.getPrediction(preds, row);\\n }\\n\\n // Get the class value from the response column for the current row\\n int cclass = alignDataIdx((int) chks[_classcol].at80(row) - cmin);\\n assert 0 <= cclass && cclass < _N : (\\\"cclass \\\" + cclass + \\\" < \\\" + _N);\\n\\n // Ignore rows with zero votes, but still update the sum of squared errors\\n if( vi[result]==0 ) {\\n cm._skippedRows++;\\n if (!local) _sum += doSSECalc(vi, preds, cclass);\\n continue;\\n }\\n\\n // Update the confusion matrix\\n cm._matrix[cclass][result]++;\\n if( result != cclass ) cm._errors++;\\n validation_rows++;\\n\\n // Update the sum of squared errors\\n if (!local) _sum += doSSECalc(vi, preds, cclass);\\n float sum = doSum(vi);\\n\\n // Binomial classification -> compute AUC, draw ROC\\n if(_N == 2 && !local) {\\n float snd = preds[2] / sum;\\n for(int i = 0; i < ModelUtils.DEFAULT_THRESHOLDS.length; i++) {\\n int p = snd >= ModelUtils.DEFAULT_THRESHOLDS[i] ? 1 : 0;\\n _cms[i][cclass][p]++; // Increase matrix\\n }\\n }\\n }\\n\\n // End of loop over rows, return confusion matrix\\n cm._rows=validation_rows;\\n return cm;\\n }\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, true, false);\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints(3594);\\n Discretize discretize1 = new Discretize();\\n discretize1.attributeIndicesTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"\\\");\\n Discretize discretize2 = new Discretize();\\n // Undeclared exception!\\n try { \\n discretize2.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testGetMoveSpeedMultiplier() {\\n assertEquals(1, proj.getMoveSpeedMultiplier(), 0.001);\\n }\",\n \"@Before\\n\\tpublic void iniciaDisciplinaConvencional() {\\n\\t\\tList<String> pos = new ArrayList<String>();\\n\\t\\tList<String> pos1 = new ArrayList<String>();\\n\\t\\tpos1.add(\\\"Disciplina 3\\\");\\n\\t\\tpos1.add(\\\"Disciplina 4\\\");\\n\\t\\tpos1.add(\\\"Disciplina 5\\\");\\n\\n\\t\\tminitElimBai_1 = new MinitestesEliminandoBaixa(\\\"Disciplina 1\\\", 5, pos,\\n\\t\\t\\t\\t1);\\n\\t\\tminitElimBai_2 = new MinitestesEliminandoBaixa(\\\"Disciplina 2\\\", 10,\\n\\t\\t\\t\\tpos1, 2);\\n\\t}\",\n \"@Test\\n public void test13() {\\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 1));\\n }\",\n \"private void computeMultiClassMCC(ConfusionMatrixElement[] elements) {\\n\\t\\tdouble Sq = matrixTotal * matrixTotal;\\n\\t\\tdouble CS = Arrays.stream(elements).map(n -> n.tp).reduce(0.0d, (n, m) -> n + m) * matrixTotal;\\n\\t\\tdouble t = 0.0d, p = 0.0d, tt = 0.0d, pp = 0.0d, tp = 0.0d;\\n\\t\\t\\n\\t\\tfor (ConfusionMatrixElement e : elements) {\\n\\t\\t\\tt = (e.tp + e.fp); \\n\\t\\t\\tp = (e.tp + e.fn);\\n\\t\\t\\t\\n\\t\\t\\ttt += (t * t);\\n\\t\\t\\tpp += (p * p);\\n\\t\\t\\ttp += (t * p);\\n\\t\\t}\\n\\t\\tmatrixMCC = (CS - tp) / (Math.sqrt(Sq - pp) * Math.sqrt(Sq - tt));\\n\\t}\",\n \"@Test\\n public void testLargeValues() {\\n double x = 123;\\n double dof = 6.4;\\n double nonCent = 100.34;\\n NonCentralChiSquaredDistribution dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.7930769).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n x = 455.038;\\n dof = 12;\\n nonCent = 444.44;\\n\\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.4961805).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n x = 999400;\\n dof = 500;\\n nonCent = 1000000;\\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.2913029).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n Locale.getISOLanguages();\\n double[][] doubleArray0 = new double[9][8];\\n double[] doubleArray1 = new double[3];\\n double[] doubleArray2 = new double[9];\\n doubleArray2[5] = (-163.0);\\n doubleArray2[8] = 1421.4058829;\\n doubleArray0[3] = doubleArray0[2];\\n doubleArray0[4] = doubleArray0[1];\\n discretize0.setOutputFormat();\\n double[] doubleArray3 = new double[2];\\n doubleArray3[0] = 1421.4058829;\\n doubleArray3[1] = (-163.0);\\n doubleArray0[6] = doubleArray2;\\n double[] doubleArray4 = new double[0];\\n doubleArray0[7] = doubleArray4;\\n double[] doubleArray5 = new double[6];\\n doubleArray5[2] = (-163.0);\\n doubleArray0[8] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test29() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[3][6];\\n discretize0.setIgnoreClass(false);\\n discretize0.m_CutPoints = doubleArray0;\\n double[] doubleArray1 = new double[4];\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints((-831));\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // -831\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test01() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"c9^n/\\\");\\n discretize0.getCapabilities();\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n }\",\n \"@Test\\n\\tpublic void testCollapseByEditDistance() {\\n\\t\\tString cellBC = \\\"ATCAGGGACAGA\\\";\\n\\t\\tString molBC = \\\"CGGGGCTC\\\";\\n\\t\\tint snpPos = 76227022;\\n\\t\\tInterval snpInterval = new Interval(\\\"HUMAN_1\\\", snpPos, snpPos, true, \\\"test\\\");\\n\\t\\tSampleGenotypeProbabilities result = new SampleGenotypeProbabilities(snpInterval, cellBC);\\n\\t\\tSNPUMIBasePileup p = getPileUpFromFile(cellBC, molBC);\\n\\t\\tresult.add(p);\\n\\n\\t\\t// let's add another pileup in that's within ed=1 and incorporates an error.\\n\\t\\t// result has only a single base because two UMIs are merged.\\n\\t\\tString molBC2 = \\\"AGGGGCTC\\\";\\n\\t\\tSNPUMIBasePileup p2 = getPileUpFromFile(cellBC, molBC2);\\n\\t\\tchar[] b2 = { 'G', 'G', 'T' };\\n\\t\\tbyte[] bases2 = new byte[b2.length];\\n\\t\\tStringUtil.charsToBytes(b2, 0, b2.length, bases2, 0);\\n\\t\\tbyte[] quals2 = { 37, 37, 10 };\\n\\t\\tp2.setBasesAndQualities(bases2, quals2);\\n\\t\\tresult.add(p2);\\n\\t\\tresult.collapseUMIs(1);\\n\\t\\tList<Byte> qualResult2 = result.getQualities();\\n\\t\\tAssert.assertEquals(qualResult2.size(), 1);\\n\\t\\tAssert.assertEquals(new Byte((byte) 8), qualResult2.get(0));\\n\\t}\",\n \"@Test\\n public void testLogLikelihoodCorrelationMatrix()\\n {\\n IDoubleArray corr = null;\\n IDoubleArray C = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n double expResult = 0.0;\\n double result = instance.logLikelihoodCorrelationMatrix(corr, C);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public void percentiles(double cutoff, MWBMatchingAlgorithm mwbm) {\\n for (int i = 0; i < this.train.numAttributes(); i++) {\\n for (int j = 0; j < this.test.numAttributes(); j++) {\\n // negative infinity counts as not present, we do this so we don't have to map\\n // between attribute indexes in weka\\n // and the result of the mwbm computation\\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\\n\\n // class attributes are not relevant\\n if (this.test.classIndex() == j) {\\n continue;\\n }\\n if (this.train.classIndex() == i) {\\n continue;\\n }\\n\\n // get percentiles\\n double trainvals[] = this.train_values.get(i);\\n double testvals[] = this.test_values.get(j);\\n\\n Arrays.sort(trainvals);\\n Arrays.sort(testvals);\\n\\n // percentiles\\n double train_p;\\n double test_p;\\n double score = 0.0;\\n for (int p = 1; p <= 9; p++) {\\n train_p = trainvals[(int) Math.ceil(trainvals.length * (p / 100))];\\n test_p = testvals[(int) Math.ceil(testvals.length * (p / 100))];\\n\\n if (train_p > test_p) {\\n score += test_p / train_p;\\n }\\n else {\\n score += train_p / test_p;\\n }\\n }\\n\\n if (score > cutoff) {\\n mwbm.setWeight(i, j, score);\\n }\\n }\\n }\\n }\",\n \"public double[] monteCarloPercentiles(){\\n if(!this.monteCarloDone)this.monteCarlo();\\n return this.randomEigenValuesPercentiles;\\n }\",\n \"@Test\\n public void test10() {\\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 3));\\n }\",\n \"@Test\\n public void testMesurerDistanceCoordonnees() {\\n System.out.println(\\\"MesurerDistanceCoordonnees\\\");\\n double lat1 = 0.0;\\n double lon1 = 0.0;\\n double lat2 = 0.0;\\n double lon2 = 0.0;\\n double expResult = 0.0;\\n double result = Utils.MesurerDistanceCoordonnees(lat1, lon1, lat2, lon2);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test13() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"\\\\tInvert matching sense of column indexes.\\\");\\n discretize0.setOutputFormat();\\n discretize0.listOptions();\\n Discretize discretize1 = new Discretize();\\n discretize1.setIgnoreClass(false);\\n String[] stringArray0 = new String[2];\\n stringArray0[0] = \\\"\\\\tInvert matching sense of column indexes.\\\";\\n stringArray0[1] = \\\"\\\";\\n Discretize.main(stringArray0);\\n String string0 = discretize1.findNumBinsTipText();\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", string0);\\n \\n discretize1.setInvertSelection(false);\\n discretize0.getBinRangesString(511);\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n \\n discretize1.getFindNumBins();\\n assertEquals(10, discretize1.getBins());\\n }\",\n \"@Test\\n public void testMetastableStates()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IIntArray expResult = null;\\n IIntArray result = instance.metastableStates(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCstepping_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCstepping(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test096() throws Throwable {\\n CostMatrix costMatrix0 = Evaluation.handleCostOption(\\\"\\\", 1281);\\n assertNull(costMatrix0);\\n }\",\n \"@Test\\n\\tpublic void evalCrispDistanceWithCrisp1(){\\n\\t\\tFuzzySet res = (CrispSet)cs1.distance(cs2);\\n\\t\\tassertTrue(res instanceof CrispSet);\\n\\t\\tCrispSet resCrisp = (CrispSet)res;\\n\\t\\t//Puntos limite\\t\\t\\n\\t\\tassertTrue(resCrisp.getLeftBoundary()==3);\\n\\t\\tassertTrue(resCrisp.getRightBoundary()==6);\\n\\t\\t//Valores extremos\\n\\t\\tassertTrue(resCrisp.getMembershipValue(0)==0);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(3)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(4)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(4.5)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(5)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(6)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(6.01)==0);\\n\\t}\",\n \"@Test\\n public void testJuvenilesSpreadFromOutsideInsideGrid() {\\n params = new CompletelySpatiallyRandomParams(0.5, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params);\\n coloniser.updateJuvenilePresenceLayers();\\n\\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 5);\\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 5);\\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 5);\\n }\",\n \"@Test\\n public void pruebaCalculCamiseta() {\\n System.out.println(\\\"prueba Calcul Camiseta\\\");\\n Camiseta camisetaTest = new Camiseta(1);\\n assertEquals(190, (camisetaTest.calculCamiseta(1)),0) ;\\n \\n Camiseta camisetaTestDos = new Camiseta(2);\\n assertEquals(1481.2, (camisetaTestDos.calculCamiseta(7)),0) ;\\n \\n Camiseta camisetaTestTres = new Camiseta(3);\\n assertEquals(1178, (camisetaTestTres.calculCamiseta(4)),0) ;\\n \\n \\n }\",\n \"public int getCostPerMile() {\\n return costPerMile;\\n }\",\n \"@Test()\\n public void testTrainWarmCFastSMO() {\\n ClassificationDataSet train = FixedProblems.getHalfCircles(250, RandomUtil.getRandom(), 0.1, 0.2);\\n\\n PlattSMO warmModel = new PlattSMO(new LinearKernel(1));\\n warmModel.setC(1);\\n warmModel.train(train);\\n\\n PlattSMO warm = new PlattSMO(new LinearKernel(1));\\n warm.setC(1e4);// too large to train efficently like noraml\\n\\n long start, end;\\n\\n start = System.currentTimeMillis();\\n warm.train(train, warmModel);\\n end = System.currentTimeMillis();\\n long warmTime = (end - start);\\n\\n PlattSMO notWarm = new PlattSMO(new LinearKernel(1));\\n notWarm.setC(1e4);// too large to train efficently like noraml\\n\\n start = System.currentTimeMillis();\\n notWarm.train(train);\\n end = System.currentTimeMillis();\\n long normTime = (end - start);\\n\\n assertTrue(warmTime < normTime * 0.75);\\n\\n }\",\n \"@Test\\n public void testConnectedComponents()\\n {\\n Collection<IIntArray> cc1 = MarkovModel.util.connectedComponents(P1);\\n assertTrue(cc1.containsAll(C1));\\n assertEquals(cc1.size(), C1.size());\\n\\n Collection<IIntArray> cc2 = MarkovModel.util.connectedComponents(P2);\\n assertTrue(cc2.containsAll(C2));\\n assertEquals(cc2.size(), C2.size());\\n\\n Collection<IIntArray> cc3 = MarkovModel.util.connectedComponents(P3);\\n assertTrue(cc3.containsAll(C3));\\n assertEquals(cc3.size(), C3.size()); \\n }\",\n \"@Test(timeout = 4000)\\n public void test26() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.m_FindNumBins = true;\\n int[] intArray0 = new int[6];\\n intArray0[0] = (-388);\\n intArray0[1] = (-2121610348);\\n intArray0[2] = 11;\\n intArray0[3] = (-1);\\n intArray0[4] = 1062;\\n intArray0[5] = 70;\\n discretize0.setAttributeIndicesArray(intArray0);\\n discretize0.setDesiredWeightOfInstancesPerInterval((-1149.15832593));\\n discretize0.getOptions();\\n assertEquals((-1149.15832593), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n }\",\n \"@Test\\r\\n\\tpublic void testAllMarkupsElectronics() throws BasePriceMustBePositiveException, \\r\\n\\t NegativePersonCountException, ProductCategoryNotSpecifiedException {\\r\\n\\t\\tNuPackEstimator estimator = new NuPackEstimator(new BigDecimal(\\\"100\\\"), 5, \\\"electronics\\\");\\r\\n\\t assertEquals(estimator.estimateFinalCost(), new BigDecimal(\\\"113.40\\\"));\\r\\n\\t}\",\n \"@Test\\n public void testGetProjectileSpeedMultiplier() {\\n assertEquals(1.3, proj.getProjectileSpeedMultiplier(), 0.001);\\n }\",\n \"public void testConcurrency() throws InterruptedException {\\n initCache();\\n\\n final ArrayList<WMSTile> wcos = new ArrayList<WMSTile>();\\n final ArrayList<Integer> test = new ArrayList<Integer>();\\n final LinkedBlockingQueue<Integer> idxs = new LinkedBlockingQueue<Integer>();\\n\\n Collection<Callable<Integer>> tasks = new ArrayList<Callable<Integer>>();\\n\\n for (int i = 0; i < 30000; i++) {\\n wcos.add(getDefaultWMSCacheObject(\\\"q\\\" + i, 50, true));\\n test.add(-1);\\n idxs.put(i);\\n\\n //put and get task\\n tasks.add(new Callable<Integer>() {\\n\\n public Integer call() throws Exception {\\n //put\\n int i = idxs.take();\\n boolean ok = wmsCache.put(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1, wcos.get(i));\\n\\n //get\\n if (ok) {\\n WMSTile wco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\\n if (wco.getCached()) {\\n if (compareWMSObjects(wco, wcos.get(i))) {\\n test.set(i, 1);\\n } else {\\n test.set(i, 0);\\n }\\n }\\n }\\n return i;\\n }\\n });\\n }\\n ExecutorService executorService = Executors.newFixedThreadPool(100);\\n executorService.invokeAll(tasks);\\n\\n //test cache cleaner operated correctly\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n\\n //test for presence of invalid test comparisons\\n int invalid = 0;\\n int valid = 0;\\n for (int i = 0; i < test.size(); i++) {\\n if (test.get(i) == 0) {\\n invalid++;\\n } else if (test.get(i) == 1) {\\n valid++;\\n }\\n }\\n assertTrue(valid > 0);\\n assertTrue(invalid == 0);\\n }\",\n \"@Test\\n public void testGetCenter() {\\n int expResult = 3;\\n int result = this.dummyDigitSize.getCenter();\\n assertEquals(expResult, result);\\n }\",\n \"@Before\\r\\n\\tpublic void setup() {\\r\\n\\t\\tMockitoAnnotations.initMocks(this);\\r\\n\\t\\tcompute(\\\"12-10-19\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"10-09-20\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"11-08-20\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"03-05-17\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"09-03-19\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"01-02-17\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t}\",\n \"@Test\\n public void testAutocorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.autocorrelation(M, observable, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testIsRateMatrix()\\n {\\n IDoubleArray K = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n boolean expResult = false;\\n boolean result = instance.isRateMatrix(K);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test28() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n doubleArray2[5] = (-236.4995664359);\\n doubleArray2[7] = 1421.4058829;\\n doubleArray0[3] = doubleArray2;\\n double[] doubleArray3 = new double[3];\\n doubleArray3[0] = 0.0;\\n doubleArray3[1] = (-236.4995664359);\\n doubleArray0[4] = doubleArray3;\\n double[] doubleArray4 = new double[21];\\n doubleArray4[0] = 1421.4058829;\\n doubleArray3[2] = (-236.4995664359);\\n doubleArray4[4] = 1.7976931348623157E308;\\n doubleArray0[7] = doubleArray4;\\n discretize0.m_NumBins = 162;\\n doubleArray0[1] = doubleArray4;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.getBinRangesString(1);\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"public void testSizeManagementWMS() {\\n initCache();\\n \\n ArrayList<WMSTile> wcos = new ArrayList<WMSTile>();\\n long putSize = 0;\\n long maxSize = 0;\\n int cacheSizeDropCount = 0;\\n for (int i = 0; i < 1000; i++) {\\n WMSTile wco = getDefaultWMSCacheObject(\\\"q\\\" + i, 50, true);\\n putSize += wco.getSize();\\n\\n wcos.add(wco);\\n long size = wmsCache.getSize();\\n\\n if (size < maxSize) {\\n maxSize = size;\\n cacheSizeDropCount++;\\n } else {\\n maxSize = size;\\n }\\n\\n boolean result = wmsCache.put(wco.getQuery(), wco.getColourmode(), PointType.POINT_1, wco);\\n\\n //test if cache is full the put was unsuccessful\\n //allow for cachecleaner to have reduced the size between put and test\\n boolean test = (wmsCache.getSize() + wco.getSize() > wmsCache.getMaxCacheSize()) == !result;\\n if (test == false) {\\n assertTrue((size + wco.getSize() > wmsCache.getMaxCacheSize()) == !result);\\n }\\n\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n }\\n\\n //test size calcuations are operating\\n assertTrue(putSize > 10000);\\n\\n //test cache cleaner was run more than once\\n assertTrue(cacheSizeDropCount > 1);\\n\\n //test cache size is under max\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n\\n //test gets. Anything that is a placeholder will be null.\\n int cachedCount = 0;\\n for (int i = 0; i < wcos.size(); i++) {\\n WMSTile getwco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\\n if (getwco.getCached()) {\\n assertTrue(compareWMSObjects(wcos.get(i), getwco));\\n cachedCount++;\\n }\\n }\\n\\n //test if at least 2 objects were cached at the end\\n assertTrue(cachedCount > 1);\\n }\",\n \"@Test\\r\\n public void testCalculateNDM() {\\r\\n System.out.println(\\\"calculateNDM\\\");\\r\\n NDM instance = null;\\r\\n Double expResult = null;\\r\\n //Double result = instance.calculateNDM();\\r\\n //assertEquals(expResult, result);\\r\\n // TODO review the generated test code and remove the default call to fail.\\r\\n //fail(\\\"The test case is a prototype.\\\");\\r\\n }\",\n \"@Test(timeout = 4000)\\n public void test098() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV(0, 0);\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[] intArray0 = new int[0];\\n resultMatrixCSV0.setRowOrder(intArray0);\\n assertArrayEquals(new int[] {}, intArray0);\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(0, intArray0.length);\\n \\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML();\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertNotNull(resultMatrixHTML0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n resultMatrixHTML0.setMeanPrec((-2615));\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n ResultMatrixHTML resultMatrixHTML1 = null;\\n try {\\n resultMatrixHTML1 = new ResultMatrixHTML(2312, (-2214));\\n fail(\\\"Expecting exception: NegativeArraySizeException\\\");\\n \\n } catch(NegativeArraySizeException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.experiment.ResultMatrix\\\", e);\\n }\\n }\",\n \"@Test\\n public void testNumCellsColonisedFromOutsideGrid() {\\n CompletelySpatiallyRandomParams params1 = new CompletelySpatiallyRandomParams(0.1, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params1);\\n assertEquals(1, coloniser.getNumCellsColonisedFromOutsideGrid());\\n\\n // if baseRate = 0.5 then 9 * 0.5 = 4.5 ~ 5 cells colonised from outside grid\\n CompletelySpatiallyRandomParams params2 = new CompletelySpatiallyRandomParams(0.5, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params2);\\n assertEquals(5, coloniser.getNumCellsColonisedFromOutsideGrid());\\n }\",\n \"@Test(timeout = 4000)\\n public void test117() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[] intArray0 = new int[1];\\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n String[] stringArray0 = new String[4];\\n stringArray0[0] = \\\"v\\\";\\n stringArray0[1] = \\\"[\\\";\\n stringArray0[2] = \\\"*\\\";\\n double[] doubleArray0 = new double[7];\\n doubleArray0[0] = (double) 0;\\n doubleArray0[1] = (double) 2;\\n doubleArray0[2] = (double) 0;\\n doubleArray0[3] = (double) 1;\\n doubleArray0[4] = (double) 2;\\n doubleArray0[5] = (double) 0;\\n ResultMatrixPlainText resultMatrixPlainText0 = new ResultMatrixPlainText();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertNotNull(resultMatrixPlainText0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n ResultMatrixLatex resultMatrixLatex0 = new ResultMatrixLatex(resultMatrixPlainText0);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertNotNull(resultMatrixLatex0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n String string0 = resultMatrixPlainText0.getSummaryTitle(216);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertNotNull(string0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"i\\\", string0);\\n \\n int[][] intArray1 = new int[8][0];\\n intArray1[0] = intArray0;\\n intArray1[1] = intArray0;\\n intArray1[2] = intArray0;\\n intArray1[3] = intArray0;\\n intArray1[4] = intArray0;\\n intArray1[5] = intArray0;\\n intArray1[6] = intArray0;\\n intArray1[7] = intArray0;\\n resultMatrixLatex0.setSummary(intArray1, intArray1);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(8, intArray1.length);\\n \\n ResultMatrixCSV resultMatrixCSV1 = new ResultMatrixCSV();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNotNull(resultMatrixCSV1);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n \\n int[] intArray2 = resultMatrixCSV1.getColOrder();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNull(intArray2);\\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n \\n boolean boolean0 = resultMatrixLatex0.getDefaultPrintColNames();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertFalse(boolean0);\\n \\n ResultMatrixSignificance resultMatrixSignificance1 = new ResultMatrixSignificance(resultMatrixSignificance0);\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getSignificanceWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance1.showStdDevTipText());\\n assertEquals(1, resultMatrixSignificance1.getRowCount());\\n assertTrue(resultMatrixSignificance1.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixSignificance1.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance1.meanWidthTipText());\\n assertFalse(resultMatrixSignificance1.getPrintColNames());\\n assertFalse(resultMatrixSignificance1.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance1.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance1.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance1.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance1.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance1.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixSignificance1.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixSignificance1.getVisibleColCount());\\n assertTrue(resultMatrixSignificance1.getPrintRowNames());\\n assertEquals(40, resultMatrixSignificance1.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance1.getCountWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance1.countWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance1.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance1.getRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance1.getRowNameWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance1.getDefaultRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultMeanWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance1.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultColNameWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance1.rowNameWidthTipText());\\n assertEquals(1, resultMatrixSignificance1.getColCount());\\n assertEquals(0, resultMatrixSignificance1.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateRowNamesTipText());\\n assertTrue(resultMatrixSignificance1.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance1.getMeanWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowAverage());\\n assertEquals(1, resultMatrixSignificance1.getVisibleRowCount());\\n assertTrue(resultMatrixSignificance1.getDefaultEnumerateColNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance1.colNameWidthTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance1.getShowAverage());\\n assertFalse(resultMatrixSignificance1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance1.meanPrecTipText());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance1.globalInfo());\\n assertEquals(2, resultMatrixSignificance1.getDefaultStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance1.getDisplayName());\\n assertEquals(0, resultMatrixSignificance1.getStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance1.showAverageTipText());\\n assertNotNull(resultMatrixSignificance1);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertFalse(resultMatrixSignificance1.equals((Object)resultMatrixSignificance0));\\n \\n String string1 = resultMatrixCSV1.toStringSummary();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNotNull(string1);\\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"-summary data not set-\\\", string1);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n assertFalse(string1.equals((Object)string0));\\n \\n boolean boolean1 = resultMatrixPlainText0.isRowName(1);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(boolean1);\\n assertTrue(boolean1 == boolean0);\\n \\n int int0 = resultMatrixLatex0.getDefaultColNameWidth();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, int0);\\n }\",\n \"int getN_estimators();\",\n \"@Test(timeout = 4000)\\n public void test048() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML(resultMatrixCSV0);\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertNotNull(resultMatrixHTML0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[][] intArray0 = new int[0][9];\\n // Undeclared exception!\\n try { \\n resultMatrixCSV0.setSummary(intArray0, intArray0);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 0\\n //\\n verifyException(\\\"weka.experiment.ResultMatrix\\\", e);\\n }\\n }\",\n \"@Test\\n public void testSmallDist() throws Exception {\\n assertTrue(\\n new ImageSimilarity(getFile(\\\"imageSimilarity/with.png\\\"))\\n .calcDistance(ImageIO.read(getFile(\\\"imageSimilarity/without.png\\\")))\\n > 0);\\n }\",\n \"double[] compute_t_Cm (double[][] t_mmnt, double thickness, double camber) {\\n double[] result = new double[15];\\n if (camber >= 0)\\n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \\n result[i] = ci15_from_javafoil_data(t_mmnt, aoa, thickness, camber);\\n else \\n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \\n result[i] = -ci15_from_javafoil_data(t_mmnt, -aoa, thickness, camber);\\n return result;\\n }\",\n \"@Test\\r\\n\\tpublic void testGetReducedCell() {\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"a\\\", new double[] { 10., 6., 4. }, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(0), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"alen\\\", 12.328828005937952, lattice1.getCMLVector3(\\r\\n\\t\\t\\t\\t0).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"b\\\", new double[] { 7., 11., 5. }, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(1), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"blen\\\", 13.96424004376894, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(1).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"c\\\", new double[] { 6.8, -4., -9. },\\r\\n\\t\\t\\t\\tlattice1.getCMLVector3(2), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"clen\\\", 11.968291440301744, lattice1.getCMLVector3(\\r\\n\\t\\t\\t\\t2).getLength(), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"vol\\\", -619.2, lattice1.getVolume(), EPS);\\r\\n\\t\\tdouble[] params = lattice1.getCellParameters();\\r\\n\\t\\tDoubleTestBase.assertEquals(\\\"params\\\", new double[] {\\r\\n\\t\\t\\t\\t12.328828005937952, 13.96424004376894, 11.968291440301744,\\r\\n\\t\\t\\t\\t104.34229064539636, 86.89206686722031, 25.025508299339933 },\\r\\n\\t\\t\\t\\tparams, EPS);\\r\\n\\r\\n\\t\\tCMLLattice reducedLattice = lattice1.getReducedCell();\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"a\\\", new double[] { 3.0, -5.0, -1.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(0), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"alen\\\", 5.916079783099616, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(0).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"b\\\", new double[] { 3.8, 1.0, -8.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(1), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"blen\\\", 8.912911982062877, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(1).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"c\\\", new double[] { 10.0, 6.0, 4.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(2), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"clen\\\", 12.328828005937952, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(2).getLength(), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"vol\\\", 619.2, reducedLattice.getVolume(), EPS);\\r\\n\\t\\tparams = reducedLattice.getCellParameters();\\r\\n\\t\\tDoubleTestBase.assertEquals(\\\"params\\\", new double[] { 5.916079783099616,\\r\\n\\t\\t\\t\\t8.912911982062877, 12.328828005937952, 83.73054962879554,\\r\\n\\t\\t\\t\\t93.14372638090103, 74.15166267765416 }, params, EPS);\\r\\n\\r\\n\\t}\",\n \"public double[] jacobi_iter(double[] x, double tolerance, int M) {\\n double[][] sContent = {{1.0, 0, 0}, {0, 1.0, 0}, {0, 0, 1.0}};\\n Matrix s = new Matrix(sContent);\\n\\n double[][] tContent = {{0, -0.5, -0.33333333}, {-0.5, 0, -0.25}, {-0.3333333333, -0.25, 0}};\\n Matrix t = new Matrix(tContent);\\n\\n //keep track of the number of iterations\\n\\n int counter = 0;\\n double tol = 10000; //initial\\n double[] tempXVector;\\n Matrix firstFirst;\\n double[] firstPart; //s^-1(-T)(Xn)\\n double[] secondPart; //s^-1(b)\\n String output = \\\"\\\";\\n\\n double tempXVectorNorm;\\n double xVectorNorm;\\n\\n for (double elem : x) {\\n output += elem + \\\", \\\";\\n }\\n System.out.println(\\\"x(0): \\\" + output);\\n while (tol > tolerance && counter < M) {\\n //perform the jacobi iterations\\n firstFirst = s.multiply(t);\\n firstPart = Matrix.matrixVectorMultiply(firstFirst, x);\\n secondPart = Matrix.matrixVectorMultiply(s, bVector);\\n tempXVector = Matrix.plus(firstPart, secondPart);\\n\\n //find norm of vectors and then subtract the\\n tempXVectorNorm = Matrix.norm(tempXVector);\\n xVectorNorm = Matrix.norm(x);\\n tol = tempXVectorNorm - xVectorNorm;\\n tol = Math.abs(tol);\\n counter++;\\n numOfIterations++;\\n x = tempXVector;\\n }\\n\\n output = \\\"\\\";\\n if (counter == M) {\\n System.out.println(\\\"Jacobi Output for x(N): null\\\");\\n System.out.println(\\\"Too many iterations without reaching a accurate answer.\\\");\\n return null;\\n }\\n for (double elem : x) {\\n output += elem + \\\", \\\";\\n }\\n System.out.println(\\\"Jacobi Output for x(N): \\\" + output);\\n System.out.println(\\\"Number of Iterations using Jacobi Algorithm: \\\" + counter + \\\"\\\\n\\\");\\n return x;\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[7][2];\\n double[] doubleArray1 = new double[7];\\n doubleArray1[0] = 0.0;\\n doubleArray1[1] = (-587.14627);\\n doubleArray1[2] = (-2542.40202289275);\\n doubleArray1[3] = (-167.679515);\\n doubleArray1[4] = (-1.0);\\n doubleArray1[5] = 1.0;\\n doubleArray1[6] = (-1.0);\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[5];\\n doubleArray2[0] = (-167.679515);\\n doubleArray2[1] = (-2692.352);\\n doubleArray2[2] = (-167.679515);\\n doubleArray2[3] = (-2542.40202289275);\\n doubleArray2[4] = (-587.14627);\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[4];\\n doubleArray3[0] = (-1.0);\\n doubleArray3[1] = (-1.0);\\n doubleArray3[2] = 1.0;\\n doubleArray3[3] = (-2542.40202289275);\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[7];\\n doubleArray4[0] = (-2692.352);\\n doubleArray4[1] = (-1.0);\\n doubleArray4[2] = (-1.0);\\n doubleArray4[3] = 0.0;\\n doubleArray4[4] = 1.0;\\n doubleArray4[5] = 0.0;\\n doubleArray4[6] = 1.0;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[1];\\n doubleArray5[0] = (-167.679515);\\n doubleArray0[4] = doubleArray5;\\n double[] doubleArray6 = new double[0];\\n doubleArray0[5] = doubleArray6;\\n double[] doubleArray7 = new double[1];\\n doubleArray7[0] = (-587.14627);\\n doubleArray0[6] = doubleArray7;\\n discretize0.m_CutPoints = doubleArray0;\\n String string0 = discretize0.invertSelectionTipText();\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.\\\", string0);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Before\\n public void setup() {\\n threeByTwo = new Matrix(new int[][] {{1, 2, 3}, {2, 5, 6}});\\n compareTwoByThree = new Matrix(new int[][] {{1, 42, 32}, {2, 15, 65}});\\n oneByOne = new Matrix(new int[][] {{4}});\\n rightOneByOne = new Matrix(new int[][] {{8}});\\n twoByFour = new Matrix(new int[][] {{1, 2, 3, 4}, {2, 5, 6, 8}});\\n twoByThree = new Matrix(new int[][] {{4, 5}, {3, 2}, {1, 1}});\\n threeByThree = new Matrix(new int[][] {{4, 5, 6}, {3, 2, 0}, {1, 1, 1}});\\n rightThreeByThree = new Matrix(new int[][] {{1, 2, 3}, {5, 2, 0}, {1, 1, 1}});\\n emptyMatrix = new Matrix(new int[0][0]);\\n // this is the known correct result of multiplying M1 by M2\\n twoByTwoResult = new Matrix(new int[][] {{13, 12}, {29, 26}});\\n threeByThreeResult = new Matrix(new int[][] {{35, 24, 18}, {13, 10, 9}, {7, 5, 4}});\\n oneByOneSum = new Matrix(new int[][] {{12}});\\n oneByOneProduct = new Matrix(new int[][] {{32}});\\n }\",\n \"@Test\\n\\tpublic void testOptimization() throws Exception {\\n\\t\\tfor(int i = 0; i < 20000000; i++) {\\n\\t\\t\\tint _300s = (int) (Math.random() * 100);\\n\\t\\t\\tint _100s = (int) (Math.random() * 100);\\n\\t\\t\\tint _50s = (int) (Math.random() * 100);\\n\\t\\t\\tint misses = (int) (Math.random() * 100);\\n\\t\\t\\t\\n\\t\\t\\tdouble acc = OsuApiScore.getAccuracy(_300s, _100s, _50s, misses);\\n\\t\\t\\t\\n\\t\\t\\tAccuracyDistribution accuracyDistribution = AccuracyDistribution.closest(\\n\\t\\t\\t\\t\\t_300s + _100s + _50s + misses, misses,\\n\\t\\t\\t\\t\\tacc);\\n\\t\\t\\t\\n\\t\\t\\tdouble rec = OsuApiScore.getAccuracy(accuracyDistribution.getX300(),\\n\\t\\t\\t\\t\\taccuracyDistribution.getX100(), accuracyDistribution.getX50(),\\n\\t\\t\\t\\t\\taccuracyDistribution.getMiss());\\n\\t\\t\\t\\n\\t\\t\\tassertEquals(acc, rec, 0d);\\n\\t\\t}\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test35() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n assertFalse(discretize0.getMakeBinary());\\n \\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n Attribute attribute0 = new Attribute(\\\"invalid CVS revision - not dots!\\\");\\n attribute0.setWeight(0.0);\\n Attribute.typeToString(attribute0);\\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\\n arrayList0.add(attribute0);\\n Instances instances0 = new Instances(\\\"UR<dj;O\\\", arrayList0, 3);\\n Instances instances1 = new Instances(instances0);\\n boolean boolean0 = discretize0.setInputFormat(instances1);\\n assertFalse(boolean0);\\n \\n String[] stringArray0 = new String[3];\\n stringArray0[0] = \\\".bsi\\\";\\n stringArray0[1] = \\\"invalid CVS revision - not dots!\\\";\\n stringArray0[2] = \\\"@end\\\";\\n discretize0.setOptions(stringArray0);\\n discretize0.setOptions(stringArray0);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n }\",\n \"@Test\\n\\tpublic void testNoCollapseByEditDistance() {\\n\\t\\tString cellBC = \\\"ATCAGGGACAGA\\\";\\n\\t\\tString molBC = \\\"CGGGGCTC\\\";\\n\\t\\tint snpPos = 76227022;\\n\\t\\tInterval snpInterval = new Interval(\\\"HUMAN_1\\\", snpPos, snpPos, true, \\\"test\\\");\\n\\t\\tSampleGenotypeProbabilities result = new SampleGenotypeProbabilities(snpInterval, cellBC);\\n\\t\\tSNPUMIBasePileup p = getPileUpFromFile(cellBC, molBC);\\n\\t\\tresult.add(p);\\n\\n\\t\\t// let's add another pileup in nowhere near the ED\\n\\t\\t// result has 2 different bases for the two pileups.\\n\\t\\tString molBC2 = \\\"ZZZZZZZZ\\\";\\n\\t\\tSNPUMIBasePileup p2 = getPileUpFromFile(cellBC, molBC2);\\n\\t\\tchar[] b2 = { 'G', 'G', 'T' };\\n\\t\\tbyte[] bases2 = new byte[b2.length];\\n\\t\\tStringUtil.charsToBytes(b2, 0, b2.length, bases2, 0);\\n\\t\\tbyte[] quals2 = { 37, 37, 10 };\\n\\t\\tp2.setBasesAndQualities(bases2, quals2);\\n\\t\\tresult.add(p2);\\n\\t\\tresult.collapseUMIs(1);\\n\\n\\t\\tList<Byte> qualResult2 = result.getQualities();\\n\\t\\tAssert.assertEquals(qualResult2.size(), 2);\\n\\t\\tAssert.assertEquals(new Byte((byte) 31), qualResult2.get(0));\\n\\t\\tAssert.assertEquals(new Byte((byte) 5), qualResult2.get(1));\\n\\t}\",\n \"@Test\\n\\tpublic void testaGetMensagem() {\\n\\t\\tAssert.assertEquals(\\n\\t\\t\\t\\t\\\"Erro no getMensagem()\\\",\\n\\t\\t\\t\\t\\\"Disciplina 1 eh avaliada com 5 minitestes, eliminando 1 dos mais baixos.\\\",\\n\\t\\t\\t\\tminitElimBai_1.getMensagem());\\n\\t\\tAssert.assertEquals(\\n\\t\\t\\t\\t\\\"Erro no getMensagem()\\\",\\n\\t\\t\\t\\t\\\"Disciplina 2 eh avaliada com 10 minitestes, eliminando 2 dos mais baixos.\\\",\\n\\t\\t\\t\\tminitElimBai_2.getMensagem());\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test050() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate(\\\"\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.areaUnderPRC((-1));\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"@Test\\n public void testQuantile() {\\n System.out.println(\\\"quantile\\\");\\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\\n instance.rand();\\n assertEquals(0, instance.quantile(0), 1E-7);\\n assertEquals(0, instance.quantile(0.001), 1E-7);\\n assertEquals(0, instance.quantile(0.01), 1E-7);\\n assertEquals(2, instance.quantile(0.1), 1E-7);\\n assertEquals(3, instance.quantile(0.2), 1E-7);\\n assertEquals(13, instance.quantile(0.9), 1E-7);\\n assertEquals(22, instance.quantile(0.99), 1E-7);\\n assertEquals(30, instance.quantile(0.999), 1E-7);\\n }\",\n \"public void testGetConformingStimulusLargeScale() throws Exception {\\n long start = System.currentTimeMillis();\\n Stimulus stimulus = new MockStimulus();\\n message.setConformingStimulus(stimulus);\\n for (int i = 0; i < TOTAL_ITERATION; i++) {\\n message.getConformingStimulus();\\n }\\n System.out.println(\\\"Calling \\\" + TOTAL_ITERATION + \\\"times getConformingStimulus method\\\"\\n + \\\" costs:\\\" + (System.currentTimeMillis() - start) + \\\" ms\\\");\\n }\",\n \"@Test\\n public void testLogLikelihood()\\n {\\n IDoubleArray T = null;\\n IDoubleArray C = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n double expResult = 0.0;\\n double result = instance.logLikelihood(T, C);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap<Integer,Integer> words)\\r\\n{\\r\\n int t=0;\\r\\n double LearningRates[] = {1, 0.1, 0.01};\\r\\n double Margins[] = {1, 0.1, 0.01};\\r\\n\\r\\n\\tint minInt =0; // Set To Your Desired Min Value\\r\\n int maxInt =2;\\r\\n\\tRandom rand = new Random();\\r\\n\\t\\r\\n \\tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\\r\\n\\r\\n \\tdouble LearningRate = LearningRates[randomNum];\\r\\n \\t\\r\\nfor(int m=0;m<LearningRates.length;m++)\\r\\n{\\r\\n for(int i=0;i<LearningRates.length;i++)\\r\\n {\\r\\n\\t LearningRate = LearningRates[i];\\r\\n\\t\\t double devAccuracyTotal = (double)0;\\r\\n\\t\\t int noOfEpochs= 20;\\r\\n\\t\\t int decreaseRate = 0;\\r\\n\\t\\t \\r\\n \\t\\t Double w[] = new Double[74500];\\r\\n\\t\\t Double min =-0.01; // Set To Your Desired Min Value\\r\\n\\t Double max = 0.01;\\r\\n\\t \\t\\tdouble smallRandNumber = min + new Random().nextDouble() * (max - min); \\r\\n\\t\\t\\tArrays.fill(w, smallRandNumber);\\r\\n\\t\\t\\t\\r\\n\\t\\t for(int epoch=0;epoch<noOfEpochs;epoch++)\\r\\n\\t\\t {\\r\\n\\t\\t\\t \\tw = learnMarginPerceptron(data,index,LearningRate,decreaseRate,w,Margins[m]);\\r\\n\\t\\t\\t \\tdecreaseRate = decreaseRate + index;\\r\\n\\t\\t\\r\\n\\t\\t }\\r\\n\\t\\t//CVSplit test\\r\\n \\tdouble cvAccuracy = testTestFile(w,testFileName,words);\\r\\n \\t\\r\\n// \\tSystem.out.println(\\\"Epoch\\\" + epoch +\\\" Dev Accuracy for Decaying Learning Rate \\\" + LearningRate + \\\" is \\\" + cvAccuracy);\\r\\n \\r\\n \\r\\n \\tSystem.out.println(\\\" ** Cv Accuracy for Margin Rate\\\" + Margins[m] + \\\" Learning Rate \\\" + LearningRate + \\\" is \\\" + cvAccuracy);\\r\\n \\tSystem.out.println(\\\" \\\");\\r\\n }\\r\\n}\\r\\n//double testAccuracy = testTestFile(w,testFileName);\\r\\n\\t//System.out.println(\\\"test Accuracy\\\" + testAccuracy);\\r\\n}\",\n \"@Test\\n void checkChangeImage() {\\n ImageViewModel imageModel = new ImageViewModel();\\n int countUp = 10;\\n int totalTime = 70;\\n\\n int i = imageModel.checkChangeImage(countUp, totalTime);\\n\\n assertEquals(1, i);\\n }\",\n \"@Test(timeout = 4000)\\n public void test04() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"9/&=Kq&\\\");\\n discretize0.getBinRangesString((-1679));\\n int int0 = (-98);\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-98));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test23() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setIgnoreClass(true);\\n discretize0.getOptions();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualFrequencyBinning((-821));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.core.Instances\\\", e);\\n }\\n }\",\n \"private void findAbdomenCM(int sliceNum) {\\r\\n \\r\\n // load up the sliceBuffer with the appropriate slice from the abdomen label image\\r\\n try {\\r\\n abdomenImage.exportData(sliceNum * sliceSize, sliceSize, sliceBuffer);\\r\\n } catch (IOException ex) {\\r\\n System.err.println(\\\"findAbdomenCM(...) Error exporting data on slice: \\\" +sliceNum);\\r\\n return;\\r\\n }\\r\\n\\r\\n int xcm = 0, ycm = 0, pixCount = 0;\\r\\n for (int idx= 0, y = 0; y < yDim; y++) {\\r\\n for (int x = 0; x < xDim; x++, idx++) {\\r\\n if (sliceBuffer[idx] == abdomenTissueLabel) {\\r\\n xcm += x;\\r\\n ycm += y;\\r\\n pixCount++;\\r\\n }\\r\\n } // end for (int x = 0; ...)\\r\\n } // end for (int idx = 0, ...)\\r\\n \\r\\n if (pixCount == 0) {\\r\\n// System.err.println(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n MipavUtil.displayError(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n // set the centerOfMass to an generate an obvious error \\r\\n centerOfMass[0] = (short)-1;\\r\\n centerOfMass[1] = (short)-1;\\r\\n return;\\r\\n }\\r\\n \\r\\n centerOfMass[0] = (short)(xcm / pixCount);\\r\\n centerOfMass[1] = (short)(ycm / pixCount);\\r\\n }\",\n \"@Test(timeout = 4000)\\n public void test13() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n String string0 = discretize0.makeBinaryTipText();\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(\\\"Make resulting attributes binary.\\\", string0);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(10, discretize0.getBins());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getMakeBinary());\\n }\",\n \"@Test(timeout = 4000)\\n public void test102() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate(\\\"\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double[][] doubleArray0 = evaluation0.confusionMatrix();\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(2, doubleArray0.length);\\n }\",\n \"@Test(timeout = 4000)\\n public void test028() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n String[] stringArray0 = new String[4];\\n stringArray0[0] = \\\"v\\\";\\n stringArray0[1] = \\\"[\\\";\\n stringArray0[2] = \\\"*\\\";\\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML(0, 16);\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(16, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertNotNull(resultMatrixHTML0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int int0 = 2298;\\n resultMatrixHTML0.setRowNameWidth(2298);\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(2298, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(16, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n resultMatrixHTML0.setRowNameWidth(1);\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(1, resultMatrixHTML0.getRowNameWidth());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(16, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n boolean boolean0 = resultMatrixHTML0.getShowAverage();\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(1, resultMatrixHTML0.getRowNameWidth());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(16, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(boolean0);\\n \\n ResultMatrixPlainText resultMatrixPlainText0 = new ResultMatrixPlainText();\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertNotNull(resultMatrixPlainText0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n boolean boolean1 = resultMatrixPlainText0.isSignificance(16);\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertFalse(boolean1);\\n assertTrue(boolean1 == boolean0);\\n \\n int int1 = resultMatrixHTML0.getColCount();\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(16, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(1, resultMatrixHTML0.getRowNameWidth());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(16, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(0, int1);\\n assertFalse(int1 == int0);\\n \\n ResultMatrixHTML resultMatrixHTML1 = null;\\n try {\\n resultMatrixHTML1 = new ResultMatrixHTML((-1157), 5);\\n fail(\\\"Expecting exception: NegativeArraySizeException\\\");\\n \\n } catch(NegativeArraySizeException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.experiment.ResultMatrix\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test18() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"%\\\");\\n FileSystemHandling.createFolder((EvoSuiteFile) null);\\n discretize0.findNumBinsTipText();\\n double[][] doubleArray0 = new double[0][7];\\n discretize0.m_CutPoints = doubleArray0;\\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, false, true);\\n discretize0.setInvertSelection(false);\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void test_IsOxideCorr() {\\n System.out.println(\\\"Testing MeasuredRatioModel's isOxideCorr()\\\");\\n MeasuredRatioModel instance = new MeasuredRatioModel();\\n boolean expResult = false;\\n boolean result = instance.isOxideCorr();\\n assertEquals(expResult, result);\\n instance = new MeasuredRatioModel(\\\"Specific\\\",new BigDecimal(\\\"213\\\"),\\\"ABS\\\",new BigDecimal(\\\"0.4324\\\"),true,true);\\n result = instance.isOxideCorr();\\n expResult=true;\\n assertEquals(expResult, result);\\n }\",\n \"@Test\\n\\tpublic final void testProcGridCoord() {\\n\\t\\tfor (int d=0; d<DIMENSIONALITY; d++) {\\n\\t\\t\\tassertTrue(0 <= block.procGridCoord(d)\\n\\t\\t\\t\\t\\t&& block.procGridCoord(d)<block.procGridSize(d));\\n\\t\\t}\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test061() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[][] intArray0 = new int[3][0];\\n int[] intArray1 = new int[1];\\n intArray1[0] = 1;\\n intArray0[0] = intArray1;\\n int[] intArray2 = new int[9];\\n assertFalse(intArray2.equals((Object)intArray1));\\n \\n intArray2[0] = 1;\\n intArray2[1] = 2;\\n intArray2[2] = 2;\\n intArray2[3] = 0;\\n intArray2[4] = 3;\\n intArray2[6] = 2;\\n intArray2[7] = 1;\\n intArray2[8] = 2;\\n intArray0[1] = intArray2;\\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n String string0 = resultMatrixSignificance0.getRevision();\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertNotNull(string0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(\\\"8034\\\", string0);\\n \\n resultMatrixSignificance0.setColHidden(1003, false);\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n ResultMatrixSignificance resultMatrixSignificance1 = new ResultMatrixSignificance();\\n assertEquals(0, resultMatrixSignificance1.getCountWidth());\\n assertEquals(1, resultMatrixSignificance1.getVisibleColCount());\\n assertFalse(resultMatrixSignificance1.getDefaultPrintColNames());\\n assertEquals(1, resultMatrixSignificance1.getColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance1.colNameWidthTipText());\\n assertEquals(2, resultMatrixSignificance1.getDefaultMeanPrec());\\n assertTrue(resultMatrixSignificance1.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance1.showAverageTipText());\\n assertEquals(40, resultMatrixSignificance1.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance1.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance1.getColNameWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance1.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance1.stdDevPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance1.printRowNamesTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance1.printColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance1.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance1.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance1.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance1.getVisibleRowCount());\\n assertTrue(resultMatrixSignificance1.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance1.getStdDevWidth());\\n assertEquals(0, resultMatrixSignificance1.getMeanWidth());\\n assertEquals(2, resultMatrixSignificance1.getDefaultStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance1.getDisplayName());\\n assertFalse(resultMatrixSignificance1.getShowAverage());\\n assertTrue(resultMatrixSignificance1.getEnumerateColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance1.globalInfo());\\n assertFalse(resultMatrixSignificance1.getPrintColNames());\\n assertFalse(resultMatrixSignificance1.getEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance1.getDefaultCountWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance1.showStdDevTipText());\\n assertEquals(1, resultMatrixSignificance1.getRowCount());\\n assertFalse(resultMatrixSignificance1.getShowStdDev());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance1.stdDevWidthTipText());\\n assertTrue(resultMatrixSignificance1.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixSignificance1.getSignificanceWidth());\\n assertEquals(2, resultMatrixSignificance1.getMeanPrec());\\n assertFalse(resultMatrixSignificance1.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance1.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance1.meanWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateRowNamesTipText());\\n assertEquals(40, resultMatrixSignificance1.getRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance1.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixSignificance1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance1.getDefaultStdDevWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance1.countWidthTipText());\\n assertNotNull(resultMatrixSignificance1);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(resultMatrixSignificance1.equals((Object)resultMatrixSignificance0));\\n \\n ResultMatrixSignificance resultMatrixSignificance2 = new ResultMatrixSignificance();\\n assertFalse(resultMatrixSignificance2.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance2.getMeanPrec());\\n assertFalse(resultMatrixSignificance2.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance2.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance2.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance2.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance2.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance2.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance2.getRowNameWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance2.meanWidthTipText());\\n assertTrue(resultMatrixSignificance2.getDefaultPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance2.meanPrecTipText());\\n assertEquals(2, resultMatrixSignificance2.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance2.getRowCount());\\n assertEquals(40, resultMatrixSignificance2.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance2.stdDevWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance2.printRowNamesTipText());\\n assertTrue(resultMatrixSignificance2.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance2.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance2.getDefaultCountWidth());\\n assertTrue(resultMatrixSignificance2.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance2.getShowAverage());\\n assertEquals(2, resultMatrixSignificance2.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance2.showAverageTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance2.colNameWidthTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance2.getDisplayName());\\n assertEquals(0, resultMatrixSignificance2.getStdDevWidth());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance2.globalInfo());\\n assertEquals(0, resultMatrixSignificance2.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance2.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance2.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance2.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance2.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance2.printColNamesTipText());\\n assertEquals(1, resultMatrixSignificance2.getColCount());\\n assertFalse(resultMatrixSignificance2.getRemoveFilterName());\\n assertFalse(resultMatrixSignificance2.getShowStdDev());\\n assertFalse(resultMatrixSignificance2.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance2.stdDevPrecTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance2.countWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance2.showStdDevTipText());\\n assertEquals(0, resultMatrixSignificance2.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixSignificance2.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance2.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance2.getSignificanceWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance2.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance2.getCountWidth());\\n assertEquals(1, resultMatrixSignificance2.getVisibleColCount());\\n assertTrue(resultMatrixSignificance2.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance2.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixSignificance2.getDefaultMeanPrec());\\n assertNotNull(resultMatrixSignificance2);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance0));\\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance1));\\n \\n int int0 = resultMatrixSignificance2.getVisibleColCount();\\n assertFalse(resultMatrixSignificance2.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance2.getMeanPrec());\\n assertFalse(resultMatrixSignificance2.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance2.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance2.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance2.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance2.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance2.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance2.getRowNameWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance2.meanWidthTipText());\\n assertTrue(resultMatrixSignificance2.getDefaultPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance2.meanPrecTipText());\\n assertEquals(2, resultMatrixSignificance2.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance2.getRowCount());\\n assertEquals(40, resultMatrixSignificance2.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance2.stdDevWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance2.printRowNamesTipText());\\n assertTrue(resultMatrixSignificance2.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance2.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance2.getDefaultCountWidth());\\n assertTrue(resultMatrixSignificance2.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance2.getShowAverage());\\n assertEquals(2, resultMatrixSignificance2.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance2.showAverageTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance2.colNameWidthTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance2.getDisplayName());\\n assertEquals(0, resultMatrixSignificance2.getStdDevWidth());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance2.globalInfo());\\n assertEquals(0, resultMatrixSignificance2.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance2.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance2.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance2.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance2.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance2.printColNamesTipText());\\n assertEquals(1, resultMatrixSignificance2.getColCount());\\n assertFalse(resultMatrixSignificance2.getRemoveFilterName());\\n assertFalse(resultMatrixSignificance2.getShowStdDev());\\n assertFalse(resultMatrixSignificance2.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance2.stdDevPrecTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance2.countWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance2.showStdDevTipText());\\n assertEquals(0, resultMatrixSignificance2.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixSignificance2.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance2.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixSignificance2.getSignificanceWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance2.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance2.getCountWidth());\\n assertEquals(1, resultMatrixSignificance2.getVisibleColCount());\\n assertTrue(resultMatrixSignificance2.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance2.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixSignificance2.getDefaultMeanPrec());\\n assertNotSame(resultMatrixSignificance2, resultMatrixSignificance0);\\n assertNotSame(resultMatrixSignificance2, resultMatrixSignificance1);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(1, int0);\\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance0));\\n assertFalse(resultMatrixSignificance2.equals((Object)resultMatrixSignificance1));\\n \\n ResultMatrixLatex resultMatrixLatex0 = new ResultMatrixLatex();\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getCountWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getRowNameWidth());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertFalse(resultMatrixLatex0.getPrintColNames());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertNotNull(resultMatrixLatex0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n boolean boolean0 = resultMatrixLatex0.isSignificance((-1));\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getCountWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getRowNameWidth());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertFalse(resultMatrixLatex0.getPrintColNames());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(boolean0);\\n }\",\n \"@Test(timeout = 4000)\\n public void test126() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n String string0 = resultMatrixCSV0.toString();\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertNotNull(string0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"Dataset,[1]\\\\nrow0,''\\\\n'[v/ /*]',''\\\\n\\\", string0);\\n \\n String[] stringArray0 = new String[4];\\n ResultMatrixGnuPlot resultMatrixGnuPlot0 = new ResultMatrixGnuPlot();\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultCountWidth());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixGnuPlot0.countWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getSignificanceWidth());\\n assertTrue(resultMatrixGnuPlot0.getPrintRowNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixGnuPlot0.getCountWidth());\\n assertEquals(50, resultMatrixGnuPlot0.getRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixGnuPlot0.significanceWidthTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultRowNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getMeanPrec());\\n assertFalse(resultMatrixGnuPlot0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixGnuPlot0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixGnuPlot0.stdDevPrecTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getColNameWidth());\\n assertEquals(0, resultMatrixGnuPlot0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixGnuPlot0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixGnuPlot0.printColNamesTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultMeanWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixGnuPlot0.colNameWidthTipText());\\n assertEquals(1, resultMatrixGnuPlot0.getVisibleColCount());\\n assertEquals(0, resultMatrixGnuPlot0.getStdDevWidth());\\n assertFalse(resultMatrixGnuPlot0.getShowAverage());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixGnuPlot0.showStdDevTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixGnuPlot0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixGnuPlot0.meanWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixGnuPlot0.showAverageTipText());\\n assertTrue(resultMatrixGnuPlot0.getPrintColNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixGnuPlot0.meanPrecTipText());\\n assertEquals(1, resultMatrixGnuPlot0.getRowCount());\\n assertEquals(\\\"GNUPlot\\\", resultMatrixGnuPlot0.getDisplayName());\\n assertFalse(resultMatrixGnuPlot0.getRemoveFilterName());\\n assertEquals(1, resultMatrixGnuPlot0.getColCount());\\n assertEquals(1, resultMatrixGnuPlot0.getVisibleRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixGnuPlot0.stdDevWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateRowNamesTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowAverage());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintRowNames());\\n assertEquals(\\\"Generates output for a data and script file for GnuPlot.\\\", resultMatrixGnuPlot0.globalInfo());\\n assertNotNull(resultMatrixGnuPlot0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n resultMatrixGnuPlot0.setSize(0, 0);\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixGnuPlot0.getRowCount());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultCountWidth());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixGnuPlot0.countWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getSignificanceWidth());\\n assertTrue(resultMatrixGnuPlot0.getPrintRowNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixGnuPlot0.getCountWidth());\\n assertEquals(50, resultMatrixGnuPlot0.getRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixGnuPlot0.significanceWidthTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getVisibleColCount());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultRowNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getMeanPrec());\\n assertFalse(resultMatrixGnuPlot0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixGnuPlot0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixGnuPlot0.stdDevPrecTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getColNameWidth());\\n assertEquals(0, resultMatrixGnuPlot0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixGnuPlot0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixGnuPlot0.printColNamesTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultMeanWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixGnuPlot0.colNameWidthTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getVisibleRowCount());\\n assertEquals(0, resultMatrixGnuPlot0.getStdDevWidth());\\n assertFalse(resultMatrixGnuPlot0.getShowAverage());\\n assertEquals(0, resultMatrixGnuPlot0.getColCount());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixGnuPlot0.showStdDevTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixGnuPlot0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixGnuPlot0.meanWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixGnuPlot0.showAverageTipText());\\n assertTrue(resultMatrixGnuPlot0.getPrintColNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixGnuPlot0.meanPrecTipText());\\n assertEquals(\\\"GNUPlot\\\", resultMatrixGnuPlot0.getDisplayName());\\n assertFalse(resultMatrixGnuPlot0.getRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixGnuPlot0.stdDevWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateRowNamesTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowAverage());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintRowNames());\\n assertEquals(\\\"Generates output for a data and script file for GnuPlot.\\\", resultMatrixGnuPlot0.globalInfo());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n int int0 = resultMatrixGnuPlot0.getDefaultColNameWidth();\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixGnuPlot0.getRowCount());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultCountWidth());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixGnuPlot0.countWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixGnuPlot0.getSignificanceWidth());\\n assertTrue(resultMatrixGnuPlot0.getPrintRowNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixGnuPlot0.getCountWidth());\\n assertEquals(50, resultMatrixGnuPlot0.getRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixGnuPlot0.significanceWidthTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getVisibleColCount());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultRowNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getMeanPrec());\\n assertFalse(resultMatrixGnuPlot0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixGnuPlot0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixGnuPlot0.stdDevPrecTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getColNameWidth());\\n assertEquals(0, resultMatrixGnuPlot0.getMeanWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixGnuPlot0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixGnuPlot0.printColNamesTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultMeanWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixGnuPlot0.colNameWidthTipText());\\n assertEquals(0, resultMatrixGnuPlot0.getVisibleRowCount());\\n assertEquals(0, resultMatrixGnuPlot0.getStdDevWidth());\\n assertFalse(resultMatrixGnuPlot0.getShowAverage());\\n assertEquals(0, resultMatrixGnuPlot0.getColCount());\\n assertEquals(0, resultMatrixGnuPlot0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixGnuPlot0.showStdDevTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixGnuPlot0.printRowNamesTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixGnuPlot0.meanWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixGnuPlot0.showAverageTipText());\\n assertTrue(resultMatrixGnuPlot0.getPrintColNames());\\n assertEquals(2, resultMatrixGnuPlot0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixGnuPlot0.meanPrecTipText());\\n assertEquals(\\\"GNUPlot\\\", resultMatrixGnuPlot0.getDisplayName());\\n assertFalse(resultMatrixGnuPlot0.getRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixGnuPlot0.stdDevWidthTipText());\\n assertFalse(resultMatrixGnuPlot0.getEnumerateRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixGnuPlot0.enumerateRowNamesTipText());\\n assertEquals(50, resultMatrixGnuPlot0.getDefaultColNameWidth());\\n assertEquals(2, resultMatrixGnuPlot0.getStdDevPrec());\\n assertFalse(resultMatrixGnuPlot0.getDefaultShowAverage());\\n assertTrue(resultMatrixGnuPlot0.getDefaultPrintRowNames());\\n assertEquals(\\\"Generates output for a data and script file for GnuPlot.\\\", resultMatrixGnuPlot0.globalInfo());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(50, int0);\\n }\",\n \"@Test\\n public void testMedian() {\\n System.out.println(\\\"median\\\");\\n ImageFilter instance = null;\\n BufferedImage expResult = null;\\n BufferedImage result = instance.median();\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testCompute() {\\n System.out.println(\\\"compute\\\");\\n ParallelMergeSort instance = null;\\n instance.compute();\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testPerturbationExpectation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray pi0 = null;\\n IDoubleArray observable = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.perturbationExpectation(M, pi0, observable, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test public void estimatedMonthlyCostTest() throws NegativeValueException {\\n PetBunny pb1 = new PetBunny(\\\"Floppy\\\", \\\"Holland Lop\\\", 3.5);\\n \\n Assert.assertEquals(6.475, pb1.estimatedMonthlyCost(), 0.00001);\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7719333","0.76359123","0.73141825","0.57383484","0.56049335","0.5368913","0.5366745","0.53475344","0.5276815","0.5211457","0.51922","0.5185153","0.51824313","0.5180554","0.51034254","0.50930893","0.50909305","0.5089265","0.508482","0.50655603","0.50131375","0.4967044","0.49259824","0.49166456","0.49056277","0.4897456","0.48964065","0.4878053","0.48741156","0.48628837","0.48621482","0.4857977","0.4856882","0.4843203","0.4839442","0.48388618","0.48254445","0.48191392","0.48160744","0.48117122","0.48064062","0.48031175","0.48019585","0.47925425","0.47874644","0.47844023","0.4783126","0.47804153","0.47622862","0.4751772","0.47323477","0.472947","0.4725859","0.4717567","0.4714627","0.4706283","0.46862835","0.46653253","0.46588334","0.46533224","0.46517122","0.46484607","0.46459064","0.46393064","0.46359068","0.46323836","0.460578","0.46015692","0.46007916","0.46004763","0.4596998","0.459289","0.45927462","0.4591392","0.45903093","0.45902893","0.45858636","0.45837918","0.45836726","0.45825702","0.45811412","0.4581066","0.4577238","0.45733365","0.45690647","0.45689315","0.4568545","0.45649377","0.45623296","0.45590258","0.4556435","0.45536804","0.45520693","0.4550323","0.45498943","0.45419466","0.45397726","0.4532619","0.45256138","0.45127285"],"string":"[\n \"0.7719333\",\n \"0.76359123\",\n \"0.73141825\",\n \"0.57383484\",\n \"0.56049335\",\n \"0.5368913\",\n \"0.5366745\",\n \"0.53475344\",\n \"0.5276815\",\n \"0.5211457\",\n \"0.51922\",\n \"0.5185153\",\n \"0.51824313\",\n \"0.5180554\",\n \"0.51034254\",\n \"0.50930893\",\n \"0.50909305\",\n \"0.5089265\",\n \"0.508482\",\n \"0.50655603\",\n \"0.50131375\",\n \"0.4967044\",\n \"0.49259824\",\n \"0.49166456\",\n \"0.49056277\",\n \"0.4897456\",\n \"0.48964065\",\n \"0.4878053\",\n \"0.48741156\",\n \"0.48628837\",\n \"0.48621482\",\n \"0.4857977\",\n \"0.4856882\",\n \"0.4843203\",\n \"0.4839442\",\n \"0.48388618\",\n \"0.48254445\",\n \"0.48191392\",\n \"0.48160744\",\n \"0.48117122\",\n \"0.48064062\",\n \"0.48031175\",\n \"0.48019585\",\n \"0.47925425\",\n \"0.47874644\",\n \"0.47844023\",\n \"0.4783126\",\n \"0.47804153\",\n \"0.47622862\",\n \"0.4751772\",\n \"0.47323477\",\n \"0.472947\",\n \"0.4725859\",\n \"0.4717567\",\n \"0.4714627\",\n \"0.4706283\",\n \"0.46862835\",\n \"0.46653253\",\n \"0.46588334\",\n \"0.46533224\",\n \"0.46517122\",\n \"0.46484607\",\n \"0.46459064\",\n \"0.46393064\",\n \"0.46359068\",\n \"0.46323836\",\n \"0.460578\",\n \"0.46015692\",\n \"0.46007916\",\n \"0.46004763\",\n \"0.4596998\",\n \"0.459289\",\n \"0.45927462\",\n \"0.4591392\",\n \"0.45903093\",\n \"0.45902893\",\n \"0.45858636\",\n \"0.45837918\",\n \"0.45836726\",\n \"0.45825702\",\n \"0.45811412\",\n \"0.4581066\",\n \"0.4577238\",\n \"0.45733365\",\n \"0.45690647\",\n \"0.45689315\",\n \"0.4568545\",\n \"0.45649377\",\n \"0.45623296\",\n \"0.45590258\",\n \"0.4556435\",\n \"0.45536804\",\n \"0.45520693\",\n \"0.4550323\",\n \"0.45498943\",\n \"0.45419466\",\n \"0.45397726\",\n \"0.4532619\",\n \"0.45256138\",\n \"0.45127285\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65835816"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":135797,"cells":{"query":{"kind":"string","value":"Test of estimateCstepping method, of class MarkovModelUtilities."},"document":{"kind":"string","value":"@Test\n public void testEstimateCstepping_Iterable_int()\n {\n Iterable<IIntArray> trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCstepping(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["@Test\n public void testEstimateT()\n {\n \n IDoubleArray counts = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateT(counts);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCstepping_IIntArray_int()\n {\n IIntArray traj = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCstepping(traj, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateC_Iterable_int()\n {\n Iterable<IIntArray> trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateC(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCmilestoning_3args_1()\n {\n Iterable<IIntArray> trajs = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCmilestoning_3args_2()\n {\n IIntArray traj = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testForwardCommittor()\n {\n IDoubleArray M = null;\n IIntArray A = null;\n IIntArray B = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.forwardCommittor(M, A, B);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test02() throws Throwable {\n Discretize discretize0 = new Discretize(\"l*{e5SNh\");\n boolean boolean0 = true;\n discretize0.m_ClassIndex = 177;\n discretize0.setUseEqualFrequency(true);\n double[][] doubleArray0 = new double[5][9];\n double[] doubleArray1 = new double[3];\n doubleArray1[0] = (double) 177;\n doubleArray1[1] = (double) 177;\n doubleArray1[2] = (double) 177;\n doubleArray0[0] = doubleArray1;\n double[] doubleArray2 = new double[2];\n doubleArray2[0] = (double) 177;\n doubleArray2[1] = (double) 177;\n doubleArray0[1] = doubleArray2;\n double[] doubleArray3 = new double[9];\n doubleArray3[0] = (double) 177;\n doubleArray3[1] = (double) 177;\n doubleArray3[2] = (double) 177;\n doubleArray3[3] = (double) 177;\n doubleArray3[4] = (double) 177;\n doubleArray3[5] = (double) 177;\n doubleArray3[6] = (double) 177;\n doubleArray3[7] = (double) 177;\n doubleArray3[8] = (double) 177;\n doubleArray0[2] = doubleArray3;\n double[] doubleArray4 = new double[8];\n doubleArray4[0] = (double) 177;\n doubleArray4[1] = (double) 177;\n doubleArray4[2] = (double) 177;\n doubleArray4[3] = (double) 177;\n doubleArray4[4] = (double) 177;\n doubleArray4[5] = (double) 177;\n doubleArray4[6] = (double) 177;\n doubleArray4[7] = (double) 177;\n doubleArray0[3] = doubleArray4;\n double[] doubleArray5 = new double[0];\n doubleArray0[4] = doubleArray5;\n discretize0.m_CutPoints = doubleArray0;\n discretize0.listOptions();\n discretize0.setIgnoreClass(true);\n String[] stringArray0 = new String[0];\n Discretize.main(stringArray0);\n discretize0.findNumBinsTipText();\n discretize0.setInvertSelection(true);\n // Undeclared exception!\n try { \n discretize0.getBinRangesString(511);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 511\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n\tpublic void testRepeatibleDynamics() throws IOException, PropertiesException {\n\n\t\t\n\t\t// INSTANTIATE benchmark \n\t\tProperties props = PropertiesUtil.setpointProperties(new File (\"src/main/resources/sim.properties\"));\n\t\tSetPointGenerator lg = new SetPointGenerator (props);\n\t\tList<ExternalDriver> externalDrivers = new ArrayList<ExternalDriver>();\n\t\texternalDrivers.add(lg);\n\t\tIndustrialBenchmarkDynamics d = new IndustrialBenchmarkDynamics (props, externalDrivers);\n\t\tRandom actionRand = new Random(System.currentTimeMillis());\n \n // 1) do 100000 random steps, in order to initialize dynamics\n\t\tfinal ActionDelta action = new ActionDelta(0.001f, 0.001f, 0.001f); \n\t\tfor (int i=0; i<INIT_STEPS; i++) {\n\t\t\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\td.step(action);\n\t\t}\n\t\t\n\t\t// 2) memorize current observable state and current markov state\n\t\tfinal ObservableState os = d.getState();\n\t\tfinal DataVector ms = d.getInternalMarkovState();\n\t\tSystem.out.println (\"init o-state: \" + os.toString());\n\t\tSystem.out.println (\"init m-state: \" + ms.toString());\n\t\t\n\t\t\n\t\t// 3) perform test trajectory and memorize states\n\t\tactionRand.setSeed(ACTION_SEED);\n\t\tDataVector oStates[] = new DataVector[MEM_STEPS];\n\t\tDataVector mStates[] = new DataVector[MEM_STEPS];\n\t\t\t\t\n\t\tfor (int i=0; i<MEM_STEPS; i++) {\n\t\t\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\td.step(action);\n\t\t\toStates[i] = d.getState();\n\t\t\tmStates[i] = d.getInternalMarkovState();\n\t\t}\n\t\t\n\t\t// 4) reset dynamics & parameters and internal markov state\n\t\td.reset();\n\t\td.setInternalMarkovState(ms);\n\t\t\n\t\t// 5) reperform test and check if values are consistent\n\t\tactionRand.setSeed(ACTION_SEED); // reproduce action sequence\n\t\tDataVector oState = null;\n\t\tDataVector mState = null;\n\t\tfor (int i=0; i<MEM_STEPS; i++) {\n\t\t\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\t\n\t\t\td.step(action);\n\t\t\toState = d.getState();\n\t\t\tmState = d.getInternalMarkovState();\n\t\t\t\n\t\t\t// check observable state\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.SetPoint), oState.getValue(ObservableStateDescription.SetPoint), 0.0001);\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.Fatigue), oState.getValue(ObservableStateDescription.Fatigue), 0.0001);\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.Consumption), oState.getValue(ObservableStateDescription.Consumption), 0.0001);\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.RewardTotal), oState.getValue(ObservableStateDescription.RewardTotal), 0.0001);\n\n\t\t\t// \n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.CurrentOperationalCost), mState.getValue(MarkovianStateDescription.CurrentOperationalCost), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent2), mState.getValue(MarkovianStateDescription.FatigueLatent2), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent1), mState.getValue(MarkovianStateDescription.FatigueLatent1), 0.0001);\n\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionGainBeta), mState.getValue(MarkovianStateDescription.EffectiveActionGainBeta), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), mState.getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveShift), mState.getValue(MarkovianStateDescription.EffectiveShift), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.MisCalibration), mState.getValue(MarkovianStateDescription.MisCalibration), 0.0001);\n\t\t\t\n\t\t\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), mState.getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), 0.0001);\n\t\t\t\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardFatigue), mState.getValue(MarkovianStateDescription.RewardFatigue), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardConsumption), mState.getValue(MarkovianStateDescription.RewardConsumption), 0.0001);\n\t\t}\n\t\t\n\t\tSystem.out.println (\"last o-state 1st trajectory: \" + oStates[oStates.length-1]);\n\t\tSystem.out.println (\"last o-state 2nd trajectory: \" + oState);\n\t\t\n\t\tSystem.out.println (\"last m-state 1st trajectory: \" + mStates[oStates.length-1]);\n\t\tSystem.out.println (\"last m-state 2nd trajectory: \" + mState);\n\t}","public void steepestDescent() {\r\n ArrayList<Atom> atoms = cluster.getAtoms();\r\n Atom a;\r\n int numAtoms = atoms.size();\r\n double potentialEnergy;\r\n double trialPotential;\r\n int i;\r\n\r\n //Increment time\r\n totalTime += currentStep;\r\n\r\n //Calculate force on each atom\r\n cluster.calculateForces();\r\n\r\n //Get energy of current configuration\r\n HashMap<String, Double> energies = cluster.getEnergies();\r\n potentialEnergy = 0.0;\r\n for(String eName : energies.keySet())\r\n potentialEnergy += energies.get(eName).doubleValue();\r\n\r\n //Save current atom positions and advance atoms to trial positions\r\n for(i = 0; i < numAtoms; i++) {\r\n a = atoms.get(i);\r\n\r\n savedLocations[i].x = a.location.x;\r\n savedLocations[i].y = a.location.y;\r\n savedLocations[i].z = a.location.z;\r\n\r\n //Compute trial locations\r\n a.location.x += a.force.x * currentStep;\r\n a.location.y += a.force.y * currentStep;\r\n a.location.z += a.force.z * currentStep;\r\n }\r\n\r\n //Get energy of the trial configuration\r\n energies = cluster.getEnergies();\r\n trialPotential = 0.0;\r\n for(String eName : energies.keySet())\r\n trialPotential += energies.get(eName).doubleValue();\r\n\r\n //Decide whether to keep trial configuration based on convergence\r\n if(trialPotential < potentialEnergy) {\r\n //Keep positions and check for convergence\r\n if(Math.abs((potentialEnergy - trialPotential) / potentialEnergy) < convergenceCriterion)\r\n isConverged = true;\r\n\r\n currentStep *= 1.2;\r\n } else {\r\n //Restore old positions\r\n for(i = 0; i < numAtoms; i++) {\r\n a = atoms.get(i);\r\n\r\n a.location.x = savedLocations[i].x;\r\n a.location.y = savedLocations[i].y;\r\n a.location.z = savedLocations[i].z;\r\n }\r\n\r\n currentStep *= 0.5;\r\n }\r\n }","@Test\n public void testMetastableStates()\n {\n IDoubleArray M = null;\n int nstates = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IIntArray expResult = null;\n IIntArray result = instance.metastableStates(M, nstates);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testBackwardCommittor()\n {\n IDoubleArray M = null;\n IIntArray A = null;\n IIntArray B = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.backwardCommittor(M, A, B);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateC_IIntArray_int()\n {\n IIntArray traj = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateC(traj, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n void makeCoffee() {\n Assertions.assertFalse(machine.makeCoffee());\n\n //try with cup\n Assertions.assertDoesNotThrow(() -> {\n machine.positionCup();\n });\n\n Assertions.assertThrows(Exception.class, () -> {\n machine.positionCup();\n });\n\n\n Assertions.assertTrue(machine.makeCoffee());\n\n //check if correct amount as been subtracted\n Assertions.assertEquals(machine.getCurrentWater(), 1000 - machine.waterPerCup);\n Assertions.assertEquals(machine.getCurrentBeans(), 800 - machine.beansPerCup);\n\n //reset\n machine.fillWater();\n machine.fillBeans();\n machine.setCupPositioned(true);\n\n //test over boundary\n Assertions.assertTrue(machine.makeCoffee());\n Assertions.assertTrue(machine.makeCoffee());\n\n //set only water to boundary values and below\n machine.setCurrentWater(0);\n Assertions.assertFalse(machine.makeCoffee());\n machine.setCurrentWater(machine.waterPerCup);\n Assertions.assertTrue(machine.makeCoffee());\n\n //reset water\n machine.fillWater();\n\n //set only beans to boundary value and below\n machine.setCurrentBeans(0);\n Assertions.assertFalse(machine.makeCoffee());\n machine.setCurrentBeans(machine.beansPerCup);\n Assertions.assertTrue(machine.makeCoffee());\n\n\n }","public void checkMovementZMinimum(ITestContext context) throws Exception {\n Reporter.testStart();\n String caseFolder=context.getName()+\"/\";\n String importFile=context.getCurrentXmlTest().getParameter(\"importFile\");\n String expectationFile=context.getCurrentXmlTest().getParameter(\"expectationFile\");\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\n String screenWithinCase=caseFolder+methodName;\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\n if(meshViewPage.isThePage()){\n\n uploadDCM(meshViewPage,importFile);\n\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\n Reporter.log(\"click Orientation Adjustment tab on left bar\");\n String text=\"0.1\";\n Reporter.log(\"select Movement step \"+text);\n Assert.assertEquals(oa.selectMovementStep(text),text,\"Cann't select \"+text);\n\n Integer clickTimes=40;\n String disResult=oa.clickDistanceMinus(clickTimes);\n String higher=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n\n clickTimes=5;\n disResult=oa.clickDistanceMinus(clickTimes);\n String middle=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n\n clickTimes=6;\n disResult=oa.clickDistanceMinus(clickTimes);//click times: 51\n String lower=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-5\",\"distance is not same as expected result\");\n\n embededCompareResult(higher,middle,lower, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\n\n disResult=oa.clickDistanceReset();\n embededScreenShot(screenWithinCase+\"_reset_\"+disResult,\"click Z reset(1 times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"0\",\"distance is not same as expected result\");\n }\n Reporter.testEnd();\n }","public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap<Integer,Integer> words)\r\n{\r\n int t=0;\r\n double LearningRates[] = {1, 0.1, 0.01};\r\n double Margins[] = {1, 0.1, 0.01};\r\n\r\n\tint minInt =0; // Set To Your Desired Min Value\r\n int maxInt =2;\r\n\tRandom rand = new Random();\r\n\t\r\n \tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\r\n\r\n \tdouble LearningRate = LearningRates[randomNum];\r\n \t\r\nfor(int m=0;m<LearningRates.length;m++)\r\n{\r\n for(int i=0;i<LearningRates.length;i++)\r\n {\r\n\t LearningRate = LearningRates[i];\r\n\t\t double devAccuracyTotal = (double)0;\r\n\t\t int noOfEpochs= 20;\r\n\t\t int decreaseRate = 0;\r\n\t\t \r\n \t\t Double w[] = new Double[74500];\r\n\t\t Double min =-0.01; // Set To Your Desired Min Value\r\n\t Double max = 0.01;\r\n\t \t\tdouble smallRandNumber = min + new Random().nextDouble() * (max - min); \r\n\t\t\tArrays.fill(w, smallRandNumber);\r\n\t\t\t\r\n\t\t for(int epoch=0;epoch<noOfEpochs;epoch++)\r\n\t\t {\r\n\t\t\t \tw = learnMarginPerceptron(data,index,LearningRate,decreaseRate,w,Margins[m]);\r\n\t\t\t \tdecreaseRate = decreaseRate + index;\r\n\t\t\r\n\t\t }\r\n\t\t//CVSplit test\r\n \tdouble cvAccuracy = testTestFile(w,testFileName,words);\r\n \t\r\n// \tSystem.out.println(\"Epoch\" + epoch +\" Dev Accuracy for Decaying Learning Rate \" + LearningRate + \" is \" + cvAccuracy);\r\n \r\n \r\n \tSystem.out.println(\" ** Cv Accuracy for Margin Rate\" + Margins[m] + \" Learning Rate \" + LearningRate + \" is \" + cvAccuracy);\r\n \tSystem.out.println(\" \");\r\n }\r\n}\r\n//double testAccuracy = testTestFile(w,testFileName);\r\n\t//System.out.println(\"test Accuracy\" + testAccuracy);\r\n}","@Test(timeout = 4000)\n public void test29() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[3][6];\n discretize0.setIgnoreClass(false);\n discretize0.m_CutPoints = doubleArray0;\n double[] doubleArray1 = new double[4];\n // Undeclared exception!\n try { \n discretize0.getCutPoints((-831));\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // -831\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","public void testStep() {\n\n DT_TractographyImage crossing = Images.getCrossing();\n\n FACT_FibreTracker tracker = new FACT_FibreTracker(crossing);\n\n Vector3D[] pds = crossing.getPDs(1,1,1);\n\n Vector3D step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 0, true);\n\n assertEquals(1.0, pds[0].dot(step.normalized()), 1E-8);\n\n assertEquals(Images.xVoxelDim / 2.0, step.mod(), 0.05);\n \n step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 0, false);\n\n assertEquals(-1.0, pds[0].dot(step.normalized()), 1E-8);\n\n step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 1, true);\n\n assertEquals(1.0, pds[1].dot(step.normalized()), 1E-8);\n\n\n for (int i = 0; i < 2; i++) {\n\n step = tracker.getNextStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), new Vector3D(pds[i].x + 0.01, pds[i].y + 0.02, pds[i].z + 0.03).normalized());\n\n assertEquals(1.0, pds[i].dot(step.normalized()), 1E-8);\n\n }\n\n \n }","@Test()\n public void testTrainWarmCFastSMO() {\n ClassificationDataSet train = FixedProblems.getHalfCircles(250, RandomUtil.getRandom(), 0.1, 0.2);\n\n PlattSMO warmModel = new PlattSMO(new LinearKernel(1));\n warmModel.setC(1);\n warmModel.train(train);\n\n PlattSMO warm = new PlattSMO(new LinearKernel(1));\n warm.setC(1e4);// too large to train efficently like noraml\n\n long start, end;\n\n start = System.currentTimeMillis();\n warm.train(train, warmModel);\n end = System.currentTimeMillis();\n long warmTime = (end - start);\n\n PlattSMO notWarm = new PlattSMO(new LinearKernel(1));\n notWarm.setC(1e4);// too large to train efficently like noraml\n\n start = System.currentTimeMillis();\n notWarm.train(train);\n end = System.currentTimeMillis();\n long normTime = (end - start);\n\n assertTrue(warmTime < normTime * 0.75);\n\n }","@Test(timeout = 4000)\n public void test03() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.setAttributeIndices(\"\");\n Filter.makeCopy(discretize0);\n discretize0.setDesiredWeightOfInstancesPerInterval(2.0);\n boolean boolean0 = true;\n discretize0.m_ClassIndex = 0;\n discretize0.setUseBinNumbers(true);\n discretize0.listOptions();\n discretize0.makeBinaryTipText();\n discretize0.getInvertSelection();\n // Undeclared exception!\n try { \n discretize0.calculateCutPointsByEqualWidthBinning((-458));\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void testStationaryDistribution()\n {\n try\n {\n IDoubleArray T = Doubles.create.fromFile(inputT1);\n IDoubleArray expResult = Doubles.create.fromFile(inputT1_pi); \n IDoubleArray pi = MarkovModel.util.stationaryDistribution(T);\n assertEqual(pi, expResult, 1e-5);\n }\n catch(IOException e)\n {\n \n e.printStackTrace();\n }\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n }","@Test\n public void testPerturbationExpectation()\n {\n IDoubleArray M = null;\n IDoubleArray pi0 = null;\n IDoubleArray observable = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.perturbationExpectation(M, pi0, observable, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void test03() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double[] doubleArray0 = new double[19];\n evaluation0.updateMargins(doubleArray0, 0, 0.0);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\n }","@Test\n void cupTesting() {\n Assertions.assertFalse(machine.isCupPositioned());\n Assertions.assertFalse(machine.removeCup());\n\n Assertions.assertDoesNotThrow(() -> machine.positionCup());\n\n Assertions.assertTrue(machine.removeCup());\n Assertions.assertFalse(machine.isCupPositioned());\n machine.setCupPositioned(true);\n Assertions.assertTrue(machine.isCupPositioned());\n\n }","public ModelParams optimize() throws Exception {\n\t\tint iterCount = 0;\n\t\tdivergence = KLDivergenceCalculator.calculate(cState, tState);\n\t\tSystem.out.println(\"KL Divergence : \" + divergence);\n\t\t// ((DivergenceChart) BeanFinder\n\t\t// .findBean(MetaConstants.BEAN_DIVERGENCE_CHART)).update(\n\t\t// iterCount, divergence);\n\t\tdouble delta[] = null;\n\t\tdouble theta[] = data.getParams().getParams();\n\t\tdouble newDivergence = 0.0d;\n\t\tdouble initialStep = data.getInitialStepSize();\n\t\tboolean optimized = false;\n\t\tdo {\n\t\t\tdelta = computeGradient(theta);\n\t\t\tfor (double step = initialStep; iterCount < data.getMaxIterations(); step /= 10) {\n\t\t\t\tSystem.out.println(\"Step size : \" + step);\n\t\t\t\tSystem.out.println(\"KL divergence before taking a step : \"\n\t\t\t\t\t\t+ KLDivergenceCalculator.calculate(cState, tState));\n\t\t\t\tfor (int i = 0; i < delta.length; i++) {\n\t\t\t\t\tif (Double.isNaN(delta[i]))\n\t\t\t\t\t\tSystem.out.print(\"delta is NAN!!\" + delta[i] + \", \");\n\t\t\t\t\ttheta[i] -= step * delta[i];\n\t\t\t\t\tSystem.out.print((theta[i]) + \", \");\n\t\t\t\t}\n\t\t\t\tSystem.out.println();\n\t\t\t\tgetNewState(theta);\n\t\t\t\titerCount++;\n\t\t\t\tnewDivergence = KLDivergenceCalculator\n\t\t\t\t\t\t.calculate(cState, tState);\n\t\t\t\tSystem.out.println(\"KL Divergence : \" + newDivergence);\n\t\t\t\t// ((DivergenceChart) BeanFinder\n\t\t\t\t// .findBean(MetaConstants.BEAN_DIVERGENCE_CHART)).update(\n\t\t\t\t// iterCount, newDivergence);\n\t\t\t\tif (newDivergence < divergence) {\n\t\t\t\t\tdivergence = newDivergence;\n\t\t\t\t\tinitialStep = step;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\t// before taking the next step size, reset theta to what it was\n\t\t\t\tfor (int i = 0; i < delta.length; i++)\n\t\t\t\t\ttheta[i] += step * delta[i];\n\t\t\t\tgetNewState(theta); // Added for checking. Not required.\n\t\t\t}\n\t\t\toptimized = optimized(theta);\n\t\t} while (!optimized && iterCount < data.getMaxIterations());\n\t\tModelParams optimParams = null;\n\t\tif (optimized) {\n\t\t\toptimParams = new ModelParams();\n\t\t\toptimParams.setParams(theta);\n\t\t}\n\t\treturn optimParams;\n\t}","@Test\n public void testEstimateCmilestoning_Iterable_int()\n {\n Iterable<IIntArray> trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test05() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[7][2];\n double[] doubleArray1 = new double[7];\n doubleArray1[0] = 0.0;\n doubleArray1[1] = (-587.14627);\n doubleArray1[2] = (-2542.40202289275);\n doubleArray1[3] = (-167.679515);\n doubleArray1[4] = (-1.0);\n doubleArray1[5] = 1.0;\n doubleArray1[6] = (-1.0);\n doubleArray0[0] = doubleArray1;\n double[] doubleArray2 = new double[5];\n doubleArray2[0] = (-167.679515);\n doubleArray2[1] = (-2692.352);\n doubleArray2[2] = (-167.679515);\n doubleArray2[3] = (-2542.40202289275);\n doubleArray2[4] = (-587.14627);\n doubleArray0[1] = doubleArray2;\n double[] doubleArray3 = new double[4];\n doubleArray3[0] = (-1.0);\n doubleArray3[1] = (-1.0);\n doubleArray3[2] = 1.0;\n doubleArray3[3] = (-2542.40202289275);\n doubleArray0[2] = doubleArray3;\n double[] doubleArray4 = new double[7];\n doubleArray4[0] = (-2692.352);\n doubleArray4[1] = (-1.0);\n doubleArray4[2] = (-1.0);\n doubleArray4[3] = 0.0;\n doubleArray4[4] = 1.0;\n doubleArray4[5] = 0.0;\n doubleArray4[6] = 1.0;\n doubleArray0[3] = doubleArray4;\n double[] doubleArray5 = new double[1];\n doubleArray5[0] = (-167.679515);\n doubleArray0[4] = doubleArray5;\n double[] doubleArray6 = new double[0];\n doubleArray0[5] = doubleArray6;\n double[] doubleArray7 = new double[1];\n doubleArray7[0] = (-587.14627);\n doubleArray0[6] = doubleArray7;\n discretize0.m_CutPoints = doubleArray0;\n String string0 = discretize0.invertSelectionTipText();\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertEquals(\"Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.\", string0);\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseEqualFrequency());\n }","@Test\r\n public void testAverageacceleration() {\r\n System.out.println(\"Average Acceleration\");\r\n \r\n // Test case one.\r\n System.out.println(\"Test case #1\"); \r\n \r\n double distance = 200;\r\n long time = 16;\r\n \r\n SceneControl instance = new SceneControl();\r\n \r\n double expResult = 0.78125;\r\n double result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case two.\r\n System.out.println(\"Test case #2\"); \r\n \r\n distance = 80;\r\n time = 200;\r\n \r\n \r\n expResult = .002;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case three.\r\n System.out.println(\"Test case #3\"); \r\n \r\n distance = 0;\r\n time = 15;\r\n \r\n \r\n expResult = 0;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case four.\r\n System.out.println(\"Test case #4\"); \r\n \r\n distance = 12;\r\n time = 1;\r\n \r\n \r\n expResult = 12;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case five.\r\n System.out.println(\"Test case #5\"); \r\n \r\n distance = 1;\r\n time = 1;\r\n \r\n \r\n expResult = 1;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case six.\r\n System.out.println(\"Test case #6\"); \r\n \r\n distance = 3;\r\n time = 8;\r\n \r\n \r\n expResult = .046875;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case seven.\r\n System.out.println(\"Test case #7\"); \r\n \r\n distance = 200;\r\n time = 1;\r\n \r\n \r\n expResult = 200;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n \r\n \r\n }","@Test\n public void testGetSetTemperaturaExteriorMinima() {\n System.out.println(\"getSetTemperaturaExteriorMinima\");\n double expResult = 10.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaExteriorMinima(expResult);\n double result = instance.getTemperaturaExteriorMinima();\n assertEquals(expResult, result, 0.0);\n }","public void checkMovementZReset(ITestContext context) {\n Reporter.testStart();\n String caseFolder=context.getName()+\"/\";\n String importFile=context.getCurrentXmlTest().getParameter(\"importFile\");\n String expectationFile=context.getCurrentXmlTest().getParameter(\"expectationFile\");\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\n String screenWithinCase=caseFolder+methodName;\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\n if(meshViewPage.isThePage()){\n\n uploadDCM(meshViewPage,importFile);\n\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\n Reporter.log(\"click Orientation Adjustment tab on left bar\");\n String text=\"0.1\";\n Reporter.log(\"select Movement step \"+text);\n Assert.assertEquals(oa.selectMovementStep(text),text,\"Cann't select \"+text);\n\n Integer clickTimes=30;\n String disResult=oa.clickDistanceMinus(clickTimes);\n embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-3\",\"distance is not same as expected result\");\n\n clickTimes=10;\n disResult=oa.clickDistancePlus(clickTimes);\n String lower=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-2\",\"distance is not same as expected result\");\n\n disResult=oa.clickDistancePlus(clickTimes);\n String middle=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-1\",\"distance is not same as expected result\");\n\n disResult=oa.clickDistanceReset();\n String higher=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z reset(1 times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"0\",\"distance is not same as expected result\");\n\n embededCompareResult(lower,middle,higher, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\n\n }\n Reporter.testEnd();\n }","private static void testTakeStep(Drunkard drunkard, int testStepTimes, int testStepSize){\n\t for (int i = 0; i < testStepTimes; i++)\n\t{\n\t ImPoint pointA = drunkard.getCurrentLoc(); \n\t\tdrunkard.takeStep();\n\t\tImPoint pointB = drunkard.getCurrentLoc();\n\t\t\n\t\tif (pointA.getX()-pointB.getX() != 0 && pointA.getY()-pointB.getY() != 0)\n\t\t{\n\t\t\tSystem.out.println(\"Not a valid compass direction!\");\n\t\t}\n\t\telse if (Math.sqrt((pointA.getX() - pointB.getX())*(pointA.getX() - pointB.getX()) + (pointA.getY() - pointB.getY())*(pointA.getY() - pointB.getY())) != testStepSize)\n\t\t{\n\t\t\tSystem.out.println(\"took step to (\" + pointB.getX() + \",\" + pointB.getY() + \"), FAILED, not a valid step\");\n\t\t}\n\t\telse\n\t\t{\n\t\t\tSystem.out.println(\"took step to (\" + pointB.getX() + \",\" + pointB.getY() + \"), SUCCEEDED\");\n\t\t}\n\t}\n\t System.out.println(); \n\t}","@Test\n public void testIsTransitionMatrix()\n {\n IDoubleArray T = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n boolean expResult = false;\n boolean result = instance.isTransitionMatrix(T);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test072() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate((String) null);\n Evaluation evaluation0 = new Evaluation(instances0);\n double[] doubleArray0 = new double[7];\n evaluation0.updateMargins(doubleArray0, 3, (-1));\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n }","@Test(timeout = 4000)\n public void test19() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.getCutPoints((-2276));\n assertFalse(discretize0.getFindNumBins());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertFalse(discretize0.getUseEqualFrequency());\n }","@Test\n public void testGiveChange() throws Exception {\n System.out.println(\"giveChange\");\n //CashRegister instance = new CashRegister();\n assertEquals(0.0, instance.giveChange(), EPSILON);\n instance.scanItem(0.55);\n instance.scanItem(1.27);\n double expResult = 0.18;\n instance.collectPayment(Money.QUARTER, 8);\n double result = instance.giveChange();\n assertEquals(expResult, result, EPSILON);\n \n }","@Test\n public void testCalcMedAmount() {\n \n //Test case 1\n \n System.out.println(\"calcMedAmount Test Case 1 - Dose is correct\");\n \n //Input Values for Test Case 1\n \n double weightInPounds = 100;\n double numberOfPills = 1;\n \n AntidoteControl instance = new AntidoteControl();\n \n double expResult = 227;\n double result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n \n \n //Test case 2\n \n System.out.println(\"calcMedAmount Test Case 2 Invalid weight\");\n \n //Input Values for Test Case 2\n \n weightInPounds = 0;\n numberOfPills = 1;\n \n // AntidoteControl instance = new AntidoteControl();\n \n expResult = -1;\n result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n \n \n //Test case 3\n \n System.out.println(\"calcMedAmount Test Case 3 Underdose\");\n \n //Input Values for Test Case 3\n \n weightInPounds = 10001;\n numberOfPills = 0;\n \n // AntidoteControl instance = new AntidoteControl();\n \n expResult = -2;\n result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n \n \n //Test case 4\n \n System.out.println(\"calcMedAmount Test Case 4 Overdose\");\n \n //Input Values for Test Case 4\n \n weightInPounds = 100;\n numberOfPills = 4;\n \n // AntidoteControl instance = new AntidoteControl();\n \n expResult = 227;\n result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n }","int calculateMST();","@Test\n public void transferCheckingtoSavingsTest(){\n assertTrue(userC.transfer(150.00, userS));\n }","@Test(timeout = 4000)\n public void test02() throws Throwable {\n Discretize discretize0 = new Discretize();\n Locale.getISOLanguages();\n double[][] doubleArray0 = new double[9][8];\n double[] doubleArray1 = new double[3];\n double[] doubleArray2 = new double[9];\n doubleArray2[5] = (-163.0);\n doubleArray2[8] = 1421.4058829;\n doubleArray0[3] = doubleArray0[2];\n doubleArray0[4] = doubleArray0[1];\n discretize0.setOutputFormat();\n double[] doubleArray3 = new double[2];\n doubleArray3[0] = 1421.4058829;\n doubleArray3[1] = (-163.0);\n doubleArray0[6] = doubleArray2;\n double[] doubleArray4 = new double[0];\n doubleArray0[7] = doubleArray4;\n double[] doubleArray5 = new double[6];\n doubleArray5[2] = (-163.0);\n doubleArray0[8] = doubleArray5;\n discretize0.m_CutPoints = doubleArray0;\n // Undeclared exception!\n try { \n discretize0.setOutputFormat();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test096() throws Throwable {\n CostMatrix costMatrix0 = Evaluation.handleCostOption(\"\", 1281);\n assertNull(costMatrix0);\n }","@Test(timeout = 4000)\n public void test19() throws Throwable {\n Discretize discretize0 = new Discretize(\"%\");\n FileSystemHandling.createFolder((EvoSuiteFile) null);\n discretize0.findNumBinsTipText();\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \"%\");\n discretize0.m_DefaultCols = \"%\";\n double[][] doubleArray0 = new double[0][7];\n discretize0.m_CutPoints = doubleArray0;\n // Undeclared exception!\n try { \n discretize0.getCutPoints(1);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 1\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void shouldCalculateProperMoveProbabilities() throws Exception {\n\n int actualCity = 0;\n boolean[] visited = new boolean[] {true, false, false, false};\n int[][] firstCriterium = new int[][] {{0, 2, 1, 2},\n {2, 2, 1, 1},\n {1, 1, 0, 1},\n {2, 1, 1, 0}};\n int[][] secondCriterium = new int[][] {{0, 3, 2, 1},\n {3, 0, 0, 0},\n {2, 0, 0, 0},\n {1, 0, 0, 0}};\n// final AttractivenessCalculator attractivenessCalculator = new AttractivenessCalculator(1.0, 0.0, 0.0, 0.0, 0.0);\n// double[][] array = new double[0][0];\n// final double[] probabilities = attractivenessCalculator.movesProbability(actualCity, visited, array, firstCriterium, secondCriterium, null, 0.0, 0.0);\n\n// assertTrue(probabilities[0] == 0.0);\n// assertTrue(probabilities[1] == 0.0);\n// assertTrue(probabilities[2] == 0.5);\n// assertTrue(probabilities[3] == 0.5);\n }","public void testMainLBGFSCachedSemiMarkov() throws Exception {\n\t\tConrad.main(new String[] {\"train\", \"test/input/zeroOrderLBFGSCachedSemiMarkov.xml\", \"test/input/zeroOrderTestShortMax.txt\", \"test/working/zeroLBGFSModelCachedSemiMarkov.ser\"});\n\t\tConrad.main(new String[] {\"test\", \"test/working/zeroLBGFSModelCachedSemiMarkov.ser\", \"test/input/zeroOrderTest.txt\", \"test/working/zeroOrderMainLBGFSCachedSemiMarkovPredicted.txt\"});\n\t\tConrad.main(new String[] {\"test\", \"test/working/zeroLBGFSModelCachedSemiMarkov.ser\", \"test/input/zeroOrderTest2.txt\", \"test/working/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\"});\n\t\tassertFilesMatch(\"test/output/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\", \"test/working/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\");\n\t}","@Test\n public void testGetSetTemperaturaExterior() {\n System.out.println(\"getSetTemperaturaExterior\");\n double expResult = 20.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaExterior(expResult);\n double result = instance.getTemperaturaExterior();\n assertEquals(expResult, result, 0.0);\n }","@Test\r\n public void gallonsCauldronHolds() {\r\n \r\n System.out.println(\"gallonsCauldronHolds\");\r\n \r\n //Test #1\r\n System.out.println(\"Test case #1\"); \r\n \r\n double diameter = 50;\r\n double depth = 25;\r\n \r\n SceneControl instance = new SceneControl();\r\n \r\n double expResult = 212.500;\r\n double result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #2\r\n System.out.println(\"Test case #2\");\r\n \r\n diameter = 15;\r\n depth = -12;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #3\r\n System.out.println(\"Test case #3\");\r\n \r\n diameter = -5;\r\n depth = 20;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #4\r\n System.out.println(\"Test case #4\");\r\n \r\n diameter = 0;\r\n depth = 30;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #5\r\n System.out.println(\"Test case #5\");\r\n \r\n diameter = 14;\r\n depth = 0;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #6\r\n System.out.println(\"Test case #6\");\r\n \r\n diameter = 1;\r\n depth = 12;\r\n \r\n expResult = .041;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #7\r\n System.out.println(\"Test case #7\");\r\n \r\n diameter = 20;\r\n depth = 1;\r\n \r\n expResult = 1.36;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n }","@Test\n public void testInbreedingCoeffForMultiallelicVC() {\n VariantContext test1 = makeVC(\"1\",Arrays.asList(Aref,T,C),\n makeG(\"s1\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s2\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s3\", T, C, 7099, 2530, 7099, 3056, 0, 14931),\n makeG(\"s4\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s5\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s6\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s7\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s8\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s9\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s10\", Aref, T, 2530, 0, 7099, 366, 3056, 14931));\n\n final AS_InbreedingCoeff testClass1 = new AS_InbreedingCoeff();\n final double ICresult1 = testClass1.calculateIC(test1, T);\n Assert.assertEquals(ICresult1, -0.4285714, DELTA_PRECISION, \"Pass\");\n final double ICresult1b = testClass1.calculateIC(test1, C);\n Assert.assertEquals(ICresult1b, -0.05263, DELTA_PRECISION, \"Pass\");\n\n //make sure that hets with different alternate alleles all get counted\n VariantContext test2 = makeVC(\"2\", Arrays.asList(Aref,T,C),\n makeG(\"s1\", Aref, C, 4878, 1623, 11297, 0, 7970, 8847),\n makeG(\"s2\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s3\", Aref, T, 3382, 0, 6364, 1817, 5867, 12246),\n makeG(\"s4\", Aref, T, 2488, 0, 9110, 3131, 9374, 12505),\n makeG(\"s5\", Aref, C, 4530, 2006, 18875, 0, 6847, 23949),\n makeG(\"s6\", Aref, T, 5325, 0, 18692, 389, 16014, 24570),\n makeG(\"s7\", Aref, T, 2936, 0, 29743, 499, 21979, 38630),\n makeG(\"s8\", Aref, T, 6902, 0, 8976, 45, 5844, 9061),\n makeG(\"s9\", Aref, T, 5732, 0, 10876, 6394, 11408, 17802),\n makeG(\"s10\", Aref, T, 2780, 0, 25045, 824, 23330, 30939));\n\n final AS_InbreedingCoeff testClass2 = new AS_InbreedingCoeff();\n final double ICresult2 = testClass2.calculateIC(test2, T);\n Assert.assertEquals(ICresult2, -0.666666, DELTA_PRECISION, \"Pass\");\n final double ICresult2b = testClass2.calculateIC(test2, C);\n Assert.assertEquals(ICresult2b, -0.111129, DELTA_PRECISION, \"Pass\");\n }","public void test_setClippingIIII() {\n}","@Test\n\tvoid testCalculateMarkTillSemesterSuccess3() {\n\t\n\t\tString semesterName = \"20193\";\n\t\t\n\t\tUser user = userService.findByUsername(\"B17DCCN123\");\n\t\tStudent student = user.getStudent();\n\t\tList<StudentResult> resultsTillSemester = resultService.findResultTillSemester(student.getId(),\n\t\t\t\tsemesterName);\n\t\t\n\t\tString tktMark = \"\";\n\t\t\n\t\tfor (StudentResult result : resultsTillSemester) {\n\t\t\tSubject subject = result.getStudentRegister().getCourse().getSubject();\n\t\t\tif (subject.getName().equals(\"Toán kinh tế\")) {\n\t\t\t\ttktMark = result.getMarkToChar();\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\t\n\t\tList<Object> listGpaResultsTillNow = MarkUtility.calculateMarkTillSemester(resultsTillSemester);\n\t\tint passedCreditsTillNow = (int) listGpaResultsTillNow.get(0);\n\t\tfloat gpaTillNow = (float) listGpaResultsTillNow.get(1);\n\t\t\n\t\tDecimalFormat df = new DecimalFormat(\"#.##\");\n\t\t\n\t\tassertTrue(\n\t\t\t\ttktMark.equals(\"C\") && \n\t\t\t\tpassedCreditsTillNow == 18 && \n\t\t\t\tdf.format(gpaTillNow).equals(\"3.19\")\n\t\t\t\t);\n\t}","float estimateCostToGoal( int current_x, int current_y, int current_z, boolean fromStart ) {\n\t\treturn 0;\n\t}","@Test\n @Tag(\"slow\")\n public void testCZPROB() {\n CuteNetlibCase.doTest(\"CZPROB.SIF\", \"2185196.6988565763\", \"3089066.71321333\", NumberContext.of(7, 4));\n }","@Test\n public void testVerser1() {\n\n //Arrange\n CompteBancaire cpt = new CompteBancaire(5000.0);\n double montant_a_verser = 1000.0;\n double excepted = 6000.0;\n\n //Act\n assertEquals(true, cpt.verser(montant_a_verser));\n\n //Assert\n assertEquals(excepted, cpt.getSolde(), 0);\n }","@Test\n public void testVirement3() {\n\n //Arrange\n CompteBancaire cpt1 = new CompteBancaire(0.0);\n CompteBancaire cpt2 = new CompteBancaire(43000.0);\n double montant_a_virer = 8000.0;\n double excepted_solde_cpt1 = 0.0;\n double excepted_solde_cpt2 = 43000.0;\n\n //Act\n assertEquals(false, cpt1.virerArgent(cpt2, montant_a_virer));\n\n //Assert\n assertEquals(excepted_solde_cpt1, cpt1.getSolde(), 0);\n assertEquals(excepted_solde_cpt2, cpt2.getSolde(), 0);\n\n }","public static void main(String[] args) {\n\n String fileEdgeList = \"../DataSource/PANCANsigEdgeList.csv\";\n String fileGtTrainingMatrix = \"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.training.10.csv\";\n// String fileGtTestingMatrix = \"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.testing.10.csv\";\n String fileGeTrainingMatrix = \"../DataSource/CrossMatrixDriver/PANCAN.GeM.4468tumors.training.10.csv\";\n String fileGeTestingMatrix = \"../DataSource/CrossMatrixDriver/PANCAN.GeM.4468tumors.testing.10.csv\";\n String fileInferDriver = \"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.InferDriver.10.withTumorID.csv\";\n// String fileDriverSGATable = \"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.DriverSGATable.10.csv\";\n \n \n DataReader dataObj = new DataReader(fileEdgeList, fileGtTrainingMatrix, fileGeTrainingMatrix);\n\n int reRun = 0;\n double T = 0.5;\n do {\n reRun += 1;\n\n EstimateParams paramObj = new EstimateParams(dataObj.edgeList, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.driverSGATable, dataObj.targetDEGTable);\n InferDriverActivation actObj = new InferDriverActivation(paramObj.mapEdgeParam, paramObj.mapSGAParam,\n dataObj.targetDEGTable, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.mapSgaDegs);\n\n\n actObj.thresholding(T);\n \n actObj.updateInferDriverTable( dataObj.driverSGATable);\n \n double change = actObj.compareMatrix(dataObj.driverSGATable);\n\n System.out.println(\"Current T is \" + T);\n System.out.println(\"Change is \" + change);\n System.out.println(\"This is the \" + reRun + \"th run\");\n if (change < 0.001 || T > 1) {\n System.out.println(\"Total times of run is \" + reRun + \". Final cut shreshold is \" + T);\n \n// dataObj.readInGtMatrix(fileGtTestingMatrix);\n dataObj.readInGeMatrix(fileGeTestingMatrix);\n InferDriverActivation actObjTesting = new InferDriverActivation(paramObj.mapEdgeParam, paramObj.mapSGAParam,\n dataObj.targetDEGTable, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.mapSgaDegs);\n \n actObjTesting.outputInferActivation(fileInferDriver, dataObj.tumorNames);\n// dataObj.outputDriverSGATable(fileDriverSGATable); //output contains tumorName, since tumor name is defined in dataObj, so no need to pass in\n\n break; \n \n } else {\n dataObj.updateDriverSGATable(actObj.inferDriverTable);\n T += 0.05;\n \n }\n\n } while (true);\n }","@Test(timeout = 4000)\n public void test36() throws Throwable {\n Discretize discretize0 = new Discretize();\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\n Properties properties0 = new Properties();\n ProtectedProperties protectedProperties0 = new ProtectedProperties(properties0);\n ProtectedProperties protectedProperties1 = new ProtectedProperties(protectedProperties0);\n Attribute attribute0 = new Attribute(\"-\", \"-\", protectedProperties1);\n arrayList0.add(attribute0);\n Instances instances0 = new Instances(\"(,Z;Xp#\\\"dxq[\", arrayList0, 1587);\n boolean boolean0 = discretize0.setInputFormat(instances0);\n boolean boolean1 = discretize0.batchFinished();\n assertFalse(discretize0.getFindNumBins());\n assertEquals(10, discretize0.getBins());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertTrue(boolean1 == boolean0);\n assertFalse(discretize0.getUseEqualFrequency());\n assertFalse(boolean1);\n }","@Test\n public void testStationaryDistributionRevQuick()\n {\n IDoubleArray T = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.stationaryDistributionRevQuick(T);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testGetSetTemperaturaInterior() {\n System.out.println(\"getSetTemperaturaInterior\");\n double expResult = 20.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaInterior(expResult);\n double result = instance.getTemperaturaInterior();\n assertEquals(expResult, result, 0.0);\n }","@Test\n public void testCorrelation()\n {\n IDoubleArray M = null;\n IDoubleArray observable1 = null;\n IDoubleArray observable2 = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test083() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getStructure();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.SFMeanEntropyGain();\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n assertEquals(Double.NaN, double0, 0.01);\n }","@Test\n public void testGetSetTemperaturaExteriorMaxima() {\n System.out.println(\"getSetTemperaturaExteriorMaxima\");\n double expResult = 20.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaExteriorMaxima(expResult);\n double result = instance.getTemperaturaExteriorMaxima();\n assertEquals(expResult, result, 0.0);\n }","@Test(timeout = 4000)\n public void test087() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.KBMeanInformation();\n assertEquals(Double.NaN, double0, 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","public void checkMovementZMaximum(ITestContext context) {\n Reporter.testStart();\n String caseFolder=context.getName()+\"/\";\n String importFile=context.getCurrentXmlTest().getParameter(\"importFile\");\n String expectationFile=context.getCurrentXmlTest().getParameter(\"expectationFile\");\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\n String screenWithinCase=caseFolder+methodName;\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\n if(meshViewPage.isThePage()){\n\n uploadDCM(meshViewPage,importFile);\n\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\n\n Reporter.log(\"click Orientation Adjustment tab on left bar\");\n String text=\"0.05\";\n Reporter.log(\"select Movement step \"+text);\n Assert.assertEquals(oa.selectMovementStep(text),text,\"Cann't select \"+text);\n\n Integer clickTimes=98;\n String valueOnLabel=oa.clickDistancePlus(clickTimes);\n String lower=embededScreenShot(screenWithinCase+\"_distance_\"+valueOnLabel,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+valueOnLabel);\n Assert.assertEquals(valueOnLabel,\"4.9\",\"distance is not same as expected result\");\n\n clickTimes=1;\n valueOnLabel=oa.clickDistancePlus();\n String middle=embededScreenShot(screenWithinCase+\"_distance_\"+valueOnLabel,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+valueOnLabel);\n Assert.assertEquals(valueOnLabel,\"4.95\",\"distance is not same as expected result\");\n\n text=\"0.1\";\n Reporter.log(\"select Movement step \"+text);\n Assert.assertEquals(oa.selectMovementStep(text),text,\"Cann't select \"+text);\n\n valueOnLabel=oa.clickDistancePlus(clickTimes);\n String higher=embededScreenShot(screenWithinCase+\"_distance_\"+valueOnLabel,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+valueOnLabel);\n Assert.assertEquals(valueOnLabel,\"5\",\"distance is not same as expected result\");\n\n embededCompareResult(lower,middle,higher, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\n\n valueOnLabel=oa.clickDistanceReset();\n embededScreenShot(screenWithinCase+\"_reset_\"+valueOnLabel,\"click Z reset(1 times), get distance is \"+valueOnLabel);\n Assert.assertEquals(valueOnLabel,\"0\",\"distance is not same as expected result\");\n }\n Reporter.testEnd();\n }","@Test(timeout = 4000)\n public void test05() throws Throwable {\n Discretize discretize0 = new Discretize(\"^yH*RVC#\\\"|tm,k2XpGN\");\n String[] stringArray0 = new String[2];\n discretize0.m_ClassIndex = 906;\n discretize0.m_IgnoreClass = true;\n stringArray0[0] = \"^yH*RVC#\\\"|tm,k2XpGN\";\n stringArray0[1] = \"^yH*RVC#\\\"|tm,k2XpGN\";\n discretize0.setOptions(stringArray0);\n discretize0.setAttributeIndices(\"^yH*RVC#\\\"|tm,k2XpGN\");\n assertFalse(discretize0.getMakeBinary());\n assertFalse(discretize0.getUseEqualFrequency());\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getFindNumBins());\n }","@Test void perfect() {\n\t\tstandardScene();\n\t\tsimulateScene(0);\n\n\t\tvar alg = new DistanceMetricTripleReprojection23();\n\n\t\tvar model = new MetricCameraTriple();\n\t\tmodel.view1.setTo(cameraA);\n\t\tmodel.view2.setTo(cameraB);\n\t\tmodel.view3.setTo(cameraC);\n\t\tmodel.view_1_to_2.setTo(truthView_1_to_i(1));\n\t\tmodel.view_1_to_3.setTo(truthView_1_to_i(2));\n\n\t\talg.setModel(model);\n\n\t\tfor (AssociatedTriple a : observations3) {\n\t\t\tassertEquals(0.0, alg.distance(a), UtilEjml.TEST_F64);\n\t\t}\n\n\t\tvar set = observations3.subList(4, 11);\n\t\tvar distances = new double[set.size()];\n\t\talg.distances(set, distances);\n\t\tfor (int i = 0; i < distances.length; i++) {\n\t\t\tassertEquals(0.0, distances[i], UtilEjml.TEST_F64);\n\t\t}\n\t}","@Test\n public void testGetTemperaturaInteriorAlvo() {\n System.out.println(\"getSetTemperaturaInteriorAlvo\");\n double expResult = 20.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaInteriorAlvo(expResult);\n double result = instance.getTemperaturaInteriorAlvo();\n assertEquals(expResult, result, 0.0);\n }","@Override\n protected boolean stoppingCriteriaSatisfied(KPMPSolution generatedSolution, RandomStepFunction stepFunction) {\n numberOfIterations++;\n numberOfIterationsWithoutImprovement++;\n KPMPSolutionChecker solutionChecker = new KPMPSolutionChecker();\n int crossingsOnOriginalPage = solutionChecker.getCrossingNumberOfEdge(bestSolution.getSpineOrder(), bestSolution.getEdgePartition(), originalPageIndex, edge);\n int crossingsOnNewPage = solutionChecker.getCrossingNumberOfEdge(generatedSolution.getSpineOrder(), generatedSolution.getEdgePartition(), newPageIndex, edge);\n if (crossingsOnNewPage < crossingsOnOriginalPage) {\n bestSolution = generatedSolution;\n numberOfIterationsWithoutImprovement = 0;\n\n crossingNumber = crossingNumber - (crossingsOnOriginalPage - crossingsOnNewPage);\n }\n return numberOfIterations >= Main.localSearchIterationLimit || numberOfIterations >= bestSolution.getEdgePartition().size() * bestSolution.getNumberOfPages() || numberOfIterationsWithoutImprovement >= Main.iterationMultiplier || ((System.nanoTime() - Main.START) / 1000000) >= (Main.secondsBeforeStop * 1000) || numberOfIterations >= Main.localSearchIterationLimit;\n }","@Test(timeout = 4000)\n public void test116() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate((String) null);\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.correct();\n assertEquals(0.0, double0, 0.01);\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n }","@Test(timeout = 4000)\n public void test086() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.SFMeanPriorEntropy();\n assertEquals(Double.NaN, double0, 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","@Test(timeout = 4000)\n public void test126() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate((String) null);\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.totalCost();\n assertEquals(0.0, double0, 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","@Test(timeout = 4000)\n public void test26() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.m_FindNumBins = true;\n int[] intArray0 = new int[6];\n intArray0[0] = (-388);\n intArray0[1] = (-2121610348);\n intArray0[2] = 11;\n intArray0[3] = (-1);\n intArray0[4] = 1062;\n intArray0[5] = 70;\n discretize0.setAttributeIndicesArray(intArray0);\n discretize0.setDesiredWeightOfInstancesPerInterval((-1149.15832593));\n discretize0.getOptions();\n assertEquals((-1149.15832593), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n }","@Override\n protected boolean stoppingCriteriaSatisfied(KPMPSolution generatedSolution, BestImprovementStepFunction stepFunction) {\n KPMPSolutionChecker solutionChecker = new KPMPSolutionChecker();\n int crossingsOnOriginalPage = solutionChecker.getCrossingNumberOfEdge(bestSolution.getSpineOrder(), bestSolution.getEdgePartition(), originalPageIndex, edge);\n int crossingsOnNewPage = solutionChecker.getCrossingNumberOfEdge(generatedSolution.getSpineOrder(), generatedSolution.getEdgePartition(), newPageIndex, edge);\n if (crossingsOnNewPage < crossingsOnOriginalPage) {\n bestSolution = generatedSolution;\n index = 0;\n pageCounter = 0;\n\n crossingNumber = crossingNumber - (crossingsOnOriginalPage - crossingsOnNewPage);\n\n //assert(crossingNumber == new KPMPSolutionChecker().getCrossingNumber(bestSolution));\n }\n if (index == bestSolution.getEdgePartition().size() && pageCounter != bestSolution.getNumberOfPages() - 1) {\n index = 0;\n pageCounter++;\n }\n return ((System.nanoTime() - Main.START) / 1000000) >= (Main.secondsBeforeStop * 1000) || index == bestSolution.getEdgePartition().size() && pageCounter == bestSolution.getNumberOfPages() - 1 || numberOfIterations >= Main.localSearchIterationLimit;\n }","@Test(timeout = 4000)\n public void test093() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n Object[] objectArray0 = new Object[2];\n evaluation0.evaluateModel((Classifier) null, instances0, objectArray0);\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n }","@Test(timeout = 4000)\n public void test11() throws Throwable {\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, true, false);\n Discretize discretize0 = new Discretize();\n discretize0.getCutPoints(3594);\n Discretize discretize1 = new Discretize();\n discretize1.attributeIndicesTipText();\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \"\");\n Discretize discretize2 = new Discretize();\n // Undeclared exception!\n try { \n discretize2.calculateCutPoints();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@ParameterizedTest\n\t@CsvSource({\n\t\t\t\"9, 1, 2, 3, 1, 10, 1, 10, 1, -1, -1, -1\",\n\t\t\t\"9, -1, -1, -1, 9, 10, 1, 10, 1, 9, -1, -1\",\n\t\t\t\"9, -1, -1, -1, 1, 10, 9, 10, 1, 9, -1, -1\",\n\t\t\t\"9, 3, 3, 3, 89, 100, 1, 10, 1, -1, -1, -1\",\n\t\t\t\"9, 1, -1, -1, 1, 10, 89, 100, 1, -1, -1, -1\",\n\t\t\t\"9, 1, 2, -1, 2, 2, 1, 10, 25, 1, 2, 9\",\n\t\t\t\"9, 3, 3, 3, 0, 2, 950, 1000, 25, 3, 3, -1\",\n\t\t\t\"9, -1, -1, -1, 9999, 10000, 950, 1000, 1, 9, 9, 9\",\n\t\t\t\"9, -1, -1, -1, 0, 1, 0, 390000, 1, -1, -1, -1\",\n\t\t\t\"4, -1, -1, -1, 100, 100, 1000, 1000, 1, 4, 4, 4\",\n\t\t\t\"5, 3, -1, -1, 100, 100, 1000, 1000, 10, 3, 5, 5\",\n\t\t\t\"5, 1, 3, 4, 100, 100, 1000, 1000, 25, 1, 3, 4\",\n\t})\n\tvoid usesExpectedMultiplier(\n\t\t\tlong consensusSec,\n\t\t\tlong old10XLevelStart,\n\t\t\tlong old25XLevelStart,\n\t\t\tlong old100XLevelStart,\n\t\t\tint firstUsed,\n\t\t\tint firstTps,\n\t\t\tint secondUsed,\n\t\t\tint secondTps,\n\t\t\tlong expectedMultiplier,\n\t\t\tlong new10XLevelStart,\n\t\t\tlong new25XLevelStart,\n\t\t\tlong new100XLevelStart\n\t) {\n\t\tfinal var aThrottle = DeterministicThrottle.withTps(firstTps);\n\t\tfinal var bThrottle = DeterministicThrottle.withTps(secondTps);\n\t\taThrottle.allow(firstUsed);\n\t\tbThrottle.allow(secondUsed);\n\t\tgiven(throttling.activeThrottlesFor(CryptoTransfer)).willReturn(List.of(aThrottle, bThrottle));\n\n\t\tsubject.resetExpectations();\n\t\tsubject.resetCongestionLevelStarts(instants(old10XLevelStart, old25XLevelStart, old100XLevelStart));\n\t\tsubject.updateMultiplier(Instant.ofEpochSecond(consensusSec));\n\t\tfinal long actualMultiplier = subject.currentMultiplier();\n\t\tfinal var starts = subject.congestionLevelStarts();\n\n\t\tassertEquals(expectedMultiplier, actualMultiplier);\n\t\tassertNullOrEquals(starts[0], new10XLevelStart);\n\t\tassertNullOrEquals(starts[1], new25XLevelStart);\n\t\tassertNullOrEquals(starts[2], new100XLevelStart);\n\t}","private void calculations() {\n\t\tSystem.out.println(\"Starting calculations\\n\");\n\t\tcalcMedian(myProt.getChain(requestedChain));\n\t\tcalcZvalue(myProt.getChain(requestedChain));\n\t\tcalcSVMweightedZvalue(myProt.getChain(requestedChain));\n\t}","@Test(timeout = 4000)\n public void test35() throws Throwable {\n Discretize discretize0 = new Discretize();\n assertFalse(discretize0.getMakeBinary());\n \n double[][] doubleArray0 = new double[8][8];\n double[] doubleArray1 = new double[3];\n doubleArray1[2] = 1421.4058829;\n doubleArray0[1] = doubleArray1;\n double[] doubleArray2 = new double[9];\n doubleArray2[1] = 1.7976931348623157E308;\n doubleArray2[2] = 0.0;\n Attribute attribute0 = new Attribute(\"invalid CVS revision - not dots!\");\n attribute0.setWeight(0.0);\n Attribute.typeToString(attribute0);\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\n arrayList0.add(attribute0);\n Instances instances0 = new Instances(\"UR<dj;O\", arrayList0, 3);\n Instances instances1 = new Instances(instances0);\n boolean boolean0 = discretize0.setInputFormat(instances1);\n assertFalse(boolean0);\n \n String[] stringArray0 = new String[3];\n stringArray0[0] = \".bsi\";\n stringArray0[1] = \"invalid CVS revision - not dots!\";\n stringArray0[2] = \"@end\";\n discretize0.setOptions(stringArray0);\n discretize0.setOptions(stringArray0);\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseEqualFrequency());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n }","@Test\n\tpublic void TEAM3_CONTROLLER_UT05() {\n\t\t// PRECONDITIONS\n\t\tScheduleMakerController smc = new ScheduleMakerController();\n\t\t\n\t\t// INPUT\n\t\tCollection<ScheduleStub> savedSchedules = new ArrayList<ScheduleStub>();\n\t\tsmc.saveSchedules(savedSchedules);\n\t\t\n\t\t// EXPECTED OUTPUT\n\t\tException exceptionThrown = null;\n\t\tdouble balance = 0.0d;\n\t\ttry {\n\t\t\tMethod getBalance = smc.getClass().getDeclaredMethod(\"getBalance\", (Class<?> []) null);\n\t\t\tObject balanceObj = getBalance.invoke(smc, (Object[]) null);\n\t\t\t\n\t\t\tif (balanceObj == null) throw new NullPointerException(\"getBalance() returned null; expected a number\");\n\t\t\telse if (!(balanceObj instanceof Number)) throw new RuntimeException(\"getBalance() did not return a number\");\n\t\t} catch (Exception ex) {\n\t\t\texceptionThrown = ex;\n\t\t}\n\t\t\n\t\tassertNull(exceptionThrown);\n\t\tassertEquals(0.0d, balance, 0.0d);\n\t}","public double calc_c() {\r\n \t\t\r\n \t\tdouble cij = 0;\r\n \t\tint sum_i = 0;\r\n \t\tint sum_j = 0;\r\n \t\tint diff = 0;\r\n \t\tint state = 0;\r\n \t\t\r\n \t\tdouble li, lri, lj, lrj;\r\n \t\t\r\n \t\tList<Integer> samestate_i = new ArrayList<>();\r\n \t\tList<Integer> samestate_j = new ArrayList<>();\r\n \t\t\r\n \t\tList<Double> gradient_i = new ArrayList<>();\r\n \t\tList<Double> gradient_j = new ArrayList<>();\r\n \t\t\r\n\t\t// if the time step is less than the window cij is initialized to zero.\r\n\t\tif(this.n <= this.w || this.w <2) {\r\n\t\t\tcij = 0;\r\n\t\t}\r\n\t\t\r\n\t\telse {\r\n\t\t\t\r\n\t\t\t// else we determine the state of the region i and region j of which\r\n\t\t\t// we are calculating the cij for\r\n\t \t\tDeterminestate di = new Determinestate(this.i, this.n);\r\n\t\t\tDeterminestate dj = new Determinestate(this.j, this.n);\r\n\t\t\t\r\n\t\t\tProbability pi = new Probability(this.i, this.n, this.w, this.neighborsize);\r\n\t\t\tProbability pj = new Probability(this.i, this.n, this.w, this.neighborsize);\r\n\t\t\t\r\n\t\t\tif(di.regionCurrentstate() == dj.regionCurrentstate()) {\r\n\t\t\t\tli = pi.getLsave().get(0);\r\n\t\t\t\tlj = pj.getLsave().get(0);\r\n\t\t\t\tstate = 0;\r\n\t\t\t}\r\n\t\t\telse {\r\n\t\t\t\tli = pi.getLsave().get(1);\r\n\t\t\t\tlj = pj.getLsave().get(1);\r\n\t\t\t\tstate =1;\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t\r\n\t\t\t// for the time window\r\n\t\t\tfor(int k=1; k<this.w; k++) {\r\n\t\t\t\t\r\n\t\t\t\t// determine the state of i and j in the in n-k time step\r\n\t \t\t\tDeterminestate dri = new Determinestate(this.i, this.n-k);\r\n\t \t\t\tDeterminestate drj = new Determinestate(this.j, this.n-k);\r\n\t \t\t\t\r\n\t \t\t\tProbability pri = new Probability(this.i, this.n, this.w, this.neighborsize);\r\n\t\t\t\tProbability prj = new Probability(this.i, this.n, this.w, this.neighborsize);\r\n\t\t\t\t\r\n\t\t\t\tlri = pri.getLsave().get(state);\r\n\t\t\t\tlrj = prj.getLsave().get(state);\r\n\t \t\t\t\r\n\t \t\t\t\r\n\t \t\t\t//if state matches for i make a list of time step in which they match and another list of gradient of Likelihood \r\n\t \t\t\tif(di.regionCurrentstate() == dri.regionCurrentstate()){\r\n\t \t\t\t\tsamestate_i.add(k);\r\n\t \t\t\t\tgradient_i.add(li - lri);\r\n\t \t\t\t}\r\n\t \t\t\t\r\n\t \t\t\t//if state matches for j make a list of time step in which they match and another list of gradient of Likelihood \r\n\t \t\t\tif(di.regionCurrentstate() == drj.regionCurrentstate()){\r\n\t \t\t\t\tsamestate_j.add(k);\r\n\t \t\t\t\tgradient_j.add(lj - lrj);\r\n\t \t\t\t}\r\n\t \t\t\t\r\n\t \t\t}\r\n\t\t}\r\n\t\t\t\r\n\t\t// if no match found return zero\r\n\t\tif(samestate_i.size() == 0 || samestate_j.size() == 0) {\r\n\t\t\tcij = 0;\r\n\t\t}\r\n\t\t\r\n\t\t// else calculate cij\r\n\t\telse {\r\n\t\t\t\r\n\t\t\t// if both have same length \r\n\t\t\tif(samestate_i.size() == samestate_j.size()) {\r\n\t\t\t\tfor(int i=0; i<samestate_i.size(); i++) {\r\n\t\t\t\t\tsum_i = sum_i + samestate_i.get(i);\r\n\t\t\t\t\tsum_j = sum_j + samestate_j.get(i);\r\n\t\t\t\t}\r\n\t\t\t\tdiff = (sum_i%this.w - sum_j%this.w);\r\n\t\t\t\tif (diff == 0) {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i, gradient_j);\r\n\t\t\t\t}\r\n\t\t\t\telse {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i, gradient_j) * Math.abs(diff/(double)this.w);\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t//if i is smaller\r\n\t\t\telse if(samestate_i.size() < samestate_j.size()) {\r\n\t\t\t\tfor(int i=0; i<samestate_i.size(); i++) {\r\n\t\t\t\t\tsum_i = sum_i + samestate_i.get(i);\r\n\t\t\t\t\tsum_j = sum_j + samestate_j.get(i);\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\tdiff = (sum_i%this.w - sum_j%this.w);\r\n\t\t\t\tif (diff == 0) {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i.subList(0, samestate_i.size()), gradient_j);\r\n\t\t\t\t}\r\n\t\t\t\telse {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i.subList(0, samestate_i.size()), gradient_j) * diff/(double)this.w;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t// if j is smaller \r\n\t\t\telse {\r\n\t\t\t\tfor(int i=0; i<samestate_j.size(); i++) {\r\n\t\t\t\t\tsum_i = sum_i + samestate_i.get(i);\r\n\t\t\t\t\tsum_j = sum_j + samestate_j.get(i);\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\tdiff = (sum_i%this.w - sum_j%this.w);\r\n\t\t\t\tif (diff == 0) {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i.subList(0, samestate_j.size()), gradient_j);\r\n\t\t\t\t}\r\n\t\t\t\telse {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i.subList(0, samestate_j.size()), gradient_j) * diff/(double)this.w;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\t\r\n\t\tif (Double.isNaN(cij)) {\r\n\t\t\treturn 0;\r\n\t\t}\r\n\t\telse {\r\n\t\t\treturn cij;\r\n\t\t}\r\n \t}","@Test\n\tvoid testCalculateMarkTillSemesterSuccess2() {\n\t\t\n\t\tString semesterName = \"20193\";\n\t\t\n\t\tUser user = userService.findByUsername(\"B16DCCN168\");\n\t\tStudent student = user.getStudent();\n\t\tList<StudentResult> resultsTillSemester = resultService.findResultTillSemester(student.getId(),\n\t\t\t\tsemesterName);\n\t\t\n\t\tString mmtMark = \"\", tthcmMark = \"\";\n\t\t\n\t\tfor (StudentResult result : resultsTillSemester) {\n\t\t\tSubject subject = result.getStudentRegister().getCourse().getSubject();\n\t\t\tif (subject.getName().equals(\"Mạng máy tính\")) {\n\t\t\t\tmmtMark = result.getMarkToChar();\n\t\t\t}\n\t\t\t\n\t\t\tif (subject.getName().equals(\"Tư tưởng Hồ Chí Minh\")) {\n\t\t\t\ttthcmMark = result.getMarkToChar();\n\t\t\t}\n\t\t}\n\t\t\n\t\tList<Object> listGpaResultsTillNow = MarkUtility.calculateMarkTillSemester(resultsTillSemester);\n\t\tint passedCreditsTillNow = (int) listGpaResultsTillNow.get(0);\n\t\tfloat gpaTillNow = (float) listGpaResultsTillNow.get(1);\n\t\t\n\t\tDecimalFormat df = new DecimalFormat(\"#.##\");\n\t\t\n\t\tassertTrue(\n\t\t\t\tmmtMark.equals(\"B\") &&\n\t\t\t\ttthcmMark.equals(\"A\") &&\n\t\t\t\tpassedCreditsTillNow == 15 &&\n\t\t\t\tdf.format(gpaTillNow).equals(\"3.64\")\n\t\t\t\t);\n\t}","@Test\n public void testWithPropagator() {\n CelestialBody sun = CelestialBodyFactory.getSun();\n final Frame eci = FramesFactory.getGCRF();\n final Frame ecef = FramesFactory.getITRF(IERSConventions.IERS_2010, true);\n AbsoluteDate date = new AbsoluteDate(2004, 1, 1, utc);\n OneAxisEllipsoid earth = new OneAxisEllipsoid(Constants.WGS84_EARTH_EQUATORIAL_RADIUS,\n Constants.WGS84_EARTH_FLATTENING, ecef);\n Orbit orbit = new KeplerianOrbit(6378137 + 400e3, 1e-3, FastMath.toRadians(50), 0, 0, 0, PositionAngle.TRUE,\n eci, date, Constants.EIGEN5C_EARTH_MU);\n final SpacecraftState ic = new SpacecraftState(orbit);\n\n final AbsoluteDate end = date.shiftedBy(5 * Constants.JULIAN_DAY);\n final AbsoluteDate resetDate = date.shiftedBy(0.8 * Constants.JULIAN_DAY + 0.1);\n\n final SpacecraftState[] lastState = new SpacecraftState[1];\n final OrekitStepHandler stepSaver = interpolator -> {\n final AbsoluteDate start = interpolator.getPreviousState().getDate();\n if (start.compareTo(resetDate) < 0) {\n lastState[0] = interpolator.getPreviousState();\n }\n };\n\n // propagate with state rest to take slightly different path\n NumericalPropagator propagator = getNumericalPropagatorWithDTM(sun, earth, ic);\n propagator.setStepHandler(stepSaver);\n propagator.propagate(resetDate);\n propagator.resetInitialState(lastState[0]);\n propagator.clearStepHandlers();\n SpacecraftState actual = propagator.propagate(end);\n\n // propagate straight through\n propagator = getNumericalPropagatorWithDTM(sun, earth, ic);\n propagator.resetInitialState(ic);\n propagator.clearStepHandlers();\n SpacecraftState expected = propagator.propagate(end);\n\n assertThat(actual.getPVCoordinates(), pvCloseTo(expected.getPVCoordinates(), 1.0));\n\n // propagate with state rest to take slightly different path\n propagator = getNumericalPropagatorWithMSIS(sun, earth, ic);\n propagator.setStepHandler(stepSaver);\n propagator.propagate(resetDate);\n propagator.resetInitialState(lastState[0]);\n propagator.clearStepHandlers();\n actual = propagator.propagate(end);\n\n // propagate straight through\n propagator = getNumericalPropagatorWithMSIS(sun, earth, ic);\n propagator.resetInitialState(ic);\n propagator.clearStepHandlers();\n expected = propagator.propagate(end);\n\n assertThat(actual.getPVCoordinates(), pvCloseTo(expected.getPVCoordinates(), 1.0));\n }","public void testSVMChunkLearnng() throws IOException, GateException {\n // Initialisation\n System.out.print(\"Testing the SVM with liner kernenl on chunk learning...\");\n File chunklearningHome = new File(new File(learningHome, \"test\"),\n \"chunklearning\");\n String configFileURL = new File(chunklearningHome, \"engines-svm.xml\")\n .getAbsolutePath();\n String corpusDirName = new File(chunklearningHome, \"data-ontonews\")\n .getAbsolutePath();\n //Remove the label list file, feature list file and chunk length files.\n String wdResults = new File(chunklearningHome,\n ConstantParameters.SUBDIRFORRESULTS).getAbsolutePath();\n emptySavedFiles(wdResults);\n String inputASN = \"Key\";\n loadSettings(configFileURL, corpusDirName, inputASN, inputASN);\n // Set the evaluation mode\n RunMode runM=RunMode.EVALUATION;\n learningApi.setLearningMode(runM);\n controller.execute();\n // Using the evaluation mode for testing\n EvaluationBasedOnDocs evaluation = learningApi.getEvaluation();\n // Compare the overall results with the correct numbers\n assertEquals(\"Wrong value for correct: \", 44, (int)Math.floor(evaluation.macroMeasuresOfResults.correct));\n assertEquals(\"Wrong value for partial: \", 10, (int)Math.floor(evaluation.macroMeasuresOfResults.partialCor));\n assertEquals(\"Wrong value for spurious: \", 11, (int)Math.floor(evaluation.macroMeasuresOfResults.spurious));\n assertEquals(\"Wrong value for missing: \", 40, (int)Math.floor(evaluation.macroMeasuresOfResults.missing));\n\n System.out.println(\"completed\");\n // Remove the resources\n clearOneTest();\n }","static void playC45() throws IOException {\n\t\tRealVector[] data = NNDataset.getData(NNDataset.HOME);\n\t\tRealVector[] label = NNDataset.getLabel(NNDataset.HOME);\n\t\tRealMatrix d = MatrixUtils.createRealMatrix(data.length, data[0].getDimension());\n\t\tfor (int i = 0; i < data.length; i++) {\n\t\t\td.setRowVector(i, data[i]);\n\t\t}\n\t\td = d.transpose(); // data = column vector\n\n\t\tRealVector l = MatrixUtils.createRealVector(new double[label.length]);\n\t\tfor (int i = 0; i < l.getDimension(); i++) {\n\t\t\tl.setEntry(i, label[i].getEntry(0));\n\t\t}\n\n\t\tDecisionNode root = training(d, l);\n\t\tString[] header = NNDataset.getHeader(NNDataset.HOME);\n\t\tprintTree(root, header);\n\n\t\tInteger lb = predict(root, data[0]);\n\t\tSystem.out.println(lb);\n\n\t\tdouble ok = MSE(root, data, label);\n\t\tSystem.out.println(\"total hit: \" + ok);\n\t}","@Test\n public void testVirement1() {\n\n //Arrange\n CompteBancaire cpt1 = new CompteBancaire(63000.0);\n CompteBancaire cpt2 = new CompteBancaire(43000.0);\n double montant_a_virer = 28000.0;\n double excepted_solde_cpt1 = 35000.0;\n double excepted_solde_cpt2 = 71000.0;\n\n //Act\n assertEquals(true, cpt1.virerArgent(cpt2, montant_a_virer));\n\n //Assert\n assertEquals(excepted_solde_cpt1, cpt1.getSolde(), 0);\n assertEquals(excepted_solde_cpt2, cpt2.getSolde(), 0);\n\n }","public void controlPointsChanged(List<Coordinates> lcp) {\n // sort the control points by x position.\n // this makes things easier later when we paint the function.\n lcp.sort(\n (point1,point2) -> Integer.compare(point1.getX(),point2.getX())\n );\n\n // collect the points in an array before doing the calculations.\n // again, this makes things easier for us.\n controlPoints=lcp.toArray(new Coordinates[0]);\n n=controlPoints.length;\n if(n<2) {\n // nothing to do here\n return;\n }\n double []X = new double[n];\n double []Y = new double[n];\n for(int i =0;i<n;i++){\n \tX[i]=controlPoints[i].getX();\n \tY[i]=controlPoints[i].getY();\n }\n // Construct the linear system S*C=T with n-2 equations.\n // The matrix S contains the left hand side of the equations on slide 13.\n // The matrix Z contains the right hand side of the equations on slide 13.\n double[][] S=new double[n-2][n-2];\n double[] Z=new double[n-2];\n \n // Fill the matrix S and the vector Z and solve S*C=T to get c_2 to c_{n-1}.\n for(int i = 0; i< n-3;i++){\n \tS[i][i]=2*delta(X,i+1,0);\n \tS[i+1][i]=delta(X,i+2,-1);\n \tS[i][i+1]=delta(X,i+1,1);\n \tZ[i]=z(Y,X,i);\n }\n S[n-2][n-2]=2*delta(X,n-1,0);\n \n C= EliminationGaussLegendre.solve(S, Z);\n \n // Calculate the n-1 coefficients b_i and d_i.\n // Note that you need all c_i here (but you know that c_1 and c_n are zero).\n //a_i = y_i;\n for(int i=1 ; i<n ; i++){\n \tD[i-1]=d(Y,X,i-1);\n \tB[i-1]=b(Y,C,X,i-1);\n }\n \n }","@Test\n public void costTest() {\n // TODO: test cost\n }","@Test\n public void testAutocorrelation()\n {\n IDoubleArray M = null;\n IDoubleArray observable = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.autocorrelation(M, observable, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void test22() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n FastVector fastVector0 = evaluation0.predictions();\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01D);\n }","public static void main(String[] args) {\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 3, 4, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 3, 5, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 3, 4, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 3, 5, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, \"cartellainesistente\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, \"cartellainesistente\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, \"cartellainesistente\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, \"cartellainesistente\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, \"strategies\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, \"strategies\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, \"strategies\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, \"strategies\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 5, 2, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 5, 2, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(6, 6, 2, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(6, 6, 2, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 3, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 3, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(7, 7, 2, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(7, 7, 2, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 4, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 4, true, null)));\n measureTime(()->checkInterruptSequential(4, 4, 3, false));\n measureTime(()->checkInterruptSequential(4, 4, 3, true));\n }","@Test\n\tpublic void TEAM3_CONTROLLER_UT06() {\n\t\t// PRECONDITIONS\n\t\tClassDetailsStub c = new ClassDetailsStub(TEST_CONFIG.TueThu);\n\t\t\n\t\tCollection<ClassDetailsStub> s = new ArrayList<ClassDetailsStub>();\n\t\ts.add(c);\n\t\t\n\t\tScheduleMakerController smc = new ScheduleMakerController();\n\t\t\n\t\t// INPUT\n\t\tCollection<ScheduleStub> savedSchedules = new ArrayList<ScheduleStub>();\n\t\tsavedSchedules.add(new ScheduleStub(s));\n\t\t\n\t\t// EXPECTED OUTPUT\n\t\tException exceptionThrown = null;\n\t\tboolean balanceGreaterThanZero = false;\n\t\ttry {\n\t\t\tObject balanceObj = null;\n\t\t\tMethod getBalance = smc.getClass().getDeclaredMethod(\"getBalance\", (Class<?> []) null);\n\t\t\t\n\t\t\tsmc.saveSchedules(savedSchedules);\n\t\t\tbalanceObj = getBalance.invoke(smc, (Object[]) null);\n\t\t\t\n\t\t\tif (balanceObj == null) throw new NullPointerException(\"getBalance() returned null; expected a number\");\n\t\t\telse if (!(balanceObj instanceof Number)) throw new RuntimeException(\"getBalance() did not return a number\");\n\t\t\t\n\t\t\tbalanceGreaterThanZero = ((Number) balanceObj).doubleValue() > 0.0d;\n\t\t} catch (Exception ex) {\n\t\t\texceptionThrown = ex;\n\t\t}\n\t\t\n\t\tassertNull(exceptionThrown);\n\t\tassertTrue(balanceGreaterThanZero);\n\t}","@Test(timeout = 4000)\n public void test28() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[8][8];\n double[] doubleArray1 = new double[3];\n doubleArray1[2] = 1421.4058829;\n doubleArray0[1] = doubleArray1;\n double[] doubleArray2 = new double[9];\n doubleArray2[1] = 1.7976931348623157E308;\n doubleArray2[2] = 0.0;\n doubleArray2[5] = (-236.4995664359);\n doubleArray2[7] = 1421.4058829;\n doubleArray0[3] = doubleArray2;\n double[] doubleArray3 = new double[3];\n doubleArray3[0] = 0.0;\n doubleArray3[1] = (-236.4995664359);\n doubleArray0[4] = doubleArray3;\n double[] doubleArray4 = new double[21];\n doubleArray4[0] = 1421.4058829;\n doubleArray3[2] = (-236.4995664359);\n doubleArray4[4] = 1.7976931348623157E308;\n doubleArray0[7] = doubleArray4;\n discretize0.m_NumBins = 162;\n doubleArray0[1] = doubleArray4;\n discretize0.m_CutPoints = doubleArray0;\n discretize0.getBinRangesString(1);\n // Undeclared exception!\n try { \n discretize0.setOutputFormat();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void test28() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\n CostMatrix costMatrix0 = costSensitiveClassifier0.getCostMatrix();\n Evaluation evaluation0 = null;\n try {\n evaluation0 = new Evaluation(instances0, costMatrix0);\n fail(\"Expecting exception: Exception\");\n \n } catch(Exception e) {\n //\n // Cost matrix not compatible with data!\n //\n }\n }","@Test\n public void testTrain_RegressionDataSet_ExecutorService() {\n System.out.println(\"train\");\n RegressionDataSet trainSet = FixedProblems.getSimpleRegression1(150, new Random(2));\n RegressionDataSet testSet = FixedProblems.getSimpleRegression1(50, new Random(3));\n\n for (boolean modification1 : new boolean[] { true, false })\n for (SupportVectorLearner.CacheMode cacheMode : SupportVectorLearner.CacheMode.values()) {\n PlattSMO smo = new PlattSMO(new RBFKernel(0.5));\n smo.setCacheMode(cacheMode);\n smo.setC(1);\n smo.setEpsilon(0.1);\n smo.setModificationOne(modification1);\n smo.train(trainSet, true);\n\n double errors = 0;\n for (int i = 0; i < testSet.size(); i++)\n errors += Math.pow(testSet.getTargetValue(i) - smo.regress(testSet.getDataPoint(i)), 2);\n assertTrue(errors / testSet.size() < 1);\n }\n }","@Override\n\tprotected void setCost(double cst) {\n\t\t\n\t}","@Test\n public void testGetMoveSpeedMultiplier() {\n assertEquals(1, proj.getMoveSpeedMultiplier(), 0.001);\n }","@Test(timeout = 4000)\n public void test11() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.invertSelectionTipText();\n boolean boolean0 = discretize0.m_UseBinNumbers;\n // Undeclared exception!\n try { \n discretize0.calculateCutPoints();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n\tpublic void test() {\n\t\tdouble delta = 0.00001;\n\t\tSnp fakeSnp1 = new Snp(\"fakeId1\", 0, 0.3);\n\t\tSnp fakeSnp2 = new Snp(\"fakeId2\", 0, 0.8);\n\t\tSnp fakeSnp3 = new Snp(\"fakeId3\", 0, 1.4);\n\t\tPascal.set.withZScore_=true;\n\t\tArrayList<Snp> geneSnps = new ArrayList<Snp>();\n\t\tgeneSnps.add(fakeSnp1);geneSnps.add(fakeSnp2);geneSnps.add(fakeSnp3);\n\t\tDenseMatrix ld= new DenseMatrix(3,3);\n\t\t//DenseMatrix crossLd= new DenseMatrix(3,2);\n\t\t\n\t\tArrayList<Double> snpScores = new ArrayList<Double>(3);\n\t\tsnpScores.add(fakeSnp1.getZscore());\n\t\tsnpScores.add(fakeSnp2.getZscore());\n\t\tsnpScores.add(fakeSnp3.getZscore());\n\t\t//ld and crossLd calculated as follows:\n\t\t// make 0.9-toeplitz mat of size 5.\n\t\t// 3,4,5 for ld-mat\n\t\t// 1,2 for crossLd-mat\n\t\tld.set(0,0,1);ld.set(1,1,1);ld.set(2,2,1);\n\t\tld.set(0,1,0.9);ld.set(1,2,0.9);\n\t\tld.set(1,0,0.9);ld.set(2,1,0.9);\n\t\tld.set(0,2,0.81);\n\t\tld.set(2,0,0.81);\n\t\tdouble[] weights = {2,2,2};\n\t\tAnalyticVegas myAnalyticObj= null;\n\t\t//UpperSymmDenseMatrix myMatToDecompose = null;\n\t\tdouble[] myEigenvals = null;\n\t\tdouble[] emptyWeights = {1,1,1};\n\t\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, emptyWeights);\n\t\tmyAnalyticObj.computeScore();\n\t\tmyEigenvals = myAnalyticObj.computeLambda();\n\t\tassertEquals(myEigenvals[0],2.74067, delta);\n\t\tassertEquals(myEigenvals[1],0.1900, delta);\n\t\tassertEquals(myEigenvals[2],0.06932, delta);\n\t\t\n\t\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, weights);\n\t\tmyAnalyticObj.computeScore();\n\t\tmyEigenvals = myAnalyticObj.computeLambda();\t\t\t\n\t\tassertEquals(myEigenvals[0], 5.481348, delta);\n\t\tassertEquals(myEigenvals[1],0.380000, delta);\n\t\tassertEquals(myEigenvals[2],0.138652, delta);\n\t\t\t\n\t\tdouble[] weights2 = {1,2,0.5};\n\t\t\n\t\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, weights2);\n\t\tmyAnalyticObj.computeScore();\n\t\tmyEigenvals = myAnalyticObj.computeLambda();\t\t\n\t\tassertEquals(myEigenvals[0],3.27694674, delta);\n\t\tassertEquals(myEigenvals[1],0.1492338, delta);\n\t\tassertEquals(myEigenvals[2],0.07381938, delta);\t\t\t\n\t\t\n\t}","@Test(timeout = 4000)\n public void test06() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[] doubleArray0 = new double[0];\n int[] intArray0 = new int[4];\n intArray0[0] = 2749;\n intArray0[1] = (-2);\n intArray0[2] = 0;\n intArray0[3] = 0;\n SparseInstance sparseInstance0 = new SparseInstance((-40.152), doubleArray0, intArray0, 919);\n // Undeclared exception!\n try { \n discretize0.input(sparseInstance0);\n fail(\"Expecting exception: IllegalStateException\");\n \n } catch(IllegalStateException e) {\n //\n // No input instance format defined\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test01() throws Throwable {\n Discretize discretize0 = new Discretize(\"c9^n/\");\n discretize0.getCapabilities();\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseBinNumbers());\n assertFalse(discretize0.getUseEqualFrequency());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n }","double cekStop(double x[][], double v0[],double v[][],double w0[],double w[][], double t[]){\n double akumY=0;\n //~ itung z_in dan z\n for(int h=0; h<jumlah_data; h++){\n for(int j=0; j<unit_hidden; j++){\n //itung sigma xi vij\n double jum_xv=0;\n for(int i=0; i<unit_input; i++){\n double cc=x[h][i]*v[i][j];\n jum_xv=jum_xv+cc;\n //System.out.println(x[h][j]);\n }\n z_in[j]=v0[j]+jum_xv;\n //itung z\n z[j]=1/(1+(double)Math.exp(-z_in[j]));\n //System.out.println(\" dan z= \"+z[j]);\n }\n \n //~ itung y_in dan y (output)\n double cxc=0;\n for(int k=0; k<unit_output; k++){\n double jum_zw=0;\n for(int j=0; j<unit_hidden; j++){\n double cc=z[j]*w[j][k];\n jum_zw=jum_zw+cc;\n }\n y_in[k]=w0[k]+jum_zw;\n y[k]=1/(1+(double)Math.exp(-y_in[k]));\n akumY = akumY + Math.pow((t[h]-y[k]),2);\n //System.out.println(t[h]+\"-\"+y[k]+\"=\"+(t[k]-y[k]));\n }\n }\n double E = 0.5 * akumY;\n //System.out.println(E);\n return E;\n }","public void testCleave() {\n final String inputFile = \"testCleave.fas\";\n final String input = TestCaseLM.getFullFilePath(inputFile).replace(\"%20\", \" \");\n\n try {\n // We need to obtain a pointer the control file before anything else.\n BufferedReader br = new BufferedReader(new FileReader(input));\n\n // First one with zero MC's.\n Enzyme e = new Enzyme(\"TestEnzyme\", \"KR\", \"P\", \"Cterm\", 0);\n Protein p = new Protein(\">sw|Q55645|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\");\n\n Protein[] result = e.cleave(p);\n boolean once = false;\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now with one miscleavage.\n e.setMiscleavages(1);\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now with three miscleavages.\n e.setMiscleavages(3);\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Now check for the edge case where there's only one amino acid after the last cleavage position\n // (used to be a bug that this amino acid was never considered).\n e.setMiscleavages(1);\n result = e.cleave(new Protein(\">sw|Q55645|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDRA\"));\n boolean foundIt = false;\n for(int i = 0; i < result.length; i++) {\n Protein lProtein = result[i];\n if(lProtein.getSequence().getSequence().equals(\"TGFDRA\")){\n foundIt = true;\n break;\n }\n }\n\n if(!foundIt) {\n fail(\"The peptide 'TGFDRA' (penultimate amino acid is cleavage site, one missed cleavage allowed) was NOT returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now Nterm position and 1 miscleavage.\n e.setPosition(Enzyme.NTERM);\n e.setMiscleavages(1);\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Next, take an enzyme that does not have restricting residus.\n e = new Enzyme(\"Test\", \"KR\", null, \"Cterm\", 0);\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // And now take an entry that has a location set and see if the cleavage is adjusted accordingly.\n e = new Enzyme(\"TestEnzyme\", \"KR\", \"P\", \"Cterm\", 1);\n p = new Protein(\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\");\n\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now take a C-terminally truncated sequence and see if it is cleaved correctly.\n e = new Enzyme(\"TestEnzyme\", \"KR\", \"P\", \"Cterm\", 1);\n p = new Protein(\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\", true, Protein.CTERMTRUNC);\n\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now take an N-terminally truncated sequence and see if it is cleaved correctly.\n e = new Enzyme(\"TestEnzyme\", \"KR\", \"P\", \"Cterm\", 1);\n p = new Protein(\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\", true, Protein.NTERMTRUNC);\n\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now take a translated sequence, containing an '_' (stopcodon).\n e = new Enzyme(\"TestEnzyme\", \"KR\", \"P\", \"Cterm\", 1);\n p = new Protein(\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHG_FVNRTGFDA\");\n\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // END of file reached.\n // Check this!\n String line = null;\n while((line = br.readLine()) != null) {\n if(!line.trim().equals(\"\")) {\n fail(\"More lines in testCleave.fas then were generated by the test cleavage!\");\n }\n }\n br.close();\n } catch(IOException ioe) {\n fail(\"IOException occurred while testing the cleave() method: '\" + ioe.getMessage() + \"'.\");\n }\n }","@Test\n public void testMetastableMemberships()\n {\n IDoubleArray M = null;\n int nstates = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.metastableMemberships(M, nstates);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testVerser2() {\n\n //Arrange\n CompteBancaire cpt = new CompteBancaire(6500.0);\n double montant_a_verser = -2000.0;\n double excepted = 6500.0;\n\n //Act\n assertEquals(false, cpt.verser(montant_a_verser));\n\n //Assert\n assertEquals(excepted, cpt.getSolde(), 0);\n\n }"],"string":"[\n \"@Test\\n public void testEstimateT()\\n {\\n \\n IDoubleArray counts = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateT(counts);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCstepping_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCstepping(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateC_Iterable_int()\\n {\\n Iterable<IIntArray> trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_3args_1()\\n {\\n Iterable<IIntArray> trajs = null;\\n Iterable<IIntArray> cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_3args_2()\\n {\\n IIntArray traj = null;\\n Iterable<IIntArray> cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testForwardCommittor()\\n {\\n IDoubleArray M = null;\\n IIntArray A = null;\\n IIntArray B = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.forwardCommittor(M, A, B);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"l*{e5SNh\\\");\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 177;\\n discretize0.setUseEqualFrequency(true);\\n double[][] doubleArray0 = new double[5][9];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[0] = (double) 177;\\n doubleArray1[1] = (double) 177;\\n doubleArray1[2] = (double) 177;\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[2];\\n doubleArray2[0] = (double) 177;\\n doubleArray2[1] = (double) 177;\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[9];\\n doubleArray3[0] = (double) 177;\\n doubleArray3[1] = (double) 177;\\n doubleArray3[2] = (double) 177;\\n doubleArray3[3] = (double) 177;\\n doubleArray3[4] = (double) 177;\\n doubleArray3[5] = (double) 177;\\n doubleArray3[6] = (double) 177;\\n doubleArray3[7] = (double) 177;\\n doubleArray3[8] = (double) 177;\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[8];\\n doubleArray4[0] = (double) 177;\\n doubleArray4[1] = (double) 177;\\n doubleArray4[2] = (double) 177;\\n doubleArray4[3] = (double) 177;\\n doubleArray4[4] = (double) 177;\\n doubleArray4[5] = (double) 177;\\n doubleArray4[6] = (double) 177;\\n doubleArray4[7] = (double) 177;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[0];\\n doubleArray0[4] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.listOptions();\\n discretize0.setIgnoreClass(true);\\n String[] stringArray0 = new String[0];\\n Discretize.main(stringArray0);\\n discretize0.findNumBinsTipText();\\n discretize0.setInvertSelection(true);\\n // Undeclared exception!\\n try { \\n discretize0.getBinRangesString(511);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 511\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n\\tpublic void testRepeatibleDynamics() throws IOException, PropertiesException {\\n\\n\\t\\t\\n\\t\\t// INSTANTIATE benchmark \\n\\t\\tProperties props = PropertiesUtil.setpointProperties(new File (\\\"src/main/resources/sim.properties\\\"));\\n\\t\\tSetPointGenerator lg = new SetPointGenerator (props);\\n\\t\\tList<ExternalDriver> externalDrivers = new ArrayList<ExternalDriver>();\\n\\t\\texternalDrivers.add(lg);\\n\\t\\tIndustrialBenchmarkDynamics d = new IndustrialBenchmarkDynamics (props, externalDrivers);\\n\\t\\tRandom actionRand = new Random(System.currentTimeMillis());\\n \\n // 1) do 100000 random steps, in order to initialize dynamics\\n\\t\\tfinal ActionDelta action = new ActionDelta(0.001f, 0.001f, 0.001f); \\n\\t\\tfor (int i=0; i<INIT_STEPS; i++) {\\n\\t\\t\\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\td.step(action);\\n\\t\\t}\\n\\t\\t\\n\\t\\t// 2) memorize current observable state and current markov state\\n\\t\\tfinal ObservableState os = d.getState();\\n\\t\\tfinal DataVector ms = d.getInternalMarkovState();\\n\\t\\tSystem.out.println (\\\"init o-state: \\\" + os.toString());\\n\\t\\tSystem.out.println (\\\"init m-state: \\\" + ms.toString());\\n\\t\\t\\n\\t\\t\\n\\t\\t// 3) perform test trajectory and memorize states\\n\\t\\tactionRand.setSeed(ACTION_SEED);\\n\\t\\tDataVector oStates[] = new DataVector[MEM_STEPS];\\n\\t\\tDataVector mStates[] = new DataVector[MEM_STEPS];\\n\\t\\t\\t\\t\\n\\t\\tfor (int i=0; i<MEM_STEPS; i++) {\\n\\t\\t\\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\td.step(action);\\n\\t\\t\\toStates[i] = d.getState();\\n\\t\\t\\tmStates[i] = d.getInternalMarkovState();\\n\\t\\t}\\n\\t\\t\\n\\t\\t// 4) reset dynamics & parameters and internal markov state\\n\\t\\td.reset();\\n\\t\\td.setInternalMarkovState(ms);\\n\\t\\t\\n\\t\\t// 5) reperform test and check if values are consistent\\n\\t\\tactionRand.setSeed(ACTION_SEED); // reproduce action sequence\\n\\t\\tDataVector oState = null;\\n\\t\\tDataVector mState = null;\\n\\t\\tfor (int i=0; i<MEM_STEPS; i++) {\\n\\t\\t\\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\t\\n\\t\\t\\td.step(action);\\n\\t\\t\\toState = d.getState();\\n\\t\\t\\tmState = d.getInternalMarkovState();\\n\\t\\t\\t\\n\\t\\t\\t// check observable state\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.SetPoint), oState.getValue(ObservableStateDescription.SetPoint), 0.0001);\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.Fatigue), oState.getValue(ObservableStateDescription.Fatigue), 0.0001);\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.Consumption), oState.getValue(ObservableStateDescription.Consumption), 0.0001);\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.RewardTotal), oState.getValue(ObservableStateDescription.RewardTotal), 0.0001);\\n\\n\\t\\t\\t// \\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.CurrentOperationalCost), mState.getValue(MarkovianStateDescription.CurrentOperationalCost), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent2), mState.getValue(MarkovianStateDescription.FatigueLatent2), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent1), mState.getValue(MarkovianStateDescription.FatigueLatent1), 0.0001);\\n\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionGainBeta), mState.getValue(MarkovianStateDescription.EffectiveActionGainBeta), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), mState.getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveShift), mState.getValue(MarkovianStateDescription.EffectiveShift), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.MisCalibration), mState.getValue(MarkovianStateDescription.MisCalibration), 0.0001);\\n\\t\\t\\t\\n\\t\\t\\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), mState.getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), 0.0001);\\n\\t\\t\\t\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardFatigue), mState.getValue(MarkovianStateDescription.RewardFatigue), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardConsumption), mState.getValue(MarkovianStateDescription.RewardConsumption), 0.0001);\\n\\t\\t}\\n\\t\\t\\n\\t\\tSystem.out.println (\\\"last o-state 1st trajectory: \\\" + oStates[oStates.length-1]);\\n\\t\\tSystem.out.println (\\\"last o-state 2nd trajectory: \\\" + oState);\\n\\t\\t\\n\\t\\tSystem.out.println (\\\"last m-state 1st trajectory: \\\" + mStates[oStates.length-1]);\\n\\t\\tSystem.out.println (\\\"last m-state 2nd trajectory: \\\" + mState);\\n\\t}\",\n \"public void steepestDescent() {\\r\\n ArrayList<Atom> atoms = cluster.getAtoms();\\r\\n Atom a;\\r\\n int numAtoms = atoms.size();\\r\\n double potentialEnergy;\\r\\n double trialPotential;\\r\\n int i;\\r\\n\\r\\n //Increment time\\r\\n totalTime += currentStep;\\r\\n\\r\\n //Calculate force on each atom\\r\\n cluster.calculateForces();\\r\\n\\r\\n //Get energy of current configuration\\r\\n HashMap<String, Double> energies = cluster.getEnergies();\\r\\n potentialEnergy = 0.0;\\r\\n for(String eName : energies.keySet())\\r\\n potentialEnergy += energies.get(eName).doubleValue();\\r\\n\\r\\n //Save current atom positions and advance atoms to trial positions\\r\\n for(i = 0; i < numAtoms; i++) {\\r\\n a = atoms.get(i);\\r\\n\\r\\n savedLocations[i].x = a.location.x;\\r\\n savedLocations[i].y = a.location.y;\\r\\n savedLocations[i].z = a.location.z;\\r\\n\\r\\n //Compute trial locations\\r\\n a.location.x += a.force.x * currentStep;\\r\\n a.location.y += a.force.y * currentStep;\\r\\n a.location.z += a.force.z * currentStep;\\r\\n }\\r\\n\\r\\n //Get energy of the trial configuration\\r\\n energies = cluster.getEnergies();\\r\\n trialPotential = 0.0;\\r\\n for(String eName : energies.keySet())\\r\\n trialPotential += energies.get(eName).doubleValue();\\r\\n\\r\\n //Decide whether to keep trial configuration based on convergence\\r\\n if(trialPotential < potentialEnergy) {\\r\\n //Keep positions and check for convergence\\r\\n if(Math.abs((potentialEnergy - trialPotential) / potentialEnergy) < convergenceCriterion)\\r\\n isConverged = true;\\r\\n\\r\\n currentStep *= 1.2;\\r\\n } else {\\r\\n //Restore old positions\\r\\n for(i = 0; i < numAtoms; i++) {\\r\\n a = atoms.get(i);\\r\\n\\r\\n a.location.x = savedLocations[i].x;\\r\\n a.location.y = savedLocations[i].y;\\r\\n a.location.z = savedLocations[i].z;\\r\\n }\\r\\n\\r\\n currentStep *= 0.5;\\r\\n }\\r\\n }\",\n \"@Test\\n public void testMetastableStates()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IIntArray expResult = null;\\n IIntArray result = instance.metastableStates(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testBackwardCommittor()\\n {\\n IDoubleArray M = null;\\n IIntArray A = null;\\n IIntArray B = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.backwardCommittor(M, A, B);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateC_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n void makeCoffee() {\\n Assertions.assertFalse(machine.makeCoffee());\\n\\n //try with cup\\n Assertions.assertDoesNotThrow(() -> {\\n machine.positionCup();\\n });\\n\\n Assertions.assertThrows(Exception.class, () -> {\\n machine.positionCup();\\n });\\n\\n\\n Assertions.assertTrue(machine.makeCoffee());\\n\\n //check if correct amount as been subtracted\\n Assertions.assertEquals(machine.getCurrentWater(), 1000 - machine.waterPerCup);\\n Assertions.assertEquals(machine.getCurrentBeans(), 800 - machine.beansPerCup);\\n\\n //reset\\n machine.fillWater();\\n machine.fillBeans();\\n machine.setCupPositioned(true);\\n\\n //test over boundary\\n Assertions.assertTrue(machine.makeCoffee());\\n Assertions.assertTrue(machine.makeCoffee());\\n\\n //set only water to boundary values and below\\n machine.setCurrentWater(0);\\n Assertions.assertFalse(machine.makeCoffee());\\n machine.setCurrentWater(machine.waterPerCup);\\n Assertions.assertTrue(machine.makeCoffee());\\n\\n //reset water\\n machine.fillWater();\\n\\n //set only beans to boundary value and below\\n machine.setCurrentBeans(0);\\n Assertions.assertFalse(machine.makeCoffee());\\n machine.setCurrentBeans(machine.beansPerCup);\\n Assertions.assertTrue(machine.makeCoffee());\\n\\n\\n }\",\n \"public void checkMovementZMinimum(ITestContext context) throws Exception {\\n Reporter.testStart();\\n String caseFolder=context.getName()+\\\"/\\\";\\n String importFile=context.getCurrentXmlTest().getParameter(\\\"importFile\\\");\\n String expectationFile=context.getCurrentXmlTest().getParameter(\\\"expectationFile\\\");\\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\\n String screenWithinCase=caseFolder+methodName;\\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\\n if(meshViewPage.isThePage()){\\n\\n uploadDCM(meshViewPage,importFile);\\n\\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\\n Reporter.log(\\\"click Orientation Adjustment tab on left bar\\\");\\n String text=\\\"0.1\\\";\\n Reporter.log(\\\"select Movement step \\\"+text);\\n Assert.assertEquals(oa.selectMovementStep(text),text,\\\"Cann't select \\\"+text);\\n\\n Integer clickTimes=40;\\n String disResult=oa.clickDistanceMinus(clickTimes);\\n String higher=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z minus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n\\n clickTimes=5;\\n disResult=oa.clickDistanceMinus(clickTimes);\\n String middle=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z minus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n\\n clickTimes=6;\\n disResult=oa.clickDistanceMinus(clickTimes);//click times: 51\\n String lower=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z minus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"-5\\\",\\\"distance is not same as expected result\\\");\\n\\n embededCompareResult(higher,middle,lower, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\\n\\n disResult=oa.clickDistanceReset();\\n embededScreenShot(screenWithinCase+\\\"_reset_\\\"+disResult,\\\"click Z reset(1 times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"0\\\",\\\"distance is not same as expected result\\\");\\n }\\n Reporter.testEnd();\\n }\",\n \"public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap<Integer,Integer> words)\\r\\n{\\r\\n int t=0;\\r\\n double LearningRates[] = {1, 0.1, 0.01};\\r\\n double Margins[] = {1, 0.1, 0.01};\\r\\n\\r\\n\\tint minInt =0; // Set To Your Desired Min Value\\r\\n int maxInt =2;\\r\\n\\tRandom rand = new Random();\\r\\n\\t\\r\\n \\tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\\r\\n\\r\\n \\tdouble LearningRate = LearningRates[randomNum];\\r\\n \\t\\r\\nfor(int m=0;m<LearningRates.length;m++)\\r\\n{\\r\\n for(int i=0;i<LearningRates.length;i++)\\r\\n {\\r\\n\\t LearningRate = LearningRates[i];\\r\\n\\t\\t double devAccuracyTotal = (double)0;\\r\\n\\t\\t int noOfEpochs= 20;\\r\\n\\t\\t int decreaseRate = 0;\\r\\n\\t\\t \\r\\n \\t\\t Double w[] = new Double[74500];\\r\\n\\t\\t Double min =-0.01; // Set To Your Desired Min Value\\r\\n\\t Double max = 0.01;\\r\\n\\t \\t\\tdouble smallRandNumber = min + new Random().nextDouble() * (max - min); \\r\\n\\t\\t\\tArrays.fill(w, smallRandNumber);\\r\\n\\t\\t\\t\\r\\n\\t\\t for(int epoch=0;epoch<noOfEpochs;epoch++)\\r\\n\\t\\t {\\r\\n\\t\\t\\t \\tw = learnMarginPerceptron(data,index,LearningRate,decreaseRate,w,Margins[m]);\\r\\n\\t\\t\\t \\tdecreaseRate = decreaseRate + index;\\r\\n\\t\\t\\r\\n\\t\\t }\\r\\n\\t\\t//CVSplit test\\r\\n \\tdouble cvAccuracy = testTestFile(w,testFileName,words);\\r\\n \\t\\r\\n// \\tSystem.out.println(\\\"Epoch\\\" + epoch +\\\" Dev Accuracy for Decaying Learning Rate \\\" + LearningRate + \\\" is \\\" + cvAccuracy);\\r\\n \\r\\n \\r\\n \\tSystem.out.println(\\\" ** Cv Accuracy for Margin Rate\\\" + Margins[m] + \\\" Learning Rate \\\" + LearningRate + \\\" is \\\" + cvAccuracy);\\r\\n \\tSystem.out.println(\\\" \\\");\\r\\n }\\r\\n}\\r\\n//double testAccuracy = testTestFile(w,testFileName);\\r\\n\\t//System.out.println(\\\"test Accuracy\\\" + testAccuracy);\\r\\n}\",\n \"@Test(timeout = 4000)\\n public void test29() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[3][6];\\n discretize0.setIgnoreClass(false);\\n discretize0.m_CutPoints = doubleArray0;\\n double[] doubleArray1 = new double[4];\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints((-831));\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // -831\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"public void testStep() {\\n\\n DT_TractographyImage crossing = Images.getCrossing();\\n\\n FACT_FibreTracker tracker = new FACT_FibreTracker(crossing);\\n\\n Vector3D[] pds = crossing.getPDs(1,1,1);\\n\\n Vector3D step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 0, true);\\n\\n assertEquals(1.0, pds[0].dot(step.normalized()), 1E-8);\\n\\n assertEquals(Images.xVoxelDim / 2.0, step.mod(), 0.05);\\n \\n step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 0, false);\\n\\n assertEquals(-1.0, pds[0].dot(step.normalized()), 1E-8);\\n\\n step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 1, true);\\n\\n assertEquals(1.0, pds[1].dot(step.normalized()), 1E-8);\\n\\n\\n for (int i = 0; i < 2; i++) {\\n\\n step = tracker.getNextStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), new Vector3D(pds[i].x + 0.01, pds[i].y + 0.02, pds[i].z + 0.03).normalized());\\n\\n assertEquals(1.0, pds[i].dot(step.normalized()), 1E-8);\\n\\n }\\n\\n \\n }\",\n \"@Test()\\n public void testTrainWarmCFastSMO() {\\n ClassificationDataSet train = FixedProblems.getHalfCircles(250, RandomUtil.getRandom(), 0.1, 0.2);\\n\\n PlattSMO warmModel = new PlattSMO(new LinearKernel(1));\\n warmModel.setC(1);\\n warmModel.train(train);\\n\\n PlattSMO warm = new PlattSMO(new LinearKernel(1));\\n warm.setC(1e4);// too large to train efficently like noraml\\n\\n long start, end;\\n\\n start = System.currentTimeMillis();\\n warm.train(train, warmModel);\\n end = System.currentTimeMillis();\\n long warmTime = (end - start);\\n\\n PlattSMO notWarm = new PlattSMO(new LinearKernel(1));\\n notWarm.setC(1e4);// too large to train efficently like noraml\\n\\n start = System.currentTimeMillis();\\n notWarm.train(train);\\n end = System.currentTimeMillis();\\n long normTime = (end - start);\\n\\n assertTrue(warmTime < normTime * 0.75);\\n\\n }\",\n \"@Test(timeout = 4000)\\n public void test03() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setAttributeIndices(\\\"\\\");\\n Filter.makeCopy(discretize0);\\n discretize0.setDesiredWeightOfInstancesPerInterval(2.0);\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 0;\\n discretize0.setUseBinNumbers(true);\\n discretize0.listOptions();\\n discretize0.makeBinaryTipText();\\n discretize0.getInvertSelection();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-458));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testStationaryDistribution()\\n {\\n try\\n {\\n IDoubleArray T = Doubles.create.fromFile(inputT1);\\n IDoubleArray expResult = Doubles.create.fromFile(inputT1_pi); \\n IDoubleArray pi = MarkovModel.util.stationaryDistribution(T);\\n assertEqual(pi, expResult, 1e-5);\\n }\\n catch(IOException e)\\n {\\n \\n e.printStackTrace();\\n }\\n // TODO review the generated test code and remove the default call to fail.\\n //fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testPerturbationExpectation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray pi0 = null;\\n IDoubleArray observable = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.perturbationExpectation(M, pi0, observable, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void test03() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double[] doubleArray0 = new double[19];\\n evaluation0.updateMargins(doubleArray0, 0, 0.0);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n }\",\n \"@Test\\n void cupTesting() {\\n Assertions.assertFalse(machine.isCupPositioned());\\n Assertions.assertFalse(machine.removeCup());\\n\\n Assertions.assertDoesNotThrow(() -> machine.positionCup());\\n\\n Assertions.assertTrue(machine.removeCup());\\n Assertions.assertFalse(machine.isCupPositioned());\\n machine.setCupPositioned(true);\\n Assertions.assertTrue(machine.isCupPositioned());\\n\\n }\",\n \"public ModelParams optimize() throws Exception {\\n\\t\\tint iterCount = 0;\\n\\t\\tdivergence = KLDivergenceCalculator.calculate(cState, tState);\\n\\t\\tSystem.out.println(\\\"KL Divergence : \\\" + divergence);\\n\\t\\t// ((DivergenceChart) BeanFinder\\n\\t\\t// .findBean(MetaConstants.BEAN_DIVERGENCE_CHART)).update(\\n\\t\\t// iterCount, divergence);\\n\\t\\tdouble delta[] = null;\\n\\t\\tdouble theta[] = data.getParams().getParams();\\n\\t\\tdouble newDivergence = 0.0d;\\n\\t\\tdouble initialStep = data.getInitialStepSize();\\n\\t\\tboolean optimized = false;\\n\\t\\tdo {\\n\\t\\t\\tdelta = computeGradient(theta);\\n\\t\\t\\tfor (double step = initialStep; iterCount < data.getMaxIterations(); step /= 10) {\\n\\t\\t\\t\\tSystem.out.println(\\\"Step size : \\\" + step);\\n\\t\\t\\t\\tSystem.out.println(\\\"KL divergence before taking a step : \\\"\\n\\t\\t\\t\\t\\t\\t+ KLDivergenceCalculator.calculate(cState, tState));\\n\\t\\t\\t\\tfor (int i = 0; i < delta.length; i++) {\\n\\t\\t\\t\\t\\tif (Double.isNaN(delta[i]))\\n\\t\\t\\t\\t\\t\\tSystem.out.print(\\\"delta is NAN!!\\\" + delta[i] + \\\", \\\");\\n\\t\\t\\t\\t\\ttheta[i] -= step * delta[i];\\n\\t\\t\\t\\t\\tSystem.out.print((theta[i]) + \\\", \\\");\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tSystem.out.println();\\n\\t\\t\\t\\tgetNewState(theta);\\n\\t\\t\\t\\titerCount++;\\n\\t\\t\\t\\tnewDivergence = KLDivergenceCalculator\\n\\t\\t\\t\\t\\t\\t.calculate(cState, tState);\\n\\t\\t\\t\\tSystem.out.println(\\\"KL Divergence : \\\" + newDivergence);\\n\\t\\t\\t\\t// ((DivergenceChart) BeanFinder\\n\\t\\t\\t\\t// .findBean(MetaConstants.BEAN_DIVERGENCE_CHART)).update(\\n\\t\\t\\t\\t// iterCount, newDivergence);\\n\\t\\t\\t\\tif (newDivergence < divergence) {\\n\\t\\t\\t\\t\\tdivergence = newDivergence;\\n\\t\\t\\t\\t\\tinitialStep = step;\\n\\t\\t\\t\\t\\tbreak;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t// before taking the next step size, reset theta to what it was\\n\\t\\t\\t\\tfor (int i = 0; i < delta.length; i++)\\n\\t\\t\\t\\t\\ttheta[i] += step * delta[i];\\n\\t\\t\\t\\tgetNewState(theta); // Added for checking. Not required.\\n\\t\\t\\t}\\n\\t\\t\\toptimized = optimized(theta);\\n\\t\\t} while (!optimized && iterCount < data.getMaxIterations());\\n\\t\\tModelParams optimParams = null;\\n\\t\\tif (optimized) {\\n\\t\\t\\toptimParams = new ModelParams();\\n\\t\\t\\toptimParams.setParams(theta);\\n\\t\\t}\\n\\t\\treturn optimParams;\\n\\t}\",\n \"@Test\\n public void testEstimateCmilestoning_Iterable_int()\\n {\\n Iterable<IIntArray> trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[7][2];\\n double[] doubleArray1 = new double[7];\\n doubleArray1[0] = 0.0;\\n doubleArray1[1] = (-587.14627);\\n doubleArray1[2] = (-2542.40202289275);\\n doubleArray1[3] = (-167.679515);\\n doubleArray1[4] = (-1.0);\\n doubleArray1[5] = 1.0;\\n doubleArray1[6] = (-1.0);\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[5];\\n doubleArray2[0] = (-167.679515);\\n doubleArray2[1] = (-2692.352);\\n doubleArray2[2] = (-167.679515);\\n doubleArray2[3] = (-2542.40202289275);\\n doubleArray2[4] = (-587.14627);\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[4];\\n doubleArray3[0] = (-1.0);\\n doubleArray3[1] = (-1.0);\\n doubleArray3[2] = 1.0;\\n doubleArray3[3] = (-2542.40202289275);\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[7];\\n doubleArray4[0] = (-2692.352);\\n doubleArray4[1] = (-1.0);\\n doubleArray4[2] = (-1.0);\\n doubleArray4[3] = 0.0;\\n doubleArray4[4] = 1.0;\\n doubleArray4[5] = 0.0;\\n doubleArray4[6] = 1.0;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[1];\\n doubleArray5[0] = (-167.679515);\\n doubleArray0[4] = doubleArray5;\\n double[] doubleArray6 = new double[0];\\n doubleArray0[5] = doubleArray6;\\n double[] doubleArray7 = new double[1];\\n doubleArray7[0] = (-587.14627);\\n doubleArray0[6] = doubleArray7;\\n discretize0.m_CutPoints = doubleArray0;\\n String string0 = discretize0.invertSelectionTipText();\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.\\\", string0);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Test\\r\\n public void testAverageacceleration() {\\r\\n System.out.println(\\\"Average Acceleration\\\");\\r\\n \\r\\n // Test case one.\\r\\n System.out.println(\\\"Test case #1\\\"); \\r\\n \\r\\n double distance = 200;\\r\\n long time = 16;\\r\\n \\r\\n SceneControl instance = new SceneControl();\\r\\n \\r\\n double expResult = 0.78125;\\r\\n double result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case two.\\r\\n System.out.println(\\\"Test case #2\\\"); \\r\\n \\r\\n distance = 80;\\r\\n time = 200;\\r\\n \\r\\n \\r\\n expResult = .002;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case three.\\r\\n System.out.println(\\\"Test case #3\\\"); \\r\\n \\r\\n distance = 0;\\r\\n time = 15;\\r\\n \\r\\n \\r\\n expResult = 0;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case four.\\r\\n System.out.println(\\\"Test case #4\\\"); \\r\\n \\r\\n distance = 12;\\r\\n time = 1;\\r\\n \\r\\n \\r\\n expResult = 12;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case five.\\r\\n System.out.println(\\\"Test case #5\\\"); \\r\\n \\r\\n distance = 1;\\r\\n time = 1;\\r\\n \\r\\n \\r\\n expResult = 1;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case six.\\r\\n System.out.println(\\\"Test case #6\\\"); \\r\\n \\r\\n distance = 3;\\r\\n time = 8;\\r\\n \\r\\n \\r\\n expResult = .046875;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case seven.\\r\\n System.out.println(\\\"Test case #7\\\"); \\r\\n \\r\\n distance = 200;\\r\\n time = 1;\\r\\n \\r\\n \\r\\n expResult = 200;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n \\r\\n \\r\\n }\",\n \"@Test\\n public void testGetSetTemperaturaExteriorMinima() {\\n System.out.println(\\\"getSetTemperaturaExteriorMinima\\\");\\n double expResult = 10.0;\\n Simulacao simulacao = new Simulacao();\\n simulacao.setSala(sala);\\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\\n instance.setTemperaturaExteriorMinima(expResult);\\n double result = instance.getTemperaturaExteriorMinima();\\n assertEquals(expResult, result, 0.0);\\n }\",\n \"public void checkMovementZReset(ITestContext context) {\\n Reporter.testStart();\\n String caseFolder=context.getName()+\\\"/\\\";\\n String importFile=context.getCurrentXmlTest().getParameter(\\\"importFile\\\");\\n String expectationFile=context.getCurrentXmlTest().getParameter(\\\"expectationFile\\\");\\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\\n String screenWithinCase=caseFolder+methodName;\\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\\n if(meshViewPage.isThePage()){\\n\\n uploadDCM(meshViewPage,importFile);\\n\\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\\n Reporter.log(\\\"click Orientation Adjustment tab on left bar\\\");\\n String text=\\\"0.1\\\";\\n Reporter.log(\\\"select Movement step \\\"+text);\\n Assert.assertEquals(oa.selectMovementStep(text),text,\\\"Cann't select \\\"+text);\\n\\n Integer clickTimes=30;\\n String disResult=oa.clickDistanceMinus(clickTimes);\\n embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z minus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"-3\\\",\\\"distance is not same as expected result\\\");\\n\\n clickTimes=10;\\n disResult=oa.clickDistancePlus(clickTimes);\\n String lower=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z plus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"-2\\\",\\\"distance is not same as expected result\\\");\\n\\n disResult=oa.clickDistancePlus(clickTimes);\\n String middle=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z plus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"-1\\\",\\\"distance is not same as expected result\\\");\\n\\n disResult=oa.clickDistanceReset();\\n String higher=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z reset(1 times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"0\\\",\\\"distance is not same as expected result\\\");\\n\\n embededCompareResult(lower,middle,higher, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\\n\\n }\\n Reporter.testEnd();\\n }\",\n \"private static void testTakeStep(Drunkard drunkard, int testStepTimes, int testStepSize){\\n\\t for (int i = 0; i < testStepTimes; i++)\\n\\t{\\n\\t ImPoint pointA = drunkard.getCurrentLoc(); \\n\\t\\tdrunkard.takeStep();\\n\\t\\tImPoint pointB = drunkard.getCurrentLoc();\\n\\t\\t\\n\\t\\tif (pointA.getX()-pointB.getX() != 0 && pointA.getY()-pointB.getY() != 0)\\n\\t\\t{\\n\\t\\t\\tSystem.out.println(\\\"Not a valid compass direction!\\\");\\n\\t\\t}\\n\\t\\telse if (Math.sqrt((pointA.getX() - pointB.getX())*(pointA.getX() - pointB.getX()) + (pointA.getY() - pointB.getY())*(pointA.getY() - pointB.getY())) != testStepSize)\\n\\t\\t{\\n\\t\\t\\tSystem.out.println(\\\"took step to (\\\" + pointB.getX() + \\\",\\\" + pointB.getY() + \\\"), FAILED, not a valid step\\\");\\n\\t\\t}\\n\\t\\telse\\n\\t\\t{\\n\\t\\t\\tSystem.out.println(\\\"took step to (\\\" + pointB.getX() + \\\",\\\" + pointB.getY() + \\\"), SUCCEEDED\\\");\\n\\t\\t}\\n\\t}\\n\\t System.out.println(); \\n\\t}\",\n \"@Test\\n public void testIsTransitionMatrix()\\n {\\n IDoubleArray T = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n boolean expResult = false;\\n boolean result = instance.isTransitionMatrix(T);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test072() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate((String) null);\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double[] doubleArray0 = new double[7];\\n evaluation0.updateMargins(doubleArray0, 3, (-1));\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints((-2276));\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Test\\n public void testGiveChange() throws Exception {\\n System.out.println(\\\"giveChange\\\");\\n //CashRegister instance = new CashRegister();\\n assertEquals(0.0, instance.giveChange(), EPSILON);\\n instance.scanItem(0.55);\\n instance.scanItem(1.27);\\n double expResult = 0.18;\\n instance.collectPayment(Money.QUARTER, 8);\\n double result = instance.giveChange();\\n assertEquals(expResult, result, EPSILON);\\n \\n }\",\n \"@Test\\n public void testCalcMedAmount() {\\n \\n //Test case 1\\n \\n System.out.println(\\\"calcMedAmount Test Case 1 - Dose is correct\\\");\\n \\n //Input Values for Test Case 1\\n \\n double weightInPounds = 100;\\n double numberOfPills = 1;\\n \\n AntidoteControl instance = new AntidoteControl();\\n \\n double expResult = 227;\\n double result = AntidoteControl.calcMedAmount(weightInPounds);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n //fail(\\\"The test case is a prototype.\\\");\\n \\n \\n //Test case 2\\n \\n System.out.println(\\\"calcMedAmount Test Case 2 Invalid weight\\\");\\n \\n //Input Values for Test Case 2\\n \\n weightInPounds = 0;\\n numberOfPills = 1;\\n \\n // AntidoteControl instance = new AntidoteControl();\\n \\n expResult = -1;\\n result = AntidoteControl.calcMedAmount(weightInPounds);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n //fail(\\\"The test case is a prototype.\\\");\\n \\n \\n //Test case 3\\n \\n System.out.println(\\\"calcMedAmount Test Case 3 Underdose\\\");\\n \\n //Input Values for Test Case 3\\n \\n weightInPounds = 10001;\\n numberOfPills = 0;\\n \\n // AntidoteControl instance = new AntidoteControl();\\n \\n expResult = -2;\\n result = AntidoteControl.calcMedAmount(weightInPounds);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n //fail(\\\"The test case is a prototype.\\\");\\n \\n \\n //Test case 4\\n \\n System.out.println(\\\"calcMedAmount Test Case 4 Overdose\\\");\\n \\n //Input Values for Test Case 4\\n \\n weightInPounds = 100;\\n numberOfPills = 4;\\n \\n // AntidoteControl instance = new AntidoteControl();\\n \\n expResult = 227;\\n result = AntidoteControl.calcMedAmount(weightInPounds);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n //fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"int calculateMST();\",\n \"@Test\\n public void transferCheckingtoSavingsTest(){\\n assertTrue(userC.transfer(150.00, userS));\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n Locale.getISOLanguages();\\n double[][] doubleArray0 = new double[9][8];\\n double[] doubleArray1 = new double[3];\\n double[] doubleArray2 = new double[9];\\n doubleArray2[5] = (-163.0);\\n doubleArray2[8] = 1421.4058829;\\n doubleArray0[3] = doubleArray0[2];\\n doubleArray0[4] = doubleArray0[1];\\n discretize0.setOutputFormat();\\n double[] doubleArray3 = new double[2];\\n doubleArray3[0] = 1421.4058829;\\n doubleArray3[1] = (-163.0);\\n doubleArray0[6] = doubleArray2;\\n double[] doubleArray4 = new double[0];\\n doubleArray0[7] = doubleArray4;\\n double[] doubleArray5 = new double[6];\\n doubleArray5[2] = (-163.0);\\n doubleArray0[8] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test096() throws Throwable {\\n CostMatrix costMatrix0 = Evaluation.handleCostOption(\\\"\\\", 1281);\\n assertNull(costMatrix0);\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"%\\\");\\n FileSystemHandling.createFolder((EvoSuiteFile) null);\\n discretize0.findNumBinsTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"%\\\");\\n discretize0.m_DefaultCols = \\\"%\\\";\\n double[][] doubleArray0 = new double[0][7];\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints(1);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 1\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void shouldCalculateProperMoveProbabilities() throws Exception {\\n\\n int actualCity = 0;\\n boolean[] visited = new boolean[] {true, false, false, false};\\n int[][] firstCriterium = new int[][] {{0, 2, 1, 2},\\n {2, 2, 1, 1},\\n {1, 1, 0, 1},\\n {2, 1, 1, 0}};\\n int[][] secondCriterium = new int[][] {{0, 3, 2, 1},\\n {3, 0, 0, 0},\\n {2, 0, 0, 0},\\n {1, 0, 0, 0}};\\n// final AttractivenessCalculator attractivenessCalculator = new AttractivenessCalculator(1.0, 0.0, 0.0, 0.0, 0.0);\\n// double[][] array = new double[0][0];\\n// final double[] probabilities = attractivenessCalculator.movesProbability(actualCity, visited, array, firstCriterium, secondCriterium, null, 0.0, 0.0);\\n\\n// assertTrue(probabilities[0] == 0.0);\\n// assertTrue(probabilities[1] == 0.0);\\n// assertTrue(probabilities[2] == 0.5);\\n// assertTrue(probabilities[3] == 0.5);\\n }\",\n \"public void testMainLBGFSCachedSemiMarkov() throws Exception {\\n\\t\\tConrad.main(new String[] {\\\"train\\\", \\\"test/input/zeroOrderLBFGSCachedSemiMarkov.xml\\\", \\\"test/input/zeroOrderTestShortMax.txt\\\", \\\"test/working/zeroLBGFSModelCachedSemiMarkov.ser\\\"});\\n\\t\\tConrad.main(new String[] {\\\"test\\\", \\\"test/working/zeroLBGFSModelCachedSemiMarkov.ser\\\", \\\"test/input/zeroOrderTest.txt\\\", \\\"test/working/zeroOrderMainLBGFSCachedSemiMarkovPredicted.txt\\\"});\\n\\t\\tConrad.main(new String[] {\\\"test\\\", \\\"test/working/zeroLBGFSModelCachedSemiMarkov.ser\\\", \\\"test/input/zeroOrderTest2.txt\\\", \\\"test/working/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\\\"});\\n\\t\\tassertFilesMatch(\\\"test/output/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\\\", \\\"test/working/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\\\");\\n\\t}\",\n \"@Test\\n public void testGetSetTemperaturaExterior() {\\n System.out.println(\\\"getSetTemperaturaExterior\\\");\\n double expResult = 20.0;\\n Simulacao simulacao = new Simulacao();\\n simulacao.setSala(sala);\\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\\n instance.setTemperaturaExterior(expResult);\\n double result = instance.getTemperaturaExterior();\\n assertEquals(expResult, result, 0.0);\\n }\",\n \"@Test\\r\\n public void gallonsCauldronHolds() {\\r\\n \\r\\n System.out.println(\\\"gallonsCauldronHolds\\\");\\r\\n \\r\\n //Test #1\\r\\n System.out.println(\\\"Test case #1\\\"); \\r\\n \\r\\n double diameter = 50;\\r\\n double depth = 25;\\r\\n \\r\\n SceneControl instance = new SceneControl();\\r\\n \\r\\n double expResult = 212.500;\\r\\n double result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #2\\r\\n System.out.println(\\\"Test case #2\\\");\\r\\n \\r\\n diameter = 15;\\r\\n depth = -12;\\r\\n \\r\\n expResult = -1;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #3\\r\\n System.out.println(\\\"Test case #3\\\");\\r\\n \\r\\n diameter = -5;\\r\\n depth = 20;\\r\\n \\r\\n expResult = -1;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #4\\r\\n System.out.println(\\\"Test case #4\\\");\\r\\n \\r\\n diameter = 0;\\r\\n depth = 30;\\r\\n \\r\\n expResult = -1;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #5\\r\\n System.out.println(\\\"Test case #5\\\");\\r\\n \\r\\n diameter = 14;\\r\\n depth = 0;\\r\\n \\r\\n expResult = -1;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #6\\r\\n System.out.println(\\\"Test case #6\\\");\\r\\n \\r\\n diameter = 1;\\r\\n depth = 12;\\r\\n \\r\\n expResult = .041;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #7\\r\\n System.out.println(\\\"Test case #7\\\");\\r\\n \\r\\n diameter = 20;\\r\\n depth = 1;\\r\\n \\r\\n expResult = 1.36;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n }\",\n \"@Test\\n public void testInbreedingCoeffForMultiallelicVC() {\\n VariantContext test1 = makeVC(\\\"1\\\",Arrays.asList(Aref,T,C),\\n makeG(\\\"s1\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\\n makeG(\\\"s2\\\", T, T, 7099, 2530, 0, 7099, 366, 3056),\\n makeG(\\\"s3\\\", T, C, 7099, 2530, 7099, 3056, 0, 14931),\\n makeG(\\\"s4\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\\n makeG(\\\"s5\\\", T, T, 7099, 2530, 0, 7099, 366, 3056),\\n makeG(\\\"s6\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\\n makeG(\\\"s7\\\", T, T, 7099, 2530, 0, 7099, 366, 3056),\\n makeG(\\\"s8\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\\n makeG(\\\"s9\\\", T, T, 7099, 2530, 0, 7099, 366, 3056),\\n makeG(\\\"s10\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931));\\n\\n final AS_InbreedingCoeff testClass1 = new AS_InbreedingCoeff();\\n final double ICresult1 = testClass1.calculateIC(test1, T);\\n Assert.assertEquals(ICresult1, -0.4285714, DELTA_PRECISION, \\\"Pass\\\");\\n final double ICresult1b = testClass1.calculateIC(test1, C);\\n Assert.assertEquals(ICresult1b, -0.05263, DELTA_PRECISION, \\\"Pass\\\");\\n\\n //make sure that hets with different alternate alleles all get counted\\n VariantContext test2 = makeVC(\\\"2\\\", Arrays.asList(Aref,T,C),\\n makeG(\\\"s1\\\", Aref, C, 4878, 1623, 11297, 0, 7970, 8847),\\n makeG(\\\"s2\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\\n makeG(\\\"s3\\\", Aref, T, 3382, 0, 6364, 1817, 5867, 12246),\\n makeG(\\\"s4\\\", Aref, T, 2488, 0, 9110, 3131, 9374, 12505),\\n makeG(\\\"s5\\\", Aref, C, 4530, 2006, 18875, 0, 6847, 23949),\\n makeG(\\\"s6\\\", Aref, T, 5325, 0, 18692, 389, 16014, 24570),\\n makeG(\\\"s7\\\", Aref, T, 2936, 0, 29743, 499, 21979, 38630),\\n makeG(\\\"s8\\\", Aref, T, 6902, 0, 8976, 45, 5844, 9061),\\n makeG(\\\"s9\\\", Aref, T, 5732, 0, 10876, 6394, 11408, 17802),\\n makeG(\\\"s10\\\", Aref, T, 2780, 0, 25045, 824, 23330, 30939));\\n\\n final AS_InbreedingCoeff testClass2 = new AS_InbreedingCoeff();\\n final double ICresult2 = testClass2.calculateIC(test2, T);\\n Assert.assertEquals(ICresult2, -0.666666, DELTA_PRECISION, \\\"Pass\\\");\\n final double ICresult2b = testClass2.calculateIC(test2, C);\\n Assert.assertEquals(ICresult2b, -0.111129, DELTA_PRECISION, \\\"Pass\\\");\\n }\",\n \"public void test_setClippingIIII() {\\n}\",\n \"@Test\\n\\tvoid testCalculateMarkTillSemesterSuccess3() {\\n\\t\\n\\t\\tString semesterName = \\\"20193\\\";\\n\\t\\t\\n\\t\\tUser user = userService.findByUsername(\\\"B17DCCN123\\\");\\n\\t\\tStudent student = user.getStudent();\\n\\t\\tList<StudentResult> resultsTillSemester = resultService.findResultTillSemester(student.getId(),\\n\\t\\t\\t\\tsemesterName);\\n\\t\\t\\n\\t\\tString tktMark = \\\"\\\";\\n\\t\\t\\n\\t\\tfor (StudentResult result : resultsTillSemester) {\\n\\t\\t\\tSubject subject = result.getStudentRegister().getCourse().getSubject();\\n\\t\\t\\tif (subject.getName().equals(\\\"Toán kinh tế\\\")) {\\n\\t\\t\\t\\ttktMark = result.getMarkToChar();\\n\\t\\t\\t\\tbreak;\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\t\\n\\t\\tList<Object> listGpaResultsTillNow = MarkUtility.calculateMarkTillSemester(resultsTillSemester);\\n\\t\\tint passedCreditsTillNow = (int) listGpaResultsTillNow.get(0);\\n\\t\\tfloat gpaTillNow = (float) listGpaResultsTillNow.get(1);\\n\\t\\t\\n\\t\\tDecimalFormat df = new DecimalFormat(\\\"#.##\\\");\\n\\t\\t\\n\\t\\tassertTrue(\\n\\t\\t\\t\\ttktMark.equals(\\\"C\\\") && \\n\\t\\t\\t\\tpassedCreditsTillNow == 18 && \\n\\t\\t\\t\\tdf.format(gpaTillNow).equals(\\\"3.19\\\")\\n\\t\\t\\t\\t);\\n\\t}\",\n \"float estimateCostToGoal( int current_x, int current_y, int current_z, boolean fromStart ) {\\n\\t\\treturn 0;\\n\\t}\",\n \"@Test\\n @Tag(\\\"slow\\\")\\n public void testCZPROB() {\\n CuteNetlibCase.doTest(\\\"CZPROB.SIF\\\", \\\"2185196.6988565763\\\", \\\"3089066.71321333\\\", NumberContext.of(7, 4));\\n }\",\n \"@Test\\n public void testVerser1() {\\n\\n //Arrange\\n CompteBancaire cpt = new CompteBancaire(5000.0);\\n double montant_a_verser = 1000.0;\\n double excepted = 6000.0;\\n\\n //Act\\n assertEquals(true, cpt.verser(montant_a_verser));\\n\\n //Assert\\n assertEquals(excepted, cpt.getSolde(), 0);\\n }\",\n \"@Test\\n public void testVirement3() {\\n\\n //Arrange\\n CompteBancaire cpt1 = new CompteBancaire(0.0);\\n CompteBancaire cpt2 = new CompteBancaire(43000.0);\\n double montant_a_virer = 8000.0;\\n double excepted_solde_cpt1 = 0.0;\\n double excepted_solde_cpt2 = 43000.0;\\n\\n //Act\\n assertEquals(false, cpt1.virerArgent(cpt2, montant_a_virer));\\n\\n //Assert\\n assertEquals(excepted_solde_cpt1, cpt1.getSolde(), 0);\\n assertEquals(excepted_solde_cpt2, cpt2.getSolde(), 0);\\n\\n }\",\n \"public static void main(String[] args) {\\n\\n String fileEdgeList = \\\"../DataSource/PANCANsigEdgeList.csv\\\";\\n String fileGtTrainingMatrix = \\\"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.training.10.csv\\\";\\n// String fileGtTestingMatrix = \\\"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.testing.10.csv\\\";\\n String fileGeTrainingMatrix = \\\"../DataSource/CrossMatrixDriver/PANCAN.GeM.4468tumors.training.10.csv\\\";\\n String fileGeTestingMatrix = \\\"../DataSource/CrossMatrixDriver/PANCAN.GeM.4468tumors.testing.10.csv\\\";\\n String fileInferDriver = \\\"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.InferDriver.10.withTumorID.csv\\\";\\n// String fileDriverSGATable = \\\"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.DriverSGATable.10.csv\\\";\\n \\n \\n DataReader dataObj = new DataReader(fileEdgeList, fileGtTrainingMatrix, fileGeTrainingMatrix);\\n\\n int reRun = 0;\\n double T = 0.5;\\n do {\\n reRun += 1;\\n\\n EstimateParams paramObj = new EstimateParams(dataObj.edgeList, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.driverSGATable, dataObj.targetDEGTable);\\n InferDriverActivation actObj = new InferDriverActivation(paramObj.mapEdgeParam, paramObj.mapSGAParam,\\n dataObj.targetDEGTable, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.mapSgaDegs);\\n\\n\\n actObj.thresholding(T);\\n \\n actObj.updateInferDriverTable( dataObj.driverSGATable);\\n \\n double change = actObj.compareMatrix(dataObj.driverSGATable);\\n\\n System.out.println(\\\"Current T is \\\" + T);\\n System.out.println(\\\"Change is \\\" + change);\\n System.out.println(\\\"This is the \\\" + reRun + \\\"th run\\\");\\n if (change < 0.001 || T > 1) {\\n System.out.println(\\\"Total times of run is \\\" + reRun + \\\". Final cut shreshold is \\\" + T);\\n \\n// dataObj.readInGtMatrix(fileGtTestingMatrix);\\n dataObj.readInGeMatrix(fileGeTestingMatrix);\\n InferDriverActivation actObjTesting = new InferDriverActivation(paramObj.mapEdgeParam, paramObj.mapSGAParam,\\n dataObj.targetDEGTable, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.mapSgaDegs);\\n \\n actObjTesting.outputInferActivation(fileInferDriver, dataObj.tumorNames);\\n// dataObj.outputDriverSGATable(fileDriverSGATable); //output contains tumorName, since tumor name is defined in dataObj, so no need to pass in\\n\\n break; \\n \\n } else {\\n dataObj.updateDriverSGATable(actObj.inferDriverTable);\\n T += 0.05;\\n \\n }\\n\\n } while (true);\\n }\",\n \"@Test(timeout = 4000)\\n public void test36() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\\n Properties properties0 = new Properties();\\n ProtectedProperties protectedProperties0 = new ProtectedProperties(properties0);\\n ProtectedProperties protectedProperties1 = new ProtectedProperties(protectedProperties0);\\n Attribute attribute0 = new Attribute(\\\"-\\\", \\\"-\\\", protectedProperties1);\\n arrayList0.add(attribute0);\\n Instances instances0 = new Instances(\\\"(,Z;Xp#\\\\\\\"dxq[\\\", arrayList0, 1587);\\n boolean boolean0 = discretize0.setInputFormat(instances0);\\n boolean boolean1 = discretize0.batchFinished();\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(10, discretize0.getBins());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertTrue(boolean1 == boolean0);\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(boolean1);\\n }\",\n \"@Test\\n public void testStationaryDistributionRevQuick()\\n {\\n IDoubleArray T = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.stationaryDistributionRevQuick(T);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testGetSetTemperaturaInterior() {\\n System.out.println(\\\"getSetTemperaturaInterior\\\");\\n double expResult = 20.0;\\n Simulacao simulacao = new Simulacao();\\n simulacao.setSala(sala);\\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\\n instance.setTemperaturaInterior(expResult);\\n double result = instance.getTemperaturaInterior();\\n assertEquals(expResult, result, 0.0);\\n }\",\n \"@Test\\n public void testCorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable1 = null;\\n IDoubleArray observable2 = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test083() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.SFMeanEntropyGain();\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, double0, 0.01);\\n }\",\n \"@Test\\n public void testGetSetTemperaturaExteriorMaxima() {\\n System.out.println(\\\"getSetTemperaturaExteriorMaxima\\\");\\n double expResult = 20.0;\\n Simulacao simulacao = new Simulacao();\\n simulacao.setSala(sala);\\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\\n instance.setTemperaturaExteriorMaxima(expResult);\\n double result = instance.getTemperaturaExteriorMaxima();\\n assertEquals(expResult, result, 0.0);\\n }\",\n \"@Test(timeout = 4000)\\n public void test087() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.KBMeanInformation();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"public void checkMovementZMaximum(ITestContext context) {\\n Reporter.testStart();\\n String caseFolder=context.getName()+\\\"/\\\";\\n String importFile=context.getCurrentXmlTest().getParameter(\\\"importFile\\\");\\n String expectationFile=context.getCurrentXmlTest().getParameter(\\\"expectationFile\\\");\\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\\n String screenWithinCase=caseFolder+methodName;\\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\\n if(meshViewPage.isThePage()){\\n\\n uploadDCM(meshViewPage,importFile);\\n\\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\\n\\n Reporter.log(\\\"click Orientation Adjustment tab on left bar\\\");\\n String text=\\\"0.05\\\";\\n Reporter.log(\\\"select Movement step \\\"+text);\\n Assert.assertEquals(oa.selectMovementStep(text),text,\\\"Cann't select \\\"+text);\\n\\n Integer clickTimes=98;\\n String valueOnLabel=oa.clickDistancePlus(clickTimes);\\n String lower=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+valueOnLabel,\\\"click Z plus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+valueOnLabel);\\n Assert.assertEquals(valueOnLabel,\\\"4.9\\\",\\\"distance is not same as expected result\\\");\\n\\n clickTimes=1;\\n valueOnLabel=oa.clickDistancePlus();\\n String middle=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+valueOnLabel,\\\"click Z plus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+valueOnLabel);\\n Assert.assertEquals(valueOnLabel,\\\"4.95\\\",\\\"distance is not same as expected result\\\");\\n\\n text=\\\"0.1\\\";\\n Reporter.log(\\\"select Movement step \\\"+text);\\n Assert.assertEquals(oa.selectMovementStep(text),text,\\\"Cann't select \\\"+text);\\n\\n valueOnLabel=oa.clickDistancePlus(clickTimes);\\n String higher=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+valueOnLabel,\\\"click Z plus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+valueOnLabel);\\n Assert.assertEquals(valueOnLabel,\\\"5\\\",\\\"distance is not same as expected result\\\");\\n\\n embededCompareResult(lower,middle,higher, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\\n\\n valueOnLabel=oa.clickDistanceReset();\\n embededScreenShot(screenWithinCase+\\\"_reset_\\\"+valueOnLabel,\\\"click Z reset(1 times), get distance is \\\"+valueOnLabel);\\n Assert.assertEquals(valueOnLabel,\\\"0\\\",\\\"distance is not same as expected result\\\");\\n }\\n Reporter.testEnd();\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n String[] stringArray0 = new String[2];\\n discretize0.m_ClassIndex = 906;\\n discretize0.m_IgnoreClass = true;\\n stringArray0[0] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n stringArray0[1] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n discretize0.setOptions(stringArray0);\\n discretize0.setAttributeIndices(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getFindNumBins());\\n }\",\n \"@Test void perfect() {\\n\\t\\tstandardScene();\\n\\t\\tsimulateScene(0);\\n\\n\\t\\tvar alg = new DistanceMetricTripleReprojection23();\\n\\n\\t\\tvar model = new MetricCameraTriple();\\n\\t\\tmodel.view1.setTo(cameraA);\\n\\t\\tmodel.view2.setTo(cameraB);\\n\\t\\tmodel.view3.setTo(cameraC);\\n\\t\\tmodel.view_1_to_2.setTo(truthView_1_to_i(1));\\n\\t\\tmodel.view_1_to_3.setTo(truthView_1_to_i(2));\\n\\n\\t\\talg.setModel(model);\\n\\n\\t\\tfor (AssociatedTriple a : observations3) {\\n\\t\\t\\tassertEquals(0.0, alg.distance(a), UtilEjml.TEST_F64);\\n\\t\\t}\\n\\n\\t\\tvar set = observations3.subList(4, 11);\\n\\t\\tvar distances = new double[set.size()];\\n\\t\\talg.distances(set, distances);\\n\\t\\tfor (int i = 0; i < distances.length; i++) {\\n\\t\\t\\tassertEquals(0.0, distances[i], UtilEjml.TEST_F64);\\n\\t\\t}\\n\\t}\",\n \"@Test\\n public void testGetTemperaturaInteriorAlvo() {\\n System.out.println(\\\"getSetTemperaturaInteriorAlvo\\\");\\n double expResult = 20.0;\\n Simulacao simulacao = new Simulacao();\\n simulacao.setSala(sala);\\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\\n instance.setTemperaturaInteriorAlvo(expResult);\\n double result = instance.getTemperaturaInteriorAlvo();\\n assertEquals(expResult, result, 0.0);\\n }\",\n \"@Override\\n protected boolean stoppingCriteriaSatisfied(KPMPSolution generatedSolution, RandomStepFunction stepFunction) {\\n numberOfIterations++;\\n numberOfIterationsWithoutImprovement++;\\n KPMPSolutionChecker solutionChecker = new KPMPSolutionChecker();\\n int crossingsOnOriginalPage = solutionChecker.getCrossingNumberOfEdge(bestSolution.getSpineOrder(), bestSolution.getEdgePartition(), originalPageIndex, edge);\\n int crossingsOnNewPage = solutionChecker.getCrossingNumberOfEdge(generatedSolution.getSpineOrder(), generatedSolution.getEdgePartition(), newPageIndex, edge);\\n if (crossingsOnNewPage < crossingsOnOriginalPage) {\\n bestSolution = generatedSolution;\\n numberOfIterationsWithoutImprovement = 0;\\n\\n crossingNumber = crossingNumber - (crossingsOnOriginalPage - crossingsOnNewPage);\\n }\\n return numberOfIterations >= Main.localSearchIterationLimit || numberOfIterations >= bestSolution.getEdgePartition().size() * bestSolution.getNumberOfPages() || numberOfIterationsWithoutImprovement >= Main.iterationMultiplier || ((System.nanoTime() - Main.START) / 1000000) >= (Main.secondsBeforeStop * 1000) || numberOfIterations >= Main.localSearchIterationLimit;\\n }\",\n \"@Test(timeout = 4000)\\n public void test116() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate((String) null);\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.correct();\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test086() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.SFMeanPriorEntropy();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test126() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate((String) null);\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.totalCost();\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test26() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.m_FindNumBins = true;\\n int[] intArray0 = new int[6];\\n intArray0[0] = (-388);\\n intArray0[1] = (-2121610348);\\n intArray0[2] = 11;\\n intArray0[3] = (-1);\\n intArray0[4] = 1062;\\n intArray0[5] = 70;\\n discretize0.setAttributeIndicesArray(intArray0);\\n discretize0.setDesiredWeightOfInstancesPerInterval((-1149.15832593));\\n discretize0.getOptions();\\n assertEquals((-1149.15832593), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n }\",\n \"@Override\\n protected boolean stoppingCriteriaSatisfied(KPMPSolution generatedSolution, BestImprovementStepFunction stepFunction) {\\n KPMPSolutionChecker solutionChecker = new KPMPSolutionChecker();\\n int crossingsOnOriginalPage = solutionChecker.getCrossingNumberOfEdge(bestSolution.getSpineOrder(), bestSolution.getEdgePartition(), originalPageIndex, edge);\\n int crossingsOnNewPage = solutionChecker.getCrossingNumberOfEdge(generatedSolution.getSpineOrder(), generatedSolution.getEdgePartition(), newPageIndex, edge);\\n if (crossingsOnNewPage < crossingsOnOriginalPage) {\\n bestSolution = generatedSolution;\\n index = 0;\\n pageCounter = 0;\\n\\n crossingNumber = crossingNumber - (crossingsOnOriginalPage - crossingsOnNewPage);\\n\\n //assert(crossingNumber == new KPMPSolutionChecker().getCrossingNumber(bestSolution));\\n }\\n if (index == bestSolution.getEdgePartition().size() && pageCounter != bestSolution.getNumberOfPages() - 1) {\\n index = 0;\\n pageCounter++;\\n }\\n return ((System.nanoTime() - Main.START) / 1000000) >= (Main.secondsBeforeStop * 1000) || index == bestSolution.getEdgePartition().size() && pageCounter == bestSolution.getNumberOfPages() - 1 || numberOfIterations >= Main.localSearchIterationLimit;\\n }\",\n \"@Test(timeout = 4000)\\n public void test093() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n Object[] objectArray0 = new Object[2];\\n evaluation0.evaluateModel((Classifier) null, instances0, objectArray0);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, true, false);\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints(3594);\\n Discretize discretize1 = new Discretize();\\n discretize1.attributeIndicesTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"\\\");\\n Discretize discretize2 = new Discretize();\\n // Undeclared exception!\\n try { \\n discretize2.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@ParameterizedTest\\n\\t@CsvSource({\\n\\t\\t\\t\\\"9, 1, 2, 3, 1, 10, 1, 10, 1, -1, -1, -1\\\",\\n\\t\\t\\t\\\"9, -1, -1, -1, 9, 10, 1, 10, 1, 9, -1, -1\\\",\\n\\t\\t\\t\\\"9, -1, -1, -1, 1, 10, 9, 10, 1, 9, -1, -1\\\",\\n\\t\\t\\t\\\"9, 3, 3, 3, 89, 100, 1, 10, 1, -1, -1, -1\\\",\\n\\t\\t\\t\\\"9, 1, -1, -1, 1, 10, 89, 100, 1, -1, -1, -1\\\",\\n\\t\\t\\t\\\"9, 1, 2, -1, 2, 2, 1, 10, 25, 1, 2, 9\\\",\\n\\t\\t\\t\\\"9, 3, 3, 3, 0, 2, 950, 1000, 25, 3, 3, -1\\\",\\n\\t\\t\\t\\\"9, -1, -1, -1, 9999, 10000, 950, 1000, 1, 9, 9, 9\\\",\\n\\t\\t\\t\\\"9, -1, -1, -1, 0, 1, 0, 390000, 1, -1, -1, -1\\\",\\n\\t\\t\\t\\\"4, -1, -1, -1, 100, 100, 1000, 1000, 1, 4, 4, 4\\\",\\n\\t\\t\\t\\\"5, 3, -1, -1, 100, 100, 1000, 1000, 10, 3, 5, 5\\\",\\n\\t\\t\\t\\\"5, 1, 3, 4, 100, 100, 1000, 1000, 25, 1, 3, 4\\\",\\n\\t})\\n\\tvoid usesExpectedMultiplier(\\n\\t\\t\\tlong consensusSec,\\n\\t\\t\\tlong old10XLevelStart,\\n\\t\\t\\tlong old25XLevelStart,\\n\\t\\t\\tlong old100XLevelStart,\\n\\t\\t\\tint firstUsed,\\n\\t\\t\\tint firstTps,\\n\\t\\t\\tint secondUsed,\\n\\t\\t\\tint secondTps,\\n\\t\\t\\tlong expectedMultiplier,\\n\\t\\t\\tlong new10XLevelStart,\\n\\t\\t\\tlong new25XLevelStart,\\n\\t\\t\\tlong new100XLevelStart\\n\\t) {\\n\\t\\tfinal var aThrottle = DeterministicThrottle.withTps(firstTps);\\n\\t\\tfinal var bThrottle = DeterministicThrottle.withTps(secondTps);\\n\\t\\taThrottle.allow(firstUsed);\\n\\t\\tbThrottle.allow(secondUsed);\\n\\t\\tgiven(throttling.activeThrottlesFor(CryptoTransfer)).willReturn(List.of(aThrottle, bThrottle));\\n\\n\\t\\tsubject.resetExpectations();\\n\\t\\tsubject.resetCongestionLevelStarts(instants(old10XLevelStart, old25XLevelStart, old100XLevelStart));\\n\\t\\tsubject.updateMultiplier(Instant.ofEpochSecond(consensusSec));\\n\\t\\tfinal long actualMultiplier = subject.currentMultiplier();\\n\\t\\tfinal var starts = subject.congestionLevelStarts();\\n\\n\\t\\tassertEquals(expectedMultiplier, actualMultiplier);\\n\\t\\tassertNullOrEquals(starts[0], new10XLevelStart);\\n\\t\\tassertNullOrEquals(starts[1], new25XLevelStart);\\n\\t\\tassertNullOrEquals(starts[2], new100XLevelStart);\\n\\t}\",\n \"private void calculations() {\\n\\t\\tSystem.out.println(\\\"Starting calculations\\\\n\\\");\\n\\t\\tcalcMedian(myProt.getChain(requestedChain));\\n\\t\\tcalcZvalue(myProt.getChain(requestedChain));\\n\\t\\tcalcSVMweightedZvalue(myProt.getChain(requestedChain));\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test35() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n assertFalse(discretize0.getMakeBinary());\\n \\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n Attribute attribute0 = new Attribute(\\\"invalid CVS revision - not dots!\\\");\\n attribute0.setWeight(0.0);\\n Attribute.typeToString(attribute0);\\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\\n arrayList0.add(attribute0);\\n Instances instances0 = new Instances(\\\"UR<dj;O\\\", arrayList0, 3);\\n Instances instances1 = new Instances(instances0);\\n boolean boolean0 = discretize0.setInputFormat(instances1);\\n assertFalse(boolean0);\\n \\n String[] stringArray0 = new String[3];\\n stringArray0[0] = \\\".bsi\\\";\\n stringArray0[1] = \\\"invalid CVS revision - not dots!\\\";\\n stringArray0[2] = \\\"@end\\\";\\n discretize0.setOptions(stringArray0);\\n discretize0.setOptions(stringArray0);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n }\",\n \"@Test\\n\\tpublic void TEAM3_CONTROLLER_UT05() {\\n\\t\\t// PRECONDITIONS\\n\\t\\tScheduleMakerController smc = new ScheduleMakerController();\\n\\t\\t\\n\\t\\t// INPUT\\n\\t\\tCollection<ScheduleStub> savedSchedules = new ArrayList<ScheduleStub>();\\n\\t\\tsmc.saveSchedules(savedSchedules);\\n\\t\\t\\n\\t\\t// EXPECTED OUTPUT\\n\\t\\tException exceptionThrown = null;\\n\\t\\tdouble balance = 0.0d;\\n\\t\\ttry {\\n\\t\\t\\tMethod getBalance = smc.getClass().getDeclaredMethod(\\\"getBalance\\\", (Class<?> []) null);\\n\\t\\t\\tObject balanceObj = getBalance.invoke(smc, (Object[]) null);\\n\\t\\t\\t\\n\\t\\t\\tif (balanceObj == null) throw new NullPointerException(\\\"getBalance() returned null; expected a number\\\");\\n\\t\\t\\telse if (!(balanceObj instanceof Number)) throw new RuntimeException(\\\"getBalance() did not return a number\\\");\\n\\t\\t} catch (Exception ex) {\\n\\t\\t\\texceptionThrown = ex;\\n\\t\\t}\\n\\t\\t\\n\\t\\tassertNull(exceptionThrown);\\n\\t\\tassertEquals(0.0d, balance, 0.0d);\\n\\t}\",\n \"public double calc_c() {\\r\\n \\t\\t\\r\\n \\t\\tdouble cij = 0;\\r\\n \\t\\tint sum_i = 0;\\r\\n \\t\\tint sum_j = 0;\\r\\n \\t\\tint diff = 0;\\r\\n \\t\\tint state = 0;\\r\\n \\t\\t\\r\\n \\t\\tdouble li, lri, lj, lrj;\\r\\n \\t\\t\\r\\n \\t\\tList<Integer> samestate_i = new ArrayList<>();\\r\\n \\t\\tList<Integer> samestate_j = new ArrayList<>();\\r\\n \\t\\t\\r\\n \\t\\tList<Double> gradient_i = new ArrayList<>();\\r\\n \\t\\tList<Double> gradient_j = new ArrayList<>();\\r\\n \\t\\t\\r\\n\\t\\t// if the time step is less than the window cij is initialized to zero.\\r\\n\\t\\tif(this.n <= this.w || this.w <2) {\\r\\n\\t\\t\\tcij = 0;\\r\\n\\t\\t}\\r\\n\\t\\t\\r\\n\\t\\telse {\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t// else we determine the state of the region i and region j of which\\r\\n\\t\\t\\t// we are calculating the cij for\\r\\n\\t \\t\\tDeterminestate di = new Determinestate(this.i, this.n);\\r\\n\\t\\t\\tDeterminestate dj = new Determinestate(this.j, this.n);\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tProbability pi = new Probability(this.i, this.n, this.w, this.neighborsize);\\r\\n\\t\\t\\tProbability pj = new Probability(this.i, this.n, this.w, this.neighborsize);\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tif(di.regionCurrentstate() == dj.regionCurrentstate()) {\\r\\n\\t\\t\\t\\tli = pi.getLsave().get(0);\\r\\n\\t\\t\\t\\tlj = pj.getLsave().get(0);\\r\\n\\t\\t\\t\\tstate = 0;\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\telse {\\r\\n\\t\\t\\t\\tli = pi.getLsave().get(1);\\r\\n\\t\\t\\t\\tlj = pj.getLsave().get(1);\\r\\n\\t\\t\\t\\tstate =1;\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t// for the time window\\r\\n\\t\\t\\tfor(int k=1; k<this.w; k++) {\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t// determine the state of i and j in the in n-k time step\\r\\n\\t \\t\\t\\tDeterminestate dri = new Determinestate(this.i, this.n-k);\\r\\n\\t \\t\\t\\tDeterminestate drj = new Determinestate(this.j, this.n-k);\\r\\n\\t \\t\\t\\t\\r\\n\\t \\t\\t\\tProbability pri = new Probability(this.i, this.n, this.w, this.neighborsize);\\r\\n\\t\\t\\t\\tProbability prj = new Probability(this.i, this.n, this.w, this.neighborsize);\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t\\tlri = pri.getLsave().get(state);\\r\\n\\t\\t\\t\\tlrj = prj.getLsave().get(state);\\r\\n\\t \\t\\t\\t\\r\\n\\t \\t\\t\\t\\r\\n\\t \\t\\t\\t//if state matches for i make a list of time step in which they match and another list of gradient of Likelihood \\r\\n\\t \\t\\t\\tif(di.regionCurrentstate() == dri.regionCurrentstate()){\\r\\n\\t \\t\\t\\t\\tsamestate_i.add(k);\\r\\n\\t \\t\\t\\t\\tgradient_i.add(li - lri);\\r\\n\\t \\t\\t\\t}\\r\\n\\t \\t\\t\\t\\r\\n\\t \\t\\t\\t//if state matches for j make a list of time step in which they match and another list of gradient of Likelihood \\r\\n\\t \\t\\t\\tif(di.regionCurrentstate() == drj.regionCurrentstate()){\\r\\n\\t \\t\\t\\t\\tsamestate_j.add(k);\\r\\n\\t \\t\\t\\t\\tgradient_j.add(lj - lrj);\\r\\n\\t \\t\\t\\t}\\r\\n\\t \\t\\t\\t\\r\\n\\t \\t\\t}\\r\\n\\t\\t}\\r\\n\\t\\t\\t\\r\\n\\t\\t// if no match found return zero\\r\\n\\t\\tif(samestate_i.size() == 0 || samestate_j.size() == 0) {\\r\\n\\t\\t\\tcij = 0;\\r\\n\\t\\t}\\r\\n\\t\\t\\r\\n\\t\\t// else calculate cij\\r\\n\\t\\telse {\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t// if both have same length \\r\\n\\t\\t\\tif(samestate_i.size() == samestate_j.size()) {\\r\\n\\t\\t\\t\\tfor(int i=0; i<samestate_i.size(); i++) {\\r\\n\\t\\t\\t\\t\\tsum_i = sum_i + samestate_i.get(i);\\r\\n\\t\\t\\t\\t\\tsum_j = sum_j + samestate_j.get(i);\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\tdiff = (sum_i%this.w - sum_j%this.w);\\r\\n\\t\\t\\t\\tif (diff == 0) {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i, gradient_j);\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\telse {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i, gradient_j) * Math.abs(diff/(double)this.w);\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t//if i is smaller\\r\\n\\t\\t\\telse if(samestate_i.size() < samestate_j.size()) {\\r\\n\\t\\t\\t\\tfor(int i=0; i<samestate_i.size(); i++) {\\r\\n\\t\\t\\t\\t\\tsum_i = sum_i + samestate_i.get(i);\\r\\n\\t\\t\\t\\t\\tsum_j = sum_j + samestate_j.get(i);\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\tdiff = (sum_i%this.w - sum_j%this.w);\\r\\n\\t\\t\\t\\tif (diff == 0) {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i.subList(0, samestate_i.size()), gradient_j);\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\telse {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i.subList(0, samestate_i.size()), gradient_j) * diff/(double)this.w;\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t// if j is smaller \\r\\n\\t\\t\\telse {\\r\\n\\t\\t\\t\\tfor(int i=0; i<samestate_j.size(); i++) {\\r\\n\\t\\t\\t\\t\\tsum_i = sum_i + samestate_i.get(i);\\r\\n\\t\\t\\t\\t\\tsum_j = sum_j + samestate_j.get(i);\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\tdiff = (sum_i%this.w - sum_j%this.w);\\r\\n\\t\\t\\t\\tif (diff == 0) {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i.subList(0, samestate_j.size()), gradient_j);\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\telse {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i.subList(0, samestate_j.size()), gradient_j) * diff/(double)this.w;\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t}\\r\\n\\t\\t}\\r\\n\\t\\t\\r\\n\\t\\tif (Double.isNaN(cij)) {\\r\\n\\t\\t\\treturn 0;\\r\\n\\t\\t}\\r\\n\\t\\telse {\\r\\n\\t\\t\\treturn cij;\\r\\n\\t\\t}\\r\\n \\t}\",\n \"@Test\\n\\tvoid testCalculateMarkTillSemesterSuccess2() {\\n\\t\\t\\n\\t\\tString semesterName = \\\"20193\\\";\\n\\t\\t\\n\\t\\tUser user = userService.findByUsername(\\\"B16DCCN168\\\");\\n\\t\\tStudent student = user.getStudent();\\n\\t\\tList<StudentResult> resultsTillSemester = resultService.findResultTillSemester(student.getId(),\\n\\t\\t\\t\\tsemesterName);\\n\\t\\t\\n\\t\\tString mmtMark = \\\"\\\", tthcmMark = \\\"\\\";\\n\\t\\t\\n\\t\\tfor (StudentResult result : resultsTillSemester) {\\n\\t\\t\\tSubject subject = result.getStudentRegister().getCourse().getSubject();\\n\\t\\t\\tif (subject.getName().equals(\\\"Mạng máy tính\\\")) {\\n\\t\\t\\t\\tmmtMark = result.getMarkToChar();\\n\\t\\t\\t}\\n\\t\\t\\t\\n\\t\\t\\tif (subject.getName().equals(\\\"Tư tưởng Hồ Chí Minh\\\")) {\\n\\t\\t\\t\\ttthcmMark = result.getMarkToChar();\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\t\\n\\t\\tList<Object> listGpaResultsTillNow = MarkUtility.calculateMarkTillSemester(resultsTillSemester);\\n\\t\\tint passedCreditsTillNow = (int) listGpaResultsTillNow.get(0);\\n\\t\\tfloat gpaTillNow = (float) listGpaResultsTillNow.get(1);\\n\\t\\t\\n\\t\\tDecimalFormat df = new DecimalFormat(\\\"#.##\\\");\\n\\t\\t\\n\\t\\tassertTrue(\\n\\t\\t\\t\\tmmtMark.equals(\\\"B\\\") &&\\n\\t\\t\\t\\ttthcmMark.equals(\\\"A\\\") &&\\n\\t\\t\\t\\tpassedCreditsTillNow == 15 &&\\n\\t\\t\\t\\tdf.format(gpaTillNow).equals(\\\"3.64\\\")\\n\\t\\t\\t\\t);\\n\\t}\",\n \"@Test\\n public void testWithPropagator() {\\n CelestialBody sun = CelestialBodyFactory.getSun();\\n final Frame eci = FramesFactory.getGCRF();\\n final Frame ecef = FramesFactory.getITRF(IERSConventions.IERS_2010, true);\\n AbsoluteDate date = new AbsoluteDate(2004, 1, 1, utc);\\n OneAxisEllipsoid earth = new OneAxisEllipsoid(Constants.WGS84_EARTH_EQUATORIAL_RADIUS,\\n Constants.WGS84_EARTH_FLATTENING, ecef);\\n Orbit orbit = new KeplerianOrbit(6378137 + 400e3, 1e-3, FastMath.toRadians(50), 0, 0, 0, PositionAngle.TRUE,\\n eci, date, Constants.EIGEN5C_EARTH_MU);\\n final SpacecraftState ic = new SpacecraftState(orbit);\\n\\n final AbsoluteDate end = date.shiftedBy(5 * Constants.JULIAN_DAY);\\n final AbsoluteDate resetDate = date.shiftedBy(0.8 * Constants.JULIAN_DAY + 0.1);\\n\\n final SpacecraftState[] lastState = new SpacecraftState[1];\\n final OrekitStepHandler stepSaver = interpolator -> {\\n final AbsoluteDate start = interpolator.getPreviousState().getDate();\\n if (start.compareTo(resetDate) < 0) {\\n lastState[0] = interpolator.getPreviousState();\\n }\\n };\\n\\n // propagate with state rest to take slightly different path\\n NumericalPropagator propagator = getNumericalPropagatorWithDTM(sun, earth, ic);\\n propagator.setStepHandler(stepSaver);\\n propagator.propagate(resetDate);\\n propagator.resetInitialState(lastState[0]);\\n propagator.clearStepHandlers();\\n SpacecraftState actual = propagator.propagate(end);\\n\\n // propagate straight through\\n propagator = getNumericalPropagatorWithDTM(sun, earth, ic);\\n propagator.resetInitialState(ic);\\n propagator.clearStepHandlers();\\n SpacecraftState expected = propagator.propagate(end);\\n\\n assertThat(actual.getPVCoordinates(), pvCloseTo(expected.getPVCoordinates(), 1.0));\\n\\n // propagate with state rest to take slightly different path\\n propagator = getNumericalPropagatorWithMSIS(sun, earth, ic);\\n propagator.setStepHandler(stepSaver);\\n propagator.propagate(resetDate);\\n propagator.resetInitialState(lastState[0]);\\n propagator.clearStepHandlers();\\n actual = propagator.propagate(end);\\n\\n // propagate straight through\\n propagator = getNumericalPropagatorWithMSIS(sun, earth, ic);\\n propagator.resetInitialState(ic);\\n propagator.clearStepHandlers();\\n expected = propagator.propagate(end);\\n\\n assertThat(actual.getPVCoordinates(), pvCloseTo(expected.getPVCoordinates(), 1.0));\\n }\",\n \"public void testSVMChunkLearnng() throws IOException, GateException {\\n // Initialisation\\n System.out.print(\\\"Testing the SVM with liner kernenl on chunk learning...\\\");\\n File chunklearningHome = new File(new File(learningHome, \\\"test\\\"),\\n \\\"chunklearning\\\");\\n String configFileURL = new File(chunklearningHome, \\\"engines-svm.xml\\\")\\n .getAbsolutePath();\\n String corpusDirName = new File(chunklearningHome, \\\"data-ontonews\\\")\\n .getAbsolutePath();\\n //Remove the label list file, feature list file and chunk length files.\\n String wdResults = new File(chunklearningHome,\\n ConstantParameters.SUBDIRFORRESULTS).getAbsolutePath();\\n emptySavedFiles(wdResults);\\n String inputASN = \\\"Key\\\";\\n loadSettings(configFileURL, corpusDirName, inputASN, inputASN);\\n // Set the evaluation mode\\n RunMode runM=RunMode.EVALUATION;\\n learningApi.setLearningMode(runM);\\n controller.execute();\\n // Using the evaluation mode for testing\\n EvaluationBasedOnDocs evaluation = learningApi.getEvaluation();\\n // Compare the overall results with the correct numbers\\n assertEquals(\\\"Wrong value for correct: \\\", 44, (int)Math.floor(evaluation.macroMeasuresOfResults.correct));\\n assertEquals(\\\"Wrong value for partial: \\\", 10, (int)Math.floor(evaluation.macroMeasuresOfResults.partialCor));\\n assertEquals(\\\"Wrong value for spurious: \\\", 11, (int)Math.floor(evaluation.macroMeasuresOfResults.spurious));\\n assertEquals(\\\"Wrong value for missing: \\\", 40, (int)Math.floor(evaluation.macroMeasuresOfResults.missing));\\n\\n System.out.println(\\\"completed\\\");\\n // Remove the resources\\n clearOneTest();\\n }\",\n \"static void playC45() throws IOException {\\n\\t\\tRealVector[] data = NNDataset.getData(NNDataset.HOME);\\n\\t\\tRealVector[] label = NNDataset.getLabel(NNDataset.HOME);\\n\\t\\tRealMatrix d = MatrixUtils.createRealMatrix(data.length, data[0].getDimension());\\n\\t\\tfor (int i = 0; i < data.length; i++) {\\n\\t\\t\\td.setRowVector(i, data[i]);\\n\\t\\t}\\n\\t\\td = d.transpose(); // data = column vector\\n\\n\\t\\tRealVector l = MatrixUtils.createRealVector(new double[label.length]);\\n\\t\\tfor (int i = 0; i < l.getDimension(); i++) {\\n\\t\\t\\tl.setEntry(i, label[i].getEntry(0));\\n\\t\\t}\\n\\n\\t\\tDecisionNode root = training(d, l);\\n\\t\\tString[] header = NNDataset.getHeader(NNDataset.HOME);\\n\\t\\tprintTree(root, header);\\n\\n\\t\\tInteger lb = predict(root, data[0]);\\n\\t\\tSystem.out.println(lb);\\n\\n\\t\\tdouble ok = MSE(root, data, label);\\n\\t\\tSystem.out.println(\\\"total hit: \\\" + ok);\\n\\t}\",\n \"@Test\\n public void testVirement1() {\\n\\n //Arrange\\n CompteBancaire cpt1 = new CompteBancaire(63000.0);\\n CompteBancaire cpt2 = new CompteBancaire(43000.0);\\n double montant_a_virer = 28000.0;\\n double excepted_solde_cpt1 = 35000.0;\\n double excepted_solde_cpt2 = 71000.0;\\n\\n //Act\\n assertEquals(true, cpt1.virerArgent(cpt2, montant_a_virer));\\n\\n //Assert\\n assertEquals(excepted_solde_cpt1, cpt1.getSolde(), 0);\\n assertEquals(excepted_solde_cpt2, cpt2.getSolde(), 0);\\n\\n }\",\n \"public void controlPointsChanged(List<Coordinates> lcp) {\\n // sort the control points by x position.\\n // this makes things easier later when we paint the function.\\n lcp.sort(\\n (point1,point2) -> Integer.compare(point1.getX(),point2.getX())\\n );\\n\\n // collect the points in an array before doing the calculations.\\n // again, this makes things easier for us.\\n controlPoints=lcp.toArray(new Coordinates[0]);\\n n=controlPoints.length;\\n if(n<2) {\\n // nothing to do here\\n return;\\n }\\n double []X = new double[n];\\n double []Y = new double[n];\\n for(int i =0;i<n;i++){\\n \\tX[i]=controlPoints[i].getX();\\n \\tY[i]=controlPoints[i].getY();\\n }\\n // Construct the linear system S*C=T with n-2 equations.\\n // The matrix S contains the left hand side of the equations on slide 13.\\n // The matrix Z contains the right hand side of the equations on slide 13.\\n double[][] S=new double[n-2][n-2];\\n double[] Z=new double[n-2];\\n \\n // Fill the matrix S and the vector Z and solve S*C=T to get c_2 to c_{n-1}.\\n for(int i = 0; i< n-3;i++){\\n \\tS[i][i]=2*delta(X,i+1,0);\\n \\tS[i+1][i]=delta(X,i+2,-1);\\n \\tS[i][i+1]=delta(X,i+1,1);\\n \\tZ[i]=z(Y,X,i);\\n }\\n S[n-2][n-2]=2*delta(X,n-1,0);\\n \\n C= EliminationGaussLegendre.solve(S, Z);\\n \\n // Calculate the n-1 coefficients b_i and d_i.\\n // Note that you need all c_i here (but you know that c_1 and c_n are zero).\\n //a_i = y_i;\\n for(int i=1 ; i<n ; i++){\\n \\tD[i-1]=d(Y,X,i-1);\\n \\tB[i-1]=b(Y,C,X,i-1);\\n }\\n \\n }\",\n \"@Test\\n public void costTest() {\\n // TODO: test cost\\n }\",\n \"@Test\\n public void testAutocorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.autocorrelation(M, observable, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void test22() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n FastVector fastVector0 = evaluation0.predictions();\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01D);\\n }\",\n \"public static void main(String[] args) {\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 3, 4, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 3, 5, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 3, 4, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 3, 5, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, \\\"cartellainesistente\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, \\\"cartellainesistente\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, \\\"cartellainesistente\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, \\\"cartellainesistente\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, \\\"strategies\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, \\\"strategies\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, \\\"strategies\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, \\\"strategies\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 5, 2, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 5, 2, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(6, 6, 2, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(6, 6, 2, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 3, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 3, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(7, 7, 2, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(7, 7, 2, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 4, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 4, true, null)));\\n measureTime(()->checkInterruptSequential(4, 4, 3, false));\\n measureTime(()->checkInterruptSequential(4, 4, 3, true));\\n }\",\n \"@Test\\n\\tpublic void TEAM3_CONTROLLER_UT06() {\\n\\t\\t// PRECONDITIONS\\n\\t\\tClassDetailsStub c = new ClassDetailsStub(TEST_CONFIG.TueThu);\\n\\t\\t\\n\\t\\tCollection<ClassDetailsStub> s = new ArrayList<ClassDetailsStub>();\\n\\t\\ts.add(c);\\n\\t\\t\\n\\t\\tScheduleMakerController smc = new ScheduleMakerController();\\n\\t\\t\\n\\t\\t// INPUT\\n\\t\\tCollection<ScheduleStub> savedSchedules = new ArrayList<ScheduleStub>();\\n\\t\\tsavedSchedules.add(new ScheduleStub(s));\\n\\t\\t\\n\\t\\t// EXPECTED OUTPUT\\n\\t\\tException exceptionThrown = null;\\n\\t\\tboolean balanceGreaterThanZero = false;\\n\\t\\ttry {\\n\\t\\t\\tObject balanceObj = null;\\n\\t\\t\\tMethod getBalance = smc.getClass().getDeclaredMethod(\\\"getBalance\\\", (Class<?> []) null);\\n\\t\\t\\t\\n\\t\\t\\tsmc.saveSchedules(savedSchedules);\\n\\t\\t\\tbalanceObj = getBalance.invoke(smc, (Object[]) null);\\n\\t\\t\\t\\n\\t\\t\\tif (balanceObj == null) throw new NullPointerException(\\\"getBalance() returned null; expected a number\\\");\\n\\t\\t\\telse if (!(balanceObj instanceof Number)) throw new RuntimeException(\\\"getBalance() did not return a number\\\");\\n\\t\\t\\t\\n\\t\\t\\tbalanceGreaterThanZero = ((Number) balanceObj).doubleValue() > 0.0d;\\n\\t\\t} catch (Exception ex) {\\n\\t\\t\\texceptionThrown = ex;\\n\\t\\t}\\n\\t\\t\\n\\t\\tassertNull(exceptionThrown);\\n\\t\\tassertTrue(balanceGreaterThanZero);\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test28() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n doubleArray2[5] = (-236.4995664359);\\n doubleArray2[7] = 1421.4058829;\\n doubleArray0[3] = doubleArray2;\\n double[] doubleArray3 = new double[3];\\n doubleArray3[0] = 0.0;\\n doubleArray3[1] = (-236.4995664359);\\n doubleArray0[4] = doubleArray3;\\n double[] doubleArray4 = new double[21];\\n doubleArray4[0] = 1421.4058829;\\n doubleArray3[2] = (-236.4995664359);\\n doubleArray4[4] = 1.7976931348623157E308;\\n doubleArray0[7] = doubleArray4;\\n discretize0.m_NumBins = 162;\\n doubleArray0[1] = doubleArray4;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.getBinRangesString(1);\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void test28() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\\n CostMatrix costMatrix0 = costSensitiveClassifier0.getCostMatrix();\\n Evaluation evaluation0 = null;\\n try {\\n evaluation0 = new Evaluation(instances0, costMatrix0);\\n fail(\\\"Expecting exception: Exception\\\");\\n \\n } catch(Exception e) {\\n //\\n // Cost matrix not compatible with data!\\n //\\n }\\n }\",\n \"@Test\\n public void testTrain_RegressionDataSet_ExecutorService() {\\n System.out.println(\\\"train\\\");\\n RegressionDataSet trainSet = FixedProblems.getSimpleRegression1(150, new Random(2));\\n RegressionDataSet testSet = FixedProblems.getSimpleRegression1(50, new Random(3));\\n\\n for (boolean modification1 : new boolean[] { true, false })\\n for (SupportVectorLearner.CacheMode cacheMode : SupportVectorLearner.CacheMode.values()) {\\n PlattSMO smo = new PlattSMO(new RBFKernel(0.5));\\n smo.setCacheMode(cacheMode);\\n smo.setC(1);\\n smo.setEpsilon(0.1);\\n smo.setModificationOne(modification1);\\n smo.train(trainSet, true);\\n\\n double errors = 0;\\n for (int i = 0; i < testSet.size(); i++)\\n errors += Math.pow(testSet.getTargetValue(i) - smo.regress(testSet.getDataPoint(i)), 2);\\n assertTrue(errors / testSet.size() < 1);\\n }\\n }\",\n \"@Override\\n\\tprotected void setCost(double cst) {\\n\\t\\t\\n\\t}\",\n \"@Test\\n public void testGetMoveSpeedMultiplier() {\\n assertEquals(1, proj.getMoveSpeedMultiplier(), 0.001);\\n }\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.invertSelectionTipText();\\n boolean boolean0 = discretize0.m_UseBinNumbers;\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n\\tpublic void test() {\\n\\t\\tdouble delta = 0.00001;\\n\\t\\tSnp fakeSnp1 = new Snp(\\\"fakeId1\\\", 0, 0.3);\\n\\t\\tSnp fakeSnp2 = new Snp(\\\"fakeId2\\\", 0, 0.8);\\n\\t\\tSnp fakeSnp3 = new Snp(\\\"fakeId3\\\", 0, 1.4);\\n\\t\\tPascal.set.withZScore_=true;\\n\\t\\tArrayList<Snp> geneSnps = new ArrayList<Snp>();\\n\\t\\tgeneSnps.add(fakeSnp1);geneSnps.add(fakeSnp2);geneSnps.add(fakeSnp3);\\n\\t\\tDenseMatrix ld= new DenseMatrix(3,3);\\n\\t\\t//DenseMatrix crossLd= new DenseMatrix(3,2);\\n\\t\\t\\n\\t\\tArrayList<Double> snpScores = new ArrayList<Double>(3);\\n\\t\\tsnpScores.add(fakeSnp1.getZscore());\\n\\t\\tsnpScores.add(fakeSnp2.getZscore());\\n\\t\\tsnpScores.add(fakeSnp3.getZscore());\\n\\t\\t//ld and crossLd calculated as follows:\\n\\t\\t// make 0.9-toeplitz mat of size 5.\\n\\t\\t// 3,4,5 for ld-mat\\n\\t\\t// 1,2 for crossLd-mat\\n\\t\\tld.set(0,0,1);ld.set(1,1,1);ld.set(2,2,1);\\n\\t\\tld.set(0,1,0.9);ld.set(1,2,0.9);\\n\\t\\tld.set(1,0,0.9);ld.set(2,1,0.9);\\n\\t\\tld.set(0,2,0.81);\\n\\t\\tld.set(2,0,0.81);\\n\\t\\tdouble[] weights = {2,2,2};\\n\\t\\tAnalyticVegas myAnalyticObj= null;\\n\\t\\t//UpperSymmDenseMatrix myMatToDecompose = null;\\n\\t\\tdouble[] myEigenvals = null;\\n\\t\\tdouble[] emptyWeights = {1,1,1};\\n\\t\\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, emptyWeights);\\n\\t\\tmyAnalyticObj.computeScore();\\n\\t\\tmyEigenvals = myAnalyticObj.computeLambda();\\n\\t\\tassertEquals(myEigenvals[0],2.74067, delta);\\n\\t\\tassertEquals(myEigenvals[1],0.1900, delta);\\n\\t\\tassertEquals(myEigenvals[2],0.06932, delta);\\n\\t\\t\\n\\t\\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, weights);\\n\\t\\tmyAnalyticObj.computeScore();\\n\\t\\tmyEigenvals = myAnalyticObj.computeLambda();\\t\\t\\t\\n\\t\\tassertEquals(myEigenvals[0], 5.481348, delta);\\n\\t\\tassertEquals(myEigenvals[1],0.380000, delta);\\n\\t\\tassertEquals(myEigenvals[2],0.138652, delta);\\n\\t\\t\\t\\n\\t\\tdouble[] weights2 = {1,2,0.5};\\n\\t\\t\\n\\t\\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, weights2);\\n\\t\\tmyAnalyticObj.computeScore();\\n\\t\\tmyEigenvals = myAnalyticObj.computeLambda();\\t\\t\\n\\t\\tassertEquals(myEigenvals[0],3.27694674, delta);\\n\\t\\tassertEquals(myEigenvals[1],0.1492338, delta);\\n\\t\\tassertEquals(myEigenvals[2],0.07381938, delta);\\t\\t\\t\\n\\t\\t\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test06() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[] doubleArray0 = new double[0];\\n int[] intArray0 = new int[4];\\n intArray0[0] = 2749;\\n intArray0[1] = (-2);\\n intArray0[2] = 0;\\n intArray0[3] = 0;\\n SparseInstance sparseInstance0 = new SparseInstance((-40.152), doubleArray0, intArray0, 919);\\n // Undeclared exception!\\n try { \\n discretize0.input(sparseInstance0);\\n fail(\\\"Expecting exception: IllegalStateException\\\");\\n \\n } catch(IllegalStateException e) {\\n //\\n // No input instance format defined\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test01() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"c9^n/\\\");\\n discretize0.getCapabilities();\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n }\",\n \"double cekStop(double x[][], double v0[],double v[][],double w0[],double w[][], double t[]){\\n double akumY=0;\\n //~ itung z_in dan z\\n for(int h=0; h<jumlah_data; h++){\\n for(int j=0; j<unit_hidden; j++){\\n //itung sigma xi vij\\n double jum_xv=0;\\n for(int i=0; i<unit_input; i++){\\n double cc=x[h][i]*v[i][j];\\n jum_xv=jum_xv+cc;\\n //System.out.println(x[h][j]);\\n }\\n z_in[j]=v0[j]+jum_xv;\\n //itung z\\n z[j]=1/(1+(double)Math.exp(-z_in[j]));\\n //System.out.println(\\\" dan z= \\\"+z[j]);\\n }\\n \\n //~ itung y_in dan y (output)\\n double cxc=0;\\n for(int k=0; k<unit_output; k++){\\n double jum_zw=0;\\n for(int j=0; j<unit_hidden; j++){\\n double cc=z[j]*w[j][k];\\n jum_zw=jum_zw+cc;\\n }\\n y_in[k]=w0[k]+jum_zw;\\n y[k]=1/(1+(double)Math.exp(-y_in[k]));\\n akumY = akumY + Math.pow((t[h]-y[k]),2);\\n //System.out.println(t[h]+\\\"-\\\"+y[k]+\\\"=\\\"+(t[k]-y[k]));\\n }\\n }\\n double E = 0.5 * akumY;\\n //System.out.println(E);\\n return E;\\n }\",\n \"public void testCleave() {\\n final String inputFile = \\\"testCleave.fas\\\";\\n final String input = TestCaseLM.getFullFilePath(inputFile).replace(\\\"%20\\\", \\\" \\\");\\n\\n try {\\n // We need to obtain a pointer the control file before anything else.\\n BufferedReader br = new BufferedReader(new FileReader(input));\\n\\n // First one with zero MC's.\\n Enzyme e = new Enzyme(\\\"TestEnzyme\\\", \\\"KR\\\", \\\"P\\\", \\\"Cterm\\\", 0);\\n Protein p = new Protein(\\\">sw|Q55645|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\\\");\\n\\n Protein[] result = e.cleave(p);\\n boolean once = false;\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now with one miscleavage.\\n e.setMiscleavages(1);\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now with three miscleavages.\\n e.setMiscleavages(3);\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Now check for the edge case where there's only one amino acid after the last cleavage position\\n // (used to be a bug that this amino acid was never considered).\\n e.setMiscleavages(1);\\n result = e.cleave(new Protein(\\\">sw|Q55645|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDRA\\\"));\\n boolean foundIt = false;\\n for(int i = 0; i < result.length; i++) {\\n Protein lProtein = result[i];\\n if(lProtein.getSequence().getSequence().equals(\\\"TGFDRA\\\")){\\n foundIt = true;\\n break;\\n }\\n }\\n\\n if(!foundIt) {\\n fail(\\\"The peptide 'TGFDRA' (penultimate amino acid is cleavage site, one missed cleavage allowed) was NOT returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now Nterm position and 1 miscleavage.\\n e.setPosition(Enzyme.NTERM);\\n e.setMiscleavages(1);\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Next, take an enzyme that does not have restricting residus.\\n e = new Enzyme(\\\"Test\\\", \\\"KR\\\", null, \\\"Cterm\\\", 0);\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // And now take an entry that has a location set and see if the cleavage is adjusted accordingly.\\n e = new Enzyme(\\\"TestEnzyme\\\", \\\"KR\\\", \\\"P\\\", \\\"Cterm\\\", 1);\\n p = new Protein(\\\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\\\");\\n\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now take a C-terminally truncated sequence and see if it is cleaved correctly.\\n e = new Enzyme(\\\"TestEnzyme\\\", \\\"KR\\\", \\\"P\\\", \\\"Cterm\\\", 1);\\n p = new Protein(\\\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\\\", true, Protein.CTERMTRUNC);\\n\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now take an N-terminally truncated sequence and see if it is cleaved correctly.\\n e = new Enzyme(\\\"TestEnzyme\\\", \\\"KR\\\", \\\"P\\\", \\\"Cterm\\\", 1);\\n p = new Protein(\\\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\\\", true, Protein.NTERMTRUNC);\\n\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now take a translated sequence, containing an '_' (stopcodon).\\n e = new Enzyme(\\\"TestEnzyme\\\", \\\"KR\\\", \\\"P\\\", \\\"Cterm\\\", 1);\\n p = new Protein(\\\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHG_FVNRTGFDA\\\");\\n\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // END of file reached.\\n // Check this!\\n String line = null;\\n while((line = br.readLine()) != null) {\\n if(!line.trim().equals(\\\"\\\")) {\\n fail(\\\"More lines in testCleave.fas then were generated by the test cleavage!\\\");\\n }\\n }\\n br.close();\\n } catch(IOException ioe) {\\n fail(\\\"IOException occurred while testing the cleave() method: '\\\" + ioe.getMessage() + \\\"'.\\\");\\n }\\n }\",\n \"@Test\\n public void testMetastableMemberships()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.metastableMemberships(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testVerser2() {\\n\\n //Arrange\\n CompteBancaire cpt = new CompteBancaire(6500.0);\\n double montant_a_verser = -2000.0;\\n double excepted = 6500.0;\\n\\n //Act\\n assertEquals(false, cpt.verser(montant_a_verser));\\n\\n //Assert\\n assertEquals(excepted, cpt.getSolde(), 0);\\n\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65949225","0.65912473","0.5986597","0.57795","0.5703691","0.563787","0.53556013","0.5318458","0.531436","0.52704465","0.5177754","0.5174197","0.5158172","0.51175845","0.51056963","0.507546","0.5049223","0.50490606","0.5048576","0.50396436","0.50091225","0.50049496","0.49835175","0.49745733","0.49732602","0.49524283","0.4951273","0.49045762","0.490148","0.49009198","0.48959324","0.48947224","0.4891426","0.48707473","0.48685265","0.48633185","0.48515907","0.48470095","0.4844898","0.48436123","0.48387545","0.4836782","0.4828989","0.4802411","0.47978875","0.47808146","0.47798273","0.4768204","0.47634593","0.47626087","0.47494087","0.47443756","0.47443378","0.4738983","0.47319967","0.47283176","0.47174108","0.47151816","0.47100234","0.47089654","0.47085842","0.4697149","0.4692755","0.46917945","0.4666193","0.46616486","0.46554494","0.46519253","0.4648634","0.4644216","0.46313304","0.46295875","0.46276626","0.46170428","0.46129498","0.46123195","0.46072134","0.46064213","0.45993474","0.45975587","0.45975453","0.45942676","0.4593302","0.4591114","0.45910373","0.45897686","0.45873484","0.45796907","0.4575925","0.45708704","0.45677796","0.45607907","0.45571607","0.45456725","0.45397073","0.45350856","0.4524383","0.45240518","0.45236713","0.45216507"],"string":"[\n \"0.65949225\",\n \"0.65912473\",\n \"0.5986597\",\n \"0.57795\",\n \"0.5703691\",\n \"0.563787\",\n \"0.53556013\",\n \"0.5318458\",\n \"0.531436\",\n \"0.52704465\",\n \"0.5177754\",\n \"0.5174197\",\n \"0.5158172\",\n \"0.51175845\",\n \"0.51056963\",\n \"0.507546\",\n \"0.5049223\",\n \"0.50490606\",\n \"0.5048576\",\n \"0.50396436\",\n \"0.50091225\",\n \"0.50049496\",\n \"0.49835175\",\n \"0.49745733\",\n \"0.49732602\",\n \"0.49524283\",\n \"0.4951273\",\n \"0.49045762\",\n \"0.490148\",\n \"0.49009198\",\n \"0.48959324\",\n \"0.48947224\",\n \"0.4891426\",\n \"0.48707473\",\n \"0.48685265\",\n \"0.48633185\",\n \"0.48515907\",\n \"0.48470095\",\n \"0.4844898\",\n \"0.48436123\",\n \"0.48387545\",\n \"0.4836782\",\n \"0.4828989\",\n \"0.4802411\",\n \"0.47978875\",\n \"0.47808146\",\n \"0.47798273\",\n \"0.4768204\",\n \"0.47634593\",\n \"0.47626087\",\n \"0.47494087\",\n \"0.47443756\",\n \"0.47443378\",\n \"0.4738983\",\n \"0.47319967\",\n \"0.47283176\",\n \"0.47174108\",\n \"0.47151816\",\n \"0.47100234\",\n \"0.47089654\",\n \"0.47085842\",\n \"0.4697149\",\n \"0.4692755\",\n \"0.46917945\",\n \"0.4666193\",\n \"0.46616486\",\n \"0.46554494\",\n \"0.46519253\",\n \"0.4648634\",\n \"0.4644216\",\n \"0.46313304\",\n \"0.46295875\",\n \"0.46276626\",\n \"0.46170428\",\n \"0.46129498\",\n \"0.46123195\",\n \"0.46072134\",\n \"0.46064213\",\n \"0.45993474\",\n \"0.45975587\",\n \"0.45975453\",\n \"0.45942676\",\n \"0.4593302\",\n \"0.4591114\",\n \"0.45910373\",\n \"0.45897686\",\n \"0.45873484\",\n \"0.45796907\",\n \"0.4575925\",\n \"0.45708704\",\n \"0.45677796\",\n \"0.45607907\",\n \"0.45571607\",\n \"0.45456725\",\n \"0.45397073\",\n \"0.45350856\",\n \"0.4524383\",\n \"0.45240518\",\n \"0.45236713\",\n \"0.45216507\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71575373"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":135798,"cells":{"query":{"kind":"string","value":"Test of estimateCstepping method, of class MarkovModelUtilities."},"document":{"kind":"string","value":"@Test\n public void testEstimateCstepping_IIntArray_int()\n {\n IIntArray traj = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCstepping(traj, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["@Test\n public void testEstimateCstepping_Iterable_int()\n {\n Iterable<IIntArray> trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCstepping(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateT()\n {\n \n IDoubleArray counts = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateT(counts);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateC_Iterable_int()\n {\n Iterable<IIntArray> trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateC(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCmilestoning_3args_1()\n {\n Iterable<IIntArray> trajs = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCmilestoning_3args_2()\n {\n IIntArray traj = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testForwardCommittor()\n {\n IDoubleArray M = null;\n IIntArray A = null;\n IIntArray B = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.forwardCommittor(M, A, B);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test02() throws Throwable {\n Discretize discretize0 = new Discretize(\"l*{e5SNh\");\n boolean boolean0 = true;\n discretize0.m_ClassIndex = 177;\n discretize0.setUseEqualFrequency(true);\n double[][] doubleArray0 = new double[5][9];\n double[] doubleArray1 = new double[3];\n doubleArray1[0] = (double) 177;\n doubleArray1[1] = (double) 177;\n doubleArray1[2] = (double) 177;\n doubleArray0[0] = doubleArray1;\n double[] doubleArray2 = new double[2];\n doubleArray2[0] = (double) 177;\n doubleArray2[1] = (double) 177;\n doubleArray0[1] = doubleArray2;\n double[] doubleArray3 = new double[9];\n doubleArray3[0] = (double) 177;\n doubleArray3[1] = (double) 177;\n doubleArray3[2] = (double) 177;\n doubleArray3[3] = (double) 177;\n doubleArray3[4] = (double) 177;\n doubleArray3[5] = (double) 177;\n doubleArray3[6] = (double) 177;\n doubleArray3[7] = (double) 177;\n doubleArray3[8] = (double) 177;\n doubleArray0[2] = doubleArray3;\n double[] doubleArray4 = new double[8];\n doubleArray4[0] = (double) 177;\n doubleArray4[1] = (double) 177;\n doubleArray4[2] = (double) 177;\n doubleArray4[3] = (double) 177;\n doubleArray4[4] = (double) 177;\n doubleArray4[5] = (double) 177;\n doubleArray4[6] = (double) 177;\n doubleArray4[7] = (double) 177;\n doubleArray0[3] = doubleArray4;\n double[] doubleArray5 = new double[0];\n doubleArray0[4] = doubleArray5;\n discretize0.m_CutPoints = doubleArray0;\n discretize0.listOptions();\n discretize0.setIgnoreClass(true);\n String[] stringArray0 = new String[0];\n Discretize.main(stringArray0);\n discretize0.findNumBinsTipText();\n discretize0.setInvertSelection(true);\n // Undeclared exception!\n try { \n discretize0.getBinRangesString(511);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 511\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n\tpublic void testRepeatibleDynamics() throws IOException, PropertiesException {\n\n\t\t\n\t\t// INSTANTIATE benchmark \n\t\tProperties props = PropertiesUtil.setpointProperties(new File (\"src/main/resources/sim.properties\"));\n\t\tSetPointGenerator lg = new SetPointGenerator (props);\n\t\tList<ExternalDriver> externalDrivers = new ArrayList<ExternalDriver>();\n\t\texternalDrivers.add(lg);\n\t\tIndustrialBenchmarkDynamics d = new IndustrialBenchmarkDynamics (props, externalDrivers);\n\t\tRandom actionRand = new Random(System.currentTimeMillis());\n \n // 1) do 100000 random steps, in order to initialize dynamics\n\t\tfinal ActionDelta action = new ActionDelta(0.001f, 0.001f, 0.001f); \n\t\tfor (int i=0; i<INIT_STEPS; i++) {\n\t\t\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\td.step(action);\n\t\t}\n\t\t\n\t\t// 2) memorize current observable state and current markov state\n\t\tfinal ObservableState os = d.getState();\n\t\tfinal DataVector ms = d.getInternalMarkovState();\n\t\tSystem.out.println (\"init o-state: \" + os.toString());\n\t\tSystem.out.println (\"init m-state: \" + ms.toString());\n\t\t\n\t\t\n\t\t// 3) perform test trajectory and memorize states\n\t\tactionRand.setSeed(ACTION_SEED);\n\t\tDataVector oStates[] = new DataVector[MEM_STEPS];\n\t\tDataVector mStates[] = new DataVector[MEM_STEPS];\n\t\t\t\t\n\t\tfor (int i=0; i<MEM_STEPS; i++) {\n\t\t\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\td.step(action);\n\t\t\toStates[i] = d.getState();\n\t\t\tmStates[i] = d.getInternalMarkovState();\n\t\t}\n\t\t\n\t\t// 4) reset dynamics & parameters and internal markov state\n\t\td.reset();\n\t\td.setInternalMarkovState(ms);\n\t\t\n\t\t// 5) reperform test and check if values are consistent\n\t\tactionRand.setSeed(ACTION_SEED); // reproduce action sequence\n\t\tDataVector oState = null;\n\t\tDataVector mState = null;\n\t\tfor (int i=0; i<MEM_STEPS; i++) {\n\t\t\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\t\n\t\t\td.step(action);\n\t\t\toState = d.getState();\n\t\t\tmState = d.getInternalMarkovState();\n\t\t\t\n\t\t\t// check observable state\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.SetPoint), oState.getValue(ObservableStateDescription.SetPoint), 0.0001);\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.Fatigue), oState.getValue(ObservableStateDescription.Fatigue), 0.0001);\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.Consumption), oState.getValue(ObservableStateDescription.Consumption), 0.0001);\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.RewardTotal), oState.getValue(ObservableStateDescription.RewardTotal), 0.0001);\n\n\t\t\t// \n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.CurrentOperationalCost), mState.getValue(MarkovianStateDescription.CurrentOperationalCost), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent2), mState.getValue(MarkovianStateDescription.FatigueLatent2), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent1), mState.getValue(MarkovianStateDescription.FatigueLatent1), 0.0001);\n\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionGainBeta), mState.getValue(MarkovianStateDescription.EffectiveActionGainBeta), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), mState.getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveShift), mState.getValue(MarkovianStateDescription.EffectiveShift), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.MisCalibration), mState.getValue(MarkovianStateDescription.MisCalibration), 0.0001);\n\t\t\t\n\t\t\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), mState.getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), 0.0001);\n\t\t\t\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardFatigue), mState.getValue(MarkovianStateDescription.RewardFatigue), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardConsumption), mState.getValue(MarkovianStateDescription.RewardConsumption), 0.0001);\n\t\t}\n\t\t\n\t\tSystem.out.println (\"last o-state 1st trajectory: \" + oStates[oStates.length-1]);\n\t\tSystem.out.println (\"last o-state 2nd trajectory: \" + oState);\n\t\t\n\t\tSystem.out.println (\"last m-state 1st trajectory: \" + mStates[oStates.length-1]);\n\t\tSystem.out.println (\"last m-state 2nd trajectory: \" + mState);\n\t}","public void steepestDescent() {\r\n ArrayList<Atom> atoms = cluster.getAtoms();\r\n Atom a;\r\n int numAtoms = atoms.size();\r\n double potentialEnergy;\r\n double trialPotential;\r\n int i;\r\n\r\n //Increment time\r\n totalTime += currentStep;\r\n\r\n //Calculate force on each atom\r\n cluster.calculateForces();\r\n\r\n //Get energy of current configuration\r\n HashMap<String, Double> energies = cluster.getEnergies();\r\n potentialEnergy = 0.0;\r\n for(String eName : energies.keySet())\r\n potentialEnergy += energies.get(eName).doubleValue();\r\n\r\n //Save current atom positions and advance atoms to trial positions\r\n for(i = 0; i < numAtoms; i++) {\r\n a = atoms.get(i);\r\n\r\n savedLocations[i].x = a.location.x;\r\n savedLocations[i].y = a.location.y;\r\n savedLocations[i].z = a.location.z;\r\n\r\n //Compute trial locations\r\n a.location.x += a.force.x * currentStep;\r\n a.location.y += a.force.y * currentStep;\r\n a.location.z += a.force.z * currentStep;\r\n }\r\n\r\n //Get energy of the trial configuration\r\n energies = cluster.getEnergies();\r\n trialPotential = 0.0;\r\n for(String eName : energies.keySet())\r\n trialPotential += energies.get(eName).doubleValue();\r\n\r\n //Decide whether to keep trial configuration based on convergence\r\n if(trialPotential < potentialEnergy) {\r\n //Keep positions and check for convergence\r\n if(Math.abs((potentialEnergy - trialPotential) / potentialEnergy) < convergenceCriterion)\r\n isConverged = true;\r\n\r\n currentStep *= 1.2;\r\n } else {\r\n //Restore old positions\r\n for(i = 0; i < numAtoms; i++) {\r\n a = atoms.get(i);\r\n\r\n a.location.x = savedLocations[i].x;\r\n a.location.y = savedLocations[i].y;\r\n a.location.z = savedLocations[i].z;\r\n }\r\n\r\n currentStep *= 0.5;\r\n }\r\n }","@Test\n public void testMetastableStates()\n {\n IDoubleArray M = null;\n int nstates = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IIntArray expResult = null;\n IIntArray result = instance.metastableStates(M, nstates);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testBackwardCommittor()\n {\n IDoubleArray M = null;\n IIntArray A = null;\n IIntArray B = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.backwardCommittor(M, A, B);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateC_IIntArray_int()\n {\n IIntArray traj = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateC(traj, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n void makeCoffee() {\n Assertions.assertFalse(machine.makeCoffee());\n\n //try with cup\n Assertions.assertDoesNotThrow(() -> {\n machine.positionCup();\n });\n\n Assertions.assertThrows(Exception.class, () -> {\n machine.positionCup();\n });\n\n\n Assertions.assertTrue(machine.makeCoffee());\n\n //check if correct amount as been subtracted\n Assertions.assertEquals(machine.getCurrentWater(), 1000 - machine.waterPerCup);\n Assertions.assertEquals(machine.getCurrentBeans(), 800 - machine.beansPerCup);\n\n //reset\n machine.fillWater();\n machine.fillBeans();\n machine.setCupPositioned(true);\n\n //test over boundary\n Assertions.assertTrue(machine.makeCoffee());\n Assertions.assertTrue(machine.makeCoffee());\n\n //set only water to boundary values and below\n machine.setCurrentWater(0);\n Assertions.assertFalse(machine.makeCoffee());\n machine.setCurrentWater(machine.waterPerCup);\n Assertions.assertTrue(machine.makeCoffee());\n\n //reset water\n machine.fillWater();\n\n //set only beans to boundary value and below\n machine.setCurrentBeans(0);\n Assertions.assertFalse(machine.makeCoffee());\n machine.setCurrentBeans(machine.beansPerCup);\n Assertions.assertTrue(machine.makeCoffee());\n\n\n }","public void checkMovementZMinimum(ITestContext context) throws Exception {\n Reporter.testStart();\n String caseFolder=context.getName()+\"/\";\n String importFile=context.getCurrentXmlTest().getParameter(\"importFile\");\n String expectationFile=context.getCurrentXmlTest().getParameter(\"expectationFile\");\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\n String screenWithinCase=caseFolder+methodName;\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\n if(meshViewPage.isThePage()){\n\n uploadDCM(meshViewPage,importFile);\n\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\n Reporter.log(\"click Orientation Adjustment tab on left bar\");\n String text=\"0.1\";\n Reporter.log(\"select Movement step \"+text);\n Assert.assertEquals(oa.selectMovementStep(text),text,\"Cann't select \"+text);\n\n Integer clickTimes=40;\n String disResult=oa.clickDistanceMinus(clickTimes);\n String higher=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n\n clickTimes=5;\n disResult=oa.clickDistanceMinus(clickTimes);\n String middle=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n\n clickTimes=6;\n disResult=oa.clickDistanceMinus(clickTimes);//click times: 51\n String lower=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-5\",\"distance is not same as expected result\");\n\n embededCompareResult(higher,middle,lower, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\n\n disResult=oa.clickDistanceReset();\n embededScreenShot(screenWithinCase+\"_reset_\"+disResult,\"click Z reset(1 times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"0\",\"distance is not same as expected result\");\n }\n Reporter.testEnd();\n }","public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap<Integer,Integer> words)\r\n{\r\n int t=0;\r\n double LearningRates[] = {1, 0.1, 0.01};\r\n double Margins[] = {1, 0.1, 0.01};\r\n\r\n\tint minInt =0; // Set To Your Desired Min Value\r\n int maxInt =2;\r\n\tRandom rand = new Random();\r\n\t\r\n \tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\r\n\r\n \tdouble LearningRate = LearningRates[randomNum];\r\n \t\r\nfor(int m=0;m<LearningRates.length;m++)\r\n{\r\n for(int i=0;i<LearningRates.length;i++)\r\n {\r\n\t LearningRate = LearningRates[i];\r\n\t\t double devAccuracyTotal = (double)0;\r\n\t\t int noOfEpochs= 20;\r\n\t\t int decreaseRate = 0;\r\n\t\t \r\n \t\t Double w[] = new Double[74500];\r\n\t\t Double min =-0.01; // Set To Your Desired Min Value\r\n\t Double max = 0.01;\r\n\t \t\tdouble smallRandNumber = min + new Random().nextDouble() * (max - min); \r\n\t\t\tArrays.fill(w, smallRandNumber);\r\n\t\t\t\r\n\t\t for(int epoch=0;epoch<noOfEpochs;epoch++)\r\n\t\t {\r\n\t\t\t \tw = learnMarginPerceptron(data,index,LearningRate,decreaseRate,w,Margins[m]);\r\n\t\t\t \tdecreaseRate = decreaseRate + index;\r\n\t\t\r\n\t\t }\r\n\t\t//CVSplit test\r\n \tdouble cvAccuracy = testTestFile(w,testFileName,words);\r\n \t\r\n// \tSystem.out.println(\"Epoch\" + epoch +\" Dev Accuracy for Decaying Learning Rate \" + LearningRate + \" is \" + cvAccuracy);\r\n \r\n \r\n \tSystem.out.println(\" ** Cv Accuracy for Margin Rate\" + Margins[m] + \" Learning Rate \" + LearningRate + \" is \" + cvAccuracy);\r\n \tSystem.out.println(\" \");\r\n }\r\n}\r\n//double testAccuracy = testTestFile(w,testFileName);\r\n\t//System.out.println(\"test Accuracy\" + testAccuracy);\r\n}","@Test(timeout = 4000)\n public void test29() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[3][6];\n discretize0.setIgnoreClass(false);\n discretize0.m_CutPoints = doubleArray0;\n double[] doubleArray1 = new double[4];\n // Undeclared exception!\n try { \n discretize0.getCutPoints((-831));\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // -831\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","public void testStep() {\n\n DT_TractographyImage crossing = Images.getCrossing();\n\n FACT_FibreTracker tracker = new FACT_FibreTracker(crossing);\n\n Vector3D[] pds = crossing.getPDs(1,1,1);\n\n Vector3D step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 0, true);\n\n assertEquals(1.0, pds[0].dot(step.normalized()), 1E-8);\n\n assertEquals(Images.xVoxelDim / 2.0, step.mod(), 0.05);\n \n step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 0, false);\n\n assertEquals(-1.0, pds[0].dot(step.normalized()), 1E-8);\n\n step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 1, true);\n\n assertEquals(1.0, pds[1].dot(step.normalized()), 1E-8);\n\n\n for (int i = 0; i < 2; i++) {\n\n step = tracker.getNextStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), new Vector3D(pds[i].x + 0.01, pds[i].y + 0.02, pds[i].z + 0.03).normalized());\n\n assertEquals(1.0, pds[i].dot(step.normalized()), 1E-8);\n\n }\n\n \n }","@Test()\n public void testTrainWarmCFastSMO() {\n ClassificationDataSet train = FixedProblems.getHalfCircles(250, RandomUtil.getRandom(), 0.1, 0.2);\n\n PlattSMO warmModel = new PlattSMO(new LinearKernel(1));\n warmModel.setC(1);\n warmModel.train(train);\n\n PlattSMO warm = new PlattSMO(new LinearKernel(1));\n warm.setC(1e4);// too large to train efficently like noraml\n\n long start, end;\n\n start = System.currentTimeMillis();\n warm.train(train, warmModel);\n end = System.currentTimeMillis();\n long warmTime = (end - start);\n\n PlattSMO notWarm = new PlattSMO(new LinearKernel(1));\n notWarm.setC(1e4);// too large to train efficently like noraml\n\n start = System.currentTimeMillis();\n notWarm.train(train);\n end = System.currentTimeMillis();\n long normTime = (end - start);\n\n assertTrue(warmTime < normTime * 0.75);\n\n }","@Test(timeout = 4000)\n public void test03() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.setAttributeIndices(\"\");\n Filter.makeCopy(discretize0);\n discretize0.setDesiredWeightOfInstancesPerInterval(2.0);\n boolean boolean0 = true;\n discretize0.m_ClassIndex = 0;\n discretize0.setUseBinNumbers(true);\n discretize0.listOptions();\n discretize0.makeBinaryTipText();\n discretize0.getInvertSelection();\n // Undeclared exception!\n try { \n discretize0.calculateCutPointsByEqualWidthBinning((-458));\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void testStationaryDistribution()\n {\n try\n {\n IDoubleArray T = Doubles.create.fromFile(inputT1);\n IDoubleArray expResult = Doubles.create.fromFile(inputT1_pi); \n IDoubleArray pi = MarkovModel.util.stationaryDistribution(T);\n assertEqual(pi, expResult, 1e-5);\n }\n catch(IOException e)\n {\n \n e.printStackTrace();\n }\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n }","@Test\n public void testPerturbationExpectation()\n {\n IDoubleArray M = null;\n IDoubleArray pi0 = null;\n IDoubleArray observable = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.perturbationExpectation(M, pi0, observable, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void test03() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double[] doubleArray0 = new double[19];\n evaluation0.updateMargins(doubleArray0, 0, 0.0);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\n }","@Test\n void cupTesting() {\n Assertions.assertFalse(machine.isCupPositioned());\n Assertions.assertFalse(machine.removeCup());\n\n Assertions.assertDoesNotThrow(() -> machine.positionCup());\n\n Assertions.assertTrue(machine.removeCup());\n Assertions.assertFalse(machine.isCupPositioned());\n machine.setCupPositioned(true);\n Assertions.assertTrue(machine.isCupPositioned());\n\n }","public ModelParams optimize() throws Exception {\n\t\tint iterCount = 0;\n\t\tdivergence = KLDivergenceCalculator.calculate(cState, tState);\n\t\tSystem.out.println(\"KL Divergence : \" + divergence);\n\t\t// ((DivergenceChart) BeanFinder\n\t\t// .findBean(MetaConstants.BEAN_DIVERGENCE_CHART)).update(\n\t\t// iterCount, divergence);\n\t\tdouble delta[] = null;\n\t\tdouble theta[] = data.getParams().getParams();\n\t\tdouble newDivergence = 0.0d;\n\t\tdouble initialStep = data.getInitialStepSize();\n\t\tboolean optimized = false;\n\t\tdo {\n\t\t\tdelta = computeGradient(theta);\n\t\t\tfor (double step = initialStep; iterCount < data.getMaxIterations(); step /= 10) {\n\t\t\t\tSystem.out.println(\"Step size : \" + step);\n\t\t\t\tSystem.out.println(\"KL divergence before taking a step : \"\n\t\t\t\t\t\t+ KLDivergenceCalculator.calculate(cState, tState));\n\t\t\t\tfor (int i = 0; i < delta.length; i++) {\n\t\t\t\t\tif (Double.isNaN(delta[i]))\n\t\t\t\t\t\tSystem.out.print(\"delta is NAN!!\" + delta[i] + \", \");\n\t\t\t\t\ttheta[i] -= step * delta[i];\n\t\t\t\t\tSystem.out.print((theta[i]) + \", \");\n\t\t\t\t}\n\t\t\t\tSystem.out.println();\n\t\t\t\tgetNewState(theta);\n\t\t\t\titerCount++;\n\t\t\t\tnewDivergence = KLDivergenceCalculator\n\t\t\t\t\t\t.calculate(cState, tState);\n\t\t\t\tSystem.out.println(\"KL Divergence : \" + newDivergence);\n\t\t\t\t// ((DivergenceChart) BeanFinder\n\t\t\t\t// .findBean(MetaConstants.BEAN_DIVERGENCE_CHART)).update(\n\t\t\t\t// iterCount, newDivergence);\n\t\t\t\tif (newDivergence < divergence) {\n\t\t\t\t\tdivergence = newDivergence;\n\t\t\t\t\tinitialStep = step;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\t// before taking the next step size, reset theta to what it was\n\t\t\t\tfor (int i = 0; i < delta.length; i++)\n\t\t\t\t\ttheta[i] += step * delta[i];\n\t\t\t\tgetNewState(theta); // Added for checking. Not required.\n\t\t\t}\n\t\t\toptimized = optimized(theta);\n\t\t} while (!optimized && iterCount < data.getMaxIterations());\n\t\tModelParams optimParams = null;\n\t\tif (optimized) {\n\t\t\toptimParams = new ModelParams();\n\t\t\toptimParams.setParams(theta);\n\t\t}\n\t\treturn optimParams;\n\t}","@Test\n public void testEstimateCmilestoning_Iterable_int()\n {\n Iterable<IIntArray> trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test05() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[7][2];\n double[] doubleArray1 = new double[7];\n doubleArray1[0] = 0.0;\n doubleArray1[1] = (-587.14627);\n doubleArray1[2] = (-2542.40202289275);\n doubleArray1[3] = (-167.679515);\n doubleArray1[4] = (-1.0);\n doubleArray1[5] = 1.0;\n doubleArray1[6] = (-1.0);\n doubleArray0[0] = doubleArray1;\n double[] doubleArray2 = new double[5];\n doubleArray2[0] = (-167.679515);\n doubleArray2[1] = (-2692.352);\n doubleArray2[2] = (-167.679515);\n doubleArray2[3] = (-2542.40202289275);\n doubleArray2[4] = (-587.14627);\n doubleArray0[1] = doubleArray2;\n double[] doubleArray3 = new double[4];\n doubleArray3[0] = (-1.0);\n doubleArray3[1] = (-1.0);\n doubleArray3[2] = 1.0;\n doubleArray3[3] = (-2542.40202289275);\n doubleArray0[2] = doubleArray3;\n double[] doubleArray4 = new double[7];\n doubleArray4[0] = (-2692.352);\n doubleArray4[1] = (-1.0);\n doubleArray4[2] = (-1.0);\n doubleArray4[3] = 0.0;\n doubleArray4[4] = 1.0;\n doubleArray4[5] = 0.0;\n doubleArray4[6] = 1.0;\n doubleArray0[3] = doubleArray4;\n double[] doubleArray5 = new double[1];\n doubleArray5[0] = (-167.679515);\n doubleArray0[4] = doubleArray5;\n double[] doubleArray6 = new double[0];\n doubleArray0[5] = doubleArray6;\n double[] doubleArray7 = new double[1];\n doubleArray7[0] = (-587.14627);\n doubleArray0[6] = doubleArray7;\n discretize0.m_CutPoints = doubleArray0;\n String string0 = discretize0.invertSelectionTipText();\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertEquals(\"Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.\", string0);\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseEqualFrequency());\n }","@Test\r\n public void testAverageacceleration() {\r\n System.out.println(\"Average Acceleration\");\r\n \r\n // Test case one.\r\n System.out.println(\"Test case #1\"); \r\n \r\n double distance = 200;\r\n long time = 16;\r\n \r\n SceneControl instance = new SceneControl();\r\n \r\n double expResult = 0.78125;\r\n double result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case two.\r\n System.out.println(\"Test case #2\"); \r\n \r\n distance = 80;\r\n time = 200;\r\n \r\n \r\n expResult = .002;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case three.\r\n System.out.println(\"Test case #3\"); \r\n \r\n distance = 0;\r\n time = 15;\r\n \r\n \r\n expResult = 0;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case four.\r\n System.out.println(\"Test case #4\"); \r\n \r\n distance = 12;\r\n time = 1;\r\n \r\n \r\n expResult = 12;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case five.\r\n System.out.println(\"Test case #5\"); \r\n \r\n distance = 1;\r\n time = 1;\r\n \r\n \r\n expResult = 1;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case six.\r\n System.out.println(\"Test case #6\"); \r\n \r\n distance = 3;\r\n time = 8;\r\n \r\n \r\n expResult = .046875;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case seven.\r\n System.out.println(\"Test case #7\"); \r\n \r\n distance = 200;\r\n time = 1;\r\n \r\n \r\n expResult = 200;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n \r\n \r\n }","@Test\n public void testGetSetTemperaturaExteriorMinima() {\n System.out.println(\"getSetTemperaturaExteriorMinima\");\n double expResult = 10.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaExteriorMinima(expResult);\n double result = instance.getTemperaturaExteriorMinima();\n assertEquals(expResult, result, 0.0);\n }","public void checkMovementZReset(ITestContext context) {\n Reporter.testStart();\n String caseFolder=context.getName()+\"/\";\n String importFile=context.getCurrentXmlTest().getParameter(\"importFile\");\n String expectationFile=context.getCurrentXmlTest().getParameter(\"expectationFile\");\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\n String screenWithinCase=caseFolder+methodName;\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\n if(meshViewPage.isThePage()){\n\n uploadDCM(meshViewPage,importFile);\n\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\n Reporter.log(\"click Orientation Adjustment tab on left bar\");\n String text=\"0.1\";\n Reporter.log(\"select Movement step \"+text);\n Assert.assertEquals(oa.selectMovementStep(text),text,\"Cann't select \"+text);\n\n Integer clickTimes=30;\n String disResult=oa.clickDistanceMinus(clickTimes);\n embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-3\",\"distance is not same as expected result\");\n\n clickTimes=10;\n disResult=oa.clickDistancePlus(clickTimes);\n String lower=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-2\",\"distance is not same as expected result\");\n\n disResult=oa.clickDistancePlus(clickTimes);\n String middle=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-1\",\"distance is not same as expected result\");\n\n disResult=oa.clickDistanceReset();\n String higher=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z reset(1 times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"0\",\"distance is not same as expected result\");\n\n embededCompareResult(lower,middle,higher, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\n\n }\n Reporter.testEnd();\n }","private static void testTakeStep(Drunkard drunkard, int testStepTimes, int testStepSize){\n\t for (int i = 0; i < testStepTimes; i++)\n\t{\n\t ImPoint pointA = drunkard.getCurrentLoc(); \n\t\tdrunkard.takeStep();\n\t\tImPoint pointB = drunkard.getCurrentLoc();\n\t\t\n\t\tif (pointA.getX()-pointB.getX() != 0 && pointA.getY()-pointB.getY() != 0)\n\t\t{\n\t\t\tSystem.out.println(\"Not a valid compass direction!\");\n\t\t}\n\t\telse if (Math.sqrt((pointA.getX() - pointB.getX())*(pointA.getX() - pointB.getX()) + (pointA.getY() - pointB.getY())*(pointA.getY() - pointB.getY())) != testStepSize)\n\t\t{\n\t\t\tSystem.out.println(\"took step to (\" + pointB.getX() + \",\" + pointB.getY() + \"), FAILED, not a valid step\");\n\t\t}\n\t\telse\n\t\t{\n\t\t\tSystem.out.println(\"took step to (\" + pointB.getX() + \",\" + pointB.getY() + \"), SUCCEEDED\");\n\t\t}\n\t}\n\t System.out.println(); \n\t}","@Test\n public void testIsTransitionMatrix()\n {\n IDoubleArray T = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n boolean expResult = false;\n boolean result = instance.isTransitionMatrix(T);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test072() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate((String) null);\n Evaluation evaluation0 = new Evaluation(instances0);\n double[] doubleArray0 = new double[7];\n evaluation0.updateMargins(doubleArray0, 3, (-1));\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n }","@Test(timeout = 4000)\n public void test19() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.getCutPoints((-2276));\n assertFalse(discretize0.getFindNumBins());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertFalse(discretize0.getUseEqualFrequency());\n }","@Test\n public void testGiveChange() throws Exception {\n System.out.println(\"giveChange\");\n //CashRegister instance = new CashRegister();\n assertEquals(0.0, instance.giveChange(), EPSILON);\n instance.scanItem(0.55);\n instance.scanItem(1.27);\n double expResult = 0.18;\n instance.collectPayment(Money.QUARTER, 8);\n double result = instance.giveChange();\n assertEquals(expResult, result, EPSILON);\n \n }","@Test\n public void testCalcMedAmount() {\n \n //Test case 1\n \n System.out.println(\"calcMedAmount Test Case 1 - Dose is correct\");\n \n //Input Values for Test Case 1\n \n double weightInPounds = 100;\n double numberOfPills = 1;\n \n AntidoteControl instance = new AntidoteControl();\n \n double expResult = 227;\n double result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n \n \n //Test case 2\n \n System.out.println(\"calcMedAmount Test Case 2 Invalid weight\");\n \n //Input Values for Test Case 2\n \n weightInPounds = 0;\n numberOfPills = 1;\n \n // AntidoteControl instance = new AntidoteControl();\n \n expResult = -1;\n result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n \n \n //Test case 3\n \n System.out.println(\"calcMedAmount Test Case 3 Underdose\");\n \n //Input Values for Test Case 3\n \n weightInPounds = 10001;\n numberOfPills = 0;\n \n // AntidoteControl instance = new AntidoteControl();\n \n expResult = -2;\n result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n \n \n //Test case 4\n \n System.out.println(\"calcMedAmount Test Case 4 Overdose\");\n \n //Input Values for Test Case 4\n \n weightInPounds = 100;\n numberOfPills = 4;\n \n // AntidoteControl instance = new AntidoteControl();\n \n expResult = 227;\n result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n }","int calculateMST();","@Test\n public void transferCheckingtoSavingsTest(){\n assertTrue(userC.transfer(150.00, userS));\n }","@Test(timeout = 4000)\n public void test02() throws Throwable {\n Discretize discretize0 = new Discretize();\n Locale.getISOLanguages();\n double[][] doubleArray0 = new double[9][8];\n double[] doubleArray1 = new double[3];\n double[] doubleArray2 = new double[9];\n doubleArray2[5] = (-163.0);\n doubleArray2[8] = 1421.4058829;\n doubleArray0[3] = doubleArray0[2];\n doubleArray0[4] = doubleArray0[1];\n discretize0.setOutputFormat();\n double[] doubleArray3 = new double[2];\n doubleArray3[0] = 1421.4058829;\n doubleArray3[1] = (-163.0);\n doubleArray0[6] = doubleArray2;\n double[] doubleArray4 = new double[0];\n doubleArray0[7] = doubleArray4;\n double[] doubleArray5 = new double[6];\n doubleArray5[2] = (-163.0);\n doubleArray0[8] = doubleArray5;\n discretize0.m_CutPoints = doubleArray0;\n // Undeclared exception!\n try { \n discretize0.setOutputFormat();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test096() throws Throwable {\n CostMatrix costMatrix0 = Evaluation.handleCostOption(\"\", 1281);\n assertNull(costMatrix0);\n }","@Test(timeout = 4000)\n public void test19() throws Throwable {\n Discretize discretize0 = new Discretize(\"%\");\n FileSystemHandling.createFolder((EvoSuiteFile) null);\n discretize0.findNumBinsTipText();\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \"%\");\n discretize0.m_DefaultCols = \"%\";\n double[][] doubleArray0 = new double[0][7];\n discretize0.m_CutPoints = doubleArray0;\n // Undeclared exception!\n try { \n discretize0.getCutPoints(1);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 1\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void shouldCalculateProperMoveProbabilities() throws Exception {\n\n int actualCity = 0;\n boolean[] visited = new boolean[] {true, false, false, false};\n int[][] firstCriterium = new int[][] {{0, 2, 1, 2},\n {2, 2, 1, 1},\n {1, 1, 0, 1},\n {2, 1, 1, 0}};\n int[][] secondCriterium = new int[][] {{0, 3, 2, 1},\n {3, 0, 0, 0},\n {2, 0, 0, 0},\n {1, 0, 0, 0}};\n// final AttractivenessCalculator attractivenessCalculator = new AttractivenessCalculator(1.0, 0.0, 0.0, 0.0, 0.0);\n// double[][] array = new double[0][0];\n// final double[] probabilities = attractivenessCalculator.movesProbability(actualCity, visited, array, firstCriterium, secondCriterium, null, 0.0, 0.0);\n\n// assertTrue(probabilities[0] == 0.0);\n// assertTrue(probabilities[1] == 0.0);\n// assertTrue(probabilities[2] == 0.5);\n// assertTrue(probabilities[3] == 0.5);\n }","public void testMainLBGFSCachedSemiMarkov() throws Exception {\n\t\tConrad.main(new String[] {\"train\", \"test/input/zeroOrderLBFGSCachedSemiMarkov.xml\", \"test/input/zeroOrderTestShortMax.txt\", \"test/working/zeroLBGFSModelCachedSemiMarkov.ser\"});\n\t\tConrad.main(new String[] {\"test\", \"test/working/zeroLBGFSModelCachedSemiMarkov.ser\", \"test/input/zeroOrderTest.txt\", \"test/working/zeroOrderMainLBGFSCachedSemiMarkovPredicted.txt\"});\n\t\tConrad.main(new String[] {\"test\", \"test/working/zeroLBGFSModelCachedSemiMarkov.ser\", \"test/input/zeroOrderTest2.txt\", \"test/working/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\"});\n\t\tassertFilesMatch(\"test/output/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\", \"test/working/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\");\n\t}","@Test\n public void testGetSetTemperaturaExterior() {\n System.out.println(\"getSetTemperaturaExterior\");\n double expResult = 20.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaExterior(expResult);\n double result = instance.getTemperaturaExterior();\n assertEquals(expResult, result, 0.0);\n }","@Test\r\n public void gallonsCauldronHolds() {\r\n \r\n System.out.println(\"gallonsCauldronHolds\");\r\n \r\n //Test #1\r\n System.out.println(\"Test case #1\"); \r\n \r\n double diameter = 50;\r\n double depth = 25;\r\n \r\n SceneControl instance = new SceneControl();\r\n \r\n double expResult = 212.500;\r\n double result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #2\r\n System.out.println(\"Test case #2\");\r\n \r\n diameter = 15;\r\n depth = -12;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #3\r\n System.out.println(\"Test case #3\");\r\n \r\n diameter = -5;\r\n depth = 20;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #4\r\n System.out.println(\"Test case #4\");\r\n \r\n diameter = 0;\r\n depth = 30;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #5\r\n System.out.println(\"Test case #5\");\r\n \r\n diameter = 14;\r\n depth = 0;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #6\r\n System.out.println(\"Test case #6\");\r\n \r\n diameter = 1;\r\n depth = 12;\r\n \r\n expResult = .041;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #7\r\n System.out.println(\"Test case #7\");\r\n \r\n diameter = 20;\r\n depth = 1;\r\n \r\n expResult = 1.36;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n }","@Test\n public void testInbreedingCoeffForMultiallelicVC() {\n VariantContext test1 = makeVC(\"1\",Arrays.asList(Aref,T,C),\n makeG(\"s1\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s2\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s3\", T, C, 7099, 2530, 7099, 3056, 0, 14931),\n makeG(\"s4\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s5\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s6\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s7\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s8\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s9\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s10\", Aref, T, 2530, 0, 7099, 366, 3056, 14931));\n\n final AS_InbreedingCoeff testClass1 = new AS_InbreedingCoeff();\n final double ICresult1 = testClass1.calculateIC(test1, T);\n Assert.assertEquals(ICresult1, -0.4285714, DELTA_PRECISION, \"Pass\");\n final double ICresult1b = testClass1.calculateIC(test1, C);\n Assert.assertEquals(ICresult1b, -0.05263, DELTA_PRECISION, \"Pass\");\n\n //make sure that hets with different alternate alleles all get counted\n VariantContext test2 = makeVC(\"2\", Arrays.asList(Aref,T,C),\n makeG(\"s1\", Aref, C, 4878, 1623, 11297, 0, 7970, 8847),\n makeG(\"s2\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s3\", Aref, T, 3382, 0, 6364, 1817, 5867, 12246),\n makeG(\"s4\", Aref, T, 2488, 0, 9110, 3131, 9374, 12505),\n makeG(\"s5\", Aref, C, 4530, 2006, 18875, 0, 6847, 23949),\n makeG(\"s6\", Aref, T, 5325, 0, 18692, 389, 16014, 24570),\n makeG(\"s7\", Aref, T, 2936, 0, 29743, 499, 21979, 38630),\n makeG(\"s8\", Aref, T, 6902, 0, 8976, 45, 5844, 9061),\n makeG(\"s9\", Aref, T, 5732, 0, 10876, 6394, 11408, 17802),\n makeG(\"s10\", Aref, T, 2780, 0, 25045, 824, 23330, 30939));\n\n final AS_InbreedingCoeff testClass2 = new AS_InbreedingCoeff();\n final double ICresult2 = testClass2.calculateIC(test2, T);\n Assert.assertEquals(ICresult2, -0.666666, DELTA_PRECISION, \"Pass\");\n final double ICresult2b = testClass2.calculateIC(test2, C);\n Assert.assertEquals(ICresult2b, -0.111129, DELTA_PRECISION, \"Pass\");\n }","public void test_setClippingIIII() {\n}","@Test\n\tvoid testCalculateMarkTillSemesterSuccess3() {\n\t\n\t\tString semesterName = \"20193\";\n\t\t\n\t\tUser user = userService.findByUsername(\"B17DCCN123\");\n\t\tStudent student = user.getStudent();\n\t\tList<StudentResult> resultsTillSemester = resultService.findResultTillSemester(student.getId(),\n\t\t\t\tsemesterName);\n\t\t\n\t\tString tktMark = \"\";\n\t\t\n\t\tfor (StudentResult result : resultsTillSemester) {\n\t\t\tSubject subject = result.getStudentRegister().getCourse().getSubject();\n\t\t\tif (subject.getName().equals(\"Toán kinh tế\")) {\n\t\t\t\ttktMark = result.getMarkToChar();\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\t\n\t\tList<Object> listGpaResultsTillNow = MarkUtility.calculateMarkTillSemester(resultsTillSemester);\n\t\tint passedCreditsTillNow = (int) listGpaResultsTillNow.get(0);\n\t\tfloat gpaTillNow = (float) listGpaResultsTillNow.get(1);\n\t\t\n\t\tDecimalFormat df = new DecimalFormat(\"#.##\");\n\t\t\n\t\tassertTrue(\n\t\t\t\ttktMark.equals(\"C\") && \n\t\t\t\tpassedCreditsTillNow == 18 && \n\t\t\t\tdf.format(gpaTillNow).equals(\"3.19\")\n\t\t\t\t);\n\t}","float estimateCostToGoal( int current_x, int current_y, int current_z, boolean fromStart ) {\n\t\treturn 0;\n\t}","@Test\n @Tag(\"slow\")\n public void testCZPROB() {\n CuteNetlibCase.doTest(\"CZPROB.SIF\", \"2185196.6988565763\", \"3089066.71321333\", NumberContext.of(7, 4));\n }","@Test\n public void testVerser1() {\n\n //Arrange\n CompteBancaire cpt = new CompteBancaire(5000.0);\n double montant_a_verser = 1000.0;\n double excepted = 6000.0;\n\n //Act\n assertEquals(true, cpt.verser(montant_a_verser));\n\n //Assert\n assertEquals(excepted, cpt.getSolde(), 0);\n }","@Test\n public void testVirement3() {\n\n //Arrange\n CompteBancaire cpt1 = new CompteBancaire(0.0);\n CompteBancaire cpt2 = new CompteBancaire(43000.0);\n double montant_a_virer = 8000.0;\n double excepted_solde_cpt1 = 0.0;\n double excepted_solde_cpt2 = 43000.0;\n\n //Act\n assertEquals(false, cpt1.virerArgent(cpt2, montant_a_virer));\n\n //Assert\n assertEquals(excepted_solde_cpt1, cpt1.getSolde(), 0);\n assertEquals(excepted_solde_cpt2, cpt2.getSolde(), 0);\n\n }","public static void main(String[] args) {\n\n String fileEdgeList = \"../DataSource/PANCANsigEdgeList.csv\";\n String fileGtTrainingMatrix = \"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.training.10.csv\";\n// String fileGtTestingMatrix = \"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.testing.10.csv\";\n String fileGeTrainingMatrix = \"../DataSource/CrossMatrixDriver/PANCAN.GeM.4468tumors.training.10.csv\";\n String fileGeTestingMatrix = \"../DataSource/CrossMatrixDriver/PANCAN.GeM.4468tumors.testing.10.csv\";\n String fileInferDriver = \"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.InferDriver.10.withTumorID.csv\";\n// String fileDriverSGATable = \"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.DriverSGATable.10.csv\";\n \n \n DataReader dataObj = new DataReader(fileEdgeList, fileGtTrainingMatrix, fileGeTrainingMatrix);\n\n int reRun = 0;\n double T = 0.5;\n do {\n reRun += 1;\n\n EstimateParams paramObj = new EstimateParams(dataObj.edgeList, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.driverSGATable, dataObj.targetDEGTable);\n InferDriverActivation actObj = new InferDriverActivation(paramObj.mapEdgeParam, paramObj.mapSGAParam,\n dataObj.targetDEGTable, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.mapSgaDegs);\n\n\n actObj.thresholding(T);\n \n actObj.updateInferDriverTable( dataObj.driverSGATable);\n \n double change = actObj.compareMatrix(dataObj.driverSGATable);\n\n System.out.println(\"Current T is \" + T);\n System.out.println(\"Change is \" + change);\n System.out.println(\"This is the \" + reRun + \"th run\");\n if (change < 0.001 || T > 1) {\n System.out.println(\"Total times of run is \" + reRun + \". Final cut shreshold is \" + T);\n \n// dataObj.readInGtMatrix(fileGtTestingMatrix);\n dataObj.readInGeMatrix(fileGeTestingMatrix);\n InferDriverActivation actObjTesting = new InferDriverActivation(paramObj.mapEdgeParam, paramObj.mapSGAParam,\n dataObj.targetDEGTable, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.mapSgaDegs);\n \n actObjTesting.outputInferActivation(fileInferDriver, dataObj.tumorNames);\n// dataObj.outputDriverSGATable(fileDriverSGATable); //output contains tumorName, since tumor name is defined in dataObj, so no need to pass in\n\n break; \n \n } else {\n dataObj.updateDriverSGATable(actObj.inferDriverTable);\n T += 0.05;\n \n }\n\n } while (true);\n }","@Test(timeout = 4000)\n public void test36() throws Throwable {\n Discretize discretize0 = new Discretize();\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\n Properties properties0 = new Properties();\n ProtectedProperties protectedProperties0 = new ProtectedProperties(properties0);\n ProtectedProperties protectedProperties1 = new ProtectedProperties(protectedProperties0);\n Attribute attribute0 = new Attribute(\"-\", \"-\", protectedProperties1);\n arrayList0.add(attribute0);\n Instances instances0 = new Instances(\"(,Z;Xp#\\\"dxq[\", arrayList0, 1587);\n boolean boolean0 = discretize0.setInputFormat(instances0);\n boolean boolean1 = discretize0.batchFinished();\n assertFalse(discretize0.getFindNumBins());\n assertEquals(10, discretize0.getBins());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertTrue(boolean1 == boolean0);\n assertFalse(discretize0.getUseEqualFrequency());\n assertFalse(boolean1);\n }","@Test\n public void testStationaryDistributionRevQuick()\n {\n IDoubleArray T = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.stationaryDistributionRevQuick(T);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testGetSetTemperaturaInterior() {\n System.out.println(\"getSetTemperaturaInterior\");\n double expResult = 20.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaInterior(expResult);\n double result = instance.getTemperaturaInterior();\n assertEquals(expResult, result, 0.0);\n }","@Test\n public void testCorrelation()\n {\n IDoubleArray M = null;\n IDoubleArray observable1 = null;\n IDoubleArray observable2 = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test083() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getStructure();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.SFMeanEntropyGain();\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n assertEquals(Double.NaN, double0, 0.01);\n }","@Test\n public void testGetSetTemperaturaExteriorMaxima() {\n System.out.println(\"getSetTemperaturaExteriorMaxima\");\n double expResult = 20.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaExteriorMaxima(expResult);\n double result = instance.getTemperaturaExteriorMaxima();\n assertEquals(expResult, result, 0.0);\n }","@Test(timeout = 4000)\n public void test087() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.KBMeanInformation();\n assertEquals(Double.NaN, double0, 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","public void checkMovementZMaximum(ITestContext context) {\n Reporter.testStart();\n String caseFolder=context.getName()+\"/\";\n String importFile=context.getCurrentXmlTest().getParameter(\"importFile\");\n String expectationFile=context.getCurrentXmlTest().getParameter(\"expectationFile\");\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\n String screenWithinCase=caseFolder+methodName;\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\n if(meshViewPage.isThePage()){\n\n uploadDCM(meshViewPage,importFile);\n\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\n\n Reporter.log(\"click Orientation Adjustment tab on left bar\");\n String text=\"0.05\";\n Reporter.log(\"select Movement step \"+text);\n Assert.assertEquals(oa.selectMovementStep(text),text,\"Cann't select \"+text);\n\n Integer clickTimes=98;\n String valueOnLabel=oa.clickDistancePlus(clickTimes);\n String lower=embededScreenShot(screenWithinCase+\"_distance_\"+valueOnLabel,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+valueOnLabel);\n Assert.assertEquals(valueOnLabel,\"4.9\",\"distance is not same as expected result\");\n\n clickTimes=1;\n valueOnLabel=oa.clickDistancePlus();\n String middle=embededScreenShot(screenWithinCase+\"_distance_\"+valueOnLabel,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+valueOnLabel);\n Assert.assertEquals(valueOnLabel,\"4.95\",\"distance is not same as expected result\");\n\n text=\"0.1\";\n Reporter.log(\"select Movement step \"+text);\n Assert.assertEquals(oa.selectMovementStep(text),text,\"Cann't select \"+text);\n\n valueOnLabel=oa.clickDistancePlus(clickTimes);\n String higher=embededScreenShot(screenWithinCase+\"_distance_\"+valueOnLabel,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+valueOnLabel);\n Assert.assertEquals(valueOnLabel,\"5\",\"distance is not same as expected result\");\n\n embededCompareResult(lower,middle,higher, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\n\n valueOnLabel=oa.clickDistanceReset();\n embededScreenShot(screenWithinCase+\"_reset_\"+valueOnLabel,\"click Z reset(1 times), get distance is \"+valueOnLabel);\n Assert.assertEquals(valueOnLabel,\"0\",\"distance is not same as expected result\");\n }\n Reporter.testEnd();\n }","@Test(timeout = 4000)\n public void test05() throws Throwable {\n Discretize discretize0 = new Discretize(\"^yH*RVC#\\\"|tm,k2XpGN\");\n String[] stringArray0 = new String[2];\n discretize0.m_ClassIndex = 906;\n discretize0.m_IgnoreClass = true;\n stringArray0[0] = \"^yH*RVC#\\\"|tm,k2XpGN\";\n stringArray0[1] = \"^yH*RVC#\\\"|tm,k2XpGN\";\n discretize0.setOptions(stringArray0);\n discretize0.setAttributeIndices(\"^yH*RVC#\\\"|tm,k2XpGN\");\n assertFalse(discretize0.getMakeBinary());\n assertFalse(discretize0.getUseEqualFrequency());\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getFindNumBins());\n }","@Test void perfect() {\n\t\tstandardScene();\n\t\tsimulateScene(0);\n\n\t\tvar alg = new DistanceMetricTripleReprojection23();\n\n\t\tvar model = new MetricCameraTriple();\n\t\tmodel.view1.setTo(cameraA);\n\t\tmodel.view2.setTo(cameraB);\n\t\tmodel.view3.setTo(cameraC);\n\t\tmodel.view_1_to_2.setTo(truthView_1_to_i(1));\n\t\tmodel.view_1_to_3.setTo(truthView_1_to_i(2));\n\n\t\talg.setModel(model);\n\n\t\tfor (AssociatedTriple a : observations3) {\n\t\t\tassertEquals(0.0, alg.distance(a), UtilEjml.TEST_F64);\n\t\t}\n\n\t\tvar set = observations3.subList(4, 11);\n\t\tvar distances = new double[set.size()];\n\t\talg.distances(set, distances);\n\t\tfor (int i = 0; i < distances.length; i++) {\n\t\t\tassertEquals(0.0, distances[i], UtilEjml.TEST_F64);\n\t\t}\n\t}","@Test\n public void testGetTemperaturaInteriorAlvo() {\n System.out.println(\"getSetTemperaturaInteriorAlvo\");\n double expResult = 20.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaInteriorAlvo(expResult);\n double result = instance.getTemperaturaInteriorAlvo();\n assertEquals(expResult, result, 0.0);\n }","@Override\n protected boolean stoppingCriteriaSatisfied(KPMPSolution generatedSolution, RandomStepFunction stepFunction) {\n numberOfIterations++;\n numberOfIterationsWithoutImprovement++;\n KPMPSolutionChecker solutionChecker = new KPMPSolutionChecker();\n int crossingsOnOriginalPage = solutionChecker.getCrossingNumberOfEdge(bestSolution.getSpineOrder(), bestSolution.getEdgePartition(), originalPageIndex, edge);\n int crossingsOnNewPage = solutionChecker.getCrossingNumberOfEdge(generatedSolution.getSpineOrder(), generatedSolution.getEdgePartition(), newPageIndex, edge);\n if (crossingsOnNewPage < crossingsOnOriginalPage) {\n bestSolution = generatedSolution;\n numberOfIterationsWithoutImprovement = 0;\n\n crossingNumber = crossingNumber - (crossingsOnOriginalPage - crossingsOnNewPage);\n }\n return numberOfIterations >= Main.localSearchIterationLimit || numberOfIterations >= bestSolution.getEdgePartition().size() * bestSolution.getNumberOfPages() || numberOfIterationsWithoutImprovement >= Main.iterationMultiplier || ((System.nanoTime() - Main.START) / 1000000) >= (Main.secondsBeforeStop * 1000) || numberOfIterations >= Main.localSearchIterationLimit;\n }","@Test(timeout = 4000)\n public void test116() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate((String) null);\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.correct();\n assertEquals(0.0, double0, 0.01);\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n }","@Test(timeout = 4000)\n public void test086() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.SFMeanPriorEntropy();\n assertEquals(Double.NaN, double0, 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","@Test(timeout = 4000)\n public void test126() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate((String) null);\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.totalCost();\n assertEquals(0.0, double0, 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","@Test(timeout = 4000)\n public void test26() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.m_FindNumBins = true;\n int[] intArray0 = new int[6];\n intArray0[0] = (-388);\n intArray0[1] = (-2121610348);\n intArray0[2] = 11;\n intArray0[3] = (-1);\n intArray0[4] = 1062;\n intArray0[5] = 70;\n discretize0.setAttributeIndicesArray(intArray0);\n discretize0.setDesiredWeightOfInstancesPerInterval((-1149.15832593));\n discretize0.getOptions();\n assertEquals((-1149.15832593), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n }","@Override\n protected boolean stoppingCriteriaSatisfied(KPMPSolution generatedSolution, BestImprovementStepFunction stepFunction) {\n KPMPSolutionChecker solutionChecker = new KPMPSolutionChecker();\n int crossingsOnOriginalPage = solutionChecker.getCrossingNumberOfEdge(bestSolution.getSpineOrder(), bestSolution.getEdgePartition(), originalPageIndex, edge);\n int crossingsOnNewPage = solutionChecker.getCrossingNumberOfEdge(generatedSolution.getSpineOrder(), generatedSolution.getEdgePartition(), newPageIndex, edge);\n if (crossingsOnNewPage < crossingsOnOriginalPage) {\n bestSolution = generatedSolution;\n index = 0;\n pageCounter = 0;\n\n crossingNumber = crossingNumber - (crossingsOnOriginalPage - crossingsOnNewPage);\n\n //assert(crossingNumber == new KPMPSolutionChecker().getCrossingNumber(bestSolution));\n }\n if (index == bestSolution.getEdgePartition().size() && pageCounter != bestSolution.getNumberOfPages() - 1) {\n index = 0;\n pageCounter++;\n }\n return ((System.nanoTime() - Main.START) / 1000000) >= (Main.secondsBeforeStop * 1000) || index == bestSolution.getEdgePartition().size() && pageCounter == bestSolution.getNumberOfPages() - 1 || numberOfIterations >= Main.localSearchIterationLimit;\n }","@Test(timeout = 4000)\n public void test093() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n Object[] objectArray0 = new Object[2];\n evaluation0.evaluateModel((Classifier) null, instances0, objectArray0);\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n }","@Test(timeout = 4000)\n public void test11() throws Throwable {\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, true, false);\n Discretize discretize0 = new Discretize();\n discretize0.getCutPoints(3594);\n Discretize discretize1 = new Discretize();\n discretize1.attributeIndicesTipText();\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \"\");\n Discretize discretize2 = new Discretize();\n // Undeclared exception!\n try { \n discretize2.calculateCutPoints();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@ParameterizedTest\n\t@CsvSource({\n\t\t\t\"9, 1, 2, 3, 1, 10, 1, 10, 1, -1, -1, -1\",\n\t\t\t\"9, -1, -1, -1, 9, 10, 1, 10, 1, 9, -1, -1\",\n\t\t\t\"9, -1, -1, -1, 1, 10, 9, 10, 1, 9, -1, -1\",\n\t\t\t\"9, 3, 3, 3, 89, 100, 1, 10, 1, -1, -1, -1\",\n\t\t\t\"9, 1, -1, -1, 1, 10, 89, 100, 1, -1, -1, -1\",\n\t\t\t\"9, 1, 2, -1, 2, 2, 1, 10, 25, 1, 2, 9\",\n\t\t\t\"9, 3, 3, 3, 0, 2, 950, 1000, 25, 3, 3, -1\",\n\t\t\t\"9, -1, -1, -1, 9999, 10000, 950, 1000, 1, 9, 9, 9\",\n\t\t\t\"9, -1, -1, -1, 0, 1, 0, 390000, 1, -1, -1, -1\",\n\t\t\t\"4, -1, -1, -1, 100, 100, 1000, 1000, 1, 4, 4, 4\",\n\t\t\t\"5, 3, -1, -1, 100, 100, 1000, 1000, 10, 3, 5, 5\",\n\t\t\t\"5, 1, 3, 4, 100, 100, 1000, 1000, 25, 1, 3, 4\",\n\t})\n\tvoid usesExpectedMultiplier(\n\t\t\tlong consensusSec,\n\t\t\tlong old10XLevelStart,\n\t\t\tlong old25XLevelStart,\n\t\t\tlong old100XLevelStart,\n\t\t\tint firstUsed,\n\t\t\tint firstTps,\n\t\t\tint secondUsed,\n\t\t\tint secondTps,\n\t\t\tlong expectedMultiplier,\n\t\t\tlong new10XLevelStart,\n\t\t\tlong new25XLevelStart,\n\t\t\tlong new100XLevelStart\n\t) {\n\t\tfinal var aThrottle = DeterministicThrottle.withTps(firstTps);\n\t\tfinal var bThrottle = DeterministicThrottle.withTps(secondTps);\n\t\taThrottle.allow(firstUsed);\n\t\tbThrottle.allow(secondUsed);\n\t\tgiven(throttling.activeThrottlesFor(CryptoTransfer)).willReturn(List.of(aThrottle, bThrottle));\n\n\t\tsubject.resetExpectations();\n\t\tsubject.resetCongestionLevelStarts(instants(old10XLevelStart, old25XLevelStart, old100XLevelStart));\n\t\tsubject.updateMultiplier(Instant.ofEpochSecond(consensusSec));\n\t\tfinal long actualMultiplier = subject.currentMultiplier();\n\t\tfinal var starts = subject.congestionLevelStarts();\n\n\t\tassertEquals(expectedMultiplier, actualMultiplier);\n\t\tassertNullOrEquals(starts[0], new10XLevelStart);\n\t\tassertNullOrEquals(starts[1], new25XLevelStart);\n\t\tassertNullOrEquals(starts[2], new100XLevelStart);\n\t}","private void calculations() {\n\t\tSystem.out.println(\"Starting calculations\\n\");\n\t\tcalcMedian(myProt.getChain(requestedChain));\n\t\tcalcZvalue(myProt.getChain(requestedChain));\n\t\tcalcSVMweightedZvalue(myProt.getChain(requestedChain));\n\t}","@Test(timeout = 4000)\n public void test35() throws Throwable {\n Discretize discretize0 = new Discretize();\n assertFalse(discretize0.getMakeBinary());\n \n double[][] doubleArray0 = new double[8][8];\n double[] doubleArray1 = new double[3];\n doubleArray1[2] = 1421.4058829;\n doubleArray0[1] = doubleArray1;\n double[] doubleArray2 = new double[9];\n doubleArray2[1] = 1.7976931348623157E308;\n doubleArray2[2] = 0.0;\n Attribute attribute0 = new Attribute(\"invalid CVS revision - not dots!\");\n attribute0.setWeight(0.0);\n Attribute.typeToString(attribute0);\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\n arrayList0.add(attribute0);\n Instances instances0 = new Instances(\"UR<dj;O\", arrayList0, 3);\n Instances instances1 = new Instances(instances0);\n boolean boolean0 = discretize0.setInputFormat(instances1);\n assertFalse(boolean0);\n \n String[] stringArray0 = new String[3];\n stringArray0[0] = \".bsi\";\n stringArray0[1] = \"invalid CVS revision - not dots!\";\n stringArray0[2] = \"@end\";\n discretize0.setOptions(stringArray0);\n discretize0.setOptions(stringArray0);\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseEqualFrequency());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n }","@Test\n\tpublic void TEAM3_CONTROLLER_UT05() {\n\t\t// PRECONDITIONS\n\t\tScheduleMakerController smc = new ScheduleMakerController();\n\t\t\n\t\t// INPUT\n\t\tCollection<ScheduleStub> savedSchedules = new ArrayList<ScheduleStub>();\n\t\tsmc.saveSchedules(savedSchedules);\n\t\t\n\t\t// EXPECTED OUTPUT\n\t\tException exceptionThrown = null;\n\t\tdouble balance = 0.0d;\n\t\ttry {\n\t\t\tMethod getBalance = smc.getClass().getDeclaredMethod(\"getBalance\", (Class<?> []) null);\n\t\t\tObject balanceObj = getBalance.invoke(smc, (Object[]) null);\n\t\t\t\n\t\t\tif (balanceObj == null) throw new NullPointerException(\"getBalance() returned null; expected a number\");\n\t\t\telse if (!(balanceObj instanceof Number)) throw new RuntimeException(\"getBalance() did not return a number\");\n\t\t} catch (Exception ex) {\n\t\t\texceptionThrown = ex;\n\t\t}\n\t\t\n\t\tassertNull(exceptionThrown);\n\t\tassertEquals(0.0d, balance, 0.0d);\n\t}","public double calc_c() {\r\n \t\t\r\n \t\tdouble cij = 0;\r\n \t\tint sum_i = 0;\r\n \t\tint sum_j = 0;\r\n \t\tint diff = 0;\r\n \t\tint state = 0;\r\n \t\t\r\n \t\tdouble li, lri, lj, lrj;\r\n \t\t\r\n \t\tList<Integer> samestate_i = new ArrayList<>();\r\n \t\tList<Integer> samestate_j = new ArrayList<>();\r\n \t\t\r\n \t\tList<Double> gradient_i = new ArrayList<>();\r\n \t\tList<Double> gradient_j = new ArrayList<>();\r\n \t\t\r\n\t\t// if the time step is less than the window cij is initialized to zero.\r\n\t\tif(this.n <= this.w || this.w <2) {\r\n\t\t\tcij = 0;\r\n\t\t}\r\n\t\t\r\n\t\telse {\r\n\t\t\t\r\n\t\t\t// else we determine the state of the region i and region j of which\r\n\t\t\t// we are calculating the cij for\r\n\t \t\tDeterminestate di = new Determinestate(this.i, this.n);\r\n\t\t\tDeterminestate dj = new Determinestate(this.j, this.n);\r\n\t\t\t\r\n\t\t\tProbability pi = new Probability(this.i, this.n, this.w, this.neighborsize);\r\n\t\t\tProbability pj = new Probability(this.i, this.n, this.w, this.neighborsize);\r\n\t\t\t\r\n\t\t\tif(di.regionCurrentstate() == dj.regionCurrentstate()) {\r\n\t\t\t\tli = pi.getLsave().get(0);\r\n\t\t\t\tlj = pj.getLsave().get(0);\r\n\t\t\t\tstate = 0;\r\n\t\t\t}\r\n\t\t\telse {\r\n\t\t\t\tli = pi.getLsave().get(1);\r\n\t\t\t\tlj = pj.getLsave().get(1);\r\n\t\t\t\tstate =1;\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t\r\n\t\t\t// for the time window\r\n\t\t\tfor(int k=1; k<this.w; k++) {\r\n\t\t\t\t\r\n\t\t\t\t// determine the state of i and j in the in n-k time step\r\n\t \t\t\tDeterminestate dri = new Determinestate(this.i, this.n-k);\r\n\t \t\t\tDeterminestate drj = new Determinestate(this.j, this.n-k);\r\n\t \t\t\t\r\n\t \t\t\tProbability pri = new Probability(this.i, this.n, this.w, this.neighborsize);\r\n\t\t\t\tProbability prj = new Probability(this.i, this.n, this.w, this.neighborsize);\r\n\t\t\t\t\r\n\t\t\t\tlri = pri.getLsave().get(state);\r\n\t\t\t\tlrj = prj.getLsave().get(state);\r\n\t \t\t\t\r\n\t \t\t\t\r\n\t \t\t\t//if state matches for i make a list of time step in which they match and another list of gradient of Likelihood \r\n\t \t\t\tif(di.regionCurrentstate() == dri.regionCurrentstate()){\r\n\t \t\t\t\tsamestate_i.add(k);\r\n\t \t\t\t\tgradient_i.add(li - lri);\r\n\t \t\t\t}\r\n\t \t\t\t\r\n\t \t\t\t//if state matches for j make a list of time step in which they match and another list of gradient of Likelihood \r\n\t \t\t\tif(di.regionCurrentstate() == drj.regionCurrentstate()){\r\n\t \t\t\t\tsamestate_j.add(k);\r\n\t \t\t\t\tgradient_j.add(lj - lrj);\r\n\t \t\t\t}\r\n\t \t\t\t\r\n\t \t\t}\r\n\t\t}\r\n\t\t\t\r\n\t\t// if no match found return zero\r\n\t\tif(samestate_i.size() == 0 || samestate_j.size() == 0) {\r\n\t\t\tcij = 0;\r\n\t\t}\r\n\t\t\r\n\t\t// else calculate cij\r\n\t\telse {\r\n\t\t\t\r\n\t\t\t// if both have same length \r\n\t\t\tif(samestate_i.size() == samestate_j.size()) {\r\n\t\t\t\tfor(int i=0; i<samestate_i.size(); i++) {\r\n\t\t\t\t\tsum_i = sum_i + samestate_i.get(i);\r\n\t\t\t\t\tsum_j = sum_j + samestate_j.get(i);\r\n\t\t\t\t}\r\n\t\t\t\tdiff = (sum_i%this.w - sum_j%this.w);\r\n\t\t\t\tif (diff == 0) {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i, gradient_j);\r\n\t\t\t\t}\r\n\t\t\t\telse {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i, gradient_j) * Math.abs(diff/(double)this.w);\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t//if i is smaller\r\n\t\t\telse if(samestate_i.size() < samestate_j.size()) {\r\n\t\t\t\tfor(int i=0; i<samestate_i.size(); i++) {\r\n\t\t\t\t\tsum_i = sum_i + samestate_i.get(i);\r\n\t\t\t\t\tsum_j = sum_j + samestate_j.get(i);\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\tdiff = (sum_i%this.w - sum_j%this.w);\r\n\t\t\t\tif (diff == 0) {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i.subList(0, samestate_i.size()), gradient_j);\r\n\t\t\t\t}\r\n\t\t\t\telse {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i.subList(0, samestate_i.size()), gradient_j) * diff/(double)this.w;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t// if j is smaller \r\n\t\t\telse {\r\n\t\t\t\tfor(int i=0; i<samestate_j.size(); i++) {\r\n\t\t\t\t\tsum_i = sum_i + samestate_i.get(i);\r\n\t\t\t\t\tsum_j = sum_j + samestate_j.get(i);\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\tdiff = (sum_i%this.w - sum_j%this.w);\r\n\t\t\t\tif (diff == 0) {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i.subList(0, samestate_j.size()), gradient_j);\r\n\t\t\t\t}\r\n\t\t\t\telse {\r\n\t\t\t\t\tcij = cosine_sim(gradient_i.subList(0, samestate_j.size()), gradient_j) * diff/(double)this.w;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\t\r\n\t\tif (Double.isNaN(cij)) {\r\n\t\t\treturn 0;\r\n\t\t}\r\n\t\telse {\r\n\t\t\treturn cij;\r\n\t\t}\r\n \t}","@Test\n\tvoid testCalculateMarkTillSemesterSuccess2() {\n\t\t\n\t\tString semesterName = \"20193\";\n\t\t\n\t\tUser user = userService.findByUsername(\"B16DCCN168\");\n\t\tStudent student = user.getStudent();\n\t\tList<StudentResult> resultsTillSemester = resultService.findResultTillSemester(student.getId(),\n\t\t\t\tsemesterName);\n\t\t\n\t\tString mmtMark = \"\", tthcmMark = \"\";\n\t\t\n\t\tfor (StudentResult result : resultsTillSemester) {\n\t\t\tSubject subject = result.getStudentRegister().getCourse().getSubject();\n\t\t\tif (subject.getName().equals(\"Mạng máy tính\")) {\n\t\t\t\tmmtMark = result.getMarkToChar();\n\t\t\t}\n\t\t\t\n\t\t\tif (subject.getName().equals(\"Tư tưởng Hồ Chí Minh\")) {\n\t\t\t\ttthcmMark = result.getMarkToChar();\n\t\t\t}\n\t\t}\n\t\t\n\t\tList<Object> listGpaResultsTillNow = MarkUtility.calculateMarkTillSemester(resultsTillSemester);\n\t\tint passedCreditsTillNow = (int) listGpaResultsTillNow.get(0);\n\t\tfloat gpaTillNow = (float) listGpaResultsTillNow.get(1);\n\t\t\n\t\tDecimalFormat df = new DecimalFormat(\"#.##\");\n\t\t\n\t\tassertTrue(\n\t\t\t\tmmtMark.equals(\"B\") &&\n\t\t\t\ttthcmMark.equals(\"A\") &&\n\t\t\t\tpassedCreditsTillNow == 15 &&\n\t\t\t\tdf.format(gpaTillNow).equals(\"3.64\")\n\t\t\t\t);\n\t}","@Test\n public void testWithPropagator() {\n CelestialBody sun = CelestialBodyFactory.getSun();\n final Frame eci = FramesFactory.getGCRF();\n final Frame ecef = FramesFactory.getITRF(IERSConventions.IERS_2010, true);\n AbsoluteDate date = new AbsoluteDate(2004, 1, 1, utc);\n OneAxisEllipsoid earth = new OneAxisEllipsoid(Constants.WGS84_EARTH_EQUATORIAL_RADIUS,\n Constants.WGS84_EARTH_FLATTENING, ecef);\n Orbit orbit = new KeplerianOrbit(6378137 + 400e3, 1e-3, FastMath.toRadians(50), 0, 0, 0, PositionAngle.TRUE,\n eci, date, Constants.EIGEN5C_EARTH_MU);\n final SpacecraftState ic = new SpacecraftState(orbit);\n\n final AbsoluteDate end = date.shiftedBy(5 * Constants.JULIAN_DAY);\n final AbsoluteDate resetDate = date.shiftedBy(0.8 * Constants.JULIAN_DAY + 0.1);\n\n final SpacecraftState[] lastState = new SpacecraftState[1];\n final OrekitStepHandler stepSaver = interpolator -> {\n final AbsoluteDate start = interpolator.getPreviousState().getDate();\n if (start.compareTo(resetDate) < 0) {\n lastState[0] = interpolator.getPreviousState();\n }\n };\n\n // propagate with state rest to take slightly different path\n NumericalPropagator propagator = getNumericalPropagatorWithDTM(sun, earth, ic);\n propagator.setStepHandler(stepSaver);\n propagator.propagate(resetDate);\n propagator.resetInitialState(lastState[0]);\n propagator.clearStepHandlers();\n SpacecraftState actual = propagator.propagate(end);\n\n // propagate straight through\n propagator = getNumericalPropagatorWithDTM(sun, earth, ic);\n propagator.resetInitialState(ic);\n propagator.clearStepHandlers();\n SpacecraftState expected = propagator.propagate(end);\n\n assertThat(actual.getPVCoordinates(), pvCloseTo(expected.getPVCoordinates(), 1.0));\n\n // propagate with state rest to take slightly different path\n propagator = getNumericalPropagatorWithMSIS(sun, earth, ic);\n propagator.setStepHandler(stepSaver);\n propagator.propagate(resetDate);\n propagator.resetInitialState(lastState[0]);\n propagator.clearStepHandlers();\n actual = propagator.propagate(end);\n\n // propagate straight through\n propagator = getNumericalPropagatorWithMSIS(sun, earth, ic);\n propagator.resetInitialState(ic);\n propagator.clearStepHandlers();\n expected = propagator.propagate(end);\n\n assertThat(actual.getPVCoordinates(), pvCloseTo(expected.getPVCoordinates(), 1.0));\n }","public void testSVMChunkLearnng() throws IOException, GateException {\n // Initialisation\n System.out.print(\"Testing the SVM with liner kernenl on chunk learning...\");\n File chunklearningHome = new File(new File(learningHome, \"test\"),\n \"chunklearning\");\n String configFileURL = new File(chunklearningHome, \"engines-svm.xml\")\n .getAbsolutePath();\n String corpusDirName = new File(chunklearningHome, \"data-ontonews\")\n .getAbsolutePath();\n //Remove the label list file, feature list file and chunk length files.\n String wdResults = new File(chunklearningHome,\n ConstantParameters.SUBDIRFORRESULTS).getAbsolutePath();\n emptySavedFiles(wdResults);\n String inputASN = \"Key\";\n loadSettings(configFileURL, corpusDirName, inputASN, inputASN);\n // Set the evaluation mode\n RunMode runM=RunMode.EVALUATION;\n learningApi.setLearningMode(runM);\n controller.execute();\n // Using the evaluation mode for testing\n EvaluationBasedOnDocs evaluation = learningApi.getEvaluation();\n // Compare the overall results with the correct numbers\n assertEquals(\"Wrong value for correct: \", 44, (int)Math.floor(evaluation.macroMeasuresOfResults.correct));\n assertEquals(\"Wrong value for partial: \", 10, (int)Math.floor(evaluation.macroMeasuresOfResults.partialCor));\n assertEquals(\"Wrong value for spurious: \", 11, (int)Math.floor(evaluation.macroMeasuresOfResults.spurious));\n assertEquals(\"Wrong value for missing: \", 40, (int)Math.floor(evaluation.macroMeasuresOfResults.missing));\n\n System.out.println(\"completed\");\n // Remove the resources\n clearOneTest();\n }","static void playC45() throws IOException {\n\t\tRealVector[] data = NNDataset.getData(NNDataset.HOME);\n\t\tRealVector[] label = NNDataset.getLabel(NNDataset.HOME);\n\t\tRealMatrix d = MatrixUtils.createRealMatrix(data.length, data[0].getDimension());\n\t\tfor (int i = 0; i < data.length; i++) {\n\t\t\td.setRowVector(i, data[i]);\n\t\t}\n\t\td = d.transpose(); // data = column vector\n\n\t\tRealVector l = MatrixUtils.createRealVector(new double[label.length]);\n\t\tfor (int i = 0; i < l.getDimension(); i++) {\n\t\t\tl.setEntry(i, label[i].getEntry(0));\n\t\t}\n\n\t\tDecisionNode root = training(d, l);\n\t\tString[] header = NNDataset.getHeader(NNDataset.HOME);\n\t\tprintTree(root, header);\n\n\t\tInteger lb = predict(root, data[0]);\n\t\tSystem.out.println(lb);\n\n\t\tdouble ok = MSE(root, data, label);\n\t\tSystem.out.println(\"total hit: \" + ok);\n\t}","@Test\n public void testVirement1() {\n\n //Arrange\n CompteBancaire cpt1 = new CompteBancaire(63000.0);\n CompteBancaire cpt2 = new CompteBancaire(43000.0);\n double montant_a_virer = 28000.0;\n double excepted_solde_cpt1 = 35000.0;\n double excepted_solde_cpt2 = 71000.0;\n\n //Act\n assertEquals(true, cpt1.virerArgent(cpt2, montant_a_virer));\n\n //Assert\n assertEquals(excepted_solde_cpt1, cpt1.getSolde(), 0);\n assertEquals(excepted_solde_cpt2, cpt2.getSolde(), 0);\n\n }","public void controlPointsChanged(List<Coordinates> lcp) {\n // sort the control points by x position.\n // this makes things easier later when we paint the function.\n lcp.sort(\n (point1,point2) -> Integer.compare(point1.getX(),point2.getX())\n );\n\n // collect the points in an array before doing the calculations.\n // again, this makes things easier for us.\n controlPoints=lcp.toArray(new Coordinates[0]);\n n=controlPoints.length;\n if(n<2) {\n // nothing to do here\n return;\n }\n double []X = new double[n];\n double []Y = new double[n];\n for(int i =0;i<n;i++){\n \tX[i]=controlPoints[i].getX();\n \tY[i]=controlPoints[i].getY();\n }\n // Construct the linear system S*C=T with n-2 equations.\n // The matrix S contains the left hand side of the equations on slide 13.\n // The matrix Z contains the right hand side of the equations on slide 13.\n double[][] S=new double[n-2][n-2];\n double[] Z=new double[n-2];\n \n // Fill the matrix S and the vector Z and solve S*C=T to get c_2 to c_{n-1}.\n for(int i = 0; i< n-3;i++){\n \tS[i][i]=2*delta(X,i+1,0);\n \tS[i+1][i]=delta(X,i+2,-1);\n \tS[i][i+1]=delta(X,i+1,1);\n \tZ[i]=z(Y,X,i);\n }\n S[n-2][n-2]=2*delta(X,n-1,0);\n \n C= EliminationGaussLegendre.solve(S, Z);\n \n // Calculate the n-1 coefficients b_i and d_i.\n // Note that you need all c_i here (but you know that c_1 and c_n are zero).\n //a_i = y_i;\n for(int i=1 ; i<n ; i++){\n \tD[i-1]=d(Y,X,i-1);\n \tB[i-1]=b(Y,C,X,i-1);\n }\n \n }","@Test\n public void costTest() {\n // TODO: test cost\n }","@Test\n public void testAutocorrelation()\n {\n IDoubleArray M = null;\n IDoubleArray observable = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.autocorrelation(M, observable, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void test22() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n FastVector fastVector0 = evaluation0.predictions();\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01D);\n }","public static void main(String[] args) {\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 3, 4, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 3, 5, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 3, 4, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 3, 5, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, \"cartellainesistente\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, \"cartellainesistente\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, \"cartellainesistente\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, \"cartellainesistente\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, \"strategies\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, \"strategies\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, \"strategies\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, \"strategies\")));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 5, 2, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 5, 2, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(6, 6, 2, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(6, 6, 2, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 3, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 3, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(7, 7, 2, true, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(7, 7, 2, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 4, false, null)));\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 4, true, null)));\n measureTime(()->checkInterruptSequential(4, 4, 3, false));\n measureTime(()->checkInterruptSequential(4, 4, 3, true));\n }","@Test\n\tpublic void TEAM3_CONTROLLER_UT06() {\n\t\t// PRECONDITIONS\n\t\tClassDetailsStub c = new ClassDetailsStub(TEST_CONFIG.TueThu);\n\t\t\n\t\tCollection<ClassDetailsStub> s = new ArrayList<ClassDetailsStub>();\n\t\ts.add(c);\n\t\t\n\t\tScheduleMakerController smc = new ScheduleMakerController();\n\t\t\n\t\t// INPUT\n\t\tCollection<ScheduleStub> savedSchedules = new ArrayList<ScheduleStub>();\n\t\tsavedSchedules.add(new ScheduleStub(s));\n\t\t\n\t\t// EXPECTED OUTPUT\n\t\tException exceptionThrown = null;\n\t\tboolean balanceGreaterThanZero = false;\n\t\ttry {\n\t\t\tObject balanceObj = null;\n\t\t\tMethod getBalance = smc.getClass().getDeclaredMethod(\"getBalance\", (Class<?> []) null);\n\t\t\t\n\t\t\tsmc.saveSchedules(savedSchedules);\n\t\t\tbalanceObj = getBalance.invoke(smc, (Object[]) null);\n\t\t\t\n\t\t\tif (balanceObj == null) throw new NullPointerException(\"getBalance() returned null; expected a number\");\n\t\t\telse if (!(balanceObj instanceof Number)) throw new RuntimeException(\"getBalance() did not return a number\");\n\t\t\t\n\t\t\tbalanceGreaterThanZero = ((Number) balanceObj).doubleValue() > 0.0d;\n\t\t} catch (Exception ex) {\n\t\t\texceptionThrown = ex;\n\t\t}\n\t\t\n\t\tassertNull(exceptionThrown);\n\t\tassertTrue(balanceGreaterThanZero);\n\t}","@Test(timeout = 4000)\n public void test28() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[8][8];\n double[] doubleArray1 = new double[3];\n doubleArray1[2] = 1421.4058829;\n doubleArray0[1] = doubleArray1;\n double[] doubleArray2 = new double[9];\n doubleArray2[1] = 1.7976931348623157E308;\n doubleArray2[2] = 0.0;\n doubleArray2[5] = (-236.4995664359);\n doubleArray2[7] = 1421.4058829;\n doubleArray0[3] = doubleArray2;\n double[] doubleArray3 = new double[3];\n doubleArray3[0] = 0.0;\n doubleArray3[1] = (-236.4995664359);\n doubleArray0[4] = doubleArray3;\n double[] doubleArray4 = new double[21];\n doubleArray4[0] = 1421.4058829;\n doubleArray3[2] = (-236.4995664359);\n doubleArray4[4] = 1.7976931348623157E308;\n doubleArray0[7] = doubleArray4;\n discretize0.m_NumBins = 162;\n doubleArray0[1] = doubleArray4;\n discretize0.m_CutPoints = doubleArray0;\n discretize0.getBinRangesString(1);\n // Undeclared exception!\n try { \n discretize0.setOutputFormat();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void test28() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\n CostMatrix costMatrix0 = costSensitiveClassifier0.getCostMatrix();\n Evaluation evaluation0 = null;\n try {\n evaluation0 = new Evaluation(instances0, costMatrix0);\n fail(\"Expecting exception: Exception\");\n \n } catch(Exception e) {\n //\n // Cost matrix not compatible with data!\n //\n }\n }","@Test\n public void testTrain_RegressionDataSet_ExecutorService() {\n System.out.println(\"train\");\n RegressionDataSet trainSet = FixedProblems.getSimpleRegression1(150, new Random(2));\n RegressionDataSet testSet = FixedProblems.getSimpleRegression1(50, new Random(3));\n\n for (boolean modification1 : new boolean[] { true, false })\n for (SupportVectorLearner.CacheMode cacheMode : SupportVectorLearner.CacheMode.values()) {\n PlattSMO smo = new PlattSMO(new RBFKernel(0.5));\n smo.setCacheMode(cacheMode);\n smo.setC(1);\n smo.setEpsilon(0.1);\n smo.setModificationOne(modification1);\n smo.train(trainSet, true);\n\n double errors = 0;\n for (int i = 0; i < testSet.size(); i++)\n errors += Math.pow(testSet.getTargetValue(i) - smo.regress(testSet.getDataPoint(i)), 2);\n assertTrue(errors / testSet.size() < 1);\n }\n }","@Override\n\tprotected void setCost(double cst) {\n\t\t\n\t}","@Test\n public void testGetMoveSpeedMultiplier() {\n assertEquals(1, proj.getMoveSpeedMultiplier(), 0.001);\n }","@Test(timeout = 4000)\n public void test11() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.invertSelectionTipText();\n boolean boolean0 = discretize0.m_UseBinNumbers;\n // Undeclared exception!\n try { \n discretize0.calculateCutPoints();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n\tpublic void test() {\n\t\tdouble delta = 0.00001;\n\t\tSnp fakeSnp1 = new Snp(\"fakeId1\", 0, 0.3);\n\t\tSnp fakeSnp2 = new Snp(\"fakeId2\", 0, 0.8);\n\t\tSnp fakeSnp3 = new Snp(\"fakeId3\", 0, 1.4);\n\t\tPascal.set.withZScore_=true;\n\t\tArrayList<Snp> geneSnps = new ArrayList<Snp>();\n\t\tgeneSnps.add(fakeSnp1);geneSnps.add(fakeSnp2);geneSnps.add(fakeSnp3);\n\t\tDenseMatrix ld= new DenseMatrix(3,3);\n\t\t//DenseMatrix crossLd= new DenseMatrix(3,2);\n\t\t\n\t\tArrayList<Double> snpScores = new ArrayList<Double>(3);\n\t\tsnpScores.add(fakeSnp1.getZscore());\n\t\tsnpScores.add(fakeSnp2.getZscore());\n\t\tsnpScores.add(fakeSnp3.getZscore());\n\t\t//ld and crossLd calculated as follows:\n\t\t// make 0.9-toeplitz mat of size 5.\n\t\t// 3,4,5 for ld-mat\n\t\t// 1,2 for crossLd-mat\n\t\tld.set(0,0,1);ld.set(1,1,1);ld.set(2,2,1);\n\t\tld.set(0,1,0.9);ld.set(1,2,0.9);\n\t\tld.set(1,0,0.9);ld.set(2,1,0.9);\n\t\tld.set(0,2,0.81);\n\t\tld.set(2,0,0.81);\n\t\tdouble[] weights = {2,2,2};\n\t\tAnalyticVegas myAnalyticObj= null;\n\t\t//UpperSymmDenseMatrix myMatToDecompose = null;\n\t\tdouble[] myEigenvals = null;\n\t\tdouble[] emptyWeights = {1,1,1};\n\t\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, emptyWeights);\n\t\tmyAnalyticObj.computeScore();\n\t\tmyEigenvals = myAnalyticObj.computeLambda();\n\t\tassertEquals(myEigenvals[0],2.74067, delta);\n\t\tassertEquals(myEigenvals[1],0.1900, delta);\n\t\tassertEquals(myEigenvals[2],0.06932, delta);\n\t\t\n\t\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, weights);\n\t\tmyAnalyticObj.computeScore();\n\t\tmyEigenvals = myAnalyticObj.computeLambda();\t\t\t\n\t\tassertEquals(myEigenvals[0], 5.481348, delta);\n\t\tassertEquals(myEigenvals[1],0.380000, delta);\n\t\tassertEquals(myEigenvals[2],0.138652, delta);\n\t\t\t\n\t\tdouble[] weights2 = {1,2,0.5};\n\t\t\n\t\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, weights2);\n\t\tmyAnalyticObj.computeScore();\n\t\tmyEigenvals = myAnalyticObj.computeLambda();\t\t\n\t\tassertEquals(myEigenvals[0],3.27694674, delta);\n\t\tassertEquals(myEigenvals[1],0.1492338, delta);\n\t\tassertEquals(myEigenvals[2],0.07381938, delta);\t\t\t\n\t\t\n\t}","@Test(timeout = 4000)\n public void test06() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[] doubleArray0 = new double[0];\n int[] intArray0 = new int[4];\n intArray0[0] = 2749;\n intArray0[1] = (-2);\n intArray0[2] = 0;\n intArray0[3] = 0;\n SparseInstance sparseInstance0 = new SparseInstance((-40.152), doubleArray0, intArray0, 919);\n // Undeclared exception!\n try { \n discretize0.input(sparseInstance0);\n fail(\"Expecting exception: IllegalStateException\");\n \n } catch(IllegalStateException e) {\n //\n // No input instance format defined\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test01() throws Throwable {\n Discretize discretize0 = new Discretize(\"c9^n/\");\n discretize0.getCapabilities();\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseBinNumbers());\n assertFalse(discretize0.getUseEqualFrequency());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n }","double cekStop(double x[][], double v0[],double v[][],double w0[],double w[][], double t[]){\n double akumY=0;\n //~ itung z_in dan z\n for(int h=0; h<jumlah_data; h++){\n for(int j=0; j<unit_hidden; j++){\n //itung sigma xi vij\n double jum_xv=0;\n for(int i=0; i<unit_input; i++){\n double cc=x[h][i]*v[i][j];\n jum_xv=jum_xv+cc;\n //System.out.println(x[h][j]);\n }\n z_in[j]=v0[j]+jum_xv;\n //itung z\n z[j]=1/(1+(double)Math.exp(-z_in[j]));\n //System.out.println(\" dan z= \"+z[j]);\n }\n \n //~ itung y_in dan y (output)\n double cxc=0;\n for(int k=0; k<unit_output; k++){\n double jum_zw=0;\n for(int j=0; j<unit_hidden; j++){\n double cc=z[j]*w[j][k];\n jum_zw=jum_zw+cc;\n }\n y_in[k]=w0[k]+jum_zw;\n y[k]=1/(1+(double)Math.exp(-y_in[k]));\n akumY = akumY + Math.pow((t[h]-y[k]),2);\n //System.out.println(t[h]+\"-\"+y[k]+\"=\"+(t[k]-y[k]));\n }\n }\n double E = 0.5 * akumY;\n //System.out.println(E);\n return E;\n }","public void testCleave() {\n final String inputFile = \"testCleave.fas\";\n final String input = TestCaseLM.getFullFilePath(inputFile).replace(\"%20\", \" \");\n\n try {\n // We need to obtain a pointer the control file before anything else.\n BufferedReader br = new BufferedReader(new FileReader(input));\n\n // First one with zero MC's.\n Enzyme e = new Enzyme(\"TestEnzyme\", \"KR\", \"P\", \"Cterm\", 0);\n Protein p = new Protein(\">sw|Q55645|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\");\n\n Protein[] result = e.cleave(p);\n boolean once = false;\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now with one miscleavage.\n e.setMiscleavages(1);\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now with three miscleavages.\n e.setMiscleavages(3);\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Now check for the edge case where there's only one amino acid after the last cleavage position\n // (used to be a bug that this amino acid was never considered).\n e.setMiscleavages(1);\n result = e.cleave(new Protein(\">sw|Q55645|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDRA\"));\n boolean foundIt = false;\n for(int i = 0; i < result.length; i++) {\n Protein lProtein = result[i];\n if(lProtein.getSequence().getSequence().equals(\"TGFDRA\")){\n foundIt = true;\n break;\n }\n }\n\n if(!foundIt) {\n fail(\"The peptide 'TGFDRA' (penultimate amino acid is cleavage site, one missed cleavage allowed) was NOT returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now Nterm position and 1 miscleavage.\n e.setPosition(Enzyme.NTERM);\n e.setMiscleavages(1);\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Next, take an enzyme that does not have restricting residus.\n e = new Enzyme(\"Test\", \"KR\", null, \"Cterm\", 0);\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // And now take an entry that has a location set and see if the cleavage is adjusted accordingly.\n e = new Enzyme(\"TestEnzyme\", \"KR\", \"P\", \"Cterm\", 1);\n p = new Protein(\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\");\n\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now take a C-terminally truncated sequence and see if it is cleaved correctly.\n e = new Enzyme(\"TestEnzyme\", \"KR\", \"P\", \"Cterm\", 1);\n p = new Protein(\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\", true, Protein.CTERMTRUNC);\n\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now take an N-terminally truncated sequence and see if it is cleaved correctly.\n e = new Enzyme(\"TestEnzyme\", \"KR\", \"P\", \"Cterm\", 1);\n p = new Protein(\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\", true, Protein.NTERMTRUNC);\n\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // Skip to the next part of the controlfile.\n br.readLine();\n once = false;\n\n // Now take a translated sequence, containing an '_' (stopcodon).\n e = new Enzyme(\"TestEnzyme\", \"KR\", \"P\", \"Cterm\", 1);\n p = new Protein(\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\", \"GHIKLMVSTRPIGASDNPKLHG_FVNRTGFDA\");\n\n result = e.cleave(p);\n for(int i = 0; i < result.length; i++) {\n once = true;\n Protein lProtein = result[i];\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\n String sequence = lProtein.getSequence().getSequence();\n Assert.assertEquals(br.readLine(), header);\n Assert.assertEquals(br.readLine(), sequence);\n }\n\n if(!once) {\n fail(\"NO peptides AT ALL were returned when cleaving '\" + p.getSequence().getSequence() + \"'!\");\n }\n\n // END of file reached.\n // Check this!\n String line = null;\n while((line = br.readLine()) != null) {\n if(!line.trim().equals(\"\")) {\n fail(\"More lines in testCleave.fas then were generated by the test cleavage!\");\n }\n }\n br.close();\n } catch(IOException ioe) {\n fail(\"IOException occurred while testing the cleave() method: '\" + ioe.getMessage() + \"'.\");\n }\n }","@Test\n public void testMetastableMemberships()\n {\n IDoubleArray M = null;\n int nstates = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.metastableMemberships(M, nstates);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testVerser2() {\n\n //Arrange\n CompteBancaire cpt = new CompteBancaire(6500.0);\n double montant_a_verser = -2000.0;\n double excepted = 6500.0;\n\n //Act\n assertEquals(false, cpt.verser(montant_a_verser));\n\n //Assert\n assertEquals(excepted, cpt.getSolde(), 0);\n\n }"],"string":"[\n \"@Test\\n public void testEstimateCstepping_Iterable_int()\\n {\\n Iterable<IIntArray> trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCstepping(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateT()\\n {\\n \\n IDoubleArray counts = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateT(counts);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateC_Iterable_int()\\n {\\n Iterable<IIntArray> trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_3args_1()\\n {\\n Iterable<IIntArray> trajs = null;\\n Iterable<IIntArray> cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_3args_2()\\n {\\n IIntArray traj = null;\\n Iterable<IIntArray> cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testForwardCommittor()\\n {\\n IDoubleArray M = null;\\n IIntArray A = null;\\n IIntArray B = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.forwardCommittor(M, A, B);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"l*{e5SNh\\\");\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 177;\\n discretize0.setUseEqualFrequency(true);\\n double[][] doubleArray0 = new double[5][9];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[0] = (double) 177;\\n doubleArray1[1] = (double) 177;\\n doubleArray1[2] = (double) 177;\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[2];\\n doubleArray2[0] = (double) 177;\\n doubleArray2[1] = (double) 177;\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[9];\\n doubleArray3[0] = (double) 177;\\n doubleArray3[1] = (double) 177;\\n doubleArray3[2] = (double) 177;\\n doubleArray3[3] = (double) 177;\\n doubleArray3[4] = (double) 177;\\n doubleArray3[5] = (double) 177;\\n doubleArray3[6] = (double) 177;\\n doubleArray3[7] = (double) 177;\\n doubleArray3[8] = (double) 177;\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[8];\\n doubleArray4[0] = (double) 177;\\n doubleArray4[1] = (double) 177;\\n doubleArray4[2] = (double) 177;\\n doubleArray4[3] = (double) 177;\\n doubleArray4[4] = (double) 177;\\n doubleArray4[5] = (double) 177;\\n doubleArray4[6] = (double) 177;\\n doubleArray4[7] = (double) 177;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[0];\\n doubleArray0[4] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.listOptions();\\n discretize0.setIgnoreClass(true);\\n String[] stringArray0 = new String[0];\\n Discretize.main(stringArray0);\\n discretize0.findNumBinsTipText();\\n discretize0.setInvertSelection(true);\\n // Undeclared exception!\\n try { \\n discretize0.getBinRangesString(511);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 511\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n\\tpublic void testRepeatibleDynamics() throws IOException, PropertiesException {\\n\\n\\t\\t\\n\\t\\t// INSTANTIATE benchmark \\n\\t\\tProperties props = PropertiesUtil.setpointProperties(new File (\\\"src/main/resources/sim.properties\\\"));\\n\\t\\tSetPointGenerator lg = new SetPointGenerator (props);\\n\\t\\tList<ExternalDriver> externalDrivers = new ArrayList<ExternalDriver>();\\n\\t\\texternalDrivers.add(lg);\\n\\t\\tIndustrialBenchmarkDynamics d = new IndustrialBenchmarkDynamics (props, externalDrivers);\\n\\t\\tRandom actionRand = new Random(System.currentTimeMillis());\\n \\n // 1) do 100000 random steps, in order to initialize dynamics\\n\\t\\tfinal ActionDelta action = new ActionDelta(0.001f, 0.001f, 0.001f); \\n\\t\\tfor (int i=0; i<INIT_STEPS; i++) {\\n\\t\\t\\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\td.step(action);\\n\\t\\t}\\n\\t\\t\\n\\t\\t// 2) memorize current observable state and current markov state\\n\\t\\tfinal ObservableState os = d.getState();\\n\\t\\tfinal DataVector ms = d.getInternalMarkovState();\\n\\t\\tSystem.out.println (\\\"init o-state: \\\" + os.toString());\\n\\t\\tSystem.out.println (\\\"init m-state: \\\" + ms.toString());\\n\\t\\t\\n\\t\\t\\n\\t\\t// 3) perform test trajectory and memorize states\\n\\t\\tactionRand.setSeed(ACTION_SEED);\\n\\t\\tDataVector oStates[] = new DataVector[MEM_STEPS];\\n\\t\\tDataVector mStates[] = new DataVector[MEM_STEPS];\\n\\t\\t\\t\\t\\n\\t\\tfor (int i=0; i<MEM_STEPS; i++) {\\n\\t\\t\\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\td.step(action);\\n\\t\\t\\toStates[i] = d.getState();\\n\\t\\t\\tmStates[i] = d.getInternalMarkovState();\\n\\t\\t}\\n\\t\\t\\n\\t\\t// 4) reset dynamics & parameters and internal markov state\\n\\t\\td.reset();\\n\\t\\td.setInternalMarkovState(ms);\\n\\t\\t\\n\\t\\t// 5) reperform test and check if values are consistent\\n\\t\\tactionRand.setSeed(ACTION_SEED); // reproduce action sequence\\n\\t\\tDataVector oState = null;\\n\\t\\tDataVector mState = null;\\n\\t\\tfor (int i=0; i<MEM_STEPS; i++) {\\n\\t\\t\\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\t\\n\\t\\t\\td.step(action);\\n\\t\\t\\toState = d.getState();\\n\\t\\t\\tmState = d.getInternalMarkovState();\\n\\t\\t\\t\\n\\t\\t\\t// check observable state\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.SetPoint), oState.getValue(ObservableStateDescription.SetPoint), 0.0001);\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.Fatigue), oState.getValue(ObservableStateDescription.Fatigue), 0.0001);\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.Consumption), oState.getValue(ObservableStateDescription.Consumption), 0.0001);\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.RewardTotal), oState.getValue(ObservableStateDescription.RewardTotal), 0.0001);\\n\\n\\t\\t\\t// \\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.CurrentOperationalCost), mState.getValue(MarkovianStateDescription.CurrentOperationalCost), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent2), mState.getValue(MarkovianStateDescription.FatigueLatent2), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent1), mState.getValue(MarkovianStateDescription.FatigueLatent1), 0.0001);\\n\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionGainBeta), mState.getValue(MarkovianStateDescription.EffectiveActionGainBeta), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), mState.getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveShift), mState.getValue(MarkovianStateDescription.EffectiveShift), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.MisCalibration), mState.getValue(MarkovianStateDescription.MisCalibration), 0.0001);\\n\\t\\t\\t\\n\\t\\t\\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), mState.getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), 0.0001);\\n\\t\\t\\t\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardFatigue), mState.getValue(MarkovianStateDescription.RewardFatigue), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardConsumption), mState.getValue(MarkovianStateDescription.RewardConsumption), 0.0001);\\n\\t\\t}\\n\\t\\t\\n\\t\\tSystem.out.println (\\\"last o-state 1st trajectory: \\\" + oStates[oStates.length-1]);\\n\\t\\tSystem.out.println (\\\"last o-state 2nd trajectory: \\\" + oState);\\n\\t\\t\\n\\t\\tSystem.out.println (\\\"last m-state 1st trajectory: \\\" + mStates[oStates.length-1]);\\n\\t\\tSystem.out.println (\\\"last m-state 2nd trajectory: \\\" + mState);\\n\\t}\",\n \"public void steepestDescent() {\\r\\n ArrayList<Atom> atoms = cluster.getAtoms();\\r\\n Atom a;\\r\\n int numAtoms = atoms.size();\\r\\n double potentialEnergy;\\r\\n double trialPotential;\\r\\n int i;\\r\\n\\r\\n //Increment time\\r\\n totalTime += currentStep;\\r\\n\\r\\n //Calculate force on each atom\\r\\n cluster.calculateForces();\\r\\n\\r\\n //Get energy of current configuration\\r\\n HashMap<String, Double> energies = cluster.getEnergies();\\r\\n potentialEnergy = 0.0;\\r\\n for(String eName : energies.keySet())\\r\\n potentialEnergy += energies.get(eName).doubleValue();\\r\\n\\r\\n //Save current atom positions and advance atoms to trial positions\\r\\n for(i = 0; i < numAtoms; i++) {\\r\\n a = atoms.get(i);\\r\\n\\r\\n savedLocations[i].x = a.location.x;\\r\\n savedLocations[i].y = a.location.y;\\r\\n savedLocations[i].z = a.location.z;\\r\\n\\r\\n //Compute trial locations\\r\\n a.location.x += a.force.x * currentStep;\\r\\n a.location.y += a.force.y * currentStep;\\r\\n a.location.z += a.force.z * currentStep;\\r\\n }\\r\\n\\r\\n //Get energy of the trial configuration\\r\\n energies = cluster.getEnergies();\\r\\n trialPotential = 0.0;\\r\\n for(String eName : energies.keySet())\\r\\n trialPotential += energies.get(eName).doubleValue();\\r\\n\\r\\n //Decide whether to keep trial configuration based on convergence\\r\\n if(trialPotential < potentialEnergy) {\\r\\n //Keep positions and check for convergence\\r\\n if(Math.abs((potentialEnergy - trialPotential) / potentialEnergy) < convergenceCriterion)\\r\\n isConverged = true;\\r\\n\\r\\n currentStep *= 1.2;\\r\\n } else {\\r\\n //Restore old positions\\r\\n for(i = 0; i < numAtoms; i++) {\\r\\n a = atoms.get(i);\\r\\n\\r\\n a.location.x = savedLocations[i].x;\\r\\n a.location.y = savedLocations[i].y;\\r\\n a.location.z = savedLocations[i].z;\\r\\n }\\r\\n\\r\\n currentStep *= 0.5;\\r\\n }\\r\\n }\",\n \"@Test\\n public void testMetastableStates()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IIntArray expResult = null;\\n IIntArray result = instance.metastableStates(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testBackwardCommittor()\\n {\\n IDoubleArray M = null;\\n IIntArray A = null;\\n IIntArray B = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.backwardCommittor(M, A, B);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateC_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n void makeCoffee() {\\n Assertions.assertFalse(machine.makeCoffee());\\n\\n //try with cup\\n Assertions.assertDoesNotThrow(() -> {\\n machine.positionCup();\\n });\\n\\n Assertions.assertThrows(Exception.class, () -> {\\n machine.positionCup();\\n });\\n\\n\\n Assertions.assertTrue(machine.makeCoffee());\\n\\n //check if correct amount as been subtracted\\n Assertions.assertEquals(machine.getCurrentWater(), 1000 - machine.waterPerCup);\\n Assertions.assertEquals(machine.getCurrentBeans(), 800 - machine.beansPerCup);\\n\\n //reset\\n machine.fillWater();\\n machine.fillBeans();\\n machine.setCupPositioned(true);\\n\\n //test over boundary\\n Assertions.assertTrue(machine.makeCoffee());\\n Assertions.assertTrue(machine.makeCoffee());\\n\\n //set only water to boundary values and below\\n machine.setCurrentWater(0);\\n Assertions.assertFalse(machine.makeCoffee());\\n machine.setCurrentWater(machine.waterPerCup);\\n Assertions.assertTrue(machine.makeCoffee());\\n\\n //reset water\\n machine.fillWater();\\n\\n //set only beans to boundary value and below\\n machine.setCurrentBeans(0);\\n Assertions.assertFalse(machine.makeCoffee());\\n machine.setCurrentBeans(machine.beansPerCup);\\n Assertions.assertTrue(machine.makeCoffee());\\n\\n\\n }\",\n \"public void checkMovementZMinimum(ITestContext context) throws Exception {\\n Reporter.testStart();\\n String caseFolder=context.getName()+\\\"/\\\";\\n String importFile=context.getCurrentXmlTest().getParameter(\\\"importFile\\\");\\n String expectationFile=context.getCurrentXmlTest().getParameter(\\\"expectationFile\\\");\\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\\n String screenWithinCase=caseFolder+methodName;\\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\\n if(meshViewPage.isThePage()){\\n\\n uploadDCM(meshViewPage,importFile);\\n\\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\\n Reporter.log(\\\"click Orientation Adjustment tab on left bar\\\");\\n String text=\\\"0.1\\\";\\n Reporter.log(\\\"select Movement step \\\"+text);\\n Assert.assertEquals(oa.selectMovementStep(text),text,\\\"Cann't select \\\"+text);\\n\\n Integer clickTimes=40;\\n String disResult=oa.clickDistanceMinus(clickTimes);\\n String higher=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z minus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n\\n clickTimes=5;\\n disResult=oa.clickDistanceMinus(clickTimes);\\n String middle=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z minus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n\\n clickTimes=6;\\n disResult=oa.clickDistanceMinus(clickTimes);//click times: 51\\n String lower=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z minus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"-5\\\",\\\"distance is not same as expected result\\\");\\n\\n embededCompareResult(higher,middle,lower, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\\n\\n disResult=oa.clickDistanceReset();\\n embededScreenShot(screenWithinCase+\\\"_reset_\\\"+disResult,\\\"click Z reset(1 times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"0\\\",\\\"distance is not same as expected result\\\");\\n }\\n Reporter.testEnd();\\n }\",\n \"public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap<Integer,Integer> words)\\r\\n{\\r\\n int t=0;\\r\\n double LearningRates[] = {1, 0.1, 0.01};\\r\\n double Margins[] = {1, 0.1, 0.01};\\r\\n\\r\\n\\tint minInt =0; // Set To Your Desired Min Value\\r\\n int maxInt =2;\\r\\n\\tRandom rand = new Random();\\r\\n\\t\\r\\n \\tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\\r\\n\\r\\n \\tdouble LearningRate = LearningRates[randomNum];\\r\\n \\t\\r\\nfor(int m=0;m<LearningRates.length;m++)\\r\\n{\\r\\n for(int i=0;i<LearningRates.length;i++)\\r\\n {\\r\\n\\t LearningRate = LearningRates[i];\\r\\n\\t\\t double devAccuracyTotal = (double)0;\\r\\n\\t\\t int noOfEpochs= 20;\\r\\n\\t\\t int decreaseRate = 0;\\r\\n\\t\\t \\r\\n \\t\\t Double w[] = new Double[74500];\\r\\n\\t\\t Double min =-0.01; // Set To Your Desired Min Value\\r\\n\\t Double max = 0.01;\\r\\n\\t \\t\\tdouble smallRandNumber = min + new Random().nextDouble() * (max - min); \\r\\n\\t\\t\\tArrays.fill(w, smallRandNumber);\\r\\n\\t\\t\\t\\r\\n\\t\\t for(int epoch=0;epoch<noOfEpochs;epoch++)\\r\\n\\t\\t {\\r\\n\\t\\t\\t \\tw = learnMarginPerceptron(data,index,LearningRate,decreaseRate,w,Margins[m]);\\r\\n\\t\\t\\t \\tdecreaseRate = decreaseRate + index;\\r\\n\\t\\t\\r\\n\\t\\t }\\r\\n\\t\\t//CVSplit test\\r\\n \\tdouble cvAccuracy = testTestFile(w,testFileName,words);\\r\\n \\t\\r\\n// \\tSystem.out.println(\\\"Epoch\\\" + epoch +\\\" Dev Accuracy for Decaying Learning Rate \\\" + LearningRate + \\\" is \\\" + cvAccuracy);\\r\\n \\r\\n \\r\\n \\tSystem.out.println(\\\" ** Cv Accuracy for Margin Rate\\\" + Margins[m] + \\\" Learning Rate \\\" + LearningRate + \\\" is \\\" + cvAccuracy);\\r\\n \\tSystem.out.println(\\\" \\\");\\r\\n }\\r\\n}\\r\\n//double testAccuracy = testTestFile(w,testFileName);\\r\\n\\t//System.out.println(\\\"test Accuracy\\\" + testAccuracy);\\r\\n}\",\n \"@Test(timeout = 4000)\\n public void test29() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[3][6];\\n discretize0.setIgnoreClass(false);\\n discretize0.m_CutPoints = doubleArray0;\\n double[] doubleArray1 = new double[4];\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints((-831));\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // -831\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"public void testStep() {\\n\\n DT_TractographyImage crossing = Images.getCrossing();\\n\\n FACT_FibreTracker tracker = new FACT_FibreTracker(crossing);\\n\\n Vector3D[] pds = crossing.getPDs(1,1,1);\\n\\n Vector3D step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 0, true);\\n\\n assertEquals(1.0, pds[0].dot(step.normalized()), 1E-8);\\n\\n assertEquals(Images.xVoxelDim / 2.0, step.mod(), 0.05);\\n \\n step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 0, false);\\n\\n assertEquals(-1.0, pds[0].dot(step.normalized()), 1E-8);\\n\\n step = tracker.getFirstStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), 1, true);\\n\\n assertEquals(1.0, pds[1].dot(step.normalized()), 1E-8);\\n\\n\\n for (int i = 0; i < 2; i++) {\\n\\n step = tracker.getNextStep(new Point3D(1.5 * Images.xVoxelDim, 1.2 * Images.yVoxelDim, 1.1 * Images.zVoxelDim), new Vector3D(pds[i].x + 0.01, pds[i].y + 0.02, pds[i].z + 0.03).normalized());\\n\\n assertEquals(1.0, pds[i].dot(step.normalized()), 1E-8);\\n\\n }\\n\\n \\n }\",\n \"@Test()\\n public void testTrainWarmCFastSMO() {\\n ClassificationDataSet train = FixedProblems.getHalfCircles(250, RandomUtil.getRandom(), 0.1, 0.2);\\n\\n PlattSMO warmModel = new PlattSMO(new LinearKernel(1));\\n warmModel.setC(1);\\n warmModel.train(train);\\n\\n PlattSMO warm = new PlattSMO(new LinearKernel(1));\\n warm.setC(1e4);// too large to train efficently like noraml\\n\\n long start, end;\\n\\n start = System.currentTimeMillis();\\n warm.train(train, warmModel);\\n end = System.currentTimeMillis();\\n long warmTime = (end - start);\\n\\n PlattSMO notWarm = new PlattSMO(new LinearKernel(1));\\n notWarm.setC(1e4);// too large to train efficently like noraml\\n\\n start = System.currentTimeMillis();\\n notWarm.train(train);\\n end = System.currentTimeMillis();\\n long normTime = (end - start);\\n\\n assertTrue(warmTime < normTime * 0.75);\\n\\n }\",\n \"@Test(timeout = 4000)\\n public void test03() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setAttributeIndices(\\\"\\\");\\n Filter.makeCopy(discretize0);\\n discretize0.setDesiredWeightOfInstancesPerInterval(2.0);\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 0;\\n discretize0.setUseBinNumbers(true);\\n discretize0.listOptions();\\n discretize0.makeBinaryTipText();\\n discretize0.getInvertSelection();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-458));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testStationaryDistribution()\\n {\\n try\\n {\\n IDoubleArray T = Doubles.create.fromFile(inputT1);\\n IDoubleArray expResult = Doubles.create.fromFile(inputT1_pi); \\n IDoubleArray pi = MarkovModel.util.stationaryDistribution(T);\\n assertEqual(pi, expResult, 1e-5);\\n }\\n catch(IOException e)\\n {\\n \\n e.printStackTrace();\\n }\\n // TODO review the generated test code and remove the default call to fail.\\n //fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testPerturbationExpectation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray pi0 = null;\\n IDoubleArray observable = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.perturbationExpectation(M, pi0, observable, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void test03() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double[] doubleArray0 = new double[19];\\n evaluation0.updateMargins(doubleArray0, 0, 0.0);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n }\",\n \"@Test\\n void cupTesting() {\\n Assertions.assertFalse(machine.isCupPositioned());\\n Assertions.assertFalse(machine.removeCup());\\n\\n Assertions.assertDoesNotThrow(() -> machine.positionCup());\\n\\n Assertions.assertTrue(machine.removeCup());\\n Assertions.assertFalse(machine.isCupPositioned());\\n machine.setCupPositioned(true);\\n Assertions.assertTrue(machine.isCupPositioned());\\n\\n }\",\n \"public ModelParams optimize() throws Exception {\\n\\t\\tint iterCount = 0;\\n\\t\\tdivergence = KLDivergenceCalculator.calculate(cState, tState);\\n\\t\\tSystem.out.println(\\\"KL Divergence : \\\" + divergence);\\n\\t\\t// ((DivergenceChart) BeanFinder\\n\\t\\t// .findBean(MetaConstants.BEAN_DIVERGENCE_CHART)).update(\\n\\t\\t// iterCount, divergence);\\n\\t\\tdouble delta[] = null;\\n\\t\\tdouble theta[] = data.getParams().getParams();\\n\\t\\tdouble newDivergence = 0.0d;\\n\\t\\tdouble initialStep = data.getInitialStepSize();\\n\\t\\tboolean optimized = false;\\n\\t\\tdo {\\n\\t\\t\\tdelta = computeGradient(theta);\\n\\t\\t\\tfor (double step = initialStep; iterCount < data.getMaxIterations(); step /= 10) {\\n\\t\\t\\t\\tSystem.out.println(\\\"Step size : \\\" + step);\\n\\t\\t\\t\\tSystem.out.println(\\\"KL divergence before taking a step : \\\"\\n\\t\\t\\t\\t\\t\\t+ KLDivergenceCalculator.calculate(cState, tState));\\n\\t\\t\\t\\tfor (int i = 0; i < delta.length; i++) {\\n\\t\\t\\t\\t\\tif (Double.isNaN(delta[i]))\\n\\t\\t\\t\\t\\t\\tSystem.out.print(\\\"delta is NAN!!\\\" + delta[i] + \\\", \\\");\\n\\t\\t\\t\\t\\ttheta[i] -= step * delta[i];\\n\\t\\t\\t\\t\\tSystem.out.print((theta[i]) + \\\", \\\");\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tSystem.out.println();\\n\\t\\t\\t\\tgetNewState(theta);\\n\\t\\t\\t\\titerCount++;\\n\\t\\t\\t\\tnewDivergence = KLDivergenceCalculator\\n\\t\\t\\t\\t\\t\\t.calculate(cState, tState);\\n\\t\\t\\t\\tSystem.out.println(\\\"KL Divergence : \\\" + newDivergence);\\n\\t\\t\\t\\t// ((DivergenceChart) BeanFinder\\n\\t\\t\\t\\t// .findBean(MetaConstants.BEAN_DIVERGENCE_CHART)).update(\\n\\t\\t\\t\\t// iterCount, newDivergence);\\n\\t\\t\\t\\tif (newDivergence < divergence) {\\n\\t\\t\\t\\t\\tdivergence = newDivergence;\\n\\t\\t\\t\\t\\tinitialStep = step;\\n\\t\\t\\t\\t\\tbreak;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t// before taking the next step size, reset theta to what it was\\n\\t\\t\\t\\tfor (int i = 0; i < delta.length; i++)\\n\\t\\t\\t\\t\\ttheta[i] += step * delta[i];\\n\\t\\t\\t\\tgetNewState(theta); // Added for checking. Not required.\\n\\t\\t\\t}\\n\\t\\t\\toptimized = optimized(theta);\\n\\t\\t} while (!optimized && iterCount < data.getMaxIterations());\\n\\t\\tModelParams optimParams = null;\\n\\t\\tif (optimized) {\\n\\t\\t\\toptimParams = new ModelParams();\\n\\t\\t\\toptimParams.setParams(theta);\\n\\t\\t}\\n\\t\\treturn optimParams;\\n\\t}\",\n \"@Test\\n public void testEstimateCmilestoning_Iterable_int()\\n {\\n Iterable<IIntArray> trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[7][2];\\n double[] doubleArray1 = new double[7];\\n doubleArray1[0] = 0.0;\\n doubleArray1[1] = (-587.14627);\\n doubleArray1[2] = (-2542.40202289275);\\n doubleArray1[3] = (-167.679515);\\n doubleArray1[4] = (-1.0);\\n doubleArray1[5] = 1.0;\\n doubleArray1[6] = (-1.0);\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[5];\\n doubleArray2[0] = (-167.679515);\\n doubleArray2[1] = (-2692.352);\\n doubleArray2[2] = (-167.679515);\\n doubleArray2[3] = (-2542.40202289275);\\n doubleArray2[4] = (-587.14627);\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[4];\\n doubleArray3[0] = (-1.0);\\n doubleArray3[1] = (-1.0);\\n doubleArray3[2] = 1.0;\\n doubleArray3[3] = (-2542.40202289275);\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[7];\\n doubleArray4[0] = (-2692.352);\\n doubleArray4[1] = (-1.0);\\n doubleArray4[2] = (-1.0);\\n doubleArray4[3] = 0.0;\\n doubleArray4[4] = 1.0;\\n doubleArray4[5] = 0.0;\\n doubleArray4[6] = 1.0;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[1];\\n doubleArray5[0] = (-167.679515);\\n doubleArray0[4] = doubleArray5;\\n double[] doubleArray6 = new double[0];\\n doubleArray0[5] = doubleArray6;\\n double[] doubleArray7 = new double[1];\\n doubleArray7[0] = (-587.14627);\\n doubleArray0[6] = doubleArray7;\\n discretize0.m_CutPoints = doubleArray0;\\n String string0 = discretize0.invertSelectionTipText();\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.\\\", string0);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Test\\r\\n public void testAverageacceleration() {\\r\\n System.out.println(\\\"Average Acceleration\\\");\\r\\n \\r\\n // Test case one.\\r\\n System.out.println(\\\"Test case #1\\\"); \\r\\n \\r\\n double distance = 200;\\r\\n long time = 16;\\r\\n \\r\\n SceneControl instance = new SceneControl();\\r\\n \\r\\n double expResult = 0.78125;\\r\\n double result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case two.\\r\\n System.out.println(\\\"Test case #2\\\"); \\r\\n \\r\\n distance = 80;\\r\\n time = 200;\\r\\n \\r\\n \\r\\n expResult = .002;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case three.\\r\\n System.out.println(\\\"Test case #3\\\"); \\r\\n \\r\\n distance = 0;\\r\\n time = 15;\\r\\n \\r\\n \\r\\n expResult = 0;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case four.\\r\\n System.out.println(\\\"Test case #4\\\"); \\r\\n \\r\\n distance = 12;\\r\\n time = 1;\\r\\n \\r\\n \\r\\n expResult = 12;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case five.\\r\\n System.out.println(\\\"Test case #5\\\"); \\r\\n \\r\\n distance = 1;\\r\\n time = 1;\\r\\n \\r\\n \\r\\n expResult = 1;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case six.\\r\\n System.out.println(\\\"Test case #6\\\"); \\r\\n \\r\\n distance = 3;\\r\\n time = 8;\\r\\n \\r\\n \\r\\n expResult = .046875;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n // Test case seven.\\r\\n System.out.println(\\\"Test case #7\\\"); \\r\\n \\r\\n distance = 200;\\r\\n time = 1;\\r\\n \\r\\n \\r\\n expResult = 200;\\r\\n result = instance.averageAcceleration(distance, time);\\r\\n assertEquals(expResult, result, -1);\\r\\n \\r\\n \\r\\n \\r\\n }\",\n \"@Test\\n public void testGetSetTemperaturaExteriorMinima() {\\n System.out.println(\\\"getSetTemperaturaExteriorMinima\\\");\\n double expResult = 10.0;\\n Simulacao simulacao = new Simulacao();\\n simulacao.setSala(sala);\\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\\n instance.setTemperaturaExteriorMinima(expResult);\\n double result = instance.getTemperaturaExteriorMinima();\\n assertEquals(expResult, result, 0.0);\\n }\",\n \"public void checkMovementZReset(ITestContext context) {\\n Reporter.testStart();\\n String caseFolder=context.getName()+\\\"/\\\";\\n String importFile=context.getCurrentXmlTest().getParameter(\\\"importFile\\\");\\n String expectationFile=context.getCurrentXmlTest().getParameter(\\\"expectationFile\\\");\\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\\n String screenWithinCase=caseFolder+methodName;\\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\\n if(meshViewPage.isThePage()){\\n\\n uploadDCM(meshViewPage,importFile);\\n\\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\\n Reporter.log(\\\"click Orientation Adjustment tab on left bar\\\");\\n String text=\\\"0.1\\\";\\n Reporter.log(\\\"select Movement step \\\"+text);\\n Assert.assertEquals(oa.selectMovementStep(text),text,\\\"Cann't select \\\"+text);\\n\\n Integer clickTimes=30;\\n String disResult=oa.clickDistanceMinus(clickTimes);\\n embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z minus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"-3\\\",\\\"distance is not same as expected result\\\");\\n\\n clickTimes=10;\\n disResult=oa.clickDistancePlus(clickTimes);\\n String lower=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z plus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"-2\\\",\\\"distance is not same as expected result\\\");\\n\\n disResult=oa.clickDistancePlus(clickTimes);\\n String middle=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z plus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"-1\\\",\\\"distance is not same as expected result\\\");\\n\\n disResult=oa.clickDistanceReset();\\n String higher=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+disResult,\\\"click Z reset(1 times), get distance is \\\"+disResult);\\n Assert.assertEquals(disResult,\\\"0\\\",\\\"distance is not same as expected result\\\");\\n\\n embededCompareResult(lower,middle,higher, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\\n\\n }\\n Reporter.testEnd();\\n }\",\n \"private static void testTakeStep(Drunkard drunkard, int testStepTimes, int testStepSize){\\n\\t for (int i = 0; i < testStepTimes; i++)\\n\\t{\\n\\t ImPoint pointA = drunkard.getCurrentLoc(); \\n\\t\\tdrunkard.takeStep();\\n\\t\\tImPoint pointB = drunkard.getCurrentLoc();\\n\\t\\t\\n\\t\\tif (pointA.getX()-pointB.getX() != 0 && pointA.getY()-pointB.getY() != 0)\\n\\t\\t{\\n\\t\\t\\tSystem.out.println(\\\"Not a valid compass direction!\\\");\\n\\t\\t}\\n\\t\\telse if (Math.sqrt((pointA.getX() - pointB.getX())*(pointA.getX() - pointB.getX()) + (pointA.getY() - pointB.getY())*(pointA.getY() - pointB.getY())) != testStepSize)\\n\\t\\t{\\n\\t\\t\\tSystem.out.println(\\\"took step to (\\\" + pointB.getX() + \\\",\\\" + pointB.getY() + \\\"), FAILED, not a valid step\\\");\\n\\t\\t}\\n\\t\\telse\\n\\t\\t{\\n\\t\\t\\tSystem.out.println(\\\"took step to (\\\" + pointB.getX() + \\\",\\\" + pointB.getY() + \\\"), SUCCEEDED\\\");\\n\\t\\t}\\n\\t}\\n\\t System.out.println(); \\n\\t}\",\n \"@Test\\n public void testIsTransitionMatrix()\\n {\\n IDoubleArray T = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n boolean expResult = false;\\n boolean result = instance.isTransitionMatrix(T);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test072() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate((String) null);\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double[] doubleArray0 = new double[7];\\n evaluation0.updateMargins(doubleArray0, 3, (-1));\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints((-2276));\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Test\\n public void testGiveChange() throws Exception {\\n System.out.println(\\\"giveChange\\\");\\n //CashRegister instance = new CashRegister();\\n assertEquals(0.0, instance.giveChange(), EPSILON);\\n instance.scanItem(0.55);\\n instance.scanItem(1.27);\\n double expResult = 0.18;\\n instance.collectPayment(Money.QUARTER, 8);\\n double result = instance.giveChange();\\n assertEquals(expResult, result, EPSILON);\\n \\n }\",\n \"@Test\\n public void testCalcMedAmount() {\\n \\n //Test case 1\\n \\n System.out.println(\\\"calcMedAmount Test Case 1 - Dose is correct\\\");\\n \\n //Input Values for Test Case 1\\n \\n double weightInPounds = 100;\\n double numberOfPills = 1;\\n \\n AntidoteControl instance = new AntidoteControl();\\n \\n double expResult = 227;\\n double result = AntidoteControl.calcMedAmount(weightInPounds);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n //fail(\\\"The test case is a prototype.\\\");\\n \\n \\n //Test case 2\\n \\n System.out.println(\\\"calcMedAmount Test Case 2 Invalid weight\\\");\\n \\n //Input Values for Test Case 2\\n \\n weightInPounds = 0;\\n numberOfPills = 1;\\n \\n // AntidoteControl instance = new AntidoteControl();\\n \\n expResult = -1;\\n result = AntidoteControl.calcMedAmount(weightInPounds);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n //fail(\\\"The test case is a prototype.\\\");\\n \\n \\n //Test case 3\\n \\n System.out.println(\\\"calcMedAmount Test Case 3 Underdose\\\");\\n \\n //Input Values for Test Case 3\\n \\n weightInPounds = 10001;\\n numberOfPills = 0;\\n \\n // AntidoteControl instance = new AntidoteControl();\\n \\n expResult = -2;\\n result = AntidoteControl.calcMedAmount(weightInPounds);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n //fail(\\\"The test case is a prototype.\\\");\\n \\n \\n //Test case 4\\n \\n System.out.println(\\\"calcMedAmount Test Case 4 Overdose\\\");\\n \\n //Input Values for Test Case 4\\n \\n weightInPounds = 100;\\n numberOfPills = 4;\\n \\n // AntidoteControl instance = new AntidoteControl();\\n \\n expResult = 227;\\n result = AntidoteControl.calcMedAmount(weightInPounds);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n //fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"int calculateMST();\",\n \"@Test\\n public void transferCheckingtoSavingsTest(){\\n assertTrue(userC.transfer(150.00, userS));\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n Locale.getISOLanguages();\\n double[][] doubleArray0 = new double[9][8];\\n double[] doubleArray1 = new double[3];\\n double[] doubleArray2 = new double[9];\\n doubleArray2[5] = (-163.0);\\n doubleArray2[8] = 1421.4058829;\\n doubleArray0[3] = doubleArray0[2];\\n doubleArray0[4] = doubleArray0[1];\\n discretize0.setOutputFormat();\\n double[] doubleArray3 = new double[2];\\n doubleArray3[0] = 1421.4058829;\\n doubleArray3[1] = (-163.0);\\n doubleArray0[6] = doubleArray2;\\n double[] doubleArray4 = new double[0];\\n doubleArray0[7] = doubleArray4;\\n double[] doubleArray5 = new double[6];\\n doubleArray5[2] = (-163.0);\\n doubleArray0[8] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test096() throws Throwable {\\n CostMatrix costMatrix0 = Evaluation.handleCostOption(\\\"\\\", 1281);\\n assertNull(costMatrix0);\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"%\\\");\\n FileSystemHandling.createFolder((EvoSuiteFile) null);\\n discretize0.findNumBinsTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"%\\\");\\n discretize0.m_DefaultCols = \\\"%\\\";\\n double[][] doubleArray0 = new double[0][7];\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints(1);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 1\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void shouldCalculateProperMoveProbabilities() throws Exception {\\n\\n int actualCity = 0;\\n boolean[] visited = new boolean[] {true, false, false, false};\\n int[][] firstCriterium = new int[][] {{0, 2, 1, 2},\\n {2, 2, 1, 1},\\n {1, 1, 0, 1},\\n {2, 1, 1, 0}};\\n int[][] secondCriterium = new int[][] {{0, 3, 2, 1},\\n {3, 0, 0, 0},\\n {2, 0, 0, 0},\\n {1, 0, 0, 0}};\\n// final AttractivenessCalculator attractivenessCalculator = new AttractivenessCalculator(1.0, 0.0, 0.0, 0.0, 0.0);\\n// double[][] array = new double[0][0];\\n// final double[] probabilities = attractivenessCalculator.movesProbability(actualCity, visited, array, firstCriterium, secondCriterium, null, 0.0, 0.0);\\n\\n// assertTrue(probabilities[0] == 0.0);\\n// assertTrue(probabilities[1] == 0.0);\\n// assertTrue(probabilities[2] == 0.5);\\n// assertTrue(probabilities[3] == 0.5);\\n }\",\n \"public void testMainLBGFSCachedSemiMarkov() throws Exception {\\n\\t\\tConrad.main(new String[] {\\\"train\\\", \\\"test/input/zeroOrderLBFGSCachedSemiMarkov.xml\\\", \\\"test/input/zeroOrderTestShortMax.txt\\\", \\\"test/working/zeroLBGFSModelCachedSemiMarkov.ser\\\"});\\n\\t\\tConrad.main(new String[] {\\\"test\\\", \\\"test/working/zeroLBGFSModelCachedSemiMarkov.ser\\\", \\\"test/input/zeroOrderTest.txt\\\", \\\"test/working/zeroOrderMainLBGFSCachedSemiMarkovPredicted.txt\\\"});\\n\\t\\tConrad.main(new String[] {\\\"test\\\", \\\"test/working/zeroLBGFSModelCachedSemiMarkov.ser\\\", \\\"test/input/zeroOrderTest2.txt\\\", \\\"test/working/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\\\"});\\n\\t\\tassertFilesMatch(\\\"test/output/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\\\", \\\"test/working/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\\\");\\n\\t}\",\n \"@Test\\n public void testGetSetTemperaturaExterior() {\\n System.out.println(\\\"getSetTemperaturaExterior\\\");\\n double expResult = 20.0;\\n Simulacao simulacao = new Simulacao();\\n simulacao.setSala(sala);\\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\\n instance.setTemperaturaExterior(expResult);\\n double result = instance.getTemperaturaExterior();\\n assertEquals(expResult, result, 0.0);\\n }\",\n \"@Test\\r\\n public void gallonsCauldronHolds() {\\r\\n \\r\\n System.out.println(\\\"gallonsCauldronHolds\\\");\\r\\n \\r\\n //Test #1\\r\\n System.out.println(\\\"Test case #1\\\"); \\r\\n \\r\\n double diameter = 50;\\r\\n double depth = 25;\\r\\n \\r\\n SceneControl instance = new SceneControl();\\r\\n \\r\\n double expResult = 212.500;\\r\\n double result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #2\\r\\n System.out.println(\\\"Test case #2\\\");\\r\\n \\r\\n diameter = 15;\\r\\n depth = -12;\\r\\n \\r\\n expResult = -1;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #3\\r\\n System.out.println(\\\"Test case #3\\\");\\r\\n \\r\\n diameter = -5;\\r\\n depth = 20;\\r\\n \\r\\n expResult = -1;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #4\\r\\n System.out.println(\\\"Test case #4\\\");\\r\\n \\r\\n diameter = 0;\\r\\n depth = 30;\\r\\n \\r\\n expResult = -1;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #5\\r\\n System.out.println(\\\"Test case #5\\\");\\r\\n \\r\\n diameter = 14;\\r\\n depth = 0;\\r\\n \\r\\n expResult = -1;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #6\\r\\n System.out.println(\\\"Test case #6\\\");\\r\\n \\r\\n diameter = 1;\\r\\n depth = 12;\\r\\n \\r\\n expResult = .041;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n //Test #7\\r\\n System.out.println(\\\"Test case #7\\\");\\r\\n \\r\\n diameter = 20;\\r\\n depth = 1;\\r\\n \\r\\n expResult = 1.36;\\r\\n result = instance.gallonsCauldronHolds(diameter, depth);\\r\\n \\r\\n assertEquals(expResult, result, 0.0001);\\r\\n \\r\\n }\",\n \"@Test\\n public void testInbreedingCoeffForMultiallelicVC() {\\n VariantContext test1 = makeVC(\\\"1\\\",Arrays.asList(Aref,T,C),\\n makeG(\\\"s1\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\\n makeG(\\\"s2\\\", T, T, 7099, 2530, 0, 7099, 366, 3056),\\n makeG(\\\"s3\\\", T, C, 7099, 2530, 7099, 3056, 0, 14931),\\n makeG(\\\"s4\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\\n makeG(\\\"s5\\\", T, T, 7099, 2530, 0, 7099, 366, 3056),\\n makeG(\\\"s6\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\\n makeG(\\\"s7\\\", T, T, 7099, 2530, 0, 7099, 366, 3056),\\n makeG(\\\"s8\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\\n makeG(\\\"s9\\\", T, T, 7099, 2530, 0, 7099, 366, 3056),\\n makeG(\\\"s10\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931));\\n\\n final AS_InbreedingCoeff testClass1 = new AS_InbreedingCoeff();\\n final double ICresult1 = testClass1.calculateIC(test1, T);\\n Assert.assertEquals(ICresult1, -0.4285714, DELTA_PRECISION, \\\"Pass\\\");\\n final double ICresult1b = testClass1.calculateIC(test1, C);\\n Assert.assertEquals(ICresult1b, -0.05263, DELTA_PRECISION, \\\"Pass\\\");\\n\\n //make sure that hets with different alternate alleles all get counted\\n VariantContext test2 = makeVC(\\\"2\\\", Arrays.asList(Aref,T,C),\\n makeG(\\\"s1\\\", Aref, C, 4878, 1623, 11297, 0, 7970, 8847),\\n makeG(\\\"s2\\\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\\n makeG(\\\"s3\\\", Aref, T, 3382, 0, 6364, 1817, 5867, 12246),\\n makeG(\\\"s4\\\", Aref, T, 2488, 0, 9110, 3131, 9374, 12505),\\n makeG(\\\"s5\\\", Aref, C, 4530, 2006, 18875, 0, 6847, 23949),\\n makeG(\\\"s6\\\", Aref, T, 5325, 0, 18692, 389, 16014, 24570),\\n makeG(\\\"s7\\\", Aref, T, 2936, 0, 29743, 499, 21979, 38630),\\n makeG(\\\"s8\\\", Aref, T, 6902, 0, 8976, 45, 5844, 9061),\\n makeG(\\\"s9\\\", Aref, T, 5732, 0, 10876, 6394, 11408, 17802),\\n makeG(\\\"s10\\\", Aref, T, 2780, 0, 25045, 824, 23330, 30939));\\n\\n final AS_InbreedingCoeff testClass2 = new AS_InbreedingCoeff();\\n final double ICresult2 = testClass2.calculateIC(test2, T);\\n Assert.assertEquals(ICresult2, -0.666666, DELTA_PRECISION, \\\"Pass\\\");\\n final double ICresult2b = testClass2.calculateIC(test2, C);\\n Assert.assertEquals(ICresult2b, -0.111129, DELTA_PRECISION, \\\"Pass\\\");\\n }\",\n \"public void test_setClippingIIII() {\\n}\",\n \"@Test\\n\\tvoid testCalculateMarkTillSemesterSuccess3() {\\n\\t\\n\\t\\tString semesterName = \\\"20193\\\";\\n\\t\\t\\n\\t\\tUser user = userService.findByUsername(\\\"B17DCCN123\\\");\\n\\t\\tStudent student = user.getStudent();\\n\\t\\tList<StudentResult> resultsTillSemester = resultService.findResultTillSemester(student.getId(),\\n\\t\\t\\t\\tsemesterName);\\n\\t\\t\\n\\t\\tString tktMark = \\\"\\\";\\n\\t\\t\\n\\t\\tfor (StudentResult result : resultsTillSemester) {\\n\\t\\t\\tSubject subject = result.getStudentRegister().getCourse().getSubject();\\n\\t\\t\\tif (subject.getName().equals(\\\"Toán kinh tế\\\")) {\\n\\t\\t\\t\\ttktMark = result.getMarkToChar();\\n\\t\\t\\t\\tbreak;\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\t\\n\\t\\tList<Object> listGpaResultsTillNow = MarkUtility.calculateMarkTillSemester(resultsTillSemester);\\n\\t\\tint passedCreditsTillNow = (int) listGpaResultsTillNow.get(0);\\n\\t\\tfloat gpaTillNow = (float) listGpaResultsTillNow.get(1);\\n\\t\\t\\n\\t\\tDecimalFormat df = new DecimalFormat(\\\"#.##\\\");\\n\\t\\t\\n\\t\\tassertTrue(\\n\\t\\t\\t\\ttktMark.equals(\\\"C\\\") && \\n\\t\\t\\t\\tpassedCreditsTillNow == 18 && \\n\\t\\t\\t\\tdf.format(gpaTillNow).equals(\\\"3.19\\\")\\n\\t\\t\\t\\t);\\n\\t}\",\n \"float estimateCostToGoal( int current_x, int current_y, int current_z, boolean fromStart ) {\\n\\t\\treturn 0;\\n\\t}\",\n \"@Test\\n @Tag(\\\"slow\\\")\\n public void testCZPROB() {\\n CuteNetlibCase.doTest(\\\"CZPROB.SIF\\\", \\\"2185196.6988565763\\\", \\\"3089066.71321333\\\", NumberContext.of(7, 4));\\n }\",\n \"@Test\\n public void testVerser1() {\\n\\n //Arrange\\n CompteBancaire cpt = new CompteBancaire(5000.0);\\n double montant_a_verser = 1000.0;\\n double excepted = 6000.0;\\n\\n //Act\\n assertEquals(true, cpt.verser(montant_a_verser));\\n\\n //Assert\\n assertEquals(excepted, cpt.getSolde(), 0);\\n }\",\n \"@Test\\n public void testVirement3() {\\n\\n //Arrange\\n CompteBancaire cpt1 = new CompteBancaire(0.0);\\n CompteBancaire cpt2 = new CompteBancaire(43000.0);\\n double montant_a_virer = 8000.0;\\n double excepted_solde_cpt1 = 0.0;\\n double excepted_solde_cpt2 = 43000.0;\\n\\n //Act\\n assertEquals(false, cpt1.virerArgent(cpt2, montant_a_virer));\\n\\n //Assert\\n assertEquals(excepted_solde_cpt1, cpt1.getSolde(), 0);\\n assertEquals(excepted_solde_cpt2, cpt2.getSolde(), 0);\\n\\n }\",\n \"public static void main(String[] args) {\\n\\n String fileEdgeList = \\\"../DataSource/PANCANsigEdgeList.csv\\\";\\n String fileGtTrainingMatrix = \\\"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.training.10.csv\\\";\\n// String fileGtTestingMatrix = \\\"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.testing.10.csv\\\";\\n String fileGeTrainingMatrix = \\\"../DataSource/CrossMatrixDriver/PANCAN.GeM.4468tumors.training.10.csv\\\";\\n String fileGeTestingMatrix = \\\"../DataSource/CrossMatrixDriver/PANCAN.GeM.4468tumors.testing.10.csv\\\";\\n String fileInferDriver = \\\"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.InferDriver.10.withTumorID.csv\\\";\\n// String fileDriverSGATable = \\\"../DataSource/CrossMatrixDriver/PANCAN.Drivercallpertumor.4468tumors.DriverSGATable.10.csv\\\";\\n \\n \\n DataReader dataObj = new DataReader(fileEdgeList, fileGtTrainingMatrix, fileGeTrainingMatrix);\\n\\n int reRun = 0;\\n double T = 0.5;\\n do {\\n reRun += 1;\\n\\n EstimateParams paramObj = new EstimateParams(dataObj.edgeList, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.driverSGATable, dataObj.targetDEGTable);\\n InferDriverActivation actObj = new InferDriverActivation(paramObj.mapEdgeParam, paramObj.mapSGAParam,\\n dataObj.targetDEGTable, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.mapSgaDegs);\\n\\n\\n actObj.thresholding(T);\\n \\n actObj.updateInferDriverTable( dataObj.driverSGATable);\\n \\n double change = actObj.compareMatrix(dataObj.driverSGATable);\\n\\n System.out.println(\\\"Current T is \\\" + T);\\n System.out.println(\\\"Change is \\\" + change);\\n System.out.println(\\\"This is the \\\" + reRun + \\\"th run\\\");\\n if (change < 0.001 || T > 1) {\\n System.out.println(\\\"Total times of run is \\\" + reRun + \\\". Final cut shreshold is \\\" + T);\\n \\n// dataObj.readInGtMatrix(fileGtTestingMatrix);\\n dataObj.readInGeMatrix(fileGeTestingMatrix);\\n InferDriverActivation actObjTesting = new InferDriverActivation(paramObj.mapEdgeParam, paramObj.mapSGAParam,\\n dataObj.targetDEGTable, dataObj.driverSGAs, dataObj.targetDEGs, dataObj.mapSgaDegs);\\n \\n actObjTesting.outputInferActivation(fileInferDriver, dataObj.tumorNames);\\n// dataObj.outputDriverSGATable(fileDriverSGATable); //output contains tumorName, since tumor name is defined in dataObj, so no need to pass in\\n\\n break; \\n \\n } else {\\n dataObj.updateDriverSGATable(actObj.inferDriverTable);\\n T += 0.05;\\n \\n }\\n\\n } while (true);\\n }\",\n \"@Test(timeout = 4000)\\n public void test36() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\\n Properties properties0 = new Properties();\\n ProtectedProperties protectedProperties0 = new ProtectedProperties(properties0);\\n ProtectedProperties protectedProperties1 = new ProtectedProperties(protectedProperties0);\\n Attribute attribute0 = new Attribute(\\\"-\\\", \\\"-\\\", protectedProperties1);\\n arrayList0.add(attribute0);\\n Instances instances0 = new Instances(\\\"(,Z;Xp#\\\\\\\"dxq[\\\", arrayList0, 1587);\\n boolean boolean0 = discretize0.setInputFormat(instances0);\\n boolean boolean1 = discretize0.batchFinished();\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(10, discretize0.getBins());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertTrue(boolean1 == boolean0);\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(boolean1);\\n }\",\n \"@Test\\n public void testStationaryDistributionRevQuick()\\n {\\n IDoubleArray T = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.stationaryDistributionRevQuick(T);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testGetSetTemperaturaInterior() {\\n System.out.println(\\\"getSetTemperaturaInterior\\\");\\n double expResult = 20.0;\\n Simulacao simulacao = new Simulacao();\\n simulacao.setSala(sala);\\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\\n instance.setTemperaturaInterior(expResult);\\n double result = instance.getTemperaturaInterior();\\n assertEquals(expResult, result, 0.0);\\n }\",\n \"@Test\\n public void testCorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable1 = null;\\n IDoubleArray observable2 = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test083() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.SFMeanEntropyGain();\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, double0, 0.01);\\n }\",\n \"@Test\\n public void testGetSetTemperaturaExteriorMaxima() {\\n System.out.println(\\\"getSetTemperaturaExteriorMaxima\\\");\\n double expResult = 20.0;\\n Simulacao simulacao = new Simulacao();\\n simulacao.setSala(sala);\\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\\n instance.setTemperaturaExteriorMaxima(expResult);\\n double result = instance.getTemperaturaExteriorMaxima();\\n assertEquals(expResult, result, 0.0);\\n }\",\n \"@Test(timeout = 4000)\\n public void test087() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.KBMeanInformation();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"public void checkMovementZMaximum(ITestContext context) {\\n Reporter.testStart();\\n String caseFolder=context.getName()+\\\"/\\\";\\n String importFile=context.getCurrentXmlTest().getParameter(\\\"importFile\\\");\\n String expectationFile=context.getCurrentXmlTest().getParameter(\\\"expectationFile\\\");\\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\\n String screenWithinCase=caseFolder+methodName;\\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\\n if(meshViewPage.isThePage()){\\n\\n uploadDCM(meshViewPage,importFile);\\n\\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\\n\\n Reporter.log(\\\"click Orientation Adjustment tab on left bar\\\");\\n String text=\\\"0.05\\\";\\n Reporter.log(\\\"select Movement step \\\"+text);\\n Assert.assertEquals(oa.selectMovementStep(text),text,\\\"Cann't select \\\"+text);\\n\\n Integer clickTimes=98;\\n String valueOnLabel=oa.clickDistancePlus(clickTimes);\\n String lower=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+valueOnLabel,\\\"click Z plus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+valueOnLabel);\\n Assert.assertEquals(valueOnLabel,\\\"4.9\\\",\\\"distance is not same as expected result\\\");\\n\\n clickTimes=1;\\n valueOnLabel=oa.clickDistancePlus();\\n String middle=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+valueOnLabel,\\\"click Z plus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+valueOnLabel);\\n Assert.assertEquals(valueOnLabel,\\\"4.95\\\",\\\"distance is not same as expected result\\\");\\n\\n text=\\\"0.1\\\";\\n Reporter.log(\\\"select Movement step \\\"+text);\\n Assert.assertEquals(oa.selectMovementStep(text),text,\\\"Cann't select \\\"+text);\\n\\n valueOnLabel=oa.clickDistancePlus(clickTimes);\\n String higher=embededScreenShot(screenWithinCase+\\\"_distance_\\\"+valueOnLabel,\\\"click Z plus(\\\"+clickTimes.toString()+\\\" times), get distance is \\\"+valueOnLabel);\\n Assert.assertEquals(valueOnLabel,\\\"5\\\",\\\"distance is not same as expected result\\\");\\n\\n embededCompareResult(lower,middle,higher, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\\n\\n valueOnLabel=oa.clickDistanceReset();\\n embededScreenShot(screenWithinCase+\\\"_reset_\\\"+valueOnLabel,\\\"click Z reset(1 times), get distance is \\\"+valueOnLabel);\\n Assert.assertEquals(valueOnLabel,\\\"0\\\",\\\"distance is not same as expected result\\\");\\n }\\n Reporter.testEnd();\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n String[] stringArray0 = new String[2];\\n discretize0.m_ClassIndex = 906;\\n discretize0.m_IgnoreClass = true;\\n stringArray0[0] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n stringArray0[1] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n discretize0.setOptions(stringArray0);\\n discretize0.setAttributeIndices(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getFindNumBins());\\n }\",\n \"@Test void perfect() {\\n\\t\\tstandardScene();\\n\\t\\tsimulateScene(0);\\n\\n\\t\\tvar alg = new DistanceMetricTripleReprojection23();\\n\\n\\t\\tvar model = new MetricCameraTriple();\\n\\t\\tmodel.view1.setTo(cameraA);\\n\\t\\tmodel.view2.setTo(cameraB);\\n\\t\\tmodel.view3.setTo(cameraC);\\n\\t\\tmodel.view_1_to_2.setTo(truthView_1_to_i(1));\\n\\t\\tmodel.view_1_to_3.setTo(truthView_1_to_i(2));\\n\\n\\t\\talg.setModel(model);\\n\\n\\t\\tfor (AssociatedTriple a : observations3) {\\n\\t\\t\\tassertEquals(0.0, alg.distance(a), UtilEjml.TEST_F64);\\n\\t\\t}\\n\\n\\t\\tvar set = observations3.subList(4, 11);\\n\\t\\tvar distances = new double[set.size()];\\n\\t\\talg.distances(set, distances);\\n\\t\\tfor (int i = 0; i < distances.length; i++) {\\n\\t\\t\\tassertEquals(0.0, distances[i], UtilEjml.TEST_F64);\\n\\t\\t}\\n\\t}\",\n \"@Test\\n public void testGetTemperaturaInteriorAlvo() {\\n System.out.println(\\\"getSetTemperaturaInteriorAlvo\\\");\\n double expResult = 20.0;\\n Simulacao simulacao = new Simulacao();\\n simulacao.setSala(sala);\\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\\n instance.setTemperaturaInteriorAlvo(expResult);\\n double result = instance.getTemperaturaInteriorAlvo();\\n assertEquals(expResult, result, 0.0);\\n }\",\n \"@Override\\n protected boolean stoppingCriteriaSatisfied(KPMPSolution generatedSolution, RandomStepFunction stepFunction) {\\n numberOfIterations++;\\n numberOfIterationsWithoutImprovement++;\\n KPMPSolutionChecker solutionChecker = new KPMPSolutionChecker();\\n int crossingsOnOriginalPage = solutionChecker.getCrossingNumberOfEdge(bestSolution.getSpineOrder(), bestSolution.getEdgePartition(), originalPageIndex, edge);\\n int crossingsOnNewPage = solutionChecker.getCrossingNumberOfEdge(generatedSolution.getSpineOrder(), generatedSolution.getEdgePartition(), newPageIndex, edge);\\n if (crossingsOnNewPage < crossingsOnOriginalPage) {\\n bestSolution = generatedSolution;\\n numberOfIterationsWithoutImprovement = 0;\\n\\n crossingNumber = crossingNumber - (crossingsOnOriginalPage - crossingsOnNewPage);\\n }\\n return numberOfIterations >= Main.localSearchIterationLimit || numberOfIterations >= bestSolution.getEdgePartition().size() * bestSolution.getNumberOfPages() || numberOfIterationsWithoutImprovement >= Main.iterationMultiplier || ((System.nanoTime() - Main.START) / 1000000) >= (Main.secondsBeforeStop * 1000) || numberOfIterations >= Main.localSearchIterationLimit;\\n }\",\n \"@Test(timeout = 4000)\\n public void test116() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate((String) null);\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.correct();\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test086() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.SFMeanPriorEntropy();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test126() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate((String) null);\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.totalCost();\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test26() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.m_FindNumBins = true;\\n int[] intArray0 = new int[6];\\n intArray0[0] = (-388);\\n intArray0[1] = (-2121610348);\\n intArray0[2] = 11;\\n intArray0[3] = (-1);\\n intArray0[4] = 1062;\\n intArray0[5] = 70;\\n discretize0.setAttributeIndicesArray(intArray0);\\n discretize0.setDesiredWeightOfInstancesPerInterval((-1149.15832593));\\n discretize0.getOptions();\\n assertEquals((-1149.15832593), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n }\",\n \"@Override\\n protected boolean stoppingCriteriaSatisfied(KPMPSolution generatedSolution, BestImprovementStepFunction stepFunction) {\\n KPMPSolutionChecker solutionChecker = new KPMPSolutionChecker();\\n int crossingsOnOriginalPage = solutionChecker.getCrossingNumberOfEdge(bestSolution.getSpineOrder(), bestSolution.getEdgePartition(), originalPageIndex, edge);\\n int crossingsOnNewPage = solutionChecker.getCrossingNumberOfEdge(generatedSolution.getSpineOrder(), generatedSolution.getEdgePartition(), newPageIndex, edge);\\n if (crossingsOnNewPage < crossingsOnOriginalPage) {\\n bestSolution = generatedSolution;\\n index = 0;\\n pageCounter = 0;\\n\\n crossingNumber = crossingNumber - (crossingsOnOriginalPage - crossingsOnNewPage);\\n\\n //assert(crossingNumber == new KPMPSolutionChecker().getCrossingNumber(bestSolution));\\n }\\n if (index == bestSolution.getEdgePartition().size() && pageCounter != bestSolution.getNumberOfPages() - 1) {\\n index = 0;\\n pageCounter++;\\n }\\n return ((System.nanoTime() - Main.START) / 1000000) >= (Main.secondsBeforeStop * 1000) || index == bestSolution.getEdgePartition().size() && pageCounter == bestSolution.getNumberOfPages() - 1 || numberOfIterations >= Main.localSearchIterationLimit;\\n }\",\n \"@Test(timeout = 4000)\\n public void test093() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n Object[] objectArray0 = new Object[2];\\n evaluation0.evaluateModel((Classifier) null, instances0, objectArray0);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, true, false);\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints(3594);\\n Discretize discretize1 = new Discretize();\\n discretize1.attributeIndicesTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"\\\");\\n Discretize discretize2 = new Discretize();\\n // Undeclared exception!\\n try { \\n discretize2.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@ParameterizedTest\\n\\t@CsvSource({\\n\\t\\t\\t\\\"9, 1, 2, 3, 1, 10, 1, 10, 1, -1, -1, -1\\\",\\n\\t\\t\\t\\\"9, -1, -1, -1, 9, 10, 1, 10, 1, 9, -1, -1\\\",\\n\\t\\t\\t\\\"9, -1, -1, -1, 1, 10, 9, 10, 1, 9, -1, -1\\\",\\n\\t\\t\\t\\\"9, 3, 3, 3, 89, 100, 1, 10, 1, -1, -1, -1\\\",\\n\\t\\t\\t\\\"9, 1, -1, -1, 1, 10, 89, 100, 1, -1, -1, -1\\\",\\n\\t\\t\\t\\\"9, 1, 2, -1, 2, 2, 1, 10, 25, 1, 2, 9\\\",\\n\\t\\t\\t\\\"9, 3, 3, 3, 0, 2, 950, 1000, 25, 3, 3, -1\\\",\\n\\t\\t\\t\\\"9, -1, -1, -1, 9999, 10000, 950, 1000, 1, 9, 9, 9\\\",\\n\\t\\t\\t\\\"9, -1, -1, -1, 0, 1, 0, 390000, 1, -1, -1, -1\\\",\\n\\t\\t\\t\\\"4, -1, -1, -1, 100, 100, 1000, 1000, 1, 4, 4, 4\\\",\\n\\t\\t\\t\\\"5, 3, -1, -1, 100, 100, 1000, 1000, 10, 3, 5, 5\\\",\\n\\t\\t\\t\\\"5, 1, 3, 4, 100, 100, 1000, 1000, 25, 1, 3, 4\\\",\\n\\t})\\n\\tvoid usesExpectedMultiplier(\\n\\t\\t\\tlong consensusSec,\\n\\t\\t\\tlong old10XLevelStart,\\n\\t\\t\\tlong old25XLevelStart,\\n\\t\\t\\tlong old100XLevelStart,\\n\\t\\t\\tint firstUsed,\\n\\t\\t\\tint firstTps,\\n\\t\\t\\tint secondUsed,\\n\\t\\t\\tint secondTps,\\n\\t\\t\\tlong expectedMultiplier,\\n\\t\\t\\tlong new10XLevelStart,\\n\\t\\t\\tlong new25XLevelStart,\\n\\t\\t\\tlong new100XLevelStart\\n\\t) {\\n\\t\\tfinal var aThrottle = DeterministicThrottle.withTps(firstTps);\\n\\t\\tfinal var bThrottle = DeterministicThrottle.withTps(secondTps);\\n\\t\\taThrottle.allow(firstUsed);\\n\\t\\tbThrottle.allow(secondUsed);\\n\\t\\tgiven(throttling.activeThrottlesFor(CryptoTransfer)).willReturn(List.of(aThrottle, bThrottle));\\n\\n\\t\\tsubject.resetExpectations();\\n\\t\\tsubject.resetCongestionLevelStarts(instants(old10XLevelStart, old25XLevelStart, old100XLevelStart));\\n\\t\\tsubject.updateMultiplier(Instant.ofEpochSecond(consensusSec));\\n\\t\\tfinal long actualMultiplier = subject.currentMultiplier();\\n\\t\\tfinal var starts = subject.congestionLevelStarts();\\n\\n\\t\\tassertEquals(expectedMultiplier, actualMultiplier);\\n\\t\\tassertNullOrEquals(starts[0], new10XLevelStart);\\n\\t\\tassertNullOrEquals(starts[1], new25XLevelStart);\\n\\t\\tassertNullOrEquals(starts[2], new100XLevelStart);\\n\\t}\",\n \"private void calculations() {\\n\\t\\tSystem.out.println(\\\"Starting calculations\\\\n\\\");\\n\\t\\tcalcMedian(myProt.getChain(requestedChain));\\n\\t\\tcalcZvalue(myProt.getChain(requestedChain));\\n\\t\\tcalcSVMweightedZvalue(myProt.getChain(requestedChain));\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test35() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n assertFalse(discretize0.getMakeBinary());\\n \\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n Attribute attribute0 = new Attribute(\\\"invalid CVS revision - not dots!\\\");\\n attribute0.setWeight(0.0);\\n Attribute.typeToString(attribute0);\\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\\n arrayList0.add(attribute0);\\n Instances instances0 = new Instances(\\\"UR<dj;O\\\", arrayList0, 3);\\n Instances instances1 = new Instances(instances0);\\n boolean boolean0 = discretize0.setInputFormat(instances1);\\n assertFalse(boolean0);\\n \\n String[] stringArray0 = new String[3];\\n stringArray0[0] = \\\".bsi\\\";\\n stringArray0[1] = \\\"invalid CVS revision - not dots!\\\";\\n stringArray0[2] = \\\"@end\\\";\\n discretize0.setOptions(stringArray0);\\n discretize0.setOptions(stringArray0);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n }\",\n \"@Test\\n\\tpublic void TEAM3_CONTROLLER_UT05() {\\n\\t\\t// PRECONDITIONS\\n\\t\\tScheduleMakerController smc = new ScheduleMakerController();\\n\\t\\t\\n\\t\\t// INPUT\\n\\t\\tCollection<ScheduleStub> savedSchedules = new ArrayList<ScheduleStub>();\\n\\t\\tsmc.saveSchedules(savedSchedules);\\n\\t\\t\\n\\t\\t// EXPECTED OUTPUT\\n\\t\\tException exceptionThrown = null;\\n\\t\\tdouble balance = 0.0d;\\n\\t\\ttry {\\n\\t\\t\\tMethod getBalance = smc.getClass().getDeclaredMethod(\\\"getBalance\\\", (Class<?> []) null);\\n\\t\\t\\tObject balanceObj = getBalance.invoke(smc, (Object[]) null);\\n\\t\\t\\t\\n\\t\\t\\tif (balanceObj == null) throw new NullPointerException(\\\"getBalance() returned null; expected a number\\\");\\n\\t\\t\\telse if (!(balanceObj instanceof Number)) throw new RuntimeException(\\\"getBalance() did not return a number\\\");\\n\\t\\t} catch (Exception ex) {\\n\\t\\t\\texceptionThrown = ex;\\n\\t\\t}\\n\\t\\t\\n\\t\\tassertNull(exceptionThrown);\\n\\t\\tassertEquals(0.0d, balance, 0.0d);\\n\\t}\",\n \"public double calc_c() {\\r\\n \\t\\t\\r\\n \\t\\tdouble cij = 0;\\r\\n \\t\\tint sum_i = 0;\\r\\n \\t\\tint sum_j = 0;\\r\\n \\t\\tint diff = 0;\\r\\n \\t\\tint state = 0;\\r\\n \\t\\t\\r\\n \\t\\tdouble li, lri, lj, lrj;\\r\\n \\t\\t\\r\\n \\t\\tList<Integer> samestate_i = new ArrayList<>();\\r\\n \\t\\tList<Integer> samestate_j = new ArrayList<>();\\r\\n \\t\\t\\r\\n \\t\\tList<Double> gradient_i = new ArrayList<>();\\r\\n \\t\\tList<Double> gradient_j = new ArrayList<>();\\r\\n \\t\\t\\r\\n\\t\\t// if the time step is less than the window cij is initialized to zero.\\r\\n\\t\\tif(this.n <= this.w || this.w <2) {\\r\\n\\t\\t\\tcij = 0;\\r\\n\\t\\t}\\r\\n\\t\\t\\r\\n\\t\\telse {\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t// else we determine the state of the region i and region j of which\\r\\n\\t\\t\\t// we are calculating the cij for\\r\\n\\t \\t\\tDeterminestate di = new Determinestate(this.i, this.n);\\r\\n\\t\\t\\tDeterminestate dj = new Determinestate(this.j, this.n);\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tProbability pi = new Probability(this.i, this.n, this.w, this.neighborsize);\\r\\n\\t\\t\\tProbability pj = new Probability(this.i, this.n, this.w, this.neighborsize);\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tif(di.regionCurrentstate() == dj.regionCurrentstate()) {\\r\\n\\t\\t\\t\\tli = pi.getLsave().get(0);\\r\\n\\t\\t\\t\\tlj = pj.getLsave().get(0);\\r\\n\\t\\t\\t\\tstate = 0;\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\telse {\\r\\n\\t\\t\\t\\tli = pi.getLsave().get(1);\\r\\n\\t\\t\\t\\tlj = pj.getLsave().get(1);\\r\\n\\t\\t\\t\\tstate =1;\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t// for the time window\\r\\n\\t\\t\\tfor(int k=1; k<this.w; k++) {\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t// determine the state of i and j in the in n-k time step\\r\\n\\t \\t\\t\\tDeterminestate dri = new Determinestate(this.i, this.n-k);\\r\\n\\t \\t\\t\\tDeterminestate drj = new Determinestate(this.j, this.n-k);\\r\\n\\t \\t\\t\\t\\r\\n\\t \\t\\t\\tProbability pri = new Probability(this.i, this.n, this.w, this.neighborsize);\\r\\n\\t\\t\\t\\tProbability prj = new Probability(this.i, this.n, this.w, this.neighborsize);\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t\\tlri = pri.getLsave().get(state);\\r\\n\\t\\t\\t\\tlrj = prj.getLsave().get(state);\\r\\n\\t \\t\\t\\t\\r\\n\\t \\t\\t\\t\\r\\n\\t \\t\\t\\t//if state matches for i make a list of time step in which they match and another list of gradient of Likelihood \\r\\n\\t \\t\\t\\tif(di.regionCurrentstate() == dri.regionCurrentstate()){\\r\\n\\t \\t\\t\\t\\tsamestate_i.add(k);\\r\\n\\t \\t\\t\\t\\tgradient_i.add(li - lri);\\r\\n\\t \\t\\t\\t}\\r\\n\\t \\t\\t\\t\\r\\n\\t \\t\\t\\t//if state matches for j make a list of time step in which they match and another list of gradient of Likelihood \\r\\n\\t \\t\\t\\tif(di.regionCurrentstate() == drj.regionCurrentstate()){\\r\\n\\t \\t\\t\\t\\tsamestate_j.add(k);\\r\\n\\t \\t\\t\\t\\tgradient_j.add(lj - lrj);\\r\\n\\t \\t\\t\\t}\\r\\n\\t \\t\\t\\t\\r\\n\\t \\t\\t}\\r\\n\\t\\t}\\r\\n\\t\\t\\t\\r\\n\\t\\t// if no match found return zero\\r\\n\\t\\tif(samestate_i.size() == 0 || samestate_j.size() == 0) {\\r\\n\\t\\t\\tcij = 0;\\r\\n\\t\\t}\\r\\n\\t\\t\\r\\n\\t\\t// else calculate cij\\r\\n\\t\\telse {\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t// if both have same length \\r\\n\\t\\t\\tif(samestate_i.size() == samestate_j.size()) {\\r\\n\\t\\t\\t\\tfor(int i=0; i<samestate_i.size(); i++) {\\r\\n\\t\\t\\t\\t\\tsum_i = sum_i + samestate_i.get(i);\\r\\n\\t\\t\\t\\t\\tsum_j = sum_j + samestate_j.get(i);\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\tdiff = (sum_i%this.w - sum_j%this.w);\\r\\n\\t\\t\\t\\tif (diff == 0) {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i, gradient_j);\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\telse {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i, gradient_j) * Math.abs(diff/(double)this.w);\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t//if i is smaller\\r\\n\\t\\t\\telse if(samestate_i.size() < samestate_j.size()) {\\r\\n\\t\\t\\t\\tfor(int i=0; i<samestate_i.size(); i++) {\\r\\n\\t\\t\\t\\t\\tsum_i = sum_i + samestate_i.get(i);\\r\\n\\t\\t\\t\\t\\tsum_j = sum_j + samestate_j.get(i);\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\tdiff = (sum_i%this.w - sum_j%this.w);\\r\\n\\t\\t\\t\\tif (diff == 0) {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i.subList(0, samestate_i.size()), gradient_j);\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\telse {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i.subList(0, samestate_i.size()), gradient_j) * diff/(double)this.w;\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t// if j is smaller \\r\\n\\t\\t\\telse {\\r\\n\\t\\t\\t\\tfor(int i=0; i<samestate_j.size(); i++) {\\r\\n\\t\\t\\t\\t\\tsum_i = sum_i + samestate_i.get(i);\\r\\n\\t\\t\\t\\t\\tsum_j = sum_j + samestate_j.get(i);\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\tdiff = (sum_i%this.w - sum_j%this.w);\\r\\n\\t\\t\\t\\tif (diff == 0) {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i.subList(0, samestate_j.size()), gradient_j);\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\telse {\\r\\n\\t\\t\\t\\t\\tcij = cosine_sim(gradient_i.subList(0, samestate_j.size()), gradient_j) * diff/(double)this.w;\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t}\\r\\n\\t\\t}\\r\\n\\t\\t\\r\\n\\t\\tif (Double.isNaN(cij)) {\\r\\n\\t\\t\\treturn 0;\\r\\n\\t\\t}\\r\\n\\t\\telse {\\r\\n\\t\\t\\treturn cij;\\r\\n\\t\\t}\\r\\n \\t}\",\n \"@Test\\n\\tvoid testCalculateMarkTillSemesterSuccess2() {\\n\\t\\t\\n\\t\\tString semesterName = \\\"20193\\\";\\n\\t\\t\\n\\t\\tUser user = userService.findByUsername(\\\"B16DCCN168\\\");\\n\\t\\tStudent student = user.getStudent();\\n\\t\\tList<StudentResult> resultsTillSemester = resultService.findResultTillSemester(student.getId(),\\n\\t\\t\\t\\tsemesterName);\\n\\t\\t\\n\\t\\tString mmtMark = \\\"\\\", tthcmMark = \\\"\\\";\\n\\t\\t\\n\\t\\tfor (StudentResult result : resultsTillSemester) {\\n\\t\\t\\tSubject subject = result.getStudentRegister().getCourse().getSubject();\\n\\t\\t\\tif (subject.getName().equals(\\\"Mạng máy tính\\\")) {\\n\\t\\t\\t\\tmmtMark = result.getMarkToChar();\\n\\t\\t\\t}\\n\\t\\t\\t\\n\\t\\t\\tif (subject.getName().equals(\\\"Tư tưởng Hồ Chí Minh\\\")) {\\n\\t\\t\\t\\ttthcmMark = result.getMarkToChar();\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\t\\n\\t\\tList<Object> listGpaResultsTillNow = MarkUtility.calculateMarkTillSemester(resultsTillSemester);\\n\\t\\tint passedCreditsTillNow = (int) listGpaResultsTillNow.get(0);\\n\\t\\tfloat gpaTillNow = (float) listGpaResultsTillNow.get(1);\\n\\t\\t\\n\\t\\tDecimalFormat df = new DecimalFormat(\\\"#.##\\\");\\n\\t\\t\\n\\t\\tassertTrue(\\n\\t\\t\\t\\tmmtMark.equals(\\\"B\\\") &&\\n\\t\\t\\t\\ttthcmMark.equals(\\\"A\\\") &&\\n\\t\\t\\t\\tpassedCreditsTillNow == 15 &&\\n\\t\\t\\t\\tdf.format(gpaTillNow).equals(\\\"3.64\\\")\\n\\t\\t\\t\\t);\\n\\t}\",\n \"@Test\\n public void testWithPropagator() {\\n CelestialBody sun = CelestialBodyFactory.getSun();\\n final Frame eci = FramesFactory.getGCRF();\\n final Frame ecef = FramesFactory.getITRF(IERSConventions.IERS_2010, true);\\n AbsoluteDate date = new AbsoluteDate(2004, 1, 1, utc);\\n OneAxisEllipsoid earth = new OneAxisEllipsoid(Constants.WGS84_EARTH_EQUATORIAL_RADIUS,\\n Constants.WGS84_EARTH_FLATTENING, ecef);\\n Orbit orbit = new KeplerianOrbit(6378137 + 400e3, 1e-3, FastMath.toRadians(50), 0, 0, 0, PositionAngle.TRUE,\\n eci, date, Constants.EIGEN5C_EARTH_MU);\\n final SpacecraftState ic = new SpacecraftState(orbit);\\n\\n final AbsoluteDate end = date.shiftedBy(5 * Constants.JULIAN_DAY);\\n final AbsoluteDate resetDate = date.shiftedBy(0.8 * Constants.JULIAN_DAY + 0.1);\\n\\n final SpacecraftState[] lastState = new SpacecraftState[1];\\n final OrekitStepHandler stepSaver = interpolator -> {\\n final AbsoluteDate start = interpolator.getPreviousState().getDate();\\n if (start.compareTo(resetDate) < 0) {\\n lastState[0] = interpolator.getPreviousState();\\n }\\n };\\n\\n // propagate with state rest to take slightly different path\\n NumericalPropagator propagator = getNumericalPropagatorWithDTM(sun, earth, ic);\\n propagator.setStepHandler(stepSaver);\\n propagator.propagate(resetDate);\\n propagator.resetInitialState(lastState[0]);\\n propagator.clearStepHandlers();\\n SpacecraftState actual = propagator.propagate(end);\\n\\n // propagate straight through\\n propagator = getNumericalPropagatorWithDTM(sun, earth, ic);\\n propagator.resetInitialState(ic);\\n propagator.clearStepHandlers();\\n SpacecraftState expected = propagator.propagate(end);\\n\\n assertThat(actual.getPVCoordinates(), pvCloseTo(expected.getPVCoordinates(), 1.0));\\n\\n // propagate with state rest to take slightly different path\\n propagator = getNumericalPropagatorWithMSIS(sun, earth, ic);\\n propagator.setStepHandler(stepSaver);\\n propagator.propagate(resetDate);\\n propagator.resetInitialState(lastState[0]);\\n propagator.clearStepHandlers();\\n actual = propagator.propagate(end);\\n\\n // propagate straight through\\n propagator = getNumericalPropagatorWithMSIS(sun, earth, ic);\\n propagator.resetInitialState(ic);\\n propagator.clearStepHandlers();\\n expected = propagator.propagate(end);\\n\\n assertThat(actual.getPVCoordinates(), pvCloseTo(expected.getPVCoordinates(), 1.0));\\n }\",\n \"public void testSVMChunkLearnng() throws IOException, GateException {\\n // Initialisation\\n System.out.print(\\\"Testing the SVM with liner kernenl on chunk learning...\\\");\\n File chunklearningHome = new File(new File(learningHome, \\\"test\\\"),\\n \\\"chunklearning\\\");\\n String configFileURL = new File(chunklearningHome, \\\"engines-svm.xml\\\")\\n .getAbsolutePath();\\n String corpusDirName = new File(chunklearningHome, \\\"data-ontonews\\\")\\n .getAbsolutePath();\\n //Remove the label list file, feature list file and chunk length files.\\n String wdResults = new File(chunklearningHome,\\n ConstantParameters.SUBDIRFORRESULTS).getAbsolutePath();\\n emptySavedFiles(wdResults);\\n String inputASN = \\\"Key\\\";\\n loadSettings(configFileURL, corpusDirName, inputASN, inputASN);\\n // Set the evaluation mode\\n RunMode runM=RunMode.EVALUATION;\\n learningApi.setLearningMode(runM);\\n controller.execute();\\n // Using the evaluation mode for testing\\n EvaluationBasedOnDocs evaluation = learningApi.getEvaluation();\\n // Compare the overall results with the correct numbers\\n assertEquals(\\\"Wrong value for correct: \\\", 44, (int)Math.floor(evaluation.macroMeasuresOfResults.correct));\\n assertEquals(\\\"Wrong value for partial: \\\", 10, (int)Math.floor(evaluation.macroMeasuresOfResults.partialCor));\\n assertEquals(\\\"Wrong value for spurious: \\\", 11, (int)Math.floor(evaluation.macroMeasuresOfResults.spurious));\\n assertEquals(\\\"Wrong value for missing: \\\", 40, (int)Math.floor(evaluation.macroMeasuresOfResults.missing));\\n\\n System.out.println(\\\"completed\\\");\\n // Remove the resources\\n clearOneTest();\\n }\",\n \"static void playC45() throws IOException {\\n\\t\\tRealVector[] data = NNDataset.getData(NNDataset.HOME);\\n\\t\\tRealVector[] label = NNDataset.getLabel(NNDataset.HOME);\\n\\t\\tRealMatrix d = MatrixUtils.createRealMatrix(data.length, data[0].getDimension());\\n\\t\\tfor (int i = 0; i < data.length; i++) {\\n\\t\\t\\td.setRowVector(i, data[i]);\\n\\t\\t}\\n\\t\\td = d.transpose(); // data = column vector\\n\\n\\t\\tRealVector l = MatrixUtils.createRealVector(new double[label.length]);\\n\\t\\tfor (int i = 0; i < l.getDimension(); i++) {\\n\\t\\t\\tl.setEntry(i, label[i].getEntry(0));\\n\\t\\t}\\n\\n\\t\\tDecisionNode root = training(d, l);\\n\\t\\tString[] header = NNDataset.getHeader(NNDataset.HOME);\\n\\t\\tprintTree(root, header);\\n\\n\\t\\tInteger lb = predict(root, data[0]);\\n\\t\\tSystem.out.println(lb);\\n\\n\\t\\tdouble ok = MSE(root, data, label);\\n\\t\\tSystem.out.println(\\\"total hit: \\\" + ok);\\n\\t}\",\n \"@Test\\n public void testVirement1() {\\n\\n //Arrange\\n CompteBancaire cpt1 = new CompteBancaire(63000.0);\\n CompteBancaire cpt2 = new CompteBancaire(43000.0);\\n double montant_a_virer = 28000.0;\\n double excepted_solde_cpt1 = 35000.0;\\n double excepted_solde_cpt2 = 71000.0;\\n\\n //Act\\n assertEquals(true, cpt1.virerArgent(cpt2, montant_a_virer));\\n\\n //Assert\\n assertEquals(excepted_solde_cpt1, cpt1.getSolde(), 0);\\n assertEquals(excepted_solde_cpt2, cpt2.getSolde(), 0);\\n\\n }\",\n \"public void controlPointsChanged(List<Coordinates> lcp) {\\n // sort the control points by x position.\\n // this makes things easier later when we paint the function.\\n lcp.sort(\\n (point1,point2) -> Integer.compare(point1.getX(),point2.getX())\\n );\\n\\n // collect the points in an array before doing the calculations.\\n // again, this makes things easier for us.\\n controlPoints=lcp.toArray(new Coordinates[0]);\\n n=controlPoints.length;\\n if(n<2) {\\n // nothing to do here\\n return;\\n }\\n double []X = new double[n];\\n double []Y = new double[n];\\n for(int i =0;i<n;i++){\\n \\tX[i]=controlPoints[i].getX();\\n \\tY[i]=controlPoints[i].getY();\\n }\\n // Construct the linear system S*C=T with n-2 equations.\\n // The matrix S contains the left hand side of the equations on slide 13.\\n // The matrix Z contains the right hand side of the equations on slide 13.\\n double[][] S=new double[n-2][n-2];\\n double[] Z=new double[n-2];\\n \\n // Fill the matrix S and the vector Z and solve S*C=T to get c_2 to c_{n-1}.\\n for(int i = 0; i< n-3;i++){\\n \\tS[i][i]=2*delta(X,i+1,0);\\n \\tS[i+1][i]=delta(X,i+2,-1);\\n \\tS[i][i+1]=delta(X,i+1,1);\\n \\tZ[i]=z(Y,X,i);\\n }\\n S[n-2][n-2]=2*delta(X,n-1,0);\\n \\n C= EliminationGaussLegendre.solve(S, Z);\\n \\n // Calculate the n-1 coefficients b_i and d_i.\\n // Note that you need all c_i here (but you know that c_1 and c_n are zero).\\n //a_i = y_i;\\n for(int i=1 ; i<n ; i++){\\n \\tD[i-1]=d(Y,X,i-1);\\n \\tB[i-1]=b(Y,C,X,i-1);\\n }\\n \\n }\",\n \"@Test\\n public void costTest() {\\n // TODO: test cost\\n }\",\n \"@Test\\n public void testAutocorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.autocorrelation(M, observable, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void test22() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n FastVector fastVector0 = evaluation0.predictions();\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01D);\\n }\",\n \"public static void main(String[] args) {\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 3, 4, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 3, 5, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 3, 4, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 3, 5, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, \\\"cartellainesistente\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, \\\"cartellainesistente\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, \\\"cartellainesistente\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, \\\"cartellainesistente\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, \\\"strategies\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, \\\"strategies\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, \\\"strategies\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, \\\"strategies\\\")));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(2, 2, 2, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(3, 3, 3, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 5, 2, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(5, 5, 2, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(6, 6, 2, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(6, 6, 2, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 3, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 3, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(7, 7, 2, true, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(7, 7, 2, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 4, false, null)));\\n tryOrPrintStackTrace(() -> measureTime(() -> testOptimalMNK(4, 4, 4, true, null)));\\n measureTime(()->checkInterruptSequential(4, 4, 3, false));\\n measureTime(()->checkInterruptSequential(4, 4, 3, true));\\n }\",\n \"@Test\\n\\tpublic void TEAM3_CONTROLLER_UT06() {\\n\\t\\t// PRECONDITIONS\\n\\t\\tClassDetailsStub c = new ClassDetailsStub(TEST_CONFIG.TueThu);\\n\\t\\t\\n\\t\\tCollection<ClassDetailsStub> s = new ArrayList<ClassDetailsStub>();\\n\\t\\ts.add(c);\\n\\t\\t\\n\\t\\tScheduleMakerController smc = new ScheduleMakerController();\\n\\t\\t\\n\\t\\t// INPUT\\n\\t\\tCollection<ScheduleStub> savedSchedules = new ArrayList<ScheduleStub>();\\n\\t\\tsavedSchedules.add(new ScheduleStub(s));\\n\\t\\t\\n\\t\\t// EXPECTED OUTPUT\\n\\t\\tException exceptionThrown = null;\\n\\t\\tboolean balanceGreaterThanZero = false;\\n\\t\\ttry {\\n\\t\\t\\tObject balanceObj = null;\\n\\t\\t\\tMethod getBalance = smc.getClass().getDeclaredMethod(\\\"getBalance\\\", (Class<?> []) null);\\n\\t\\t\\t\\n\\t\\t\\tsmc.saveSchedules(savedSchedules);\\n\\t\\t\\tbalanceObj = getBalance.invoke(smc, (Object[]) null);\\n\\t\\t\\t\\n\\t\\t\\tif (balanceObj == null) throw new NullPointerException(\\\"getBalance() returned null; expected a number\\\");\\n\\t\\t\\telse if (!(balanceObj instanceof Number)) throw new RuntimeException(\\\"getBalance() did not return a number\\\");\\n\\t\\t\\t\\n\\t\\t\\tbalanceGreaterThanZero = ((Number) balanceObj).doubleValue() > 0.0d;\\n\\t\\t} catch (Exception ex) {\\n\\t\\t\\texceptionThrown = ex;\\n\\t\\t}\\n\\t\\t\\n\\t\\tassertNull(exceptionThrown);\\n\\t\\tassertTrue(balanceGreaterThanZero);\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test28() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n doubleArray2[5] = (-236.4995664359);\\n doubleArray2[7] = 1421.4058829;\\n doubleArray0[3] = doubleArray2;\\n double[] doubleArray3 = new double[3];\\n doubleArray3[0] = 0.0;\\n doubleArray3[1] = (-236.4995664359);\\n doubleArray0[4] = doubleArray3;\\n double[] doubleArray4 = new double[21];\\n doubleArray4[0] = 1421.4058829;\\n doubleArray3[2] = (-236.4995664359);\\n doubleArray4[4] = 1.7976931348623157E308;\\n doubleArray0[7] = doubleArray4;\\n discretize0.m_NumBins = 162;\\n doubleArray0[1] = doubleArray4;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.getBinRangesString(1);\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void test28() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\\n CostMatrix costMatrix0 = costSensitiveClassifier0.getCostMatrix();\\n Evaluation evaluation0 = null;\\n try {\\n evaluation0 = new Evaluation(instances0, costMatrix0);\\n fail(\\\"Expecting exception: Exception\\\");\\n \\n } catch(Exception e) {\\n //\\n // Cost matrix not compatible with data!\\n //\\n }\\n }\",\n \"@Test\\n public void testTrain_RegressionDataSet_ExecutorService() {\\n System.out.println(\\\"train\\\");\\n RegressionDataSet trainSet = FixedProblems.getSimpleRegression1(150, new Random(2));\\n RegressionDataSet testSet = FixedProblems.getSimpleRegression1(50, new Random(3));\\n\\n for (boolean modification1 : new boolean[] { true, false })\\n for (SupportVectorLearner.CacheMode cacheMode : SupportVectorLearner.CacheMode.values()) {\\n PlattSMO smo = new PlattSMO(new RBFKernel(0.5));\\n smo.setCacheMode(cacheMode);\\n smo.setC(1);\\n smo.setEpsilon(0.1);\\n smo.setModificationOne(modification1);\\n smo.train(trainSet, true);\\n\\n double errors = 0;\\n for (int i = 0; i < testSet.size(); i++)\\n errors += Math.pow(testSet.getTargetValue(i) - smo.regress(testSet.getDataPoint(i)), 2);\\n assertTrue(errors / testSet.size() < 1);\\n }\\n }\",\n \"@Override\\n\\tprotected void setCost(double cst) {\\n\\t\\t\\n\\t}\",\n \"@Test\\n public void testGetMoveSpeedMultiplier() {\\n assertEquals(1, proj.getMoveSpeedMultiplier(), 0.001);\\n }\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.invertSelectionTipText();\\n boolean boolean0 = discretize0.m_UseBinNumbers;\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n\\tpublic void test() {\\n\\t\\tdouble delta = 0.00001;\\n\\t\\tSnp fakeSnp1 = new Snp(\\\"fakeId1\\\", 0, 0.3);\\n\\t\\tSnp fakeSnp2 = new Snp(\\\"fakeId2\\\", 0, 0.8);\\n\\t\\tSnp fakeSnp3 = new Snp(\\\"fakeId3\\\", 0, 1.4);\\n\\t\\tPascal.set.withZScore_=true;\\n\\t\\tArrayList<Snp> geneSnps = new ArrayList<Snp>();\\n\\t\\tgeneSnps.add(fakeSnp1);geneSnps.add(fakeSnp2);geneSnps.add(fakeSnp3);\\n\\t\\tDenseMatrix ld= new DenseMatrix(3,3);\\n\\t\\t//DenseMatrix crossLd= new DenseMatrix(3,2);\\n\\t\\t\\n\\t\\tArrayList<Double> snpScores = new ArrayList<Double>(3);\\n\\t\\tsnpScores.add(fakeSnp1.getZscore());\\n\\t\\tsnpScores.add(fakeSnp2.getZscore());\\n\\t\\tsnpScores.add(fakeSnp3.getZscore());\\n\\t\\t//ld and crossLd calculated as follows:\\n\\t\\t// make 0.9-toeplitz mat of size 5.\\n\\t\\t// 3,4,5 for ld-mat\\n\\t\\t// 1,2 for crossLd-mat\\n\\t\\tld.set(0,0,1);ld.set(1,1,1);ld.set(2,2,1);\\n\\t\\tld.set(0,1,0.9);ld.set(1,2,0.9);\\n\\t\\tld.set(1,0,0.9);ld.set(2,1,0.9);\\n\\t\\tld.set(0,2,0.81);\\n\\t\\tld.set(2,0,0.81);\\n\\t\\tdouble[] weights = {2,2,2};\\n\\t\\tAnalyticVegas myAnalyticObj= null;\\n\\t\\t//UpperSymmDenseMatrix myMatToDecompose = null;\\n\\t\\tdouble[] myEigenvals = null;\\n\\t\\tdouble[] emptyWeights = {1,1,1};\\n\\t\\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, emptyWeights);\\n\\t\\tmyAnalyticObj.computeScore();\\n\\t\\tmyEigenvals = myAnalyticObj.computeLambda();\\n\\t\\tassertEquals(myEigenvals[0],2.74067, delta);\\n\\t\\tassertEquals(myEigenvals[1],0.1900, delta);\\n\\t\\tassertEquals(myEigenvals[2],0.06932, delta);\\n\\t\\t\\n\\t\\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, weights);\\n\\t\\tmyAnalyticObj.computeScore();\\n\\t\\tmyEigenvals = myAnalyticObj.computeLambda();\\t\\t\\t\\n\\t\\tassertEquals(myEigenvals[0], 5.481348, delta);\\n\\t\\tassertEquals(myEigenvals[1],0.380000, delta);\\n\\t\\tassertEquals(myEigenvals[2],0.138652, delta);\\n\\t\\t\\t\\n\\t\\tdouble[] weights2 = {1,2,0.5};\\n\\t\\t\\n\\t\\tmyAnalyticObj= new AnalyticVegas(snpScores, ld, weights2);\\n\\t\\tmyAnalyticObj.computeScore();\\n\\t\\tmyEigenvals = myAnalyticObj.computeLambda();\\t\\t\\n\\t\\tassertEquals(myEigenvals[0],3.27694674, delta);\\n\\t\\tassertEquals(myEigenvals[1],0.1492338, delta);\\n\\t\\tassertEquals(myEigenvals[2],0.07381938, delta);\\t\\t\\t\\n\\t\\t\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test06() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[] doubleArray0 = new double[0];\\n int[] intArray0 = new int[4];\\n intArray0[0] = 2749;\\n intArray0[1] = (-2);\\n intArray0[2] = 0;\\n intArray0[3] = 0;\\n SparseInstance sparseInstance0 = new SparseInstance((-40.152), doubleArray0, intArray0, 919);\\n // Undeclared exception!\\n try { \\n discretize0.input(sparseInstance0);\\n fail(\\\"Expecting exception: IllegalStateException\\\");\\n \\n } catch(IllegalStateException e) {\\n //\\n // No input instance format defined\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test01() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"c9^n/\\\");\\n discretize0.getCapabilities();\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n }\",\n \"double cekStop(double x[][], double v0[],double v[][],double w0[],double w[][], double t[]){\\n double akumY=0;\\n //~ itung z_in dan z\\n for(int h=0; h<jumlah_data; h++){\\n for(int j=0; j<unit_hidden; j++){\\n //itung sigma xi vij\\n double jum_xv=0;\\n for(int i=0; i<unit_input; i++){\\n double cc=x[h][i]*v[i][j];\\n jum_xv=jum_xv+cc;\\n //System.out.println(x[h][j]);\\n }\\n z_in[j]=v0[j]+jum_xv;\\n //itung z\\n z[j]=1/(1+(double)Math.exp(-z_in[j]));\\n //System.out.println(\\\" dan z= \\\"+z[j]);\\n }\\n \\n //~ itung y_in dan y (output)\\n double cxc=0;\\n for(int k=0; k<unit_output; k++){\\n double jum_zw=0;\\n for(int j=0; j<unit_hidden; j++){\\n double cc=z[j]*w[j][k];\\n jum_zw=jum_zw+cc;\\n }\\n y_in[k]=w0[k]+jum_zw;\\n y[k]=1/(1+(double)Math.exp(-y_in[k]));\\n akumY = akumY + Math.pow((t[h]-y[k]),2);\\n //System.out.println(t[h]+\\\"-\\\"+y[k]+\\\"=\\\"+(t[k]-y[k]));\\n }\\n }\\n double E = 0.5 * akumY;\\n //System.out.println(E);\\n return E;\\n }\",\n \"public void testCleave() {\\n final String inputFile = \\\"testCleave.fas\\\";\\n final String input = TestCaseLM.getFullFilePath(inputFile).replace(\\\"%20\\\", \\\" \\\");\\n\\n try {\\n // We need to obtain a pointer the control file before anything else.\\n BufferedReader br = new BufferedReader(new FileReader(input));\\n\\n // First one with zero MC's.\\n Enzyme e = new Enzyme(\\\"TestEnzyme\\\", \\\"KR\\\", \\\"P\\\", \\\"Cterm\\\", 0);\\n Protein p = new Protein(\\\">sw|Q55645|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\\\");\\n\\n Protein[] result = e.cleave(p);\\n boolean once = false;\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now with one miscleavage.\\n e.setMiscleavages(1);\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now with three miscleavages.\\n e.setMiscleavages(3);\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Now check for the edge case where there's only one amino acid after the last cleavage position\\n // (used to be a bug that this amino acid was never considered).\\n e.setMiscleavages(1);\\n result = e.cleave(new Protein(\\\">sw|Q55645|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDRA\\\"));\\n boolean foundIt = false;\\n for(int i = 0; i < result.length; i++) {\\n Protein lProtein = result[i];\\n if(lProtein.getSequence().getSequence().equals(\\\"TGFDRA\\\")){\\n foundIt = true;\\n break;\\n }\\n }\\n\\n if(!foundIt) {\\n fail(\\\"The peptide 'TGFDRA' (penultimate amino acid is cleavage site, one missed cleavage allowed) was NOT returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now Nterm position and 1 miscleavage.\\n e.setPosition(Enzyme.NTERM);\\n e.setMiscleavages(1);\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Next, take an enzyme that does not have restricting residus.\\n e = new Enzyme(\\\"Test\\\", \\\"KR\\\", null, \\\"Cterm\\\", 0);\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // And now take an entry that has a location set and see if the cleavage is adjusted accordingly.\\n e = new Enzyme(\\\"TestEnzyme\\\", \\\"KR\\\", \\\"P\\\", \\\"Cterm\\\", 1);\\n p = new Protein(\\\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\\\");\\n\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now take a C-terminally truncated sequence and see if it is cleaved correctly.\\n e = new Enzyme(\\\"TestEnzyme\\\", \\\"KR\\\", \\\"P\\\", \\\"Cterm\\\", 1);\\n p = new Protein(\\\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\\\", true, Protein.CTERMTRUNC);\\n\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now take an N-terminally truncated sequence and see if it is cleaved correctly.\\n e = new Enzyme(\\\"TestEnzyme\\\", \\\"KR\\\", \\\"P\\\", \\\"Cterm\\\", 1);\\n p = new Protein(\\\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHGFVNRTGFDA\\\", true, Protein.NTERMTRUNC);\\n\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // Skip to the next part of the controlfile.\\n br.readLine();\\n once = false;\\n\\n // Now take a translated sequence, containing an '_' (stopcodon).\\n e = new Enzyme(\\\"TestEnzyme\\\", \\\"KR\\\", \\\"P\\\", \\\"Cterm\\\", 1);\\n p = new Protein(\\\">sw|Q55645 (15-45)|TEST_HUMAN Test Protein for the cleave() method.\\\", \\\"GHIKLMVSTRPIGASDNPKLHG_FVNRTGFDA\\\");\\n\\n result = e.cleave(p);\\n for(int i = 0; i < result.length; i++) {\\n once = true;\\n Protein lProtein = result[i];\\n String header = lProtein.getHeader().getFullHeaderWithAddenda();\\n String sequence = lProtein.getSequence().getSequence();\\n Assert.assertEquals(br.readLine(), header);\\n Assert.assertEquals(br.readLine(), sequence);\\n }\\n\\n if(!once) {\\n fail(\\\"NO peptides AT ALL were returned when cleaving '\\\" + p.getSequence().getSequence() + \\\"'!\\\");\\n }\\n\\n // END of file reached.\\n // Check this!\\n String line = null;\\n while((line = br.readLine()) != null) {\\n if(!line.trim().equals(\\\"\\\")) {\\n fail(\\\"More lines in testCleave.fas then were generated by the test cleavage!\\\");\\n }\\n }\\n br.close();\\n } catch(IOException ioe) {\\n fail(\\\"IOException occurred while testing the cleave() method: '\\\" + ioe.getMessage() + \\\"'.\\\");\\n }\\n }\",\n \"@Test\\n public void testMetastableMemberships()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.metastableMemberships(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testVerser2() {\\n\\n //Arrange\\n CompteBancaire cpt = new CompteBancaire(6500.0);\\n double montant_a_verser = -2000.0;\\n double excepted = 6500.0;\\n\\n //Act\\n assertEquals(false, cpt.verser(montant_a_verser));\\n\\n //Assert\\n assertEquals(excepted, cpt.getSolde(), 0);\\n\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71575373","0.65949225","0.5986597","0.57795","0.5703691","0.563787","0.53556013","0.5318458","0.531436","0.52704465","0.5177754","0.5174197","0.5158172","0.51175845","0.51056963","0.507546","0.5049223","0.50490606","0.5048576","0.50396436","0.50091225","0.50049496","0.49835175","0.49745733","0.49732602","0.49524283","0.4951273","0.49045762","0.490148","0.49009198","0.48959324","0.48947224","0.4891426","0.48707473","0.48685265","0.48633185","0.48515907","0.48470095","0.4844898","0.48436123","0.48387545","0.4836782","0.4828989","0.4802411","0.47978875","0.47808146","0.47798273","0.4768204","0.47634593","0.47626087","0.47494087","0.47443756","0.47443378","0.4738983","0.47319967","0.47283176","0.47174108","0.47151816","0.47100234","0.47089654","0.47085842","0.4697149","0.4692755","0.46917945","0.4666193","0.46616486","0.46554494","0.46519253","0.4648634","0.4644216","0.46313304","0.46295875","0.46276626","0.46170428","0.46129498","0.46123195","0.46072134","0.46064213","0.45993474","0.45975587","0.45975453","0.45942676","0.4593302","0.4591114","0.45910373","0.45897686","0.45873484","0.45796907","0.4575925","0.45708704","0.45677796","0.45607907","0.45571607","0.45456725","0.45397073","0.45350856","0.4524383","0.45240518","0.45236713","0.45216507"],"string":"[\n \"0.71575373\",\n \"0.65949225\",\n \"0.5986597\",\n \"0.57795\",\n \"0.5703691\",\n \"0.563787\",\n \"0.53556013\",\n \"0.5318458\",\n \"0.531436\",\n \"0.52704465\",\n \"0.5177754\",\n \"0.5174197\",\n \"0.5158172\",\n \"0.51175845\",\n \"0.51056963\",\n \"0.507546\",\n \"0.5049223\",\n \"0.50490606\",\n \"0.5048576\",\n \"0.50396436\",\n \"0.50091225\",\n \"0.50049496\",\n \"0.49835175\",\n \"0.49745733\",\n \"0.49732602\",\n \"0.49524283\",\n \"0.4951273\",\n \"0.49045762\",\n \"0.490148\",\n \"0.49009198\",\n \"0.48959324\",\n \"0.48947224\",\n \"0.4891426\",\n \"0.48707473\",\n \"0.48685265\",\n \"0.48633185\",\n \"0.48515907\",\n \"0.48470095\",\n \"0.4844898\",\n \"0.48436123\",\n \"0.48387545\",\n \"0.4836782\",\n \"0.4828989\",\n \"0.4802411\",\n \"0.47978875\",\n \"0.47808146\",\n \"0.47798273\",\n \"0.4768204\",\n \"0.47634593\",\n \"0.47626087\",\n \"0.47494087\",\n \"0.47443756\",\n \"0.47443378\",\n \"0.4738983\",\n \"0.47319967\",\n \"0.47283176\",\n \"0.47174108\",\n \"0.47151816\",\n \"0.47100234\",\n \"0.47089654\",\n \"0.47085842\",\n \"0.4697149\",\n \"0.4692755\",\n \"0.46917945\",\n \"0.4666193\",\n \"0.46616486\",\n \"0.46554494\",\n \"0.46519253\",\n \"0.4648634\",\n \"0.4644216\",\n \"0.46313304\",\n \"0.46295875\",\n \"0.46276626\",\n \"0.46170428\",\n \"0.46129498\",\n \"0.46123195\",\n \"0.46072134\",\n \"0.46064213\",\n \"0.45993474\",\n \"0.45975587\",\n \"0.45975453\",\n \"0.45942676\",\n \"0.4593302\",\n \"0.4591114\",\n \"0.45910373\",\n \"0.45897686\",\n \"0.45873484\",\n \"0.45796907\",\n \"0.4575925\",\n \"0.45708704\",\n \"0.45677796\",\n \"0.45607907\",\n \"0.45571607\",\n \"0.45456725\",\n \"0.45397073\",\n \"0.45350856\",\n \"0.4524383\",\n \"0.45240518\",\n \"0.45236713\",\n \"0.45216507\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65912473"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":135799,"cells":{"query":{"kind":"string","value":"Test of logLikelihood method, of class MarkovModelUtilities."},"document":{"kind":"string","value":"@Test\n public void testLogLikelihood()\n {\n IDoubleArray T = null;\n IDoubleArray C = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n double expResult = 0.0;\n double result = instance.logLikelihood(T, C);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["@Test\n public void testLogLikelihoodCorrelationMatrix()\n {\n IDoubleArray corr = null;\n IDoubleArray C = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n double expResult = 0.0;\n double result = instance.logLikelihoodCorrelationMatrix(corr, C);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","public void computeLogLikelihood(double[] betas, Instances instances) {\n //Basic implementation done in the prior class.\n super.computelogLikelihood(betas, instances);\n }","@Test\n public void testLogP() {\n System.out.println(\"logP\");\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\n instance.rand();\n assertEquals(Math.log(0.027), instance.logp(0), 1E-7);\n assertEquals(Math.log(0.0567), instance.logp(1), 1E-7);\n assertEquals(Math.log(0.07938), instance.logp(2), 1E-7);\n assertEquals(Math.log(0.09261), instance.logp(3), 1E-7);\n assertEquals(Math.log(0.05033709), instance.logp(10), 1E-7);\n }","@Test\n public void testEstimateT()\n {\n \n IDoubleArray counts = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateT(counts);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","public abstract double getLikelihood(String command);","com.google.privacy.dlp.v2.Likelihood getLikelihood();","int getLikelihoodValue();","public void test2 (List<List<String>> sentences) {\n double totalLogProb = 0;\n double totalNumTokens = 0;\n for (List<String> sentence : sentences) {\n totalNumTokens += sentence.size();\n double sentenceLogProb = sentenceLogProb2(sentence);\n totalLogProb += sentenceLogProb;\n }\n double perplexity = Math.exp(-totalLogProb / totalNumTokens);\n System.out.println(\"Word Perplexity = \" + perplexity );\n }","public double empiricalLikelihood(int numSamples, ArrayList<ArrayList<Integer>> testing) {\n\t\tNCRPNode[] path = new NCRPNode[numLevels];\n\t\tNCRPNode node;\n\t\t\n\t\tpath[0] = rootNode;\n\n\t\tArrayList<Integer> fs;\n\t\tint sample, level, type, token, doc, seqLen;\n\n\t\tDirichlet dirichlet = new Dirichlet(numLevels, alpha);\n\t\tdouble[] levelWeights;\n\t\t//dictionary\n\t\tdouble[] multinomial = new double[numTypes];\n\n\t\tdouble[][] likelihoods = new double[ testing.size() ][ numSamples ];\n\t\t\n\t\t//for each sample\n\t\tfor (sample = 0; sample < numSamples; sample++) {\n\t\t\tArrays.fill(multinomial, 0.0);\n\n\t\t\t//select a path\n\t\t\tfor (level = 1; level < numLevels; level++) {\n\t\t\t\tpath[level] = path[level-1].selectExisting();\n\t\t\t}\n\t \n\t\t\t//sample level weights\n\t\t\tlevelWeights = dirichlet.nextDistribution();\n\t \n\t\t\t//for each words in dictionary\n\t\t\tfor (type = 0; type < numTypes; type++) {\n\t\t\t\t//for each topic\n\t\t\t\tfor (level = 0; level < numLevels; level++) {\n\t\t\t\t\tnode = path[level];\n\t\t\t\t\tmultinomial[type] +=\n\t\t\t\t\t\tlevelWeights[level] * \n\t\t\t\t\t\t(eta + node.typeCounts[type]) /\n\t\t\t\t\t\t(etaSum + node.totalTokens);\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t\t//convert to log\n\t\t\tfor (type = 0; type < numTypes; type++) {\n\t\t\t\tmultinomial[type] = Math.log(multinomial[type]);\n\t\t\t}\n\n\t\t\t//calculate document likelihoods \n\t\t\tfor (doc=0; doc<testing.size(); doc++) {\n fs = testing.get(doc);\n seqLen = fs.size();\n \n for (token = 0; token < seqLen; token++) {\n type = fs.get(token);\n likelihoods[doc][sample] += multinomial[type];\n }\n }\n\t\t}\n\t\n double averageLogLikelihood = 0.0;\n double logNumSamples = Math.log(numSamples);\n for (doc=0; doc<testing.size(); doc++) {\n \t\n \t//find the max for normalization, avoid overflow of sum\n double max = Double.NEGATIVE_INFINITY;\n for (sample = 0; sample < numSamples; sample++) {\n if (likelihoods[doc][sample] > max) {\n max = likelihoods[doc][sample];\n }\n }\n\n double sum = 0.0;\n //normalize \n for (sample = 0; sample < numSamples; sample++) {\n sum += Math.exp(likelihoods[doc][sample] - max);\n }\n\n //calc average\n averageLogLikelihood += Math.log(sum) + max - logNumSamples;\n }\n\n\t\treturn averageLogLikelihood;\n }","@Test(timeout = 4000)\n public void test14() throws Throwable {\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\n naiveBayesMultinomialText0.m_lnorm = (double) (-2);\n double double0 = naiveBayesMultinomialText0.getLNorm();\n assertEquals((-2.0), double0, 0.01);\n }","void calculateLogLikelihoods(double[] fPartials, double[] fFrequencies, double[] fOutLogLikelihoods)\n\t{\n int v = 0;\n\t\tfor (int k = 0; k < m_nPatterns; k++) {\n\n double sum = 0.0;\n\t\t\tfor (int i = 0; i < m_nStates; i++) {\n\n\t\t\t\tsum += fFrequencies[i] * fPartials[v];\n\t\t\t\tv++;\n\t\t\t}\n fOutLogLikelihoods[k] = Math.log(sum) + getLogScalingFactor(k);\n\t\t}\n\t}","@Test\n public void whenLogarithmicFunctionThenLogarithmicResults() {\n FunctionMethod functionMethod = new FunctionMethod();\n List<Double> result = functionMethod.diapason(16, 18, x -> Math.log(x) / Math.log(2.0));\n List<Double> expected = Arrays.asList(4D, 4.08746284125034D);\n assertThat(result, is(expected));\n }","@Test(timeout = 4000)\n public void test68() throws Throwable {\n LovinsStemmer lovinsStemmer0 = new LovinsStemmer();\n String string0 = lovinsStemmer0.stemString(\"probToLogOdds: probability must be in [0,1] \");\n assertEquals(\"probtologod: prob must be in [0,1] \", string0);\n }","@Test(timeout = 4000)\n public void test40() throws Throwable {\n LovinsStemmer lovinsStemmer0 = new LovinsStemmer();\n String string0 = lovinsStemmer0.stemString(\"probToLogOdds: probability must be in [0,1] \");\n assertEquals(\"probtologod: prob must be in [0,1] \", string0);\n }","public void logThis(String log, Theory theory)\n {\n }","@Test(timeout = 4000)\n public void test42() throws Throwable {\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\n naiveBayesMultinomialText0.setLNorm(551.149718887);\n assertEquals(551.149718887, naiveBayesMultinomialText0.getLNorm(), 0.01);\n }","@Tutorial(showSource = true, showSignature = true, showLink = true, linkPrefix = \"src/test/java/\")\n @Override\n public void process(ProcessorContext context)\n {\n samples.add(model.likelihood.parameters.max.getValue() - observation.getValue());\n }","@Test\n public void test03() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double[] doubleArray0 = new double[19];\n evaluation0.updateMargins(doubleArray0, 0, 0.0);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\n }","@SuppressWarnings(\"deprecation\")\n\t@Override\n public double calculateLogP() {\n logP = 0.0;\n // jeffreys Prior!\n if (jeffreys) {\n logP += -Math.log(getChainValue(0));\n }\n for (int i = 1; i < chainParameter.getDimension(); i++) {\n final double mean = getChainValue(i - 1);\n final double x = getChainValue(i);\n\n if (useLogNormal) {\n\t final double sigma = 1.0 / shape; // shape = precision\n\t // convert mean to log space\n\t final double M = Math.log(mean) - (0.5 * sigma * sigma);\n\t logNormal.setMeanAndStdDev(M, sigma);\n\t logP += logNormal.logDensity(x);\n } else {\n final double scale = mean / shape;\n gamma.setBeta(scale);\n logP += gamma.logDensity(x);\n }\n }\n return logP;\n }","@Test(timeout = 4000)\n public void test13() throws Throwable {\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\n assertEquals(2.0, naiveBayesMultinomialText0.getLNorm(), 0.01);\n \n naiveBayesMultinomialText0.setLNorm(0);\n double double0 = naiveBayesMultinomialText0.getLNorm();\n assertEquals(0.0, double0, 0.01);\n }","public abstract double log2Prior(double betaForDimension, int dimension);","@Test\n public void testPerturbationExpectation()\n {\n IDoubleArray M = null;\n IDoubleArray pi0 = null;\n IDoubleArray observable = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.perturbationExpectation(M, pi0, observable, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","public List<Float> randomLikelihood() {\n\t\tList<Float> likelihood = new ArrayList<Float>();\n\t\tlikelihood.add((float) getRandomInteger(-200, 0));\n\t\treturn(likelihood);\n\t}","@Test\r\n public void TestFindLog() {\r\n\r\n int index = r.nextInt(100);\r\n Assert.assertEquals(logs[index], logDocument.findLog(index));\r\n\r\n }","@Test\n public void testMetastableStates()\n {\n IDoubleArray M = null;\n int nstates = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IIntArray expResult = null;\n IIntArray result = instance.metastableStates(M, nstates);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testMMI() {\n System.out.println(\"MMI\");\n int a = 0;\n int m = 0;\n int expResult = 0;\n int result = utilsHill.MMI(a, m);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testStationaryDistribution()\n {\n try\n {\n IDoubleArray T = Doubles.create.fromFile(inputT1);\n IDoubleArray expResult = Doubles.create.fromFile(inputT1_pi); \n IDoubleArray pi = MarkovModel.util.stationaryDistribution(T);\n assertEqual(pi, expResult, 1e-5);\n }\n catch(IOException e)\n {\n \n e.printStackTrace();\n }\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test051() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n evaluation0.addNumericTrainClass(0.0, Double.NaN);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","@Test\n public void test33() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.correct();\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\n assertEquals(0.0, double0, 0.01D);\n }","@Test(timeout = 4000)\n public void test026() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getStructure();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.recall((-9));\n assertEquals(0.0, double0, 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","@Test\n public void testMetastableMemberships()\n {\n IDoubleArray M = null;\n int nstates = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.metastableMemberships(M, nstates);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void test22() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n FastVector fastVector0 = evaluation0.predictions();\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01D);\n }","@Test\n public void testTrain_RegressionDataSet() {\n System.out.println(\"train\");\n RegressionDataSet trainSet = FixedProblems.getSimpleRegression1(150, new Random(2));\n RegressionDataSet testSet = FixedProblems.getSimpleRegression1(50, new Random(3));\n\n for (boolean modification1 : new boolean[] { true, false })\n for (SupportVectorLearner.CacheMode cacheMode : SupportVectorLearner.CacheMode.values()) {\n PlattSMO smo = new PlattSMO(new RBFKernel(0.5));\n smo.setCacheMode(cacheMode);\n smo.setC(1);\n smo.setEpsilon(0.1);\n smo.setModificationOne(modification1);\n smo.train(trainSet);\n\n double errors = 0;\n for (int i = 0; i < testSet.size(); i++)\n errors += Math.pow(testSet.getTargetValue(i) - smo.regress(testSet.getDataPoint(i)), 2);\n assertTrue(errors / testSet.size() < 1);\n }\n }","@Test\n public void testTrain() {\n System.out.println(\"train\");\n TokenizedLine tokenizedLine = null;\n ArrayList<Words> expResult = null;\n ArrayList<Words> result = Training.train(tokenizedLine);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n \n }","@Test(timeout = 4000)\n public void test012() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getStructure();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.priorEntropy();\n assertEquals(0.0, double0, 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","@Test\n public void testIsRateMatrix()\n {\n IDoubleArray K = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n boolean expResult = false;\n boolean result = instance.isRateMatrix(K);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","public double getLogProb(double value) {\n checkHasParams();\n if (value < 0) {\n return Double.NEGATIVE_INFINITY;\n } else {\n return logLambda - lambda * value;\n }\n }","@Test\n public void test36() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.incorrect();\n assertEquals(0.0, double0, 0.01D);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\n }","public static void main(String[] args) throws IOException{\n \n int cells = 16;\n MnistManager mm = new MnistManager(Config.MNIST_TRAIN_IMAGES, Config.MNIST_TRAIN_LABELS);\n \n // Grab first image\n mm.setCurrent(1);\n int[][] image = mm.readPixelMatrix();\n BufferedImage bimg = mm.readImage();\n \n ImageHelper.printImage(mm.readImage(), \"llf_test.png\");\n System.out.println(\"The number is: \" + mm.readLabel());\n \n // Make sure the cell size is correct -> 7 pixels\n int cellSize = image.length / (int)Math.sqrt(cells);\n System.out.println(\"The size of the cells are \" + cellSize + \" pixels\");\n \n // Check that the cell iteration is correct\n for(int i = 0; i < image.length; i += cellSize) {\n for(int j = 0; j < image[0].length; j += cellSize) {\n String info = \"Top left X: \"+i+\" Y: \"+j+\" Bottom right X: \"+(i + cellSize)+\" Y: \"+(j + cellSize);\n System.out.println(info);\n double b = slope(i, j, cellSize, image);\n System.out.println(b);\n System.out.println(\"\\n\\n\");\n }\n }\n }","@Test\n public void testEstimateCmilestoning_3args_1()\n {\n Iterable<IIntArray> trajs = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","void log(Log log);","public void testLogAccuracy() {\r\n // log the message\r\n Exception cause = new Exception(\"some error\");\r\n String message = \"some message\";\r\n log.log(Level.ALL, cause, message);\r\n\r\n // check the result\r\n String result = AccuracyTestsHelper.getFileContent(FILE);\r\n assertTrue(result.indexOf(\"some message\") >= 0);\r\n assertTrue(result.indexOf(\"some error\") >= 0);\r\n }","public void createLog(double initialFitness, double mst) {\n createLog(initialFitness, mst, null, null);\n }","@Override\n public double calculateLogP() {\n\n double fastLogP = logLhoodAllGeneTreesInSMCTree(getInverseGammaMixture(),\n popPriorScaleInput.get().getValue(), false);\n\n if (debugFlag && numberofdebugchecks < maxnumberofdebugchecks) {\n double robustLogP = logLhoodAllGeneTreesInSMCTree(getInverseGammaMixture(),\n popPriorScaleInput.get().getValue(), true);\n if (Math.abs(fastLogP - robustLogP) > 1e-12) {\n System.err.println(\"BUG in calculateLogP() in PIOMSCoalescentDistribution\");\n throw new RuntimeException(\"Fatal STACEY error.\");\n\n }\n numberofdebugchecks++;\n }\n logP = fastLogP;\n return logP;\n }","void log() {\n\t\tm_drivetrain.log();\n\t\tm_forklift.log();\n\t}","@Test(timeout = 4000)\n public void test093() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n Object[] objectArray0 = new Object[2];\n evaluation0.evaluateModel((Classifier) null, instances0, objectArray0);\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n }","@Test(timeout = 4000)\n public void test02() throws Throwable {\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate();\n naiveBayesMultinomialText0.m_leplace = (double) (-1);\n naiveBayesMultinomialText0.buildClassifier(instances0);\n assertEquals(0, naiveBayesMultinomialText0.getPeriodicPruning());\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\n assertFalse(naiveBayesMultinomialText0.getNormalizeDocLength());\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\n assertEquals(2.0, naiveBayesMultinomialText0.getLNorm(), 0.01);\n }","@Test\n public void test21() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.errorRate();\n assertEquals(Double.NaN, double0, 0.01D);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\n }","public double logZero(double x);","@Test\n public void test29() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0, (CostMatrix) null);\n double double0 = evaluation0.pctCorrect();\n assertEquals(Double.NaN, double0, 0.01D);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\n }","@Test(timeout = 4000)\n public void test059() throws Throwable {\n NaiveBayesMultinomial naiveBayesMultinomial0 = new NaiveBayesMultinomial();\n String[] stringArray0 = new String[2];\n try { \n Evaluation.evaluateModel((Classifier) naiveBayesMultinomial0, stringArray0);\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.core.Utils\", e);\n }\n }","@Test\n public void testMean() {\n System.out.println(\"mean\");\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\n instance.rand();\n assertEquals(7, instance.mean(), 1E-7);\n }","public static double exact(double mu, double lambda, double bl, MSAPoset msa)\n { \n LinearizedAlignment linearizedMSA = new LinearizedAlignment(msa);\n PIPTreeNode root = PIPTreeNode.nextUnlabelled();\n PIPTreeNode \n n1 = PIPTreeNode.withLabel(PIPMain.ta.toString()),\n n2 = PIPTreeNode.withLabel(PIPMain.tb.toString());\n UndirectedGraph<PIPTreeNode, UnorderedPair<PIPTreeNode,PIPTreeNode>> topo = GraphUtils.newUndirectedGraph();\n topo.addVertex(n1);\n topo.addVertex(n2);\n topo.addVertex(root);\n topo.addEdge(n1, root);\n topo.addEdge(n2, root);\n Map<UnorderedPair<PIPTreeNode,PIPTreeNode>, Double> bls = Maps.newLinkedHashMap();\n bls.put(UnorderedPair.of(n1, root), fraction * bl);\n bls.put(UnorderedPair.of(n2, root), (1.0-fraction)*bl);\n \n double [][] trivialRateMtx = new double[][]{{1}};\n Indexer<Character> trivialIndex = new Indexer<Character>();\n trivialIndex.addToIndex(PIPProcess.star);\n \n PoissonParameters poissonParams = new PoissonParameters(trivialIndex, trivialRateMtx, lambda, mu);\n \n PIPLikelihoodCalculator calculator = new PIPLikelihoodCalculator(poissonParams, linearizedMSA, topo, bls, root);\n double logJoint = calculator.computeDataLogProbabilityGivenTree(); \n \n double statRate = lambda / mu;\n PoissonDistribution pd = new PoissonDistribution(statRate);\n double logPrior = Math.log(pd.probability(msa.sequences().get(PIPMain.ta).length()));\n return logJoint - logPrior;\n }","@Test\n public void test18() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.KBMeanInformation();\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\n }","public double getMMLScore(Value.Model m, Value stats, Value params) {\n Value.Vector x = (Value.Vector)((Value.Structured)stats).cmpnt(0);\n return -m_LL + Math.log(x.length())*getNumParams(null);\n }","long countByExample(BehaveLogExample example);","@Test(timeout = 4000)\n public void test03() throws Throwable {\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\n \n naiveBayesMultinomialText0.m_norm = 715.85816;\n MultiClassClassifierUpdateable multiClassClassifierUpdateable0 = new MultiClassClassifierUpdateable();\n Capabilities capabilities0 = multiClassClassifierUpdateable0.getCapabilities();\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\n Instances instances0 = testInstances0.generate();\n naiveBayesMultinomialText0.buildClassifier(instances0);\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\n }","@Test(timeout = 4000)\n public void test045() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n NaiveBayesMultinomialUpdateable naiveBayesMultinomialUpdateable0 = new NaiveBayesMultinomialUpdateable();\n try { \n evaluation0.evaluateModel((Classifier) naiveBayesMultinomialUpdateable0, instances0, (Object[]) null);\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.classifiers.Evaluation\", e);\n }\n }","@Test\n public void test01() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate((String) null);\n Evaluation evaluation0 = new Evaluation(instances0);\n evaluation0.addNumericTrainClass((-1459.149165489484), ',');\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\n }","@Test(timeout = 4000)\n public void test125() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\n try { \n evaluation0.evaluateModelOnceAndRecordPrediction((Classifier) costSensitiveClassifier0, (Instance) null);\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.classifiers.Evaluation\", e);\n }\n }","@Test\n public void testEstimateCmilestoning_3args_2()\n {\n IIntArray traj = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\r\n public void testCalculateNDM() {\r\n System.out.println(\"calculateNDM\");\r\n NDM instance = null;\r\n Double expResult = null;\r\n //Double result = instance.calculateNDM();\r\n //assertEquals(expResult, result);\r\n // TODO review the generated test code and remove the default call to fail.\r\n //fail(\"The test case is a prototype.\");\r\n }","@Test(timeout = 4000)\n public void test101() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate((String) null);\n Evaluation evaluation0 = new Evaluation(instances0);\n NaiveBayesMultinomial naiveBayesMultinomial0 = new NaiveBayesMultinomial();\n MockRandom mockRandom0 = new MockRandom((-1));\n try { \n evaluation0.crossValidateModel((Classifier) naiveBayesMultinomial0, instances0, (-1), (Random) mockRandom0, (Object[]) testInstances0.DEFAULT_WORDS);\n fail(\"Expecting exception: IllegalArgumentException\");\n \n } catch(IllegalArgumentException e) {\n //\n // Number of folds must be greater than 1\n //\n verifyException(\"weka.core.Instances\", e);\n }\n }","private void log(String l) {\n if (LOG) {\n System.out.println(\"RotationMatrixTest.\" + l);\n }\n }","@Test(timeout = 4000)\n public void test022() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getStructure();\n Evaluation evaluation0 = new Evaluation(instances0);\n evaluation0.toMatrixString(\"getPreBuiltClassifiers\");\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n }","public static void main(String[] args) {\r\n // feed the generator a fixed random value for repeatable behavior\r\n MarkovTextGeneratorLoL gen = new MarkovTextGeneratorLoL(new Random(42));\r\n //String textString = \"hi there hi Leo\";\r\n //String textString = \"\";\r\n \r\n String textString = \"Hello. Hello there. This is a test. Hello there. Hello Bob. Test again.\";\r\n System.out.println(textString);\r\n gen.train(textString);\r\n System.out.println(gen);\r\n System.out.println(\"Generator: \" + gen.generateText(0));\r\n String textString2 = \"You say yes, I say no, \" +\r\n \"You say stop, and I say go, go, go, \" +\r\n \"Oh no. You say goodbye and I say hello, hello, hello, \" +\r\n \"I don't know why you say goodbye, I say hello, hello, hello, \" +\r\n \"I don't know why you say goodbye, I say hello. \" +\r\n \"I say high, you say low, \" +\r\n \"You say why, and I say I don't know. \" +\r\n \"Oh no. \" +\r\n \"You say goodbye and I say hello, hello, hello. \" +\r\n \"I don't know why you say goodbye, I say hello, hello, hello, \" +\r\n \"I don't know why you say goodbye, I say hello. \" +\r\n \"Why, why, why, why, why, why, \" +\r\n \"Do you say goodbye. \" +\r\n \"Oh no. \" +\r\n \"You say goodbye and I say hello, hello, hello. \" +\r\n \"I don't know why you say goodbye, I say hello, hello, hello, \" +\r\n \"I don't know why you say goodbye, I say hello. \" +\r\n \"You say yes, I say no, \" +\r\n \"You say stop and I say go, go, go. \" +\r\n \"Oh, oh no. \" +\r\n \"You say goodbye and I say hello, hello, hello. \" +\r\n \"I don't know why you say goodbye, I say hello, hello, hello, \" +\r\n \"I don't know why you say goodbye, I say hello, hello, hello, \" +\r\n \"I don't know why you say goodbye, I say hello, hello, hello,\";\r\n System.out.println(textString2);\r\n gen.retrain(textString2);\r\n System.out.println(gen);\r\n System.out.println(gen.generateText(20));\r\n }","@Test\n public void testForwardCommittor()\n {\n IDoubleArray M = null;\n IIntArray A = null;\n IIntArray B = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.forwardCommittor(M, A, B);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testNpara() {\n System.out.println(\"npara\");\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\n instance.rand();\n assertEquals(2, instance.npara());\n }","public double getInstanceReward(Instance selectedInstance, boolean doBernoulliRewards, boolean isTrainingInstance, boolean verbose)\n {\n double[] probDist = classifyInstance(selectedInstance, false); \n \n //Determine Expected Reward from the returned Probability Distribution\n //Note that the following 5-point Likert scale was used\n //1 - Extremely unchallenging for me\n //2 - Somewhat unchallenging for me\n //3 - Appropriately challenging for me\n //4 - Somewhat challenging for me\n //5 - Extremely challenging for me\n double[] rewardsPerClass = new double[]{0, 1d/3d, 1, 1d/3d, 0}; //rewards per class as defined in paper, section 4.2 Phase 1.\n double expectedReward = (probDist[0] * rewardsPerClass[0]) +\n (probDist[1] * rewardsPerClass[1]) +\n (probDist[2] * rewardsPerClass[2]) +\n (probDist[3] * rewardsPerClass[3]) +\n (probDist[4] * rewardsPerClass[4]);\n \n if (verbose) {\n System.out.println(\"\");\n System.out.println(\"getInstanceReward called()...\");\n System.out.println(\"-probability of instance being class 1: \" + probDist[0]);\n System.out.println(\"-probability of instance being class 2: \" + probDist[1]);\n System.out.println(\"-probability of instance being class 3: \" + probDist[2]);\n System.out.println(\"-probability of instance being class 4: \" + probDist[3]);\n System.out.println(\"-probability of instance being class 5: \" + probDist[4]);\n System.out.println(\"-the expected reward for this level segment was determined to be: \" + expectedReward );\n System.out.println(\"-done\");\n }\n \n// //Determine rewards according to Bernoulli scheme / proportional reward\n// double reward = 0.0;\n// if (doBernoulliRewards) {\n// if (verbose) System.out.println(\"-returning reward of 1 with probablity of \" + probChallenging + \", else reward of 0 (Bernoulli rewards)\");\n// boolean returnBernoulliReward;\n// if ( Math.random() <= probChallenging ) returnBernoulliReward = true;\n// else returnBernoulliReward = false;\n// if (verbose) System.out.println(\"-boolean returnBernoulliReward: \" + returnBernoulliReward);\n// if (returnBernoulliReward) reward = 1.0;\n// else reward = 0.0; \n// }\n// else {\n// if (verbose) System.out.println(\"-returning reward \" + probChallenging + \" (regular non-Bernoulli rewards)\");\n// reward = probChallenging;\n// }\n// if (verbose) System.out.println(\"-done\");\n \n return expectedReward;\n }","@Test(timeout = 4000)\n public void test28() throws Throwable {\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\n try { \n naiveBayesMultinomialText0.distributionForInstance((Instance) null);\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n }\n }","@Test\n public void test23() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n SerializedClassifier serializedClassifier0 = new SerializedClassifier();\n Object[] objectArray0 = new Object[25];\n double[] doubleArray0 = evaluation0.evaluateModel((Classifier) serializedClassifier0, instances0, objectArray0);\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01D);\n }","@Test(timeout = 4000)\n public void test094() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getStructure();\n NaiveBayesMultinomialUpdateable naiveBayesMultinomialUpdateable0 = new NaiveBayesMultinomialUpdateable();\n Evaluation evaluation0 = new Evaluation(instances0);\n Object[] objectArray0 = new Object[0];\n double[] doubleArray0 = evaluation0.evaluateModel((Classifier) naiveBayesMultinomialUpdateable0, instances0, objectArray0);\n try { \n evaluation0.evaluateModelOnce(doubleArray0, (Instance) null);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 0\n //\n verifyException(\"weka.classifiers.Evaluation\", e);\n }\n }","@Test(timeout = 4000)\n public void test54() throws Throwable {\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\n double double0 = naiveBayesMultinomialText0.getLNorm();\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\n assertEquals(0, naiveBayesMultinomialText0.getPeriodicPruning());\n assertEquals(2.0, double0, 0.01);\n assertFalse(naiveBayesMultinomialText0.getNormalizeDocLength());\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\n assertFalse(naiveBayesMultinomialText0.getUseWordFrequencies());\n }","@Test\n public void fastLogitInversesSigmoidCorrectly()\n {\n\n double maxDiff1 = Double.MIN_VALUE;\n double maxDiff2 = Double.MIN_VALUE;\n\n for (int k = 0; k < 20; k++)\n {\n double x = (double) k / 2 - 5;\n double fastSigmoidAlternative1 = fastSigmoidAlternative(x);\n double fastLogitAlternative1 = fastLogitAlternative(fastSigmoidAlternative1);\n double diff1 = abs(x - fastLogitAlternative1);\n if (!Double.isNaN(diff1) && !Double.isInfinite(diff1))\n maxDiff1 = max(diff1, maxDiff1);\n for (int i = 0; i < 20; i++)\n {\n for (int j = 0; j < 20; j++)\n {\n double rangeLow = (double) i / 10 - 1;\n double rangeHigh = (double) j / 10;\n double fastSigmoidAlternative2 = fastSigmoidAlternative(x, rangeLow, rangeHigh);\n double fastLogitAlternative2 = fastLogitAlternative(fastSigmoidAlternative2, rangeLow, rangeHigh);\n double diff2 = abs(x - fastLogitAlternative2);\n if (!Double.isNaN(diff2) && !Double.isInfinite(diff2) && ! DoubleMath.fuzzyEquals(rangeLow, rangeHigh, 1E-10))\n maxDiff2 = max(diff2, maxDiff2);\n }\n }\n }\n\n // Verify that x = logit(sigmoid(x))\n assertThat(maxDiff1, closeTo(0, .0000000001));\n assertThat(maxDiff2, closeTo(0, .0000000001));\n }","public void testPLSOM() throws Exception\r\n {\r\n System.err.println(\"PLSOM test\");\r\n PLSOM plsom = new PLSOM(2,mapWidth,mapHeight);\r\n plsom.setNeighbourhoodRange(60);\r\n double [] tmp = new double[2];\r\n Random r = new Random(1);\r\n long mean = 0;\r\n int count = 2000; \r\n //train\r\n for ( int x = 0;x< count;x++ )\r\n {\r\n //random input\r\n tmp[0] = r.nextDouble()*2-1;\r\n tmp[1] = r.nextDouble()*2-1;\r\n long start = System.nanoTime();\r\n plsom.train(tmp);\r\n long diff = System.nanoTime()-start;\r\n mean += diff;\r\n }\r\n double meanTrainTime = mean/((double)count*1000);\r\n //test\r\n mean = 0;\r\n int sqrtCount = (int)Math.sqrt(count);\r\n for ( int x = 0;x< sqrtCount;x++ )\r\n {\r\n for ( int y = 0;y<sqrtCount;y++ )\r\n {\r\n tmp[0] = (x/(double)sqrtCount)*2-1;\r\n tmp[1] = (y/(double)sqrtCount)*2-1;\r\n plsom.setInput(tmp);\r\n long start = System.nanoTime();\r\n plsom.classify();\r\n long diff = System.nanoTime()-start;\r\n mean += diff;\r\n }\r\n }\r\n double meanTestTime = mean/((double)count*1000);\r\n System.err.println(\"Train mean query time is: \" + meanTrainTime + \" microseconds.\");\r\n System.err.println(\"Test mean query time is: \" + meanTestTime + \" microseconds.\");\r\n }","public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap<Integer,Integer> words)\r\n{\r\n int t=0;\r\n double LearningRates[] = {1, 0.1, 0.01};\r\n double Margins[] = {1, 0.1, 0.01};\r\n\r\n\tint minInt =0; // Set To Your Desired Min Value\r\n int maxInt =2;\r\n\tRandom rand = new Random();\r\n\t\r\n \tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\r\n\r\n \tdouble LearningRate = LearningRates[randomNum];\r\n \t\r\nfor(int m=0;m<LearningRates.length;m++)\r\n{\r\n for(int i=0;i<LearningRates.length;i++)\r\n {\r\n\t LearningRate = LearningRates[i];\r\n\t\t double devAccuracyTotal = (double)0;\r\n\t\t int noOfEpochs= 20;\r\n\t\t int decreaseRate = 0;\r\n\t\t \r\n \t\t Double w[] = new Double[74500];\r\n\t\t Double min =-0.01; // Set To Your Desired Min Value\r\n\t Double max = 0.01;\r\n\t \t\tdouble smallRandNumber = min + new Random().nextDouble() * (max - min); \r\n\t\t\tArrays.fill(w, smallRandNumber);\r\n\t\t\t\r\n\t\t for(int epoch=0;epoch<noOfEpochs;epoch++)\r\n\t\t {\r\n\t\t\t \tw = learnMarginPerceptron(data,index,LearningRate,decreaseRate,w,Margins[m]);\r\n\t\t\t \tdecreaseRate = decreaseRate + index;\r\n\t\t\r\n\t\t }\r\n\t\t//CVSplit test\r\n \tdouble cvAccuracy = testTestFile(w,testFileName,words);\r\n \t\r\n// \tSystem.out.println(\"Epoch\" + epoch +\" Dev Accuracy for Decaying Learning Rate \" + LearningRate + \" is \" + cvAccuracy);\r\n \r\n \r\n \tSystem.out.println(\" ** Cv Accuracy for Margin Rate\" + Margins[m] + \" Learning Rate \" + LearningRate + \" is \" + cvAccuracy);\r\n \tSystem.out.println(\" \");\r\n }\r\n}\r\n//double testAccuracy = testTestFile(w,testFileName);\r\n\t//System.out.println(\"test Accuracy\" + testAccuracy);\r\n}","public void setLogModel(LogModel[] param) {\n validateLogModel(param);\n\n localLogModelTracker = true;\n\n this.localLogModel = param;\n }","@Test\n public void test30() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n boolean boolean0 = evaluation0.getDiscardPredictions();\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\n assertFalse(boolean0);\n }","public void setLogModel(LogModel param) {\n this.localLogModel = param;\n }","protected void validateLogModel(LogModel[] param) {\n }","@Test(timeout = 4000)\n public void test003() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.weightedFalsePositiveRate();\n assertEquals(Double.NaN, double0, 0.01);\n }","@Test(timeout = 4000)\n public void test031() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getStructure();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.numFalseNegatives((-1436));\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n assertEquals(0.0, double0, 0.01);\n }","@Test\n\tpublic void testRepeatibleDynamics() throws IOException, PropertiesException {\n\n\t\t\n\t\t// INSTANTIATE benchmark \n\t\tProperties props = PropertiesUtil.setpointProperties(new File (\"src/main/resources/sim.properties\"));\n\t\tSetPointGenerator lg = new SetPointGenerator (props);\n\t\tList<ExternalDriver> externalDrivers = new ArrayList<ExternalDriver>();\n\t\texternalDrivers.add(lg);\n\t\tIndustrialBenchmarkDynamics d = new IndustrialBenchmarkDynamics (props, externalDrivers);\n\t\tRandom actionRand = new Random(System.currentTimeMillis());\n \n // 1) do 100000 random steps, in order to initialize dynamics\n\t\tfinal ActionDelta action = new ActionDelta(0.001f, 0.001f, 0.001f); \n\t\tfor (int i=0; i<INIT_STEPS; i++) {\n\t\t\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\td.step(action);\n\t\t}\n\t\t\n\t\t// 2) memorize current observable state and current markov state\n\t\tfinal ObservableState os = d.getState();\n\t\tfinal DataVector ms = d.getInternalMarkovState();\n\t\tSystem.out.println (\"init o-state: \" + os.toString());\n\t\tSystem.out.println (\"init m-state: \" + ms.toString());\n\t\t\n\t\t\n\t\t// 3) perform test trajectory and memorize states\n\t\tactionRand.setSeed(ACTION_SEED);\n\t\tDataVector oStates[] = new DataVector[MEM_STEPS];\n\t\tDataVector mStates[] = new DataVector[MEM_STEPS];\n\t\t\t\t\n\t\tfor (int i=0; i<MEM_STEPS; i++) {\n\t\t\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\td.step(action);\n\t\t\toStates[i] = d.getState();\n\t\t\tmStates[i] = d.getInternalMarkovState();\n\t\t}\n\t\t\n\t\t// 4) reset dynamics & parameters and internal markov state\n\t\td.reset();\n\t\td.setInternalMarkovState(ms);\n\t\t\n\t\t// 5) reperform test and check if values are consistent\n\t\tactionRand.setSeed(ACTION_SEED); // reproduce action sequence\n\t\tDataVector oState = null;\n\t\tDataVector mState = null;\n\t\tfor (int i=0; i<MEM_STEPS; i++) {\n\t\t\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\n\t\t\t\n\t\t\td.step(action);\n\t\t\toState = d.getState();\n\t\t\tmState = d.getInternalMarkovState();\n\t\t\t\n\t\t\t// check observable state\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.SetPoint), oState.getValue(ObservableStateDescription.SetPoint), 0.0001);\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.Fatigue), oState.getValue(ObservableStateDescription.Fatigue), 0.0001);\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.Consumption), oState.getValue(ObservableStateDescription.Consumption), 0.0001);\n\t\t\tassertEquals (oStates[i].getValue(ObservableStateDescription.RewardTotal), oState.getValue(ObservableStateDescription.RewardTotal), 0.0001);\n\n\t\t\t// \n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.CurrentOperationalCost), mState.getValue(MarkovianStateDescription.CurrentOperationalCost), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent2), mState.getValue(MarkovianStateDescription.FatigueLatent2), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent1), mState.getValue(MarkovianStateDescription.FatigueLatent1), 0.0001);\n\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionGainBeta), mState.getValue(MarkovianStateDescription.EffectiveActionGainBeta), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), mState.getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveShift), mState.getValue(MarkovianStateDescription.EffectiveShift), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.MisCalibration), mState.getValue(MarkovianStateDescription.MisCalibration), 0.0001);\n\t\t\t\n\t\t\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), mState.getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), 0.0001);\n\t\t\t\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardFatigue), mState.getValue(MarkovianStateDescription.RewardFatigue), 0.0001);\n\t\t\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardConsumption), mState.getValue(MarkovianStateDescription.RewardConsumption), 0.0001);\n\t\t}\n\t\t\n\t\tSystem.out.println (\"last o-state 1st trajectory: \" + oStates[oStates.length-1]);\n\t\tSystem.out.println (\"last o-state 2nd trajectory: \" + oState);\n\t\t\n\t\tSystem.out.println (\"last m-state 1st trajectory: \" + mStates[oStates.length-1]);\n\t\tSystem.out.println (\"last m-state 2nd trajectory: \" + mState);\n\t}","@Test\n public void testSlip() {\n // Setup\n\n // Run the test\n modelUnderTest.slip();\n\n // Verify the results\n }","@Test(timeout = 4000)\n public void test025() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getDataSet();\n Evaluation evaluation0 = new Evaluation(instances0);\n evaluation0.setPriors(instances0);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","void log();","public boolean updateLog(LogModel Log);","@Test(timeout = 4000)\n public void test048() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getStructure();\n Evaluation evaluation0 = new Evaluation(instances0);\n evaluation0.areaUnderROC(17);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","protected abstract void evaluate(Vector target, List<Vector> predictions);","@Test(timeout = 4000)\n public void test51() throws Throwable {\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\n naiveBayesMultinomialText0.getTokenizer();\n assertEquals(0, naiveBayesMultinomialText0.getPeriodicPruning());\n assertEquals(2.0, naiveBayesMultinomialText0.getLNorm(), 0.01);\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\n assertFalse(naiveBayesMultinomialText0.getUseWordFrequencies());\n assertFalse(naiveBayesMultinomialText0.getNormalizeDocLength());\n }","public static double computeMleLikelihood(double[] T, double PIuni, double mu, double sigma) {\n\n double range = 100;\n double likelihood = Double.NEGATIVE_INFINITY;\n if (PIuni >= 0 && PIuni < 100) {\n PIuni = PIuni / 100;\n // loop over the elements the x array\n likelihood = 0; // init sum value\n for (int i = 0; i < T.length; i++) {\n double temp = (T[i] - mu) / sigma;\n temp = Math.exp(-0.5 * temp * temp);\n temp = temp / (SQRT2PI * sigma);\n temp = (PIuni / range) + (1 - PIuni) * temp;\n temp = Math.abs(temp);\n temp = Math.log(temp);\n likelihood = likelihood + temp;\n }\n }\n\n return likelihood;\n }","public void test_0020() {\n int nFactors = 2;\n FactorAnalysis instance = new FactorAnalysis(data, nFactors);\n\n assertEquals(instance.nObs(), 18., 1e-15);\n assertEquals(instance.nVariables(), 6., 1e-15);\n assertEquals(instance.nFactors(), 2., 1e-15);\n\n FAEstimator estimators = instance.getEstimators(400);\n\n Vector uniqueness = estimators.psi();\n assertArrayEquals(\n new double[]{0.005, 0.114, 0.642, 0.742, 0.005, 0.097},\n uniqueness.toArray(),\n 2e-2);\n\n int dof = estimators.dof();\n assertEquals(dof, 4);\n\n double fitted = estimators.logLikelihood();\n assertEquals(fitted, 1.803, 1e-3);\n\n Matrix loadings = estimators.loadings();\n assertTrue(AreMatrices.equal(\n new DenseMatrix(new double[][]{\n {0.971, 0.228},\n {0.917, 0.213},\n {0.429, 0.418},\n {0.363, 0.355},\n {0.254, 0.965},\n {0.205, 0.928}\n }),\n loadings,\n 1e-3));\n\n double testStats = estimators.statistics();\n assertEquals(testStats, 23.14, 1e-2);\n\n double pValue = estimators.pValue();\n assertEquals(pValue, 0.000119, 1e-6);//R: 1-pchisq(23.14, df=4) = 0.0001187266\n\n// System.out.println(uniqueness.toString());\n// System.out.println(fitted);\n// System.out.println(loadings.toString());\n }","@Test\r\n public void testCheckLIS() {\r\n System.out.println(\"checkLIS\");\r\n String word = \"\";\r\n IwRepoDAOMarkLogicImplStud instance = new IwRepoDAOMarkLogicImplStud();\r\n boolean expResult = false;\r\n boolean result = instance.checkLIS(word);\r\n assertEquals(expResult, result);\r\n // TODO review the generated test code and remove the default call to fail.\r\n fail(\"The test case is a prototype.\");\r\n }","@Test(timeout = 4000)\n public void test06() throws Throwable {\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\n PrecomputedKernelMatrixKernel precomputedKernelMatrixKernel0 = new PrecomputedKernelMatrixKernel();\n File file0 = precomputedKernelMatrixKernel0.getKernelMatrixFile();\n naiveBayesMultinomialText0.setStopwords(file0);\n File file1 = naiveBayesMultinomialText0.getStopwords();\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\n assertFalse(naiveBayesMultinomialText0.getUseWordFrequencies());\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\n assertEquals(2.0, naiveBayesMultinomialText0.getLNorm(), 0.01);\n assertFalse(naiveBayesMultinomialText0.getNormalizeDocLength());\n assertFalse(file1.exists());\n assertEquals(0, naiveBayesMultinomialText0.getPeriodicPruning());\n assertFalse(naiveBayesMultinomialText0.getUseStopList());\n }","@Test\r\n public void testGetLIS() {\r\n System.out.println(\"getLIS\");\r\n String word = \"\";\r\n IwRepoDAOMarkLogicImplStud instance = new IwRepoDAOMarkLogicImplStud();\r\n ImageWord expResult = null;\r\n ImageWord result = instance.getLIS(word);\r\n assertEquals(expResult, result);\r\n // TODO review the generated test code and remove the default call to fail.\r\n fail(\"The test case is a prototype.\");\r\n }","public final void log() throws ArithmeticException {\n\t\tif ((mksa&_LOG) != 0) throw new ArithmeticException\n\t\t(\"****Unit: log(\" + symbol + \")\") ;\n\t\tvalue = AstroMath.log(value);\n\t\tmksa |= _log;\n\t\tif (symbol != null) \n\t\t\tsymbol = \"[\" + symbol + \"]\";\t// Enough to indicate log\n\t}","@Test\n public void testCorrelation()\n {\n IDoubleArray M = null;\n IDoubleArray observable1 = null;\n IDoubleArray observable2 = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test81() throws Throwable {\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\n String string0 = naiveBayesMultinomialText0.LNormTipText();\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\n assertEquals(2.0, naiveBayesMultinomialText0.getLNorm(), 0.01);\n assertEquals(\"The LNorm to use for document length normalization.\", string0);\n assertEquals(0, naiveBayesMultinomialText0.getPeriodicPruning());\n assertFalse(naiveBayesMultinomialText0.getNormalizeDocLength());\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\n assertFalse(naiveBayesMultinomialText0.getUseWordFrequencies());\n }","@Test(timeout = 4000)\n public void test077() throws Throwable {\n String[] stringArray0 = new String[2];\n stringArray0[1] = \"\";\n Evaluation.main(stringArray0);\n NaiveBayesMultinomial naiveBayesMultinomial0 = new NaiveBayesMultinomial();\n try { \n Evaluation.evaluateModel((Classifier) naiveBayesMultinomial0, stringArray0);\n fail(\"Expecting exception: Exception\");\n \n } catch(Exception e) {\n //\n // \n // Weka exception: No training file and no object input file given.\n // \n // General options:\n // \n // -h or -help\n // \\tOutput help information.\n // -synopsis or -info\n // \\tOutput synopsis for classifier (use in conjunction with -h)\n // -t <name of training file>\n // \\tSets training file.\n // -T <name of test file>\n // \\tSets test file. If missing, a cross-validation will be performed\n // \\ton the training data.\n // -c <class index>\n // \\tSets index of class attribute (default: last).\n // -x <number of folds>\n // \\tSets number of folds for cross-validation (default: 10).\n // -no-cv\n // \\tDo not perform any cross validation.\n // -split-percentage <percentage>\n // \\tSets the percentage for the train/test set split, e.g., 66.\n // -preserve-order\n // \\tPreserves the order in the percentage split.\n // -s <random number seed>\n // \\tSets random number seed for cross-validation or percentage split\n // \\t(default: 1).\n // -m <name of file with cost matrix>\n // \\tSets file with cost matrix.\n // -l <name of input file>\n // \\tSets model input file. In case the filename ends with '.xml',\n // \\ta PMML file is loaded or, if that fails, options are loaded\n // \\tfrom the XML file.\n // -d <name of output file>\n // \\tSets model output file. In case the filename ends with '.xml',\n // \\tonly the options are saved to the XML file, not the model.\n // -v\n // \\tOutputs no statistics for training data.\n // -o\n // \\tOutputs statistics only, not the classifier.\n // -i\n // \\tOutputs detailed information-retrieval statistics for each class.\n // -k\n // \\tOutputs information-theoretic statistics.\n // -classifications \\\"weka.classifiers.evaluation.output.prediction.AbstractOutput + options\\\"\n // \\tUses the specified class for generating the classification output.\n // \\tE.g.: weka.classifiers.evaluation.output.prediction.PlainText\n // -p range\n // \\tOutputs predictions for test instances (or the train instances if\n // \\tno test instances provided and -no-cv is used), along with the \n // \\tattributes in the specified range (and nothing else). \n // \\tUse '-p 0' if no attributes are desired.\n // \\tDeprecated: use \\\"-classifications ...\\\" instead.\n // -distribution\n // \\tOutputs the distribution instead of only the prediction\n // \\tin conjunction with the '-p' option (only nominal classes).\n // \\tDeprecated: use \\\"-classifications ...\\\" instead.\n // -r\n // \\tOnly outputs cumulative margin distribution.\n // -xml filename | xml-string\n // \\tRetrieves the options from the XML-data instead of the command line.\n // -threshold-file <file>\n // \\tThe file to save the threshold data to.\n // \\tThe format is determined by the extensions, e.g., '.arff' for ARFF \n // \\tformat or '.csv' for CSV.\n // -threshold-label <label>\n // \\tThe class label to determine the threshold data for\n // \\t(default is the first label)\n // \n // Options specific to weka.classifiers.bayes.NaiveBayesMultinomial:\n // \n // -D\n // \\tIf set, classifier is run in debug mode and\n // \\tmay output additional info to the console\n //\n verifyException(\"weka.classifiers.Evaluation\", e);\n }\n }","@Test(timeout = 4000)\n public void test083() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getStructure();\n Evaluation evaluation0 = new Evaluation(instances0);\n double double0 = evaluation0.SFMeanEntropyGain();\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n assertEquals(Double.NaN, double0, 0.01);\n }"],"string":"[\n \"@Test\\n public void testLogLikelihoodCorrelationMatrix()\\n {\\n IDoubleArray corr = null;\\n IDoubleArray C = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n double expResult = 0.0;\\n double result = instance.logLikelihoodCorrelationMatrix(corr, C);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public void computeLogLikelihood(double[] betas, Instances instances) {\\n //Basic implementation done in the prior class.\\n super.computelogLikelihood(betas, instances);\\n }\",\n \"@Test\\n public void testLogP() {\\n System.out.println(\\\"logP\\\");\\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\\n instance.rand();\\n assertEquals(Math.log(0.027), instance.logp(0), 1E-7);\\n assertEquals(Math.log(0.0567), instance.logp(1), 1E-7);\\n assertEquals(Math.log(0.07938), instance.logp(2), 1E-7);\\n assertEquals(Math.log(0.09261), instance.logp(3), 1E-7);\\n assertEquals(Math.log(0.05033709), instance.logp(10), 1E-7);\\n }\",\n \"@Test\\n public void testEstimateT()\\n {\\n \\n IDoubleArray counts = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateT(counts);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public abstract double getLikelihood(String command);\",\n \"com.google.privacy.dlp.v2.Likelihood getLikelihood();\",\n \"int getLikelihoodValue();\",\n \"public void test2 (List<List<String>> sentences) {\\n double totalLogProb = 0;\\n double totalNumTokens = 0;\\n for (List<String> sentence : sentences) {\\n totalNumTokens += sentence.size();\\n double sentenceLogProb = sentenceLogProb2(sentence);\\n totalLogProb += sentenceLogProb;\\n }\\n double perplexity = Math.exp(-totalLogProb / totalNumTokens);\\n System.out.println(\\\"Word Perplexity = \\\" + perplexity );\\n }\",\n \"public double empiricalLikelihood(int numSamples, ArrayList<ArrayList<Integer>> testing) {\\n\\t\\tNCRPNode[] path = new NCRPNode[numLevels];\\n\\t\\tNCRPNode node;\\n\\t\\t\\n\\t\\tpath[0] = rootNode;\\n\\n\\t\\tArrayList<Integer> fs;\\n\\t\\tint sample, level, type, token, doc, seqLen;\\n\\n\\t\\tDirichlet dirichlet = new Dirichlet(numLevels, alpha);\\n\\t\\tdouble[] levelWeights;\\n\\t\\t//dictionary\\n\\t\\tdouble[] multinomial = new double[numTypes];\\n\\n\\t\\tdouble[][] likelihoods = new double[ testing.size() ][ numSamples ];\\n\\t\\t\\n\\t\\t//for each sample\\n\\t\\tfor (sample = 0; sample < numSamples; sample++) {\\n\\t\\t\\tArrays.fill(multinomial, 0.0);\\n\\n\\t\\t\\t//select a path\\n\\t\\t\\tfor (level = 1; level < numLevels; level++) {\\n\\t\\t\\t\\tpath[level] = path[level-1].selectExisting();\\n\\t\\t\\t}\\n\\t \\n\\t\\t\\t//sample level weights\\n\\t\\t\\tlevelWeights = dirichlet.nextDistribution();\\n\\t \\n\\t\\t\\t//for each words in dictionary\\n\\t\\t\\tfor (type = 0; type < numTypes; type++) {\\n\\t\\t\\t\\t//for each topic\\n\\t\\t\\t\\tfor (level = 0; level < numLevels; level++) {\\n\\t\\t\\t\\t\\tnode = path[level];\\n\\t\\t\\t\\t\\tmultinomial[type] +=\\n\\t\\t\\t\\t\\t\\tlevelWeights[level] * \\n\\t\\t\\t\\t\\t\\t(eta + node.typeCounts[type]) /\\n\\t\\t\\t\\t\\t\\t(etaSum + node.totalTokens);\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t}\\n\\n\\t\\t\\t//convert to log\\n\\t\\t\\tfor (type = 0; type < numTypes; type++) {\\n\\t\\t\\t\\tmultinomial[type] = Math.log(multinomial[type]);\\n\\t\\t\\t}\\n\\n\\t\\t\\t//calculate document likelihoods \\n\\t\\t\\tfor (doc=0; doc<testing.size(); doc++) {\\n fs = testing.get(doc);\\n seqLen = fs.size();\\n \\n for (token = 0; token < seqLen; token++) {\\n type = fs.get(token);\\n likelihoods[doc][sample] += multinomial[type];\\n }\\n }\\n\\t\\t}\\n\\t\\n double averageLogLikelihood = 0.0;\\n double logNumSamples = Math.log(numSamples);\\n for (doc=0; doc<testing.size(); doc++) {\\n \\t\\n \\t//find the max for normalization, avoid overflow of sum\\n double max = Double.NEGATIVE_INFINITY;\\n for (sample = 0; sample < numSamples; sample++) {\\n if (likelihoods[doc][sample] > max) {\\n max = likelihoods[doc][sample];\\n }\\n }\\n\\n double sum = 0.0;\\n //normalize \\n for (sample = 0; sample < numSamples; sample++) {\\n sum += Math.exp(likelihoods[doc][sample] - max);\\n }\\n\\n //calc average\\n averageLogLikelihood += Math.log(sum) + max - logNumSamples;\\n }\\n\\n\\t\\treturn averageLogLikelihood;\\n }\",\n \"@Test(timeout = 4000)\\n public void test14() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n naiveBayesMultinomialText0.m_lnorm = (double) (-2);\\n double double0 = naiveBayesMultinomialText0.getLNorm();\\n assertEquals((-2.0), double0, 0.01);\\n }\",\n \"void calculateLogLikelihoods(double[] fPartials, double[] fFrequencies, double[] fOutLogLikelihoods)\\n\\t{\\n int v = 0;\\n\\t\\tfor (int k = 0; k < m_nPatterns; k++) {\\n\\n double sum = 0.0;\\n\\t\\t\\tfor (int i = 0; i < m_nStates; i++) {\\n\\n\\t\\t\\t\\tsum += fFrequencies[i] * fPartials[v];\\n\\t\\t\\t\\tv++;\\n\\t\\t\\t}\\n fOutLogLikelihoods[k] = Math.log(sum) + getLogScalingFactor(k);\\n\\t\\t}\\n\\t}\",\n \"@Test\\n public void whenLogarithmicFunctionThenLogarithmicResults() {\\n FunctionMethod functionMethod = new FunctionMethod();\\n List<Double> result = functionMethod.diapason(16, 18, x -> Math.log(x) / Math.log(2.0));\\n List<Double> expected = Arrays.asList(4D, 4.08746284125034D);\\n assertThat(result, is(expected));\\n }\",\n \"@Test(timeout = 4000)\\n public void test68() throws Throwable {\\n LovinsStemmer lovinsStemmer0 = new LovinsStemmer();\\n String string0 = lovinsStemmer0.stemString(\\\"probToLogOdds: probability must be in [0,1] \\\");\\n assertEquals(\\\"probtologod: prob must be in [0,1] \\\", string0);\\n }\",\n \"@Test(timeout = 4000)\\n public void test40() throws Throwable {\\n LovinsStemmer lovinsStemmer0 = new LovinsStemmer();\\n String string0 = lovinsStemmer0.stemString(\\\"probToLogOdds: probability must be in [0,1] \\\");\\n assertEquals(\\\"probtologod: prob must be in [0,1] \\\", string0);\\n }\",\n \"public void logThis(String log, Theory theory)\\n {\\n }\",\n \"@Test(timeout = 4000)\\n public void test42() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n naiveBayesMultinomialText0.setLNorm(551.149718887);\\n assertEquals(551.149718887, naiveBayesMultinomialText0.getLNorm(), 0.01);\\n }\",\n \"@Tutorial(showSource = true, showSignature = true, showLink = true, linkPrefix = \\\"src/test/java/\\\")\\n @Override\\n public void process(ProcessorContext context)\\n {\\n samples.add(model.likelihood.parameters.max.getValue() - observation.getValue());\\n }\",\n \"@Test\\n public void test03() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double[] doubleArray0 = new double[19];\\n evaluation0.updateMargins(doubleArray0, 0, 0.0);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n }\",\n \"@SuppressWarnings(\\\"deprecation\\\")\\n\\t@Override\\n public double calculateLogP() {\\n logP = 0.0;\\n // jeffreys Prior!\\n if (jeffreys) {\\n logP += -Math.log(getChainValue(0));\\n }\\n for (int i = 1; i < chainParameter.getDimension(); i++) {\\n final double mean = getChainValue(i - 1);\\n final double x = getChainValue(i);\\n\\n if (useLogNormal) {\\n\\t final double sigma = 1.0 / shape; // shape = precision\\n\\t // convert mean to log space\\n\\t final double M = Math.log(mean) - (0.5 * sigma * sigma);\\n\\t logNormal.setMeanAndStdDev(M, sigma);\\n\\t logP += logNormal.logDensity(x);\\n } else {\\n final double scale = mean / shape;\\n gamma.setBeta(scale);\\n logP += gamma.logDensity(x);\\n }\\n }\\n return logP;\\n }\",\n \"@Test(timeout = 4000)\\n public void test13() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n assertEquals(2.0, naiveBayesMultinomialText0.getLNorm(), 0.01);\\n \\n naiveBayesMultinomialText0.setLNorm(0);\\n double double0 = naiveBayesMultinomialText0.getLNorm();\\n assertEquals(0.0, double0, 0.01);\\n }\",\n \"public abstract double log2Prior(double betaForDimension, int dimension);\",\n \"@Test\\n public void testPerturbationExpectation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray pi0 = null;\\n IDoubleArray observable = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.perturbationExpectation(M, pi0, observable, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public List<Float> randomLikelihood() {\\n\\t\\tList<Float> likelihood = new ArrayList<Float>();\\n\\t\\tlikelihood.add((float) getRandomInteger(-200, 0));\\n\\t\\treturn(likelihood);\\n\\t}\",\n \"@Test\\r\\n public void TestFindLog() {\\r\\n\\r\\n int index = r.nextInt(100);\\r\\n Assert.assertEquals(logs[index], logDocument.findLog(index));\\r\\n\\r\\n }\",\n \"@Test\\n public void testMetastableStates()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IIntArray expResult = null;\\n IIntArray result = instance.metastableStates(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testMMI() {\\n System.out.println(\\\"MMI\\\");\\n int a = 0;\\n int m = 0;\\n int expResult = 0;\\n int result = utilsHill.MMI(a, m);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testStationaryDistribution()\\n {\\n try\\n {\\n IDoubleArray T = Doubles.create.fromFile(inputT1);\\n IDoubleArray expResult = Doubles.create.fromFile(inputT1_pi); \\n IDoubleArray pi = MarkovModel.util.stationaryDistribution(T);\\n assertEqual(pi, expResult, 1e-5);\\n }\\n catch(IOException e)\\n {\\n \\n e.printStackTrace();\\n }\\n // TODO review the generated test code and remove the default call to fail.\\n //fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test051() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.addNumericTrainClass(0.0, Double.NaN);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"@Test\\n public void test33() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.correct();\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n assertEquals(0.0, double0, 0.01D);\\n }\",\n \"@Test(timeout = 4000)\\n public void test026() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.recall((-9));\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"@Test\\n public void testMetastableMemberships()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.metastableMemberships(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void test22() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n FastVector fastVector0 = evaluation0.predictions();\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01D);\\n }\",\n \"@Test\\n public void testTrain_RegressionDataSet() {\\n System.out.println(\\\"train\\\");\\n RegressionDataSet trainSet = FixedProblems.getSimpleRegression1(150, new Random(2));\\n RegressionDataSet testSet = FixedProblems.getSimpleRegression1(50, new Random(3));\\n\\n for (boolean modification1 : new boolean[] { true, false })\\n for (SupportVectorLearner.CacheMode cacheMode : SupportVectorLearner.CacheMode.values()) {\\n PlattSMO smo = new PlattSMO(new RBFKernel(0.5));\\n smo.setCacheMode(cacheMode);\\n smo.setC(1);\\n smo.setEpsilon(0.1);\\n smo.setModificationOne(modification1);\\n smo.train(trainSet);\\n\\n double errors = 0;\\n for (int i = 0; i < testSet.size(); i++)\\n errors += Math.pow(testSet.getTargetValue(i) - smo.regress(testSet.getDataPoint(i)), 2);\\n assertTrue(errors / testSet.size() < 1);\\n }\\n }\",\n \"@Test\\n public void testTrain() {\\n System.out.println(\\\"train\\\");\\n TokenizedLine tokenizedLine = null;\\n ArrayList<Words> expResult = null;\\n ArrayList<Words> result = Training.train(tokenizedLine);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n \\n }\",\n \"@Test(timeout = 4000)\\n public void test012() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.priorEntropy();\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"@Test\\n public void testIsRateMatrix()\\n {\\n IDoubleArray K = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n boolean expResult = false;\\n boolean result = instance.isRateMatrix(K);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public double getLogProb(double value) {\\n checkHasParams();\\n if (value < 0) {\\n return Double.NEGATIVE_INFINITY;\\n } else {\\n return logLambda - lambda * value;\\n }\\n }\",\n \"@Test\\n public void test36() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.incorrect();\\n assertEquals(0.0, double0, 0.01D);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n }\",\n \"public static void main(String[] args) throws IOException{\\n \\n int cells = 16;\\n MnistManager mm = new MnistManager(Config.MNIST_TRAIN_IMAGES, Config.MNIST_TRAIN_LABELS);\\n \\n // Grab first image\\n mm.setCurrent(1);\\n int[][] image = mm.readPixelMatrix();\\n BufferedImage bimg = mm.readImage();\\n \\n ImageHelper.printImage(mm.readImage(), \\\"llf_test.png\\\");\\n System.out.println(\\\"The number is: \\\" + mm.readLabel());\\n \\n // Make sure the cell size is correct -> 7 pixels\\n int cellSize = image.length / (int)Math.sqrt(cells);\\n System.out.println(\\\"The size of the cells are \\\" + cellSize + \\\" pixels\\\");\\n \\n // Check that the cell iteration is correct\\n for(int i = 0; i < image.length; i += cellSize) {\\n for(int j = 0; j < image[0].length; j += cellSize) {\\n String info = \\\"Top left X: \\\"+i+\\\" Y: \\\"+j+\\\" Bottom right X: \\\"+(i + cellSize)+\\\" Y: \\\"+(j + cellSize);\\n System.out.println(info);\\n double b = slope(i, j, cellSize, image);\\n System.out.println(b);\\n System.out.println(\\\"\\\\n\\\\n\\\");\\n }\\n }\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_3args_1()\\n {\\n Iterable<IIntArray> trajs = null;\\n Iterable<IIntArray> cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"void log(Log log);\",\n \"public void testLogAccuracy() {\\r\\n // log the message\\r\\n Exception cause = new Exception(\\\"some error\\\");\\r\\n String message = \\\"some message\\\";\\r\\n log.log(Level.ALL, cause, message);\\r\\n\\r\\n // check the result\\r\\n String result = AccuracyTestsHelper.getFileContent(FILE);\\r\\n assertTrue(result.indexOf(\\\"some message\\\") >= 0);\\r\\n assertTrue(result.indexOf(\\\"some error\\\") >= 0);\\r\\n }\",\n \"public void createLog(double initialFitness, double mst) {\\n createLog(initialFitness, mst, null, null);\\n }\",\n \"@Override\\n public double calculateLogP() {\\n\\n double fastLogP = logLhoodAllGeneTreesInSMCTree(getInverseGammaMixture(),\\n popPriorScaleInput.get().getValue(), false);\\n\\n if (debugFlag && numberofdebugchecks < maxnumberofdebugchecks) {\\n double robustLogP = logLhoodAllGeneTreesInSMCTree(getInverseGammaMixture(),\\n popPriorScaleInput.get().getValue(), true);\\n if (Math.abs(fastLogP - robustLogP) > 1e-12) {\\n System.err.println(\\\"BUG in calculateLogP() in PIOMSCoalescentDistribution\\\");\\n throw new RuntimeException(\\\"Fatal STACEY error.\\\");\\n\\n }\\n numberofdebugchecks++;\\n }\\n logP = fastLogP;\\n return logP;\\n }\",\n \"void log() {\\n\\t\\tm_drivetrain.log();\\n\\t\\tm_forklift.log();\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test093() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n Object[] objectArray0 = new Object[2];\\n evaluation0.evaluateModel((Classifier) null, instances0, objectArray0);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n naiveBayesMultinomialText0.m_leplace = (double) (-1);\\n naiveBayesMultinomialText0.buildClassifier(instances0);\\n assertEquals(0, naiveBayesMultinomialText0.getPeriodicPruning());\\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\\n assertFalse(naiveBayesMultinomialText0.getNormalizeDocLength());\\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\\n assertEquals(2.0, naiveBayesMultinomialText0.getLNorm(), 0.01);\\n }\",\n \"@Test\\n public void test21() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.errorRate();\\n assertEquals(Double.NaN, double0, 0.01D);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n }\",\n \"public double logZero(double x);\",\n \"@Test\\n public void test29() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0, (CostMatrix) null);\\n double double0 = evaluation0.pctCorrect();\\n assertEquals(Double.NaN, double0, 0.01D);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n }\",\n \"@Test(timeout = 4000)\\n public void test059() throws Throwable {\\n NaiveBayesMultinomial naiveBayesMultinomial0 = new NaiveBayesMultinomial();\\n String[] stringArray0 = new String[2];\\n try { \\n Evaluation.evaluateModel((Classifier) naiveBayesMultinomial0, stringArray0);\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.core.Utils\\\", e);\\n }\\n }\",\n \"@Test\\n public void testMean() {\\n System.out.println(\\\"mean\\\");\\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\\n instance.rand();\\n assertEquals(7, instance.mean(), 1E-7);\\n }\",\n \"public static double exact(double mu, double lambda, double bl, MSAPoset msa)\\n { \\n LinearizedAlignment linearizedMSA = new LinearizedAlignment(msa);\\n PIPTreeNode root = PIPTreeNode.nextUnlabelled();\\n PIPTreeNode \\n n1 = PIPTreeNode.withLabel(PIPMain.ta.toString()),\\n n2 = PIPTreeNode.withLabel(PIPMain.tb.toString());\\n UndirectedGraph<PIPTreeNode, UnorderedPair<PIPTreeNode,PIPTreeNode>> topo = GraphUtils.newUndirectedGraph();\\n topo.addVertex(n1);\\n topo.addVertex(n2);\\n topo.addVertex(root);\\n topo.addEdge(n1, root);\\n topo.addEdge(n2, root);\\n Map<UnorderedPair<PIPTreeNode,PIPTreeNode>, Double> bls = Maps.newLinkedHashMap();\\n bls.put(UnorderedPair.of(n1, root), fraction * bl);\\n bls.put(UnorderedPair.of(n2, root), (1.0-fraction)*bl);\\n \\n double [][] trivialRateMtx = new double[][]{{1}};\\n Indexer<Character> trivialIndex = new Indexer<Character>();\\n trivialIndex.addToIndex(PIPProcess.star);\\n \\n PoissonParameters poissonParams = new PoissonParameters(trivialIndex, trivialRateMtx, lambda, mu);\\n \\n PIPLikelihoodCalculator calculator = new PIPLikelihoodCalculator(poissonParams, linearizedMSA, topo, bls, root);\\n double logJoint = calculator.computeDataLogProbabilityGivenTree(); \\n \\n double statRate = lambda / mu;\\n PoissonDistribution pd = new PoissonDistribution(statRate);\\n double logPrior = Math.log(pd.probability(msa.sequences().get(PIPMain.ta).length()));\\n return logJoint - logPrior;\\n }\",\n \"@Test\\n public void test18() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.KBMeanInformation();\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n }\",\n \"public double getMMLScore(Value.Model m, Value stats, Value params) {\\n Value.Vector x = (Value.Vector)((Value.Structured)stats).cmpnt(0);\\n return -m_LL + Math.log(x.length())*getNumParams(null);\\n }\",\n \"long countByExample(BehaveLogExample example);\",\n \"@Test(timeout = 4000)\\n public void test03() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\\n \\n naiveBayesMultinomialText0.m_norm = 715.85816;\\n MultiClassClassifierUpdateable multiClassClassifierUpdateable0 = new MultiClassClassifierUpdateable();\\n Capabilities capabilities0 = multiClassClassifierUpdateable0.getCapabilities();\\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\\n Instances instances0 = testInstances0.generate();\\n naiveBayesMultinomialText0.buildClassifier(instances0);\\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test045() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n NaiveBayesMultinomialUpdateable naiveBayesMultinomialUpdateable0 = new NaiveBayesMultinomialUpdateable();\\n try { \\n evaluation0.evaluateModel((Classifier) naiveBayesMultinomialUpdateable0, instances0, (Object[]) null);\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.classifiers.Evaluation\\\", e);\\n }\\n }\",\n \"@Test\\n public void test01() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate((String) null);\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.addNumericTrainClass((-1459.149165489484), ',');\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n }\",\n \"@Test(timeout = 4000)\\n public void test125() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\\n try { \\n evaluation0.evaluateModelOnceAndRecordPrediction((Classifier) costSensitiveClassifier0, (Instance) null);\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.classifiers.Evaluation\\\", e);\\n }\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_3args_2()\\n {\\n IIntArray traj = null;\\n Iterable<IIntArray> cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\r\\n public void testCalculateNDM() {\\r\\n System.out.println(\\\"calculateNDM\\\");\\r\\n NDM instance = null;\\r\\n Double expResult = null;\\r\\n //Double result = instance.calculateNDM();\\r\\n //assertEquals(expResult, result);\\r\\n // TODO review the generated test code and remove the default call to fail.\\r\\n //fail(\\\"The test case is a prototype.\\\");\\r\\n }\",\n \"@Test(timeout = 4000)\\n public void test101() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate((String) null);\\n Evaluation evaluation0 = new Evaluation(instances0);\\n NaiveBayesMultinomial naiveBayesMultinomial0 = new NaiveBayesMultinomial();\\n MockRandom mockRandom0 = new MockRandom((-1));\\n try { \\n evaluation0.crossValidateModel((Classifier) naiveBayesMultinomial0, instances0, (-1), (Random) mockRandom0, (Object[]) testInstances0.DEFAULT_WORDS);\\n fail(\\\"Expecting exception: IllegalArgumentException\\\");\\n \\n } catch(IllegalArgumentException e) {\\n //\\n // Number of folds must be greater than 1\\n //\\n verifyException(\\\"weka.core.Instances\\\", e);\\n }\\n }\",\n \"private void log(String l) {\\n if (LOG) {\\n System.out.println(\\\"RotationMatrixTest.\\\" + l);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test022() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.toMatrixString(\\\"getPreBuiltClassifiers\\\");\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"public static void main(String[] args) {\\r\\n // feed the generator a fixed random value for repeatable behavior\\r\\n MarkovTextGeneratorLoL gen = new MarkovTextGeneratorLoL(new Random(42));\\r\\n //String textString = \\\"hi there hi Leo\\\";\\r\\n //String textString = \\\"\\\";\\r\\n \\r\\n String textString = \\\"Hello. Hello there. This is a test. Hello there. Hello Bob. Test again.\\\";\\r\\n System.out.println(textString);\\r\\n gen.train(textString);\\r\\n System.out.println(gen);\\r\\n System.out.println(\\\"Generator: \\\" + gen.generateText(0));\\r\\n String textString2 = \\\"You say yes, I say no, \\\" +\\r\\n \\\"You say stop, and I say go, go, go, \\\" +\\r\\n \\\"Oh no. You say goodbye and I say hello, hello, hello, \\\" +\\r\\n \\\"I don't know why you say goodbye, I say hello, hello, hello, \\\" +\\r\\n \\\"I don't know why you say goodbye, I say hello. \\\" +\\r\\n \\\"I say high, you say low, \\\" +\\r\\n \\\"You say why, and I say I don't know. \\\" +\\r\\n \\\"Oh no. \\\" +\\r\\n \\\"You say goodbye and I say hello, hello, hello. \\\" +\\r\\n \\\"I don't know why you say goodbye, I say hello, hello, hello, \\\" +\\r\\n \\\"I don't know why you say goodbye, I say hello. \\\" +\\r\\n \\\"Why, why, why, why, why, why, \\\" +\\r\\n \\\"Do you say goodbye. \\\" +\\r\\n \\\"Oh no. \\\" +\\r\\n \\\"You say goodbye and I say hello, hello, hello. \\\" +\\r\\n \\\"I don't know why you say goodbye, I say hello, hello, hello, \\\" +\\r\\n \\\"I don't know why you say goodbye, I say hello. \\\" +\\r\\n \\\"You say yes, I say no, \\\" +\\r\\n \\\"You say stop and I say go, go, go. \\\" +\\r\\n \\\"Oh, oh no. \\\" +\\r\\n \\\"You say goodbye and I say hello, hello, hello. \\\" +\\r\\n \\\"I don't know why you say goodbye, I say hello, hello, hello, \\\" +\\r\\n \\\"I don't know why you say goodbye, I say hello, hello, hello, \\\" +\\r\\n \\\"I don't know why you say goodbye, I say hello, hello, hello,\\\";\\r\\n System.out.println(textString2);\\r\\n gen.retrain(textString2);\\r\\n System.out.println(gen);\\r\\n System.out.println(gen.generateText(20));\\r\\n }\",\n \"@Test\\n public void testForwardCommittor()\\n {\\n IDoubleArray M = null;\\n IIntArray A = null;\\n IIntArray B = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.forwardCommittor(M, A, B);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testNpara() {\\n System.out.println(\\\"npara\\\");\\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\\n instance.rand();\\n assertEquals(2, instance.npara());\\n }\",\n \"public double getInstanceReward(Instance selectedInstance, boolean doBernoulliRewards, boolean isTrainingInstance, boolean verbose)\\n {\\n double[] probDist = classifyInstance(selectedInstance, false); \\n \\n //Determine Expected Reward from the returned Probability Distribution\\n //Note that the following 5-point Likert scale was used\\n //1 - Extremely unchallenging for me\\n //2 - Somewhat unchallenging for me\\n //3 - Appropriately challenging for me\\n //4 - Somewhat challenging for me\\n //5 - Extremely challenging for me\\n double[] rewardsPerClass = new double[]{0, 1d/3d, 1, 1d/3d, 0}; //rewards per class as defined in paper, section 4.2 Phase 1.\\n double expectedReward = (probDist[0] * rewardsPerClass[0]) +\\n (probDist[1] * rewardsPerClass[1]) +\\n (probDist[2] * rewardsPerClass[2]) +\\n (probDist[3] * rewardsPerClass[3]) +\\n (probDist[4] * rewardsPerClass[4]);\\n \\n if (verbose) {\\n System.out.println(\\\"\\\");\\n System.out.println(\\\"getInstanceReward called()...\\\");\\n System.out.println(\\\"-probability of instance being class 1: \\\" + probDist[0]);\\n System.out.println(\\\"-probability of instance being class 2: \\\" + probDist[1]);\\n System.out.println(\\\"-probability of instance being class 3: \\\" + probDist[2]);\\n System.out.println(\\\"-probability of instance being class 4: \\\" + probDist[3]);\\n System.out.println(\\\"-probability of instance being class 5: \\\" + probDist[4]);\\n System.out.println(\\\"-the expected reward for this level segment was determined to be: \\\" + expectedReward );\\n System.out.println(\\\"-done\\\");\\n }\\n \\n// //Determine rewards according to Bernoulli scheme / proportional reward\\n// double reward = 0.0;\\n// if (doBernoulliRewards) {\\n// if (verbose) System.out.println(\\\"-returning reward of 1 with probablity of \\\" + probChallenging + \\\", else reward of 0 (Bernoulli rewards)\\\");\\n// boolean returnBernoulliReward;\\n// if ( Math.random() <= probChallenging ) returnBernoulliReward = true;\\n// else returnBernoulliReward = false;\\n// if (verbose) System.out.println(\\\"-boolean returnBernoulliReward: \\\" + returnBernoulliReward);\\n// if (returnBernoulliReward) reward = 1.0;\\n// else reward = 0.0; \\n// }\\n// else {\\n// if (verbose) System.out.println(\\\"-returning reward \\\" + probChallenging + \\\" (regular non-Bernoulli rewards)\\\");\\n// reward = probChallenging;\\n// }\\n// if (verbose) System.out.println(\\\"-done\\\");\\n \\n return expectedReward;\\n }\",\n \"@Test(timeout = 4000)\\n public void test28() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n try { \\n naiveBayesMultinomialText0.distributionForInstance((Instance) null);\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n }\\n }\",\n \"@Test\\n public void test23() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n SerializedClassifier serializedClassifier0 = new SerializedClassifier();\\n Object[] objectArray0 = new Object[25];\\n double[] doubleArray0 = evaluation0.evaluateModel((Classifier) serializedClassifier0, instances0, objectArray0);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01D);\\n }\",\n \"@Test(timeout = 4000)\\n public void test094() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n NaiveBayesMultinomialUpdateable naiveBayesMultinomialUpdateable0 = new NaiveBayesMultinomialUpdateable();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n Object[] objectArray0 = new Object[0];\\n double[] doubleArray0 = evaluation0.evaluateModel((Classifier) naiveBayesMultinomialUpdateable0, instances0, objectArray0);\\n try { \\n evaluation0.evaluateModelOnce(doubleArray0, (Instance) null);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 0\\n //\\n verifyException(\\\"weka.classifiers.Evaluation\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test54() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n double double0 = naiveBayesMultinomialText0.getLNorm();\\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\\n assertEquals(0, naiveBayesMultinomialText0.getPeriodicPruning());\\n assertEquals(2.0, double0, 0.01);\\n assertFalse(naiveBayesMultinomialText0.getNormalizeDocLength());\\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\\n assertFalse(naiveBayesMultinomialText0.getUseWordFrequencies());\\n }\",\n \"@Test\\n public void fastLogitInversesSigmoidCorrectly()\\n {\\n\\n double maxDiff1 = Double.MIN_VALUE;\\n double maxDiff2 = Double.MIN_VALUE;\\n\\n for (int k = 0; k < 20; k++)\\n {\\n double x = (double) k / 2 - 5;\\n double fastSigmoidAlternative1 = fastSigmoidAlternative(x);\\n double fastLogitAlternative1 = fastLogitAlternative(fastSigmoidAlternative1);\\n double diff1 = abs(x - fastLogitAlternative1);\\n if (!Double.isNaN(diff1) && !Double.isInfinite(diff1))\\n maxDiff1 = max(diff1, maxDiff1);\\n for (int i = 0; i < 20; i++)\\n {\\n for (int j = 0; j < 20; j++)\\n {\\n double rangeLow = (double) i / 10 - 1;\\n double rangeHigh = (double) j / 10;\\n double fastSigmoidAlternative2 = fastSigmoidAlternative(x, rangeLow, rangeHigh);\\n double fastLogitAlternative2 = fastLogitAlternative(fastSigmoidAlternative2, rangeLow, rangeHigh);\\n double diff2 = abs(x - fastLogitAlternative2);\\n if (!Double.isNaN(diff2) && !Double.isInfinite(diff2) && ! DoubleMath.fuzzyEquals(rangeLow, rangeHigh, 1E-10))\\n maxDiff2 = max(diff2, maxDiff2);\\n }\\n }\\n }\\n\\n // Verify that x = logit(sigmoid(x))\\n assertThat(maxDiff1, closeTo(0, .0000000001));\\n assertThat(maxDiff2, closeTo(0, .0000000001));\\n }\",\n \"public void testPLSOM() throws Exception\\r\\n {\\r\\n System.err.println(\\\"PLSOM test\\\");\\r\\n PLSOM plsom = new PLSOM(2,mapWidth,mapHeight);\\r\\n plsom.setNeighbourhoodRange(60);\\r\\n double [] tmp = new double[2];\\r\\n Random r = new Random(1);\\r\\n long mean = 0;\\r\\n int count = 2000; \\r\\n //train\\r\\n for ( int x = 0;x< count;x++ )\\r\\n {\\r\\n //random input\\r\\n tmp[0] = r.nextDouble()*2-1;\\r\\n tmp[1] = r.nextDouble()*2-1;\\r\\n long start = System.nanoTime();\\r\\n plsom.train(tmp);\\r\\n long diff = System.nanoTime()-start;\\r\\n mean += diff;\\r\\n }\\r\\n double meanTrainTime = mean/((double)count*1000);\\r\\n //test\\r\\n mean = 0;\\r\\n int sqrtCount = (int)Math.sqrt(count);\\r\\n for ( int x = 0;x< sqrtCount;x++ )\\r\\n {\\r\\n for ( int y = 0;y<sqrtCount;y++ )\\r\\n {\\r\\n tmp[0] = (x/(double)sqrtCount)*2-1;\\r\\n tmp[1] = (y/(double)sqrtCount)*2-1;\\r\\n plsom.setInput(tmp);\\r\\n long start = System.nanoTime();\\r\\n plsom.classify();\\r\\n long diff = System.nanoTime()-start;\\r\\n mean += diff;\\r\\n }\\r\\n }\\r\\n double meanTestTime = mean/((double)count*1000);\\r\\n System.err.println(\\\"Train mean query time is: \\\" + meanTrainTime + \\\" microseconds.\\\");\\r\\n System.err.println(\\\"Test mean query time is: \\\" + meanTestTime + \\\" microseconds.\\\");\\r\\n }\",\n \"public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap<Integer,Integer> words)\\r\\n{\\r\\n int t=0;\\r\\n double LearningRates[] = {1, 0.1, 0.01};\\r\\n double Margins[] = {1, 0.1, 0.01};\\r\\n\\r\\n\\tint minInt =0; // Set To Your Desired Min Value\\r\\n int maxInt =2;\\r\\n\\tRandom rand = new Random();\\r\\n\\t\\r\\n \\tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\\r\\n\\r\\n \\tdouble LearningRate = LearningRates[randomNum];\\r\\n \\t\\r\\nfor(int m=0;m<LearningRates.length;m++)\\r\\n{\\r\\n for(int i=0;i<LearningRates.length;i++)\\r\\n {\\r\\n\\t LearningRate = LearningRates[i];\\r\\n\\t\\t double devAccuracyTotal = (double)0;\\r\\n\\t\\t int noOfEpochs= 20;\\r\\n\\t\\t int decreaseRate = 0;\\r\\n\\t\\t \\r\\n \\t\\t Double w[] = new Double[74500];\\r\\n\\t\\t Double min =-0.01; // Set To Your Desired Min Value\\r\\n\\t Double max = 0.01;\\r\\n\\t \\t\\tdouble smallRandNumber = min + new Random().nextDouble() * (max - min); \\r\\n\\t\\t\\tArrays.fill(w, smallRandNumber);\\r\\n\\t\\t\\t\\r\\n\\t\\t for(int epoch=0;epoch<noOfEpochs;epoch++)\\r\\n\\t\\t {\\r\\n\\t\\t\\t \\tw = learnMarginPerceptron(data,index,LearningRate,decreaseRate,w,Margins[m]);\\r\\n\\t\\t\\t \\tdecreaseRate = decreaseRate + index;\\r\\n\\t\\t\\r\\n\\t\\t }\\r\\n\\t\\t//CVSplit test\\r\\n \\tdouble cvAccuracy = testTestFile(w,testFileName,words);\\r\\n \\t\\r\\n// \\tSystem.out.println(\\\"Epoch\\\" + epoch +\\\" Dev Accuracy for Decaying Learning Rate \\\" + LearningRate + \\\" is \\\" + cvAccuracy);\\r\\n \\r\\n \\r\\n \\tSystem.out.println(\\\" ** Cv Accuracy for Margin Rate\\\" + Margins[m] + \\\" Learning Rate \\\" + LearningRate + \\\" is \\\" + cvAccuracy);\\r\\n \\tSystem.out.println(\\\" \\\");\\r\\n }\\r\\n}\\r\\n//double testAccuracy = testTestFile(w,testFileName);\\r\\n\\t//System.out.println(\\\"test Accuracy\\\" + testAccuracy);\\r\\n}\",\n \"public void setLogModel(LogModel[] param) {\\n validateLogModel(param);\\n\\n localLogModelTracker = true;\\n\\n this.localLogModel = param;\\n }\",\n \"@Test\\n public void test30() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n boolean boolean0 = evaluation0.getDiscardPredictions();\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n assertFalse(boolean0);\\n }\",\n \"public void setLogModel(LogModel param) {\\n this.localLogModel = param;\\n }\",\n \"protected void validateLogModel(LogModel[] param) {\\n }\",\n \"@Test(timeout = 4000)\\n public void test003() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.weightedFalsePositiveRate();\\n assertEquals(Double.NaN, double0, 0.01);\\n }\",\n \"@Test(timeout = 4000)\\n public void test031() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.numFalseNegatives((-1436));\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(0.0, double0, 0.01);\\n }\",\n \"@Test\\n\\tpublic void testRepeatibleDynamics() throws IOException, PropertiesException {\\n\\n\\t\\t\\n\\t\\t// INSTANTIATE benchmark \\n\\t\\tProperties props = PropertiesUtil.setpointProperties(new File (\\\"src/main/resources/sim.properties\\\"));\\n\\t\\tSetPointGenerator lg = new SetPointGenerator (props);\\n\\t\\tList<ExternalDriver> externalDrivers = new ArrayList<ExternalDriver>();\\n\\t\\texternalDrivers.add(lg);\\n\\t\\tIndustrialBenchmarkDynamics d = new IndustrialBenchmarkDynamics (props, externalDrivers);\\n\\t\\tRandom actionRand = new Random(System.currentTimeMillis());\\n \\n // 1) do 100000 random steps, in order to initialize dynamics\\n\\t\\tfinal ActionDelta action = new ActionDelta(0.001f, 0.001f, 0.001f); \\n\\t\\tfor (int i=0; i<INIT_STEPS; i++) {\\n\\t\\t\\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\td.step(action);\\n\\t\\t}\\n\\t\\t\\n\\t\\t// 2) memorize current observable state and current markov state\\n\\t\\tfinal ObservableState os = d.getState();\\n\\t\\tfinal DataVector ms = d.getInternalMarkovState();\\n\\t\\tSystem.out.println (\\\"init o-state: \\\" + os.toString());\\n\\t\\tSystem.out.println (\\\"init m-state: \\\" + ms.toString());\\n\\t\\t\\n\\t\\t\\n\\t\\t// 3) perform test trajectory and memorize states\\n\\t\\tactionRand.setSeed(ACTION_SEED);\\n\\t\\tDataVector oStates[] = new DataVector[MEM_STEPS];\\n\\t\\tDataVector mStates[] = new DataVector[MEM_STEPS];\\n\\t\\t\\t\\t\\n\\t\\tfor (int i=0; i<MEM_STEPS; i++) {\\n\\t\\t\\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\td.step(action);\\n\\t\\t\\toStates[i] = d.getState();\\n\\t\\t\\tmStates[i] = d.getInternalMarkovState();\\n\\t\\t}\\n\\t\\t\\n\\t\\t// 4) reset dynamics & parameters and internal markov state\\n\\t\\td.reset();\\n\\t\\td.setInternalMarkovState(ms);\\n\\t\\t\\n\\t\\t// 5) reperform test and check if values are consistent\\n\\t\\tactionRand.setSeed(ACTION_SEED); // reproduce action sequence\\n\\t\\tDataVector oState = null;\\n\\t\\tDataVector mState = null;\\n\\t\\tfor (int i=0; i<MEM_STEPS; i++) {\\n\\t\\t\\taction.setDeltaGain(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaVelocity(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\taction.setDeltaShift(2.f*(actionRand.nextFloat()-0.5f));\\n\\t\\t\\t\\n\\t\\t\\td.step(action);\\n\\t\\t\\toState = d.getState();\\n\\t\\t\\tmState = d.getInternalMarkovState();\\n\\t\\t\\t\\n\\t\\t\\t// check observable state\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.SetPoint), oState.getValue(ObservableStateDescription.SetPoint), 0.0001);\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.Fatigue), oState.getValue(ObservableStateDescription.Fatigue), 0.0001);\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.Consumption), oState.getValue(ObservableStateDescription.Consumption), 0.0001);\\n\\t\\t\\tassertEquals (oStates[i].getValue(ObservableStateDescription.RewardTotal), oState.getValue(ObservableStateDescription.RewardTotal), 0.0001);\\n\\n\\t\\t\\t// \\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.CurrentOperationalCost), mState.getValue(MarkovianStateDescription.CurrentOperationalCost), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent2), mState.getValue(MarkovianStateDescription.FatigueLatent2), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.FatigueLatent1), mState.getValue(MarkovianStateDescription.FatigueLatent1), 0.0001);\\n\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionGainBeta), mState.getValue(MarkovianStateDescription.EffectiveActionGainBeta), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), mState.getValue(MarkovianStateDescription.EffectiveActionVelocityAlpha), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.EffectiveShift), mState.getValue(MarkovianStateDescription.EffectiveShift), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.MisCalibration), mState.getValue(MarkovianStateDescription.MisCalibration), 0.0001);\\n\\t\\t\\t\\n\\t\\t\\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), mState.getValue(SetPointGeneratorStateDescription.SetPointChangeRatePerStep), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointCurrentSteps), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), mState.getValue(SetPointGeneratorStateDescription.SetPointLastSequenceSteps), 0.0001);\\n\\t\\t\\t\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardFatigue), mState.getValue(MarkovianStateDescription.RewardFatigue), 0.0001);\\n\\t\\t\\tassertEquals (mStates[i].getValue(MarkovianStateDescription.RewardConsumption), mState.getValue(MarkovianStateDescription.RewardConsumption), 0.0001);\\n\\t\\t}\\n\\t\\t\\n\\t\\tSystem.out.println (\\\"last o-state 1st trajectory: \\\" + oStates[oStates.length-1]);\\n\\t\\tSystem.out.println (\\\"last o-state 2nd trajectory: \\\" + oState);\\n\\t\\t\\n\\t\\tSystem.out.println (\\\"last m-state 1st trajectory: \\\" + mStates[oStates.length-1]);\\n\\t\\tSystem.out.println (\\\"last m-state 2nd trajectory: \\\" + mState);\\n\\t}\",\n \"@Test\\n public void testSlip() {\\n // Setup\\n\\n // Run the test\\n modelUnderTest.slip();\\n\\n // Verify the results\\n }\",\n \"@Test(timeout = 4000)\\n public void test025() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.setPriors(instances0);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"void log();\",\n \"public boolean updateLog(LogModel Log);\",\n \"@Test(timeout = 4000)\\n public void test048() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.areaUnderROC(17);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"protected abstract void evaluate(Vector target, List<Vector> predictions);\",\n \"@Test(timeout = 4000)\\n public void test51() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n naiveBayesMultinomialText0.getTokenizer();\\n assertEquals(0, naiveBayesMultinomialText0.getPeriodicPruning());\\n assertEquals(2.0, naiveBayesMultinomialText0.getLNorm(), 0.01);\\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\\n assertFalse(naiveBayesMultinomialText0.getUseWordFrequencies());\\n assertFalse(naiveBayesMultinomialText0.getNormalizeDocLength());\\n }\",\n \"public static double computeMleLikelihood(double[] T, double PIuni, double mu, double sigma) {\\n\\n double range = 100;\\n double likelihood = Double.NEGATIVE_INFINITY;\\n if (PIuni >= 0 && PIuni < 100) {\\n PIuni = PIuni / 100;\\n // loop over the elements the x array\\n likelihood = 0; // init sum value\\n for (int i = 0; i < T.length; i++) {\\n double temp = (T[i] - mu) / sigma;\\n temp = Math.exp(-0.5 * temp * temp);\\n temp = temp / (SQRT2PI * sigma);\\n temp = (PIuni / range) + (1 - PIuni) * temp;\\n temp = Math.abs(temp);\\n temp = Math.log(temp);\\n likelihood = likelihood + temp;\\n }\\n }\\n\\n return likelihood;\\n }\",\n \"public void test_0020() {\\n int nFactors = 2;\\n FactorAnalysis instance = new FactorAnalysis(data, nFactors);\\n\\n assertEquals(instance.nObs(), 18., 1e-15);\\n assertEquals(instance.nVariables(), 6., 1e-15);\\n assertEquals(instance.nFactors(), 2., 1e-15);\\n\\n FAEstimator estimators = instance.getEstimators(400);\\n\\n Vector uniqueness = estimators.psi();\\n assertArrayEquals(\\n new double[]{0.005, 0.114, 0.642, 0.742, 0.005, 0.097},\\n uniqueness.toArray(),\\n 2e-2);\\n\\n int dof = estimators.dof();\\n assertEquals(dof, 4);\\n\\n double fitted = estimators.logLikelihood();\\n assertEquals(fitted, 1.803, 1e-3);\\n\\n Matrix loadings = estimators.loadings();\\n assertTrue(AreMatrices.equal(\\n new DenseMatrix(new double[][]{\\n {0.971, 0.228},\\n {0.917, 0.213},\\n {0.429, 0.418},\\n {0.363, 0.355},\\n {0.254, 0.965},\\n {0.205, 0.928}\\n }),\\n loadings,\\n 1e-3));\\n\\n double testStats = estimators.statistics();\\n assertEquals(testStats, 23.14, 1e-2);\\n\\n double pValue = estimators.pValue();\\n assertEquals(pValue, 0.000119, 1e-6);//R: 1-pchisq(23.14, df=4) = 0.0001187266\\n\\n// System.out.println(uniqueness.toString());\\n// System.out.println(fitted);\\n// System.out.println(loadings.toString());\\n }\",\n \"@Test\\r\\n public void testCheckLIS() {\\r\\n System.out.println(\\\"checkLIS\\\");\\r\\n String word = \\\"\\\";\\r\\n IwRepoDAOMarkLogicImplStud instance = new IwRepoDAOMarkLogicImplStud();\\r\\n boolean expResult = false;\\r\\n boolean result = instance.checkLIS(word);\\r\\n assertEquals(expResult, result);\\r\\n // TODO review the generated test code and remove the default call to fail.\\r\\n fail(\\\"The test case is a prototype.\\\");\\r\\n }\",\n \"@Test(timeout = 4000)\\n public void test06() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n PrecomputedKernelMatrixKernel precomputedKernelMatrixKernel0 = new PrecomputedKernelMatrixKernel();\\n File file0 = precomputedKernelMatrixKernel0.getKernelMatrixFile();\\n naiveBayesMultinomialText0.setStopwords(file0);\\n File file1 = naiveBayesMultinomialText0.getStopwords();\\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\\n assertFalse(naiveBayesMultinomialText0.getUseWordFrequencies());\\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\\n assertEquals(2.0, naiveBayesMultinomialText0.getLNorm(), 0.01);\\n assertFalse(naiveBayesMultinomialText0.getNormalizeDocLength());\\n assertFalse(file1.exists());\\n assertEquals(0, naiveBayesMultinomialText0.getPeriodicPruning());\\n assertFalse(naiveBayesMultinomialText0.getUseStopList());\\n }\",\n \"@Test\\r\\n public void testGetLIS() {\\r\\n System.out.println(\\\"getLIS\\\");\\r\\n String word = \\\"\\\";\\r\\n IwRepoDAOMarkLogicImplStud instance = new IwRepoDAOMarkLogicImplStud();\\r\\n ImageWord expResult = null;\\r\\n ImageWord result = instance.getLIS(word);\\r\\n assertEquals(expResult, result);\\r\\n // TODO review the generated test code and remove the default call to fail.\\r\\n fail(\\\"The test case is a prototype.\\\");\\r\\n }\",\n \"public final void log() throws ArithmeticException {\\n\\t\\tif ((mksa&_LOG) != 0) throw new ArithmeticException\\n\\t\\t(\\\"****Unit: log(\\\" + symbol + \\\")\\\") ;\\n\\t\\tvalue = AstroMath.log(value);\\n\\t\\tmksa |= _log;\\n\\t\\tif (symbol != null) \\n\\t\\t\\tsymbol = \\\"[\\\" + symbol + \\\"]\\\";\\t// Enough to indicate log\\n\\t}\",\n \"@Test\\n public void testCorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable1 = null;\\n IDoubleArray observable2 = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test81() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n String string0 = naiveBayesMultinomialText0.LNormTipText();\\n assertEquals(3.0, naiveBayesMultinomialText0.getMinWordFrequency(), 0.01);\\n assertEquals(2.0, naiveBayesMultinomialText0.getLNorm(), 0.01);\\n assertEquals(\\\"The LNorm to use for document length normalization.\\\", string0);\\n assertEquals(0, naiveBayesMultinomialText0.getPeriodicPruning());\\n assertFalse(naiveBayesMultinomialText0.getNormalizeDocLength());\\n assertEquals(1.0, naiveBayesMultinomialText0.getNorm(), 0.01);\\n assertFalse(naiveBayesMultinomialText0.getUseWordFrequencies());\\n }\",\n \"@Test(timeout = 4000)\\n public void test077() throws Throwable {\\n String[] stringArray0 = new String[2];\\n stringArray0[1] = \\\"\\\";\\n Evaluation.main(stringArray0);\\n NaiveBayesMultinomial naiveBayesMultinomial0 = new NaiveBayesMultinomial();\\n try { \\n Evaluation.evaluateModel((Classifier) naiveBayesMultinomial0, stringArray0);\\n fail(\\\"Expecting exception: Exception\\\");\\n \\n } catch(Exception e) {\\n //\\n // \\n // Weka exception: No training file and no object input file given.\\n // \\n // General options:\\n // \\n // -h or -help\\n // \\\\tOutput help information.\\n // -synopsis or -info\\n // \\\\tOutput synopsis for classifier (use in conjunction with -h)\\n // -t <name of training file>\\n // \\\\tSets training file.\\n // -T <name of test file>\\n // \\\\tSets test file. If missing, a cross-validation will be performed\\n // \\\\ton the training data.\\n // -c <class index>\\n // \\\\tSets index of class attribute (default: last).\\n // -x <number of folds>\\n // \\\\tSets number of folds for cross-validation (default: 10).\\n // -no-cv\\n // \\\\tDo not perform any cross validation.\\n // -split-percentage <percentage>\\n // \\\\tSets the percentage for the train/test set split, e.g., 66.\\n // -preserve-order\\n // \\\\tPreserves the order in the percentage split.\\n // -s <random number seed>\\n // \\\\tSets random number seed for cross-validation or percentage split\\n // \\\\t(default: 1).\\n // -m <name of file with cost matrix>\\n // \\\\tSets file with cost matrix.\\n // -l <name of input file>\\n // \\\\tSets model input file. In case the filename ends with '.xml',\\n // \\\\ta PMML file is loaded or, if that fails, options are loaded\\n // \\\\tfrom the XML file.\\n // -d <name of output file>\\n // \\\\tSets model output file. In case the filename ends with '.xml',\\n // \\\\tonly the options are saved to the XML file, not the model.\\n // -v\\n // \\\\tOutputs no statistics for training data.\\n // -o\\n // \\\\tOutputs statistics only, not the classifier.\\n // -i\\n // \\\\tOutputs detailed information-retrieval statistics for each class.\\n // -k\\n // \\\\tOutputs information-theoretic statistics.\\n // -classifications \\\\\\\"weka.classifiers.evaluation.output.prediction.AbstractOutput + options\\\\\\\"\\n // \\\\tUses the specified class for generating the classification output.\\n // \\\\tE.g.: weka.classifiers.evaluation.output.prediction.PlainText\\n // -p range\\n // \\\\tOutputs predictions for test instances (or the train instances if\\n // \\\\tno test instances provided and -no-cv is used), along with the \\n // \\\\tattributes in the specified range (and nothing else). \\n // \\\\tUse '-p 0' if no attributes are desired.\\n // \\\\tDeprecated: use \\\\\\\"-classifications ...\\\\\\\" instead.\\n // -distribution\\n // \\\\tOutputs the distribution instead of only the prediction\\n // \\\\tin conjunction with the '-p' option (only nominal classes).\\n // \\\\tDeprecated: use \\\\\\\"-classifications ...\\\\\\\" instead.\\n // -r\\n // \\\\tOnly outputs cumulative margin distribution.\\n // -xml filename | xml-string\\n // \\\\tRetrieves the options from the XML-data instead of the command line.\\n // -threshold-file <file>\\n // \\\\tThe file to save the threshold data to.\\n // \\\\tThe format is determined by the extensions, e.g., '.arff' for ARFF \\n // \\\\tformat or '.csv' for CSV.\\n // -threshold-label <label>\\n // \\\\tThe class label to determine the threshold data for\\n // \\\\t(default is the first label)\\n // \\n // Options specific to weka.classifiers.bayes.NaiveBayesMultinomial:\\n // \\n // -D\\n // \\\\tIf set, classifier is run in debug mode and\\n // \\\\tmay output additional info to the console\\n //\\n verifyException(\\\"weka.classifiers.Evaluation\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test083() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.SFMeanEntropyGain();\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, double0, 0.01);\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7427038","0.66404974","0.6166927","0.5927131","0.5869066","0.56961393","0.55797553","0.5571208","0.5482358","0.54607975","0.5375718","0.5269194","0.52524656","0.5225311","0.52244943","0.5209235","0.51835144","0.51666826","0.51612955","0.51324403","0.5106958","0.509174","0.50861907","0.5084256","0.506817","0.50583553","0.50580794","0.5053669","0.50403553","0.5035418","0.5027856","0.5024973","0.50182116","0.5015083","0.50058436","0.49936283","0.49896488","0.49802783","0.4972849","0.4968762","0.4967253","0.4961648","0.49538022","0.4934286","0.4924379","0.4920171","0.4918766","0.49144915","0.49132162","0.49102497","0.49024618","0.49003986","0.48910427","0.48906612","0.489039","0.4887172","0.48870128","0.4872422","0.4865407","0.48559827","0.4851867","0.4851198","0.4828478","0.4820917","0.48162195","0.4810992","0.47851023","0.47831306","0.47681564","0.476582","0.47635767","0.4761168","0.47605193","0.47571817","0.47495136","0.47488818","0.47471437","0.47452116","0.47361922","0.47353965","0.47307587","0.47250813","0.47142136","0.47136307","0.4709314","0.47092596","0.4701","0.47009462","0.46907344","0.4687461","0.4684473","0.46735126","0.46668038","0.4666704","0.46660745","0.465778","0.46495485","0.46448883","0.46397632","0.4635751"],"string":"[\n \"0.7427038\",\n \"0.66404974\",\n \"0.6166927\",\n \"0.5927131\",\n \"0.5869066\",\n \"0.56961393\",\n \"0.55797553\",\n \"0.5571208\",\n \"0.5482358\",\n \"0.54607975\",\n \"0.5375718\",\n \"0.5269194\",\n \"0.52524656\",\n \"0.5225311\",\n \"0.52244943\",\n \"0.5209235\",\n \"0.51835144\",\n \"0.51666826\",\n \"0.51612955\",\n \"0.51324403\",\n \"0.5106958\",\n \"0.509174\",\n \"0.50861907\",\n \"0.5084256\",\n \"0.506817\",\n \"0.50583553\",\n \"0.50580794\",\n \"0.5053669\",\n \"0.50403553\",\n \"0.5035418\",\n \"0.5027856\",\n \"0.5024973\",\n \"0.50182116\",\n \"0.5015083\",\n \"0.50058436\",\n \"0.49936283\",\n \"0.49896488\",\n \"0.49802783\",\n \"0.4972849\",\n \"0.4968762\",\n \"0.4967253\",\n \"0.4961648\",\n \"0.49538022\",\n \"0.4934286\",\n \"0.4924379\",\n \"0.4920171\",\n \"0.4918766\",\n \"0.49144915\",\n \"0.49132162\",\n \"0.49102497\",\n \"0.49024618\",\n \"0.49003986\",\n \"0.48910427\",\n \"0.48906612\",\n \"0.489039\",\n \"0.4887172\",\n \"0.48870128\",\n \"0.4872422\",\n \"0.4865407\",\n \"0.48559827\",\n \"0.4851867\",\n \"0.4851198\",\n \"0.4828478\",\n \"0.4820917\",\n \"0.48162195\",\n \"0.4810992\",\n \"0.47851023\",\n \"0.47831306\",\n \"0.47681564\",\n \"0.476582\",\n \"0.47635767\",\n \"0.4761168\",\n \"0.47605193\",\n \"0.47571817\",\n \"0.47495136\",\n \"0.47488818\",\n \"0.47471437\",\n \"0.47452116\",\n \"0.47361922\",\n \"0.47353965\",\n \"0.47307587\",\n \"0.47250813\",\n \"0.47142136\",\n \"0.47136307\",\n \"0.4709314\",\n \"0.47092596\",\n \"0.4701\",\n \"0.47009462\",\n \"0.46907344\",\n \"0.4687461\",\n \"0.4684473\",\n \"0.46735126\",\n \"0.46668038\",\n \"0.4666704\",\n \"0.46660745\",\n \"0.465778\",\n \"0.46495485\",\n \"0.46448883\",\n \"0.46397632\",\n \"0.4635751\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.88419306"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":1357,"numItemsPerPage":100,"numTotalItems":136513,"offset":135700,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MzU2MDExNSwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1qYXZhLXYxIiwiZXhwIjoxNzczNTYzNzE1LCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.UjdMgXTtsSLKnZp9O910p5KOT_-kRww_o1B-kaQFUQooSa2TgewPQX0SFZevbn5NV_woq3_-h_PHIHvEw_o7DQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":136513,"numBytesParquet":1558040430}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 7 33.1k | document stringlengths 7 335k | metadata dict | negatives listlengths 3 101 | negative_scores listlengths 3 101 | document_score stringlengths 3 10 | document_rank stringclasses 102 values |
|---|---|---|---|---|---|---|
MODIFIES: this EFFECTS: return the information of the agent searched | public void searchRecruited(String name)
throws ImpossibleAgentInListException, ImpossibleAgentException, IOException {
recruitedAgents.load(file);
if (recruitedAgents.contains(recruitedAgents.getAgent(name))) {
searchAgent(name);
} else {
throw new ImpossibleAgentInListException();
}
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void searchAgent(String name) throws ImpossibleAgentException {\n if (rhineLifeAgents.contains(rhineLifeAgents.getAgent(name))) {\n Agent a = rhineLifeAgents.getAgent(name);\n a.getInfo();\n } else if (rhodeIslandAgent.contains(rhodeIslandAgent.getAgent(name))) {\n ... | [
"0.5797074",
"0.5643062",
"0.56214124",
"0.55864406",
"0.5512807",
"0.54693806",
"0.5444098",
"0.5440309",
"0.54208416",
"0.5414219",
"0.53796494",
"0.53231055",
"0.5309439",
"0.53027093",
"0.52747273",
"0.52382225",
"0.52283645",
"0.5211649",
"0.51818776",
"0.51641953",
"0.5... | 0.0 | -1 |
TODO it need adding dates checking in condition | public List<Property> findAllForReport(List<KufType> value, UUID balanceHolder, UUID orgCat, Date from, Date to) {
EntityManager em = getEntityManagerFactory().createEntityManager();
CriteriaBuilder cb = em.getCriteriaBuilder();
try {
CriteriaQuery<Property> cq = cb.createQuery(Property.class);
Root<Property> c = cq.from(Property.class);
cq.select(c);
Predicate condition = null;
if (value != null) {
condition = c.get("kuf").in(value);
}
if (user.getId() != SuperUser.ID) {
if (condition != null) {
condition = cb.and(c.get("readers").in(user.getId()), condition);
} else {
condition = c.get("readers").in(user.getId());
}
}
if (balanceHolder != null) {
if (condition != null) {
condition = cb.and(cb.equal(c.get("balanceHolder").get("id"), balanceHolder), condition);
} else {
condition = cb.equal(c.get("balanceHolder").get("id"), balanceHolder);
}
} else if (orgCat != null) {
try {
OrgCategoryDAO ocDao = new OrgCategoryDAO(getSession());
OrgCategory orgCatEntity = ocDao.findById(orgCat);
OrganizationDAO oDao = new OrganizationDAO(getSession());
List<OrgCategory> cats = new ArrayList<OrgCategory>();
cats.add(orgCatEntity);
ViewPage<Organization> orgs = oDao.findAllByOrgCategory(cats, 0, 0);
if (condition != null) {
condition = cb.and(c.get("balanceHolder").in(orgs.getResult()), condition);
} else {
condition = c.get("balanceHolder").in(orgs);
}
} catch (DAOException e) {
Server.logger.errorLogEntry(e);
}
}
cq.where(condition);
Query query = em.createQuery(cq);
return query.getResultList();
} finally {
em.close();
}
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"boolean isFilterByDate();",
"private boolean checkdates(java.util.Date date,java.util.Date date2)\n\t{\n\t\tif (date==null||date2==null) return true;\n\t\tif (!date.after(date2)&&!date.before(date2))return true;\n\t\treturn date.before(date2);\n\t}",
"public boolean checkDate(){\n Calendar c = Calendar.... | [
"0.6649518",
"0.6519971",
"0.6331176",
"0.62747604",
"0.6203099",
"0.6196772",
"0.6172704",
"0.61385167",
"0.611988",
"0.61196566",
"0.6096047",
"0.6082339",
"0.60802305",
"0.60414314",
"0.6038433",
"0.6036333",
"0.6021552",
"0.6020461",
"0.60038483",
"0.59946954",
"0.5981261... | 0.0 | -1 |
Saves an employee into the database | public void save(Session session, Employee emp) {
Transaction t = session.beginTransaction();
session.saveOrUpdate(emp);
t.commit();
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void saveEmployee(Employe employee) {\n\t\t\n\t}",
"@Override\r\n\tpublic void saveEmployee(Employee employee) {\n\t\t\r\n\t}",
"void save(Employee employee);",
"@Override\r\n\tpublic void post(Employee employee) {\n\t\t empdao.save(employee);\r\n\t}",
"@Override\n\tpublic void save(Employee employe... | [
"0.83124775",
"0.8043714",
"0.80424213",
"0.80404353",
"0.7989981",
"0.7964904",
"0.7884041",
"0.781862",
"0.7751335",
"0.7683169",
"0.7589738",
"0.74561846",
"0.74288136",
"0.7398181",
"0.7337196",
"0.73068655",
"0.7269084",
"0.7265552",
"0.7238759",
"0.72239184",
"0.7192147... | 0.6352838 | 79 |
Updates an employee of the database | public Employee update(Session session, Employee emp) {
Employee bbddEmp;
Transaction t = session.beginTransaction();
Query query = session.createQuery("from Employee where"
+ " name = :name and nifnie = :nifnie and companyEntryDate=:"
+ "companyEntryDate ");
query.setParameter("name", emp.getName());
query.setParameter("nifnie", emp.getNifnie());
query.setDate("companyEntryDate", emp.getCompanyEntryDate());
bbddEmp = (Employee) query.list().get(0);
updateColumns(emp, bbddEmp);
session.update(bbddEmp);
t.commit();
return bbddEmp;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void updateEmployee(Employe employee) {\n\t\t\n\t}",
"public void update(Employee e) {\n\t\t\r\n\t}",
"void update(EmployeeDetail detail) throws DBException;",
"public abstract void updateEmployee(int id, Employee emp) throws DatabaseExeption;",
"public void update(Employee employee){\n emplo... | [
"0.84486455",
"0.8208561",
"0.8186445",
"0.81023794",
"0.80797845",
"0.8011271",
"0.7939111",
"0.7909142",
"0.79037815",
"0.78889173",
"0.7818739",
"0.7810154",
"0.777519",
"0.77683026",
"0.7752693",
"0.7733819",
"0.7731944",
"0.77310914",
"0.7695744",
"0.7693276",
"0.7676042... | 0.67576486 | 71 |
Checks if an employee exists in the database | public boolean exists(Session session, Employee emp) {
List<Category> list;
Transaction t = session.beginTransaction();
Query query = session.createQuery("from Employee where"
+ " name = :name and nifnie = :nifnie and companyEntryDate=:"
+ "companyEntryDate ");
query.setParameter("name", emp.getName());
query.setParameter("nifnie", emp.getNifnie());
query.setDate("companyEntryDate", emp.getCompanyEntryDate());
list = query.list();
t.commit();
return !list.isEmpty();
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public boolean employeeExists() {\n\t\tResultSet r = super.query(\"SELECT \" + C.COLUMN_ID + \" FROM \" + C.TABLE_EMPLOYEE\n\t\t\t\t+ \" WHERE \" + C.COLUMN_ID + \"=\" + this.getID() + \" LIMIT 1\");\n\t\tif (r == null)\n\t\t\treturn false;\n\t\ttry {\n\t\t\tsuper.setRecentError(null);\n\t\t\treturn r.first();\n\t... | [
"0.8014229",
"0.73062676",
"0.7248659",
"0.7248659",
"0.68654305",
"0.6838001",
"0.68220377",
"0.6706755",
"0.668229",
"0.66005003",
"0.66005003",
"0.6587141",
"0.64801115",
"0.6468892",
"0.64128923",
"0.63886976",
"0.6369855",
"0.63690907",
"0.6344128",
"0.6342424",
"0.63404... | 0.67455256 | 7 |
Updates the employee bbddEmp by setting in it the values of the employee memoryEmp | public void updateColumns(Employee memoryEmp, Employee bbddEmp) {
bbddEmp.setBankAccount(memoryEmp.getbAC());
bbddEmp.setCategory(memoryEmp.getCategory());
bbddEmp.setCodigoCuenta(memoryEmp.getCodigoCuenta());
bbddEmp.setCompany(memoryEmp.getCompany());
bbddEmp.setCompanyEntryDate(memoryEmp.getCompanyEntryDate());
bbddEmp.setCompanyEntryLocalDate(memoryEmp.getCompanyEntryLocalDate());
bbddEmp.setCompanyName(memoryEmp.getCompanyName());
bbddEmp.setEmail(memoryEmp.getEmail());
bbddEmp.setExtraPayroll(memoryEmp.getExtraPayroll());
bbddEmp.setExtraProRate(memoryEmp.getExtraProRate());
bbddEmp.setID(memoryEmp.getID());
bbddEmp.setIban(memoryEmp.getIban());
bbddEmp.setNominas(memoryEmp.getNominas());
bbddEmp.setPayroll(memoryEmp.getPayroll());
bbddEmp.setSurname1(memoryEmp.getSurname1());
bbddEmp.setSurname2(memoryEmp.getSurname2());
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void updateEmp(Emp emp) {\n\t\t\n\t}",
"public void updateEmployee(Employe employee) {\n\t\t\n\t}",
"@Override\n\tpublic void updateEmployee() {\n\n\t}",
"public void updateemployee(Employeee em) {\n\t\temployeerepo.save(em);\n\t}",
"public void update(Employee e) {\n\t\t\r\n\t}",
"@Override\n\tpu... | [
"0.7214944",
"0.6658637",
"0.64828134",
"0.63875806",
"0.6366684",
"0.6276053",
"0.62002176",
"0.6186173",
"0.61748135",
"0.6167372",
"0.6141892",
"0.6127679",
"0.6110889",
"0.6104005",
"0.60957205",
"0.6077869",
"0.6062936",
"0.6059084",
"0.6031682",
"0.60042894",
"0.6003849... | 0.778142 | 0 |
Created by Jan Shair on 31/01/2017. | @Singleton
@Component(modules={AppModule.class, NetModule.class})
public interface NetComponent {
void inject(App application);
void inject(MainActivity activity);
SharedPreferenceUtil provideSharedPreferences();
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public final void mo51373a() {\n }",
"@Override\n public void perish() {\n \n }",
"private stendhal() {\n\t}",
"@Override\n public void func_104112_b() {\n \n }",
"@Override\n\tpublic void comer() {\n\t\t\n\t}",
"@Override\r\n\tpublic void tires() {\n\t\t\r\n\t}",
"@Ove... | [
"0.58478326",
"0.5797519",
"0.5680036",
"0.566044",
"0.54909265",
"0.54683006",
"0.5467793",
"0.5467793",
"0.546279",
"0.54534084",
"0.5445787",
"0.5442567",
"0.5406574",
"0.5387817",
"0.5384442",
"0.5381082",
"0.53529936",
"0.5348938",
"0.53425455",
"0.5338641",
"0.5338145",... | 0.0 | -1 |
display/center the jdialog when the button is pressed | @Override
public void actionPerformed(ActionEvent e) {
frame.setVisible(false);
new Register();
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void actionPerformed(java.awt.event.ActionEvent e) {\n\t\t\t\t\tJDialog jDialog = getPomocDialog();\n\t\t\t\t\tjDialog.setTitle(\"Pomoc\");\n\t\t\t\t\tjDialog.pack();\n\t\t\t\t\tPoint loc = getHlavneOkno().getLocation();\n\t\t\t\t\tloc.translate(20, 20);\n\t\t\t\t\tjDialog.setLocation(loc);\n\t\t\t\t\tjDial... | [
"0.7413791",
"0.72059286",
"0.7138436",
"0.70818603",
"0.7044508",
"0.6962745",
"0.69540095",
"0.69082975",
"0.68524224",
"0.68418694",
"0.6765784",
"0.6749975",
"0.6695033",
"0.6666761",
"0.66460615",
"0.66325337",
"0.6618835",
"0.66072375",
"0.6594632",
"0.65826946",
"0.656... | 0.0 | -1 |
Method to replace the child fragment | public void replaceChildFragment(Fragment parentFragment, Fragment fragment, int containerId, String strFragmentTag, boolean flagIsAddToBackStack) {
FragmentManager fragmentManager = parentFragment.getChildFragmentManager();
FragmentTransaction fragmentTransaction = fragmentManager.beginTransaction();
fragmentTransaction.replace(containerId, fragment, strFragmentTag);
if (flagIsAddToBackStack)
fragmentTransaction.addToBackStack(null);
fragmentTransaction.commit();
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"private void replaceFragment(int pos) {\n Fragment fragment = null;\n switch (pos) {\n case 0:\n //mis tarjetas\n fragment = new CardsActivity();\n break;\n case 1:\n //buscador online\n fragment = new Ca... | [
"0.7135298",
"0.7012213",
"0.69645244",
"0.68708926",
"0.6861415",
"0.6857358",
"0.6848308",
"0.6839962",
"0.6827649",
"0.67639214",
"0.6728757",
"0.6714596",
"0.6708843",
"0.6706346",
"0.6702917",
"0.6646055",
"0.6641206",
"0.66341186",
"0.66334754",
"0.6600974",
"0.660056",... | 0.6817295 | 9 |
MSH21.1 = PHLabReportAck, MSH16 is present and valued 'AL'. No error/alert expected | @Test
public void test1() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message1 = new File(classLoader.getResource(message1FileName).getFile());
String messageString = FileUtils.readFileToString(message1);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(0, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void test13() throws Exception {\n SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);\n ClassLoader classLoader = getClass().getClassLoader();\n File message13 = new File(classLoader.getResource(message13FileName).getFile());\n String messageStrin... | [
"0.5746755",
"0.5716969",
"0.5679747",
"0.5676038",
"0.56277496",
"0.5589825",
"0.55146587",
"0.54585576",
"0.54254806",
"0.5414846",
"0.54041314",
"0.5400012",
"0.5368732",
"0.53669244",
"0.53662956",
"0.53630555",
"0.53437966",
"0.5321463",
"0.5310733",
"0.530074",
"0.52998... | 0.54224753 | 9 |
MSH21.1 = PHLabReportAck, MSH16 is present and valued 'NE'. No error/alert | @Test
public void test2() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message2 = new File(classLoader.getResource(message2FileName).getFile());
String messageString = FileUtils.readFileToString(message2);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(0, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void test13() throws Exception {\n SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);\n ClassLoader classLoader = getClass().getClassLoader();\n File message13 = new File(classLoader.getResource(message13FileName).getFile());\n String messageStrin... | [
"0.5686338",
"0.5626648",
"0.55724156",
"0.55721295",
"0.555067",
"0.54942447",
"0.5482454",
"0.5476184",
"0.54705966",
"0.5469264",
"0.5429496",
"0.5417011",
"0.53503567",
"0.53436357",
"0.5325179",
"0.531314",
"0.5312265",
"0.53077364",
"0.527877",
"0.5277185",
"0.5266691",... | 0.50714874 | 44 |
MSH21.1 = PHLabReportAck, MSH16 is present and valued 'ER'. No error/alert alert expected | @Test
public void test3() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message3 = new File(classLoader.getResource(message3FileName).getFile());
String messageString = FileUtils.readFileToString(message3);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(0, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void test13() throws Exception {\n SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);\n ClassLoader classLoader = getClass().getClassLoader();\n File message13 = new File(classLoader.getResource(message13FileName).getFile());\n String messageStrin... | [
"0.60983485",
"0.59624845",
"0.59433264",
"0.5918614",
"0.5853592",
"0.57830495",
"0.56864345",
"0.5673985",
"0.5650687",
"0.5649031",
"0.5643198",
"0.5639503",
"0.5625838",
"0.56102395",
"0.55852455",
"0.55077475",
"0.5409869",
"0.5353464",
"0.5351459",
"0.53372824",
"0.5325... | 0.5592601 | 14 |
MSH21.1 = PHLabReportAck, MSH16 is present and valued 'SU'. No error/alert | @Test
public void test4() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message4 = new File(classLoader.getResource(message4FileName).getFile());
String messageString = FileUtils.readFileToString(message4);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(0, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void m6607X() {\n try {\n HashMap hashMap = new HashMap();\n hashMap.put(\"is_rename\", \"0\");\n C1390G.m6779b(\"A107|1|3|10\", hashMap);\n HashMap hashMap2 = new HashMap();\n hashMap2.put(\"mark_num\", String.valueOf(this.f5418ga));\n ... | [
"0.56689787",
"0.56416655",
"0.54996353",
"0.54984546",
"0.5496727",
"0.54800785",
"0.5474895",
"0.54597574",
"0.5406907",
"0.5380975",
"0.53386635",
"0.5331083",
"0.5297106",
"0.5295433",
"0.5284907",
"0.527891",
"0.52477443",
"0.5234338",
"0.52292305",
"0.52030635",
"0.5200... | 0.519282 | 21 |
MSH21 Not present, MSH16 present and valued NE. Two errors, no alert expected. | @Test
public void test5() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message5 = new File(classLoader.getResource(message5FileName).getFile());
String messageString = FileUtils.readFileToString(message5);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(2, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void m5015e() {\n if (C1663a.m5979a().m5993N() && (this.f3996b == null || \"\".equals(this.f3996b))) {\n C2201w.m8371a((int) C0965R.string.ota_low_version, 0);\n } else {\n this.f3997c.m4989a();\n }\n }",
"public static void m5831h() {\n if (f4669a != n... | [
"0.5837577",
"0.5803697",
"0.57654977",
"0.5758043",
"0.5741833",
"0.5683225",
"0.56741506",
"0.56531656",
"0.5618555",
"0.5603432",
"0.5577549",
"0.5541876",
"0.5540666",
"0.5534815",
"0.55136675",
"0.5467361",
"0.5455391",
"0.5448795",
"0.5445361",
"0.5439777",
"0.543365",
... | 0.0 | -1 |
MSH21 Not present, MSH16 not present. Two errors, no alert expected. | @Test
public void test6() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message6 = new File(classLoader.getResource(message6FileName).getFile());
String messageString = FileUtils.readFileToString(message6);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(2, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"private void m50411m() {\n m50398f(false);\n mo38886a(this.f30720S.getString(C10232R.string.app_name), this.f30720S.getString(C10232R.string.msg_alert_something_went_wrong));\n }",
"public void validateRpd13s7()\n {\n // This guideline cannot be automatically tested.\n }",
"public... | [
"0.56478155",
"0.5631602",
"0.56026715",
"0.5591999",
"0.5591474",
"0.5566383",
"0.55658376",
"0.55613774",
"0.5518713",
"0.5486247",
"0.5476801",
"0.5446047",
"0.54448915",
"0.544354",
"0.5423961",
"0.54091924",
"0.54090035",
"0.5404829",
"0.5389915",
"0.53891826",
"0.538374... | 0.0 | -1 |
MSH21.1 = PHLabReportAck, MSH16 is present and valued 'AB'. One error, no alert expected | @Test
public void test7() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message7 = new File(classLoader.getResource(message7FileName).getFile());
String messageString = FileUtils.readFileToString(message7);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(1, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void m6607X() {\n try {\n HashMap hashMap = new HashMap();\n hashMap.put(\"is_rename\", \"0\");\n C1390G.m6779b(\"A107|1|3|10\", hashMap);\n HashMap hashMap2 = new HashMap();\n hashMap2.put(\"mark_num\", String.valueOf(this.f5418ga));\n ... | [
"0.5760446",
"0.57159054",
"0.5699122",
"0.56056166",
"0.5583114",
"0.5473406",
"0.54666275",
"0.54441696",
"0.5391585",
"0.5389862",
"0.5386463",
"0.5373202",
"0.53516847",
"0.5348851",
"0.5342484",
"0.5334529",
"0.53320205",
"0.53297293",
"0.53091305",
"0.53000385",
"0.5299... | 0.54602754 | 7 |
MSH21.1 = PHLabReportAck, MSH16 is not present. Two errors, no alert expected | @Test
public void test8() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message8 = new File(classLoader.getResource(message8FileName).getFile());
String messageString = FileUtils.readFileToString(message8);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(2, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void test13() throws Exception {\n SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);\n ClassLoader classLoader = getClass().getClassLoader();\n File message13 = new File(classLoader.getResource(message13FileName).getFile());\n String messageStrin... | [
"0.6250409",
"0.618904",
"0.6084767",
"0.60504955",
"0.5954539",
"0.59502906",
"0.5845462",
"0.58181536",
"0.5813489",
"0.5807858",
"0.58029526",
"0.5788721",
"0.5769314",
"0.5766205",
"0.5715835",
"0.5682614",
"0.56803924",
"0.5609862",
"0.558002",
"0.55461484",
"0.54527354"... | 0.5805402 | 10 |
MSH21.1 not present, MSH16 is valued AL. Two errors, no alert expected | @Test
public void test9() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message9 = new File(classLoader.getResource(message9FileName).getFile());
String messageString = FileUtils.readFileToString(message9);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(2, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"void mo7352a(C1655s sVar, AdError adError);",
"private void m6601R() {\n if (AppFeature.f5609e) {\n this.f5399S.mo6174f(\"0\");\n }\n this.f5399S.mo6172e(\"1\");\n }",
"protected final void checkExtensionValueSpec119612Vers020101(ITSLObject tsl, ServiceHistoryInstance shi, bo... | [
"0.60178906",
"0.59933954",
"0.57308155",
"0.57057154",
"0.56433445",
"0.56027436",
"0.55833495",
"0.5541542",
"0.5539028",
"0.5516238",
"0.5504279",
"0.54884666",
"0.54685444",
"0.54624075",
"0.5442817",
"0.5438596",
"0.54291946",
"0.5421452",
"0.5421049",
"0.540996",
"0.540... | 0.0 | -1 |
MSH21.1 present, but different from PHLabReportAck, MSH16 is valued 'AL'. One error, no alert expected. | @Test
public void test10() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message10 = new File(classLoader.getResource(message10FileName).getFile());
String messageString = FileUtils.readFileToString(message10);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(1, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void m6607X() {\n try {\n HashMap hashMap = new HashMap();\n hashMap.put(\"is_rename\", \"0\");\n C1390G.m6779b(\"A107|1|3|10\", hashMap);\n HashMap hashMap2 = new HashMap();\n hashMap2.put(\"mark_num\", String.valueOf(this.f5418ga));\n ... | [
"0.5642843",
"0.55487996",
"0.54839253",
"0.5451315",
"0.5427114",
"0.54184747",
"0.5409517",
"0.5399814",
"0.5352692",
"0.5344209",
"0.52950364",
"0.5270139",
"0.5257946",
"0.52564025",
"0.52492577",
"0.52166677",
"0.5208688",
"0.5183381",
"0.5155556",
"0.5152402",
"0.515037... | 0.50532913 | 33 |
MSH21 not present, MSH16 is valued 'AL'. Three errors, no alert expected. | @Test
public void test11() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message11 = new File(classLoader.getResource(message11FileName).getFile());
String messageString = FileUtils.readFileToString(message11);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(3, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"void mo7352a(C1655s sVar, AdError adError);",
"private void m6601R() {\n if (AppFeature.f5609e) {\n this.f5399S.mo6174f(\"0\");\n }\n this.f5399S.mo6172e(\"1\");\n }",
"public static int m84097AS(String str) {\n AppMethodBeat.m2504i(3207);\n if (\"fingerPrint\".... | [
"0.6054209",
"0.58542305",
"0.5748732",
"0.5734768",
"0.5710863",
"0.56333417",
"0.5610367",
"0.56021065",
"0.55711657",
"0.5524169",
"0.5520859",
"0.55063516",
"0.548001",
"0.5478589",
"0.547029",
"0.5464033",
"0.5448373",
"0.54445034",
"0.5399206",
"0.5391242",
"0.53771836"... | 0.5304495 | 34 |
MSH21.1 not present, MSH16 valued 'NE'. One error, no alert expected. | @Test
public void test12() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message12 = new File(classLoader.getResource(message12FileName).getFile());
String messageString = FileUtils.readFileToString(message12);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(1, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\r\n public void testInvalidMinusAssignmentExpressionWithSIUnits1() throws IOException {\n checkError(\"varD_S-=4km\", \"0xA0177\");\r\n }",
"@Test\r\n public void testInvalidRegularAssignmentExpressionWithSIUnits1() throws IOException {\n checkError(\"varD_KMe2perHmSe4 = 3 mm/ks^3h^2... | [
"0.584148",
"0.5744597",
"0.57345164",
"0.5711805",
"0.5711552",
"0.56838316",
"0.5683623",
"0.5682532",
"0.56523746",
"0.5623423",
"0.56200755",
"0.5588116",
"0.5541649",
"0.55366135",
"0.5521495",
"0.5489406",
"0.5474654",
"0.5474548",
"0.5470106",
"0.54674596",
"0.54652536... | 0.5130154 | 56 |
MSH21.1 not present, MSH16 not present. One error, no alert expected. | @Test
public void test13() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message13 = new File(classLoader.getResource(message13FileName).getFile());
String messageString = FileUtils.readFileToString(message13);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(1, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void validateRpd13s7()\n {\n // This guideline cannot be automatically tested.\n }",
"public static void m5831h() {\n if (f4669a != null) {\n f4669a.m12578a(\"Back_web_sucecess_incorrect_num\", null);\n }\n Answers.getInstance().logCustom(new CustomEvent(\"Back... | [
"0.5801291",
"0.57316214",
"0.57235587",
"0.5721573",
"0.5703738",
"0.5700092",
"0.56535363",
"0.5650405",
"0.56209844",
"0.5606911",
"0.5595981",
"0.5594909",
"0.55779505",
"0.5574252",
"0.5546643",
"0.5544935",
"0.55351245",
"0.55204713",
"0.55070275",
"0.5504566",
"0.55023... | 0.5512753 | 18 |
MSH21.1 present, but different from 'PHLabReportAck', MSH16 valued 'NE'. No error, no alert expected. | @Test
public void test14() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message14 = new File(classLoader.getResource(message14FileName).getFile());
String messageString = FileUtils.readFileToString(message14);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(0, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void test13() throws Exception {\n SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);\n ClassLoader classLoader = getClass().getClassLoader();\n File message13 = new File(classLoader.getResource(message13FileName).getFile());\n String messageStrin... | [
"0.5955283",
"0.58620965",
"0.57509834",
"0.5717829",
"0.56428117",
"0.5579113",
"0.5572328",
"0.54944676",
"0.5487572",
"0.54720014",
"0.5439973",
"0.54395586",
"0.5426946",
"0.5426726",
"0.5375653",
"0.53705794",
"0.52890146",
"0.5273237",
"0.52514553",
"0.52454877",
"0.523... | 0.5742603 | 3 |
MSH21.1 present, but different from 'PHLabReportAck', MSH16 not present. No error, no alert expected. | @Test
public void test15() throws Exception {
SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);
ClassLoader classLoader = getClass().getClassLoader();
File message15 = new File(classLoader.getResource(message15FileName).getFile());
String messageString = FileUtils.readFileToString(message15);
Report report = validator.check(messageString, "ORU_R01");
Set<String> keys = report.getEntries().keySet();
int errors = 0;
int alerts = 0;
for (String key : keys) {
List<Entry> entries = report.getEntries().get(key);
if (entries != null && entries.size() > 0) {
System.out.println("*** " + key + " ***");
for (Entry entry : entries) {
switch (entry.getClassification()) {
case "Error":
Util.printEntry(entry);
errors++;
break;
case "Alert":
Util.printEntry(entry);
alerts++;
break;
}
}
}
}
assertEquals(0, errors);
assertEquals(0, alerts);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void test13() throws Exception {\n SyncHL7Validator validator = Util.createValidator(profiles, constraints, null, valueSets);\n ClassLoader classLoader = getClass().getClassLoader();\n File message13 = new File(classLoader.getResource(message13FileName).getFile());\n String messageStrin... | [
"0.5947164",
"0.58747685",
"0.5755639",
"0.5721783",
"0.56545997",
"0.5643998",
"0.5490188",
"0.54895216",
"0.5489191",
"0.5455424",
"0.5453558",
"0.5436306",
"0.54210204",
"0.5420528",
"0.5408527",
"0.5387252",
"0.523633",
"0.52335364",
"0.5200824",
"0.5164473",
"0.5127857",... | 0.565584 | 4 |
This method will make a copy of the environment and not a copy of the creatures in the environment | public Environment(Environment e){
this.creatureList = new ArrayList<Creature>();
sandboxMap = new int[e.sandboxMap.length][e.sandboxMap[0].length];
for (int i = 0; i < sandboxMap.length; i++){
for (int j = 0; j < sandboxMap[i].length; j++){
sandboxMap[i][j] = e.sandboxMap[i][j];
}
}
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void createEnvironment() {\n //create a list of collidable objects and a list of sprite objects\n List<Collidable> collidables = new ArrayList<Collidable>();\n List<Sprite> spriteList = new ArrayList<Sprite>();\n //update the game's environment to be the collidables list.\n ... | [
"0.62736577",
"0.61426365",
"0.59756565",
"0.57616735",
"0.56631196",
"0.56631196",
"0.56335837",
"0.5588786",
"0.554314",
"0.552496",
"0.5519837",
"0.541511",
"0.5384807",
"0.535702",
"0.53533536",
"0.5332816",
"0.52818954",
"0.52789146",
"0.5273526",
"0.52642274",
"0.525000... | 0.6518455 | 0 |
Does the main work provided by this plugin. | public void execute() throws MojoExecutionException
{
if (projectVersion == null) { throw new MojoExecutionException("projectVersion cannot be null."); }
String squirrelsqlVersion = projectVersion;
String timestampPattern = "yyyyMMdd_kkmm";
if (!projectVersion.toLowerCase().endsWith("-snapshot"))
{
SimpleDateFormat sdf = new SimpleDateFormat(timestampPattern);
try
{
String date = sdf.format(new Date());
squirrelsqlVersion = "Snapshot-" + date;
}
catch (IllegalStateException e)
{
log.error("Could not convert date format pattern " + timestampPattern);
throw e;
}
}
Properties props = project.getProperties();
props.put(VERSION_PROPERTY_KEY, squirrelsqlVersion);
System.setProperty(VERSION_PROPERTY_KEY, squirrelsqlVersion);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"protected void internalRun() {\n work();\n }",
"public void run() {\r\n\t\t// overwrite in a subclass.\r\n\t}",
"@Override\n\tprotected void postRun() {\n\n\t}",
"public static void setup()\n\t{\n\t\tregisterEvent(calico.plugins.events.clients.ClientConnect.class);\n\t\tregisterEvent(calico.plugins... | [
"0.6197215",
"0.612493",
"0.610911",
"0.6072115",
"0.6054889",
"0.6054889",
"0.6037613",
"0.60367626",
"0.6033538",
"0.6018043",
"0.60163593",
"0.60044044",
"0.6004092",
"0.6003603",
"0.5993083",
"0.5985791",
"0.5955702",
"0.59390473",
"0.59337413",
"0.59277177",
"0.59100956"... | 0.0 | -1 |
/ In: A Card array representing winnings from either player Out: An integer containing the score calculated from the winnings. | static int calculateScore(Card[] winnings) {
int score = 0;
for (Card card : winnings)
if (card != null)
score++;
else
break;
return score;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void displayPlayerWinnings()\n {\n for(int card = 0; card < playerCardsWon; card++)\n {\n System.out.println(playerWinnings[card].toString());\n }\n }",
"public int trickWinner(){\n Card.NUMBER one = player1Play.getValue();\n Card.NUMBER two = player2Play.getValue(... | [
"0.6462858",
"0.64117765",
"0.61951417",
"0.6172232",
"0.6166382",
"0.6143474",
"0.6137308",
"0.61175525",
"0.6107694",
"0.60899746",
"0.6064572",
"0.60437274",
"0.6034763",
"0.6023471",
"0.5987517",
"0.5974352",
"0.59734625",
"0.59468454",
"0.5946048",
"0.59399337",
"0.59250... | 0.7184643 | 0 |
/ In: [1] The Card array to add the won cards to. [2] An arbitrary number of Card objects representing the won cards. Out: Nothing | static void addToWinnings(Card[] winnings, Card... cards) {
//Find the first null position in the winnings array and place
//each card in that position.
for (int i = 0; i < cards.length; i++)
for (int j = 0; j < winnings.length; j++)
if (winnings[j] == null) {
winnings[j] = new Card(cards[i]);
break;
}
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void addCard(Card card, GamePlayer GP){\n for(int i=0; i<4; i++){\n if(cardsPlayed[i] == null){\n cardsPlayed[i]=card;\n if(i == 0){\n suit = card.getSuit();\n }\n break;\n }\n }\n }",
"pu... | [
"0.6687374",
"0.6335549",
"0.62825334",
"0.62699974",
"0.61374",
"0.6080177",
"0.6020294",
"0.6008968",
"0.59937924",
"0.5969053",
"0.5966191",
"0.5898055",
"0.58941305",
"0.5882039",
"0.5839654",
"0.58384454",
"0.5838033",
"0.58259493",
"0.5818611",
"0.5806106",
"0.5778634",... | 0.7316227 | 0 |
/ In: A Card object representing the card that the human player picked Out: An integer representing the position in the computer's hand of the card it chooses to play. This function makes this game very hard to win. The computer knows which card the human chooses to play. It chooses which card to play based on this. | static int getComputerCard(Card playerCard) {
//The computer will iterate through different possible cards it might choose to play.
//This represents a chosen card at any given time.
Card possibleCard = null;
//The position in the computer's hand where the possibleCard is stored.
int cardPosition = 0;
//True if the computer has a card of higher value than the player's.
boolean hasHigherCard = false;
//Iterate through the computer's hand, trying to find a card higher than the player's
for (int i = 0; i < highCardGame.getHand(0).getNumCards(); i++) {
if (playerCard.compareTo(highCardGame.getHand(0).inspectCard(i)) < 0) {
//The computer has a higher card.
if (possibleCard != null) {
//If this card is lower than the possible card, but can still beat the player, then replace possible card.
if (possibleCard.compareTo(highCardGame.getHand(0).inspectCard(i)) > 0) {
possibleCard = new Card(highCardGame.getHand(0).inspectCard(i));
cardPosition = i;
}
} else {
//If the computer has not yet chosen a possible card, choose this one.
possibleCard = new Card(highCardGame.getHand(0).inspectCard(i));
hasHigherCard = true;
cardPosition = i;
}
}
}
if (!hasHigherCard) {
//If the computer does not have a card that can beat the player, then feed the lowest card
//that the computer has to the player.
for (int i = 0; i < highCardGame.getHand(0).getNumCards(); i++)
if (playerCard.compareTo(highCardGame.getHand(0).inspectCard(i)) >= 0) {
if (possibleCard != null) {
if (possibleCard.compareTo(highCardGame.getHand(0).inspectCard(i)) > 0) {
possibleCard = new Card(highCardGame.getHand(0).inspectCard(i));
cardPosition = i;
}
} else {
possibleCard = highCardGame.getHand(0).inspectCard(i);
cardPosition = i;
}
}
}
return cardPosition;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Card PlayCard() {\n\t Card ret = null;\n\t Card card;\n\t if (mCardCount > 0) {\n\t\t // To work out what card to play, see what cards can be played\n\t\t \tCard[] playableCards;\n\t\t \tplayableCards = mRules.GetPlayableCards();\n\t\t \tCard[] selectedCards;\n\t\t \tselectedCards = new Card[8];\n\t... | [
"0.74133784",
"0.7336695",
"0.72532815",
"0.7204127",
"0.71322954",
"0.7028041",
"0.6975031",
"0.69401246",
"0.6867712",
"0.6841518",
"0.66775084",
"0.6670005",
"0.6644365",
"0.66268",
"0.66077554",
"0.65754086",
"0.6542449",
"0.65315914",
"0.6519754",
"0.6505161",
"0.6491849... | 0.7635844 | 0 |
/ In: A Card object Out: An integer representing that card's value | static int getCardPointValue(Card card) {
if (card.errorFlag)
return -1;
String values = new String(Card.validCardValues);
return cardPointValues[values.indexOf(card.getValue())];
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public int getCardValue(int card) {\n\t\tint result = card;\n\t\tswitch (card) {\n\t\tcase 11:\n\t\tcase 12:\n\t\tcase 13:\n\t\t\tresult = 10;\n\t\t}\n\t\treturn result;\n\t}",
"public int getCardValue()\n {\n return cardValue; \n }",
"public int getValue() {\n\t\tif (!this.isCard()) {\n\t\t\tSyst... | [
"0.7797333",
"0.7639451",
"0.75987756",
"0.75157064",
"0.71641886",
"0.7119415",
"0.7090305",
"0.70451254",
"0.70416135",
"0.7000696",
"0.6959939",
"0.69179726",
"0.6876641",
"0.6811983",
"0.6801134",
"0.67904955",
"0.67904955",
"0.67695904",
"0.6720403",
"0.6720403",
"0.6699... | 0.66931444 | 25 |
/ In: A card object that the caller wants an image for Out: An Icon containing the image | public static Icon getIcon(Card card) {
//Load all of the card icons if they haven't been already.
if (!GUICard.iconsLoaded)
GUICard.loadCardIcons();
//return the appropriate card icon.
return iconCards[valueAsInt(card)][suitAsInt(card)];
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public static ImageIcon getCardImage(Card card) {\r\n // Use image order, which is different from value order.\r\n String CardImageFileName = card.getCardImg();\r\n String path = String.format(IMAGE_PATH_FORMAT, CardImageFileName);\r\n return getIcon(path);\r\n }",
"public ImageIco... | [
"0.7342288",
"0.7312227",
"0.7186173",
"0.6828969",
"0.68220353",
"0.67514676",
"0.67374176",
"0.6726198",
"0.6726198",
"0.668909",
"0.6658882",
"0.6658882",
"0.6650844",
"0.66359836",
"0.661377",
"0.660889",
"0.66031843",
"0.6590617",
"0.65781283",
"0.6539564",
"0.6497865",
... | 0.6397766 | 22 |
/ In: Nothing Out: Nothing Loads all of teh card icons from the images directory. If the images directory is not in the right place, then the user is prompted to select a directory where the images can be found. | private static void loadCardIcons() {
//If the images folder doesn't exist,
if (!(new File(GUICard.iconFolderPath).exists())) {
//Prompt the user for a valid image folder.
JOptionPane.showMessageDialog(null, "By deafult ../images/ is used to store card icon images, but ../images/ does not exist. Press OK to select the folder where card icon images are stored. Press cancel in the forthcoming dialog window to exit this program.");
JFileChooser chooser = new JFileChooser(".");
chooser.setFileSelectionMode(JFileChooser.DIRECTORIES_ONLY);
chooser.setMultiSelectionEnabled(false);
chooser.showDialog(null, "Select");
File selectedFile = chooser.getSelectedFile();
//Exit the program if a valid image folder is not provided.
if (selectedFile == null)
System.exit(0);
GUICard.iconFolderPath = selectedFile.getPath();
System.out.println(iconFolderPath);
}
//Load each of the cards into the appropriate position in the iconCards array.
for (int i = 0; i < Card.validCardValues.length; i++)
for (int j = 0; j < VALID_SUITS.length; j++) {
//If a card cannot be loaded, tell the user and exit the application.
if (!new File(iconFolderPath + "/" + Card.validCardValues[i] + VALID_SUITS[j] + ".gif").exists()) {
JOptionPane.showMessageDialog(null, Card.validCardValues[i] + VALID_SUITS[j] + ".gif could not be found in the icon folder. Program execution will now stop.");
System.exit(0);
}
iconCards[i][j] = new ImageIcon(iconFolderPath + "/" + Card.validCardValues[i] + VALID_SUITS[j] + ".gif");
}
//Load the back of the card icon.
iconBack = new ImageIcon(iconFolderPath + "/BK.gif");
GUICard.iconsLoaded = true; //Make sure this function is not called again.
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"static void loadCardIcons()\n {\n if(iconsLoaded == false)\n {\n //arrays for suits and ranks\n String[] suits = {\"C\", \"D\", \"H\", \"S\"};\n String[] ranks = {\"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \n \"T\", \"A\", \"J\", \"Q\", \"K\", \"X\"... | [
"0.7295657",
"0.6593426",
"0.62699306",
"0.6261478",
"0.6183413",
"0.60783195",
"0.59495616",
"0.5944569",
"0.5923273",
"0.5884669",
"0.58257747",
"0.578596",
"0.5752771",
"0.57385707",
"0.5725863",
"0.5718007",
"0.5679597",
"0.56509405",
"0.5635986",
"0.5618256",
"0.5599619"... | 0.76824 | 0 |
/ In: A card object Out: An integer representing the row in iconCards that contains that value. | private static int valueAsInt(Card card) {
String values = new String(Card.validCardValues);
return values.indexOf(card.getValue());
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"static public Icon getIcon(Card card)\n {\n loadCardIcons();\n // return iconCards[valueAsInt(card)][suitAsInt(card)];\n return iconCards[suitAsInt(card)][valueAsInt(card)];\n // return iconCards[0][0];\n }",
"private int highCard() {\n\t\tint highCard = 0;\n\t\tfor (int counter... | [
"0.61320114",
"0.5796064",
"0.5733073",
"0.5701161",
"0.5677203",
"0.56761986",
"0.56631476",
"0.5645274",
"0.5643325",
"0.56427467",
"0.5636376",
"0.5634097",
"0.55950063",
"0.5576622",
"0.55512327",
"0.5510892",
"0.5497961",
"0.5494974",
"0.54868525",
"0.54862183",
"0.54583... | 0.5779312 | 2 |
/ In: A card object Out: An integer representing the column in the iconCards array that contains that suit. | private static int suitAsInt(Card card) {
return card.getSuit().ordinal();
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public int suitNum() {\r\n return this.i;\r\n }",
"public static int suit(int card){\n //check if card is out of bounds\n if(card < 1 || card > 52){\n return 0;\n }\n //subtract 1 from card to include multiples of 13\n //add 1 so that it matches up with... | [
"0.6613794",
"0.64540774",
"0.63238305",
"0.6136769",
"0.6111638",
"0.6047704",
"0.6031042",
"0.6004407",
"0.5999935",
"0.59928435",
"0.5985066",
"0.59542304",
"0.59542304",
"0.5936215",
"0.5932439",
"0.59319526",
"0.59311265",
"0.5905127",
"0.5901253",
"0.5870276",
"0.584280... | 0.7137165 | 0 |
/ In: Nothing Out: An Icon object containing the back card icon. | public static Icon getBackCardIcon() {
//Load all of the icons if they have not been already.
if (!GUICard.iconsLoaded)
GUICard.loadCardIcons();
return GUICard.iconBack;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"static public Icon getBackCardIcon()\n {\n iconBack = new ImageIcon(\"images/\" + \"BK.gif\");\n return iconBack;\n }",
"private Image getBackImage() {\n return getImage(\"playing-cards/blue-back.png\");\n }",
"public GImage getBack(){\r\n \t\t return back;\r\n\t }",
"public Node rend... | [
"0.80608755",
"0.76674515",
"0.6967381",
"0.6703697",
"0.65202326",
"0.64692855",
"0.6465936",
"0.6453745",
"0.64483213",
"0.6413046",
"0.63774824",
"0.6375087",
"0.6358919",
"0.63274366",
"0.6324186",
"0.63198376",
"0.615984",
"0.61485595",
"0.6148182",
"0.6134997",
"0.61014... | 0.8431247 | 0 |
/ In: [1] A String represetning the desired window title [2] An integer representing the number of cards per hand [3] An integer value representing the number of players playing on the table Out: Nothing. | public CardTable(String title, int numCardsPerHand, int numPlayers) {
super(); //Call JFrame's constructor.
this.handPanels = new JPanel[numPlayers];
//Verify that the input is valid. Fix it if it is not.
if (numCardsPerHand < 0 || numCardsPerHand > CardTable.MAX_CARDS_PER_HAND)
this.numCardsPerHand = 20;
this.numCardsPerHand = numCardsPerHand;
if (numPlayers < 2 || numPlayers > CardTable.MAX_PLAYERS)
this.numPlayers = numPlayers;
if (title == null)
title = "";
//Set some of the window's attributes.
this.setTitle(title);
this.setSize(800, 600);
this.setMinimumSize(new Dimension(800, 600));
this.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
//The card table will use a BorderLayout style. This allows each panel
//To have a different height. This allows for a larger play area and smaller
//hand areas.
BorderLayout layout = new BorderLayout();
this.setLayout(layout);
//Both the comptuer and human hand panels will use the flow layout.
FlowLayout flowLayout = new FlowLayout(FlowLayout.LEFT);
//Crate a titled border for the display of labels indicating
//what each panel is for.
TitledBorder border = new TitledBorder("Computer Hand");
this.handPanels[0] = new JPanel();
this.handPanels[0].setLayout(flowLayout);
this.handPanels[0].setPreferredSize(new Dimension((int) this.getMinimumSize().getWidth() - 50, 105));
//Use a JScrollPane in case the cards per hand is greater than can be displayed in the panel
//without a scroll bar.
JScrollPane scrollComputerHand = new JScrollPane(this.handPanels[0]);
scrollComputerHand.setVerticalScrollBarPolicy(JScrollPane.VERTICAL_SCROLLBAR_NEVER);
scrollComputerHand.setBorder(border);
this.add(scrollComputerHand, BorderLayout.NORTH);
//Create the playing area.
border = new TitledBorder("Playing Area");
//The play area will use a grid layout, so that the played cards, labels, and
//status text can be displayed in neat columns.
GridLayout gridLayoutCardsArea = new GridLayout(1, 2);
GridLayout gridLayoutStatusArea = new GridLayout(1, 1);
pnlPlayArea = new JPanel();
pnlPlayArea.setBorder(border);
layout = new BorderLayout();
pnlPlayArea.setLayout(layout);
pnlTimer = new JPanel();
pnlTimer.setLayout(gridLayoutStatusArea);
pnlPlayedCards = new JPanel();
pnlPlayedCards.setLayout(gridLayoutCardsArea);
pnlPlayerText = new JPanel();
pnlPlayerText.setLayout(gridLayoutCardsArea);
pnlStatusText = new JPanel();
pnlStatusText.setLayout(gridLayoutStatusArea);
pnlPlayedCards.setPreferredSize(new Dimension((int) this.getMinimumSize().getWidth() - 50, 150));
pnlPlayerText.setPreferredSize(new Dimension(100, 30));
pnlStatusText.setPreferredSize(new Dimension(100, 30));
pnlPlayArea.add(pnlTimer, BorderLayout.EAST);
pnlPlayArea.add(pnlPlayedCards, BorderLayout.NORTH);
pnlPlayArea.add(pnlPlayerText, BorderLayout.CENTER);
pnlPlayArea.add(pnlStatusText, BorderLayout.SOUTH);
this.add(pnlPlayArea, BorderLayout.CENTER);
///Create the human's hand area.
border = new TitledBorder("Human Hand");
this.handPanels[1] = new JPanel();
this.handPanels[1].setLayout(flowLayout);
this.handPanels[1].setPreferredSize(new Dimension((int) this.getMinimumSize().getWidth() - 50, 105));
JScrollPane scrollHumanHand = new JScrollPane(this.handPanels[1]);
scrollHumanHand.setVerticalScrollBarPolicy(JScrollPane.VERTICAL_SCROLLBAR_NEVER);
scrollHumanHand.setBorder(border);
this.add(scrollHumanHand, BorderLayout.SOUTH);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void Title ()\n {\n\tTitle = new Panel ();\n\tTitle.setBackground (Color.white);\n\tchessboard (Title);\n\tp_screen.add (\"1\", Title);\n }",
"public void display_title_screen() {\n parent.textAlign(CENTER);\n parent.textFont(myFont);\n parent.textSize(di... | [
"0.6151294",
"0.58792484",
"0.58282477",
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"0.5602944",
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"0.5396598",
"0.5374344",
"0.5368026",
"0.5363961",
"0.53489673",
"0.5348... | 0.5474014 | 7 |
/ In: A MouseEvent object Out: Nothing Fires when the mouse enters a card or status JLabel | public void mouseEntered(MouseEvent e) {
JLabel source = (JLabel)e.getSource();
view.highlightLabel(source);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Override\n public void mouseEntered(MouseEvent e) {\n JLabel x = (JLabel) e.getSource();\n x.setForeground(Color.BLUE);\n levelPanel.setCursor(new Cursor(Cursor.HAND_CURSOR));\n }",
"public void mouseEntered( MouseEvent event ){}",
"public void mouseEntered (MouseEvent e) {}",
"@O... | [
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"0.7100797",
"0.70764434",
"0.7075516",
"0.7072613",
"... | 0.70120543 | 26 |
/ In: A MouseEvent object Out: Nothing Fires when the mouse exits a card or status JLabel | public void mouseExited(MouseEvent e) {
JLabel source = (JLabel)e.getSource();
view.deHighlightLabel(source);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void mouseExited(MouseEvent event){\t\n\t\t// We left the card without releasing the mouse press, so we're not counting it as a click\n\t\tclickflag = false;\n\t}",
"@Override\n public void mouseExited(MouseEvent e) {\n JLabel x = (JLabel) e.getSource();\n x.setForeground(Color.black);\n ... | [
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"0.7176047",
"0.7176047... | 0.72586095 | 13 |
/ In: A MouseEvent object Out: Nothing Fires when the mouse clicks a card or status JLabel | public void mouseClicked(MouseEvent e) {
//The source will always be a JLabel
JLabel source = (JLabel)e.getSource();
if(source == view.statusText){
model.initGame();
return;
}
for(int playerHand = 0; playerHand < view.playerHands.length; playerHand++){
for(int card = 0; card < model.highCardGame.getHand(playerHand).getNumCards(); card++){
if(view.playerHands[playerHand][card].getIcon() == View.GUICard.getBackCardIcon())
continue;
if(view.playerHands[playerHand][card] == source){
//A card was clicked.
Model.playCard(playerHand, card);
return;
}
}
}
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void handleMouseClicked(MouseEvent e, CardLabel clickedLabel){\n\t\tif(status == Status.YOUR_TURN || status == Status.YOUR_FIRST_TURN){\n\t\t\tCardTile clickedTile = you.getCardTile(clickedLabel.getPlace(), clickedLabel.getSeqNo());\n\t\t\tif(clickedTile == null)\n\t\t\t\tclickedTile = opponent.getCardTile(... | [
"0.7299617",
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"0.7102388",
"0.7102388",
"0.7101252",
"0.70930505",
... | 0.7356572 | 0 |
The only time this is called is when the timer ticks. | public void actionPerformed(ActionEvent e){
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void tick(){\r\n if(timer != 0)\r\n timer++;\r\n }",
"@Override\n\tpublic boolean tick() {\n\t\treturn false;\n\t}",
"@Override\n public void onTick(long arg0) {\n }",
"public void timer() \r\n\t{\r\n\t\tSeconds.tick();\r\n\t \r... | [
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"0.6649144",
"0.6649144",
"0.6649144",
"0.6649144... | 0.0 | -1 |
The deck can consist of multiple packs of cards. / Deck(int) In: An integer specifying the number of packs to build the deck from. Out: Nothing Description: This is a constructor that will build a deck composed of the specified number of packs. | public Deck(int numPacks) {
//Build the master pack.
this.allocateMasterPack();
//If the user wants more packs than are available, give them the max.
if (numPacks > Deck.MAX_PACKS)
this.init(Deck.MAX_PACKS);
//If the user wants 0 or less packs, give them one.
else if (numPacks < 1)
this.init(1);
else
//Otherwise, build the deck with the specified number of packs.
this.init(numPacks);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Deck(int numDecks) {\n\t\t// Loop through number of decks, suits, and rank\n\t\tfor (int k = 0; k<numDecks; k++) {\n\t\t\tfor (int i = 1; i <= 4; i++) {\n\t\t\t\tfor (int j = 1; j <= 13; j++) {\n\t\t\t\t\t// Add Card object to linked list\n\t\t\t\t\tdeck.add(new Card(i,j));\n\t\t\t\t\tsize++;\n\t\t\t\t}\n\t... | [
"0.74440926",
"0.72974277",
"0.72418183",
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"0.68453455",
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"0.65884733",
"0.65632474",
"0.65601015",
"0.... | 0.77669257 | 0 |
/ Deck() In: None Out: Nothing Description: This default constructor builds a deck with one pack. | public Deck() {
this.allocateMasterPack();
this.init(1);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Deck()\n\t{\n\t\t//call other Constructor defining one deck with out shuffling\n\t\tthis(false);\n\t}",
"public Deck() {\n generateDeckOfCards();\n }",
"public Deck()\n {\n this(1);\n }",
"public Deck() {\n this.deck = new LinkedList<>();\n }",
"public Deck()\r\n\t{\r\n\... | [
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"0.68891686",
"0.6815698",
"0.677... | 0.8034931 | 2 |
/ void init(int) In: An integer whose value is the number of packs to build the deck from. Out: Nothing Description: This will initialize the cards array data member to a complete deck built from the specified number of packs. | public void init(int numPacks) {
//Initialize the cards array.
this.cards = new Card[numPacks * Deck.MAX_CARDS_IN_PACK];
//Until the total number of cards are reached, keep adding cards from the
//master pack.
for (int i = 0; i < numPacks * Deck.MAX_CARDS_IN_PACK; i++) {
this.cards[i] = this.masterPack[i % Deck.MAX_CARDS_IN_PACK];
}
//Set the top card to the last card allocated.
this.topCard = numPacks * Deck.MAX_CARDS_IN_PACK;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void init(int numPacks)\n {\n int k, pack;\n\n if (numPacks < 1 || numPacks > 6)\n numPacks = 1;\n\n // hand over the masterPack cards to our deck\n for (pack = 0; pack < numPacks; pack++)\n for (k = 0; k < 52; k++)\n cards[pack*52 + k] = masterPack[k];\n\n ... | [
"0.7720376",
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"0.6663122",
"0.66154337",
"0.65838605",... | 0.8200314 | 0 |
/ void shuffle() In: Nothing Out: Nothing Description: This uses a FisherYates shuffle to shuffle all of the cards in the deck. | public void shuffle() {
//Beginning with the top card, decrement i until i is 0.
for (int i = this.topCard - 1; i >= 0; i--) {
Card tmpCard = this.cards[i]; //Store the card at i, since it will be overwritten.
//Choose a random card position from within the deck.
int randomPosition = (int) (Math.random() * (this.topCard - 1));
//Take the card from the random position and store it in the ith position.
this.cards[i] = this.cards[randomPosition];
//Take the card from the ith position, and put it into the randomly chosen position.
this.cards[randomPosition] = tmpCard;
//The cards have now been swapped.
}
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void shuffle(){\n Collections.shuffle(this.deckOfCards);\n }",
"public void shuffle() {\n Collections.shuffle(deck);\n }",
"public void shuffle() {\n Collections.shuffle(deck);\n }",
"public void shuffle() {\r\n\t\tCollections.shuffle(cards);\r\n\t}",
"public void shuffl... | [
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"0.7663968",
"0.764409... | 0.6843067 | 56 |
/ Card dealCard() In: Nothing Out: A copy of the Card object on the top of the deck. Description: This function makes a copy of the card on the top of the deck, removes that card from the deck, and returns the copy to the caller. | public Card dealCard() {
//Return an invalid card if there are no cards in the deck.
if (this.topCard < 0)
return new Card('0', Card.Suit.spades);
else {
//Create a copy of the card on the top of the deck.
Card card = new Card(this.cards[this.topCard - 1]);
//Set the actual card on the top of the deck to null, to destroy it.
this.cards[this.topCard - 1] = null;
//The topCard is now one less than it was.
this.topCard--;
//return the copy.
return card;
}
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Card deal()\r\n\t{\r\n\t\tCard card = deck.remove(0);\r\n\t\treturn card;\r\n\t}",
"@Override\r\n public ICard dealCard() {\r\n //get the first card from top of the deck \r\n ICard top = this.deck[ZERO];\r\n //shift cards to the left, because we get the first one \r\n for (i... | [
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"0.7264972",
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"0.71542543",
"0.71174306",
"0.709... | 0.83310425 | 0 |
/ int getTopCard() In: Nothing Out: An integer whose value is the position of the top card in the deck. Description: This is a basic accessor function. | public int getTopCard() {
return this.topCard;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Card getTopCard(){\n\t\treturn getCard(4);\n\t\t\n\t}",
"public Card getTopCard() {\n\t\treturn topCard;\n\t}",
"public Card topDeckCard(){\n Preconditions.checkArgument(!isDeckEmpty());\n return deck.topCard();\n }",
"public Card topCard() {\n Preconditions.checkArgument(!card... | [
"0.86437345",
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"0.7082367... | 0.86228305 | 1 |
/ Card inspectCard(int) In: An integer representing the position of the card to be inspected. Out: A copy of the card at the specified position, or an invalid card if there is no card in that position. Description: This function returns a Card object whose values are equal to the card in the specified position. | public Card inspectCard(int k) {
//If k is invalid, return an invalid card.
if (k >= this.topCard || k < 0)
return new Card('0', Card.Suit.spades);
else
//Otherwise, return a copy of the card in position k.
return new Card(this.cards[k]);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"ICard getCard(int cardIndex) throws IllegalArgumentException;",
"public Card inspectCard(int k) {\n if (k >= this.numCards || k < 0)\n return new Card('0', Card.Suit.spades);\n else\n return new Card(this.myCards[k]);\n }",
"public Card inspectCard(int k)\r\n {\r\n if (0 <= k... | [
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"0.55499786",
... | 0.70188725 | 0 |
/ void allocateMasterPack() In: Nothing Out: Nothing Description: This function fills the masterPack if it is not already filled. It fills the pack with valid card values. | private static void allocateMasterPack() {
//If Deck.masterPack is null, then it needs to be filled, otherwise, nothing needs to be done.
if (Deck.masterPack != null) {
//For each suit, fill the masterPack with each valid card value from that suit.
for (int i = 0; i < Card.Suit.values().length; i++) {
for (int j = 0; j < Card.validCardValues.length; j++) {
Deck.masterPack[i * Card.validCardValues.length + j] = new Card(Card.validCardValues[j], Card.Suit.values()[i]);
}
}
}
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"private void createFullPackOfCards()\r\n {\r\n // Todo\r\n\r\n // Using a for-loop, add all the Card instances to cards.\r\n for ( int i = 0; i < NOOFCARDSINFULLPACK; i ++) {\r\n addTopCard( new Card(i));\r\n }\r\n }",
"public Cards( boolean fullPack)\r\n {\r\n ... | [
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"0.48766658",
"0.48218... | 0.8778677 | 0 |
/ In: A card object to add to the deck Out: A boolean value indicating whether the card was able to be added to the deck | public boolean addCard(Card card) {
int cardCount = 0;
//Check to see if the deck already has the maximum number of cards
//of this type.
for (Card cardInDeck : this.cards)
if (cardInDeck.equals(card))
cardCount++;
//Return false is the card will not fit, or if it is invalid.
if (cardCount >= this.numPacks || this.topCard >= this.MAX_CARDS || card.errorFlag)
return false;
this.topCard++;
//Add the card object to the deck.
this.cards[topCard - 1] = new Card(card);
return true;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"boolean canBeAdded(ICard toBeAddedCard);",
"abstract boolean allowedToAdd(Card card);",
"@Override\n\tpublic boolean addCard(UnoCard card){\n\t\treturn this.cardList.add(card);\n\t}",
"public boolean addCard(Card card)\n {\n if (numOccurrences(card) >= numPacks)\n return false;\n\n cards[... | [
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"0.6737587",
"0.67046255",
"0.6634... | 0.78867936 | 3 |
/ In: Nothing Out: An itneger indicating the number of cards in the deck. | public int getNumCards() {
return this.topCard;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public int size()\r\n\t{\r\n\t\treturn deck.size();\r\n\t}",
"public int size() {\n return deck.size();\n }",
"public int GetDeckSize(){\n System.out.println(\"DECK \" + deck.size());\n\n return deck.size();\n }",
"public int getSizeOfDeck(){\n return cardDeck.size();\n }... | [
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"0.7562073",
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"0.7542975",
"0.7495451",
"0.7490... | 0.70613724 | 34 |
/ In: A card object to remove from the deck Out: A boolean value indicating if the card was able to be removed from the deck. | public boolean removeCard(Card card) {
//Iterate through the deck to find the card.
for (int i = 0; i < this.cards.length; i++)
if (this.cards[i].equals(card)) {
//If the card is found, then remove it from the deck.
//replace it with the topCard
this.cards[i] = new Card(this.cards[topCard - 1]);
this.topCard--;
return true;
}
return false;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"boolean canBeRemoved(int cardIndex);",
"public boolean removePlayerCard(GreenPlayerCard card) {\r\n\t\treturn playerCards.remove(card);\r\n\t}",
"public boolean remove(Card c) {\n if (cards.remove(c)) {\n orginalOrder.remove(c);\n cardsPerSuit[c.getSuit().ordinal()]--;\n ... | [
"0.8007521",
"0.7359825",
"0.7223539",
"0.70988977",
"0.7027925",
"0.696867",
"0.68102944",
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"0.6410577",
"0.631593",
"0.63143784",
"0.6314293",
"0.62414086... | 0.7639033 | 1 |
/ In: Nothing Out: Nothing Sorts the cards in the deck | public void sort() {
Card.arraySort(this.cards, this.topCard);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Override\n\tpublic void sortDeck() {\n\t\tfor(int i = 0; i < getCount() - 1; i++) {\n\t\t\tfor(int j = i + 1; j < getCount(); j++) {\n\t\t\t\tif(aktuellesDeck[i].compareTo(aktuellesDeck[j]) == 1) {\n\t\t\t\t\tSkatCard speicherCard = new SkatCard(aktuellesDeck[i].getSuit(), aktuellesDeck[i].getRank());\n\t\t\t\t\t... | [
"0.79624015",
"0.70584756",
"0.70002025",
"0.69680345",
"0.69117165",
"0.68548816",
"0.6849615",
"0.68272465",
"0.6815935",
"0.6762261",
"0.66790026",
"0.6655873",
"0.66402763",
"0.6582242",
"0.65807754",
"0.6524183",
"0.6495335",
"0.6494394",
"0.64909464",
"0.6490516",
"0.64... | 0.65897924 | 13 |
/ In: An object Out: An int indicating if the object is less than, greater than, or equal to the object performing the comparison. | public int compareTo(Object t) {
if (t.getClass() != this.getClass())
return 1;
Card c = (Card) t;
String strRanks = new String(valueRanks);
if (strRanks.indexOf(c.getValue()) < 0)
return 1;
if (strRanks.indexOf(c.getValue()) < strRanks.indexOf(this.getValue()))
return 1;
if (strRanks.indexOf(c.getValue()) == strRanks.indexOf(this.getValue()))
return 0;
if (strRanks.indexOf(c.getValue()) > strRanks.indexOf(this.getValue()))
return -1;
return 1;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void testObjCompare()\n {\n assertEquals( Comparator.EQUAL, Util.objCompare(null,null) );\n assertEquals( Comparator.LESS, Util.objCompare(new Integer(10), new Integer(20)) );\n assertEquals( Comparator.GREATER, Util.objCompare(new Integer(25), new Integer(20)) );\n ... | [
"0.65292716",
"0.6297877",
"0.6267485",
"0.6261624",
"0.6260348",
"0.6205341",
"0.61652577",
"0.6133865",
"0.6121487",
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"0.6052482",
"0.6016914",
"0.5979752",
"0.5953252",
"0.59520644",
"0.5946798... | 0.0 | -1 |
/ In: [1] An array of card objects to be sorted [2] The number of objects in parameter 1 This uses a bubble sort to sort the cards in the cards array. | static void arraySort(Card[] cards, int arraySize) {
//Swapped will change to true if any swapping occurs in the
//loop below.
boolean swapped = false;
do {
swapped = false;
//Go through each element in the array
for (int i = 1; i < arraySize; i++) {
//If an element is larger thant he one after it,
if (cards[i - 1].compareTo(cards[i]) > 0) {
//Swap those elements.
Card tmpCard = new Card(cards[i - 1]);
cards[i - 1] = new Card(cards[i]);
cards[i] = new Card(tmpCard);
swapped = true;
}
}
} while (swapped); //Continue until this loop runs with no swapping.
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"void sort() {\n Card.arraySort(this.myCards, numCards);\n }",
"void sort()\n {\n Card.arraySort(myCards, numCards);\n }",
"public void sort()\r\n {\r\n Card.arraySort(myCards, myCards.length);\r\n }",
"void sort()\n {\n Card.arraySort(cards, topCard);\n }",
"public void so... | [
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"0.754212",
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"0.6888639",
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"0.6128282",
"0.61053646",
"0.60689086",
"0.60689086",
"0.60156065",
... | 0.7091985 | 4 |
/ Card(char, Suit) In: A char representing the card's value, and a Suit representing the card's suit. Out: Nothing Description: This is a constructor that takes a value and a suit for a card. This will create a card of the specified value and suit. | public Card(char value, Suit suit) {
this.set(value, suit);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Card(String value, String suit) {\n this.value = value;\n this.suit = suit;\n }",
"public Card(String suit, int value)\n {\n this.suit = suit;\n this.value = value;\n \n }",
"public Card(String suit, int value) {\n this.value = value;\n this.sui... | [
"0.8432872",
"0.83356154",
"0.82767296",
"0.8250045",
"0.82058215",
"0.81192964",
"0.78494835",
"0.7819709",
"0.78012484",
"0.77987427",
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"0.7528765",
"0.74874",
"0.7459573",
"0.74167913",
"0.72875106",
"0.721... | 0.8885586 | 0 |
/ Card() In: Nothing Out: Nothing Description: This is a default constructor that takes no values. It will create an Ace of Spades. | public Card() {
this.set('A', Suit.spades);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Card()\n {\n suit = 0;\n rank = Card.ACE;\n }",
"public Card () {}",
"public Card()\n {}",
"public Card()\n {\n\t// Local constants\n\n\t// Local variables\n\n\t/************ Start Card Method *****************/\n\n\t// Set face value to null\n\tfaceValue = null;\n\n\t// Set true val... | [
"0.8083181",
"0.78388685",
"0.7741092",
"0.7693176",
"0.7386531",
"0.73556036",
"0.7325361",
"0.7291956",
"0.72799057",
"0.7235718",
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"0.7198685",
"0.7107009",
"0.70863193",
"0.7056683",
"0.705652",
"0.69922423",
"0.69753164",
"0.6928167",
"0.68986636",
"0.6893225... | 0.66517246 | 34 |
/ Card(Card) In: A Card object Out: Nothing Description: This is a copy constructor that returns a NEW card with the same values as the card passed into it. | public Card(Card card) {
this.set(card.value, card.suit);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Card copy(){\n return new Card(Suits, Value);\n }",
"public Card makeCopy(){\n return new Card(vimage);\n }",
"public Card(Card orig){\n this.cardType = orig.cardType;\n this.cardRank = orig.cardRank;\n this.cardSuit = orig.cardSuit;\n scaleCard(this.cardT... | [
"0.81357294",
"0.7956693",
"0.74072963",
"0.71553856",
"0.6979305",
"0.69130486",
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"0.6432499",
"0.6426862",
"0.64100784",
"0.6401489",
"0.6398346",
"0.6380537... | 0.77016807 | 2 |
/ boolean set(char, Suit) In: A char representing the card's value and a Suit representing the card's suit. Out: True if the value and suit are valid, false if otherwise. Description: This set's the card's suit and value, if they are valid. Otherwise, it sets the card's errorFlag to true. | public boolean set(char value, Suit suit) {
if (Card.isValid(value, suit)) {
this.errorFlag = false;
this.value = value;
this.suit = suit;
return true;
} else {
this.errorFlag = true;
return false;
}
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public boolean setSuit(char inSuit) {\r\n\t\tinSuit = Character.toUpperCase(inSuit);\r\n\t\tboolean status = false;\r\n\t\t\r\n\t\tswitch (inSuit) {\r\n\t\tcase 'S':\r\n\t\tcase 'H':\r\n\t\tcase 'C':\r\n\t\tcase 'D':\r\n\t\tcase 'T':\r\n\t\tcase '1':\r\n\t\tcase '2':\r\n\t\tcase '3':\r\n\t\t\tsuit = inSuit;\r\n\t\... | [
"0.7162835",
"0.6860577",
"0.64396065",
"0.6414689",
"0.6374922",
"0.63528794",
"0.62139255",
"0.6165588",
"0.6156942",
"0.61393034",
"0.6121284",
"0.6074872",
"0.6030928",
"0.5999517",
"0.5886699",
"0.57635945",
"0.5734741",
"0.5650587",
"0.56468314",
"0.5615969",
"0.5557574... | 0.86791205 | 0 |
/ boolean isValid(char, Suit) In: A char representing the card's value and a Suit representing its suit. Out: True if the value is valid, false if otherwise. Description: This function determines whether the value passed to it is a valid value for a card. It checks the value against the valid values stored in Card.validCardValues. | private static boolean isValid(char value, Suit suit) {
for (char validValue : Card.validCardValues)
if (String.valueOf(validValue).toLowerCase().equals(String.valueOf(value).toLowerCase()))
return true;
return false;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public boolean set(char value, Suit suit) {\n if (Card.isValid(value, suit)) {\n this.errorFlag = false;\n this.value = value;\n this.suit = suit;\n return true;\n } else {\n this.errorFlag = true;\n return false;\n }\n }",
"private boolean isValid... | [
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"0.63586426",
"0.61521864",
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"0.6106851",
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"0.56152344",
"0.56008995",
"0.55958503",
"0.55825245",
"0.55... | 0.80211437 | 0 |
/ char getValue() In: Nothing Out: A char holding the card's value. Description: This is an accessor for the card's value. | public char getValue() {
return value;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public char getValue()\n\t{\n\t\treturn value;\n\t}",
"public char charValue() {\n return value;\n }",
"public String getCardValue() {\n return Cvalue;\n }",
"public char Get() {\n\t\treturn myChar;\n\t}",
"private byte getValue() {\n\t\treturn value;\n\t}",
"public byte getValue() {\... | [
"0.858945",
"0.81979924",
"0.77326256",
"0.74724793",
"0.7369775",
"0.7121566",
"0.70561546",
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"0.6973804",
"0.678774",
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"0.66451484",
"0.663381",
"0.6624049",
"0.661490... | 0.86300623 | 0 |
/ Suit getSuit() In: Nothing Out: The card's suit type. Description: This is an accessor for the card's suit. | public Suit getSuit() {
return this.suit;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Suit getSuit() {\n\t\treturn suit;\n\t}",
"public Suit getSuit() {\r\n\t\treturn this.suit;\r\n\t}",
"public Suit getSuit() {\n return suit;\n }",
"public Suits getSuit() {\n\t\treturn suit;\n\t}",
"public String getSuit()\n {\n\t// Local constants\n\n\t// Local variables\n\n\t/**********... | [
"0.8536454",
"0.8474654",
"0.84272766",
"0.83966964",
"0.8372117",
"0.83508027",
"0.8328799",
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"0.81395745",
"0.8139469",
"0.80968326",
"0.80612725",
"0.8054439",
"0.8054439... | 0.83545864 | 5 |
/ String toString() In: Nothing Out: A String object containing the value and suit of the card, or [INVALID CARD] if the errorFlag is set to true. Description: This returns the card's value to the caller in String form. | public String toString() {
if (this.errorFlag == true)
return "[INVALID CARD]";
else
return this.value + " of " + suit.toString();
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Override\n public String toString() {\n CardValue cardValue = new CardValue(value).invoke();\n\n // Change the value to the actual name when printing out\n // the card.\n\n return cardValue.getValueName() + \" of \" + suit;\n }",
"public String toString(){\n String card ... | [
"0.805353",
"0.7934607",
"0.76563036",
"0.7561195",
"0.7301019",
"0.7280859",
"0.7203827",
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"0.6951121",
"0.68677795",
"0.6864208",
"0.6852423",
"0.68263215... | 0.9040196 | 0 |
/ In: A card object Out: A boolean value. True if the cards are equal, false if otherwise. | public boolean equals(Card c) {
if (this.getValue() == c.getValue() && this.getSuit() == c.getSuit())
return true;
return false;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public boolean equals(Card card){\n return (this.getCardValue() == card.getCardValue());\n }",
"boolean similarCard(Card c);",
"public static boolean sameCard(Card card1, Card card2){\r\n return (card1.rank == card2.rank && card1.suit == card2.suit);\r\n }",
"@Override\n\tpublic boolean e... | [
"0.7866549",
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"0.6844733",
"0.68346256",
"0.6812787",
"0.680465",
"0.6801684",... | 0.7082593 | 11 |
/ Hand() In: Nothing Out: Nothing Description: The default constructor for Hand does not actually do anything. | public Hand() {
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Hand(){}",
"public Hand () //No arg\n\t{\n\t\tthis.cardsInHand = 0;\n\t\tthis.handSize = 5;\n\t}",
"public Hand() {\r\n\t\t\r\n\t}",
"public Hand() {\n this.hole1 = new BlankCard();\n this.hole2 = new BlankCard();\n showCards = false;\n }",
"public Hand()\n\t{\n\t\thand = new... | [
"0.89953953",
"0.88530725",
"0.8732816",
"0.8170031",
"0.8075864",
"0.79682744",
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"0.6948191",
"0.69450325",
"0.67640656",
"0.6757502",
"0.6718334... | 0.8659175 | 3 |
/ In: Nothing Out: Nothing Sorts all of the cards in the hand object. | void sort() {
Card.arraySort(this.myCards, numCards);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void sortHand(){\n\t\thand.sort(PlayingCard.PlayingCardComparator_ACEHIGH);\n\t}",
"public void sortHand(){\n Arrays.sort(Hand);\n }",
"@Override\n\t\n\tpublic void sortHand(PlayingCard[][] cards) {\n\t\tfor(int k = 0; k < cards.length; k++) {\n\t\t\tPlayingCard [] hand = cards[k];\n\t\t\t\n\t... | [
"0.81867355",
"0.793171",
"0.7595467",
"0.7454618",
"0.70789516",
"0.70459694",
"0.69861615",
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"0.65760446",
"0.65411997",
"0.652425",
"0.64995813",
"0.64744747",
"0.64438... | 0.7109022 | 4 |
/ void resetHand() In: Nothing Out: Nothing Description: This sets the hand to its default state, containing no cards. | public void resetHand() {
this.myCards = new Card[MAX_CARDS];
this.numCards = 0;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void resetHand()\r\n\t{\r\n\t\thandCount = 0;\r\n\t\tstand = false;\r\n\t}",
"public void resetHand()\r\n {\r\n myCards = new Card[MAX_CARDS];\r\n numCards = 0;\r\n }",
"public void clearHand() {\n\t\t_hand.clearHand();\n\n\t}",
"public void resetActiveHand() {\n\t\tif (activeHand != nul... | [
"0.8879864",
"0.8376941",
"0.8209056",
"0.8142579",
"0.80779594",
"0.7847258",
"0.78375757",
"0.7788418",
"0.77542",
"0.7730402",
"0.7715235",
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"0.71566236",
"0.69952655",
"0.6926683",
"0.69198036",
"0.6895407",
"0.6874462",
"0.6844765"... | 0.8608125 | 1 |
/ boolean takeCard(Card) In: A Card object Out: True if there is room in the hand for the card, false if otherwise Description: This takes a Card object and places a copy of that object into the hand. | public boolean takeCard(Card card) {
if (this.numCards >= MAX_CARDS)
return false;
else {
this.myCards[numCards] = new Card(card);
this.numCards++;
return true;
}
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public boolean takeCard(Card card)\r\n {\r\n if (card != null && numCards < MAX_CARDS)\r\n {\r\n myCards[numCards++] = new Card(card.getValue(), card.getSuit());\r\n return true;\r\n }\r\n return false;\r\n }",
"public void takeACard(Card card){\n hand.add(card)... | [
"0.77605283",
"0.72874486",
"0.6534812",
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"0.6289991",
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"0.60492224",
"0.60350263",
"0.60309064",
"0.6022675",
"0.601... | 0.75303763 | 1 |
/ Card playCard() In: Nothing Out: A Card object with the same values as the card on the top of the hand. Description: This creates a copy of the first card in the hand and returns it to the caller. | public Card playCard() {
Card card = this.myCards[this.numCards - 1];
this.myCards[this.numCards - 1] = null;
this.numCards--;
return card;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Card playCard()\r\n {\r\n Card removedCard = new Card('E', Card.Suit.valueOf(\"SPADES\"));\r\n if (numCards <= 0)\r\n {\r\n return removedCard;\r\n }\r\n Card playedCard = new Card(myCards[numCards - 1].getValue(),\r\n myCards[numCards - 1].getSuit());\r\n ... | [
"0.745069",
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"0.66066... | 0.7698171 | 0 |
/ String toString() In: Nothing Out: A String object containing the cards in the hand. Description: This will provide a textual representation of the data contained in hand to the caller. | public String toString() {
String handString = "( ";
for (int i = 0; i < this.numCards; i++) {
handString += this.myCards[i].toString();
if (i != this.numCards - 1)
handString += ", ";
}
handString += " )";
return handString;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public String toString(){\n return \"Hand: \" + cards; \n }",
"@Override\n public String toString() {\n String hand = \"\";\n for (int i = 0; i < cards.size(); i++) {\n hand += \"(\"+(i+1)+\")\"+cards.get(i).toString() + \", \";\n }\n return hand;\n }",
"p... | [
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"0.72842854",
"0.7262117",
"0.7223... | 0.8699887 | 1 |
/ int getNumCards() In: Nothing Out: An integer whose value is the number of cards in the hand. Description: This is a basic accessor function. | public int getNumCards() {
return this.numCards;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public int getNumCards()\r\n {\r\n return numCards;\r\n }",
"public int getNumberOfCards()\n\t{\n\t\treturn numCards;\n\t}",
"public int getNumberOfCards() {\r\n\t\treturn numberOfCards;\r\n\t}",
"public int numOfCards() {\n return cards.size();\n }",
"public int getNumCards() {\n return ... | [
"0.90650165",
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"0.73145163",
"0.72804326",
"0.7267997",
"0.72283125"... | 0.90009344 | 1 |
/ Card inspectCard(int) In: An integer representing the position of the card to be inspected. Out: A copy of the card at the specified position, or an invalid card if there is no card in that position. Description: This function returns a Card object whose values are equal to the card in the specified position. | public Card inspectCard(int k) {
if (k >= this.numCards || k < 0)
return new Card('0', Card.Suit.spades);
else
return new Card(this.myCards[k]);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Card inspectCard(int k) {\n //If k is invalid, return an invalid card.\n if (k >= this.topCard || k < 0)\n return new Card('0', Card.Suit.spades);\n else\n //Otherwise, return a copy of the card in position k.\n return new Card(this.cards[k]);\n }",
"ICard getCard(... | [
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"0.55750513",
"0.55499786"... | 0.666091 | 2 |
/ In: An integer specifying the position of the card to play in the hand Out: The Card object representing the card in that position This plays a card and removes it from the hand. | public Card playCard(int k) {
//If k is invalid, return an invalid card.
if (k >= this.numCards || k < 0)
return new Card('0', Card.Suit.spades);
else {
//Return the card in that position, and
//move all of the cards after that card
//back by one position.
Card card = new Card(this.myCards[k]);
for (int i = k + 1; i < this.numCards; i++) {
this.myCards[i - 1] = this.myCards[i];
this.myCards[i] = null;
}
this.numCards--;
return card;
}
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void playCard(PlayingCard card){\n\t\tplayed = hand.remove(hand.indexOf(card));\t\t\n\t}",
"public Card playCard(int index)\n\t{\n\t\treturn currentHand.remove(index);\n\t}",
"public Card playCard() {\n Card card = this.myCards[this.numCards - 1];\n this.myCards[this.numCards - 1] = null;\n ... | [
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... | 0.6246212 | 56 |
an array holding the cards not used in the game. e.g. pinochle does not use cards 28 of any suit | public CardGameFramework(int numPacks, int numJokersPerPack,
int numUnusedCardsPerPack, Card[] unusedCardsPerPack,
int numPlayers, int numCardsPerHand) {
int k;
// filter bad values
if (numPacks < 1 || numPacks > 6)
numPacks = 1;
if (numJokersPerPack < 0 || numJokersPerPack > 4)
numJokersPerPack = 0;
if (numUnusedCardsPerPack < 0 || numUnusedCardsPerPack > 50) // > 1 card
numUnusedCardsPerPack = 0;
if (numPlayers < 1 || numPlayers > MAX_PLAYERS)
numPlayers = 4;
// one of many ways to assure at least one full deal to all players
if (numCardsPerHand < 1 ||
numCardsPerHand > numPacks * (52 - numUnusedCardsPerPack)
/ numPlayers)
numCardsPerHand = numPacks * (52 - numUnusedCardsPerPack) / numPlayers;
// allocate
this.unusedCardsPerPack = new Card[numUnusedCardsPerPack];
this.hand = new Hand[numPlayers];
for (k = 0; k < numPlayers; k++)
this.hand[k] = new Hand();
deck = new Deck(numPacks);
// assign to members
this.numPacks = numPacks;
this.numJokersPerPack = numJokersPerPack;
this.numUnusedCardsPerPack = numUnusedCardsPerPack;
this.numPlayers = numPlayers;
this.numCardsPerHand = numCardsPerHand;
for (k = 0; k < numUnusedCardsPerPack; k++)
this.unusedCardsPerPack[k] = unusedCardsPerPack[k];
// prepare deck and shuffle
newGame();
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void discard(){\n for(int i = 1 ; i <= 110 ; i++) cardDeck.remove(i);\n }",
"public static Card[] getStandardDeck() {\n Card[] deck = new Card[52];\n int s = 0;\n for (SUIT suit : SUIT.values()) {\n for (int i = 12; i > -1; i--) {\n deck[s + i] = new Card(suit, i);\n }\... | [
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"0.6033108",
"0.6031214",
"0.60240513",
"0.601284",
"0.6005197",... | 0.56107503 | 77 |
constructor overload/default for game like bridge | public CardGameFramework() {
this(1, 0, 0, null, 4, 13);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Bridge() {\n }",
"public PlayableGame() {\r\n\r\n }",
"public Game() {}",
"private Game() {}",
"public MiniGame() {\n\n\t}",
"public Game() {\n\n\t}",
"public BeanGame () {\n\t}",
"public OrchardGame() {\n\t\t\n\t}",
"public CardGameFramework()\n {\n this(1, 0, 0, null, 4, 13);\... | [
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"0.65541834",
"0.65359926",
"0.6500396",
"0.64950... | 0.6670086 | 11 |
hands start from 0 like arrays on error return automatic empty hand | public Hand getHand(int k) {
if (k < 0 || k >= numPlayers)
return new Hand();
return hand[k];
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void emptyHand();",
"public void clearHand() {\n\t\tthis.aHand = new Chips[aHand.length];\n\t}",
"public Hand() {\n cards = new int[16];\n numSoft = 0;\n score = 0;\n numCards = 0;\n }",
"public void resetHand()\r\n\t{\r\n\t\thandCount = 0;\r\n\t\tstand = false;\r\n\t}",
"public void re... | [
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"0.57890004",
"0.57505774",
"0.5719783",
"0.5707962... | 0.52064997 | 63 |
returns false if not enough cards, but deals what it can | public boolean deal() {
int k, j;
boolean enoughCards;
// clear all hands
for (j = 0; j < numPlayers; j++)
hand[j].resetHand();
enoughCards = true;
for (k = 0; k < numCardsPerHand && enoughCards; k++) {
for (j = 0; j < numPlayers; j++)
if (deck.getNumCards() > 0)
hand[j].takeCard(deck.dealCard());
else {
enoughCards = false;
break;
}
}
return enoughCards;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public boolean hasMoreCards() {\n if (cardHold == 0)\n return false;\n else\n return true;\n }",
"public boolean canUseCard() {\n \t\tif (usedCard)\n \t\t\treturn false;\n \n \t\tfor (int i = 0; i < cards.length; i++) {\n \t\t\tif (cards[i] > 0)\n \t\t\t\treturn true;\n \t\... | [
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"0.676518",
"0.67414284",
"0.67396975",
"0.672669... | 0.72341585 | 2 |
Adds an event listener allowing to control using the includeExisting if events will be fired for existing space instances as well. | void add(SpaceInstanceAddedEventListener eventListener, boolean includeExisting); | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"void remove(SpaceInstanceAddedEventListener eventListener);",
"public void addScopeRegistrationListener(ScopeEventListener listener);",
"public void addGameEventListener(GameEventListener gameEventListener);",
"public void addSpaceStation()\n\t{\n\t\t//if a space station is already spawned, one will not be a... | [
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"0.5092585",
"0.5091604",
"0.50904536... | 0.81633794 | 0 |
Removes the event listener. | void remove(SpaceInstanceAddedEventListener eventListener); | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Override\n public void removeListener() {\n this.mListener = null;\n }",
"public synchronized void removeEventListener(InputListener listener) {\n\t\tlisteners.remove(listener);\n\t}",
"public void unregisterListener() {\n\t\tthis.listener = null;\n\t}",
"public void removeListener(T listener... | [
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"0.73888344",
"0.7308889",
"0.7291135",
"0.7288516"... | 0.0 | -1 |
Inflate the menu; this adds items to the action bar if it is present. | @Override
public boolean onCreateOptionsMenu( Menu menu ) {
getMenuInflater().inflate( R.menu.menu_main, menu );
return true;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Override\n public boolean onCreateOptionsMenu(Menu menu) {\n \tMenuInflater inflater = getMenuInflater();\n \tinflater.inflate(R.menu.main_activity_actions, menu);\n \treturn super.onCreateOptionsMenu(menu);\n }",
"@Override\n public void onCreateOptionsMenu(Menu menu, MenuInflater inflater) {... | [
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"0.68745375",
"0.68621665",
"0.6862... | 0.0 | -1 |
Handle action bar item clicks here. The action bar will automatically handle clicks on the Home/Up button, so long as you specify a parent activity in AndroidManifest.xml. | @Override
public boolean onOptionsItemSelected( MenuItem item ) {
int id = item.getItemId();
//noinspection SimplifiableIfStatement
if ( id == R.id.action_settings ) {
return true;
}
return super.onOptionsItemSelected( item );
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Override\n public boolean onOptionsItemSelected(MenuItem item) { Handle action bar item clicks here. The action bar will\n // automatically handle clicks on the Home/Up button, so long\n // as you specify a parent activity in AndroidManifest.xml.\n\n //\n // HANDLE BACK BUTTON\n ... | [
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"0.76221544",
"0.7584775",
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"0.73788774",
"0.7370012",
"0.7362617",
"0.73555875",
"0.73451835... | 0.0 | -1 |
Show 3 total pages. | @Override
public int getCount() {
return 3;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Override\n\t\tpublic int getCount() {\n\t\t\t// Show 3 total pages.\n\t\t\treturn 3;\n\t\t}",
"@Override\n // This returns the number of pages we have within our view pager\n public int getCount() {\n return 3;\n }",
"int getPagesAmount();",
"long getAmountPage();",
"int ge... | [
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"0.66357523",
"0.65496594",
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"0.5858472",
"0.58557075",
"0.58179796",
"0.5745283",
"0.573... | 0.0 | -1 |
Random in a rectangle constructor | public UniformInBoxPointSequence2D(int size, double width, double height) {
super((List<T>) generateRandomPoints(size, width, height));
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"private void generateNewRect() {\n\t\t\n\t\tif(rectengleList.size() < getLevel()){\n\t\tRandom mRandom = new Random();\n\t\tint height = getHeight();\n\t\tint width = getWidth();\n\n\t\tint cx = (int) ((mRandom.nextInt() % (width * 0.8)) + (width * 0.1));\n\t\tint cy = (int) ((mRandom.nextInt() % (height * 0.8)) +... | [
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"0.64203405",
"0.6419499",
"0.64001966",
"0.63969076",
"0.... | 0.57907474 | 85 |
Access catalog component and retrieve transportation data from the database | public List searchTransportation(String origin, String destination,
Locale locale) throws HTMLActionException {
List transportation = null;
List transportationBean = new ArrayList();
// call catalog component
try {
CatalogFacade catalogFacade = new CatalogFacade();
transportation = catalogFacade.getTransportations(origin,
destination, locale);
// Catch catalog exceptions and re-throw them as
// mini-app application defined exceptions.
} catch (Exception e) {
throw new HTMLActionException(
"Transportation Search Exception:: Catalog Exception accessing catalog component: "
+ e);
}
for (int i = 0; i < transportation.size(); ++i) {
transportationBean
.add(new TransportationBean(
((com.sun.j2ee.blueprints.catalog.Transportation) transportation
.get(i)).getTransportationId(),
((com.sun.j2ee.blueprints.catalog.Transportation) transportation
.get(i)).getName(),
((com.sun.j2ee.blueprints.catalog.Transportation) transportation
.get(i)).getDescription(),
((com.sun.j2ee.blueprints.catalog.Transportation) transportation
.get(i)).getImageURI(),
((com.sun.j2ee.blueprints.catalog.Transportation) transportation
.get(i)).getPrice(),
((com.sun.j2ee.blueprints.catalog.Transportation) transportation
.get(i)).getOrigin(),
((com.sun.j2ee.blueprints.catalog.Transportation) transportation
.get(i)).getDestination(),
((com.sun.j2ee.blueprints.catalog.Transportation) transportation
.get(i)).getCarrier(),
((com.sun.j2ee.blueprints.catalog.Transportation) transportation
.get(i)).getDepartureTime(),
((com.sun.j2ee.blueprints.catalog.Transportation) transportation
.get(i)).getArrivalTime(),
((com.sun.j2ee.blueprints.catalog.Transportation) transportation
.get(i)).getTravelClass()));
}
return transportationBean;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public Catalog getCatalog();",
"Catalog getCatalog();",
"Catalog getCatalog();",
"public IFuture<ItemInfo[]> getCatalog()\n\t{\n\t\tfinal Future<ItemInfo[]> ret = new Future<ItemInfo[]>();\n\t\tShopCapa shop = (ShopCapa)capa.getPojoCapability();\n\t\tret.setResult(shop.getCatalog().toArray(new ItemInfo[shop.... | [
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"0.5453166",
"0.5445034",
"0.5437932",
"0.5434837",
"0.5417516",... | 0.5222284 | 45 |
Methode permettant d'enregistrer un article | public int enregistrerArticleService (Articles article); | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"Article createArticle();",
"void addArticle(Article a);",
"public Article save(Article article);",
"@Override\r\n\tpublic void post(Article article) {\n\t\tarticleDao.post(article);\r\n\t\t\r\n\t}",
"@Override\n public void addArticle(Article article) {\n\n remoteDataSource.addArticle(article);\n... | [
"0.6656692",
"0.66264576",
"0.6606174",
"0.62208515",
"0.61754256",
"0.61641866",
"0.6161898",
"0.6082797",
"0.6004809",
"0.59706455",
"0.5965409",
"0.58827925",
"0.572729",
"0.5716294",
"0.5716084",
"0.56834406",
"0.56571025",
"0.5617801",
"0.561055",
"0.5575123",
"0.5573643... | 0.73849684 | 0 |
methode affichant tous les articles | public List<Articles> listArticlesService(); | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void setupArticlesList();",
"@Override\n public List<Article> getArticles() {\n List<Article> articles=articleDAO.getArticles();\n knownArticles.addAll(articles);\n return articles;\n }",
"private void getArticles() throws Exception {\n\t\t\tHttpClient httpClient = new Default... | [
"0.7531823",
"0.6890471",
"0.6810702",
"0.6808935",
"0.6779658",
"0.6739215",
"0.6661401",
"0.66375303",
"0.66054255",
"0.6595976",
"0.65347564",
"0.6529423",
"0.64996946",
"0.6492299",
"0.6446378",
"0.6408855",
"0.63760704",
"0.63101846",
"0.6270289",
"0.6242385",
"0.6229333... | 0.6610807 | 8 |
Methode de recherche d'un article | public List<Articles> RechercheArticlesService(String nomColonne, String valeurRecherchee); | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Override\r\n\tpublic Set<Article> search(String keyworkds) {\n\t\treturn null;\r\n\t}",
"private void getBySearch(HttpServletRequest request, HttpServletResponse response, ArticleService artService) {\n\t\tString search = request.getParameter(\"search\");\n\t\tList<Article> list = new ArrayList<Article>();\n\t\... | [
"0.6833042",
"0.68012106",
"0.6753014",
"0.6658464",
"0.65671915",
"0.64542717",
"0.6431282",
"0.6418926",
"0.6418926",
"0.64134014",
"0.63446",
"0.6334951",
"0.6328676",
"0.62986994",
"0.62965757",
"0.62397575",
"0.62154883",
"0.62120056",
"0.61448735",
"0.61131424",
"0.6106... | 0.6873489 | 0 |
TODO: Warning this method won't work in the case the id fields are not set | @Override
public boolean equals(Object object) {
if (!(object instanceof Matricula)) {
return false;
}
Matricula other = (Matricula) object;
if ((this.idMatricula == null && other.idMatricula != null) || (this.idMatricula != null && !this.idMatricula.equals(other.idMatricula))) {
return false;
}
return true;
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"private void setId(Integer id) { this.id = id; }",
"private Integer getId() { return this.id; }",
"public void setId(int id){ this.id = id; }",
"public void setId(Long id) {this.id = id;}",
"public void setId(Long id) {this.id = id;}",
"public void setID(String idIn) {this.id = idIn;}",
"public void se... | [
"0.6896886",
"0.6838461",
"0.67056817",
"0.66419715",
"0.66419715",
"0.6592331",
"0.6579151",
"0.6579151",
"0.6574321",
"0.6574321",
"0.6574321",
"0.6574321",
"0.6574321",
"0.6574321",
"0.65624106",
"0.65624106",
"0.65441847",
"0.65243006",
"0.65154546",
"0.6487427",
"0.64778... | 0.0 | -1 |
This endpoint method uses a combination of XPath expressions and marshalling to handle message with a <GetPatientsRequest> payload. | @PayloadRoot(localPart = GET_PATIENTS_REQUEST, namespace = MESSAGES_NAMESPACE)
@Namespace(prefix = "m", uri = MESSAGES_NAMESPACE)
@ResponsePayload
public JAXBElement<id.co.kmn.services.wsdl.server.schema.PatientInfo> getPatients(@XPathParam("//m:reqKeyword") String reqKeyword,
@XPathParam("//m:reqClinicId") String reqClinicId,
@XPathParam("//m:reqPageNumber") int reqPageNumber,
@XPathParam("//m:reqRowPerPage") int reqRowPerPage)
throws DatatypeConfigurationException, ParserConfigurationException {
if (logger.isDebugEnabled()) {
logger.debug("Received GetPatientsRequest '" + reqKeyword + "' id: '" + reqClinicId + "' page: "
+ reqPageNumber + "reqRowPerPage: " + reqRowPerPage);
}
PatientInfo patientInfo = kmnServiceMethod.getPatientInfo(reqKeyword, reqClinicId, reqPageNumber, reqRowPerPage);
return objectFactory.createGetPatientsResponse(SchemaConversionUtils.toSchemaType(patientInfo));
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@POST\n @Path(\"/getRequests\")\n @Produces(MediaType.APPLICATION_JSON)\n public ArrayList<Relative> getRequests(@HeaderParam(\"patientemail\") String patientEmail) {\n\n RequestDaoImpl reqDaoImpl = new RequestDaoImpl();\n ArrayList<Relative> relatives = reqDaoImpl.getRequests(patientEmail);... | [
"0.6143542",
"0.5734837",
"0.56783193",
"0.5543806",
"0.55051374",
"0.54933226",
"0.54809237",
"0.5428957",
"0.5426681",
"0.53851765",
"0.531561",
"0.52995414",
"0.5206264",
"0.5184552",
"0.5163061",
"0.5143694",
"0.51237094",
"0.51046866",
"0.5064082",
"0.50426173",
"0.50328... | 0.6896153 | 0 |
Test of isConnected method, of class MarkovModelUtilities. | @Test
public void testIsConnected()
{
assertEquals(MarkovModel.util.isConnected(P1), true);
assertEquals(MarkovModel.util.isConnected(P2), false);
assertEquals(MarkovModel.util.isConnected(P3), false);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void testConnectedComponents()\n {\n Collection<IIntArray> cc1 = MarkovModel.util.connectedComponents(P1);\n assertTrue(cc1.containsAll(C1));\n assertEquals(cc1.size(), C1.size());\n\n Collection<IIntArray> cc2 = MarkovModel.util.connectedComponents(P2);\n as... | [
"0.71300125",
"0.70772636",
"0.6927711",
"0.6890222",
"0.6792548",
"0.6787096",
"0.6772003",
"0.67700887",
"0.67700887",
"0.67700887",
"0.67700887",
"0.67700887",
"0.67700887",
"0.67700887",
"0.67700887",
"0.65796477",
"0.65796477",
"0.65796477",
"0.65796477",
"0.65796477",
"... | 0.83215606 | 0 |
Test of connectedComponents method, of class MarkovModelUtilities. | @Test
public void testConnectedComponents()
{
Collection<IIntArray> cc1 = MarkovModel.util.connectedComponents(P1);
assertTrue(cc1.containsAll(C1));
assertEquals(cc1.size(), C1.size());
Collection<IIntArray> cc2 = MarkovModel.util.connectedComponents(P2);
assertTrue(cc2.containsAll(C2));
assertEquals(cc2.size(), C2.size());
Collection<IIntArray> cc3 = MarkovModel.util.connectedComponents(P3);
assertTrue(cc3.containsAll(C3));
assertEquals(cc3.size(), C3.size());
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void testConnected() {\n System.out.println(\"**** connected *****\");\n int v = 9;\n int w = 12;\n ConnectedComponentsDepthSearch instance = new ConnectedComponentsDepthSearch(g);\n boolean expResult = true;\n boolean result = instance.connected(v, w);\n... | [
"0.67552066",
"0.6620726",
"0.6540942",
"0.6509026",
"0.64283943",
"0.62315524",
"0.5980144",
"0.5909983",
"0.5846662",
"0.5763573",
"0.5756352",
"0.57287776",
"0.5717743",
"0.57047963",
"0.5691166",
"0.5622625",
"0.55941814",
"0.5566277",
"0.55284333",
"0.5518904",
"0.549194... | 0.8467201 | 0 |
Test of giantComponent method, of class MarkovModelUtilities. | @Test
public void testGiantComponent()
{
assertEquals(MarkovModel.util.giantComponent(P1), giant1);
assertEquals(MarkovModel.util.giantComponent(P2), giant2);
assertEquals(MarkovModel.util.giantComponent(P3), giant3);
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"public void testGetConformingStimulusLargeScale() throws Exception {\n long start = System.currentTimeMillis();\n Stimulus stimulus = new MockStimulus();\n message.setConformingStimulus(stimulus);\n for (int i = 0; i < TOTAL_ITERATION; i++) {\n message.getConformingStimulus()... | [
"0.61025536",
"0.5821381",
"0.564987",
"0.55952024",
"0.55868876",
"0.55392677",
"0.5526498",
"0.54585916",
"0.54564625",
"0.543615",
"0.5392976",
"0.5391787",
"0.5372625",
"0.53592974",
"0.5339858",
"0.5334331",
"0.5314421",
"0.5305266",
"0.52997833",
"0.5299271",
"0.5291776... | 0.8315709 | 0 |
Test of estimateC method, of class MarkovModelUtilities. | @Test
public void testEstimateC_Iterable_int()
{
Iterable<IIntArray> trajs = null;
int lag = 0;
MarkovModelUtilities instance = new MarkovModelUtilities();
IDoubleArray expResult = null;
IDoubleArray result = instance.estimateC(trajs, lag);
assertEquals(expResult, result);
// TODO review the generated test code and remove the default call to fail.
fail("The test case is a prototype.");
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void testEstimateT()\n {\n \n IDoubleArray counts = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateT(counts);\n assertEquals(expResult, result);\n // ... | [
"0.7009995",
"0.63965464",
"0.6255407",
"0.6167512",
"0.6111849",
"0.5991579",
"0.5817513",
"0.5549115",
"0.5527761",
"0.54302907",
"0.53835",
"0.5354121",
"0.52574354",
"0.525396",
"0.52325606",
"0.51910627",
"0.5172708",
"0.5146926",
"0.5145967",
"0.5142142",
"0.51306146",
... | 0.703813 | 0 |
Test of estimateCmilestoning method, of class MarkovModelUtilities. | @Test
public void testEstimateCmilestoning_3args_1()
{
Iterable<IIntArray> trajs = null;
Iterable<IIntArray> cores = null;
int lag = 0;
MarkovModelUtilities instance = new MarkovModelUtilities();
IDoubleArray expResult = null;
IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);
assertEquals(expResult, result);
// TODO review the generated test code and remove the default call to fail.
fail("The test case is a prototype.");
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void testEstimateCmilestoning_3args_2()\n {\n IIntArray traj = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.esti... | [
"0.76359123",
"0.73141825",
"0.65835816",
"0.57383484",
"0.56049335",
"0.5368913",
"0.5366745",
"0.53475344",
"0.5276815",
"0.5211457",
"0.51922",
"0.5185153",
"0.51824313",
"0.5180554",
"0.51034254",
"0.50930893",
"0.50909305",
"0.5089265",
"0.508482",
"0.50655603",
"0.50131... | 0.7719333 | 0 |
Test of estimateCmilestoning method, of class MarkovModelUtilities. | @Test
public void testEstimateCmilestoning_Iterable_int()
{
Iterable<IIntArray> trajs = null;
int lag = 0;
MarkovModelUtilities instance = new MarkovModelUtilities();
IDoubleArray expResult = null;
IDoubleArray result = instance.estimateCmilestoning(trajs, lag);
assertEquals(expResult, result);
// TODO review the generated test code and remove the default call to fail.
fail("The test case is a prototype.");
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void testEstimateCmilestoning_3args_1()\n {\n Iterable<IIntArray> trajs = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = in... | [
"0.7720083",
"0.7637022",
"0.6585873",
"0.5737402",
"0.5602599",
"0.53692174",
"0.536536",
"0.53475755",
"0.5277167",
"0.5211567",
"0.519184",
"0.51858693",
"0.51833606",
"0.518084",
"0.5102803",
"0.5094376",
"0.50911736",
"0.508913",
"0.50863725",
"0.50679743",
"0.5012965",
... | 0.7315861 | 2 |
Test of estimateC method, of class MarkovModelUtilities. | @Test
public void testEstimateC_IIntArray_int()
{
IIntArray traj = null;
int lag = 0;
MarkovModelUtilities instance = new MarkovModelUtilities();
IDoubleArray expResult = null;
IDoubleArray result = instance.estimateC(traj, lag);
assertEquals(expResult, result);
// TODO review the generated test code and remove the default call to fail.
fail("The test case is a prototype.");
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void testEstimateC_Iterable_int()\n {\n Iterable<IIntArray> trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateC(trajs, lag);\n assertEqua... | [
"0.7036593",
"0.7006459",
"0.62546384",
"0.61666447",
"0.61112034",
"0.59915125",
"0.58168507",
"0.5547312",
"0.55261725",
"0.54291964",
"0.5382521",
"0.5353092",
"0.5255702",
"0.52556413",
"0.5232925",
"0.5190478",
"0.5171634",
"0.5145396",
"0.5144711",
"0.5140281",
"0.51295... | 0.63952476 | 2 |
Test of estimateCmilestoning method, of class MarkovModelUtilities. | @Test
public void testEstimateCmilestoning_3args_2()
{
IIntArray traj = null;
Iterable<IIntArray> cores = null;
int lag = 0;
MarkovModelUtilities instance = new MarkovModelUtilities();
IDoubleArray expResult = null;
IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);
assertEquals(expResult, result);
// TODO review the generated test code and remove the default call to fail.
fail("The test case is a prototype.");
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void testEstimateCmilestoning_3args_1()\n {\n Iterable<IIntArray> trajs = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = in... | [
"0.7719048",
"0.7314452",
"0.6584927",
"0.5736506",
"0.5605106",
"0.5367599",
"0.53661937",
"0.53501797",
"0.5276743",
"0.5212073",
"0.5192482",
"0.51843244",
"0.5181949",
"0.5180885",
"0.5106315",
"0.5093115",
"0.50918853",
"0.5088555",
"0.5084552",
"0.50662374",
"0.50125676... | 0.76356745 | 1 |
Test of estimateCmilestoning method, of class MarkovModelUtilities. | @Test
public void testEstimateCmilestoning_IIntArray_int()
{
IIntArray traj = null;
int lag = 0;
MarkovModelUtilities instance = new MarkovModelUtilities();
IDoubleArray expResult = null;
IDoubleArray result = instance.estimateCmilestoning(traj, lag);
assertEquals(expResult, result);
// TODO review the generated test code and remove the default call to fail.
fail("The test case is a prototype.");
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void testEstimateCmilestoning_3args_1()\n {\n Iterable<IIntArray> trajs = null;\n Iterable<IIntArray> cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = in... | [
"0.7719333",
"0.76359123",
"0.73141825",
"0.57383484",
"0.56049335",
"0.5368913",
"0.5366745",
"0.53475344",
"0.5276815",
"0.5211457",
"0.51922",
"0.5185153",
"0.51824313",
"0.5180554",
"0.51034254",
"0.50930893",
"0.50909305",
"0.5089265",
"0.508482",
"0.50655603",
"0.501313... | 0.65835816 | 3 |
Test of estimateCstepping method, of class MarkovModelUtilities. | @Test
public void testEstimateCstepping_Iterable_int()
{
Iterable<IIntArray> trajs = null;
int lag = 0;
MarkovModelUtilities instance = new MarkovModelUtilities();
IDoubleArray expResult = null;
IDoubleArray result = instance.estimateCstepping(trajs, lag);
assertEquals(expResult, result);
// TODO review the generated test code and remove the default call to fail.
fail("The test case is a prototype.");
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void testEstimateT()\n {\n \n IDoubleArray counts = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateT(counts);\n assertEquals(expResult, result);\n // ... | [
"0.65949225",
"0.65912473",
"0.5986597",
"0.57795",
"0.5703691",
"0.563787",
"0.53556013",
"0.5318458",
"0.531436",
"0.52704465",
"0.5177754",
"0.5174197",
"0.5158172",
"0.51175845",
"0.51056963",
"0.507546",
"0.5049223",
"0.50490606",
"0.5048576",
"0.50396436",
"0.50091225",... | 0.71575373 | 0 |
Test of estimateCstepping method, of class MarkovModelUtilities. | @Test
public void testEstimateCstepping_IIntArray_int()
{
IIntArray traj = null;
int lag = 0;
MarkovModelUtilities instance = new MarkovModelUtilities();
IDoubleArray expResult = null;
IDoubleArray result = instance.estimateCstepping(traj, lag);
assertEquals(expResult, result);
// TODO review the generated test code and remove the default call to fail.
fail("The test case is a prototype.");
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void testEstimateCstepping_Iterable_int()\n {\n Iterable<IIntArray> trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCstepping(trajs, lag);\n ... | [
"0.71575373",
"0.65949225",
"0.5986597",
"0.57795",
"0.5703691",
"0.563787",
"0.53556013",
"0.5318458",
"0.531436",
"0.52704465",
"0.5177754",
"0.5174197",
"0.5158172",
"0.51175845",
"0.51056963",
"0.507546",
"0.5049223",
"0.50490606",
"0.5048576",
"0.50396436",
"0.50091225",... | 0.65912473 | 2 |
Test of logLikelihood method, of class MarkovModelUtilities. | @Test
public void testLogLikelihood()
{
IDoubleArray T = null;
IDoubleArray C = null;
MarkovModelUtilities instance = new MarkovModelUtilities();
double expResult = 0.0;
double result = instance.logLikelihood(T, C);
assertEquals(expResult, result, 0.0);
// TODO review the generated test code and remove the default call to fail.
fail("The test case is a prototype.");
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"@Test\n public void testLogLikelihoodCorrelationMatrix()\n {\n IDoubleArray corr = null;\n IDoubleArray C = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n double expResult = 0.0;\n double result = instance.logLikelihoodCorrelationMatrix(corr, C);\n ... | [
"0.7427038",
"0.66404974",
"0.6166927",
"0.5927131",
"0.5869066",
"0.56961393",
"0.55797553",
"0.5571208",
"0.5482358",
"0.54607975",
"0.5375718",
"0.5269194",
"0.52524656",
"0.5225311",
"0.52244943",
"0.5209235",
"0.51835144",
"0.51666826",
"0.51612955",
"0.51324403",
"0.510... | 0.88419306 | 0 |
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