\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 // -z <class name>\n // \\tOnly outputs the source representation of the classifier,\n // \\tgiving it the supplied name.\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.rules.OneR:\n // \n // -B <minimum bucket size>\n // \\tThe minimum number of objects in a bucket (default: 6).\n //\n verifyException(\"weka.classifiers.Evaluation\", e);\n }\n }","title":""},{"docid":"91a66ea86998d9c52f727783b48bc275","score":"0.58417916","text":"public double entropy();","title":""},{"docid":"b2b86baa91c96f4f28b4919289068f8c","score":"0.5826284","text":"@Test(timeout = 4000)\n public void test83() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n assertNotNull(testInstances0);\n assertEquals(2, testInstances0.getNumNominalValues());\n assertEquals(20, testInstances0.getNumInstances());\n assertEquals(10, testInstances0.getNumInstancesRelational());\n assertEquals(0, testInstances0.getNumDate());\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\n assertEquals(0, testInstances0.getNumRelationalNumeric());\n assertEquals(1, testInstances0.getSeed());\n assertFalse(testInstances0.getMultiInstance());\n assertEquals(0, testInstances0.getNumRelationalString());\n assertEquals(1, testInstances0.getNumRelationalNominal());\n assertEquals(2, testInstances0.getNumAttributes());\n assertEquals(0, testInstances0.getNumRelational());\n assertEquals(0, testInstances0.getNumString());\n assertEquals(0, testInstances0.getNumNumeric());\n assertEquals(1, testInstances0.getClassType());\n assertEquals((-1), testInstances0.getClassIndex());\n assertEquals(0, testInstances0.getNumRelationalDate());\n assertEquals(1, testInstances0.getNumNominal());\n assertEquals(\"Testdata\", testInstances0.getRelation());\n assertEquals(\" \", testInstances0.getWordSeparators());\n assertEquals(2, testInstances0.getNumClasses());\n assertFalse(testInstances0.getNoClass());\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertEquals((-2), TestInstances.NO_CLASS);\n \n EvoSuiteFile evoSuiteFile0 = new EvoSuiteFile(\"/home/ubuntu/wekafiles/props\");\n boolean boolean0 = FileSystemHandling.appendStringToFile(evoSuiteFile0, \" \");\n assertTrue(boolean0);\n \n GaussianProcesses gaussianProcesses0 = new GaussianProcesses();\n assertNotNull(gaussianProcesses0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n \n Capabilities capabilities0 = gaussianProcesses0.getCapabilities();\n assertNotNull(capabilities0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertTrue(capabilities0.hasDependencies());\n assertEquals(1, capabilities0.getMinimumNumberInstances());\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n \n TestInstances testInstances1 = TestInstances.forCapabilities(capabilities0);\n assertNotNull(testInstances1);\n assertNotSame(testInstances1, testInstances0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertTrue(capabilities0.hasDependencies());\n assertEquals(1, capabilities0.getMinimumNumberInstances());\n assertEquals((-1), testInstances1.getClassIndex());\n assertEquals(1, testInstances1.getSeed());\n assertEquals(0, testInstances1.getClassType());\n assertEquals(3, testInstances1.getNumAttributes());\n assertEquals(1, testInstances1.getNumRelationalNominal());\n assertEquals(1, testInstances1.getNumNumeric());\n assertEquals(0, testInstances1.getNumRelationalString());\n assertEquals(1, testInstances1.getNumRelationalNumeric());\n assertEquals(0, testInstances1.getNumString());\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\n assertEquals(20, testInstances1.getNumInstances());\n assertFalse(testInstances1.getNoClass());\n assertFalse(testInstances1.getMultiInstance());\n assertEquals(2, testInstances1.getNumClasses());\n assertEquals(\"Testdata\", testInstances1.getRelation());\n assertEquals(\" \", testInstances1.getWordSeparators());\n assertEquals(0, testInstances1.getNumRelational());\n assertEquals(0, testInstances1.getNumRelationalDate());\n assertEquals(1, testInstances1.getNumNominal());\n assertEquals(10, testInstances1.getNumInstancesRelational());\n assertEquals(2, testInstances1.getNumNominalValues());\n assertEquals(0, testInstances1.getNumDate());\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n assertEquals((-2), TestInstances.NO_CLASS);\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertFalse(testInstances1.equals((Object)testInstances0));\n \n Enumeration enumeration0 = testInstances0.listOptions();\n assertNotNull(enumeration0);\n assertNotSame(testInstances0, testInstances1);\n assertEquals(2, testInstances0.getNumNominalValues());\n assertEquals(20, testInstances0.getNumInstances());\n assertEquals(10, testInstances0.getNumInstancesRelational());\n assertEquals(0, testInstances0.getNumDate());\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\n assertEquals(0, testInstances0.getNumRelationalNumeric());\n assertEquals(1, testInstances0.getSeed());\n assertFalse(testInstances0.getMultiInstance());\n assertEquals(0, testInstances0.getNumRelationalString());\n assertEquals(1, testInstances0.getNumRelationalNominal());\n assertEquals(2, testInstances0.getNumAttributes());\n assertEquals(0, testInstances0.getNumRelational());\n assertEquals(0, testInstances0.getNumString());\n assertEquals(0, testInstances0.getNumNumeric());\n assertEquals(1, testInstances0.getClassType());\n assertEquals((-1), testInstances0.getClassIndex());\n assertEquals(0, testInstances0.getNumRelationalDate());\n assertEquals(1, testInstances0.getNumNominal());\n assertEquals(\"Testdata\", testInstances0.getRelation());\n assertEquals(\" \", testInstances0.getWordSeparators());\n assertEquals(2, testInstances0.getNumClasses());\n assertFalse(testInstances0.getNoClass());\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertEquals((-2), TestInstances.NO_CLASS);\n assertFalse(testInstances0.equals((Object)testInstances1));\n \n Instances instances0 = testInstances1.generate();\n assertNotNull(instances0);\n assertNotSame(testInstances1, testInstances0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertTrue(capabilities0.hasDependencies());\n assertEquals(1, capabilities0.getMinimumNumberInstances());\n assertEquals((-1), testInstances1.getClassIndex());\n assertEquals(1, testInstances1.getSeed());\n assertEquals(0, testInstances1.getClassType());\n assertEquals(3, testInstances1.getNumAttributes());\n assertEquals(1, testInstances1.getNumRelationalNominal());\n assertEquals(1, testInstances1.getNumNumeric());\n assertEquals(0, testInstances1.getNumRelationalString());\n assertEquals(1, testInstances1.getNumRelationalNumeric());\n assertEquals(0, testInstances1.getNumString());\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\n assertEquals(20, testInstances1.getNumInstances());\n assertFalse(testInstances1.getNoClass());\n assertFalse(testInstances1.getMultiInstance());\n assertEquals(2, testInstances1.getNumClasses());\n assertEquals(\"Testdata\", testInstances1.getRelation());\n assertEquals(\" \", testInstances1.getWordSeparators());\n assertEquals(0, testInstances1.getNumRelational());\n assertEquals(0, testInstances1.getNumRelationalDate());\n assertEquals(1, testInstances1.getNumNominal());\n assertEquals(10, testInstances1.getNumInstancesRelational());\n assertEquals(2, testInstances1.getNumNominalValues());\n assertEquals(0, testInstances1.getNumDate());\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\n assertEquals(3, instances0.numAttributes());\n assertEquals(2, instances0.classIndex());\n assertEquals(20, instances0.numInstances());\n assertEquals(20, instances0.size());\n assertEquals(\"Testdata\", instances0.relationName());\n assertEquals(1, instances0.numClasses());\n assertFalse(instances0.checkForStringAttributes());\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n assertEquals((-2), TestInstances.NO_CLASS);\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertFalse(testInstances1.equals((Object)testInstances0));\n \n Evaluation evaluation0 = new Evaluation(instances0);\n assertNotNull(evaluation0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertTrue(capabilities0.hasDependencies());\n assertEquals(1, capabilities0.getMinimumNumberInstances());\n assertEquals((-1), testInstances1.getClassIndex());\n assertEquals(1, testInstances1.getSeed());\n assertEquals(0, testInstances1.getClassType());\n assertEquals(3, testInstances1.getNumAttributes());\n assertEquals(1, testInstances1.getNumRelationalNominal());\n assertEquals(1, testInstances1.getNumNumeric());\n assertEquals(0, testInstances1.getNumRelationalString());\n assertEquals(1, testInstances1.getNumRelationalNumeric());\n assertEquals(0, testInstances1.getNumString());\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\n assertEquals(20, testInstances1.getNumInstances());\n assertFalse(testInstances1.getNoClass());\n assertFalse(testInstances1.getMultiInstance());\n assertEquals(2, testInstances1.getNumClasses());\n assertEquals(\"Testdata\", testInstances1.getRelation());\n assertEquals(\" \", testInstances1.getWordSeparators());\n assertEquals(0, testInstances1.getNumRelational());\n assertEquals(0, testInstances1.getNumRelationalDate());\n assertEquals(1, testInstances1.getNumNominal());\n assertEquals(10, testInstances1.getNumInstancesRelational());\n assertEquals(2, testInstances1.getNumNominalValues());\n assertEquals(0, testInstances1.getNumDate());\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\n assertEquals(3, instances0.numAttributes());\n assertEquals(2, instances0.classIndex());\n assertEquals(20, instances0.numInstances());\n assertEquals(20, instances0.size());\n assertEquals(\"Testdata\", instances0.relationName());\n assertEquals(1, instances0.numClasses());\n assertFalse(instances0.checkForStringAttributes());\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01);\n assertEquals(0.0, evaluation0.correct(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01);\n assertFalse(evaluation0.getDiscardPredictions());\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01);\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01);\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01);\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01);\n assertEquals(0.0, evaluation0.numInstances(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01);\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n assertEquals((-2), TestInstances.NO_CLASS);\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertFalse(testInstances1.equals((Object)testInstances0));\n \n double double0 = (-2257.885429600286);\n try { \n evaluation0.priorEntropy();\n fail(\"Expecting exception: Exception\");\n \n } catch(Exception e) {\n //\n // Can't compute entropy of class prior: class numeric!\n //\n verifyException(\"weka.classifiers.Evaluation\", e);\n }\n }","title":""},{"docid":"e8a595b410448f016b851fb0d66288a6","score":"0.5825243","text":"@Test(timeout = 4000)\n public void test053() throws Throwable {\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\n Instances instances0 = textDirectoryLoader0.getStructure();\n Evaluation evaluation0 = new Evaluation(instances0);\n char[] charArray0 = new char[1];\n String string0 = evaluation0.num2ShortID(30, charArray0, 30);\n assertEquals(\"\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\", string0);\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n }","title":""},{"docid":"9b589339ba3824617207f7add7e44aea","score":"0.58200485","text":"@Test(timeout = 4000)\n public void test71() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n assertNotNull(testInstances0);\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\n assertEquals(0, testInstances0.getNumRelationalDate());\n assertEquals(0, testInstances0.getNumRelational());\n assertEquals(2, testInstances0.getNumNominalValues());\n assertEquals(20, testInstances0.getNumInstances());\n assertEquals(2, testInstances0.getNumAttributes());\n assertEquals(2, testInstances0.getNumClasses());\n assertEquals(0, testInstances0.getNumNumeric());\n assertFalse(testInstances0.getNoClass());\n assertEquals(1, testInstances0.getNumRelationalNominal());\n assertEquals((-1), testInstances0.getClassIndex());\n assertEquals(1, testInstances0.getClassType());\n assertEquals(0, testInstances0.getNumString());\n assertEquals(\"Testdata\", testInstances0.getRelation());\n assertEquals(0, testInstances0.getNumRelationalNumeric());\n assertEquals(\" \", testInstances0.getWordSeparators());\n assertEquals(1, testInstances0.getNumNominal());\n assertEquals(0, testInstances0.getNumDate());\n assertEquals(0, testInstances0.getNumRelationalString());\n assertFalse(testInstances0.getMultiInstance());\n assertEquals(10, testInstances0.getNumInstancesRelational());\n assertEquals(1, testInstances0.getSeed());\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertEquals((-2), TestInstances.NO_CLASS);\n \n Instances instances0 = testInstances0.generate();\n assertNotNull(instances0);\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\n assertEquals(0, testInstances0.getNumRelationalDate());\n assertEquals(0, testInstances0.getNumRelational());\n assertEquals(2, testInstances0.getNumNominalValues());\n assertEquals(20, testInstances0.getNumInstances());\n assertEquals(2, testInstances0.getNumAttributes());\n assertEquals(2, testInstances0.getNumClasses());\n assertEquals(0, testInstances0.getNumNumeric());\n assertFalse(testInstances0.getNoClass());\n assertEquals(1, testInstances0.getNumRelationalNominal());\n assertEquals((-1), testInstances0.getClassIndex());\n assertEquals(1, testInstances0.getClassType());\n assertEquals(0, testInstances0.getNumString());\n assertEquals(\"Testdata\", testInstances0.getRelation());\n assertEquals(0, testInstances0.getNumRelationalNumeric());\n assertEquals(\" \", testInstances0.getWordSeparators());\n assertEquals(1, testInstances0.getNumNominal());\n assertEquals(0, testInstances0.getNumDate());\n assertEquals(0, testInstances0.getNumRelationalString());\n assertFalse(testInstances0.getMultiInstance());\n assertEquals(10, testInstances0.getNumInstancesRelational());\n assertEquals(1, testInstances0.getSeed());\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\n assertEquals(\"Testdata\", instances0.relationName());\n assertEquals(2, instances0.numAttributes());\n assertFalse(instances0.checkForStringAttributes());\n assertEquals(20, instances0.size());\n assertEquals(2, instances0.numClasses());\n assertEquals(1, instances0.classIndex());\n assertEquals(20, instances0.numInstances());\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertEquals((-2), TestInstances.NO_CLASS);\n \n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\n assertNotNull(costSensitiveClassifier0);\n assertEquals(\"The random number seed to be used.\", costSensitiveClassifier0.seedTipText());\n assertFalse(costSensitiveClassifier0.getDebug());\n assertEquals(\"A metaclassifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class). Performance can often be improved by using a Bagged classifier to improve the probability estimates of the base classifier.\", costSensitiveClassifier0.globalInfo());\n assertEquals(\"Sets the directory where cost files are loaded from. This option is used when the costMatrixSource is set to \\\"On Demand\\\".\", costSensitiveClassifier0.onDemandDirectoryTipText());\n assertEquals(\"The base classifier to be used.\", costSensitiveClassifier0.classifierTipText());\n assertEquals(\"Sets the cost matrix explicitly. This matrix is used if the costMatrixSource property is set to \\\"Supplied\\\".\", costSensitiveClassifier0.costMatrixTipText());\n assertEquals(0, costSensitiveClassifier0.graphType());\n assertFalse(costSensitiveClassifier0.getMinimizeExpectedCost());\n assertEquals(1, costSensitiveClassifier0.getSeed());\n assertEquals(\"Sets whether the minimum expected cost criteria will be used. If this is false, the training data will be reweighted according to the costs assigned to each class. If true, the minimum expected cost criteria will be used.\", costSensitiveClassifier0.minimizeExpectedCostTipText());\n assertEquals(\"If set to true, classifier may output additional info to the console.\", costSensitiveClassifier0.debugTipText());\n assertEquals(1, CostSensitiveClassifier.MATRIX_ON_DEMAND);\n assertEquals(2, CostSensitiveClassifier.MATRIX_SUPPLIED);\n \n GaussianProcesses gaussianProcesses0 = new GaussianProcesses();\n assertNotNull(gaussianProcesses0);\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n \n CostMatrix costMatrix0 = new CostMatrix(2);\n assertNotNull(costMatrix0);\n assertEquals(2, costMatrix0.size());\n assertEquals(2, costMatrix0.numRows());\n assertEquals(2, costMatrix0.numColumns());\n \n Evaluation evaluation0 = new Evaluation(instances0, costMatrix0);\n assertNotNull(evaluation0);\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\n assertEquals(0, testInstances0.getNumRelationalDate());\n assertEquals(0, testInstances0.getNumRelational());\n assertEquals(2, testInstances0.getNumNominalValues());\n assertEquals(20, testInstances0.getNumInstances());\n assertEquals(2, testInstances0.getNumAttributes());\n assertEquals(2, testInstances0.getNumClasses());\n assertEquals(0, testInstances0.getNumNumeric());\n assertFalse(testInstances0.getNoClass());\n assertEquals(1, testInstances0.getNumRelationalNominal());\n assertEquals((-1), testInstances0.getClassIndex());\n assertEquals(1, testInstances0.getClassType());\n assertEquals(0, testInstances0.getNumString());\n assertEquals(\"Testdata\", testInstances0.getRelation());\n assertEquals(0, testInstances0.getNumRelationalNumeric());\n assertEquals(\" \", testInstances0.getWordSeparators());\n assertEquals(1, testInstances0.getNumNominal());\n assertEquals(0, testInstances0.getNumDate());\n assertEquals(0, testInstances0.getNumRelationalString());\n assertFalse(testInstances0.getMultiInstance());\n assertEquals(10, testInstances0.getNumInstancesRelational());\n assertEquals(1, testInstances0.getSeed());\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\n assertEquals(\"Testdata\", instances0.relationName());\n assertEquals(2, instances0.numAttributes());\n assertFalse(instances0.checkForStringAttributes());\n assertEquals(20, instances0.size());\n assertEquals(2, instances0.numClasses());\n assertEquals(1, instances0.classIndex());\n assertEquals(20, instances0.numInstances());\n assertEquals(2, costMatrix0.size());\n assertEquals(2, costMatrix0.numRows());\n assertEquals(2, costMatrix0.numColumns());\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01);\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedFalsePositiveRate(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01);\n assertEquals(1.0, evaluation0.kappa(), 0.01);\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01);\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\n assertEquals(0.0, evaluation0.numInstances(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedTruePositiveRate(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01);\n assertEquals(Double.NaN, evaluation0.unweightedMicroFmeasure(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedFMeasure(), 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n assertEquals(0.0, evaluation0.correct(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01);\n assertFalse(evaluation0.getDiscardPredictions());\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedTrueNegativeRate(), 0.01);\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01);\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedRecall(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedPrecision(), 0.01);\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedFalseNegativeRate(), 0.01);\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertEquals((-2), TestInstances.NO_CLASS);\n \n double[] doubleArray0 = new double[5];\n doubleArray0[0] = (double) (-2);\n doubleArray0[0] = (double) 0;\n doubleArray0[2] = 1346.641846338;\n doubleArray0[3] = (double) 1;\n FileSystemHandling fileSystemHandling0 = new FileSystemHandling();\n assertNotNull(fileSystemHandling0);\n \n SparseInstance sparseInstance0 = new SparseInstance(2.0, doubleArray0);\n assertNotNull(sparseInstance0);\n assertEquals(5, doubleArray0.length);\n assertEquals(2.0, sparseInstance0.weight(), 0.01);\n assertEquals(2, sparseInstance0.numValues());\n assertEquals(5, sparseInstance0.numAttributes());\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\n assertArrayEquals(new double[] {0.0, 0.0, 1346.641846338, 1.0, 0.0}, doubleArray0, 0.01);\n \n boolean boolean0 = FileSystemHandling.shouldThrowIOException((EvoSuiteFile) null);\n assertFalse(boolean0);\n \n sparseInstance0.setDataset(instances0);\n assertEquals(5, doubleArray0.length);\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\n assertEquals(0, testInstances0.getNumRelationalDate());\n assertEquals(0, testInstances0.getNumRelational());\n assertEquals(2, testInstances0.getNumNominalValues());\n assertEquals(20, testInstances0.getNumInstances());\n assertEquals(2, testInstances0.getNumAttributes());\n assertEquals(2, testInstances0.getNumClasses());\n assertEquals(0, testInstances0.getNumNumeric());\n assertFalse(testInstances0.getNoClass());\n assertEquals(1, testInstances0.getNumRelationalNominal());\n assertEquals((-1), testInstances0.getClassIndex());\n assertEquals(1, testInstances0.getClassType());\n assertEquals(0, testInstances0.getNumString());\n assertEquals(\"Testdata\", testInstances0.getRelation());\n assertEquals(0, testInstances0.getNumRelationalNumeric());\n assertEquals(\" \", testInstances0.getWordSeparators());\n assertEquals(1, testInstances0.getNumNominal());\n assertEquals(0, testInstances0.getNumDate());\n assertEquals(0, testInstances0.getNumRelationalString());\n assertFalse(testInstances0.getMultiInstance());\n assertEquals(10, testInstances0.getNumInstancesRelational());\n assertEquals(1, testInstances0.getSeed());\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\n assertEquals(\"Testdata\", instances0.relationName());\n assertEquals(2, instances0.numAttributes());\n assertFalse(instances0.checkForStringAttributes());\n assertEquals(20, instances0.size());\n assertEquals(2, instances0.numClasses());\n assertEquals(1, instances0.classIndex());\n assertEquals(20, instances0.numInstances());\n assertEquals(2.0, sparseInstance0.weight(), 0.01);\n assertEquals(2, sparseInstance0.numClasses());\n assertEquals(2, sparseInstance0.numValues());\n assertEquals(5, sparseInstance0.numAttributes());\n assertEquals(1, sparseInstance0.classIndex());\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertEquals((-2), TestInstances.NO_CLASS);\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\n assertArrayEquals(new double[] {0.0, 0.0, 1346.641846338, 1.0, 0.0}, doubleArray0, 0.01);\n \n double double0 = evaluation0.evaluationForSingleInstance(doubleArray0, sparseInstance0, true);\n assertEquals(5, doubleArray0.length);\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\n assertEquals(0, testInstances0.getNumRelationalDate());\n assertEquals(0, testInstances0.getNumRelational());\n assertEquals(2, testInstances0.getNumNominalValues());\n assertEquals(20, testInstances0.getNumInstances());\n assertEquals(2, testInstances0.getNumAttributes());\n assertEquals(2, testInstances0.getNumClasses());\n assertEquals(0, testInstances0.getNumNumeric());\n assertFalse(testInstances0.getNoClass());\n assertEquals(1, testInstances0.getNumRelationalNominal());\n assertEquals((-1), testInstances0.getClassIndex());\n assertEquals(1, testInstances0.getClassType());\n assertEquals(0, testInstances0.getNumString());\n assertEquals(\"Testdata\", testInstances0.getRelation());\n assertEquals(0, testInstances0.getNumRelationalNumeric());\n assertEquals(\" \", testInstances0.getWordSeparators());\n assertEquals(1, testInstances0.getNumNominal());\n assertEquals(0, testInstances0.getNumDate());\n assertEquals(0, testInstances0.getNumRelationalString());\n assertFalse(testInstances0.getMultiInstance());\n assertEquals(10, testInstances0.getNumInstancesRelational());\n assertEquals(1, testInstances0.getSeed());\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\n assertEquals(\"Testdata\", instances0.relationName());\n assertEquals(2, instances0.numAttributes());\n assertFalse(instances0.checkForStringAttributes());\n assertEquals(20, instances0.size());\n assertEquals(2, instances0.numClasses());\n assertEquals(1, instances0.classIndex());\n assertEquals(20, instances0.numInstances());\n assertEquals(2, costMatrix0.size());\n assertEquals(2, costMatrix0.numRows());\n assertEquals(2, costMatrix0.numColumns());\n assertEquals(0.0, evaluation0.SFMeanPriorEntropy(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedFalsePositiveRate(), 0.01);\n assertEquals(100.0, evaluation0.pctUnclassified(), 0.01);\n assertEquals(1.0, evaluation0.kappa(), 0.01);\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\n assertEquals(0.0, evaluation0.sizeOfPredictedRegions(), 0.01);\n assertEquals(0.0, evaluation0.pctIncorrect(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\n assertEquals(2.0, evaluation0.unclassified(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\n assertEquals(2.0, evaluation0.numInstances(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedTruePositiveRate(), 0.01);\n assertEquals(Double.NaN, evaluation0.unweightedMicroFmeasure(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedFMeasure(), 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n assertEquals(0.0, evaluation0.pctCorrect(), 0.01);\n assertEquals(0.0, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\n assertEquals(0.0, evaluation0.correct(), 0.01);\n assertFalse(evaluation0.getDiscardPredictions());\n assertEquals(0.0, evaluation0.meanPriorAbsoluteError(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedTrueNegativeRate(), 0.01);\n assertEquals(0.0, evaluation0.rootMeanPriorSquaredError(), 0.01);\n assertEquals(2.0, evaluation0.totalCost(), 0.01);\n assertEquals(1.0, evaluation0.avgCost(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedRecall(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedPrecision(), 0.01);\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\n assertEquals(Double.NaN, evaluation0.weightedFalseNegativeRate(), 0.01);\n assertEquals(1.0, evaluation0.errorRate(), 0.01);\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n assertEquals(2.0, sparseInstance0.weight(), 0.01);\n assertEquals(2, sparseInstance0.numClasses());\n assertEquals(2, sparseInstance0.numValues());\n assertEquals(5, sparseInstance0.numAttributes());\n assertEquals(1, sparseInstance0.classIndex());\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertEquals((-2), TestInstances.NO_CLASS);\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\n assertEquals(2.0, double0, 0.01);\n assertArrayEquals(new double[] {0.0, 0.0, 1346.641846338, 1.0, 0.0}, doubleArray0, 0.01);\n }","title":""},{"docid":"b228a8f8b0176954cc32762cab155432","score":"0.5817314","text":"@Test(timeout = 4000)\n public void test33() throws Throwable {\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\n Capabilities capabilities0 = naiveBayesMultinomialText0.getCapabilities();\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\n Instances instances0 = testInstances0.generate();\n naiveBayesMultinomialText0.buildClassifier(instances0);\n FileSystemHandling fileSystemHandling0 = new FileSystemHandling();\n Random.setNextRandom((-2));\n naiveBayesMultinomialText0.pruneDictionary();\n BinarySparseInstance binarySparseInstance0 = new BinarySparseInstance((-1), (int[]) null, (-2));\n naiveBayesMultinomialText0.distributionForInstance(binarySparseInstance0);\n }","title":""},{"docid":"ac8813ec4dbfde89daf3adc582b6adc5","score":"0.5817058","text":"@Test\n public void organicMetricsTest() {\n // TODO: test organicMetrics\n }","title":""},{"docid":"b70787907f58c71aeae32523cb612d5f","score":"0.5801642","text":"@Test(timeout = 4000)\n public void test74() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n assertNotNull(testInstances0);\n assertEquals(10, testInstances0.getNumInstancesRelational());\n assertEquals(0, testInstances0.getNumRelationalString());\n assertFalse(testInstances0.getNoClass());\n assertFalse(testInstances0.getMultiInstance());\n assertEquals(1, testInstances0.getNumRelationalNominal());\n assertEquals(0, testInstances0.getNumNumeric());\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\n assertEquals(0, testInstances0.getNumRelationalNumeric());\n assertEquals(0, testInstances0.getNumString());\n assertEquals(2, testInstances0.getNumAttributes());\n assertEquals(2, testInstances0.getNumClasses());\n assertEquals((-1), testInstances0.getClassIndex());\n assertEquals(1, testInstances0.getClassType());\n assertEquals(1, testInstances0.getSeed());\n assertEquals(20, testInstances0.getNumInstances());\n assertEquals(0, testInstances0.getNumRelational());\n assertEquals(\"Testdata\", testInstances0.getRelation());\n assertEquals(\" \", testInstances0.getWordSeparators());\n assertEquals(0, testInstances0.getNumRelationalDate());\n assertEquals(0, testInstances0.getNumDate());\n assertEquals(1, testInstances0.getNumNominal());\n assertEquals(2, testInstances0.getNumNominalValues());\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertEquals((-2), TestInstances.NO_CLASS);\n \n EvoSuiteFile evoSuiteFile0 = new EvoSuiteFile(\"/home/ubuntu/wekafiles/props\");\n boolean boolean0 = FileSystemHandling.appendStringToFile(evoSuiteFile0, \" \");\n assertTrue(boolean0);\n \n GaussianProcesses gaussianProcesses0 = new GaussianProcesses();\n assertNotNull(gaussianProcesses0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n \n Capabilities capabilities0 = gaussianProcesses0.getCapabilities();\n assertNotNull(capabilities0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(1, capabilities0.getMinimumNumberInstances());\n assertTrue(capabilities0.hasDependencies());\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n \n TestInstances testInstances1 = TestInstances.forCapabilities(capabilities0);\n assertNotNull(testInstances1);\n assertNotSame(testInstances1, testInstances0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(1, capabilities0.getMinimumNumberInstances());\n assertTrue(capabilities0.hasDependencies());\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\n assertEquals(2, testInstances1.getNumClasses());\n assertEquals(20, testInstances1.getNumInstances());\n assertEquals(0, testInstances1.getNumRelationalDate());\n assertEquals(0, testInstances1.getNumRelational());\n assertEquals(2, testInstances1.getNumNominalValues());\n assertEquals(0, testInstances1.getNumDate());\n assertFalse(testInstances1.getMultiInstance());\n assertEquals(10, testInstances1.getNumInstancesRelational());\n assertEquals(1, testInstances1.getSeed());\n assertEquals((-1), testInstances1.getClassIndex());\n assertEquals(1, testInstances1.getNumRelationalNominal());\n assertEquals(1, testInstances1.getNumRelationalNumeric());\n assertEquals(0, testInstances1.getNumString());\n assertEquals(0, testInstances1.getNumRelationalString());\n assertEquals(1, testInstances1.getNumNominal());\n assertEquals(\"Testdata\", testInstances1.getRelation());\n assertEquals(0, testInstances1.getClassType());\n assertEquals(\" \", testInstances1.getWordSeparators());\n assertEquals(3, testInstances1.getNumAttributes());\n assertEquals(1, testInstances1.getNumNumeric());\n assertFalse(testInstances1.getNoClass());\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n assertEquals((-2), TestInstances.NO_CLASS);\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertFalse(testInstances1.equals((Object)testInstances0));\n \n Instances instances0 = testInstances1.generate();\n assertNotNull(instances0);\n assertNotSame(testInstances1, testInstances0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(1, capabilities0.getMinimumNumberInstances());\n assertTrue(capabilities0.hasDependencies());\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\n assertEquals(2, testInstances1.getNumClasses());\n assertEquals(20, testInstances1.getNumInstances());\n assertEquals(0, testInstances1.getNumRelationalDate());\n assertEquals(0, testInstances1.getNumRelational());\n assertEquals(2, testInstances1.getNumNominalValues());\n assertEquals(0, testInstances1.getNumDate());\n assertFalse(testInstances1.getMultiInstance());\n assertEquals(10, testInstances1.getNumInstancesRelational());\n assertEquals(1, testInstances1.getSeed());\n assertEquals((-1), testInstances1.getClassIndex());\n assertEquals(1, testInstances1.getNumRelationalNominal());\n assertEquals(1, testInstances1.getNumRelationalNumeric());\n assertEquals(0, testInstances1.getNumString());\n assertEquals(0, testInstances1.getNumRelationalString());\n assertEquals(1, testInstances1.getNumNominal());\n assertEquals(\"Testdata\", testInstances1.getRelation());\n assertEquals(0, testInstances1.getClassType());\n assertEquals(\" \", testInstances1.getWordSeparators());\n assertEquals(3, testInstances1.getNumAttributes());\n assertEquals(1, testInstances1.getNumNumeric());\n assertFalse(testInstances1.getNoClass());\n assertEquals(\"Testdata\", instances0.relationName());\n assertEquals(3, instances0.numAttributes());\n assertEquals(2, instances0.classIndex());\n assertEquals(1, instances0.numClasses());\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\n assertEquals(20, instances0.size());\n assertFalse(instances0.checkForStringAttributes());\n assertEquals(20, instances0.numInstances());\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n assertEquals((-2), TestInstances.NO_CLASS);\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertFalse(testInstances1.equals((Object)testInstances0));\n \n String string0 = instances0.getRevision();\n assertNotNull(string0);\n assertNotSame(testInstances1, testInstances0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(1, capabilities0.getMinimumNumberInstances());\n assertTrue(capabilities0.hasDependencies());\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\n assertEquals(2, testInstances1.getNumClasses());\n assertEquals(20, testInstances1.getNumInstances());\n assertEquals(0, testInstances1.getNumRelationalDate());\n assertEquals(0, testInstances1.getNumRelational());\n assertEquals(2, testInstances1.getNumNominalValues());\n assertEquals(0, testInstances1.getNumDate());\n assertFalse(testInstances1.getMultiInstance());\n assertEquals(10, testInstances1.getNumInstancesRelational());\n assertEquals(1, testInstances1.getSeed());\n assertEquals((-1), testInstances1.getClassIndex());\n assertEquals(1, testInstances1.getNumRelationalNominal());\n assertEquals(1, testInstances1.getNumRelationalNumeric());\n assertEquals(0, testInstances1.getNumString());\n assertEquals(0, testInstances1.getNumRelationalString());\n assertEquals(1, testInstances1.getNumNominal());\n assertEquals(\"Testdata\", testInstances1.getRelation());\n assertEquals(0, testInstances1.getClassType());\n assertEquals(\" \", testInstances1.getWordSeparators());\n assertEquals(3, testInstances1.getNumAttributes());\n assertEquals(1, testInstances1.getNumNumeric());\n assertFalse(testInstances1.getNoClass());\n assertEquals(\"Testdata\", instances0.relationName());\n assertEquals(3, instances0.numAttributes());\n assertEquals(2, instances0.classIndex());\n assertEquals(1, instances0.numClasses());\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\n assertEquals(20, instances0.size());\n assertFalse(instances0.checkForStringAttributes());\n assertEquals(20, instances0.numInstances());\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n assertEquals((-2), TestInstances.NO_CLASS);\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertEquals(\"9186\", string0);\n assertFalse(testInstances1.equals((Object)testInstances0));\n \n Evaluation evaluation0 = new Evaluation(instances0);\n assertNotNull(evaluation0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(1, capabilities0.getMinimumNumberInstances());\n assertTrue(capabilities0.hasDependencies());\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\n assertEquals(2, testInstances1.getNumClasses());\n assertEquals(20, testInstances1.getNumInstances());\n assertEquals(0, testInstances1.getNumRelationalDate());\n assertEquals(0, testInstances1.getNumRelational());\n assertEquals(2, testInstances1.getNumNominalValues());\n assertEquals(0, testInstances1.getNumDate());\n assertFalse(testInstances1.getMultiInstance());\n assertEquals(10, testInstances1.getNumInstancesRelational());\n assertEquals(1, testInstances1.getSeed());\n assertEquals((-1), testInstances1.getClassIndex());\n assertEquals(1, testInstances1.getNumRelationalNominal());\n assertEquals(1, testInstances1.getNumRelationalNumeric());\n assertEquals(0, testInstances1.getNumString());\n assertEquals(0, testInstances1.getNumRelationalString());\n assertEquals(1, testInstances1.getNumNominal());\n assertEquals(\"Testdata\", testInstances1.getRelation());\n assertEquals(0, testInstances1.getClassType());\n assertEquals(\" \", testInstances1.getWordSeparators());\n assertEquals(3, testInstances1.getNumAttributes());\n assertEquals(1, testInstances1.getNumNumeric());\n assertFalse(testInstances1.getNoClass());\n assertEquals(\"Testdata\", instances0.relationName());\n assertEquals(3, instances0.numAttributes());\n assertEquals(2, instances0.classIndex());\n assertEquals(1, instances0.numClasses());\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\n assertEquals(20, instances0.size());\n assertFalse(instances0.checkForStringAttributes());\n assertEquals(20, instances0.numInstances());\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01);\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\n assertFalse(evaluation0.getDiscardPredictions());\n assertEquals(0.0, evaluation0.correct(), 0.01);\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01);\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01);\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\n assertEquals(0.0, evaluation0.numInstances(), 0.01);\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01);\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n assertEquals((-2), TestInstances.NO_CLASS);\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertFalse(testInstances1.equals((Object)testInstances0));\n \n evaluation0.addNumericTrainClass((-2), (-1190.7));\n assertNotSame(testInstances1, testInstances0);\n assertEquals(\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\", gaussianProcesses0.globalInfo());\n assertEquals(\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\", gaussianProcesses0.noiseTipText());\n assertEquals(\"Determines how/if the data will be transformed.\", gaussianProcesses0.filterTypeTipText());\n assertFalse(gaussianProcesses0.getDebug());\n assertEquals(\"The kernel to use.\", gaussianProcesses0.kernelTipText());\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\n assertEquals(\"If set to true, classifier may output additional info to the console.\", gaussianProcesses0.debugTipText());\n assertEquals(1, capabilities0.getMinimumNumberInstances());\n assertTrue(capabilities0.hasDependencies());\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\n assertEquals(2, testInstances1.getNumClasses());\n assertEquals(20, testInstances1.getNumInstances());\n assertEquals(0, testInstances1.getNumRelationalDate());\n assertEquals(0, testInstances1.getNumRelational());\n assertEquals(2, testInstances1.getNumNominalValues());\n assertEquals(0, testInstances1.getNumDate());\n assertFalse(testInstances1.getMultiInstance());\n assertEquals(10, testInstances1.getNumInstancesRelational());\n assertEquals(1, testInstances1.getSeed());\n assertEquals((-1), testInstances1.getClassIndex());\n assertEquals(1, testInstances1.getNumRelationalNominal());\n assertEquals(1, testInstances1.getNumRelationalNumeric());\n assertEquals(0, testInstances1.getNumString());\n assertEquals(0, testInstances1.getNumRelationalString());\n assertEquals(1, testInstances1.getNumNominal());\n assertEquals(\"Testdata\", testInstances1.getRelation());\n assertEquals(0, testInstances1.getClassType());\n assertEquals(\" \", testInstances1.getWordSeparators());\n assertEquals(3, testInstances1.getNumAttributes());\n assertEquals(1, testInstances1.getNumNumeric());\n assertFalse(testInstances1.getNoClass());\n assertEquals(\"Testdata\", instances0.relationName());\n assertEquals(3, instances0.numAttributes());\n assertEquals(2, instances0.classIndex());\n assertEquals(1, instances0.numClasses());\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\n assertEquals(20, instances0.size());\n assertFalse(instances0.checkForStringAttributes());\n assertEquals(20, instances0.numInstances());\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01);\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\n assertFalse(evaluation0.getDiscardPredictions());\n assertEquals(0.0, evaluation0.correct(), 0.01);\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01);\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01);\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01);\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\n assertEquals(0.0, evaluation0.numInstances(), 0.01);\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01);\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01);\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\n assertEquals(2, GaussianProcesses.FILTER_NONE);\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\n assertEquals((-2), TestInstances.NO_CLASS);\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\n assertFalse(testInstances1.equals((Object)testInstances0));\n \n boolean boolean1 = FileSystemHandling.shouldAllThrowIOExceptions();\n assertTrue(boolean1);\n assertTrue(boolean1 == boolean0);\n \n double double0 = evaluation0.m_ConfLevel;\n assertEquals(0.95, double0, 0.01);\n \n try { \n evaluation0.toCumulativeMarginDistributionString();\n fail(\"Expecting exception: Exception\");\n \n } catch(Exception e) {\n //\n // Class must be nominal for margin distributions\n //\n verifyException(\"weka.classifiers.Evaluation\", e);\n }\n }","title":""},{"docid":"a430fb783a7ff89cdcb17e2172af7921","score":"0.57985556","text":"private static int best_test_by_ENTROPY(State state, boolean debug){\n\t\treturn best_test_by_ENTROPY(state, null, debug);\n\t}","title":""},{"docid":"70817b48e538cfba0471676ef222ea77","score":"0.5789501","text":"@Test(timeout = 4000)\n public void test061() throws Throwable {\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertNotNull(resultMatrixSignificance0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML(resultMatrixSignificance0);\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(1, resultMatrixHTML0.getRowCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(40, resultMatrixHTML0.getRowNameWidth());\n assertEquals(1, resultMatrixHTML0.getColCount());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertNotNull(resultMatrixHTML0);\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(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n resultMatrixHTML0.setCountWidth(0);\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(1, resultMatrixHTML0.getRowCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(40, resultMatrixHTML0.getRowNameWidth());\n assertEquals(1, resultMatrixHTML0.getColCount());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\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(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n resultMatrixHTML0.setStdDevWidth(1);\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(1, resultMatrixHTML0.getStdDevWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(1, resultMatrixHTML0.getRowCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(40, resultMatrixHTML0.getRowNameWidth());\n assertEquals(1, resultMatrixHTML0.getColCount());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\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(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n String string0 = resultMatrixHTML0.toStringSummary();\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(1, resultMatrixHTML0.getStdDevWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(1, resultMatrixHTML0.getRowCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(40, resultMatrixHTML0.getRowNameWidth());\n assertEquals(1, resultMatrixHTML0.getColCount());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertNotNull(string0);\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(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(\"-summary data not set-\", string0);\n \n int int0 = resultMatrixHTML0.getSignificance((-469), 5);\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(1, resultMatrixHTML0.getStdDevWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(1, resultMatrixHTML0.getRowCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(40, resultMatrixHTML0.getRowNameWidth());\n assertEquals(1, resultMatrixHTML0.getColCount());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\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(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(0, int0);\n \n String[] stringArray0 = new String[2];\n stringArray0[0] = \"v\";\n stringArray0[1] = \" \";\n ResultMatrixCSV.main(stringArray0);\n assertEquals(2, stringArray0.length);\n }","title":""}],"string":"[\n {\n \"docid\": \"0ef0b4c562e56cbcdb375b4e19c4662a\",\n \"score\": \"0.732844\",\n \"text\": \"@Test(timeout = 4000)\\n public void test077() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.coverageOfTestCasesByPredictedRegions();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"311bfd93411d3022d7be7423142884d0\",\n \"score\": \"0.7102444\",\n \"text\": \"@Test(timeout = 4000)\\n public void test072() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.priorEntropy();\\n assertEquals(0.9940302114769565, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63f5a89231356f64df6cd03a0cfe2fa5\",\n \"score\": \"0.6911122\",\n \"text\": \"@Test(timeout = 4000)\\n public void test07() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\\n costSensitiveClassifier0.getCostMatrix();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.addNumericTrainClass((-2), 106.953);\\n FileSystemHandling.shouldAllThrowIOExceptions();\\n double double0 = evaluation0.m_SumKBInfo;\\n evaluation0.addNumericTrainClass(2391.1061813378874, 2);\\n FileSystemHandling.shouldThrowIOException((EvoSuiteFile) null);\\n double[] doubleArray0 = evaluation0.makeDistribution(Double.NaN);\\n assertEquals(2, doubleArray0.length);\\n assertArrayEquals(new double[] {0.0, 0.0}, doubleArray0, 0.01);\\n \\n evaluation0.addNumericTrainClass((-1.7976931348623157E308), (-1));\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48e354021a6347c87f4beb456dc91fc3\",\n \"score\": \"0.69065744\",\n \"text\": \"@Test(timeout = 4000)\\n public void test098() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.numInstances();\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a352b1796de63c3882bf3f3d716df432\",\n \"score\": \"0.6905144\",\n \"text\": \"@Test(timeout = 4000)\\n public void test085() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n CostMatrix costMatrix0 = new CostMatrix(0);\\n Evaluation evaluation0 = new Evaluation(instances0, costMatrix0);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ebdcbde28559e8e93872209a73f58df\",\n \"score\": \"0.68968016\",\n \"text\": \"@Test(timeout = 4000)\\n public void test076() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.m_CoverageStatisticsAvailable = false;\\n double double0 = evaluation0.coverageOfTestCasesByPredictedRegions();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"413a317ccf70e6f8bade26b70c73bb46\",\n \"score\": \"0.689352\",\n \"text\": \"@Test(timeout = 4000)\\n public void test073() 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.SFSchemeEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c70c0178f30b4965991373a73c7e344\",\n \"score\": \"0.6872428\",\n \"text\": \"@Test(timeout = 4000)\\n public void test071() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.KBInformation();\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c115c66f1b9be9f4e0708b307a1fcd7\",\n \"score\": \"0.68606895\",\n \"text\": \"@Test(timeout = 4000)\\n public void test094() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n String string0 = evaluation0.toSummaryString();\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(\\\"\\\\nTotal Number of Instances 0 \\\\n\\\", string0);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22cb065fa52c7386c72c4729fe4ffa5c\",\n \"score\": \"0.68094593\",\n \"text\": \"@Test(timeout = 4000)\\n public void test010() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.m_SumPriorEntropy = (double) 1L;\\n double double0 = evaluation0.SFEntropyGain();\\n assertEquals(1.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(1.0, double0, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f78fa1b7a56d2f07f2c006f3f66df660\",\n \"score\": \"0.6785704\",\n \"text\": \"@Test(timeout = 4000)\\n public void test095() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.pctIncorrect();\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(Double.NaN, double0, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7495d4a3af3f0eb3e7d456a1a40ea9e2\",\n \"score\": \"0.67755497\",\n \"text\": \"@Test(timeout = 4000)\\n public void test093() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.pctUnclassified();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"111ee9344165d2893e5992509bc3d929\",\n \"score\": \"0.6759826\",\n \"text\": \"@Test(timeout = 4000)\\n public void test070() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.SFPriorEntropy();\\n assertEquals(0.0, double0, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d1cd9cba337f3187b3697348f0cd5f7\",\n \"score\": \"0.66820306\",\n \"text\": \"@Test(timeout = 4000)\\n public void test075() throws Throwable {\\n Capabilities capabilities0 = new Capabilities((CapabilitiesHandler) null);\\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\\n Instances instances0 = testInstances0.generate(\\\"d$Qj[b\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.sizeOfPredictedRegions();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac50f61e949d6330bb93b69d22d9afe1\",\n \"score\": \"0.6681225\",\n \"text\": \"@Test(timeout = 4000)\\n public void test058() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n LinearRegression linearRegression0 = new LinearRegression();\\n boolean boolean0 = evaluation0.equals(linearRegression0);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertFalse(boolean0);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"312e44392d856a5e4d98b32820485ee8\",\n \"score\": \"0.6632121\",\n \"text\": \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n GaussianProcesses gaussianProcesses0 = new GaussianProcesses();\\n double[] doubleArray0 = new double[5];\\n doubleArray0[0] = (double) (-2);\\n doubleArray0[0] = (double) 0;\\n doubleArray0[3] = (double) 0;\\n doubleArray0[4] = (double) 1;\\n Double double0 = new Double((-1));\\n Capabilities capabilities0 = new Capabilities(gaussianProcesses0);\\n TestInstances testInstances1 = TestInstances.forCapabilities(capabilities0);\\n Instances instances0 = testInstances1.generate();\\n CostMatrix costMatrix0 = new CostMatrix(2);\\n Evaluation evaluation0 = new Evaluation(instances0, costMatrix0);\\n double double1 = evaluation0.SFMeanPriorEntropy();\\n assertEquals(Double.NaN, double1, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c9ea0aa7b2ae49d45fea5310844ae9\",\n \"score\": \"0.6631574\",\n \"text\": \"@Test(timeout = 4000)\\n public void test015() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.toMatrixString(\\\".names\\\");\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8cdc1896faf5edd3081e2fda9130f7d\",\n \"score\": \"0.6620126\",\n \"text\": \"@Test(timeout = 4000)\\n public void test013() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.truePositiveRate(1);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(0.0, double0, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6939cfbafd9d7c7895c301502835b0\",\n \"score\": \"0.6587169\",\n \"text\": \"@Test(timeout = 4000)\\n public void test092() 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, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fc676906d2953201bf2a910e8afa995\",\n \"score\": \"0.658711\",\n \"text\": \"@Test(timeout = 4000)\\n public void test068() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.SFSchemeEntropy();\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4457d790f16defc14bfca646c13b2954\",\n \"score\": \"0.65617466\",\n \"text\": \"@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 String string0 = evaluation0.getRevision();\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(\\\"9101\\\", string0);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6919915dd25901b13ce57de0e09e97af\",\n \"score\": \"0.65563965\",\n \"text\": \"@Test(timeout = 4000)\\n public void test069() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\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 \"title\": \"\"\n },\n {\n \"docid\": \"a179fae97e6cc11828fcc7beb2a78c5d\",\n \"score\": \"0.6551893\",\n \"text\": \"@Test(timeout = 4000)\\n public void test019() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.precision(9);\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddb48af09658377af9f6d2520432bbf9\",\n \"score\": \"0.6538658\",\n \"text\": \"@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, (CostMatrix) null);\\n double double0 = evaluation0.pctCorrect();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847ad798f965658f52dd6f87f416a204\",\n \"score\": \"0.65347224\",\n \"text\": \"@Test(timeout = 4000)\\n public void test089() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate(\\\"debugTidpText\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.getHeader();\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3cc77154b8aebf0a59510886c72a7ae\",\n \"score\": \"0.64978695\",\n \"text\": \"@Test(timeout = 4000)\\n public void test100() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.totalCost();\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b52bdf19ee05f734c8313d7cf2d586d6\",\n \"score\": \"0.6488762\",\n \"text\": \"@Test(timeout = 4000)\\n public void test017() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.recall((-1623689244));\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(0.0, double0, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c86cb68194753f032cead75ac227e56\",\n \"score\": \"0.64778054\",\n \"text\": \"@Test(timeout = 4000)\\n public void test097() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n String string0 = evaluation0.toClassDetailsString();\\n assertEquals(\\\"=== Detailed Accuracy By Class ===\\\\n\\\\n TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class\\\\n 0 0 0 0 0 0 ? ? class1\\\\n 0 0 0 0 0 0 ? ? class2\\\\nWeighted Avg. NaN NaN NaN NaN NaN NaN NaN NaN \\\\n\\\", string0);\\n assertEquals(Double.NaN, evaluation0.unweightedMicroFmeasure(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb43a2129f3fb5f67f43f88d15fb197\",\n \"score\": \"0.64752865\",\n \"text\": \"@Test(timeout = 4000)\\n public void test027() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.falseNegativeRate((-2876));\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27143d3523899fa61dd0b73986d35d1b\",\n \"score\": \"0.6454713\",\n \"text\": \"@Test(timeout = 4000)\\n public void test021() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.numFalseNegatives((-1));\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(0.0, double0, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"291e8b6bd149e9ef25eb6d671206632c\",\n \"score\": \"0.64068663\",\n \"text\": \"@Test(timeout = 4000)\\n public void test090() throws Throwable {\\n DecisionStump decisionStump0 = new DecisionStump();\\n Capabilities capabilities0 = new Capabilities(decisionStump0);\\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\\n Instances instances0 = testInstances0.generate(\\\"setOptions\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double[] doubleArray0 = evaluation0.getClassPriors();\\n assertEquals(2, doubleArray0.length);\\n assertNotNull(doubleArray0);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5251aa92d235ab3fc8baf51f10565d3a\",\n \"score\": \"0.6366907\",\n \"text\": \"@Test(timeout = 4000)\\n public void test031() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.areaUnderROC((-2122));\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80fddb7f6e848458ded36e40f853e266\",\n \"score\": \"0.634305\",\n \"text\": \"@Test(timeout = 4000)\\n public void test15() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.KBMeanInformation();\\n instances0.deleteStringAttributes();\\n Stack<BinarySparseInstance> stack0 = new Stack<BinarySparseInstance>();\\n instances0.retainAll(stack0);\\n FileSystemHandling fileSystemHandling0 = new FileSystemHandling();\\n evaluation0.updateNumericScores((double[]) null, (double[]) null, 0.0);\\n evaluation0.setDiscardPredictions(false);\\n Evaluation evaluation1 = new Evaluation(instances0);\\n double double1 = evaluation1.pctIncorrect();\\n assertEquals(Double.NaN, double1, 0.01);\\n \\n double double2 = evaluation0.correct();\\n assertNotEquals(double2, double0, 0.01);\\n assertEquals(0.0, double2, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d76deb1a103756aab6505f9b6f190f0\",\n \"score\": \"0.6342288\",\n \"text\": \"@Test(timeout = 4000)\\n public void test066() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.SFEntropyGain();\\n assertEquals(0.0, double0, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b5febd4bf8274a68c5803a88be582d\",\n \"score\": \"0.63161397\",\n \"text\": \"@Test(timeout = 4000)\\n public void test020() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.numTruePositives((-476));\\n assertEquals(0.0, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"891c57836412cbba501c2b85214f78ac\",\n \"score\": \"0.63097304\",\n \"text\": \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate(\\\" \\\");\\n FileSystemHandling.createFolder((EvoSuiteFile) null);\\n FileSystemHandling.appendDataToFile((EvoSuiteFile) null, (byte[]) null);\\n Evaluation evaluation0 = new Evaluation(instances0, (CostMatrix) null);\\n testInstances0.setNumNominalValues(13);\\n String string0 = evaluation0.toSummaryString(\\\"together\\\", true);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(\\\"together\\\\nTotal Number of Instances 0 \\\\n\\\", string0);\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a9037d415373d0e0e8566eb409d6f34\",\n \"score\": \"0.6272177\",\n \"text\": \"@Test(timeout = 4000)\\n public void test032() throws Throwable {\\n DecisionStump decisionStump0 = new DecisionStump();\\n Capabilities capabilities0 = new Capabilities(decisionStump0);\\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\\n Instances instances0 = testInstances0.generate(\\\"setOptions\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.areaUnderPRC((-1));\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad0fe8e64dd811d790fc09f17921b8f1\",\n \"score\": \"0.6271128\",\n \"text\": \"@Test(timeout = 4000)\\n public void test025() throws Throwable {\\n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\\n String string0 = Evaluation.getGlobalInfo(costSensitiveClassifier0);\\n assertEquals(\\\"\\\\nSynopsis for weka.classifiers.meta.CostSensitiveClassifier:\\\\n\\\\nA metaclassifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class). Performance can often be improved by using a Bagged classifier to improve the probability estimates of the base classifier.\\\", string0);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb1fefed1d026035b839ebcce38ac0f\",\n \"score\": \"0.62397355\",\n \"text\": \"@Test(timeout = 4000)\\n public void test14() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\" \\\\\\\"3XB^u3'zgS\\\");\\n assertNotNull(discretize0);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertFalse(discretize0.getIgnoreClass());\\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.\\\", discretize0.invertSelectionTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n \\n String[] stringArray0 = discretize0.getOptions();\\n assertNotNull(stringArray0);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertFalse(discretize0.getIgnoreClass());\\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.\\\", discretize0.invertSelectionTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(6, stringArray0.length);\\n \\n discretize0.setFindNumBins(false);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertFalse(discretize0.getIgnoreClass());\\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.\\\", discretize0.invertSelectionTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n \\n Filter.runFilter(discretize0, stringArray0);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertFalse(discretize0.getIgnoreClass());\\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.\\\", discretize0.invertSelectionTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(6, stringArray0.length);\\n \\n double[] doubleArray0 = discretize0.getCutPoints(3424);\\n assertNull(doubleArray0);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertFalse(discretize0.getIgnoreClass());\\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.\\\", discretize0.invertSelectionTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n \\n discretize0.setOutputFormat();\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertFalse(discretize0.getIgnoreClass());\\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.\\\", discretize0.invertSelectionTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91dce1a841f64a2ad4ec458e917eb6fa\",\n \"score\": \"0.62252116\",\n \"text\": \"@Test(timeout = 4000)\\n public void test01() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\")s|Y)U-\\\");\\n discretize0.getAttributeIndices();\\n discretize0.m_UseEqualFrequency = false;\\n discretize0.m_MakeBinary = true;\\n discretize0.setInvertSelection(true);\\n discretize0.setFindNumBins(true);\\n discretize0.setBins(89);\\n discretize0.getBins();\\n discretize0.attributeIndicesTipText();\\n discretize0.getBinRangesString(89);\\n discretize0.setOutputFormat();\\n discretize0.makeBinaryTipText();\\n assertEquals(89, discretize0.getBins());\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c81e8affeb3261b97a58519fa456fe45\",\n \"score\": \"0.6210561\",\n \"text\": \"@Test(timeout = 4000)\\n public void test030() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.areaUnderROC(689);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"766e9cb50b2e0acf7e3ec968151cca2a\",\n \"score\": \"0.6196148\",\n \"text\": \"@Test(timeout = 4000)\\n public void test059() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n boolean boolean0 = evaluation0.equals((Object) null);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertFalse(boolean0);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e85ac581cd896c915054a658cc0089\",\n \"score\": \"0.61926144\",\n \"text\": \"@Test(timeout = 4000)\\n public void test088() throws Throwable {\\n Capabilities capabilities0 = new Capabilities((CapabilitiesHandler) null);\\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\\n Instances instances0 = testInstances0.generate(\\\"d$Qj[b\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.relativeAbsoluteError();\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"229dc415529b6094457c75eae6da63c2\",\n \"score\": \"0.6164477\",\n \"text\": \"@Test(timeout = 4000)\\n public void test062() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.weightedFalseNegativeRate();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa30cdac394d8febfc9468fb3636dcda\",\n \"score\": \"0.61527556\",\n \"text\": \"@Test(timeout = 4000)\\n public void test091() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n String string0 = evaluation0.toMatrixString();\\n assertEquals(\\\"=== Confusion Matrix ===\\\\n\\\\n a b <-- classified as\\\\n 0 0 | a = class1\\\\n 0 0 | b = class2\\\\n\\\", string0);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d3cfccf560cac4a4d5e4c861d5301ee\",\n \"score\": \"0.6149333\",\n \"text\": \"@Test(timeout = 4000)\\n public void test17() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n assertNotNull(discretize0);\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getIgnoreClass());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n \\n discretize0.m_MakeBinary = true;\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertTrue(discretize0.getMakeBinary());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getIgnoreClass());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n \\n try { \\n discretize0.setInputFormat((Instances) null);\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.Filter\\\", e);\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e5a538c8b080cbdc25d726f0ae5e20d\",\n \"score\": \"0.61492455\",\n \"text\": \"@Test(timeout = 4000)\\n public void test006() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.weightedAreaUnderPRC();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db934cf30c261b2f51024401540021cd\",\n \"score\": \"0.6145863\",\n \"text\": \"@Test(timeout = 4000)\\n public void test065() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n String string0 = evaluation0.toCumulativeMarginDistributionString();\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(\\\" -1 0 \\\\n\\\", string0);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c37386debba6a94bb2c0cb7c83429bdc\",\n \"score\": \"0.61389935\",\n \"text\": \"@Test(timeout = 4000)\\n public void test04() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.m_NumBins = (-1805);\\n double[][] doubleArray0 = new double[5][6];\\n double[] doubleArray1 = new double[5];\\n doubleArray1[0] = (double) (-1805);\\n doubleArray1[1] = (double) (-1805);\\n doubleArray1[2] = (double) (-1805);\\n doubleArray1[3] = (-342.201671628);\\n doubleArray1[4] = (double) (-1805);\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[4];\\n doubleArray2[0] = (-342.201671628);\\n doubleArray2[1] = 1655.152;\\n doubleArray2[2] = (double) (-1805);\\n doubleArray2[3] = (double) (-1805);\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[0];\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[5];\\n doubleArray4[0] = (double) (-1805);\\n doubleArray4[1] = 1655.152;\\n doubleArray4[2] = (-342.201671628);\\n doubleArray4[3] = (-342.201671628);\\n doubleArray4[4] = (double) (-1805);\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[0];\\n doubleArray0[4] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.m_DefaultCols = \\\"\\\";\\n discretize0.globalInfo();\\n discretize0.m_DesiredWeightOfInstancesPerInterval = 0.0;\\n discretize0.setFindNumBins(false);\\n // Undeclared exception!\\n try { \\n discretize0.getBinRangesString((-461));\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // -461\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b219f9f1250c4cf195d4755149dbe39f\",\n \"score\": \"0.6136088\",\n \"text\": \"@Test(timeout = 4000)\\n public void test62() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n assertNotNull(testInstances0);\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(0, testInstances0.getNumRelationalNumeric());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertFalse(testInstances0.getNoClass());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n \\n EvoSuiteFile evoSuiteFile0 = new EvoSuiteFile(\\\"/home/ubuntu/wekafiles/props\\\");\\n boolean boolean0 = FileSystemHandling.appendStringToFile(evoSuiteFile0, \\\" \\\");\\n assertTrue(boolean0);\\n \\n GaussianProcesses gaussianProcesses0 = new GaussianProcesses();\\n assertNotNull(gaussianProcesses0);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n \\n Capabilities capabilities0 = gaussianProcesses0.getCapabilities();\\n assertNotNull(capabilities0);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n \\n TestInstances testInstances1 = TestInstances.forCapabilities(capabilities0);\\n assertNotNull(testInstances1);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(0, testInstances1.getNumDate());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n Instances instances0 = testInstances1.generate();\\n assertNotNull(instances0);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(0, testInstances1.getNumDate());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(20, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n String string0 = instances0.getRevision();\\n assertNotNull(string0);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(0, testInstances1.getNumDate());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(20, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals(\\\"9186\\\", string0);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n GaussianProcesses gaussianProcesses1 = new GaussianProcesses();\\n assertNotNull(gaussianProcesses1);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses1.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses1.filterTypeTipText());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses1.noiseTipText());\\n assertFalse(gaussianProcesses1.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses1.kernelTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses1.debugTipText());\\n assertEquals(1.0, gaussianProcesses1.getNoise(), 0.01);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertFalse(gaussianProcesses1.equals((Object)gaussianProcesses0));\\n \\n Instance instance0 = null;\\n CostMatrix costMatrix0 = new CostMatrix(1);\\n assertNotNull(costMatrix0);\\n assertEquals(1, costMatrix0.numRows());\\n assertEquals(1, costMatrix0.size());\\n assertEquals(1, costMatrix0.numColumns());\\n \\n Evaluation evaluation0 = null;\\n try {\\n evaluation0 = new Evaluation(instances0, costMatrix0);\\n fail(\\\"Expecting exception: Exception\\\");\\n \\n } catch(Throwable e) {\\n //\\n // Class has to be nominal if cost matrix given!\\n //\\n verifyException(\\\"weka.classifiers.Evaluation\\\", e);\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1e36450e471a7a805b40f749853b8ee\",\n \"score\": \"0.61336786\",\n \"text\": \"@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.setNumericPriorsFromBuffer();\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb9ab833c1167e5ff255d8b2d8e8a5e3\",\n \"score\": \"0.6123995\",\n \"text\": \"@Test(timeout = 4000)\\n public void test14() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\\n CostMatrix costMatrix0 = new CostMatrix(2);\\n Evaluation evaluation0 = new Evaluation(instances0, costMatrix0);\\n Evaluation evaluation1 = new Evaluation(instances0, costMatrix0);\\n assertTrue(evaluation1.equals((Object)evaluation0));\\n \\n M5Rules m5Rules0 = new M5Rules();\\n boolean boolean0 = evaluation0.equals(m5Rules0);\\n assertFalse(boolean0);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10fffce3a1dd5ecb2b627ee46e7fd0a0\",\n \"score\": \"0.61168677\",\n \"text\": \"@Test(timeout = 4000)\\n public void test067() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.SFMeanSchemeEntropy();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44792e2f4a82392517c95c86e4643575\",\n \"score\": \"0.61135006\",\n \"text\": \"@Test\\n public void testUrgencyCalculator() {\\n System.out.println(\\\"urgencyCalculator\\\");\\n int maxLevel = 1;\\n int noOfLevels = 1;\\n int expResult = 1;\\n int result = WordDetector.urgencyCalculator(maxLevel, noOfLevels);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n \\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e79302ea2156d4bf8915aad1fd64fc3f\",\n \"score\": \"0.610611\",\n \"text\": \"@Test(timeout = 4000)\\n public void test008() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.rootMeanPriorSquaredError();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5274fccdf72c25d70e236d5ccadaf3\",\n \"score\": \"0.6105006\",\n \"text\": \"@Test(timeout = 4000)\\n public void test12() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\")s|Y)U-\\\");\\n assertNotNull(discretize0);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(10, discretize0.getBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n \\n String string0 = discretize0.getAttributeIndices();\\n assertNotNull(string0);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(10, discretize0.getBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\")s|Y)U-\\\", string0);\\n \\n discretize0.m_UseEqualFrequency = false;\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(10, discretize0.getBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n \\n discretize0.m_MakeBinary = true;\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(10, discretize0.getBins());\\n assertTrue(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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n \\n discretize0.setInvertSelection(true);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(10, discretize0.getBins());\\n assertTrue(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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n \\n discretize0.setFindNumBins(true);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(10, discretize0.getBins());\\n assertTrue(discretize0.getMakeBinary());\\n assertTrue(discretize0.getFindNumBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n \\n discretize0.setBins(89);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(89, discretize0.getBins());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertTrue(discretize0.getMakeBinary());\\n assertTrue(discretize0.getFindNumBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n \\n int int0 = discretize0.getBins();\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(89, discretize0.getBins());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertTrue(discretize0.getMakeBinary());\\n assertTrue(discretize0.getFindNumBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(89, int0);\\n \\n String string1 = discretize0.attributeIndicesTipText();\\n assertNotNull(string1);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(89, discretize0.getBins());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertTrue(discretize0.getMakeBinary());\\n assertTrue(discretize0.getFindNumBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", string1);\\n assertFalse(string1.equals((Object)string0));\\n \\n String string2 = discretize0.getBinRangesString(89);\\n assertNull(string2);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(89, discretize0.getBins());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertTrue(discretize0.getMakeBinary());\\n assertTrue(discretize0.getFindNumBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n \\n String string3 = discretize0.makeBinaryTipText();\\n assertNotNull(string3);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(89, discretize0.getBins());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertTrue(discretize0.getMakeBinary());\\n assertTrue(discretize0.getFindNumBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Make resulting attributes binary.\\\", string3);\\n assertFalse(string3.equals((Object)string1));\\n assertFalse(string3.equals((Object)string0));\\n \\n String[] stringArray0 = discretize0.getOptions();\\n assertNotNull(stringArray0);\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(89, discretize0.getBins());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.isNewBatch());\\n assertTrue(discretize0.getMakeBinary());\\n assertTrue(discretize0.getFindNumBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(9, stringArray0.length);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26648fa3c33292cef9b1f4a05690a870\",\n \"score\": \"0.6097868\",\n \"text\": \"@Test\\n public void test38() throws Throwable {\\n RegressionByDiscretization regressionByDiscretization0 = new RegressionByDiscretization();\\n assertEquals(\\\"The density estimator to use.\\\", regressionByDiscretization0.estimatorTypeTipText());\\n assertFalse(regressionByDiscretization0.getDebug());\\n assertFalse(regressionByDiscretization0.getDeleteEmptyBins());\\n assertFalse(regressionByDiscretization0.getUseEqualFrequency());\\n assertEquals(\\\"Whether to delete empty bins after discretization.\\\", regressionByDiscretization0.deleteEmptyBinsTipText());\\n assertEquals(\\\"Number of bins for discretization.\\\", regressionByDiscretization0.numBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", regressionByDiscretization0.useEqualFrequencyTipText());\\n assertEquals(\\\"The base classifier to be used.\\\", regressionByDiscretization0.classifierTipText());\\n assertEquals(10, regressionByDiscretization0.getNumBins());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", regressionByDiscretization0.debugTipText());\\n assertEquals(\\\"Whether to minimize absolute error.\\\", regressionByDiscretization0.minimizeAbsoluteErrorTipText());\\n assertFalse(regressionByDiscretization0.getMinimizeAbsoluteError());\\n assertNotNull(regressionByDiscretization0);\\n assertEquals(0, regressionByDiscretization0.ESTIMATOR_HISTOGRAM);\\n assertEquals(1, regressionByDiscretization0.ESTIMATOR_KERNEL);\\n assertEquals(2, regressionByDiscretization0.ESTIMATOR_NORMAL);\\n \\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n assertEquals(\\\"Whether to print additional debug information to the console.\\\", textDirectoryLoader0.debugTipText());\\n assertEquals(\\\"Whether to store the filename in an additional attribute.\\\", textDirectoryLoader0.outputFilenameTipText());\\n assertFalse(textDirectoryLoader0.getOutputFilename());\\n assertEquals(\\\"\\\", textDirectoryLoader0.getCharSet());\\n assertEquals(\\\"Loads all text files in a directory and uses the subdirectory names as class labels. The content of the text files will be stored in a String attribute, the filename can be stored as well.\\\", textDirectoryLoader0.globalInfo());\\n assertFalse(textDirectoryLoader0.getDebug());\\n assertEquals(\\\"Directories\\\", textDirectoryLoader0.getFileDescription());\\n assertEquals(\\\"The character set to use when reading text files (eg UTF-8) - leave blank to use the default character set.\\\", textDirectoryLoader0.charSetTipText());\\n assertNotNull(textDirectoryLoader0);\\n \\n Instances instances0 = textDirectoryLoader0.getStructure();\\n assertEquals(0, instances0.numClasses());\\n assertEquals(1, instances0.classIndex());\\n assertEquals(\\\"_Users_fabiopalomba_Documents_workspace_LunchEvosuite\\\", instances0.relationName());\\n assertTrue(instances0.checkForStringAttributes());\\n assertEquals(0, instances0.numInstances());\\n assertEquals(2, instances0.numAttributes());\\n assertEquals(0, instances0.size());\\n assertEquals(0.0, instances0.sumOfWeights(), 0.01D);\\n assertEquals(\\\"Whether to print additional debug information to the console.\\\", textDirectoryLoader0.debugTipText());\\n assertEquals(\\\"Whether to store the filename in an additional attribute.\\\", textDirectoryLoader0.outputFilenameTipText());\\n assertFalse(textDirectoryLoader0.getOutputFilename());\\n assertEquals(\\\"\\\", textDirectoryLoader0.getCharSet());\\n assertEquals(\\\"Loads all text files in a directory and uses the subdirectory names as class labels. The content of the text files will be stored in a String attribute, the filename can be stored as well.\\\", textDirectoryLoader0.globalInfo());\\n assertFalse(textDirectoryLoader0.getDebug());\\n assertEquals(\\\"Directories\\\", textDirectoryLoader0.getFileDescription());\\n assertEquals(\\\"The character set to use when reading text files (eg UTF-8) - leave blank to use the default character set.\\\", textDirectoryLoader0.charSetTipText());\\n assertNotNull(instances0);\\n \\n Evaluation evaluation0 = new Evaluation(instances0);\\n assertEquals(0, instances0.numClasses());\\n assertEquals(1, instances0.classIndex());\\n assertEquals(\\\"_Users_fabiopalomba_Documents_workspace_LunchEvosuite\\\", instances0.relationName());\\n assertTrue(instances0.checkForStringAttributes());\\n assertEquals(0, instances0.numInstances());\\n assertEquals(2, instances0.numAttributes());\\n assertEquals(0, instances0.size());\\n assertEquals(0.0, instances0.sumOfWeights(), 0.01D);\\n assertEquals(\\\"Whether to print additional debug information to the console.\\\", textDirectoryLoader0.debugTipText());\\n assertEquals(\\\"Whether to store the filename in an additional attribute.\\\", textDirectoryLoader0.outputFilenameTipText());\\n assertFalse(textDirectoryLoader0.getOutputFilename());\\n assertEquals(\\\"\\\", textDirectoryLoader0.getCharSet());\\n assertEquals(\\\"Loads all text files in a directory and uses the subdirectory names as class labels. The content of the text files will be stored in a String attribute, the filename can be stored as well.\\\", textDirectoryLoader0.globalInfo());\\n assertFalse(textDirectoryLoader0.getDebug());\\n assertEquals(\\\"Directories\\\", textDirectoryLoader0.getFileDescription());\\n assertEquals(\\\"The character set to use when reading text files (eg UTF-8) - leave blank to use the default character set.\\\", textDirectoryLoader0.charSetTipText());\\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01D);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01D);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01D);\\n assertEquals(1.0, evaluation0.kappa(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedFalseNegativeRate(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedFalsePositiveRate(), 0.01D);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedMatthewsCorrelation(), 0.01D);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedRecall(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedPrecision(), 0.01D);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01D);\\n assertEquals(0.0, evaluation0.numInstances(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedTrueNegativeRate(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.unweightedMicroFmeasure(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01D);\\n assertEquals(0.0, evaluation0.correct(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01D);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(Double.NaN, evaluation0.weightedFMeasure(), 0.01D);\\n assertEquals(0.0, evaluation0.unclassified(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedTruePositiveRate(), 0.01D);\\n assertNotNull(evaluation0);\\n \\n MockRandom mockRandom0 = new MockRandom();\\n assertNotNull(mockRandom0);\\n \\n Object[] objectArray0 = new Object[12];\\n try {\\n evaluation0.crossValidateModel((Classifier) regressionByDiscretization0, instances0, 5298, (Random) mockRandom0, objectArray0);\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f100211018cc39162bbd51ff163d719\",\n \"score\": \"0.60713863\",\n \"text\": \"@Test(timeout = 4000)\\n public void test09() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"OAV4x1$~: D=WW:?\\\");\\n assertNotNull(discretize0);\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\n \\n String string0 = \\\"pjgDj2%^u4@f.1yT^2\\\";\\n discretize0.setUseEqualFrequency(true);\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertTrue(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\n \\n Range range0 = new Range(\\\"pjgDj2%^u4@f.1yT^2\\\");\\n assertNotNull(range0);\\n \\n boolean boolean0 = range0.getInvert();\\n assertFalse(boolean0);\\n \\n discretize0.m_DiscretizeCols = range0;\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertTrue(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\n \\n discretize0.m_FindNumBins = false;\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertTrue(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\n \\n String string1 = discretize0.invertSelectionTipText();\\n assertNotNull(string1);\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertTrue(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\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.\\\", string1);\\n assertFalse(string1.equals((Object)string0));\\n \\n discretize0.setUseEqualFrequency(false);\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\n \\n double double0 = (-144.0);\\n double[] doubleArray0 = new double[2];\\n doubleArray0[0] = (-144.0);\\n doubleArray0[1] = (-144.0);\\n discretize0.setUseBinNumbers(false);\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\n \\n int[] intArray0 = new int[1];\\n intArray0[0] = 60;\\n SparseInstance sparseInstance0 = null;\\n try {\\n sparseInstance0 = new SparseInstance((-144.0), doubleArray0, intArray0, 60);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 1\\n //\\n verifyException(\\\"weka.core.SparseInstance\\\", e);\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ee66377986f25926220ed4af7f26665\",\n \"score\": \"0.60633904\",\n \"text\": \"@Test(timeout = 4000)\\n public void test063() throws Throwable {\\n DecisionStump decisionStump0 = new DecisionStump();\\n Capabilities capabilities0 = new Capabilities(decisionStump0);\\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\\n Instances instances0 = testInstances0.generate(\\\"setOptions\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.weightedTrueNegativeRate();\\n assertEquals(Double.NaN, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"010ba632925f766149bfd668fcbf0c5b\",\n \"score\": \"0.6040576\",\n \"text\": \"@Test(timeout = 4000)\\n public void test03() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Consumer<Object> consumer0 = (Consumer<Object>) mock(Consumer.class, new ViolatedAssumptionAnswer());\\n instances0.forEach(consumer0);\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.weightedMatthewsCorrelation();\\n double double1 = evaluation0.SFMeanPriorEntropy();\\n assertEquals(double1, double0, 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(Double.NaN, double1, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a9ffaa4a298b3121a29cdf60a2492f7\",\n \"score\": \"0.6037189\",\n \"text\": \"@Test\\n public void probabilityTest() {\\n // TODO: test probability\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"562894c90bdb1569ea917833f859403a\",\n \"score\": \"0.60329765\",\n \"text\": \"@Test(timeout = 4000)\\n public void test13() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\")s|Y)U-\\\");\\n assertNotNull(discretize0);\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getFindNumBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getMakeBinary());\\n \\n ArrayList<Attribute> arrayList0 = new ArrayList<Attribute>();\\n assertNotNull(arrayList0);\\n assertEquals(0, arrayList0.size());\\n assertTrue(arrayList0.isEmpty());\\n \\n Instances instances0 = new Instances(\\\")s|Y)U-\\\", arrayList0, 1259);\\n assertNotNull(instances0);\\n assertEquals(0, arrayList0.size());\\n assertTrue(arrayList0.isEmpty());\\n assertEquals(0, instances0.numAttributes());\\n assertEquals(0.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(0, instances0.size());\\n assertEquals(\\\")s|Y)U-\\\", instances0.relationName());\\n assertEquals(0, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals((-1), instances0.classIndex());\\n \\n Capabilities capabilities0 = discretize0.getCapabilities();\\n assertNotNull(capabilities0);\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getFindNumBins());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(capabilities0.hasDependencies());\\n assertEquals(0, capabilities0.getMinimumNumberInstances());\\n \\n Discretize discretize1 = new Discretize(\\\"o<6)I1\\\");\\n assertNotNull(discretize1);\\n assertFalse(discretize1.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize1.makeBinaryTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize1.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize1.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize1.ignoreClassTipText());\\n assertFalse(discretize1.getUseBinNumbers());\\n assertFalse(discretize1.isFirstBatchDone());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize1.findNumBinsTipText());\\n assertEquals(10, discretize1.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize1.globalInfo());\\n assertEquals((-1.0), discretize1.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Number of bins.\\\", discretize1.binsTipText());\\n assertFalse(discretize1.getMakeBinary());\\n assertFalse(discretize1.isOutputFormatDefined());\\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.\\\", discretize1.invertSelectionTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize1.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize1.useBinNumbersTipText());\\n assertFalse(discretize1.getIgnoreClass());\\n assertFalse(discretize1.getFindNumBins());\\n assertTrue(discretize1.isNewBatch());\\n assertFalse(discretize1.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize1.equals((Object)discretize0));\\n \\n String string0 = discretize1.useBinNumbersTipText();\\n assertNotNull(string0);\\n assertFalse(discretize1.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize1.makeBinaryTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize1.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize1.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize1.ignoreClassTipText());\\n assertFalse(discretize1.getUseBinNumbers());\\n assertFalse(discretize1.isFirstBatchDone());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize1.findNumBinsTipText());\\n assertEquals(10, discretize1.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize1.globalInfo());\\n assertEquals((-1.0), discretize1.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Number of bins.\\\", discretize1.binsTipText());\\n assertFalse(discretize1.getMakeBinary());\\n assertFalse(discretize1.isOutputFormatDefined());\\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.\\\", discretize1.invertSelectionTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize1.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize1.useBinNumbersTipText());\\n assertFalse(discretize1.getIgnoreClass());\\n assertFalse(discretize1.getFindNumBins());\\n assertTrue(discretize1.isNewBatch());\\n assertFalse(discretize1.mayRemoveInstanceAfterFirstBatchDone());\\n assertNotSame(discretize1, discretize0);\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", string0);\\n assertFalse(discretize1.equals((Object)discretize0));\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fde3af92932e545c9925b51ae293cc0\",\n \"score\": \"0.60186595\",\n \"text\": \"@Test(timeout = 4000)\\n public void test16() throws Throwable {\\n boolean boolean0 = FileSystemHandling.setPermissions((EvoSuiteFile) null, false, false, false);\\n assertFalse(boolean0);\\n \\n Discretize discretize0 = new Discretize(\\\"\\\");\\n assertNotNull(discretize0);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n \\n boolean boolean1 = FileSystemHandling.shouldThrowIOException((EvoSuiteFile) null);\\n assertFalse(boolean1);\\n assertTrue(boolean1 == boolean0);\\n \\n discretize0.m_FindNumBins = true;\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n \\n discretize0.setDesiredWeightOfInstancesPerInterval((-2050));\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-2050.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n \\n String[] stringArray0 = new String[0];\\n Discretize.main(stringArray0);\\n assertEquals(0, stringArray0.length);\\n \\n discretize0.m_UseBinNumbers = false;\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-2050.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n \\n String string0 = discretize0.getBinRangesString((-2050));\\n assertNull(string0);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-2050.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n \\n String[] stringArray1 = discretize0.getOptions();\\n assertNotNull(stringArray1);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-2050.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertNotSame(stringArray1, stringArray0);\\n assertEquals(5, stringArray1.length);\\n assertFalse(stringArray1.equals((Object)stringArray0));\\n \\n discretize0.setOptions(stringArray1);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-2050.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertNotSame(stringArray1, stringArray0);\\n assertEquals(5, stringArray1.length);\\n assertFalse(stringArray1.equals((Object)stringArray0));\\n \\n String[] stringArray2 = discretize0.getOptions();\\n assertNotNull(stringArray2);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-2050.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertNotSame(stringArray2, stringArray0);\\n assertNotSame(stringArray2, stringArray1);\\n assertEquals(5, stringArray2.length);\\n assertFalse(stringArray2.equals((Object)stringArray0));\\n assertFalse(stringArray2.equals((Object)stringArray1));\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8363b386cc5ff0f52db25811d65532\",\n \"score\": \"0.6014696\",\n \"text\": \"@Test(timeout = 4000)\\n public void test016() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getDataSet();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n String string0 = evaluation0.toClassDetailsString(\\\"H!a/E|y7 \\\");\\n assertEquals(\\\"H!a/E|y7 \\\\n TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class\\\\nWeighted Avg. NaN NaN NaN NaN NaN NaN NaN NaN \\\\n\\\", string0);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aae1763ac4c7530534b08281ce627ff6\",\n \"score\": \"0.60029316\",\n \"text\": \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n assertNotNull(discretize0);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n \\n String string0 = discretize0.getAttributeIndices();\\n assertNotNull(string0);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"first-last\\\", string0);\\n \\n String[] stringArray0 = new String[2];\\n stringArray0[0] = \\\"first-last\\\";\\n stringArray0[1] = \\\"first-last\\\";\\n discretize0.setOptions(stringArray0);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(2, stringArray0.length);\\n \\n int int0 = 98;\\n double[] doubleArray0 = discretize0.getCutPoints(98);\\n assertNull(doubleArray0);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n \\n String string1 = discretize0.findNumBinsTipText();\\n assertNotNull(string1);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", string1);\\n assertFalse(string1.equals((Object)string0));\\n \\n String[] stringArray1 = discretize0.getOptions();\\n assertNotNull(stringArray1);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertNotSame(stringArray1, stringArray0);\\n assertEquals(6, stringArray1.length);\\n assertFalse(stringArray1.equals((Object)stringArray0));\\n \\n Capabilities capabilities0 = discretize0.getCapabilities();\\n assertNotNull(capabilities0);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(capabilities0.hasDependencies());\\n assertEquals(0, capabilities0.getMinimumNumberInstances());\\n \\n String string2 = discretize0.invertSelectionTipText();\\n assertNotNull(string2);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\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.\\\", string2);\\n assertFalse(string2.equals((Object)string0));\\n assertFalse(string2.equals((Object)string1));\\n \\n int int1 = (-1012);\\n String string3 = discretize0.getBinRangesString((-1012));\\n assertNull(string3);\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n \\n // Undeclared exception!\\n try { \\n discretize0.getOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // No output format defined.\\n //\\n verifyException(\\\"weka.filters.Filter\\\", e);\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1118c8ebeec2dd7cf9cb0f099fadf856\",\n \"score\": \"0.59983355\",\n \"text\": \"@Test(timeout = 4000)\\n public void test007() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate(\\\"debugTidpText\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.rootMeanSquaredError();\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(Double.NaN, double0, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e75605d420dadee906b3740c1af1827\",\n \"score\": \"0.5989377\",\n \"text\": \"public void testWilcoxonSumRank(){\\n\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(3,2,2));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(4,2,2));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(5,2,2));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(6,2,2));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(7,2,2));\\n\\n /*\\n Test Asserts for group size [2,3] from W Stats from 3 -> 9\\n Expected Value from https://amstat.tandfonline.com/doi/pdf/10.1080/10691898.2010.11889486?needAccess=true page 12 Figure 3.\\n */\\n\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(3,2,3));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(4,2,3));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(5,2,3));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(6,2,3));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(7,2,3));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(8,2,3));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(9,2,3));\\n\\n /*\\n Test Asserts for group size [2,4] from W Stats from 3 -> 11\\n Expected Value from https://amstat.tandfonline.com/doi/pdf/10.1080/10691898.2010.11889486?needAccess=true page 12 Figure 3.\\n */\\n\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(3,2,4));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(4,2,4));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(5,2,4));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(6,2,4));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(7,2,4));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(8,2,4));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(9,2,4));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(10,2,4));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(11,2,4));\\n\\n /*\\n Test Asserts for group size [2,5] from W Stats from 3 -> 13\\n Expected Value from https://amstat.tandfonline.com/doi/pdf/10.1080/10691898.2010.11889486?needAccess=true page 12 Figure 3.\\n */\\n\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(3,2,5));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(4,2,5));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(5,2,5));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(6,2,5));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(7,2,5));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(8,2,5));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(9,2,5));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(10,2,5));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(11,2,5));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(12,2,5));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(13,2,5));\\n\\n /*\\n Test Asserts for group size [2,6] from W Stats from 3 -> 15\\n Expected Value from https://amstat.tandfonline.com/doi/pdf/10.1080/10691898.2010.11889486?needAccess=true page 12 Figure 3.\\n */\\n\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(3,2,6));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(4,2,6));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(5,2,6));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(6,2,6));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(7,2,6));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(8,2,6));\\n assertEquals(4, ProbabilityDistribution.WilcoxonSumRank(9,2,6));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(10,2,6));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(11,2,6));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(12,2,6));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(13,2,6));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(14,2,6));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(15,2,6));\\n\\n /*\\n Test Asserts for group size [3,3] from W Stats from 6 -> 15\\n Expected Value from https://amstat.tandfonline.com/doi/pdf/10.1080/10691898.2010.11889486?needAccess=true page 12 Figure 3.\\n */\\n\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(6,3,3));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(7,3,3));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(8,3,3));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(9,3,3));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(10,3,3));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(11,3,3));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(12,3,3));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(13,3,3));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(14,3,3));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(15,3,3));\\n\\n /*\\n Test Asserts for group size [3,4] from W Stats from 6 -> 18\\n Expected Value from https://amstat.tandfonline.com/doi/pdf/10.1080/10691898.2010.11889486?needAccess=true page 12 Figure 3.\\n */\\n\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(6,3,4));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(7,3,4));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(8,3,4));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(9,3,4));\\n assertEquals(4, ProbabilityDistribution.WilcoxonSumRank(10,3,4));\\n assertEquals(4, ProbabilityDistribution.WilcoxonSumRank(11,3,4));\\n assertEquals(5, ProbabilityDistribution.WilcoxonSumRank(12,3,4));\\n assertEquals(4, ProbabilityDistribution.WilcoxonSumRank(13,3,4));\\n assertEquals(4, ProbabilityDistribution.WilcoxonSumRank(14,3,4));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(15,3,4));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(16,3,4));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(17,3,4));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(18,3,4));\\n\\n /*\\n Test Asserts for group size [3,5] from W Stats from 6 -> 21\\n Expected Value from https://amstat.tandfonline.com/doi/pdf/10.1080/10691898.2010.11889486?needAccess=true page 12 Figure 3.\\n */\\n\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(6,3,5));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(7,3,5));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(8,3,5));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(9,3,5));\\n assertEquals(4, ProbabilityDistribution.WilcoxonSumRank(10,3,5));\\n assertEquals(5, ProbabilityDistribution.WilcoxonSumRank(11,3,5));\\n assertEquals(6, ProbabilityDistribution.WilcoxonSumRank(12,3,5));\\n assertEquals(6, ProbabilityDistribution.WilcoxonSumRank(13,3,5));\\n assertEquals(6, ProbabilityDistribution.WilcoxonSumRank(14,3,5));\\n assertEquals(6, ProbabilityDistribution.WilcoxonSumRank(15,3,5));\\n assertEquals(5, ProbabilityDistribution.WilcoxonSumRank(16,3,5));\\n assertEquals(4, ProbabilityDistribution.WilcoxonSumRank(17,3,5));\\n assertEquals(3, ProbabilityDistribution.WilcoxonSumRank(18,3,5));\\n assertEquals(2, ProbabilityDistribution.WilcoxonSumRank(19,3,5));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(20,3,5));\\n assertEquals(1, ProbabilityDistribution.WilcoxonSumRank(21,3,5));\\n\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aff1678df9569785c5250ad9154b180\",\n \"score\": \"0.5980926\",\n \"text\": \"@Test(timeout = 4000)\\n public void test026() throws Throwable {\\n Capabilities capabilities0 = new Capabilities((CapabilitiesHandler) null);\\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\\n Instances instances0 = testInstances0.generate((String) null);\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.falseNegativeRate(1388);\\n assertEquals(0.0, double0, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9401147b99a2247fccb4864b0e19bd31\",\n \"score\": \"0.59637845\",\n \"text\": \"@Test\\n public void geneSummaryTest() {\\n // TODO: test geneSummary\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc0fa32e84ff2d669a04762680e1f7a3\",\n \"score\": \"0.5962953\",\n \"text\": \"@Test(timeout = 4000)\\n public void test37() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n naiveBayesMultinomialText0.m_normalize = true;\\n naiveBayesMultinomialText0.m_lowercaseTokens = true;\\n Capabilities capabilities0 = naiveBayesMultinomialText0.getCapabilities();\\n capabilities0.clone();\\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\\n capabilities0.getRevision();\\n Instances instances0 = testInstances0.generate();\\n naiveBayesMultinomialText0.buildClassifier(instances0);\\n FileSystemHandling fileSystemHandling0 = new FileSystemHandling();\\n NaiveBayesMultinomialText naiveBayesMultinomialText1 = new NaiveBayesMultinomialText();\\n LinkedHashMap<Integer, LinkedHashMap<String, NaiveBayesMultinomialText.Count>> linkedHashMap0 = new LinkedHashMap<Integer, LinkedHashMap<String, NaiveBayesMultinomialText.Count>>();\\n FileSystemHandling fileSystemHandling1 = new FileSystemHandling();\\n Integer integer0 = new Integer(420);\\n LinkedHashMap<String, NaiveBayesMultinomialText.Count> linkedHashMap1 = new LinkedHashMap<String, NaiveBayesMultinomialText.Count>();\\n linkedHashMap0.put(integer0, linkedHashMap1);\\n FileSystemHandling fileSystemHandling2 = new FileSystemHandling();\\n FileSystemHandling.shouldThrowIOException((EvoSuiteFile) null);\\n naiveBayesMultinomialText1.pruneDictionary();\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c75bc774749b44d7e8a181617b2a9f70\",\n \"score\": \"0.5960899\",\n \"text\": \"@Test(timeout = 4000)\\n public void test08() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n assertNotNull(discretize0);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\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.\\\", discretize0.invertSelectionTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n \\n // Undeclared exception!\\n try { \\n discretize0.input((Instance) null);\\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 \"title\": \"\"\n },\n {\n \"docid\": \"8cf838153ec97e04d5365212a7111b70\",\n \"score\": \"0.59575397\",\n \"text\": \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n boolean boolean0 = FileSystemHandling.shouldAllThrowIOExceptions();\\n assertTrue(boolean0);\\n \\n Discretize discretize0 = new Discretize();\\n assertNotNull(discretize0);\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getIgnoreClass());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n \\n double[][] doubleArray0 = new double[9][2];\\n double[] doubleArray1 = new double[6];\\n doubleArray1[0] = 0.0;\\n doubleArray1[1] = 0.0;\\n doubleArray1[2] = 1739.8155945888;\\n doubleArray1[3] = (-2176.18184943642);\\n doubleArray1[4] = 0.0;\\n doubleArray1[5] = 3505.4410971102;\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[2];\\n assertFalse(doubleArray2.equals((Object)doubleArray1));\\n \\n doubleArray2[0] = 0.0;\\n doubleArray2[1] = 1739.8155945888;\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[4];\\n assertFalse(doubleArray3.equals((Object)doubleArray2));\\n assertFalse(doubleArray3.equals((Object)doubleArray1));\\n \\n doubleArray3[0] = 3505.4410971102;\\n doubleArray3[1] = 0.0;\\n doubleArray3[2] = 1739.8155945888;\\n doubleArray3[3] = 1739.8155945888;\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[4];\\n assertFalse(doubleArray4.equals((Object)doubleArray1));\\n assertFalse(doubleArray4.equals((Object)doubleArray2));\\n assertFalse(doubleArray4.equals((Object)doubleArray3));\\n \\n doubleArray4[0] = (-2176.18184943642);\\n doubleArray4[1] = (-2176.18184943642);\\n doubleArray4[2] = 0.0;\\n doubleArray4[3] = 0.0;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[9];\\n assertFalse(doubleArray5.equals((Object)doubleArray1));\\n assertFalse(doubleArray5.equals((Object)doubleArray2));\\n assertFalse(doubleArray5.equals((Object)doubleArray4));\\n assertFalse(doubleArray5.equals((Object)doubleArray3));\\n \\n doubleArray5[0] = 3505.4410971102;\\n doubleArray5[1] = (-2176.18184943642);\\n doubleArray5[2] = 0.0;\\n doubleArray5[3] = (-2176.18184943642);\\n doubleArray5[4] = 0.0;\\n doubleArray5[5] = 0.0;\\n doubleArray5[6] = (-2176.18184943642);\\n doubleArray5[7] = 3505.4410971102;\\n doubleArray5[8] = 3505.4410971102;\\n doubleArray0[4] = doubleArray5;\\n double[] doubleArray6 = new double[6];\\n assertFalse(doubleArray6.equals((Object)doubleArray1));\\n assertFalse(doubleArray6.equals((Object)doubleArray5));\\n assertFalse(doubleArray6.equals((Object)doubleArray3));\\n assertFalse(doubleArray6.equals((Object)doubleArray2));\\n assertFalse(doubleArray6.equals((Object)doubleArray4));\\n \\n doubleArray6[0] = 0.0;\\n doubleArray6[1] = 4.5;\\n doubleArray6[2] = 0.0;\\n doubleArray6[3] = (-2176.18184943642);\\n doubleArray6[4] = 3505.4410971102;\\n doubleArray6[5] = 0.0;\\n doubleArray0[5] = doubleArray6;\\n double[] doubleArray7 = new double[5];\\n assertFalse(doubleArray7.equals((Object)doubleArray6));\\n assertFalse(doubleArray7.equals((Object)doubleArray1));\\n assertFalse(doubleArray7.equals((Object)doubleArray2));\\n assertFalse(doubleArray7.equals((Object)doubleArray5));\\n assertFalse(doubleArray7.equals((Object)doubleArray4));\\n assertFalse(doubleArray7.equals((Object)doubleArray3));\\n \\n doubleArray7[0] = 0.0;\\n doubleArray7[1] = 1739.8155945888;\\n doubleArray7[2] = (-2176.18184943642);\\n doubleArray7[3] = 3505.4410971102;\\n doubleArray7[4] = 3505.4410971102;\\n doubleArray0[6] = doubleArray7;\\n double[] doubleArray8 = new double[7];\\n assertFalse(doubleArray8.equals((Object)doubleArray3));\\n assertFalse(doubleArray8.equals((Object)doubleArray7));\\n assertFalse(doubleArray8.equals((Object)doubleArray5));\\n assertFalse(doubleArray8.equals((Object)doubleArray1));\\n assertFalse(doubleArray8.equals((Object)doubleArray4));\\n assertFalse(doubleArray8.equals((Object)doubleArray2));\\n assertFalse(doubleArray8.equals((Object)doubleArray6));\\n \\n doubleArray8[0] = 3505.4410971102;\\n doubleArray8[1] = (-2176.18184943642);\\n doubleArray8[2] = 3505.4410971102;\\n doubleArray8[3] = 4.5;\\n doubleArray8[4] = 4.5;\\n doubleArray8[5] = 1739.8155945888;\\n doubleArray8[6] = 1739.8155945888;\\n doubleArray0[7] = doubleArray8;\\n double[] doubleArray9 = new double[8];\\n assertFalse(doubleArray9.equals((Object)doubleArray3));\\n assertFalse(doubleArray9.equals((Object)doubleArray8));\\n assertFalse(doubleArray9.equals((Object)doubleArray4));\\n assertFalse(doubleArray9.equals((Object)doubleArray2));\\n assertFalse(doubleArray9.equals((Object)doubleArray6));\\n assertFalse(doubleArray9.equals((Object)doubleArray5));\\n assertFalse(doubleArray9.equals((Object)doubleArray1));\\n assertFalse(doubleArray9.equals((Object)doubleArray7));\\n \\n doubleArray9[0] = 0.0;\\n doubleArray9[1] = 1739.8155945888;\\n doubleArray9[2] = 0.0;\\n doubleArray9[3] = 1739.8155945888;\\n doubleArray9[4] = 0.0;\\n doubleArray9[5] = 0.0;\\n doubleArray9[6] = (-2176.18184943642);\\n doubleArray9[7] = 0.0;\\n doubleArray0[8] = doubleArray9;\\n discretize0.m_CutPoints = doubleArray0;\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertFalse(discretize0.getIgnoreClass());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n \\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 \"title\": \"\"\n },\n {\n \"docid\": \"844f9f4793adb3d7db925cf8f5ad73fc\",\n \"score\": \"0.59541446\",\n \"text\": \"@Test(timeout = 4000)\\n public void test18() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n assertNotNull(discretize0);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n \\n discretize0.m_UseBinNumbers = true;\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertTrue(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n \\n Discretize discretize1 = (Discretize)Filter.makeCopy(discretize0);\\n assertNotNull(discretize1);\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertTrue(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize1.getFindNumBins());\\n assertFalse(discretize1.getMakeBinary());\\n assertEquals(\\\"Number of bins.\\\", discretize1.binsTipText());\\n assertFalse(discretize1.isOutputFormatDefined());\\n assertFalse(discretize1.getIgnoreClass());\\n assertFalse(discretize1.mayRemoveInstanceAfterFirstBatchDone());\\n assertTrue(discretize1.getUseBinNumbers());\\n assertEquals(10, discretize1.getBins());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize1.makeBinaryTipText());\\n assertFalse(discretize1.getUseEqualFrequency());\\n assertTrue(discretize1.isNewBatch());\\n assertEquals((-1.0), discretize1.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize1.globalInfo());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize1.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize1.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize1.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize1.attributeIndicesTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize1.useEqualFrequencyTipText());\\n assertFalse(discretize1.isFirstBatchDone());\\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.\\\", discretize1.invertSelectionTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize1.findNumBinsTipText());\\n assertNotSame(discretize0, discretize1);\\n assertNotSame(discretize1, discretize0);\\n assertFalse(discretize1.equals((Object)discretize0));\\n \\n String[] stringArray0 = discretize0.getOptions();\\n assertNotNull(stringArray0);\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertTrue(discretize0.getUseBinNumbers());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.getIgnoreClass());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertNotSame(discretize0, discretize1);\\n assertEquals(7, stringArray0.length);\\n assertFalse(discretize0.equals((Object)discretize1));\\n \\n // Undeclared exception!\\n try { \\n discretize0.batchFinished();\\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 \"title\": \"\"\n },\n {\n \"docid\": \"712ee89bfedfdd886e7e0e2af6e323a1\",\n \"score\": \"0.59377915\",\n \"text\": \"@Test\\n\\tpublic void test_1_1() {\\n\\t\\tdouble expectedI = sigmaI;\\n\\t\\tdouble actualI = nc.getSigmaI();\\n\\t\\tassertEquals(expectedI, actualI, 0);\\n\\n\\t\\tdouble expectedX = sigmaX;\\n\\t\\tdouble actualX = nc.getSigmaX();\\n\\t\\tassertEquals(expectedX, actualX, 0);\\n\\n\\t\\tdouble expectedR = r;\\n\\t\\tdouble actualR = nc.getR();\\n\\t\\tassertEquals(expectedR, actualR, 0);\\n\\n\\t\\tint expectedL = l;\\n\\t\\tdouble actualL = nc.getL();\\n\\t\\tassertEquals(expectedL, actualL, 0);\\n\\n\\t\\tdouble expectedThreshold = nCutThreshold;\\n\\t\\tdouble actualThreshold = nc.getNCutThreshold();\\n\\t\\tassertEquals(expectedThreshold, actualThreshold, 0);\\n\\n\\t\\tint expectedNClusters = nClusters;\\n\\t\\tint actualNClusters = nc.getNClusters();\\n\\t\\tassertEquals(expectedNClusters, actualNClusters);\\n\\n\\t\\tint expectedCutType = nCutType;\\n\\t\\tint actualCutType = nc.getNCutType();\\n\\t\\tassertEquals(expectedCutType, actualCutType);\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f2748f72d543276b02759a5378455f4\",\n \"score\": \"0.59362435\",\n \"text\": \"@Test(timeout = 4000)\\n public void test61() throws Throwable {\\n boolean boolean0 = FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"P7<[g'<+nG\\\");\\n assertFalse(boolean0);\\n \\n double[] doubleArray0 = new double[9];\\n SVMLightLoader sVMLightLoader0 = new SVMLightLoader();\\n assertNotNull(sVMLightLoader0);\\n assertEquals(\\\"svm light data files\\\", sVMLightLoader0.getFileDescription());\\n assertEquals(\\\"Reads a source that is in svm light format.\\\\n\\\\nFor more information about svm light see:\\\\n\\\\nhttp://svmlight.joachims.org/\\\", sVMLightLoader0.globalInfo());\\n assertEquals(\\\"http://\\\", sVMLightLoader0.retrieveURL());\\n assertFalse(sVMLightLoader0.getUseRelativePath());\\n assertEquals(\\\".dat\\\", sVMLightLoader0.getFileExtension());\\n assertEquals(\\\"Use relative rather than absolute paths\\\", sVMLightLoader0.useRelativePathTipText());\\n \\n String string0 = sVMLightLoader0.getRevision();\\n assertNotNull(string0);\\n assertEquals(\\\"svm light data files\\\", sVMLightLoader0.getFileDescription());\\n assertEquals(\\\"Reads a source that is in svm light format.\\\\n\\\\nFor more information about svm light see:\\\\n\\\\nhttp://svmlight.joachims.org/\\\", sVMLightLoader0.globalInfo());\\n assertEquals(\\\"http://\\\", sVMLightLoader0.retrieveURL());\\n assertFalse(sVMLightLoader0.getUseRelativePath());\\n assertEquals(\\\".dat\\\", sVMLightLoader0.getFileExtension());\\n assertEquals(\\\"Use relative rather than absolute paths\\\", sVMLightLoader0.useRelativePathTipText());\\n assertEquals(\\\"8034\\\", string0);\\n \\n doubleArray0[1] = 1.0E75;\\n doubleArray0[2] = 1.0E75;\\n LMT lMT0 = new LMT();\\n assertNotNull(lMT0);\\n assertFalse(lMT0.getErrorOnProbabilities());\\n assertEquals(0.0, lMT0.getWeightTrimBeta(), 0.01);\\n assertEquals(\\\"Use heuristic that avoids cross-validating the number of Logit-Boost iterations at every node. When fitting the logistic regression functions at a node, LMT has to determine the number of LogitBoost iterations to run. Originally, this number was cross-validated at every node in the tree. To save time, this heuristic cross-validates the number only once and then uses that number at every node in the tree. Usually this does not decrease accuracy but improves runtime considerably.\\\", lMT0.fastRegressionTipText());\\n assertEquals(\\\"The AIC is used to determine when to stop LogitBoost iterations. The default is not to use AIC.\\\", lMT0.useAICTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", lMT0.debugTipText());\\n assertEquals(\\\"Set the beta value used for weight trimming in LogitBoost. Only instances carrying (1 - beta)% of the weight from previous iteration are used in the next iteration. Set to 0 for no weight trimming. The default value is 0.\\\", lMT0.weightTrimBetaTipText());\\n assertFalse(lMT0.getUseAIC());\\n assertEquals(15, lMT0.getMinNumInstances());\\n assertEquals(\\\"Convert all nominal attributes to binary ones before building the tree. This means that all splits in the final tree will be binary.\\\", lMT0.convertNominalTipText());\\n assertFalse(lMT0.getDebug());\\n assertEquals(\\\"Set splitting criterion based on the residuals of LogitBoost. There are two possible splitting criteria for LMT: the default is to use the C4.5 splitting criterion that uses information gain on the class variable. The other splitting criterion tries to improve the purity in the residuals produces when fitting the logistic regression functions. The choice of the splitting criterion does not usually affect classification accuracy much, but can produce different trees.\\\", lMT0.splitOnResidualsTipText());\\n assertEquals(1, lMT0.graphType());\\n assertFalse(lMT0.getConvertNominal());\\n assertEquals(\\\"Set a fixed number of iterations for LogitBoost. If >= 0, this sets a fixed number of LogitBoost iterations that is used everywhere in the tree. If < 0, the number is cross-validated.\\\", lMT0.numBoostingIterationsTipText());\\n assertEquals(\\\"Minimize error on probabilities instead of misclassification error when cross-validating the number of LogitBoost iterations. When set, the number of LogitBoost iterations is chosen that minimizes the root mean squared error instead of the misclassification error.\\\", lMT0.errorOnProbabilitiesTipText());\\n assertFalse(lMT0.getSplitOnResiduals());\\n assertEquals(\\\"Set the minimum number of instances at which a node is considered for splitting. The default value is 15.\\\", lMT0.minNumInstancesTipText());\\n assertTrue(lMT0.getFastRegression());\\n assertEquals((-1), lMT0.getNumBoostingIterations());\\n \\n String string1 = Evaluation.makeOptionString(lMT0, true);\\n assertNotNull(string1);\\n assertFalse(lMT0.getErrorOnProbabilities());\\n assertEquals(0.0, lMT0.getWeightTrimBeta(), 0.01);\\n assertEquals(\\\"Use heuristic that avoids cross-validating the number of Logit-Boost iterations at every node. When fitting the logistic regression functions at a node, LMT has to determine the number of LogitBoost iterations to run. Originally, this number was cross-validated at every node in the tree. To save time, this heuristic cross-validates the number only once and then uses that number at every node in the tree. Usually this does not decrease accuracy but improves runtime considerably.\\\", lMT0.fastRegressionTipText());\\n assertEquals(\\\"The AIC is used to determine when to stop LogitBoost iterations. The default is not to use AIC.\\\", lMT0.useAICTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", lMT0.debugTipText());\\n assertEquals(\\\"Set the beta value used for weight trimming in LogitBoost. Only instances carrying (1 - beta)% of the weight from previous iteration are used in the next iteration. Set to 0 for no weight trimming. The default value is 0.\\\", lMT0.weightTrimBetaTipText());\\n assertFalse(lMT0.getUseAIC());\\n assertEquals(15, lMT0.getMinNumInstances());\\n assertEquals(\\\"Convert all nominal attributes to binary ones before building the tree. This means that all splits in the final tree will be binary.\\\", lMT0.convertNominalTipText());\\n assertFalse(lMT0.getDebug());\\n assertEquals(\\\"Set splitting criterion based on the residuals of LogitBoost. There are two possible splitting criteria for LMT: the default is to use the C4.5 splitting criterion that uses information gain on the class variable. The other splitting criterion tries to improve the purity in the residuals produces when fitting the logistic regression functions. The choice of the splitting criterion does not usually affect classification accuracy much, but can produce different trees.\\\", lMT0.splitOnResidualsTipText());\\n assertEquals(1, lMT0.graphType());\\n assertFalse(lMT0.getConvertNominal());\\n assertEquals(\\\"Set a fixed number of iterations for LogitBoost. If >= 0, this sets a fixed number of LogitBoost iterations that is used everywhere in the tree. If < 0, the number is cross-validated.\\\", lMT0.numBoostingIterationsTipText());\\n assertEquals(\\\"Minimize error on probabilities instead of misclassification error when cross-validating the number of LogitBoost iterations. When set, the number of LogitBoost iterations is chosen that minimizes the root mean squared error instead of the misclassification error.\\\", lMT0.errorOnProbabilitiesTipText());\\n assertFalse(lMT0.getSplitOnResiduals());\\n assertEquals(\\\"Set the minimum number of instances at which a node is considered for splitting. The default value is 15.\\\", lMT0.minNumInstancesTipText());\\n assertTrue(lMT0.getFastRegression());\\n assertEquals((-1), lMT0.getNumBoostingIterations());\\n assertFalse(string1.equals((Object)string0));\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a870f9aa2f079b9312dff496da2dab10\",\n \"score\": \"0.59200066\",\n \"text\": \"@Test\\n public void test51() throws Throwable {\\n Vote vote0 = new Vote();\\n assertEquals(\\\"The combination rule used.\\\", vote0.combinationRuleTipText());\\n assertEquals(\\\"The pre-built serialized classifiers to include. Multiple serialized classifiers can be included alongside those that are built from scratch when this classifier runs. Note that it does not make sense to include pre-built classifiers in a cross-validation since they are static and their models do not change from fold to fold.\\\", vote0.preBuiltClassifiersTipText());\\n assertFalse(vote0.getDebug());\\n assertEquals(\\\"The base classifiers to be used.\\\", vote0.classifiersTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", vote0.debugTipText());\\n assertEquals(1, vote0.getSeed());\\n assertEquals(\\\"The random number seed to be used.\\\", vote0.seedTipText());\\n assertNotNull(vote0);\\n assertEquals(5, vote0.MAX_RULE);\\n assertEquals(3, vote0.MAJORITY_VOTING_RULE);\\n assertEquals(4, vote0.MIN_RULE);\\n assertEquals(1, vote0.AVERAGE_RULE);\\n assertEquals(6, vote0.MEDIAN_RULE);\\n assertEquals(2, vote0.PRODUCT_RULE);\\n \\n Capabilities capabilities0 = vote0.getCapabilities();\\n assertEquals(\\\"The combination rule used.\\\", vote0.combinationRuleTipText());\\n assertEquals(\\\"The pre-built serialized classifiers to include. Multiple serialized classifiers can be included alongside those that are built from scratch when this classifier runs. Note that it does not make sense to include pre-built classifiers in a cross-validation since they are static and their models do not change from fold to fold.\\\", vote0.preBuiltClassifiersTipText());\\n assertFalse(vote0.getDebug());\\n assertEquals(\\\"The base classifiers to be used.\\\", vote0.classifiersTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", vote0.debugTipText());\\n assertEquals(1, vote0.getSeed());\\n assertEquals(\\\"The random number seed to be used.\\\", vote0.seedTipText());\\n assertEquals(0, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertNotNull(capabilities0);\\n assertEquals(5, vote0.MAX_RULE);\\n assertEquals(3, vote0.MAJORITY_VOTING_RULE);\\n assertEquals(4, vote0.MIN_RULE);\\n assertEquals(1, vote0.AVERAGE_RULE);\\n assertEquals(6, vote0.MEDIAN_RULE);\\n assertEquals(2, vote0.PRODUCT_RULE);\\n \\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertFalse(testInstances0.getMultiInstance());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(1, testInstances0.getNumRelationalString());\\n assertEquals(1, testInstances0.getNumRelationalNumeric());\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(1, testInstances0.getNumNumeric());\\n assertEquals(6, testInstances0.getNumAttributes());\\n assertEquals(1, testInstances0.getNumString());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals(-1, testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals(4, testInstances0.getNumClasses());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(1, testInstances0.getNumRelationalDate());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(1, testInstances0.getNumRelational());\\n assertEquals(1, testInstances0.getNumDate());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(\\\"The combination rule used.\\\", vote0.combinationRuleTipText());\\n assertEquals(\\\"The pre-built serialized classifiers to include. Multiple serialized classifiers can be included alongside those that are built from scratch when this classifier runs. Note that it does not make sense to include pre-built classifiers in a cross-validation since they are static and their models do not change from fold to fold.\\\", vote0.preBuiltClassifiersTipText());\\n assertFalse(vote0.getDebug());\\n assertEquals(\\\"The base classifiers to be used.\\\", vote0.classifiersTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", vote0.debugTipText());\\n assertEquals(1, vote0.getSeed());\\n assertEquals(\\\"The random number seed to be used.\\\", vote0.seedTipText());\\n assertEquals(0, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertNotNull(testInstances0);\\n assertEquals((-2), testInstances0.NO_CLASS);\\n assertEquals((-1), testInstances0.CLASS_IS_LAST);\\n assertEquals(5, vote0.MAX_RULE);\\n assertEquals(3, vote0.MAJORITY_VOTING_RULE);\\n assertEquals(4, vote0.MIN_RULE);\\n assertEquals(1, vote0.AVERAGE_RULE);\\n assertEquals(6, vote0.MEDIAN_RULE);\\n assertEquals(2, vote0.PRODUCT_RULE);\\n \\n Instances instances0 = testInstances0.generate(\\\"YBZq GriN<\\\");\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertFalse(testInstances0.getMultiInstance());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(1, testInstances0.getNumRelationalString());\\n assertEquals(1, testInstances0.getNumRelationalNumeric());\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(1, testInstances0.getNumNumeric());\\n assertEquals(6, testInstances0.getNumAttributes());\\n assertEquals(1, testInstances0.getNumString());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals(-1, testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals(4, testInstances0.getNumClasses());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(1, testInstances0.getNumRelationalDate());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(1, testInstances0.getNumRelational());\\n assertEquals(1, testInstances0.getNumDate());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(\\\"The combination rule used.\\\", vote0.combinationRuleTipText());\\n assertEquals(\\\"The pre-built serialized classifiers to include. Multiple serialized classifiers can be included alongside those that are built from scratch when this classifier runs. Note that it does not make sense to include pre-built classifiers in a cross-validation since they are static and their models do not change from fold to fold.\\\", vote0.preBuiltClassifiersTipText());\\n assertFalse(vote0.getDebug());\\n assertEquals(\\\"The base classifiers to be used.\\\", vote0.classifiersTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", vote0.debugTipText());\\n assertEquals(1, vote0.getSeed());\\n assertEquals(\\\"The random number seed to be used.\\\", vote0.seedTipText());\\n assertEquals(5, instances0.classIndex());\\n assertEquals(4, instances0.numClasses());\\n assertEquals(20, instances0.size());\\n assertTrue(instances0.checkForStringAttributes());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(6, instances0.numAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01D);\\n assertEquals(0, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertNotNull(instances0);\\n assertEquals((-2), testInstances0.NO_CLASS);\\n assertEquals((-1), testInstances0.CLASS_IS_LAST);\\n assertEquals(5, vote0.MAX_RULE);\\n assertEquals(3, vote0.MAJORITY_VOTING_RULE);\\n assertEquals(4, vote0.MIN_RULE);\\n assertEquals(1, vote0.AVERAGE_RULE);\\n assertEquals(6, vote0.MEDIAN_RULE);\\n assertEquals(2, vote0.PRODUCT_RULE);\\n \\n Evaluation evaluation0 = new Evaluation(instances0);\\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedTruePositiveRate(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedFalsePositiveRate(), 0.01D);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedPrecision(), 0.01D);\\n assertEquals(1.0, evaluation0.kappa(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01D);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedTrueNegativeRate(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01D);\\n assertEquals(0.0, evaluation0.numInstances(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedFMeasure(), 0.01D);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01D);\\n assertEquals(0.0, evaluation0.correct(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.unweightedMicroFmeasure(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedMatthewsCorrelation(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01D);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01D);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n assertEquals(0.0, evaluation0.unclassified(), 0.01D);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedFalseNegativeRate(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedRecall(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01D);\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertFalse(testInstances0.getMultiInstance());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(1, testInstances0.getNumRelationalString());\\n assertEquals(1, testInstances0.getNumRelationalNumeric());\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(1, testInstances0.getNumNumeric());\\n assertEquals(6, testInstances0.getNumAttributes());\\n assertEquals(1, testInstances0.getNumString());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals(-1, testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals(4, testInstances0.getNumClasses());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(1, testInstances0.getNumRelationalDate());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(1, testInstances0.getNumRelational());\\n assertEquals(1, testInstances0.getNumDate());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(\\\"The combination rule used.\\\", vote0.combinationRuleTipText());\\n assertEquals(\\\"The pre-built serialized classifiers to include. Multiple serialized classifiers can be included alongside those that are built from scratch when this classifier runs. Note that it does not make sense to include pre-built classifiers in a cross-validation since they are static and their models do not change from fold to fold.\\\", vote0.preBuiltClassifiersTipText());\\n assertFalse(vote0.getDebug());\\n assertEquals(\\\"The base classifiers to be used.\\\", vote0.classifiersTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", vote0.debugTipText());\\n assertEquals(1, vote0.getSeed());\\n assertEquals(\\\"The random number seed to be used.\\\", vote0.seedTipText());\\n assertEquals(5, instances0.classIndex());\\n assertEquals(4, instances0.numClasses());\\n assertEquals(20, instances0.size());\\n assertTrue(instances0.checkForStringAttributes());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(6, instances0.numAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01D);\\n assertEquals(0, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertNotNull(evaluation0);\\n assertEquals((-2), testInstances0.NO_CLASS);\\n assertEquals((-1), testInstances0.CLASS_IS_LAST);\\n assertEquals(5, vote0.MAX_RULE);\\n assertEquals(3, vote0.MAJORITY_VOTING_RULE);\\n assertEquals(4, vote0.MIN_RULE);\\n assertEquals(1, vote0.AVERAGE_RULE);\\n assertEquals(6, vote0.MEDIAN_RULE);\\n assertEquals(2, vote0.PRODUCT_RULE);\\n \\n double double0 = evaluation0.pctUnclassified();\\n assertEquals(Double.NaN, double0, 0.01D);\\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedTruePositiveRate(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedFalsePositiveRate(), 0.01D);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedPrecision(), 0.01D);\\n assertEquals(1.0, evaluation0.kappa(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01D);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedTrueNegativeRate(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01D);\\n assertEquals(0.0, evaluation0.numInstances(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedFMeasure(), 0.01D);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01D);\\n assertEquals(0.0, evaluation0.correct(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.unweightedMicroFmeasure(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedMatthewsCorrelation(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01D);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01D);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01D);\\n assertEquals(0.0, evaluation0.unclassified(), 0.01D);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedFalseNegativeRate(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.weightedRecall(), 0.01D);\\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01D);\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertFalse(testInstances0.getMultiInstance());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(1, testInstances0.getNumRelationalString());\\n assertEquals(1, testInstances0.getNumRelationalNumeric());\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(1, testInstances0.getNumNumeric());\\n assertEquals(6, testInstances0.getNumAttributes());\\n assertEquals(1, testInstances0.getNumString());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals(-1, testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals(4, testInstances0.getNumClasses());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(1, testInstances0.getNumRelationalDate());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(1, testInstances0.getNumRelational());\\n assertEquals(1, testInstances0.getNumDate());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(\\\"The combination rule used.\\\", vote0.combinationRuleTipText());\\n assertEquals(\\\"The pre-built serialized classifiers to include. Multiple serialized classifiers can be included alongside those that are built from scratch when this classifier runs. Note that it does not make sense to include pre-built classifiers in a cross-validation since they are static and their models do not change from fold to fold.\\\", vote0.preBuiltClassifiersTipText());\\n assertFalse(vote0.getDebug());\\n assertEquals(\\\"The base classifiers to be used.\\\", vote0.classifiersTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", vote0.debugTipText());\\n assertEquals(1, vote0.getSeed());\\n assertEquals(\\\"The random number seed to be used.\\\", vote0.seedTipText());\\n assertEquals(5, instances0.classIndex());\\n assertEquals(4, instances0.numClasses());\\n assertEquals(20, instances0.size());\\n assertTrue(instances0.checkForStringAttributes());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(6, instances0.numAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01D);\\n assertEquals(0, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals((-2), testInstances0.NO_CLASS);\\n assertEquals((-1), testInstances0.CLASS_IS_LAST);\\n assertEquals(5, vote0.MAX_RULE);\\n assertEquals(3, vote0.MAJORITY_VOTING_RULE);\\n assertEquals(4, vote0.MIN_RULE);\\n assertEquals(1, vote0.AVERAGE_RULE);\\n assertEquals(6, vote0.MEDIAN_RULE);\\n assertEquals(2, vote0.PRODUCT_RULE);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ebfacd1265a155f192ac5fc4091d27d\",\n \"score\": \"0.59177905\",\n \"text\": \"@Test(timeout = 4000)\\n public void test07() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.getUseEqualFrequency();\\n double[][] doubleArray0 = new double[9][8];\\n double[] doubleArray1 = new double[0];\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[8];\\n doubleArray2[0] = 2.0;\\n doubleArray2[1] = (-2083.37);\\n doubleArray2[2] = 2460.510197234556;\\n doubleArray2[3] = 1794.63394275;\\n doubleArray2[4] = 395.9359308698316;\\n doubleArray2[5] = 0.0;\\n doubleArray2[6] = 3138.168325;\\n doubleArray2[7] = 1.7976931348623157E308;\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[2];\\n doubleArray3[0] = 1794.63394275;\\n doubleArray3[1] = 0.0;\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[4];\\n doubleArray4[0] = 2460.510197234556;\\n doubleArray4[1] = 2460.510197234556;\\n doubleArray4[2] = (-2083.37);\\n doubleArray4[3] = 2.0;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[4];\\n doubleArray5[0] = 395.9359308698316;\\n doubleArray5[1] = (-2083.37);\\n doubleArray5[2] = 395.9359308698316;\\n doubleArray5[3] = 1.7976931348623157E308;\\n doubleArray0[4] = doubleArray5;\\n double[] doubleArray6 = new double[8];\\n doubleArray6[0] = 2.0;\\n doubleArray6[1] = 0.0;\\n doubleArray6[2] = 3138.168325;\\n doubleArray6[3] = 0.0;\\n doubleArray6[4] = 2.0;\\n doubleArray6[5] = (-851.947634);\\n doubleArray6[6] = 2460.510197234556;\\n doubleArray6[7] = 2460.510197234556;\\n doubleArray0[5] = doubleArray6;\\n double[] doubleArray7 = new double[8];\\n doubleArray7[0] = 0.0;\\n doubleArray7[1] = 1.7976931348623157E308;\\n doubleArray7[2] = 0.0;\\n doubleArray7[3] = 395.9359308698316;\\n doubleArray7[4] = 3138.168325;\\n doubleArray7[5] = 395.9359308698316;\\n doubleArray7[6] = 1794.63394275;\\n doubleArray7[7] = (-2083.37);\\n doubleArray0[6] = doubleArray7;\\n double[] doubleArray8 = new double[5];\\n doubleArray8[0] = 0.0;\\n doubleArray8[1] = 3138.168325;\\n doubleArray8[2] = 0.0;\\n doubleArray8[3] = 2460.510197234556;\\n doubleArray8[4] = 2.0;\\n doubleArray0[7] = doubleArray8;\\n double[] doubleArray9 = new double[9];\\n doubleArray9[0] = 3138.168325;\\n doubleArray9[1] = 0.0;\\n doubleArray9[2] = 2808.287974275146;\\n doubleArray9[3] = 0.0;\\n doubleArray9[4] = 1794.63394275;\\n doubleArray9[5] = 1140.36509;\\n doubleArray9[6] = (-851.947634);\\n doubleArray9[7] = 395.9359308698316;\\n doubleArray9[8] = (-2083.37);\\n doubleArray0[8] = doubleArray9;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.getInvertSelection();\\n discretize0.setMakeBinary(false);\\n discretize0.m_UseBinNumbers = false;\\n Filter.makeCopies(discretize0, 3512);\\n discretize0.setUseEqualFrequency(false);\\n discretize0.setUseEqualFrequency(false);\\n Filter.makeCopies(discretize0, 3512);\\n discretize0.desiredWeightOfInstancesPerIntervalTipText();\\n discretize0.findNumBinsTipText();\\n discretize0.getOptions();\\n int int0 = 0;\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning(0);\\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 \"title\": \"\"\n },\n {\n \"docid\": \"9f7a4026acff73e5fb25fc212e08602f\",\n \"score\": \"0.59121954\",\n \"text\": \"@Test(timeout = 4000)\\n public void test089() throws Throwable {\\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n resultMatrixSignificance0.setMeanPrec((-1413));\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n boolean boolean0 = resultMatrixSignificance0.getDefaultShowStdDev();\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertFalse(boolean0);\\n \\n String string0 = resultMatrixSignificance0.significanceWidthTipText();\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertNotNull(string0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", string0);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf71c89060ca7f1a2ed9816a0f636b9b\",\n \"score\": \"0.59112376\",\n \"text\": \"@Test(timeout = 4000)\\n public void test005() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n double double0 = evaluation0.weightedAreaUnderROC();\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, double0, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce5081703f42b8b0bde3956100227be0\",\n \"score\": \"0.59020966\",\n \"text\": \"public static void doCase14() throws IOException {\\n LearningUnit unit = new LearningUnit(\\\"hana_t\\\", \\\"hana_p\\\", \\\"hana_v\\\");\\r\\n //LearningUnit unit = new LearningUnit(\\\"n_hana_t\\\", \\\"n_hana_p\\\", \\\"n_hana_v\\\");\\r\\n //System.out.println(\\\"[1 generation] unit.getModelPrediction( 2, 5, 140, true);\\\");\\r\\n //unit.getModelPrediction( 2, 2, 5, 140, false);\\r\\n //System.out.println(\\\"[2 generation] unit.getModelPrediction( 2, 5, 140, true);\\\");\\r\\n //unit.getModelPrediction( 2, 3, 10, 100, true);\\r\\n\\r\\n //System.out.println(\\\"[2 generation] unit.getModelPrediction( 0, 16.07936507936508d, 157.26392961876832d, 49.826001955034215d, false); \\\");\\r\\n //unit.getModelPrediction( 0, 16.07936507936508d, 157.26392961876832d, 49.826001955034215d, false);\\r\\n //System.out.println(\\\"[2 generation] unit.getModelPrediction( 0, 5, 198, 165, false);\\\");\\r\\n //unit.getModelPrediction( 2, 5, 198, 165, false, null, new int[0], new int[0]);\\r\\n// System.out.println(\\\"doCase13 _ftest1_\\\");\\r\\n// int[] _exclude_feature = new int[3];\\r\\n// _exclude_feature[0] = 0;\\r\\n// _exclude_feature[1] = 1;\\r\\n// _exclude_feature[2] = 2;\\r\\n// unit.getModelPrediction( 2, 30, 121.606061d, 170.237537d, false, \\\"_ftest1_\\\", _exclude_feature, new int[0]);\\r\\n//\\r\\n// int[] _exclude_feature2 = new int[2];\\r\\n// _exclude_feature2[0] = 0;\\r\\n// _exclude_feature2[1] = 1;\\r\\n// System.out.println(\\\"doCase13 _ftest2_\\\");\\r\\n// unit.getModelPrediction( 2, 30, 121.606061d, 170.237537d, false, \\\"_ftest2_\\\", _exclude_feature2, new int[0]);\\r\\n\\r\\n System.out.println(\\\"doCase14 all\\\");\\r\\n// int[] _exclude_instance1 = new int[1];\\r\\n// for (int i = 0; i < _exclude_instance1.length; i++) {\\r\\n// _exclude_instance1[i] = i;\\r\\n// }\\r\\n int[] _features = new int[4];\\r\\n _features[0] = 24;\\r\\n _features[1] = 27;\\r\\n _features[2] = 28;\\r\\n _features[3] = 36;\\r\\n\\r\\n int[] _instances = new int[3];\\r\\n _instances[0] = 1;\\r\\n _instances[1] = 4;\\r\\n _instances[2] = 9;\\r\\n //unit.getModelPrediction( 2, 30, 121.606061d, 170.237537d, false, \\\"_ftest2_\\\", _features, new int[0]);\\r\\n //unit.getModelPrediction( 2, 30, 121.606061d, 170.237537d, false, \\\"_ftest2_\\\", new int[0], new int[0]);\\r\\n unit.getModelPrediction( 2, 11, 21.50588d, 43.92157d, false, \\\"_ex4_\\\", _features, _instances);\\r\\n //unit.getModelPrediction( 2, 30, 121.606061d, 170.237537d, false, \\\"_ftest2_\\\", new int[0], _exclude_instance1);\\r\\n\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c2c5e1da1095f1b5dcf0e9b0ffca081\",\n \"score\": \"0.5901615\",\n \"text\": \"@Test(timeout = 4000)\\n public void test81() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n assertNotNull(testInstances0);\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(0, testInstances0.getNumRelationalNumeric());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals(1, testInstances0.getSeed());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n \\n boolean boolean0 = FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\" \\\");\\n assertFalse(boolean0);\\n \\n GaussianProcesses gaussianProcesses0 = new GaussianProcesses();\\n assertNotNull(gaussianProcesses0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n \\n Capabilities capabilities0 = gaussianProcesses0.getCapabilities();\\n assertNotNull(capabilities0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n \\n TestInstances testInstances1 = TestInstances.forCapabilities(capabilities0);\\n assertNotNull(testInstances1);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n Instances instances0 = testInstances1.generate();\\n assertNotNull(instances0);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(20, instances0.size());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n String string0 = instances0.getRevision();\\n assertNotNull(string0);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(20, instances0.size());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals(\\\"9186\\\", string0);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n Evaluation evaluation0 = new Evaluation(instances0);\\n assertNotNull(evaluation0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(20, instances0.size());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01);\\n assertEquals(0.0, evaluation0.numInstances(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n double double0 = evaluation0.m_Correct;\\n assertEquals(0.0, double0, 0.01);\\n \\n testInstances0.setNumRelationalNumeric(35);\\n assertNotSame(testInstances0, testInstances1);\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(35, testInstances0.getNumRelationalNumeric());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals(1, testInstances0.getSeed());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertFalse(testInstances0.equals((Object)testInstances1));\\n \\n FileSystemHandling fileSystemHandling0 = new FileSystemHandling();\\n assertNotNull(fileSystemHandling0);\\n \\n double[] doubleArray0 = new double[4];\\n doubleArray0[0] = (double) (-1);\\n doubleArray0[1] = (double) 2;\\n doubleArray0[2] = 1.0E-5;\\n doubleArray0[3] = (double) (-1);\\n SparseInstance sparseInstance0 = new SparseInstance(1.0E-5, doubleArray0);\\n assertNotNull(sparseInstance0);\\n assertEquals(4, doubleArray0.length);\\n assertEquals(4, sparseInstance0.numValues());\\n assertEquals(4, sparseInstance0.numAttributes());\\n assertEquals(1.0E-5, sparseInstance0.weight(), 0.01);\\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\\n assertArrayEquals(new double[] {(-1.0), 2.0, 1.0E-5, (-1.0)}, doubleArray0, 0.01);\\n \\n sparseInstance0.setDataset(instances0);\\n assertEquals(4, doubleArray0.length);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(4, sparseInstance0.numValues());\\n assertEquals(4, sparseInstance0.numAttributes());\\n assertEquals(2, sparseInstance0.classIndex());\\n assertEquals(1, sparseInstance0.numClasses());\\n assertEquals(1.0E-5, sparseInstance0.weight(), 0.01);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(20, instances0.size());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertArrayEquals(new double[] {(-1.0), 2.0, 1.0E-5, (-1.0)}, doubleArray0, 0.01);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n Evaluation evaluation1 = new Evaluation(instances0, (CostMatrix) null);\\n assertNotNull(evaluation1);\\n assertEquals(Double.NaN, evaluation1.pctCorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation1.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation1.SFPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation1.meanPriorAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation1.correct(), 0.01);\\n assertEquals(Double.NaN, evaluation1.SFMeanEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation1.rootRelativeSquaredError(), 0.01);\\n assertEquals(0.0, evaluation1.unclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation1.sizeOfPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation1.numInstances(), 0.01);\\n assertEquals(0.0, evaluation1.SFSchemeEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation1.avgCost(), 0.01);\\n assertEquals(Double.NaN, evaluation1.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation1.errorRate(), 0.01);\\n assertEquals(Double.NaN, evaluation1.pctUnclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation1.pctIncorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation1.SFMeanPriorEntropy(), 0.01);\\n assertEquals(0.0, evaluation1.incorrect(), 0.01);\\n assertFalse(evaluation1.getDiscardPredictions());\\n assertEquals(Double.NaN, evaluation1.relativeAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation1.meanAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation1.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(0.0, evaluation1.totalCost(), 0.01);\\n assertEquals(Double.NaN, evaluation1.rootMeanSquaredError(), 0.01);\\n assertEquals(0.0, evaluation1.SFEntropyGain(), 0.01);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(20, instances0.size());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertTrue(evaluation1.equals((Object)evaluation0));\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n evaluation1.updatePriors(sparseInstance0);\\n assertEquals(4, doubleArray0.length);\\n assertNotSame(evaluation1, evaluation0);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(4, sparseInstance0.numValues());\\n assertEquals(4, sparseInstance0.numAttributes());\\n assertEquals(2, sparseInstance0.classIndex());\\n assertEquals(1, sparseInstance0.numClasses());\\n assertEquals(1.0E-5, sparseInstance0.weight(), 0.01);\\n assertEquals(Double.NaN, evaluation1.pctCorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation1.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation1.SFPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation1.meanPriorAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation1.correct(), 0.01);\\n assertEquals(Double.NaN, evaluation1.SFMeanEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation1.rootRelativeSquaredError(), 0.01);\\n assertEquals(0.0, evaluation1.unclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation1.sizeOfPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation1.numInstances(), 0.01);\\n assertEquals(0.0, evaluation1.SFSchemeEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation1.avgCost(), 0.01);\\n assertEquals(Double.NaN, evaluation1.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation1.errorRate(), 0.01);\\n assertEquals(Double.NaN, evaluation1.pctUnclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation1.pctIncorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation1.SFMeanPriorEntropy(), 0.01);\\n assertEquals(0.0, evaluation1.incorrect(), 0.01);\\n assertFalse(evaluation1.getDiscardPredictions());\\n assertEquals(Double.NaN, evaluation1.relativeAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation1.meanAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation1.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(0.0, evaluation1.totalCost(), 0.01);\\n assertEquals(Double.NaN, evaluation1.rootMeanSquaredError(), 0.01);\\n assertEquals(0.0, evaluation1.SFEntropyGain(), 0.01);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(20, instances0.size());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertArrayEquals(new double[] {(-1.0), 2.0, 1.0E-5, (-1.0)}, doubleArray0, 0.01);\\n assertTrue(evaluation1.equals((Object)evaluation0));\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n double double1 = evaluation0.m_SumErr;\\n assertEquals(0.0, double1, 0.01);\\n assertEquals(double1, double0, 0.01);\\n \\n try { \\n evaluation0.toClassDetailsString(\\\"weka/core/Capabilities.props\\\");\\n fail(\\\"Expecting exception: Exception\\\");\\n \\n } catch(Exception e) {\\n //\\n // Evaluation: No per class statistics possible!\\n //\\n verifyException(\\\"weka.classifiers.Evaluation\\\", e);\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e334f6172144d784a38a8c988c9c4ed2\",\n \"score\": \"0.5900304\",\n \"text\": \"@Test\\n public void test_method_getNumValues_6_branch_0()\\n {\\n System.out.println(\\\"Now Testing Method:getNumValues Branch:0\\\");\\n\\n //Constructor\\n StatisticUtils instance = new StatisticUtils();\\n\\n instance.add(1.0);\\n\\n //Check Test Verification Points\\n assertEquals(1, instance.getNumValues(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c1c7eba9604f64cf85b3ae55305a6c\",\n \"score\": \"0.58881503\",\n \"text\": \"@Test\\n public void costTest() {\\n // TODO: test cost\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36f6f0bfb3bd5e62312fd6f8f611cc06\",\n \"score\": \"0.5883104\",\n \"text\": \"@Test(timeout = 4000)\\n public void test21() throws Throwable {\\n boolean boolean0 = FileSystemHandling.shouldAllThrowIOExceptions();\\n assertTrue(boolean0);\\n \\n Discretize discretize0 = new Discretize(\\\"UY;p<f;]\\\");\\n assertNotNull(discretize0);\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseBinNumbers());\\n \\n discretize0.m_FindNumBins = true;\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertTrue(discretize0.getFindNumBins());\\n \\n discretize0.setOutputFormat();\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertTrue(discretize0.getFindNumBins());\\n \\n SystemInUtil.addInputLine(\\\"UY;p<f;]\\\");\\n discretize0.setIgnoreClass(true);\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertTrue(discretize0.getIgnoreClass());\\n assertTrue(discretize0.getFindNumBins());\\n \\n String[] stringArray0 = discretize0.getOptions();\\n assertNotNull(stringArray0);\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertTrue(discretize0.getIgnoreClass());\\n assertTrue(discretize0.getFindNumBins());\\n assertEquals(8, stringArray0.length);\\n \\n discretize0.setOutputFormat();\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertTrue(discretize0.isNewBatch());\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(10, discretize0.getBins());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertTrue(discretize0.getIgnoreClass());\\n assertTrue(discretize0.getFindNumBins());\\n \\n System.setCurrentTimeMillis(2L);\\n int int0 = 2;\\n // Undeclared exception!\\n try { \\n discretize0.findNumBins(2);\\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 \"title\": \"\"\n },\n {\n \"docid\": \"dba79695cbe8a646e7d64d20d6d00a48\",\n \"score\": \"0.5883055\",\n \"text\": \"@Test(timeout = 4000)\\n public void test10() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n testInstances0.generate();\\n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\\n GaussianProcesses gaussianProcesses0 = new GaussianProcesses();\\n CostMatrix costMatrix0 = new CostMatrix(2);\\n Instances instances0 = testInstances0.generate();\\n ConverterUtils.DataSource converterUtils_DataSource0 = new ConverterUtils.DataSource(instances0);\\n Instance instance0 = converterUtils_DataSource0.nextElement(instances0);\\n Evaluation evaluation0 = new Evaluation(instances0, costMatrix0);\\n evaluation0.m_ClassPriors = null;\\n C45Loader c45Loader0 = new C45Loader();\\n costSensitiveClassifier0.distributionForInstance(instance0);\\n evaluation0.SFMeanEntropyGain();\\n SerializedInstancesLoader serializedInstancesLoader0 = new SerializedInstancesLoader();\\n try { \\n serializedInstancesLoader0.getStructure();\\n fail(\\\"Expecting exception: IOException\\\");\\n \\n } catch(IOException e) {\\n //\\n // No source has been specified\\n //\\n verifyException(\\\"weka.core.converters.SerializedInstancesLoader\\\", e);\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47fd59da3f40be237f6a9820f6defd42\",\n \"score\": \"0.5880111\",\n \"text\": \"@Test(timeout = 4000)\\n public void test82() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n assertNotNull(testInstances0);\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertEquals(0, testInstances0.getNumRelationalNumeric());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n \\n EvoSuiteFile evoSuiteFile0 = new EvoSuiteFile(\\\"/home/ubuntu/wekafiles/props\\\");\\n boolean boolean0 = FileSystemHandling.appendStringToFile(evoSuiteFile0, \\\" \\\");\\n assertTrue(boolean0);\\n \\n GaussianProcesses gaussianProcesses0 = new GaussianProcesses();\\n assertNotNull(gaussianProcesses0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n \\n Capabilities capabilities0 = gaussianProcesses0.getCapabilities();\\n assertNotNull(capabilities0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n \\n TestInstances testInstances1 = TestInstances.forCapabilities(capabilities0);\\n assertNotNull(testInstances1);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n Instances instances0 = testInstances1.generate();\\n assertNotNull(instances0);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(20, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n String string0 = instances0.getRevision();\\n assertNotNull(string0);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(20, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals(\\\"9186\\\", string0);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n Evaluation evaluation0 = new Evaluation(instances0);\\n assertNotNull(evaluation0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(20, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation0.numInstances(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n evaluation0.addNumericTrainClass((-2), (-1190.7));\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(20, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation0.numInstances(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n boolean boolean1 = FileSystemHandling.shouldAllThrowIOExceptions();\\n assertTrue(boolean1);\\n assertTrue(boolean1 == boolean0);\\n \\n double double0 = evaluation0.m_Correct;\\n assertEquals(0.0, double0, 0.01);\\n \\n testInstances1.setNumRelationalNumeric(0);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(0, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n double[] doubleArray0 = new double[3];\\n doubleArray0[0] = (-1190.7);\\n doubleArray0[1] = (double) 1;\\n FileSystemHandling fileSystemHandling0 = new FileSystemHandling();\\n assertNotNull(fileSystemHandling0);\\n \\n doubleArray0[2] = (double) 2;\\n SparseInstance sparseInstance0 = new SparseInstance((-931.1138872916), doubleArray0);\\n assertNotNull(sparseInstance0);\\n assertEquals(3, doubleArray0.length);\\n assertEquals(3, sparseInstance0.numValues());\\n assertEquals((-931.1138872916), sparseInstance0.weight(), 0.01);\\n assertEquals(3, sparseInstance0.numAttributes());\\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\\n assertArrayEquals(new double[] {(-1190.7), 1.0, 2.0}, doubleArray0, 0.01);\\n \\n sparseInstance0.setDataset(instances0);\\n assertEquals(3, doubleArray0.length);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(1, sparseInstance0.numClasses());\\n assertEquals(3, sparseInstance0.numValues());\\n assertEquals((-931.1138872916), sparseInstance0.weight(), 0.01);\\n assertEquals(3, sparseInstance0.numAttributes());\\n assertEquals(2, sparseInstance0.classIndex());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(0, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(20, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertArrayEquals(new double[] {(-1190.7), 1.0, 2.0}, doubleArray0, 0.01);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n double double1 = evaluation0.evaluationForSingleInstance(doubleArray0, sparseInstance0, true);\\n assertEquals(3, doubleArray0.length);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(1, sparseInstance0.numClasses());\\n assertEquals(3, sparseInstance0.numValues());\\n assertEquals((-931.1138872916), sparseInstance0.weight(), 0.01);\\n assertEquals(3, sparseInstance0.numAttributes());\\n assertEquals(2, sparseInstance0.classIndex());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(0, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(20, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(1192.7, evaluation0.rootMeanSquaredError(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(-0.0, evaluation0.pctCorrect(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(1192.7, evaluation0.errorRate(), 0.01);\\n assertEquals((-931.1138872916), evaluation0.numInstances(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(1192.7, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(-0.0, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(78017.98855939323, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertEquals(-0.0, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(1.5287499998696148, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(1.5287499998696148, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(-0.0, evaluation0.avgCost(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(78017.98855939323, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(-0.0, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(-0.0, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1190.7), double1, 0.01);\\n assertArrayEquals(new double[] {(-1190.7), 1.0, 2.0}, doubleArray0, 0.01);\\n assertNotEquals(double1, double0, 0.01);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n double double2 = evaluation0.correlationCoefficient();\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(0, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(20, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(1192.7, evaluation0.rootMeanSquaredError(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(-0.0, evaluation0.pctCorrect(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(1192.7, evaluation0.errorRate(), 0.01);\\n assertEquals((-931.1138872916), evaluation0.numInstances(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(1192.7, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(-0.0, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(78017.98855939323, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertEquals(-0.0, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(1.5287499998696148, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(1.5287499998696148, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(-0.0, evaluation0.avgCost(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(78017.98855939323, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(-0.0, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(-0.0, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals(0.0, double2, 0.01);\\n assertEquals(double2, double0, 0.01);\\n assertNotEquals(double2, double1, 0.01);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n double double3 = evaluation0.errorRate();\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(0, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(20, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(1192.7, evaluation0.rootMeanSquaredError(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(-0.0, evaluation0.pctCorrect(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(1192.7, evaluation0.errorRate(), 0.01);\\n assertEquals((-931.1138872916), evaluation0.numInstances(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(1192.7, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(-0.0, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(78017.98855939323, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertEquals(-0.0, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(1.5287499998696148, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(1.5287499998696148, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(-0.0, evaluation0.avgCost(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(78017.98855939323, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(-0.0, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(-0.0, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals(1192.7, double3, 0.01);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n assertNotEquals(double3, double2, 0.01);\\n assertNotEquals(double3, double0, 0.01);\\n assertNotEquals(double3, double1, 0.01);\\n \\n double double4 = evaluation0.SFMeanEntropyGain();\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(0, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(20, instances0.numInstances());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(1192.7, evaluation0.rootMeanSquaredError(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(-0.0, evaluation0.pctCorrect(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(1192.7, evaluation0.errorRate(), 0.01);\\n assertEquals((-931.1138872916), evaluation0.numInstances(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(1192.7, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(-0.0, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(78017.98855939323, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertEquals(-0.0, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(1.5287499998696148, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(1.5287499998696148, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(-0.0, evaluation0.avgCost(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(78017.98855939323, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(-0.0, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertEquals(-0.0, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(-0.0, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals(-0.0, double4, 0.01);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n assertNotEquals(double4, double3, 0.01);\\n assertEquals(double4, double0, 0.01);\\n assertEquals(double4, double2, 0.01);\\n assertNotEquals(double4, double1, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89707bdd146c2bb33004800ec4946416\",\n \"score\": \"0.5878557\",\n \"text\": \"@Test\\n public void testCalculateProbability2() {\\n this.repository.save(new Document(\\\"lasku\\\", 5L, \\\"viitenumero\\\"));\\n this.repository.save(new Document(\\\"lasku\\\", 5L, \\\"IBAN\\\"));\\n this.repository.save(new Document(\\\"saft\\\", 1L, \\\"viitenumero\\\"));\\n this.repository.save(new Document(\\\"saft\\\", 6L, \\\"selvitys\\\"));\\n this.repository.save(new Document(\\\"saft\\\", 4L, \\\"annetusta\\\"));\\n List<String> words = new ArrayList<>(Arrays.asList(\\n \\\"IBAN\\\", \\\"selvitys\\\"));\\n \\n HashMap<String, Long> counts = inst.preprocessTotalCounts();\\n \\n double result1 = inst.calculateProbability(words, \\\"lasku\\\", counts);\\n double result2 = inst.calculateProbability(words, \\\"saft\\\", counts);\\n assertTrue(result1 < result2);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"935fa3c09aede380233e60792678202f\",\n \"score\": \"0.5874469\",\n \"text\": \"@Test\\n public void estimateTest() {\\n // TODO: test estimate\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6462c742b16d0e9f3578077fb6a21844\",\n \"score\": \"0.5871443\",\n \"text\": \"@Test(timeout = 4000)\\n public void test20() throws Throwable {\\n boolean boolean0 = FileSystemHandling.setPermissions((EvoSuiteFile) null, true, true, true);\\n assertFalse(boolean0);\\n \\n Discretize discretize0 = new Discretize(\\\" \\\\\\\"3XB^u3'zgS\\\");\\n assertNotNull(discretize0);\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n \\n String[] stringArray0 = discretize0.getOptions();\\n assertNotNull(stringArray0);\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(6, stringArray0.length);\\n \\n discretize0.setFindNumBins(false);\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n \\n Filter.runFilter(discretize0, stringArray0);\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n assertEquals(6, stringArray0.length);\\n \\n double[][] doubleArray0 = new double[9][0];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[0] = (-2415.169742893789);\\n doubleArray1[1] = 0.0;\\n doubleArray1[2] = 1575.41;\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[5];\\n assertFalse(doubleArray2.equals((Object)doubleArray1));\\n \\n doubleArray2[0] = 0.0;\\n doubleArray2[1] = 1575.41;\\n doubleArray2[2] = 0.0;\\n doubleArray2[3] = 1575.41;\\n doubleArray2[4] = 1575.41;\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[8];\\n assertFalse(doubleArray3.equals((Object)doubleArray1));\\n assertFalse(doubleArray3.equals((Object)doubleArray2));\\n \\n doubleArray3[0] = 1575.41;\\n doubleArray3[1] = 1575.41;\\n doubleArray3[2] = 0.0;\\n doubleArray3[3] = 0.0;\\n doubleArray3[4] = 0.0;\\n doubleArray3[5] = 0.0;\\n doubleArray3[6] = 0.0;\\n doubleArray3[7] = 0.0;\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[3];\\n assertFalse(doubleArray4.equals((Object)doubleArray2));\\n assertFalse(doubleArray4.equals((Object)doubleArray3));\\n assertFalse(doubleArray4.equals((Object)doubleArray1));\\n \\n doubleArray4[0] = 0.0;\\n doubleArray4[1] = 0.0;\\n doubleArray4[2] = 2436.8;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[2];\\n assertFalse(doubleArray5.equals((Object)doubleArray4));\\n assertFalse(doubleArray5.equals((Object)doubleArray2));\\n assertFalse(doubleArray5.equals((Object)doubleArray1));\\n assertFalse(doubleArray5.equals((Object)doubleArray3));\\n \\n doubleArray5[0] = 0.0;\\n doubleArray5[1] = 1575.41;\\n doubleArray0[4] = doubleArray5;\\n double[] doubleArray6 = new double[6];\\n assertFalse(doubleArray6.equals((Object)doubleArray3));\\n assertFalse(doubleArray6.equals((Object)doubleArray5));\\n assertFalse(doubleArray6.equals((Object)doubleArray4));\\n assertFalse(doubleArray6.equals((Object)doubleArray1));\\n assertFalse(doubleArray6.equals((Object)doubleArray2));\\n \\n doubleArray6[0] = 1575.41;\\n doubleArray6[1] = 0.0;\\n doubleArray6[2] = 0.0;\\n doubleArray6[3] = 0.0;\\n doubleArray6[4] = (-2415.169742893789);\\n doubleArray6[5] = 0.0;\\n doubleArray0[5] = doubleArray6;\\n double[] doubleArray7 = new double[7];\\n assertFalse(doubleArray7.equals((Object)doubleArray3));\\n assertFalse(doubleArray7.equals((Object)doubleArray1));\\n assertFalse(doubleArray7.equals((Object)doubleArray6));\\n assertFalse(doubleArray7.equals((Object)doubleArray5));\\n assertFalse(doubleArray7.equals((Object)doubleArray4));\\n assertFalse(doubleArray7.equals((Object)doubleArray2));\\n \\n doubleArray7[0] = 151.20495842;\\n doubleArray7[1] = 2436.8;\\n doubleArray7[2] = 2436.8;\\n doubleArray7[3] = 2436.8;\\n doubleArray7[4] = 0.0;\\n doubleArray7[5] = 0.0;\\n doubleArray7[6] = (-2415.169742893789);\\n doubleArray0[6] = doubleArray7;\\n double[] doubleArray8 = new double[5];\\n assertFalse(doubleArray8.equals((Object)doubleArray3));\\n assertFalse(doubleArray8.equals((Object)doubleArray1));\\n assertFalse(doubleArray8.equals((Object)doubleArray6));\\n assertFalse(doubleArray8.equals((Object)doubleArray5));\\n assertFalse(doubleArray8.equals((Object)doubleArray4));\\n assertFalse(doubleArray8.equals((Object)doubleArray2));\\n assertFalse(doubleArray8.equals((Object)doubleArray7));\\n \\n doubleArray8[0] = 0.0;\\n doubleArray8[1] = 0.0;\\n doubleArray8[2] = 2436.8;\\n doubleArray8[3] = 0.0;\\n doubleArray8[4] = 0.0;\\n doubleArray0[7] = doubleArray8;\\n double[] doubleArray9 = new double[4];\\n assertFalse(doubleArray9.equals((Object)doubleArray1));\\n assertFalse(doubleArray9.equals((Object)doubleArray7));\\n assertFalse(doubleArray9.equals((Object)doubleArray8));\\n assertFalse(doubleArray9.equals((Object)doubleArray2));\\n assertFalse(doubleArray9.equals((Object)doubleArray3));\\n assertFalse(doubleArray9.equals((Object)doubleArray6));\\n assertFalse(doubleArray9.equals((Object)doubleArray5));\\n assertFalse(doubleArray9.equals((Object)doubleArray4));\\n \\n doubleArray9[0] = 0.0;\\n doubleArray9[1] = 2436.8;\\n doubleArray9[2] = 0.0;\\n doubleArray9[3] = 2436.8;\\n doubleArray0[8] = doubleArray9;\\n discretize0.m_CutPoints = doubleArray0;\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getIgnoreClass());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(\\\"An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.\\\", discretize0.globalInfo());\\n assertEquals(\\\"Make resulting attributes binary.\\\", discretize0.makeBinaryTipText());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.isOutputFormatDefined());\\n assertEquals(\\\"If set to true, equal-frequency binning will be used instead of equal-width binning.\\\", discretize0.useEqualFrequencyTipText());\\n assertEquals(\\\"Number of bins.\\\", discretize0.binsTipText());\\n assertFalse(discretize0.getFindNumBins());\\n assertTrue(discretize0.isNewBatch());\\n assertFalse(discretize0.mayRemoveInstanceAfterFirstBatchDone());\\n assertEquals(\\\"Specify range of attributes to act on. This is a comma separated list of attribute indices, with \\\\\\\"first\\\\\\\" and \\\\\\\"last\\\\\\\" valid values. Specify an inclusive range with \\\\\\\"-\\\\\\\". E.g: \\\\\\\"first-3,5,6-10,last\\\\\\\".\\\", discretize0.attributeIndicesTipText());\\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.\\\", discretize0.invertSelectionTipText());\\n assertEquals(\\\"Sets the desired weight of instances per interval for equal-frequency binning.\\\", discretize0.desiredWeightOfInstancesPerIntervalTipText());\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", discretize0.findNumBinsTipText());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Use bin numbers (eg BXofY) rather than ranges for for discretized attributes\\\", discretize0.useBinNumbersTipText());\\n assertEquals(\\\"The class index will be unset temporarily before the filter is applied.\\\", discretize0.ignoreClassTipText());\\n assertFalse(discretize0.isFirstBatchDone());\\n \\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints(3424);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 3424\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75ba9898a7088053bac6f64b52a29b26\",\n \"score\": \"0.5867155\",\n \"text\": \"@Test(timeout = 4000)\\n public void test075() throws Throwable {\\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance(10, 10);\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\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 display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n ResultMatrixPlainText resultMatrixPlainText0 = new ResultMatrixPlainText(resultMatrixSignificance0);\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\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 display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(0, resultMatrixPlainText0.getCountWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(10, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getPrintColNames());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(10, resultMatrixPlainText0.getVisibleRowCount());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(10, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(40, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertEquals(10, resultMatrixPlainText0.getColCount());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertNotNull(resultMatrixPlainText0);\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n resultMatrixPlainText0.m_MeanWidth = 10;\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\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 display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(0, resultMatrixPlainText0.getCountWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(10, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getPrintColNames());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(10, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(10, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(10, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(40, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertEquals(10, resultMatrixPlainText0.getColCount());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n \\n ResultMatrixSignificance resultMatrixSignificance1 = new ResultMatrixSignificance(resultMatrixPlainText0);\\n assertFalse(resultMatrixSignificance1.equals((Object)resultMatrixSignificance0));\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\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 display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(0, resultMatrixPlainText0.getCountWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(10, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getPrintColNames());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(10, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(10, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(10, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(40, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertEquals(10, resultMatrixPlainText0.getColCount());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance1.countWidthTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultSignificanceWidth());\\n assertEquals(2, resultMatrixSignificance1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance1.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance1.showAverageTipText());\\n assertTrue(resultMatrixSignificance1.getPrintRowNames());\\n assertEquals(10, resultMatrixSignificance1.getVisibleColCount());\\n assertFalse(resultMatrixSignificance1.getDefaultRemoveFilterName());\\n assertEquals(10, resultMatrixSignificance1.getColCount());\\n assertEquals(2, resultMatrixSignificance1.getDefaultStdDevPrec());\\n assertEquals(0, resultMatrixSignificance1.getDefaultColNameWidth());\\n assertEquals(40, resultMatrixSignificance1.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance1.meanPrecTipText());\\n assertFalse(resultMatrixSignificance1.getDefaultPrintColNames());\\n assertEquals(10, resultMatrixSignificance1.getMeanWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance1.colNameWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultCountWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultMeanWidth());\\n assertFalse(resultMatrixSignificance1.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance1.getRemoveFilterName());\\n assertTrue(resultMatrixSignificance1.getDefaultEnumerateColNames());\\n assertEquals(40, resultMatrixSignificance1.getRowNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateRowNamesTipText());\\n assertTrue(resultMatrixSignificance1.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance1.meanWidthTipText());\\n assertEquals(2, resultMatrixSignificance1.getStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance1.getDisplayName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance1.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance1.significanceWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance1.showStdDevTipText());\\n assertEquals(10, resultMatrixSignificance1.getRowCount());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance1.globalInfo());\\n assertEquals(0, resultMatrixSignificance1.getStdDevWidth());\\n assertFalse(resultMatrixSignificance1.getShowAverage());\\n assertEquals(10, resultMatrixSignificance1.getVisibleRowCount());\\n assertTrue(resultMatrixSignificance1.getEnumerateColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance1.stdDevPrecTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance1.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance1.getColNameWidth());\\n assertFalse(resultMatrixSignificance1.getPrintColNames());\\n assertEquals(0, resultMatrixSignificance1.getCountWidth());\\n assertEquals(0, resultMatrixSignificance1.getSignificanceWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance1.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance1.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance1.getMeanPrec());\\n assertNotNull(resultMatrixSignificance1);\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n String string0 = resultMatrixPlainText0.getRevision();\\n assertFalse(resultMatrixSignificance0.equals((Object)resultMatrixSignificance1));\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\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 display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(0, resultMatrixPlainText0.getCountWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(10, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getPrintColNames());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(10, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(10, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(10, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(40, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertEquals(10, resultMatrixPlainText0.getColCount());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertNotNull(string0);\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"8034\\\", string0);\\n assertNotSame(resultMatrixSignificance0, resultMatrixSignificance1);\\n \\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV(resultMatrixSignificance0);\\n assertFalse(resultMatrixSignificance0.equals((Object)resultMatrixSignificance1));\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\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 display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(10, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(10, resultMatrixCSV0.getRowCount());\\n assertEquals(40, resultMatrixCSV0.getRowNameWidth());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(10, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(10, resultMatrixCSV0.getColCount());\\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(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\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.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertNotNull(resultMatrixCSV0);\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n resultMatrixCSV0.clear();\\n assertFalse(resultMatrixSignificance0.equals((Object)resultMatrixSignificance1));\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\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 display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(10, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(10, resultMatrixCSV0.getRowCount());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(10, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(10, resultMatrixCSV0.getColCount());\\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(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\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.getDefaultSignificanceWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertNotSame(resultMatrixSignificance0, resultMatrixSignificance1);\\n \\n resultMatrixPlainText0.setRowName(0, \\\"*\\\");\\n assertFalse(resultMatrixSignificance0.equals((Object)resultMatrixSignificance1));\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\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 display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(0, resultMatrixPlainText0.getCountWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(10, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getPrintColNames());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(10, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(10, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(10, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(40, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertEquals(10, resultMatrixPlainText0.getColCount());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertNotSame(resultMatrixSignificance0, resultMatrixSignificance1);\\n \\n String string1 = resultMatrixSignificance1.getRevision();\\n assertFalse(resultMatrixSignificance0.equals((Object)resultMatrixSignificance1));\\n assertFalse(resultMatrixSignificance1.equals((Object)resultMatrixSignificance0));\\n assertTrue(string1.equals((Object)string0));\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\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 display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(0, resultMatrixPlainText0.getCountWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(10, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getPrintColNames());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(10, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(10, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(10, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(40, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertEquals(10, resultMatrixPlainText0.getColCount());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance1.countWidthTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultSignificanceWidth());\\n assertEquals(2, resultMatrixSignificance1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance1.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance1.showAverageTipText());\\n assertTrue(resultMatrixSignificance1.getPrintRowNames());\\n assertEquals(10, resultMatrixSignificance1.getVisibleColCount());\\n assertFalse(resultMatrixSignificance1.getDefaultRemoveFilterName());\\n assertEquals(10, resultMatrixSignificance1.getColCount());\\n assertEquals(2, resultMatrixSignificance1.getDefaultStdDevPrec());\\n assertEquals(0, resultMatrixSignificance1.getDefaultColNameWidth());\\n assertEquals(40, resultMatrixSignificance1.getDefaultRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance1.meanPrecTipText());\\n assertFalse(resultMatrixSignificance1.getDefaultPrintColNames());\\n assertEquals(10, resultMatrixSignificance1.getMeanWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowAverage());\\n assertFalse(resultMatrixSignificance1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance1.colNameWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultCountWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultMeanWidth());\\n assertFalse(resultMatrixSignificance1.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance1.getRemoveFilterName());\\n assertTrue(resultMatrixSignificance1.getDefaultEnumerateColNames());\\n assertEquals(40, resultMatrixSignificance1.getRowNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateRowNamesTipText());\\n assertTrue(resultMatrixSignificance1.getDefaultPrintRowNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance1.meanWidthTipText());\\n assertEquals(2, resultMatrixSignificance1.getStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance1.getDisplayName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance1.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance1.significanceWidthTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance1.showStdDevTipText());\\n assertEquals(10, resultMatrixSignificance1.getRowCount());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance1.globalInfo());\\n assertEquals(0, resultMatrixSignificance1.getStdDevWidth());\\n assertFalse(resultMatrixSignificance1.getShowAverage());\\n assertEquals(10, resultMatrixSignificance1.getVisibleRowCount());\\n assertTrue(resultMatrixSignificance1.getEnumerateColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance1.stdDevPrecTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance1.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance1.getColNameWidth());\\n assertFalse(resultMatrixSignificance1.getPrintColNames());\\n assertEquals(0, resultMatrixSignificance1.getCountWidth());\\n assertEquals(0, resultMatrixSignificance1.getSignificanceWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance1.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance1.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance1.getMeanPrec());\\n assertNotNull(string1);\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(\\\"8034\\\", string1);\\n assertNotSame(resultMatrixSignificance0, resultMatrixSignificance1);\\n assertNotSame(resultMatrixSignificance1, resultMatrixSignificance0);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"552baa41ce717a3493e2099bb024fd51\",\n \"score\": \"0.58611244\",\n \"text\": \"@Test(timeout = 4000)\\n public void test073() throws Throwable {\\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance(10, 10);\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n ResultMatrixPlainText resultMatrixPlainText0 = new ResultMatrixPlainText(resultMatrixSignificance0);\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(10, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(10, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(40, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\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.getDefaultStdDevWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\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 assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getCountWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertFalse(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(10, resultMatrixPlainText0.getRowCount());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertNotNull(resultMatrixPlainText0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n resultMatrixPlainText0.m_MeanWidth = 10;\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(10, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(10, resultMatrixPlainText0.getMeanWidth());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(10, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(40, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\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.getDefaultStdDevWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\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 assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getCountWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertFalse(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(10, resultMatrixPlainText0.getRowCount());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n \\n String string0 = resultMatrixPlainText0.getRevision();\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(10, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(10, resultMatrixPlainText0.getMeanWidth());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(10, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(40, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\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.getDefaultStdDevWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\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 assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getCountWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertFalse(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(10, resultMatrixPlainText0.getRowCount());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertNotNull(string0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"8034\\\", string0);\\n \\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV(resultMatrixSignificance0);\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\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(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(10, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(10, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(10, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(40, resultMatrixCSV0.getRowNameWidth());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(10, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\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 assertNotNull(resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n resultMatrixCSV0.clear();\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\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(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(10, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(10, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(10, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(10, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\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(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n resultMatrixPlainText0.setRowName((-3458), \\\"*\\\");\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(10, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(10, resultMatrixPlainText0.getMeanWidth());\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(10, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(40, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\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.getDefaultStdDevWidth());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\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 assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixPlainText0.getCountWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertFalse(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(10, resultMatrixPlainText0.getRowCount());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n String string1 = resultMatrixSignificance0.getRevision();\\n assertTrue(string1.equals((Object)string0));\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(10, resultMatrixSignificance0.getVisibleColCount());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(10, resultMatrixSignificance0.getColCount());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertEquals(10, resultMatrixSignificance0.getRowCount());\\n assertNotNull(string1);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"8034\\\", string1);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"206156121f60b8239e2afe9a80705aee\",\n \"score\": \"0.5860301\",\n \"text\": \"@Test(timeout = 4000)\\n public void test56() throws Throwable {\\n OneR oneR0 = new OneR();\\n assertNotNull(oneR0);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", oneR0.debugTipText());\\n assertEquals(6, oneR0.getMinBucketSize());\\n assertEquals(\\\"The minimum bucket size used for discretizing numeric attributes.\\\", oneR0.minBucketSizeTipText());\\n assertFalse(oneR0.getDebug());\\n \\n String[] stringArray0 = new String[1];\\n stringArray0[0] = \\\"getMeasure\\\";\\n try { \\n Evaluation.evaluateModel((Classifier) oneR0, 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 // -z <class name>\\n // \\\\tOnly outputs the source representation of the classifier,\\n // \\\\tgiving it the supplied name.\\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.rules.OneR:\\n // \\n // -B <minimum bucket size>\\n // \\\\tThe minimum number of objects in a bucket (default: 6).\\n //\\n verifyException(\\\"weka.classifiers.Evaluation\\\", e);\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91a66ea86998d9c52f727783b48bc275\",\n \"score\": \"0.58417916\",\n \"text\": \"public double entropy();\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b86baa91c96f4f28b4919289068f8c\",\n \"score\": \"0.5826284\",\n \"text\": \"@Test(timeout = 4000)\\n public void test83() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n assertNotNull(testInstances0);\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(0, testInstances0.getNumRelationalNumeric());\\n assertEquals(1, testInstances0.getSeed());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n \\n EvoSuiteFile evoSuiteFile0 = new EvoSuiteFile(\\\"/home/ubuntu/wekafiles/props\\\");\\n boolean boolean0 = FileSystemHandling.appendStringToFile(evoSuiteFile0, \\\" \\\");\\n assertTrue(boolean0);\\n \\n GaussianProcesses gaussianProcesses0 = new GaussianProcesses();\\n assertNotNull(gaussianProcesses0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n \\n Capabilities capabilities0 = gaussianProcesses0.getCapabilities();\\n assertNotNull(capabilities0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n \\n TestInstances testInstances1 = TestInstances.forCapabilities(capabilities0);\\n assertNotNull(testInstances1);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertFalse(testInstances1.getNoClass());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n Enumeration enumeration0 = testInstances0.listOptions();\\n assertNotNull(enumeration0);\\n assertNotSame(testInstances0, testInstances1);\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(0, testInstances0.getNumRelationalNumeric());\\n assertEquals(1, testInstances0.getSeed());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertFalse(testInstances0.equals((Object)testInstances1));\\n \\n Instances instances0 = testInstances1.generate();\\n assertNotNull(instances0);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertFalse(testInstances1.getNoClass());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(20, instances0.size());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(1, instances0.numClasses());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n Evaluation evaluation0 = new Evaluation(instances0);\\n assertNotNull(evaluation0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertFalse(testInstances1.getNoClass());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(20, instances0.size());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(1, instances0.numClasses());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01);\\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01);\\n assertEquals(0.0, evaluation0.numInstances(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n double double0 = (-2257.885429600286);\\n try { \\n evaluation0.priorEntropy();\\n fail(\\\"Expecting exception: Exception\\\");\\n \\n } catch(Exception e) {\\n //\\n // Can't compute entropy of class prior: class numeric!\\n //\\n verifyException(\\\"weka.classifiers.Evaluation\\\", e);\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8a595b410448f016b851fb0d66288a6\",\n \"score\": \"0.5825243\",\n \"text\": \"@Test(timeout = 4000)\\n public void test053() throws Throwable {\\n TextDirectoryLoader textDirectoryLoader0 = new TextDirectoryLoader();\\n Instances instances0 = textDirectoryLoader0.getStructure();\\n Evaluation evaluation0 = new Evaluation(instances0);\\n char[] charArray0 = new char[1];\\n String string0 = evaluation0.num2ShortID(30, charArray0, 30);\\n assertEquals(\\\"\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\\u0000\\\", string0);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b589339ba3824617207f7add7e44aea\",\n \"score\": \"0.58200485\",\n \"text\": \"@Test(timeout = 4000)\\n public void test71() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n assertNotNull(testInstances0);\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(0, testInstances0.getNumRelationalNumeric());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n \\n Instances instances0 = testInstances0.generate();\\n assertNotNull(instances0);\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(0, testInstances0.getNumRelationalNumeric());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(2, instances0.numAttributes());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.size());\\n assertEquals(2, instances0.numClasses());\\n assertEquals(1, instances0.classIndex());\\n assertEquals(20, instances0.numInstances());\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n \\n CostSensitiveClassifier costSensitiveClassifier0 = new CostSensitiveClassifier();\\n assertNotNull(costSensitiveClassifier0);\\n assertEquals(\\\"The random number seed to be used.\\\", costSensitiveClassifier0.seedTipText());\\n assertFalse(costSensitiveClassifier0.getDebug());\\n assertEquals(\\\"A metaclassifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class). Performance can often be improved by using a Bagged classifier to improve the probability estimates of the base classifier.\\\", costSensitiveClassifier0.globalInfo());\\n assertEquals(\\\"Sets the directory where cost files are loaded from. This option is used when the costMatrixSource is set to \\\\\\\"On Demand\\\\\\\".\\\", costSensitiveClassifier0.onDemandDirectoryTipText());\\n assertEquals(\\\"The base classifier to be used.\\\", costSensitiveClassifier0.classifierTipText());\\n assertEquals(\\\"Sets the cost matrix explicitly. This matrix is used if the costMatrixSource property is set to \\\\\\\"Supplied\\\\\\\".\\\", costSensitiveClassifier0.costMatrixTipText());\\n assertEquals(0, costSensitiveClassifier0.graphType());\\n assertFalse(costSensitiveClassifier0.getMinimizeExpectedCost());\\n assertEquals(1, costSensitiveClassifier0.getSeed());\\n assertEquals(\\\"Sets whether the minimum expected cost criteria will be used. If this is false, the training data will be reweighted according to the costs assigned to each class. If true, the minimum expected cost criteria will be used.\\\", costSensitiveClassifier0.minimizeExpectedCostTipText());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", costSensitiveClassifier0.debugTipText());\\n assertEquals(1, CostSensitiveClassifier.MATRIX_ON_DEMAND);\\n assertEquals(2, CostSensitiveClassifier.MATRIX_SUPPLIED);\\n \\n GaussianProcesses gaussianProcesses0 = new GaussianProcesses();\\n assertNotNull(gaussianProcesses0);\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n \\n CostMatrix costMatrix0 = new CostMatrix(2);\\n assertNotNull(costMatrix0);\\n assertEquals(2, costMatrix0.size());\\n assertEquals(2, costMatrix0.numRows());\\n assertEquals(2, costMatrix0.numColumns());\\n \\n Evaluation evaluation0 = new Evaluation(instances0, costMatrix0);\\n assertNotNull(evaluation0);\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(0, testInstances0.getNumRelationalNumeric());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(2, instances0.numAttributes());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.size());\\n assertEquals(2, instances0.numClasses());\\n assertEquals(1, instances0.classIndex());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(2, costMatrix0.size());\\n assertEquals(2, costMatrix0.numRows());\\n assertEquals(2, costMatrix0.numColumns());\\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01);\\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedFalsePositiveRate(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(1.0, evaluation0.kappa(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.numInstances(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedTruePositiveRate(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.unweightedMicroFmeasure(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedFMeasure(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedTrueNegativeRate(), 0.01);\\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedRecall(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedPrecision(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedFalseNegativeRate(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n \\n double[] doubleArray0 = new double[5];\\n doubleArray0[0] = (double) (-2);\\n doubleArray0[0] = (double) 0;\\n doubleArray0[2] = 1346.641846338;\\n doubleArray0[3] = (double) 1;\\n FileSystemHandling fileSystemHandling0 = new FileSystemHandling();\\n assertNotNull(fileSystemHandling0);\\n \\n SparseInstance sparseInstance0 = new SparseInstance(2.0, doubleArray0);\\n assertNotNull(sparseInstance0);\\n assertEquals(5, doubleArray0.length);\\n assertEquals(2.0, sparseInstance0.weight(), 0.01);\\n assertEquals(2, sparseInstance0.numValues());\\n assertEquals(5, sparseInstance0.numAttributes());\\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\\n assertArrayEquals(new double[] {0.0, 0.0, 1346.641846338, 1.0, 0.0}, doubleArray0, 0.01);\\n \\n boolean boolean0 = FileSystemHandling.shouldThrowIOException((EvoSuiteFile) null);\\n assertFalse(boolean0);\\n \\n sparseInstance0.setDataset(instances0);\\n assertEquals(5, doubleArray0.length);\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(0, testInstances0.getNumRelationalNumeric());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(2, instances0.numAttributes());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.size());\\n assertEquals(2, instances0.numClasses());\\n assertEquals(1, instances0.classIndex());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(2.0, sparseInstance0.weight(), 0.01);\\n assertEquals(2, sparseInstance0.numClasses());\\n assertEquals(2, sparseInstance0.numValues());\\n assertEquals(5, sparseInstance0.numAttributes());\\n assertEquals(1, sparseInstance0.classIndex());\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\\n assertArrayEquals(new double[] {0.0, 0.0, 1346.641846338, 1.0, 0.0}, doubleArray0, 0.01);\\n \\n double double0 = evaluation0.evaluationForSingleInstance(doubleArray0, sparseInstance0, true);\\n assertEquals(5, doubleArray0.length);\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertFalse(testInstances0.getNoClass());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(0, testInstances0.getNumRelationalNumeric());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(2, instances0.numAttributes());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.size());\\n assertEquals(2, instances0.numClasses());\\n assertEquals(1, instances0.classIndex());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(2, costMatrix0.size());\\n assertEquals(2, costMatrix0.numRows());\\n assertEquals(2, costMatrix0.numColumns());\\n assertEquals(0.0, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedFalsePositiveRate(), 0.01);\\n assertEquals(100.0, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(1.0, evaluation0.kappa(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(0.0, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertEquals(2.0, evaluation0.unclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(2.0, evaluation0.numInstances(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedTruePositiveRate(), 0.01);\\n assertEquals(Double.NaN, evaluation0.unweightedMicroFmeasure(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedFMeasure(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.pctCorrect(), 0.01);\\n assertEquals(0.0, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(0.0, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedTrueNegativeRate(), 0.01);\\n assertEquals(0.0, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(2.0, evaluation0.totalCost(), 0.01);\\n assertEquals(1.0, evaluation0.avgCost(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedRecall(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedPrecision(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.weightedFalseNegativeRate(), 0.01);\\n assertEquals(1.0, evaluation0.errorRate(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(2.0, sparseInstance0.weight(), 0.01);\\n assertEquals(2, sparseInstance0.numClasses());\\n assertEquals(2, sparseInstance0.numValues());\\n assertEquals(5, sparseInstance0.numAttributes());\\n assertEquals(1, sparseInstance0.classIndex());\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals(6, AbstractInstance.s_numericAfterDecimalPoint);\\n assertEquals(2.0, double0, 0.01);\\n assertArrayEquals(new double[] {0.0, 0.0, 1346.641846338, 1.0, 0.0}, doubleArray0, 0.01);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b228a8f8b0176954cc32762cab155432\",\n \"score\": \"0.5817314\",\n \"text\": \"@Test(timeout = 4000)\\n public void test33() throws Throwable {\\n NaiveBayesMultinomialText naiveBayesMultinomialText0 = new NaiveBayesMultinomialText();\\n Capabilities capabilities0 = naiveBayesMultinomialText0.getCapabilities();\\n TestInstances testInstances0 = TestInstances.forCapabilities(capabilities0);\\n Instances instances0 = testInstances0.generate();\\n naiveBayesMultinomialText0.buildClassifier(instances0);\\n FileSystemHandling fileSystemHandling0 = new FileSystemHandling();\\n Random.setNextRandom((-2));\\n naiveBayesMultinomialText0.pruneDictionary();\\n BinarySparseInstance binarySparseInstance0 = new BinarySparseInstance((-1), (int[]) null, (-2));\\n naiveBayesMultinomialText0.distributionForInstance(binarySparseInstance0);\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac8813ec4dbfde89daf3adc582b6adc5\",\n \"score\": \"0.5817058\",\n \"text\": \"@Test\\n public void organicMetricsTest() {\\n // TODO: test organicMetrics\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70787907f58c71aeae32523cb612d5f\",\n \"score\": \"0.5801642\",\n \"text\": \"@Test(timeout = 4000)\\n public void test74() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n assertNotNull(testInstances0);\\n assertEquals(10, testInstances0.getNumInstancesRelational());\\n assertEquals(0, testInstances0.getNumRelationalString());\\n assertFalse(testInstances0.getNoClass());\\n assertFalse(testInstances0.getMultiInstance());\\n assertEquals(1, testInstances0.getNumRelationalNominal());\\n assertEquals(0, testInstances0.getNumNumeric());\\n assertEquals(2, testInstances0.getNumRelationalNominalValues());\\n assertEquals(0, testInstances0.getNumRelationalNumeric());\\n assertEquals(0, testInstances0.getNumString());\\n assertEquals(2, testInstances0.getNumAttributes());\\n assertEquals(2, testInstances0.getNumClasses());\\n assertEquals((-1), testInstances0.getClassIndex());\\n assertEquals(1, testInstances0.getClassType());\\n assertEquals(1, testInstances0.getSeed());\\n assertEquals(20, testInstances0.getNumInstances());\\n assertEquals(0, testInstances0.getNumRelational());\\n assertEquals(\\\"Testdata\\\", testInstances0.getRelation());\\n assertEquals(\\\" \\\", testInstances0.getWordSeparators());\\n assertEquals(0, testInstances0.getNumRelationalDate());\\n assertEquals(0, testInstances0.getNumDate());\\n assertEquals(1, testInstances0.getNumNominal());\\n assertEquals(2, testInstances0.getNumNominalValues());\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n \\n EvoSuiteFile evoSuiteFile0 = new EvoSuiteFile(\\\"/home/ubuntu/wekafiles/props\\\");\\n boolean boolean0 = FileSystemHandling.appendStringToFile(evoSuiteFile0, \\\" \\\");\\n assertTrue(boolean0);\\n \\n GaussianProcesses gaussianProcesses0 = new GaussianProcesses();\\n assertNotNull(gaussianProcesses0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n \\n Capabilities capabilities0 = gaussianProcesses0.getCapabilities();\\n assertNotNull(capabilities0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n \\n TestInstances testInstances1 = TestInstances.forCapabilities(capabilities0);\\n assertNotNull(testInstances1);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n Instances instances0 = testInstances1.generate();\\n assertNotNull(instances0);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n String string0 = instances0.getRevision();\\n assertNotNull(string0);\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertEquals(\\\"9186\\\", string0);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n Evaluation evaluation0 = new Evaluation(instances0);\\n assertNotNull(evaluation0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.numInstances(), 0.01);\\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n evaluation0.addNumericTrainClass((-2), (-1190.7));\\n assertNotSame(testInstances1, testInstances0);\\n assertEquals(\\\" Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.\\\", gaussianProcesses0.globalInfo());\\n assertEquals(\\\"The level of Gaussian Noise (added to the diagonal of the Covariance Matrix), after the target has been normalized/standardized/left unchanged).\\\", gaussianProcesses0.noiseTipText());\\n assertEquals(\\\"Determines how/if the data will be transformed.\\\", gaussianProcesses0.filterTypeTipText());\\n assertFalse(gaussianProcesses0.getDebug());\\n assertEquals(\\\"The kernel to use.\\\", gaussianProcesses0.kernelTipText());\\n assertEquals(1.0, gaussianProcesses0.getNoise(), 0.01);\\n assertEquals(\\\"If set to true, classifier may output additional info to the console.\\\", gaussianProcesses0.debugTipText());\\n assertEquals(1, capabilities0.getMinimumNumberInstances());\\n assertTrue(capabilities0.hasDependencies());\\n assertEquals(2, testInstances1.getNumRelationalNominalValues());\\n assertEquals(2, testInstances1.getNumClasses());\\n assertEquals(20, testInstances1.getNumInstances());\\n assertEquals(0, testInstances1.getNumRelationalDate());\\n assertEquals(0, testInstances1.getNumRelational());\\n assertEquals(2, testInstances1.getNumNominalValues());\\n assertEquals(0, testInstances1.getNumDate());\\n assertFalse(testInstances1.getMultiInstance());\\n assertEquals(10, testInstances1.getNumInstancesRelational());\\n assertEquals(1, testInstances1.getSeed());\\n assertEquals((-1), testInstances1.getClassIndex());\\n assertEquals(1, testInstances1.getNumRelationalNominal());\\n assertEquals(1, testInstances1.getNumRelationalNumeric());\\n assertEquals(0, testInstances1.getNumString());\\n assertEquals(0, testInstances1.getNumRelationalString());\\n assertEquals(1, testInstances1.getNumNominal());\\n assertEquals(\\\"Testdata\\\", testInstances1.getRelation());\\n assertEquals(0, testInstances1.getClassType());\\n assertEquals(\\\" \\\", testInstances1.getWordSeparators());\\n assertEquals(3, testInstances1.getNumAttributes());\\n assertEquals(1, testInstances1.getNumNumeric());\\n assertFalse(testInstances1.getNoClass());\\n assertEquals(\\\"Testdata\\\", instances0.relationName());\\n assertEquals(3, instances0.numAttributes());\\n assertEquals(2, instances0.classIndex());\\n assertEquals(1, instances0.numClasses());\\n assertEquals(20.0, instances0.sumOfWeights(), 0.01);\\n assertEquals(20, instances0.size());\\n assertFalse(instances0.checkForStringAttributes());\\n assertEquals(20, instances0.numInstances());\\n assertEquals(Double.NaN, evaluation0.meanAbsoluteError(), 0.01);\\n assertEquals(0.0, evaluation0.totalCost(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctCorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanSchemeEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctUnclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.sizeOfPredictedRegions(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootRelativeSquaredError(), 0.01);\\n assertFalse(evaluation0.getDiscardPredictions());\\n assertEquals(0.0, evaluation0.correct(), 0.01);\\n assertEquals(0.0, evaluation0.unclassified(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanPriorEntropy(), 0.01);\\n assertEquals(Double.NaN, evaluation0.SFMeanEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.relativeAbsoluteError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanSquaredError(), 0.01);\\n assertEquals(Double.NaN, evaluation0.rootMeanPriorSquaredError(), 0.01);\\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\\n assertEquals(Double.NaN, evaluation0.avgCost(), 0.01);\\n assertEquals(0.0, evaluation0.SFSchemeEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.numInstances(), 0.01);\\n assertEquals(Double.NaN, evaluation0.coverageOfTestCasesByPredictedRegions(), 0.01);\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n assertEquals(0.0, evaluation0.incorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.errorRate(), 0.01);\\n assertEquals(Double.NaN, evaluation0.pctIncorrect(), 0.01);\\n assertEquals(Double.NaN, evaluation0.meanPriorAbsoluteError(), 0.01);\\n assertEquals(1, GaussianProcesses.FILTER_STANDARDIZE);\\n assertEquals(2, GaussianProcesses.FILTER_NONE);\\n assertEquals(0, GaussianProcesses.FILTER_NORMALIZE);\\n assertEquals((-2), TestInstances.NO_CLASS);\\n assertEquals((-1), TestInstances.CLASS_IS_LAST);\\n assertFalse(testInstances1.equals((Object)testInstances0));\\n \\n boolean boolean1 = FileSystemHandling.shouldAllThrowIOExceptions();\\n assertTrue(boolean1);\\n assertTrue(boolean1 == boolean0);\\n \\n double double0 = evaluation0.m_ConfLevel;\\n assertEquals(0.95, double0, 0.01);\\n \\n try { \\n evaluation0.toCumulativeMarginDistributionString();\\n fail(\\\"Expecting exception: Exception\\\");\\n \\n } catch(Exception e) {\\n //\\n // Class must be nominal for margin distributions\\n //\\n verifyException(\\\"weka.classifiers.Evaluation\\\", e);\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a430fb783a7ff89cdcb17e2172af7921\",\n \"score\": \"0.57985556\",\n \"text\": \"private static int best_test_by_ENTROPY(State state, boolean debug){\\n\\t\\treturn best_test_by_ENTROPY(state, null, debug);\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70817b48e538cfba0471676ef222ea77\",\n \"score\": \"0.5789501\",\n \"text\": \"@Test(timeout = 4000)\\n public void test061() throws Throwable {\\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML(resultMatrixSignificance0);\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixHTML0.getRowNameWidth());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertNotNull(resultMatrixHTML0);\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n resultMatrixHTML0.setCountWidth(0);\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixHTML0.getRowNameWidth());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n resultMatrixHTML0.setStdDevWidth(1);\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(1, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixHTML0.getRowNameWidth());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n String string0 = resultMatrixHTML0.toStringSummary();\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(1, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixHTML0.getRowNameWidth());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertNotNull(string0);\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"-summary data not set-\\\", string0);\\n \\n int int0 = resultMatrixHTML0.getSignificance((-469), 5);\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(1, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(40, resultMatrixHTML0.getRowNameWidth());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\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(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(0, int0);\\n \\n String[] stringArray0 = new String[2];\\n stringArray0[0] = \\\"v\\\";\\n stringArray0[1] = \\\" \\\";\\n ResultMatrixCSV.main(stringArray0);\\n assertEquals(2, stringArray0.length);\\n }\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":984998,"cells":{"query_id":{"kind":"string","value":"25197b4cf814864ecd440f23b9149cb3"},"query":{"kind":"string","value":"TODO Autogenerated method stub"},"positive_passages":{"kind":"list like","value":[{"docid":"64ec1cd2e8837d0bce24303eec6c77e5","score":"0.0","text":"public static void main(String[] args) {\n\t\tVehicle car1=new Car(\"bike\");\r\n\t\tBufferedReader reader=new BufferedReader(new InputStreamReader(System.in));\r\n\t\tString input = null;\r\n\t\ttry {\r\n\t\t\twhile(input!=\"quit\"){\r\n\t\t\t\tSystem.out.println(\"Which method would you like to call?\");\r\n\t\t\t\tinput=reader.readLine();\r\n\t\t\t\tif(input.equals(\"turn\")){\r\n\t\t\t\t\t((Car) car1).turn(\"left\");\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\telse if(input.equals(\"Change lane\")){\r\n\t\t\t\t\t((Car)car1).changeLanes(\"left\");\r\n\t\t\t\t}\r\n\t\t\t\telse if(input.equals(\"go\")){\r\n\t\t\t\t\t((Car)car1).go();\r\n\t\t\t\t}\r\n\t\t\t\telse if(input.equals(\"radar back\")){\r\n\t\t\t\t\t((Car)car1).radarBack((Car) car1);\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\telse if(input.equals(\"radar front\")){\r\n\t\t\t\t\t((Car)car1).radarFront((Car) car1);}}}\r\n\t\t catch (IOException e) {\r\n\t\t\t// TODO Auto-generated catch block\r\n\t\t\te.printStackTrace();\r\n\t\t}\r\n\t\t\r\n\t}","title":""}],"string":"[\n {\n \"docid\": \"64ec1cd2e8837d0bce24303eec6c77e5\",\n \"score\": \"0.0\",\n \"text\": \"public static void main(String[] args) {\\n\\t\\tVehicle car1=new Car(\\\"bike\\\");\\r\\n\\t\\tBufferedReader reader=new BufferedReader(new InputStreamReader(System.in));\\r\\n\\t\\tString input = null;\\r\\n\\t\\ttry {\\r\\n\\t\\t\\twhile(input!=\\\"quit\\\"){\\r\\n\\t\\t\\t\\tSystem.out.println(\\\"Which method would you like to call?\\\");\\r\\n\\t\\t\\t\\tinput=reader.readLine();\\r\\n\\t\\t\\t\\tif(input.equals(\\\"turn\\\")){\\r\\n\\t\\t\\t\\t\\t((Car) car1).turn(\\\"left\\\");\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\telse if(input.equals(\\\"Change lane\\\")){\\r\\n\\t\\t\\t\\t\\t((Car)car1).changeLanes(\\\"left\\\");\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\telse if(input.equals(\\\"go\\\")){\\r\\n\\t\\t\\t\\t\\t((Car)car1).go();\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\telse if(input.equals(\\\"radar back\\\")){\\r\\n\\t\\t\\t\\t\\t((Car)car1).radarBack((Car) car1);\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\t\\telse if(input.equals(\\\"radar front\\\")){\\r\\n\\t\\t\\t\\t\\t((Car)car1).radarFront((Car) car1);}}}\\r\\n\\t\\t catch (IOException e) {\\r\\n\\t\\t\\t// TODO Auto-generated catch block\\r\\n\\t\\t\\te.printStackTrace();\\r\\n\\t\\t}\\r\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"63a51b9825ac81396e1d7ad0b9f3ed82","score":"0.6866318","text":"@Override\n\tpublic void 군무() {\n\t\t\n\t}","title":""},{"docid":"15ae679aae74424c73fde647cf4332f6","score":"0.68461496","text":"@Override\r\n\t\t\tpublic void hissetmek() {\n\r\n\t\t\t}","title":""},{"docid":"76a80470fd21f5a9092c258ed3a8a632","score":"0.68199706","text":"private static void 로또번호리스트() {\n\t\t\r\n\t}","title":""},{"docid":"bfad11e436c98998f434617a4d2907e5","score":"0.6690517","text":"@Override\r\n\tpublic void adharno() {\n\t\t\r\n\t}","title":""},{"docid":"33d41636b65afa8267c9085dae3d1a2d","score":"0.6679176","text":"private void apparence() {\r\n\r\n\t}","title":""},{"docid":"9969c2cd0a26f223be9eea348d1c6160","score":"0.6571234","text":"private static void 로또수동생성() {\n\t\t\r\n\t}","title":""},{"docid":"0b5b11f4251ace8b3d0f5ead186f7beb","score":"0.65677965","text":"@Override\n\tpublic void comer() {\n\t\t\n\t}","title":""},{"docid":"5f8d37aee09bc1e9959f768d6eb0183c","score":"0.6481211","text":"@Override\r\n\t\t\tpublic void annadir() {\n\r\n\t\t\t}","title":""},{"docid":"1dd3d9bdd67d8cc8bddfe021796a9286","score":"0.6433148","text":"@Override\n protected void lodate() {\n\n }","title":""},{"docid":"b62655d47438ae6875a06d0702721dbc","score":"0.6424808","text":"@Override\r\n\t\t\tpublic void koklama() {\n\r\n\t\t\t}","title":""},{"docid":"d92d5fe4c6efcfe66d53457e76817518","score":"0.64108074","text":"@Override\r\n\t\t\tprotected void init() {\n\t\t\t\t\r\n\t\t\t}","title":""},{"docid":"5435fed6c0a9345524c30343d8ed9829","score":"0.63912827","text":"@Override\r\n\tpublic void entrenar() {\n\t\t\r\n\t}","title":""},{"docid":"dda3b5a5b8e9e69b6eec0807bf162606","score":"0.63636446","text":"@Override\r\n\tpublic void rozmnozovat() {\n\t\t\r\n\t}","title":""},{"docid":"00075c50e26b18237953cd05ffd5428b","score":"0.63032013","text":"@Override\n\tpublic void beber() {\n\t\t\n\t}","title":""},{"docid":"1f7c82e188acf30d59f88faf08cf24ac","score":"0.623686","text":"@Override\r\n\t\t\tpublic void ayuda() {\n\r\n\t\t\t}","title":""},{"docid":"1f7c82e188acf30d59f88faf08cf24ac","score":"0.623686","text":"@Override\r\n\t\t\tpublic void ayuda() {\n\r\n\t\t\t}","title":""},{"docid":"76e3d99118a00e74a082f6a955778f7d","score":"0.62293875","text":"Grensesnitt(){}","title":""},{"docid":"61358eff9372995c8157b02d47a44a6a","score":"0.6186996","text":"@Override\n\tpublic void gravarBd() {\n\t\t\n\t}","title":""},{"docid":"bf8fefe7b85b49281edb456698bfd556","score":"0.616781","text":"@Override\r\n\tpublic void descansar() {\n\t\t\r\n\t}","title":""},{"docid":"10ead2e988bf977cb8edb2bacf974891","score":"0.61546874","text":"@Override\n\tprotected void init() {\n\t\t\n\t}","title":""},{"docid":"10ead2e988bf977cb8edb2bacf974891","score":"0.61546874","text":"@Override\n\tprotected void init() {\n\t\t\n\t}","title":""},{"docid":"c612bcadafd393d086f44fd19558a8be","score":"0.61533993","text":"@Override\r\n\tpublic void inicialize() {\n\t\t\r\n\t}","title":""},{"docid":"106ce0ada7b978eeffda7ec9cccc29e2","score":"0.61470556","text":"public void дразнит() {\n\n\t}","title":""},{"docid":"a6e9388ba580a030c48b527221f8e81a","score":"0.6139653","text":"@Override\n\tpublic void actuar() {\n\t\t\n\t}","title":""},{"docid":"958663dc976180cba7b91b2a4ebcb522","score":"0.61089844","text":"@Override\n\tpublic void atualiza() {\n\n\t}","title":""},{"docid":"2bd152bb33ecbe797b9818e175c5d4ec","score":"0.6105675","text":"public void mo55659a() {\n }","title":""},{"docid":"42693116fba02f3f07be1ab752c6d9bc","score":"0.6063955","text":"@Override\r\n\tpublic void concentrarse() {\n\t\t\r\n\t}","title":""},{"docid":"cce15f647f795017e06f2ca544477ccd","score":"0.6063628","text":"@Override\n\tpublic void chamCong() {\n\n\t}","title":""},{"docid":"c95af00aee2fa41e8a03e3640ca30750","score":"0.6041561","text":"@Override\n\tpublic void ligar() {\n\t\t\n\t}","title":""},{"docid":"b7d3c2b49702a5ed673b243bf8d2b7c0","score":"0.60215324","text":"@Override\n\tprotected void funcionario() {\n\t\t\n\t}","title":""},{"docid":"0d996fa345e18b74a73dce6394fc0b15","score":"0.60198766","text":"@Override\r\n\tpublic void sunricers() {\n\t\t\r\n\t}","title":""},{"docid":"a6c2db17f79f35f8fc3b8e99eee9ab48","score":"0.60124713","text":"@Override\r\n\tprotected void init() {\n\t\t\r\n\t}","title":""},{"docid":"27c725838f280928d869cc973588317c","score":"0.6001773","text":"@Override\r\n\tpublic void doSelfOpertion() {\n\t\t\r\n\t}","title":""},{"docid":"f350fb57d7c81ec6d70f2643960940a8","score":"0.5986621","text":"@Override \r\n public void init() {\n \r\n }","title":""},{"docid":"4f035da85a44bb830509e046190e6ee5","score":"0.59682363","text":"@Override\r\n\tpublic void rajastanroyals() {\n\t\t\r\n\t}","title":""},{"docid":"a0486d5f3890f3b56ce5a39122c4bdd7","score":"0.59527355","text":"@Override\r\n\tvoid metodo4() {\n\t\t\r\n\t}","title":""},{"docid":"c744960aa067b28111e99db9ddf6aeed","score":"0.59477377","text":"@Override\r\n\tpublic void fragancia() {\n\t\t\r\n\t}","title":""},{"docid":"288fd3ee71b736e770bbef790831c263","score":"0.59301406","text":"User mo18615e();","title":""},{"docid":"6f1e0cfaa7350cf143896dace56978f5","score":"0.59270483","text":"@Override\n\tpublic void respirar() {\n\t\t\n\t}","title":""},{"docid":"8b18fd12dbb5303a665e92c25393fb78","score":"0.59239364","text":"@Override\n\tprotected void initData() {\n\t\t\n\t}","title":""},{"docid":"9808ba3c113b79c3677f2c08c09e60a1","score":"0.59020513","text":"@Override\n\tpublic void resert() {\n\t\t\n\t}","title":""},{"docid":"416a053368cfd700d9d09414458911cd","score":"0.58986443","text":"@Override\n\tpublic void assamblyOfControlhead() {\n\t\t\n\t\t\n\t\t\n\t\n\n\t}","title":""},{"docid":"c9b74342d0ae0e22bc7119b60588c66a","score":"0.5897873","text":"@Override\n\t\t\tpublic int getType() {\n\t\t\t\treturn 0;\n\t\t\t}","title":""},{"docid":"c9b74342d0ae0e22bc7119b60588c66a","score":"0.5897873","text":"@Override\n\t\t\tpublic int getType() {\n\t\t\t\treturn 0;\n\t\t\t}","title":""},{"docid":"77d32d2a2070d9c3117ff146ac04f114","score":"0.5891585","text":"@Override\r\n\tpublic void climaChuvoso() {\n\r\n\t}","title":""},{"docid":"77f859c241fd5e1d997f67c3b890833e","score":"0.5886342","text":"@Override\n\tpublic void dormir() {\n\t\t\n\t}","title":""},{"docid":"40577cf33330bd70c08bc8e310b4436d","score":"0.58706117","text":"@Override\n\tpublic void falar() {\n\n\t}","title":""},{"docid":"79c707f3d28263fcc89902b0527d5c14","score":"0.58528596","text":"public void mo736a() {\n }","title":""},{"docid":"42c3f8e63385f8e328b6828c1f7243d4","score":"0.584987","text":"@Override\r\n\t\tpublic void method_4() {\n\t\t\t\r\n\t\t}","title":""},{"docid":"60e50abec8df6d4d5a80de13ba750171","score":"0.5842782","text":"@Override\r\n\tpublic void retour() {\n\t\t\r\n\t}","title":""},{"docid":"debc6b4612a5fe9f0680a6bff73f25e6","score":"0.58388203","text":"@Override\r\n\t\tpublic void emettreSon() {\n\t\t\t\r\n\t\t}","title":""},{"docid":"d56c498b25d1ea77dc09f49efcf80f6c","score":"0.58347356","text":"@Override\r\n\tpublic void aumentaFatorAcidade() {\n\r\n\t}","title":""},{"docid":"a5d63cf5eb472d80fba18b0149c96501","score":"0.5825308","text":"@Override\n\tpublic void memberSecession() {\n\t\t\n\t}","title":""},{"docid":"f4f7bcdf27874d7a17a80f57f8df43e0","score":"0.5822819","text":"public void mo10744d() {\n }","title":""},{"docid":"d45e7311f809d3019d7382af887cb3cf","score":"0.5799729","text":"@Override\r\n\tpublic void dehlidervels() {\n\t\t\r\n\t}","title":""},{"docid":"ce91051d32625345f2bf3562abbb93de","score":"0.5794177","text":"@Override\n\tprotected void inicializar() {\n\t}","title":""},{"docid":"89b37f5fcca98bdfa7c243fc99d06675","score":"0.5790634","text":"@Override\r\n\tpublic void reanudar() {\n\t\t\r\n\t}","title":""},{"docid":"54b1134554bc066c34ed30a72adb660c","score":"0.57844025","text":"@Override\n\tprotected void initData() {\n\n\t}","title":""},{"docid":"54b1134554bc066c34ed30a72adb660c","score":"0.57844025","text":"@Override\n\tprotected void initData() {\n\n\t}","title":""},{"docid":"54b1134554bc066c34ed30a72adb660c","score":"0.57844025","text":"@Override\n\tprotected void initData() {\n\n\t}","title":""},{"docid":"2ded89a6dfb4a5cfce4bf00ee7993921","score":"0.5774479","text":"@Override\n\tpublic void init() {\n\t\t\n\t}","title":""},{"docid":"2ded89a6dfb4a5cfce4bf00ee7993921","score":"0.5774479","text":"@Override\n\tpublic void init() {\n\t\t\n\t}","title":""},{"docid":"2ded89a6dfb4a5cfce4bf00ee7993921","score":"0.5774479","text":"@Override\n\tpublic void init() {\n\t\t\n\t}","title":""},{"docid":"2ded89a6dfb4a5cfce4bf00ee7993921","score":"0.5774479","text":"@Override\n\tpublic void init() {\n\t\t\n\t}","title":""},{"docid":"2ded89a6dfb4a5cfce4bf00ee7993921","score":"0.5774479","text":"@Override\n\tpublic void init() {\n\t\t\n\t}","title":""},{"docid":"2ded89a6dfb4a5cfce4bf00ee7993921","score":"0.5774479","text":"@Override\n\tpublic void init() {\n\t\t\n\t}","title":""},{"docid":"2ded89a6dfb4a5cfce4bf00ee7993921","score":"0.5774479","text":"@Override\n\tpublic void init() {\n\t\t\n\t}","title":""},{"docid":"2ded89a6dfb4a5cfce4bf00ee7993921","score":"0.5774479","text":"@Override\n\tpublic void init() {\n\t\t\n\t}","title":""},{"docid":"2ded89a6dfb4a5cfce4bf00ee7993921","score":"0.5774479","text":"@Override\n\tpublic void init() {\n\t\t\n\t}","title":""},{"docid":"4a3a3aeaffc4b367a8aa8488e4b59610","score":"0.57720804","text":"public void mo10856b() {\n }","title":""},{"docid":"1c10afc999966d89d84d1fb549910281","score":"0.5770528","text":"@Override \n\t\tprotected void parse() {\t\t\t\n\t\t}","title":""},{"docid":"a46afb3a57efda28ee1b37c4a9b1960f","score":"0.57642525","text":"private void getEx() {\n\r\n\t}","title":""},{"docid":"5e6804b1ba880a8cc8a98006ca4ba35b","score":"0.5761362","text":"@Override\n public void init() {\n\n }","title":""},{"docid":"518a761521ca9f5cb8a8055b32b72cda","score":"0.57596046","text":"@Override\n\tprotected void initialize() {\n\t\t\n\t}","title":""},{"docid":"518a761521ca9f5cb8a8055b32b72cda","score":"0.57596046","text":"@Override\n\tprotected void initialize() {\n\t\t\n\t}","title":""},{"docid":"518a761521ca9f5cb8a8055b32b72cda","score":"0.57596046","text":"@Override\n\tprotected void initialize() {\n\t\t\n\t}","title":""},{"docid":"b95bbed498007dfeba9b078798c02e53","score":"0.5754483","text":"@Override\r\n\tpublic void exibir() {\n\t\t\r\n\t}","title":""},{"docid":"03b93c6668ea7af171bbcdb3af74bb4a","score":"0.57524616","text":"@Override\n public void bicar() {\n }","title":""},{"docid":"b2cde12cf50e0ec60f909684878e936c","score":"0.57514966","text":"@Override\n\tpublic void wykonajZamknij() {\n\n\t}","title":""},{"docid":"2d614ed2ee5b3120f5ee8b427542ddc3","score":"0.575025","text":"@Override\r\n\tpublic void init() {\n\t\t\r\n\t}","title":""},{"docid":"b143ffd983be4c8f41702b8f934b6f32","score":"0.57337177","text":"@Override\r\n\tpublic void init() {\n\r\n\t}","title":""},{"docid":"b143ffd983be4c8f41702b8f934b6f32","score":"0.57337177","text":"@Override\r\n\tpublic void init() {\n\r\n\t}","title":""},{"docid":"a53d232dac89259f961cbc93762f65b2","score":"0.5731746","text":"@Override\r\n\t\t\tvoid n() {\n\t\t\t\t\r\n\t\t\t}","title":""},{"docid":"cf193ee51fc2ac8c94c94264e19ff99c","score":"0.57304704","text":"@Override\n public boolean cannibale() {\n return false;\n }","title":""},{"docid":"cf193ee51fc2ac8c94c94264e19ff99c","score":"0.57304704","text":"@Override\n public boolean cannibale() {\n return false;\n }","title":""},{"docid":"3b072172b17a5e5ab0081f24545795f2","score":"0.57235396","text":"public void przymocowac() {\n\t\t\n\t}","title":""},{"docid":"17e507b0166d99f88c8237ad30d967b7","score":"0.5716563","text":"public void zamowic() {\n\t\t\n\t}","title":""},{"docid":"a937c607590931387a357d03c3b0eef4","score":"0.5715831","text":"@Override\n\tprotected void initialize() {\n\n\t}","title":""},{"docid":"a937c607590931387a357d03c3b0eef4","score":"0.5715831","text":"@Override\n\tprotected void initialize() {\n\n\t}","title":""},{"docid":"7bbf3f0011f317b7faa88562e06495b7","score":"0.5714223","text":"@Override\r\n public void reculer() {\n }","title":""},{"docid":"1ba6f6b334c51461b3fe944110e91ef5","score":"0.5707728","text":"@Override\n\tpublic void comerHojas() {\n\t\t\n\t}","title":""},{"docid":"39132efb6b42f8ec625d96ff6226d80b","score":"0.56991017","text":"@Override\n\tprotected void lazyLoad() {\n\t\t\n\t}","title":""},{"docid":"2d9072b54f55f18557f92b99be58ec2f","score":"0.56886935","text":"@Override\r\n public String toString() {\n\treturn null;\r\n }","title":""},{"docid":"0ca16e78cddd3c7d9de74c662606bbac","score":"0.5686997","text":"public void mo4570b() {\n }","title":""},{"docid":"359579516b65b36ff8c4344d8363a3ed","score":"0.5686103","text":"@Override\r\n\tprotected void initData() {\n\t}","title":""},{"docid":"09488e6c7d4190869b37b126a578249f","score":"0.56821704","text":"@Override\r\n\t/**\r\n\t * \r\n\t */\r\n\tpublic int size() {\n\t\treturn 0;\r\n\t}","title":""},{"docid":"b6c641fa6ba40a5b14cc33cb07aa0671","score":"0.5669649","text":"@Override\n\tprotected void initData() {\n\t}","title":""},{"docid":"b6c641fa6ba40a5b14cc33cb07aa0671","score":"0.5669649","text":"@Override\n\tprotected void initData() {\n\t}","title":""},{"docid":"001227e6b58a587f9eb4f77b15fc3588","score":"0.5668976","text":"@Override\n\tprotected void local() {\n\t\t\n\t}","title":""},{"docid":"9d94df716ebd35f7ec07df01763ae94c","score":"0.5655942","text":"@Override\n\tpublic void init() {\n\n\t}","title":""},{"docid":"9d94df716ebd35f7ec07df01763ae94c","score":"0.5655942","text":"@Override\n\tpublic void init() {\n\n\t}","title":""}],"string":"[\n {\n \"docid\": \"63a51b9825ac81396e1d7ad0b9f3ed82\",\n \"score\": \"0.6866318\",\n \"text\": \"@Override\\n\\tpublic void 군무() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15ae679aae74424c73fde647cf4332f6\",\n \"score\": \"0.68461496\",\n \"text\": \"@Override\\r\\n\\t\\t\\tpublic void hissetmek() {\\n\\r\\n\\t\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a80470fd21f5a9092c258ed3a8a632\",\n \"score\": \"0.68199706\",\n \"text\": \"private static void 로또번호리스트() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfad11e436c98998f434617a4d2907e5\",\n \"score\": \"0.6690517\",\n \"text\": \"@Override\\r\\n\\tpublic void adharno() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33d41636b65afa8267c9085dae3d1a2d\",\n \"score\": \"0.6679176\",\n \"text\": \"private void apparence() {\\r\\n\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9969c2cd0a26f223be9eea348d1c6160\",\n \"score\": \"0.6571234\",\n \"text\": \"private static void 로또수동생성() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b5b11f4251ace8b3d0f5ead186f7beb\",\n \"score\": \"0.65677965\",\n \"text\": \"@Override\\n\\tpublic void comer() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f8d37aee09bc1e9959f768d6eb0183c\",\n \"score\": \"0.6481211\",\n \"text\": \"@Override\\r\\n\\t\\t\\tpublic void annadir() {\\n\\r\\n\\t\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dd3d9bdd67d8cc8bddfe021796a9286\",\n \"score\": \"0.6433148\",\n \"text\": \"@Override\\n protected void lodate() {\\n\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b62655d47438ae6875a06d0702721dbc\",\n \"score\": \"0.6424808\",\n \"text\": \"@Override\\r\\n\\t\\t\\tpublic void koklama() {\\n\\r\\n\\t\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d92d5fe4c6efcfe66d53457e76817518\",\n \"score\": \"0.64108074\",\n \"text\": \"@Override\\r\\n\\t\\t\\tprotected void init() {\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5435fed6c0a9345524c30343d8ed9829\",\n \"score\": \"0.63912827\",\n \"text\": \"@Override\\r\\n\\tpublic void entrenar() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda3b5a5b8e9e69b6eec0807bf162606\",\n \"score\": \"0.63636446\",\n \"text\": \"@Override\\r\\n\\tpublic void rozmnozovat() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00075c50e26b18237953cd05ffd5428b\",\n \"score\": \"0.63032013\",\n \"text\": \"@Override\\n\\tpublic void beber() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f7c82e188acf30d59f88faf08cf24ac\",\n \"score\": \"0.623686\",\n \"text\": \"@Override\\r\\n\\t\\t\\tpublic void ayuda() {\\n\\r\\n\\t\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f7c82e188acf30d59f88faf08cf24ac\",\n \"score\": \"0.623686\",\n \"text\": \"@Override\\r\\n\\t\\t\\tpublic void ayuda() {\\n\\r\\n\\t\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76e3d99118a00e74a082f6a955778f7d\",\n \"score\": \"0.62293875\",\n \"text\": \"Grensesnitt(){}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61358eff9372995c8157b02d47a44a6a\",\n \"score\": \"0.6186996\",\n \"text\": \"@Override\\n\\tpublic void gravarBd() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8fefe7b85b49281edb456698bfd556\",\n \"score\": \"0.616781\",\n \"text\": \"@Override\\r\\n\\tpublic void descansar() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ead2e988bf977cb8edb2bacf974891\",\n \"score\": \"0.61546874\",\n \"text\": \"@Override\\n\\tprotected void init() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ead2e988bf977cb8edb2bacf974891\",\n \"score\": 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\"42693116fba02f3f07be1ab752c6d9bc\",\n \"score\": \"0.6063955\",\n \"text\": \"@Override\\r\\n\\tpublic void concentrarse() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cce15f647f795017e06f2ca544477ccd\",\n \"score\": \"0.6063628\",\n \"text\": \"@Override\\n\\tpublic void chamCong() {\\n\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c95af00aee2fa41e8a03e3640ca30750\",\n \"score\": \"0.6041561\",\n \"text\": \"@Override\\n\\tpublic void ligar() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d3c2b49702a5ed673b243bf8d2b7c0\",\n \"score\": \"0.60215324\",\n \"text\": \"@Override\\n\\tprotected void funcionario() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d996fa345e18b74a73dce6394fc0b15\",\n \"score\": \"0.60198766\",\n \"text\": \"@Override\\r\\n\\tpublic void sunricers() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6c2db17f79f35f8fc3b8e99eee9ab48\",\n \"score\": \"0.60124713\",\n \"text\": \"@Override\\r\\n\\tprotected void init() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27c725838f280928d869cc973588317c\",\n \"score\": \"0.6001773\",\n \"text\": \"@Override\\r\\n\\tpublic void doSelfOpertion() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f350fb57d7c81ec6d70f2643960940a8\",\n \"score\": \"0.5986621\",\n \"text\": \"@Override \\r\\n public void init() {\\n \\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f035da85a44bb830509e046190e6ee5\",\n \"score\": \"0.59682363\",\n \"text\": \"@Override\\r\\n\\tpublic void rajastanroyals() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0486d5f3890f3b56ce5a39122c4bdd7\",\n \"score\": \"0.59527355\",\n \"text\": \"@Override\\r\\n\\tvoid metodo4() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c744960aa067b28111e99db9ddf6aeed\",\n \"score\": \"0.59477377\",\n \"text\": \"@Override\\r\\n\\tpublic void fragancia() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"288fd3ee71b736e770bbef790831c263\",\n \"score\": \"0.59301406\",\n \"text\": \"User mo18615e();\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f1e0cfaa7350cf143896dace56978f5\",\n \"score\": \"0.59270483\",\n \"text\": \"@Override\\n\\tpublic void respirar() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b18fd12dbb5303a665e92c25393fb78\",\n \"score\": \"0.59239364\",\n \"text\": \"@Override\\n\\tprotected void initData() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9808ba3c113b79c3677f2c08c09e60a1\",\n \"score\": \"0.59020513\",\n \"text\": \"@Override\\n\\tpublic void resert() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"416a053368cfd700d9d09414458911cd\",\n \"score\": \"0.58986443\",\n \"text\": \"@Override\\n\\tpublic void assamblyOfControlhead() {\\n\\t\\t\\n\\t\\t\\n\\t\\t\\n\\t\\n\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9b74342d0ae0e22bc7119b60588c66a\",\n \"score\": \"0.5897873\",\n \"text\": \"@Override\\n\\t\\t\\tpublic int getType() {\\n\\t\\t\\t\\treturn 0;\\n\\t\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9b74342d0ae0e22bc7119b60588c66a\",\n \"score\": \"0.5897873\",\n \"text\": \"@Override\\n\\t\\t\\tpublic int getType() {\\n\\t\\t\\t\\treturn 0;\\n\\t\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d32d2a2070d9c3117ff146ac04f114\",\n \"score\": \"0.5891585\",\n \"text\": \"@Override\\r\\n\\tpublic void climaChuvoso() {\\n\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f859c241fd5e1d997f67c3b890833e\",\n \"score\": \"0.5886342\",\n \"text\": \"@Override\\n\\tpublic void dormir() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40577cf33330bd70c08bc8e310b4436d\",\n \"score\": \"0.58706117\",\n \"text\": \"@Override\\n\\tpublic void falar() {\\n\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c707f3d28263fcc89902b0527d5c14\",\n \"score\": \"0.58528596\",\n \"text\": \"public void mo736a() {\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42c3f8e63385f8e328b6828c1f7243d4\",\n \"score\": \"0.584987\",\n \"text\": \"@Override\\r\\n\\t\\tpublic void method_4() {\\n\\t\\t\\t\\r\\n\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60e50abec8df6d4d5a80de13ba750171\",\n \"score\": \"0.5842782\",\n \"text\": \"@Override\\r\\n\\tpublic void retour() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"debc6b4612a5fe9f0680a6bff73f25e6\",\n \"score\": \"0.58388203\",\n \"text\": \"@Override\\r\\n\\t\\tpublic void emettreSon() {\\n\\t\\t\\t\\r\\n\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d56c498b25d1ea77dc09f49efcf80f6c\",\n \"score\": \"0.58347356\",\n \"text\": \"@Override\\r\\n\\tpublic void aumentaFatorAcidade() {\\n\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5d63cf5eb472d80fba18b0149c96501\",\n \"score\": \"0.5825308\",\n \"text\": \"@Override\\n\\tpublic void memberSecession() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4f7bcdf27874d7a17a80f57f8df43e0\",\n \"score\": \"0.5822819\",\n \"text\": \"public void mo10744d() {\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45e7311f809d3019d7382af887cb3cf\",\n \"score\": \"0.5799729\",\n \"text\": \"@Override\\r\\n\\tpublic void dehlidervels() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce91051d32625345f2bf3562abbb93de\",\n \"score\": \"0.5794177\",\n \"text\": \"@Override\\n\\tprotected void inicializar() {\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89b37f5fcca98bdfa7c243fc99d06675\",\n \"score\": \"0.5790634\",\n \"text\": \"@Override\\r\\n\\tpublic void reanudar() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54b1134554bc066c34ed30a72adb660c\",\n \"score\": \"0.57844025\",\n \"text\": \"@Override\\n\\tprotected void initData() {\\n\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54b1134554bc066c34ed30a72adb660c\",\n \"score\": \"0.57844025\",\n \"text\": \"@Override\\n\\tprotected void initData() {\\n\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54b1134554bc066c34ed30a72adb660c\",\n \"score\": \"0.57844025\",\n \"text\": \"@Override\\n\\tprotected void initData() {\\n\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ded89a6dfb4a5cfce4bf00ee7993921\",\n \"score\": \"0.5774479\",\n \"text\": \"@Override\\n\\tpublic void init() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ded89a6dfb4a5cfce4bf00ee7993921\",\n \"score\": \"0.5774479\",\n \"text\": \"@Override\\n\\tpublic void init() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ded89a6dfb4a5cfce4bf00ee7993921\",\n \"score\": \"0.5774479\",\n \"text\": \"@Override\\n\\tpublic void init() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ded89a6dfb4a5cfce4bf00ee7993921\",\n \"score\": \"0.5774479\",\n \"text\": \"@Override\\n\\tpublic void init() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ded89a6dfb4a5cfce4bf00ee7993921\",\n \"score\": \"0.5774479\",\n \"text\": \"@Override\\n\\tpublic void init() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ded89a6dfb4a5cfce4bf00ee7993921\",\n \"score\": \"0.5774479\",\n \"text\": \"@Override\\n\\tpublic void init() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ded89a6dfb4a5cfce4bf00ee7993921\",\n \"score\": \"0.5774479\",\n \"text\": \"@Override\\n\\tpublic void init() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ded89a6dfb4a5cfce4bf00ee7993921\",\n \"score\": \"0.5774479\",\n \"text\": \"@Override\\n\\tpublic void init() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ded89a6dfb4a5cfce4bf00ee7993921\",\n \"score\": \"0.5774479\",\n \"text\": \"@Override\\n\\tpublic void init() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a3a3aeaffc4b367a8aa8488e4b59610\",\n \"score\": \"0.57720804\",\n \"text\": \"public void mo10856b() {\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c10afc999966d89d84d1fb549910281\",\n \"score\": \"0.5770528\",\n \"text\": \"@Override \\n\\t\\tprotected void parse() {\\t\\t\\t\\n\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a46afb3a57efda28ee1b37c4a9b1960f\",\n \"score\": \"0.57642525\",\n \"text\": \"private void getEx() {\\n\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e6804b1ba880a8cc8a98006ca4ba35b\",\n \"score\": \"0.5761362\",\n \"text\": \"@Override\\n public void init() {\\n\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518a761521ca9f5cb8a8055b32b72cda\",\n \"score\": \"0.57596046\",\n \"text\": \"@Override\\n\\tprotected void initialize() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518a761521ca9f5cb8a8055b32b72cda\",\n \"score\": \"0.57596046\",\n \"text\": \"@Override\\n\\tprotected void initialize() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518a761521ca9f5cb8a8055b32b72cda\",\n \"score\": \"0.57596046\",\n \"text\": \"@Override\\n\\tprotected void initialize() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b95bbed498007dfeba9b078798c02e53\",\n \"score\": \"0.5754483\",\n \"text\": \"@Override\\r\\n\\tpublic void exibir() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b93c6668ea7af171bbcdb3af74bb4a\",\n \"score\": \"0.57524616\",\n \"text\": \"@Override\\n public void bicar() {\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2cde12cf50e0ec60f909684878e936c\",\n \"score\": \"0.57514966\",\n \"text\": \"@Override\\n\\tpublic void wykonajZamknij() {\\n\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d614ed2ee5b3120f5ee8b427542ddc3\",\n \"score\": \"0.575025\",\n \"text\": \"@Override\\r\\n\\tpublic void init() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b143ffd983be4c8f41702b8f934b6f32\",\n \"score\": \"0.57337177\",\n \"text\": \"@Override\\r\\n\\tpublic void init() {\\n\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b143ffd983be4c8f41702b8f934b6f32\",\n \"score\": \"0.57337177\",\n \"text\": \"@Override\\r\\n\\tpublic void init() {\\n\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a53d232dac89259f961cbc93762f65b2\",\n \"score\": \"0.5731746\",\n \"text\": \"@Override\\r\\n\\t\\t\\tvoid n() {\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf193ee51fc2ac8c94c94264e19ff99c\",\n \"score\": \"0.57304704\",\n \"text\": \"@Override\\n public boolean cannibale() {\\n return false;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf193ee51fc2ac8c94c94264e19ff99c\",\n \"score\": \"0.57304704\",\n \"text\": \"@Override\\n public boolean cannibale() {\\n return false;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b072172b17a5e5ab0081f24545795f2\",\n \"score\": \"0.57235396\",\n \"text\": \"public void przymocowac() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17e507b0166d99f88c8237ad30d967b7\",\n \"score\": \"0.5716563\",\n \"text\": \"public void zamowic() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a937c607590931387a357d03c3b0eef4\",\n \"score\": \"0.5715831\",\n \"text\": \"@Override\\n\\tprotected void initialize() {\\n\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a937c607590931387a357d03c3b0eef4\",\n \"score\": \"0.5715831\",\n \"text\": \"@Override\\n\\tprotected void initialize() {\\n\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bbf3f0011f317b7faa88562e06495b7\",\n \"score\": \"0.5714223\",\n \"text\": \"@Override\\r\\n public void reculer() {\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ba6f6b334c51461b3fe944110e91ef5\",\n \"score\": \"0.5707728\",\n \"text\": \"@Override\\n\\tpublic void comerHojas() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39132efb6b42f8ec625d96ff6226d80b\",\n \"score\": \"0.56991017\",\n \"text\": \"@Override\\n\\tprotected void lazyLoad() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9072b54f55f18557f92b99be58ec2f\",\n \"score\": \"0.56886935\",\n \"text\": \"@Override\\r\\n public String toString() {\\n\\treturn null;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ca16e78cddd3c7d9de74c662606bbac\",\n \"score\": \"0.5686997\",\n \"text\": \"public void mo4570b() {\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"359579516b65b36ff8c4344d8363a3ed\",\n \"score\": \"0.5686103\",\n \"text\": \"@Override\\r\\n\\tprotected void initData() {\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09488e6c7d4190869b37b126a578249f\",\n \"score\": \"0.56821704\",\n \"text\": \"@Override\\r\\n\\t/**\\r\\n\\t * \\r\\n\\t */\\r\\n\\tpublic int size() {\\n\\t\\treturn 0;\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c641fa6ba40a5b14cc33cb07aa0671\",\n \"score\": \"0.5669649\",\n \"text\": \"@Override\\n\\tprotected void initData() {\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c641fa6ba40a5b14cc33cb07aa0671\",\n \"score\": \"0.5669649\",\n \"text\": \"@Override\\n\\tprotected void initData() {\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"001227e6b58a587f9eb4f77b15fc3588\",\n \"score\": \"0.5668976\",\n \"text\": \"@Override\\n\\tprotected void local() {\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d94df716ebd35f7ec07df01763ae94c\",\n \"score\": \"0.5655942\",\n \"text\": \"@Override\\n\\tpublic void init() {\\n\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d94df716ebd35f7ec07df01763ae94c\",\n \"score\": \"0.5655942\",\n \"text\": \"@Override\\n\\tpublic void init() {\\n\\n\\t}\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":984999,"cells":{"query_id":{"kind":"string","value":"60d39b68e0097a160c67382de81d055c"},"query":{"kind":"string","value":"This method was generated by MyBatis Generator. This method sets the value of the database column persons.city"},"positive_passages":{"kind":"list like","value":[{"docid":"8f9e1ff83c70e12f8c00f3971c32ce26","score":"0.6988063","text":"public void setCity(String city) {\n this.city = city;\n }","title":""}],"string":"[\n {\n \"docid\": \"8f9e1ff83c70e12f8c00f3971c32ce26\",\n \"score\": \"0.6988063\",\n \"text\": \"public void setCity(String city) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d0957d02c4d7e51a775bf0f53575804a","score":"0.7239875","text":"public void setCity(String city);","title":""},{"docid":"577df40dc05208b7a1a6b693ea45de45","score":"0.72193974","text":"public void setCity(String city) {\r\n\t this.city=city;\r\n }","title":""},{"docid":"474faf4685e89ca7cd07e27aed9f5a79","score":"0.71690977","text":"void setCity(java.lang.String city);","title":""},{"docid":"4d400b34d9c5c26c04931603d1d784fc","score":"0.71372074","text":"public void setCity(String newCity){\n this.city = newCity;\n }","title":""},{"docid":"3a4a3969b1a4a0323ad172db09292329","score":"0.7115246","text":"public void setCity(String newCity) { \n city = newCity;\n }","title":""},{"docid":"c299d3591b101abbaf987a9ab3d47281","score":"0.70939416","text":"public void setCity(String city) {\n \tthis.city = city ;\n }","title":""},{"docid":"7c68963c8f1e9ef564e78fd41be055ef","score":"0.7085715","text":"public void setCity(String city)\n {\n this.city = city;\n }","title":""},{"docid":"28fea776a7abd505886d87f86cd726be","score":"0.7069768","text":"public void setCity(String city){\n this.city = city;\n }","title":""},{"docid":"54368cae114d2f5c4507af08ae03b611","score":"0.70615137","text":"public void setCity(String city) {\r\n this.city = city;\r\n }","title":""},{"docid":"4c06dee834dc5dd0feaff704b0c40ca1","score":"0.70595026","text":"public void setCity(String city) {\n this.city = city;\n }","title":""},{"docid":"e6b4d8473102091c3cc5387ace7b9214","score":"0.70461637","text":"public void setCity (String City);","title":""},{"docid":"e6b4d8473102091c3cc5387ace7b9214","score":"0.70461637","text":"public void setCity (String City);","title":""},{"docid":"ea40583e961279c8d0811dd5fe2d3998","score":"0.70115954","text":"public void setCity( String city ) {\n this.city = city;\n }","title":""},{"docid":"8c4fd73b7021f750f3de751d52c55b17","score":"0.6954568","text":"public void setCity(java.lang.String city)\r\n {\r\n synchronized (monitor())\r\n {\r\n check_orphaned();\r\n org.apache.xmlbeans.SimpleValue target = null;\r\n target = (org.apache.xmlbeans.SimpleValue)get_store().find_element_user(CITY$0, 0);\r\n if (target == null)\r\n {\r\n target = (org.apache.xmlbeans.SimpleValue)get_store().add_element_user(CITY$0);\r\n }\r\n target.setStringValue(city);\r\n }\r\n }","title":""},{"docid":"76e7b79f3742fcd0d2721876ac632cdb","score":"0.69529563","text":"public void setCity(String city) {\n\t\t\r\n\t}","title":""},{"docid":"5a5039900532d2f8a6f653bfa31ab771","score":"0.694721","text":"public void setCity(java.lang.String city)\r\n {\r\n synchronized (monitor())\r\n {\r\n check_orphaned();\r\n org.apache.xmlbeans.SimpleValue target = null;\r\n target = (org.apache.xmlbeans.SimpleValue)get_store().find_element_user(CITY$2, 0);\r\n if (target == null)\r\n {\r\n target = (org.apache.xmlbeans.SimpleValue)get_store().add_element_user(CITY$2);\r\n }\r\n target.setStringValue(city);\r\n }\r\n }","title":""},{"docid":"83813d94d66fb56cf579e9824b09a85f","score":"0.6892375","text":"public void setCity(String city){\n\t\tthis.city = city;\n\t}","title":""},{"docid":"48baedcd69290095bbf5d3a03db03cad","score":"0.68861943","text":"public void setCity(String city) {\n\t\tthis.city = city;\t\t\n\t}","title":""},{"docid":"dd7b208ac16de1db89351541e952e882","score":"0.6868467","text":"public void setCity(java.lang.String city) {\r\n this.city = city;\r\n }","title":""},{"docid":"dd7b208ac16de1db89351541e952e882","score":"0.6868467","text":"public void setCity(java.lang.String city) {\r\n this.city = city;\r\n }","title":""},{"docid":"dd7b208ac16de1db89351541e952e882","score":"0.6868467","text":"public void setCity(java.lang.String city) {\r\n this.city = city;\r\n }","title":""},{"docid":"8acf0bf41ccfabb2a383af02c893417e","score":"0.6849177","text":"public void setCity(java.lang.String value);","title":""},{"docid":"4fa2d281294bcf6ab0e96033728ad028","score":"0.68371606","text":"public void setCity(String city) {\r\n\t\tthis.city = city;\r\n\t}","title":""},{"docid":"ba8f3b3f85103c672b7dfd0106419a64","score":"0.68006766","text":"public void setCity(java.lang.String city) {\n this.city = city;\n }","title":""},{"docid":"ba8f3b3f85103c672b7dfd0106419a64","score":"0.68006766","text":"public void setCity(java.lang.String city) {\n this.city = city;\n }","title":""},{"docid":"ba8f3b3f85103c672b7dfd0106419a64","score":"0.68006766","text":"public void setCity(java.lang.String city) {\n this.city = city;\n }","title":""},{"docid":"ba8f3b3f85103c672b7dfd0106419a64","score":"0.68006766","text":"public void setCity(java.lang.String city) {\n this.city = city;\n }","title":""},{"docid":"ba8f3b3f85103c672b7dfd0106419a64","score":"0.68006766","text":"public void setCity(java.lang.String city) {\n this.city = city;\n }","title":""},{"docid":"ba8f3b3f85103c672b7dfd0106419a64","score":"0.68006766","text":"public void setCity(java.lang.String city) {\n this.city = city;\n }","title":""},{"docid":"7ae7e1ce12349f3baa2a4bf91f4a71e0","score":"0.6751961","text":"public void changeStoreCity(String city){\n \tthis.city=city;\n }","title":""},{"docid":"2edd16d4859c16904e18b1d65c27996a","score":"0.67197347","text":"public void setCity(String city) {\n\t\tthis.city = city;\n\t}","title":""},{"docid":"2edd16d4859c16904e18b1d65c27996a","score":"0.67197347","text":"public void setCity(String city) {\n\t\tthis.city = city;\n\t}","title":""},{"docid":"2edd16d4859c16904e18b1d65c27996a","score":"0.67197347","text":"public void setCity(String city) {\n\t\tthis.city = city;\n\t}","title":""},{"docid":"2edd16d4859c16904e18b1d65c27996a","score":"0.67197347","text":"public void setCity(String city) {\n\t\tthis.city = city;\n\t}","title":""},{"docid":"2edd16d4859c16904e18b1d65c27996a","score":"0.67197347","text":"public void setCity(String city) {\n\t\tthis.city = city;\n\t}","title":""},{"docid":"2edd16d4859c16904e18b1d65c27996a","score":"0.67197347","text":"public void setCity(String city) {\n\t\tthis.city = city;\n\t}","title":""},{"docid":"2edd16d4859c16904e18b1d65c27996a","score":"0.67197347","text":"public void setCity(String city) {\n\t\tthis.city = city;\n\t}","title":""},{"docid":"2edd16d4859c16904e18b1d65c27996a","score":"0.67197347","text":"public void setCity(String city) {\n\t\tthis.city = city;\n\t}","title":""},{"docid":"cbd4060461f1e6228cb0956e4c678750","score":"0.6713961","text":"public void setCity(City city) {\n\t\tthis.city = city;\n\t}","title":""},{"docid":"cbd4060461f1e6228cb0956e4c678750","score":"0.6713961","text":"public void setCity(City city) {\n\t\tthis.city = city;\n\t}","title":""},{"docid":"c2e3cef6350757537c431bb7e4d63acb","score":"0.67084336","text":"public void setCity(Integer city) {\n this.city = city;\n }","title":""},{"docid":"c2e3cef6350757537c431bb7e4d63acb","score":"0.67084336","text":"public void setCity(Integer city) {\n this.city = city;\n }","title":""},{"docid":"c2e3cef6350757537c431bb7e4d63acb","score":"0.67084336","text":"public void setCity(Integer city) {\n this.city = city;\n }","title":""},{"docid":"35e01552e53ceeff82d2da243f3f5326","score":"0.6667102","text":"public abstract void setCity(String city);","title":""},{"docid":"4d2987673367cc07c69dd4d2b6f0c893","score":"0.6626711","text":"public void setCity(String c) {\n\t\tm_City = c;\n\t}","title":""},{"docid":"cafd536283912d62f7166e83ca9b5fe9","score":"0.662652","text":"public void xsetCity(org.apache.xmlbeans.XmlString city)\r\n {\r\n synchronized (monitor())\r\n {\r\n check_orphaned();\r\n org.apache.xmlbeans.XmlString target = null;\r\n target = (org.apache.xmlbeans.XmlString)get_store().find_element_user(CITY$0, 0);\r\n if (target == null)\r\n {\r\n target = (org.apache.xmlbeans.XmlString)get_store().add_element_user(CITY$0);\r\n }\r\n target.set(city);\r\n }\r\n }","title":""},{"docid":"3100b24f7eb6f0d5ece01445adac1157","score":"0.6625753","text":"public void setCity(java.lang.String newCity) {\n \tcity = newCity;\n }","title":""},{"docid":"eab9e54549a9ed0833c983b79343c4cc","score":"0.6612893","text":"public void xsetCity(org.apache.xmlbeans.XmlString city)\r\n {\r\n synchronized (monitor())\r\n {\r\n check_orphaned();\r\n org.apache.xmlbeans.XmlString target = null;\r\n target = (org.apache.xmlbeans.XmlString)get_store().find_element_user(CITY$2, 0);\r\n if (target == null)\r\n {\r\n target = (org.apache.xmlbeans.XmlString)get_store().add_element_user(CITY$2);\r\n }\r\n target.set(city);\r\n }\r\n }","title":""},{"docid":"01b1f3d3517042372b1d57057fd38aa5","score":"0.6586898","text":"public void setLocationCity (String someCity) {\r\n\t\tlocationCity = someCity;\r\n\t}","title":""},{"docid":"9de010c9717905a48e2459256acb9174","score":"0.65030676","text":"public cascading.avro.pojo.EmbeddedPojo.Builder setCity(java.lang.CharSequence value) {\n validate(fields()[1], value);\n this.City = value;\n fieldSetFlags()[1] = true;\n return this; \n }","title":""},{"docid":"8b864a4f55685039325148e2d5ed96e3","score":"0.64969265","text":"public void setCityInternal(java.lang.String value);","title":""},{"docid":"53fdc079c81c46bef0154003746bf847","score":"0.64953643","text":"public void setCity(String city) {\r\n\t\tthis.city = city == null ? null : city.trim();\r\n\t}","title":""},{"docid":"9ef04401683c21e84a5aa977a53df7bc","score":"0.6466979","text":"public void setCity(String city) {\n this.city = city == null ? null : city.trim();\n }","title":""},{"docid":"9ef04401683c21e84a5aa977a53df7bc","score":"0.6466979","text":"public void setCity(String city) {\n this.city = city == null ? null : city.trim();\n }","title":""},{"docid":"9ef04401683c21e84a5aa977a53df7bc","score":"0.6466979","text":"public void setCity(String city) {\n this.city = city == null ? null : city.trim();\n }","title":""},{"docid":"9ef04401683c21e84a5aa977a53df7bc","score":"0.6466979","text":"public void setCity(String city) {\n this.city = city == null ? null : city.trim();\n }","title":""},{"docid":"9ef04401683c21e84a5aa977a53df7bc","score":"0.6466979","text":"public void setCity(String city) {\n this.city = city == null ? null : city.trim();\n }","title":""},{"docid":"29c39fa83daf822fae3b3d226fad6370","score":"0.64592814","text":"public void setCityName(String cityName) {\r\n\t_cityName=cityName;\r\n}","title":""},{"docid":"c255887af9fd9979ade13256f5a0e131","score":"0.64146954","text":"public void setLocCity(String locCity) {\n this.locCity = locCity;\n }","title":""},{"docid":"8ed938cfa084fcd03408bbade89af36e","score":"0.63994014","text":"public void setCityId(int value) {\n this.cityId = value;\n }","title":""},{"docid":"c5198a505fb2d73e3fdc8adad1118786","score":"0.63624257","text":"public void setCity(java.lang.CharSequence value) {\n this.city = value;\n }","title":""},{"docid":"3375a4ca6256cbf85be159db6e31d881","score":"0.6359369","text":"public void setCity(java.lang.CharSequence value) {\n this.City = value;\n }","title":""},{"docid":"c22ccc6790cb086ac3fca59de665e1a5","score":"0.635655","text":"public void setJobCity(String city) {\n\t\tSelect stateDropList = new Select(driver.findElement(jobCity));\n\t\tstateDropList.selectByVisibleText(city);\n\t\t\n\t}","title":""},{"docid":"9e8e70d9878a3e0fc9363ff55d228c18","score":"0.6308096","text":"public void editCity(String new_city)\n\t{\n\t\tcity = new_city;\n\t}","title":""},{"docid":"5300072495abb454040a03f1d370889a","score":"0.62641984","text":"public void setAccount_city(String account_city) {\n this.account_city = account_city;\n }","title":""},{"docid":"1ee624edbf8602277af2f9e1d8fc5a98","score":"0.62497133","text":"@JsonSetter(\"city\")\r\n public void setCity (String value) { \r\n this.city = value;\r\n }","title":""},{"docid":"f6b43230abdd1f96b12f145040c190d8","score":"0.61898255","text":"public br.com.hitbra.marketplace.avro.shops.ContactInformations.Builder setCity(java.lang.CharSequence value) {\n validate(fields()[0], value);\n this.city = value;\n fieldSetFlags()[0] = true;\n return this; \n }","title":""},{"docid":"98cb3b6a3921f44f8ed53e9e97d1d19f","score":"0.61312306","text":"public void setCityName(String city) {\n\t\t_cityName = city;\n\t}","title":""},{"docid":"b0ab3f3db0731030ea38a1df312354ed","score":"0.6095276","text":"public BaseAddressBuilder city(@Nullable final String city) {\n this.city = city;\n return this;\n }","title":""},{"docid":"903f13f8477550314d2f0dea5ad7505e","score":"0.6087617","text":"public String getCity() {\r\n\t return city;\r\n }","title":""},{"docid":"09ab125867cafcedf6ac5b4f2399dee3","score":"0.60750556","text":"public void setCityId(Integer cityId) {\n this.cityId = cityId;\n }","title":""},{"docid":"09ab125867cafcedf6ac5b4f2399dee3","score":"0.60750556","text":"public void setCityId(Integer cityId) {\n this.cityId = cityId;\n }","title":""},{"docid":"c8dd99056578891e5ab2663281e122c1","score":"0.6054126","text":"public String getCity() {return city;}","title":""},{"docid":"1209a29deae5a9ba4a177d72421aebe1","score":"0.6006239","text":"public String getCity() {\r\n return this.city;\r\n }","title":""},{"docid":"1209a29deae5a9ba4a177d72421aebe1","score":"0.6006239","text":"public String getCity() {\r\n return this.city;\r\n }","title":""},{"docid":"2b927af38060e71b6d8935e9a72acc24","score":"0.60017073","text":"@Override\n\tpublic void changeCity(City city) {\n\t\trefreshProductInfo();\n\t\tchangeChooseCity();\n\t}","title":""},{"docid":"8d5b05fc4d2164ac5290e2cf1b36a351","score":"0.59958154","text":"public void setCitySt(String theCitySt)\n {\n citySt = theCitySt;\n }","title":""},{"docid":"7e4dd5be23b9d6e92c9418dd2df17b3c","score":"0.599099","text":"public void setCity_id(String city_id) {\r\n this.city_id = city_id == null ? null : city_id.trim();\r\n }","title":""},{"docid":"ca19db351d479dd50e1ea8c2c4cb8554","score":"0.59850466","text":"public void setCity(String strCity){\r\n \tcity.clear();\r\n \tcity.sendKeys(strCity); \r\n }","title":""},{"docid":"70a0774c1bd72bfc3bfcc8b99ecf7963","score":"0.5981168","text":"public void setWarehouseCity(java.lang.String param){\r\n \r\n this.localWarehouseCity=param;\r\n \r\n\r\n }","title":""},{"docid":"70a0774c1bd72bfc3bfcc8b99ecf7963","score":"0.5981168","text":"public void setWarehouseCity(java.lang.String param){\r\n \r\n this.localWarehouseCity=param;\r\n \r\n\r\n }","title":""},{"docid":"70a0774c1bd72bfc3bfcc8b99ecf7963","score":"0.5981168","text":"public void setWarehouseCity(java.lang.String param){\r\n \r\n this.localWarehouseCity=param;\r\n \r\n\r\n }","title":""},{"docid":"4d027bde9da3d0d8fa2fcfc2812e1e4d","score":"0.5957235","text":"@Override\r\n\tpublic void updatecity() {\n\t\t\r\n\t}","title":""},{"docid":"250bd8cafef121e45b0b0707cc9e3400","score":"0.59532565","text":"public String getCity(){\r\n return this.City;\r\n }","title":""},{"docid":"06e02014ca437ad14a0560259f5a2f86","score":"0.5948878","text":"public String getCity_id() {\r\n return city_id;\r\n }","title":""},{"docid":"e807aee880c548654d0a6361232db06a","score":"0.59460354","text":"public int getCityId() {\n return cityId;\n }","title":""},{"docid":"dacf7af0476cb628d6ce440bae1cc3c5","score":"0.5942092","text":"public String getCity(){\n return this.city;\n }","title":""},{"docid":"dacf7af0476cb628d6ce440bae1cc3c5","score":"0.5942092","text":"public String getCity(){\n return this.city;\n }","title":""},{"docid":"101d6ab5a9df79f7acd20fd2722481d5","score":"0.5932412","text":"public void setCity(String contactCity) \r\n\t{\r\n\t\tif(contactCity != null)\r\n\t\t{\r\n\t\t\tcity = contactCity;\r\n\t\t}\r\n\t\telse if((city == null)&& (contactCity == null))\r\n\t\t{\r\n\t\t\tthrow new IllegalArgumentException(\"The city must be entered\");\r\n\t\t}\r\n\t}","title":""},{"docid":"82a8bdb1dfddc85d2c290d4c9abb8773","score":"0.59316087","text":"void xsetCity(org.apache.xmlbeans.XmlString city);","title":""},{"docid":"dfe6e968dc6ebbcff996c7f7893d324d","score":"0.59237653","text":"public void setCapital(City capital)\n\t{\n\t\tthis.capital = capital;\n\t}","title":""},{"docid":"77f2f8cb480f6e198daf316527b74b80","score":"0.59220964","text":"public void setCityName(String cityName) {\n this.cityName = cityName;\n }","title":""}],"string":"[\n {\n \"docid\": \"d0957d02c4d7e51a775bf0f53575804a\",\n \"score\": \"0.7239875\",\n \"text\": \"public void setCity(String city);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"577df40dc05208b7a1a6b693ea45de45\",\n \"score\": \"0.72193974\",\n \"text\": \"public void setCity(String city) {\\r\\n\\t this.city=city;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"474faf4685e89ca7cd07e27aed9f5a79\",\n \"score\": \"0.71690977\",\n \"text\": \"void setCity(java.lang.String city);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d400b34d9c5c26c04931603d1d784fc\",\n \"score\": \"0.71372074\",\n \"text\": \"public void setCity(String newCity){\\n this.city = newCity;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a4a3969b1a4a0323ad172db09292329\",\n \"score\": \"0.7115246\",\n \"text\": \"public void setCity(String newCity) { \\n city = newCity;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c299d3591b101abbaf987a9ab3d47281\",\n \"score\": \"0.70939416\",\n \"text\": \"public void setCity(String city) {\\n \\tthis.city = city ;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c68963c8f1e9ef564e78fd41be055ef\",\n \"score\": \"0.7085715\",\n \"text\": \"public void setCity(String city)\\n {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28fea776a7abd505886d87f86cd726be\",\n \"score\": \"0.7069768\",\n \"text\": \"public void setCity(String city){\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54368cae114d2f5c4507af08ae03b611\",\n \"score\": \"0.70615137\",\n \"text\": \"public void setCity(String city) {\\r\\n this.city = city;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c06dee834dc5dd0feaff704b0c40ca1\",\n \"score\": \"0.70595026\",\n \"text\": \"public void setCity(String city) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6b4d8473102091c3cc5387ace7b9214\",\n \"score\": \"0.70461637\",\n \"text\": \"public void setCity (String City);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6b4d8473102091c3cc5387ace7b9214\",\n \"score\": \"0.70461637\",\n \"text\": \"public void setCity (String City);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea40583e961279c8d0811dd5fe2d3998\",\n \"score\": \"0.70115954\",\n \"text\": \"public void setCity( String city ) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c4fd73b7021f750f3de751d52c55b17\",\n \"score\": \"0.6954568\",\n \"text\": \"public void setCity(java.lang.String city)\\r\\n {\\r\\n synchronized (monitor())\\r\\n {\\r\\n check_orphaned();\\r\\n org.apache.xmlbeans.SimpleValue target = null;\\r\\n target = (org.apache.xmlbeans.SimpleValue)get_store().find_element_user(CITY$0, 0);\\r\\n if (target == null)\\r\\n {\\r\\n target = (org.apache.xmlbeans.SimpleValue)get_store().add_element_user(CITY$0);\\r\\n }\\r\\n target.setStringValue(city);\\r\\n }\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76e7b79f3742fcd0d2721876ac632cdb\",\n \"score\": \"0.69529563\",\n \"text\": \"public void setCity(String city) {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a5039900532d2f8a6f653bfa31ab771\",\n \"score\": \"0.694721\",\n \"text\": \"public void setCity(java.lang.String city)\\r\\n {\\r\\n synchronized (monitor())\\r\\n {\\r\\n check_orphaned();\\r\\n org.apache.xmlbeans.SimpleValue target = null;\\r\\n target = (org.apache.xmlbeans.SimpleValue)get_store().find_element_user(CITY$2, 0);\\r\\n if (target == null)\\r\\n {\\r\\n target = (org.apache.xmlbeans.SimpleValue)get_store().add_element_user(CITY$2);\\r\\n }\\r\\n target.setStringValue(city);\\r\\n }\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83813d94d66fb56cf579e9824b09a85f\",\n \"score\": \"0.6892375\",\n \"text\": \"public void setCity(String city){\\n\\t\\tthis.city = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48baedcd69290095bbf5d3a03db03cad\",\n \"score\": \"0.68861943\",\n \"text\": \"public void setCity(String city) {\\n\\t\\tthis.city = city;\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd7b208ac16de1db89351541e952e882\",\n \"score\": \"0.6868467\",\n \"text\": \"public void setCity(java.lang.String city) {\\r\\n this.city = city;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd7b208ac16de1db89351541e952e882\",\n \"score\": \"0.6868467\",\n \"text\": \"public void setCity(java.lang.String city) {\\r\\n this.city = city;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd7b208ac16de1db89351541e952e882\",\n \"score\": \"0.6868467\",\n \"text\": \"public void setCity(java.lang.String city) {\\r\\n this.city = city;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8acf0bf41ccfabb2a383af02c893417e\",\n \"score\": \"0.6849177\",\n \"text\": \"public void setCity(java.lang.String value);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa2d281294bcf6ab0e96033728ad028\",\n \"score\": \"0.68371606\",\n \"text\": \"public void setCity(String city) {\\r\\n\\t\\tthis.city = city;\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8f3b3f85103c672b7dfd0106419a64\",\n \"score\": \"0.68006766\",\n \"text\": \"public void setCity(java.lang.String city) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8f3b3f85103c672b7dfd0106419a64\",\n \"score\": \"0.68006766\",\n \"text\": \"public void setCity(java.lang.String city) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8f3b3f85103c672b7dfd0106419a64\",\n \"score\": \"0.68006766\",\n \"text\": \"public void setCity(java.lang.String city) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8f3b3f85103c672b7dfd0106419a64\",\n \"score\": \"0.68006766\",\n \"text\": \"public void setCity(java.lang.String city) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8f3b3f85103c672b7dfd0106419a64\",\n \"score\": \"0.68006766\",\n \"text\": \"public void setCity(java.lang.String city) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8f3b3f85103c672b7dfd0106419a64\",\n \"score\": \"0.68006766\",\n \"text\": \"public void setCity(java.lang.String city) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ae7e1ce12349f3baa2a4bf91f4a71e0\",\n \"score\": \"0.6751961\",\n \"text\": \"public void changeStoreCity(String city){\\n \\tthis.city=city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edd16d4859c16904e18b1d65c27996a\",\n \"score\": \"0.67197347\",\n \"text\": \"public void setCity(String city) {\\n\\t\\tthis.city = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edd16d4859c16904e18b1d65c27996a\",\n \"score\": \"0.67197347\",\n \"text\": \"public void setCity(String city) {\\n\\t\\tthis.city = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edd16d4859c16904e18b1d65c27996a\",\n \"score\": \"0.67197347\",\n \"text\": \"public void setCity(String city) {\\n\\t\\tthis.city = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edd16d4859c16904e18b1d65c27996a\",\n \"score\": \"0.67197347\",\n \"text\": \"public void setCity(String city) {\\n\\t\\tthis.city = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edd16d4859c16904e18b1d65c27996a\",\n \"score\": \"0.67197347\",\n \"text\": \"public void setCity(String city) {\\n\\t\\tthis.city = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edd16d4859c16904e18b1d65c27996a\",\n \"score\": \"0.67197347\",\n \"text\": \"public void setCity(String city) {\\n\\t\\tthis.city = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edd16d4859c16904e18b1d65c27996a\",\n \"score\": \"0.67197347\",\n \"text\": \"public void setCity(String city) {\\n\\t\\tthis.city = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edd16d4859c16904e18b1d65c27996a\",\n \"score\": \"0.67197347\",\n \"text\": \"public void setCity(String city) {\\n\\t\\tthis.city = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbd4060461f1e6228cb0956e4c678750\",\n \"score\": \"0.6713961\",\n \"text\": \"public void setCity(City city) {\\n\\t\\tthis.city = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbd4060461f1e6228cb0956e4c678750\",\n \"score\": \"0.6713961\",\n \"text\": \"public void setCity(City city) {\\n\\t\\tthis.city = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2e3cef6350757537c431bb7e4d63acb\",\n \"score\": \"0.67084336\",\n \"text\": \"public void setCity(Integer city) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2e3cef6350757537c431bb7e4d63acb\",\n \"score\": \"0.67084336\",\n \"text\": \"public void setCity(Integer city) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2e3cef6350757537c431bb7e4d63acb\",\n \"score\": \"0.67084336\",\n \"text\": \"public void setCity(Integer city) {\\n this.city = city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e01552e53ceeff82d2da243f3f5326\",\n \"score\": \"0.6667102\",\n \"text\": \"public abstract void setCity(String city);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d2987673367cc07c69dd4d2b6f0c893\",\n \"score\": \"0.6626711\",\n \"text\": \"public void setCity(String c) {\\n\\t\\tm_City = c;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cafd536283912d62f7166e83ca9b5fe9\",\n \"score\": \"0.662652\",\n \"text\": \"public void xsetCity(org.apache.xmlbeans.XmlString city)\\r\\n {\\r\\n synchronized (monitor())\\r\\n {\\r\\n check_orphaned();\\r\\n org.apache.xmlbeans.XmlString target = null;\\r\\n target = (org.apache.xmlbeans.XmlString)get_store().find_element_user(CITY$0, 0);\\r\\n if (target == null)\\r\\n {\\r\\n target = (org.apache.xmlbeans.XmlString)get_store().add_element_user(CITY$0);\\r\\n }\\r\\n target.set(city);\\r\\n }\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3100b24f7eb6f0d5ece01445adac1157\",\n \"score\": \"0.6625753\",\n \"text\": \"public void setCity(java.lang.String newCity) {\\n \\tcity = newCity;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eab9e54549a9ed0833c983b79343c4cc\",\n \"score\": \"0.6612893\",\n \"text\": \"public void xsetCity(org.apache.xmlbeans.XmlString city)\\r\\n {\\r\\n synchronized (monitor())\\r\\n {\\r\\n check_orphaned();\\r\\n org.apache.xmlbeans.XmlString target = null;\\r\\n target = (org.apache.xmlbeans.XmlString)get_store().find_element_user(CITY$2, 0);\\r\\n if (target == null)\\r\\n {\\r\\n target = (org.apache.xmlbeans.XmlString)get_store().add_element_user(CITY$2);\\r\\n }\\r\\n target.set(city);\\r\\n }\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01b1f3d3517042372b1d57057fd38aa5\",\n \"score\": \"0.6586898\",\n \"text\": \"public void setLocationCity (String someCity) {\\r\\n\\t\\tlocationCity = someCity;\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9de010c9717905a48e2459256acb9174\",\n \"score\": \"0.65030676\",\n \"text\": \"public cascading.avro.pojo.EmbeddedPojo.Builder setCity(java.lang.CharSequence value) {\\n validate(fields()[1], value);\\n this.City = value;\\n fieldSetFlags()[1] = true;\\n return this; \\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b864a4f55685039325148e2d5ed96e3\",\n \"score\": \"0.64969265\",\n \"text\": \"public void setCityInternal(java.lang.String value);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53fdc079c81c46bef0154003746bf847\",\n \"score\": \"0.64953643\",\n \"text\": \"public void setCity(String city) {\\r\\n\\t\\tthis.city = city == null ? null : city.trim();\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef04401683c21e84a5aa977a53df7bc\",\n \"score\": \"0.6466979\",\n \"text\": \"public void setCity(String city) {\\n this.city = city == null ? null : city.trim();\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef04401683c21e84a5aa977a53df7bc\",\n \"score\": \"0.6466979\",\n \"text\": \"public void setCity(String city) {\\n this.city = city == null ? null : city.trim();\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef04401683c21e84a5aa977a53df7bc\",\n \"score\": \"0.6466979\",\n \"text\": \"public void setCity(String city) {\\n this.city = city == null ? null : city.trim();\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef04401683c21e84a5aa977a53df7bc\",\n \"score\": \"0.6466979\",\n \"text\": \"public void setCity(String city) {\\n this.city = city == null ? null : city.trim();\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef04401683c21e84a5aa977a53df7bc\",\n \"score\": \"0.6466979\",\n \"text\": \"public void setCity(String city) {\\n this.city = city == null ? null : city.trim();\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29c39fa83daf822fae3b3d226fad6370\",\n \"score\": \"0.64592814\",\n \"text\": \"public void setCityName(String cityName) {\\r\\n\\t_cityName=cityName;\\r\\n}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c255887af9fd9979ade13256f5a0e131\",\n \"score\": \"0.64146954\",\n \"text\": \"public void setLocCity(String locCity) {\\n this.locCity = locCity;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ed938cfa084fcd03408bbade89af36e\",\n \"score\": \"0.63994014\",\n \"text\": \"public void setCityId(int value) {\\n this.cityId = value;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5198a505fb2d73e3fdc8adad1118786\",\n \"score\": \"0.63624257\",\n \"text\": \"public void setCity(java.lang.CharSequence value) {\\n this.city = value;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3375a4ca6256cbf85be159db6e31d881\",\n \"score\": \"0.6359369\",\n \"text\": \"public void setCity(java.lang.CharSequence value) {\\n this.City = value;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c22ccc6790cb086ac3fca59de665e1a5\",\n \"score\": \"0.635655\",\n \"text\": \"public void setJobCity(String city) {\\n\\t\\tSelect stateDropList = new Select(driver.findElement(jobCity));\\n\\t\\tstateDropList.selectByVisibleText(city);\\n\\t\\t\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e8e70d9878a3e0fc9363ff55d228c18\",\n \"score\": \"0.6308096\",\n \"text\": \"public void editCity(String new_city)\\n\\t{\\n\\t\\tcity = new_city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5300072495abb454040a03f1d370889a\",\n \"score\": \"0.62641984\",\n \"text\": \"public void setAccount_city(String account_city) {\\n this.account_city = account_city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ee624edbf8602277af2f9e1d8fc5a98\",\n \"score\": \"0.62497133\",\n \"text\": \"@JsonSetter(\\\"city\\\")\\r\\n public void setCity (String value) { \\r\\n this.city = value;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6b43230abdd1f96b12f145040c190d8\",\n \"score\": \"0.61898255\",\n \"text\": \"public br.com.hitbra.marketplace.avro.shops.ContactInformations.Builder setCity(java.lang.CharSequence value) {\\n validate(fields()[0], value);\\n this.city = value;\\n fieldSetFlags()[0] = true;\\n return this; \\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98cb3b6a3921f44f8ed53e9e97d1d19f\",\n \"score\": \"0.61312306\",\n \"text\": \"public void setCityName(String city) {\\n\\t\\t_cityName = city;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0ab3f3db0731030ea38a1df312354ed\",\n \"score\": \"0.6095276\",\n \"text\": \"public BaseAddressBuilder city(@Nullable final String city) {\\n this.city = city;\\n return this;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"903f13f8477550314d2f0dea5ad7505e\",\n \"score\": \"0.6087617\",\n \"text\": \"public String getCity() {\\r\\n\\t return city;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09ab125867cafcedf6ac5b4f2399dee3\",\n \"score\": \"0.60750556\",\n \"text\": \"public void setCityId(Integer cityId) {\\n this.cityId = cityId;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09ab125867cafcedf6ac5b4f2399dee3\",\n \"score\": \"0.60750556\",\n \"text\": \"public void setCityId(Integer cityId) {\\n this.cityId = cityId;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8dd99056578891e5ab2663281e122c1\",\n \"score\": \"0.6054126\",\n \"text\": \"public String getCity() {return city;}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1209a29deae5a9ba4a177d72421aebe1\",\n \"score\": \"0.6006239\",\n \"text\": \"public String getCity() {\\r\\n return this.city;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1209a29deae5a9ba4a177d72421aebe1\",\n \"score\": \"0.6006239\",\n \"text\": \"public String getCity() {\\r\\n return this.city;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b927af38060e71b6d8935e9a72acc24\",\n \"score\": \"0.60017073\",\n \"text\": \"@Override\\n\\tpublic void changeCity(City city) {\\n\\t\\trefreshProductInfo();\\n\\t\\tchangeChooseCity();\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d5b05fc4d2164ac5290e2cf1b36a351\",\n \"score\": \"0.59958154\",\n \"text\": \"public void setCitySt(String theCitySt)\\n {\\n citySt = theCitySt;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e4dd5be23b9d6e92c9418dd2df17b3c\",\n \"score\": \"0.599099\",\n \"text\": \"public void setCity_id(String city_id) {\\r\\n this.city_id = city_id == null ? null : city_id.trim();\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca19db351d479dd50e1ea8c2c4cb8554\",\n \"score\": \"0.59850466\",\n \"text\": \"public void setCity(String strCity){\\r\\n \\tcity.clear();\\r\\n \\tcity.sendKeys(strCity); \\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70a0774c1bd72bfc3bfcc8b99ecf7963\",\n \"score\": \"0.5981168\",\n \"text\": \"public void setWarehouseCity(java.lang.String param){\\r\\n \\r\\n this.localWarehouseCity=param;\\r\\n \\r\\n\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70a0774c1bd72bfc3bfcc8b99ecf7963\",\n \"score\": \"0.5981168\",\n \"text\": \"public void setWarehouseCity(java.lang.String param){\\r\\n \\r\\n this.localWarehouseCity=param;\\r\\n \\r\\n\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70a0774c1bd72bfc3bfcc8b99ecf7963\",\n \"score\": \"0.5981168\",\n \"text\": \"public void setWarehouseCity(java.lang.String param){\\r\\n \\r\\n this.localWarehouseCity=param;\\r\\n \\r\\n\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d027bde9da3d0d8fa2fcfc2812e1e4d\",\n \"score\": \"0.5957235\",\n \"text\": \"@Override\\r\\n\\tpublic void updatecity() {\\n\\t\\t\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"250bd8cafef121e45b0b0707cc9e3400\",\n \"score\": \"0.59532565\",\n \"text\": \"public String getCity(){\\r\\n return this.City;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06e02014ca437ad14a0560259f5a2f86\",\n \"score\": \"0.5948878\",\n \"text\": \"public String getCity_id() {\\r\\n return city_id;\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e807aee880c548654d0a6361232db06a\",\n \"score\": \"0.59460354\",\n \"text\": \"public int getCityId() {\\n return cityId;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dacf7af0476cb628d6ce440bae1cc3c5\",\n \"score\": \"0.5942092\",\n \"text\": \"public String getCity(){\\n return this.city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dacf7af0476cb628d6ce440bae1cc3c5\",\n \"score\": \"0.5942092\",\n \"text\": \"public String getCity(){\\n return this.city;\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"101d6ab5a9df79f7acd20fd2722481d5\",\n \"score\": \"0.5932412\",\n \"text\": \"public void setCity(String contactCity) \\r\\n\\t{\\r\\n\\t\\tif(contactCity != null)\\r\\n\\t\\t{\\r\\n\\t\\t\\tcity = contactCity;\\r\\n\\t\\t}\\r\\n\\t\\telse if((city == null)&& (contactCity == null))\\r\\n\\t\\t{\\r\\n\\t\\t\\tthrow new IllegalArgumentException(\\\"The city must be entered\\\");\\r\\n\\t\\t}\\r\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a8bdb1dfddc85d2c290d4c9abb8773\",\n \"score\": \"0.59316087\",\n \"text\": \"void xsetCity(org.apache.xmlbeans.XmlString city);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfe6e968dc6ebbcff996c7f7893d324d\",\n \"score\": \"0.59237653\",\n \"text\": \"public void setCapital(City capital)\\n\\t{\\n\\t\\tthis.capital = capital;\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f2f8cb480f6e198daf316527b74b80\",\n \"score\": \"0.59220964\",\n \"text\": \"public void setCityName(String cityName) {\\n this.cityName = cityName;\\n }\",\n \"title\": \"\"\n 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query_id stringlengths 32 32 | query stringlengths 7 129k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
4d367088c1472640f844fd4379a43278 | / This is the mapping that allows the user to make use of the credits service. The credit is purchased by the client and the client then has access to trailers. The details of credits is not clear but must plan for a. per trip credits b. time range credits, i.e over a period of x years serve the page with all the credi... | [
{
"docid": "d8e13d8cc30ada01abf8cdea107b4de9",
"score": "0.5230914",
"text": "@RequestMapping({\"/users/{username}/credit\"})\n public String profileCreditPage(){\n return \"profile/credit\";\n }",
"title": ""
}
] | [
{
"docid": "839b05e3cd75360d0f05af7e50d89730",
"score": "0.66831076",
"text": "public void credit();",
"title": ""
},
{
"docid": "709a4edd8a2ae73f4ba0042efe806c5e",
"score": "0.6529838",
"text": "@Override\n\tpublic void payCredit() {\n\n\t}",
"title": ""
},
{
"docid": "5... |
e261c24feb1462ce94408fb8de174f9e | Uses regex to check if valid email and password | [
{
"docid": "475cb2304c132f1c542f66fdfce172ca",
"score": "0.6956487",
"text": "private int isValidLogin(String email, String pass) {\n Matcher emailMatcher = emailPattern.matcher(email);\n Matcher passMatcher = passPattern.matcher(pass);\n if(!emailMatcher.matches()){\n re... | [
{
"docid": "9fc1f6e4c9f27014c3e8ed6b295d1c26",
"score": "0.76356673",
"text": "public void validateValidEmailAndPassword() {\n\t\t\r\n\t}",
"title": ""
},
{
"docid": "b0030be4d6417966b3cf414dea8405b3",
"score": "0.75267303",
"text": "private Boolean checkValidEmailAndPassword(Boolean... |
b39318473bf880248c12b1a867b80487 | this method returns the number of tabs in the tab strip | [
{
"docid": "5ab67a060f40db68fb80297850d476de",
"score": "0.0",
"text": "@Override\n public int getCount() {\n return Title.length;\n }",
"title": ""
}
] | [
{
"docid": "05951fec280327ad2bf0324579b2ef05",
"score": "0.77627754",
"text": "public int getTabCount() {\n return tabs.size();\n }",
"title": ""
},
{
"docid": "46607bcf68098cca0304387a42eb3ac3",
"score": "0.75373304",
"text": "public int getTabCount() { return tabArray.siz... |
2b817369cbf941fd88e535558851dfe8 | Get the connectivityStatus property: Represents the connectivity status of the connected cluster. | [
{
"docid": "09a51a2fcf1f31a4aad1f67c8bde2fd4",
"score": "0.84470993",
"text": "public ConnectivityStatus connectivityStatus() {\n return this.connectivityStatus;\n }",
"title": ""
}
] | [
{
"docid": "cf4dc03515b759cef9c2e9b64148db93",
"score": "0.79681885",
"text": "public ClusterConnectionStatus clusterConnectionStatus() {\n return this.clusterConnectionStatus;\n }",
"title": ""
},
{
"docid": "cb9b3fd6ec3f821f2e0cba6c9849bded",
"score": "0.72748774",
"text"... |
a203091be962354de4f9d0d3d71b14b2 | Subroutine returns transposed version of given 2D array. T[j][i] = M[i][j] | [
{
"docid": "07635cc360dda8f3137cd34e32cfb80f",
"score": "0.75373733",
"text": "public static int[][] transpose(int[][] M) {\n int[][] T = new int[M[0].length][M.length];\n for (int i = 0; i < M.length; i++) \n for (int j = 0; j < M[i].length; j++ ) \n T[j][i] = M[i][j];\n return T;\... | [
{
"docid": "54d9781321e358094f662846db1868d6",
"score": "0.7633798",
"text": "public static double [][] transposeMatrix(double [][] m){\n\t\tdouble [][] m2 = new double[m[0].length][m.length];\n\t\t\n\t\tfor(int i = 0; i < m.length; i++){\n\t\t\tfor(int j = 0; j < m[i].length; j++){\n\t\t\t\tm2[j][i] = ... |
da20e9be60c0767bbad7b484f750f789 | read the csv using this hand dandy library | [
{
"docid": "dddb57a1f34b84a3152b3ee16bab36bf",
"score": "0.0",
"text": "public static double[] runTestFile(DecisionTree tree, String filename){\n CSVReader reader = null;\n List<String[]> trainingData = null;\n try {\n reader = new CSVReader(new FileReader(filename));\n trainingData =... | [
{
"docid": "d2c44272407639ffecef67e71dffbd91",
"score": "0.7007884",
"text": "public void ReadFromCSVFile() throws IOException {\n\t FileReader fr = new FileReader(\"D:\\\\java\\\\AddressBook-System\\\\AddressBookData.csv\");\n\t \n\t // Declaring loop variable\n\t Scanner sc = new ... |
ad02c256b30ba28de55ac4588b46bddb | Add a Column entity which column name is "ipfix" to the Row entity of attributes. | [
{
"docid": "393f0885e0b1f778d09f573248cf7ce2",
"score": "0.58741766",
"text": "public void setIpfix(Uuid ipfix) {\n ColumnDescription columndesc = new ColumnDescription(FlowSampleCollectorSetColumn.IPFIX.columnName(),\n \"setIpfix\", Ver... | [
{
"docid": "d3b157f490bd2d8d31077adb17f2329d",
"score": "0.66607773",
"text": "public Column getIpfixColumn() {\n ColumnDescription columndesc = new ColumnDescription(FlowSampleCollectorSetColumn.IPFIX.columnName(),\n \"getIpfixColumn\",... |
8388d8d9a1caa7b6b76ea5408b4763f8 | optional .AuthenticationChallenge authenticationChallenge = 2; | [
{
"docid": "446d7d49e7a20af5c49f0ab93204083f",
"score": "0.5450157",
"text": "public Builder clearAuthenticationChallenge() {\n if (authenticationChallengeBuilder_ == null) {\n authenticationChallenge_ = com.github.yeriomin.playstoreapi.AuthenticationChallenge.getDefaultInstance();\n ... | [
{
"docid": "ef9d9419c4c6ad4c745b56b477dc2ad9",
"score": "0.64700353",
"text": "public boolean authenticate(HttpServletResponse response) throws IOException, ServletException {\n/* 315 */ return _getHttpServletRequest().authenticate(response);\n/* */ }",
"title": ""
},
{
"docid": "d... |
c5677eb48060c718837c737a5c9ec277 | whether to enforce mobile policy | [
{
"docid": "de7a4f7744939bc4109845145740cbd5",
"score": "0.0",
"text": "@ZAttr(id=833)\n public Map<String,Object> setFeatureMobilePolicyEnabled(boolean zimbraFeatureMobilePolicyEnabled, Map<String,Object> attrs) {\n if (attrs == null) attrs = new HashMap<String,Object>();\n attrs.put(P... | [
{
"docid": "e0c58752aecb53145110be7b7797c106",
"score": "0.6418706",
"text": "@ZAttr(id=1300)\n public int getMobilePolicyAllowBrowser() {\n return getIntAttr(Provisioning.A_zimbraMobilePolicyAllowBrowser, 1);\n }",
"title": ""
},
{
"docid": "265f8ebaf5d3f7ad067aac2573e775f9",
... |
25197b4cf814864ecd440f23b9149cb3 | TODO Autogenerated method stub | [
{
"docid": "ca3c3861f02167c0502562ccf76aac4b",
"score": "0.0",
"text": "public void setServletResponse(HttpServletResponse arg0) {\n\t\t\r\n\t}",
"title": ""
}
] | [
{
"docid": "d7194e467f51e022c107531d8614b6a3",
"score": "0.6905833",
"text": "@Override\r\n\tpublic void anular() {\n\t\t\r\n\t}",
"title": ""
},
{
"docid": "941cb2826e3c700358fcaba402e6bb01",
"score": "0.66775143",
"text": "@Override\r\n\tpublic void hissetmek() {\n\r\n\t}",
"ti... |
b23d31aac62d65cbc60b61fa78236b77 | This method was generated by MyBatis Generator. This method sets the value of the database column fms_docmain.CREATETIME | [
{
"docid": "e76dea7909bfcf8a347d54e20d4f8a5b",
"score": "0.6574576",
"text": "public void setCreatetime(Date createtime) {\n\t\tthis.createtime = createtime;\n\t}",
"title": ""
}
] | [
{
"docid": "a094234a5aefe134ce46802b4f39f0d4",
"score": "0.6844457",
"text": "public void setCreate_time(Date create_time) {\n this.create_time = create_time;\n }",
"title": ""
},
{
"docid": "a094234a5aefe134ce46802b4f39f0d4",
"score": "0.6844457",
"text": "public void setC... |
4a5a32ee3958d951c8fc0b452a3280ee | Replace the next line with any behavior you would like | [
{
"docid": "2ccbbc9759c8f025791fce501c367947",
"score": "0.0",
"text": "public void onHitWall(HitWallEvent e) {\n\t\tback(20);\n\t}",
"title": ""
}
] | [
{
"docid": "d9a70fce7804c4106f84101de484e70f",
"score": "0.60831225",
"text": "@Override\r\n\tprotected String modifyOneLine(String line) {\n\t\tString result=null;\r\n\t\tint index=line.indexOf(\"//\");\r\n\t\t//�����ж��Ƿ����\r\n\t\tif(index==-1)\r\n\t\t{\r\n\t\t\tresult=line;\r\n\t\t}//�����ھ�ִ������IJ... |
25197b4cf814864ecd440f23b9149cb3 | TODO Autogenerated method stub | [
{
"docid": "5d51778970d25f8febf6108a7d447958",
"score": "0.0",
"text": "@Override\n\tpublic void setDiffFirst(boolean flag) {\n\t\t\n\t}",
"title": ""
}
] | [
{
"docid": "c4efc9f9911178a27ec9261384d5f141",
"score": "0.66616714",
"text": "public void mo12644zk() {\n }",
"title": ""
},
{
"docid": "81758c2988d9979c7d4b3fd5b3cce0e5",
"score": "0.6566483",
"text": "@Override\r\n\tpublic void pular() {\n\r\n\t}",
"title": ""
},
{
... |
083a763d278062b98ae314df9944471d | Lance un de dont le resultat sera compris entre 1 inclus et 6 inclus | [
{
"docid": "2aacffe78e0024ae877d29ea39ade8c0",
"score": "0.0",
"text": "@Override\n\tpublic int lancerDe() {\n\t\tint min = 1;\n\t\tint max = 6;\n\t\treturn min + (int)(Math.random() * ((max - min) + 1));\n\t}",
"title": ""
}
] | [
{
"docid": "b918e86e8a0a14047bd2a3377dc5a834",
"score": "0.6262823",
"text": "public void coomprovarPremi(int [] numeroClient, int [] numeroSorteig, int reintegroJugador\n , int reintegroBombo, int complementari, String noReintegrament){\n opcioPremi = 0;\n int contador = 0;\n ... |
65f82c59c0044d16fce75084573fb97e | BUSCAR TODOS OS GRUPOS DO BANCO | [
{
"docid": "6ed3f4024ea8b2b0a74b5cc06727096d",
"score": "0.0",
"text": "private void listargrupos() {\n\n final GrupoCtrl grupoCtrl = new GrupoCtrl(ConexaoSQlite.getInstanciaConexao(GerenciarGrupo.this));\n\n this.grupoList = new ArrayList<>();\n grupoList = grupoCtrl.getListaGrupos... | [
{
"docid": "8b7e0a1c629858e027d2db8e5febb2b0",
"score": "0.6473545",
"text": "private BonecrusherAlterno(){\n\t\tthis.ataque = 30;\n\t\tthis.distAtaque = 3;\n\t\tthis.velocidad = 8;\n\t}",
"title": ""
},
{
"docid": "d65a28bbaef0b2782a031c453c266e27",
"score": "0.6438139",
"text": "@O... |
a4c9abb1efd2d95e54418b208c73d169 | Sets the start time to the given starttime | [
{
"docid": "984bdb92d2ff665bbab319c67a29f573",
"score": "0.79092306",
"text": "private void setStartTime(LocalTime startTime) {\n\t\tif(startTime != null) {\n\t\t\tthis.startTime = startTime;\n\t\t} else {\n\t\t\tthrow new IllegalArgumentException(\"The start time can't be empty!\");\n\t\t}\n\t}",
"... | [
{
"docid": "a02a3ac651767696bcca11956be12bcd",
"score": "0.865518",
"text": "void setStarttime(java.lang.String starttime);",
"title": ""
},
{
"docid": "eb23cf80bab5898ec74a8c1427d5eea6",
"score": "0.8515928",
"text": "public void setStarttime(Date starttime) {\r\n this.startt... |
378279fd05213bc64aefcce57c17dc8b | Shifts are done with modulo arithmetic (so that we can avoid checks overhead). | [
{
"docid": "89566aad8fab1ff92c152de8313b9157",
"score": "0.0",
"text": "public GPoint fromThisRelative(GPoint pos) {\n return pos.withDeltas(this.x, this.y);\n }",
"title": ""
}
] | [
{
"docid": "470e55de5cb65a8137c6f355e0b74662",
"score": "0.636711",
"text": "public abstract void shift();",
"title": ""
},
{
"docid": "d433c697e0e26638cdb8364c632a5bd6",
"score": "0.63267493",
"text": "private static int rol(int num, int cnt) {\n return (num << cnt) | (num >>... |
2e2c280fa84450a837f1db5621509fe1 | This function returns a copy of the instance | [
{
"docid": "6859cc4bb73726b7e66d19e289d9ddc7",
"score": "0.0",
"text": "public NeuralVector clone() {\n\t\treturn new NeuralVector(rowVector);\n\t}",
"title": ""
}
] | [
{
"docid": "613dd7d507f25420ca5d259f3a82d6b8",
"score": "0.7667386",
"text": "@Override\r\n public Object copy()\r\n {\n Object o = null;\r\n try\r\n {\r\n o = this.clone();\r\n }\r\n catch (CloneNotSupportedException e)\r\n {\r\n // TODO Auto-generated catch block\r\n ... |
f4d2d78bb96da8b41969016c091848f5 | Here green tea has an ingridient green_mixture, in this case the machine should reject the people who are trying to order It; Here 3 people came and ordered green_tea since no ingridient it dont need to wait 5 seconds to get the ingridients it can simply reject this order so the time it takes to execute this is almost ... | [
{
"docid": "c40a66150d68531122f223aa7208278a",
"score": "0.55025554",
"text": "private static void testWhenIngridientSpecifiedIsnotthereinTheVendorMachine() throws IOException {\n VendingMachine vendingMachine = makeVendingMachine();\n User user1 = new User(vendingMachine, \"green_tea\");\... | [
{
"docid": "6cf01a02ff18a7048e3fbea2159e1ba7",
"score": "0.6513724",
"text": "private static void testQueingMechanismWhenIngridientsNotEnoughThenRejectSome() throws IOException {\n VendingMachine vendingMachine = makeVendingMachine();\n User user1 = new User(vendingMachine, \"hot_tea\");\n... |
2376a35b7d80059bfee0bb7f1f555ef4 | The operation to start a virtual machine. | [
{
"docid": "4c19eb95f0460bab67d54851ab4dd460",
"score": "0.56545633",
"text": "@ServiceMethod(returns = ReturnType.SINGLE)\n SyncPoller<PollResult<Void>, Void> beginStart(String resourceGroupName, String vmName);",
"title": ""
}
] | [
{
"docid": "5cb01f8a40436fd888ac846e4993eb08",
"score": "0.6750639",
"text": "@ServiceMethod(returns = ReturnType.SINGLE)\n void start(String resourceGroupName, String vmName);",
"title": ""
},
{
"docid": "0d613e5dd006a24e354c53b92627f437",
"score": "0.6670447",
"text": "@ServiceM... |
28cfc855346bdd621a00096bc886076d | Creat a new builder instance. | [
{
"docid": "95137c846eb4d664a156cd25f5b1678f",
"score": "0.0",
"text": "protected MapBuilder() {\n }",
"title": ""
}
] | [
{
"docid": "70a8dfed31a6d286b8779bc3611c6197",
"score": "0.81701916",
"text": "public static Builder create() {\n\t\treturn new Builder();\n\t}",
"title": ""
},
{
"docid": "d2a1dcdc1e9b25e918926fcfdd244a55",
"score": "0.77372026",
"text": "public Builder() {}",
"title": ""
},
... |
70b8345f95ea62f68edaef3221ff7c1d | init views used for activity | [
{
"docid": "70d2ce48f691d33f94bbbe8d555e5967",
"score": "0.77563566",
"text": "protected void initViews(Bundle savedInstanceState) {\n\t}",
"title": ""
}
] | [
{
"docid": "43f75d067eaf5193d7fc13b24c708a1e",
"score": "0.8128274",
"text": "private void initView() {\n\t\tmBtnBack=(Button) findViewById(R.id.btn_back);\n\t\tmTVContentType=(TextView) findViewById(R.id.tv_content_type);\n\t\tmGVContent=(GridView) findViewById(R.id.gv_content_detail);\n\t\tmLoadingLay... |
8a66708dee82b4b08315f6eb7ad0afee | fastest solution on leetcode, please look at the code | [
{
"docid": "6405553d8011276421ff0234c540eb05",
"score": "0.0",
"text": "public List<String> topKFrequent(String[] words, int k) {\n if (words == null || words.length == 0) {\n return new ArrayList<String>();\n }\n Map<String, Integer> map = getMap(words);\n Priorit... | [
{
"docid": "6380eeb89175375285143a5d604c078d",
"score": "0.6526363",
"text": "public static void solve() throws Exception\n\t{\n\t\tint n = scan.nextInt();\n\t\tint k = scan.nextInt();\n\t\tInteger[] nums = new Integer[n];\n\t\tint[] w = new int[k];\n\t\tfor(int i=0;i<n;i++)\n\t\t{\n\t\t\tnums[i]=scan.n... |
6a43065f29af104e758be21f1f49976a | Get the number of bytes necessary to represent the passed char array as an UTF8 string. | [
{
"docid": "2fa1c52e150a0417dabc5efa8a2eca5e",
"score": "0.71090996",
"text": "@Nonnegative\n public static int getUTF8ByteCount (@Nullable final char [] aChars)\n {\n int nCount = 0;\n if (aChars != null)\n for (final char c : aChars)\n nCount += getUTF8ByteCount (c);\n return nC... | [
{
"docid": "93f86ffb167d7d51e9e72b6a3064f759",
"score": "0.7016445",
"text": "@Nonnegative\n public static int getUTF8ByteCount (@Nullable final String s)\n {\n return s == null ? 0 : getUTF8ByteCount (s.toCharArray ());\n }",
"title": ""
},
{
"docid": "a75aae5a15b432a51dae8f57d43a67d3... |
25197b4cf814864ecd440f23b9149cb3 | TODO Autogenerated method stub | [
{
"docid": "b468d7a409f4952a1e500d89ca1506d8",
"score": "0.0",
"text": "@Override\n public void onNothingSelected(AdapterView<?> arg0) {\n }",
"title": ""
}
] | [
{
"docid": "c4efc9f9911178a27ec9261384d5f141",
"score": "0.66613346",
"text": "public void mo12644zk() {\n }",
"title": ""
},
{
"docid": "81758c2988d9979c7d4b3fd5b3cce0e5",
"score": "0.65680635",
"text": "@Override\r\n\tpublic void pular() {\n\r\n\t}",
"title": ""
},
{... |
25197b4cf814864ecd440f23b9149cb3 | TODO Autogenerated method stub | [
{
"docid": "0eeeaff3fbffeea4a9d13de5962bb521",
"score": "0.0",
"text": "@Override\n\tpublic List<Translator> findAllById(Iterable<UUID> ids) {\n\t\treturn null;\n\t}",
"title": ""
}
] | [
{
"docid": "3d9823aba51891281b4bbd4b1818b970",
"score": "0.6671074",
"text": "@Override\r\n\tpublic void comer() \r\n\t{\n\t\t\r\n\t}",
"title": ""
},
{
"docid": "0b5b11f4251ace8b3d0f5ead186f7beb",
"score": "0.6567672",
"text": "@Override\n\tpublic void comer() {\n\t\t\n\t}",
"ti... |
ea8683784e08fa3e5591d7164e6cf728 | / 102: / 103: | [
{
"docid": "c993ac10e6ebe9316a4afd0e121bd010",
"score": "0.0",
"text": "@SideOnly(Side.CLIENT)\r\n/* 104: */ public List<String> getToolTip()\r\n/* 105: */ {\r\n/* 106:115 */ if (this.curMax > 0)\r\n/* 107: */ {\r\n/* 108:116 */ List l = new ArrayList();\r\n/* 109:117 */ ... | [
{
"docid": "ce28ddd4d9b73216b76497bacb679c73",
"score": "0.5795509",
"text": "public int method_4144() {\r\n return 9;\r\n }",
"title": ""
},
{
"docid": "85f02832a0e63d2286d0eeccdd25067f",
"score": "0.5646376",
"text": "protected String bn()\r\n/* 49: */ {\r\n/* 50: 56 ... |
178fa37250120961739bcfc7bceaf841 | Returns the part of speech tag of the lexeme | [
{
"docid": "96c743b2b6a732790dcec83fe49cb2ca",
"score": "0.0",
"text": "public String getPos() {\n return pos.name();\n }",
"title": ""
}
] | [
{
"docid": "53075f1046785c91c723dad462cad6cc",
"score": "0.65883696",
"text": "private String getTagname() {\n String s = \"\";\n nextChar(); //First character in the Tagname\n while(c != ' ' && c != '<' && c != -1) {\n s = s + (char) c;\n nextChar();\n ... |
3a6a5ac4b01011c2dc8b786ee8fc4b58 | Check for the next even item. | [
{
"docid": "3e07e4f9676a56306e2efab19f5df8c4",
"score": "0.58287436",
"text": "public boolean hasNext() {\n boolean result = false;\n while (index < numbers.length) {\n if (numbers[index] % 2 == 0) {\n result = true;\n break;\n }\n ... | [
{
"docid": "7dc01bb4249cce9cd418042b8fe861cb",
"score": "0.6385658",
"text": "@Test\r\n\tpublic void testHasNext2() {\r\n\t\tIterator<Integer> itr = list2.iterator();\r\n\t\tassertFalse(itr.hasNext());\r\n\t}",
"title": ""
},
{
"docid": "ccf37a964248ba36c2f75bd1392e1505",
"score": "0.637... |
25197b4cf814864ecd440f23b9149cb3 | TODO Autogenerated method stub | [
{
"docid": "b1c51886ffa53e2a2c54c3f172ba5cb5",
"score": "0.0",
"text": "@Override\r\n\tpublic List loadMasterDataSet(int pageSize, int startRow) {\n\t\treturn null;\r\n\t}",
"title": ""
}
] | [
{
"docid": "74a4209c94e08d9f5eb0b60e580f5e04",
"score": "0.68867725",
"text": "@Override\r\n\tpublic void comit() {\n\t\t\r\n\t}",
"title": ""
},
{
"docid": "b645eacb329adc01490ab5013a6c4f7f",
"score": "0.67295986",
"text": "@Override\n\t\t\tpublic void perfom() {\n\n\t\t\t}",
"t... |
82b13f929fc1c55406d1e5b6bb2dfeba | Busca un usuario por su nombre | [
{
"docid": "423d544bb11639a1f89c52e581bb5f31",
"score": "0.6782444",
"text": "public Usuario buscarUsuario(String nombreUsuario) throws MyException;",
"title": ""
}
] | [
{
"docid": "7664f316e74b33e43398ce14c9f7b10d",
"score": "0.79426116",
"text": "Usuario buscarUsername(String username);",
"title": ""
},
{
"docid": "226d6e649832b691bbe4cf4aa5737164",
"score": "0.6952763",
"text": "public String findUsername(String name);",
"title": ""
},
{
... |
add212c734af747864cbb9ae4270cf37 | called in the on mouse dragged event of a rectangle. It moves all rectangles and textAreas with the mouse position. | [
{
"docid": "da66e9e032c1a1ed00129fe941a22493",
"score": "0.71140707",
"text": "private void drag(Pane pane) {\r\n\t\tdouble prevHeight = 0;\r\n\t\tRectangle2D primaryScreenBounds = Screen.getPrimary().getVisualBounds();\r\n\r\n\t\tint mouseX = MouseInfo.getPointerInfo().getLocation().x;\r\n\t\tint mouse... | [
{
"docid": "4e173ab57ca0b2c62dc9c78aeaac4e76",
"score": "0.7094713",
"text": "private void handleMovingRectangle(){\n float offsetX = startX - layoutRect.left;\n float offsetY = startY - layoutRect.top;\n\n left = endX - offsetX;\n if (left < this.getLeft()) {... |
e519febe8a53f780b1d43199a02b8665 | The number of DataNodes to replicate the data to when writing to the HDFS cluster. | [
{
"docid": "f8af3f0183b0ace244b8de2d3128bc7a",
"score": "0.5512369",
"text": "public Integer getReplicationFactor() {\n return this.replicationFactor;\n }",
"title": ""
}
] | [
{
"docid": "94b4bd5ebd5d0e6c11428711e530b646",
"score": "0.6657928",
"text": "public long numNodes(){\n return numNodes;\n }",
"title": ""
},
{
"docid": "27be22b6e5903487da21bd5d20907bca",
"score": "0.65630513",
"text": "public int getNumberOfNodes();",
"title": ""
},... |
e62907d6d2185489b7a3c802a8e82117 | Fonction qui retourne la taille du noeud. | [
{
"docid": "ab56c917b55b1db7ae7ca3b4a0b68fe1",
"score": "0.0",
"text": "public int getSize(){\n if(this.nameNode.length() == 0 || this.nameNode.length() <= 4){\n return 40;\n }\n return this.nameNode.length()+40;\n }",
"title": ""
}
] | [
{
"docid": "2e80b1fa5aa6f074b8baf835a7c09477",
"score": "0.71176916",
"text": "@Override\n public int taille() {\n return taille;\n }",
"title": ""
},
{
"docid": "1055014d7ceda93eea18099c9af9180b",
"score": "0.6700378",
"text": "public abstract int taille();",
"title... |
95e72035d3c334f7631b71187fd767a4 | Tests EditLogFileOutputStream doesn't throw NullPointerException on being closed twice. See | [
{
"docid": "ce007e98a14146713cdb87cd3dadb3f2",
"score": "0.84117705",
"text": "public void testEditLogFileOutputStreamCloses()\r\n throws IOException,NullPointerException {\r\n System.out.println(\"Testing EditLogFileOutputStream doesn't throw \" +\r\n \"NullPointerException ... | [
{
"docid": "3f6fc62538642d60dcae107175446ea1",
"score": "0.60592103",
"text": "void openOrReopenStream(Logger log) {\n if (stream != null && nextReopen.expired()) {\n stream.flush();\n stream.close();\n stream = null;\n }\n if (stream == null) {\n try {\n FileOutputSt... |
31a67917d57f6337de9970bc24b4014f | Prints all the active lists. | [
{
"docid": "15266ebd556753ff5362962ff488506c",
"score": "0.77377117",
"text": "private void printActiveLists() {\n debugPrint(\" CombinedActiveList: \" + combinedActiveList.size());\n for (Iterator i = linguist.getFeatureStreams(); i.hasNext();) {\n FeatureStream stream = (Featu... | [
{
"docid": "a523dd481afcbec793807ee1dfd3ab69",
"score": "0.7215351",
"text": "public void printAll()\n {\n for(int i = 0; i < list.size(); i++)\n {\n String string = list.get(i).toString();\n \n System.out.println(string);\n }\n }",
"title"... |
cc9770f1e074e5cf9852f7ebae215cdf | uint64 version = 1; | [
{
"docid": "fde0d5ade4a22562112abcc0ad412770",
"score": "0.0",
"text": "public long getVersion() {\n return version_;\n }",
"title": ""
}
] | [
{
"docid": "e749261b45dba4784d50c98fdb49abe8",
"score": "0.6973533",
"text": "Integer version();",
"title": ""
},
{
"docid": "dcedd791d239cb7c6098bbf8c9832651",
"score": "0.6711172",
"text": "protected abstract Long version();",
"title": ""
},
{
"docid": "8cb0c0c3b42dce29... |
ea8e7d29b8cc587bbfd28dc52454f8d7 | Shows the imageView holding the control scheme by adding it to the raceViewPane. | [
{
"docid": "bfb6db6b6d55566568a46e60abededbf",
"score": "0.7846011",
"text": "private void showControlScheme() {\n imageView.setLayoutX((pane.getWidth() / 2) - (image.getWidth() / 2));\n imageView.setVisible(true);\n controlsVisible = true;\n transition.setByY(image.getHeight... | [
{
"docid": "5851dcb491da4c9e964e3d6fdcb0ec34",
"score": "0.67551976",
"text": "private void loadControlScheme() {\n String path = \"/images/controlScheme.png\";\n image = new Image(path);\n imageView = new ImageView(image);\n imageView.setPreserveRatio(true);\n pane.ge... |
8e1b677ccfb44b72aa2be415cbf1f0c2 | / This method formalizes the indices for reading the information about the donors in the file | [
{
"docid": "caa951b57a03f939fb27ae888389924d",
"score": "0.46630934",
"text": "@Override\n\tprotected void setValidIndex(Donor donor, String[] field) {\n\t\t//Variable to store the result of index validation\n\t\tboolean validationResult;\n\t\t\n\t\tvalidationResult = validIndex(this.indexDonorCpfCnpj);... | [
{
"docid": "042fe19caa8ee00938d27cee6bfc15fc",
"score": "0.59164923",
"text": "static void indexDocs(IndexWriter writer, File file)\n throws IOException {\n if (file.canRead()) {\n if (file.isDirectory()) {\n String[] files = file.list();\n // an IO... |
c5716a00280fefb6f13831a877940678 | Opening the Url based upon the value from the option.property file for TEST.ENVIRONMENT.OPEN | [
{
"docid": "d238bfe91c22a60b40de6fee079e9092",
"score": "0.76026446",
"text": "private static String OpenTheURL(String URL)\r\n\t{\r\n\t\ttry{\r\n\t\tif (URL.equalsIgnoreCase(option.getProperty(\"QA.VARIABLE\")))\r\n\t\t{\r\n\t\tdriver.get(option.getProperty(\"QA.URL.ENVIRONMENT\"));\r\n\t\t}else if (UR... | [
{
"docid": "019a5dd1d4ed56e8a2cb2e78f46cb499",
"score": "0.62790823",
"text": "public void openURL(String websiteURLKey) {\r\n\t\tprop = new Properties();\r\n\r\n\t\ttry {\r\n\t\t\tprop.load(new FileInputStream(\"src/main/java/Config/config.properties\"));\r\n\t\t} catch (Exception e) {\r\n\t\t\te.print... |
da7906f4f1a14926691f6287fdb8c896 | Find one policy rule. | [
{
"docid": "2eb6a0f1948459ace91388b2b8debe85",
"score": "0.8080284",
"text": "PolicyRule findOneByName(String name);",
"title": ""
}
] | [
{
"docid": "9a115d4f9b8fdcf842a21fcf8e0b53e7",
"score": "0.633108",
"text": "Rule findById(Long id);",
"title": ""
},
{
"docid": "3ad7ed7973c2c4a86fd804c54b1d2c49",
"score": "0.6005357",
"text": "public Rule getRuleByID(String id) {\n\t\tif (id == null) {\n\t\t\treturn null;\n\t\t}\n... |
59cf9195575feecf20ca33ffd00473c8 | / The input is the longest phrase we can get before seeing a punctuation. Therefore, we need to add all possible phrases that can be found from the input phrase to the trie. | [
{
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{
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{
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25197b4cf814864ecd440f23b9149cb3 | TODO Autogenerated method stub | [
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"docid": "a6b1690ef19947ad8c363e86ec459996",
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"text": "@Override\r\n protected void setupDatabase() {\n switch(mCalculateCycle) {\r\n case CYCLE_WEEK:\r\n setTableName(DatabaseConfig.DEFAULT_LISTED_TECH_WEEK);\r\n break;\r\n case CYCLE_MONTH... | [
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{
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5e185150f8b61ce5105970ef4a853eb6 | This method is called from within the constructor to initialize the form. WARNING: Do NOT modify this code. The content of this method is always regenerated by the Form Editor. | [
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{
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19e186211b93836fb7ad9ac6da6e04b7 | Instantiates a new Fancy message. | [
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"title": ""
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] | [
{
"docid": "df68addc10b0c5844df5f9c3a9ea3036",
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{
"docid": "70f980f3484f55deab00f70dfc815781",
"score": "0.7375724",
"text": "public FancyMessage(FancyMessage fancyMessage) {\n t... |
849b954e3c184197abed170cdffeda4a | This method was generated by MyBatis Generator. This method returns the value of the database column third_part.unique_id | [
{
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{
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bdc5426d0b8b77c2dad15b14792c2aa9 | Attack using the currently equipped weapon and the angle of view to the target. | [
{
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"score": "0.7517751",
"text": "protected void attack(Entity target) {\n // Get the weapon of the subject.\n Weapon weapon = subject.getWeapon();\n\n // The subject cannot attack without a weapon.\n if (weapon == null) {\n ... | [
{
"docid": "34861a2115e5dc70d44e03559debfc7c",
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{
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{
"docid": "1d36d6aafdf57... |
9ba0f37156e0608e6e78b931b7a9e476 | applies a filter to an image | [
{
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"text": "public static final void applyFilter(BufferedImage img, ImageFilter filter) {\n FilteredImageSource src = new FilteredImageSource(img.getSource(),\n filter);\n ... | [
{
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"text": "Image apply(Image input);",
"title": ""
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8a97b6a9251fd3ff204caf109fee5bcc | This method was generated by MyBatis Generator. This method corresponds to the database table baseinfo_company | [
{
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"title": ""
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0696d30781e79309df385cde62000fb9 | Fills the hand back to 7 | [
{
"docid": "d174fd72146088937c38735a855d9e43",
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"text": "public void fillHand() {\r\n\t\tgetMaxTurnID();\r\n\t\tif (maxTurn == 1 && myTurn) {\r\n\t\t\tdbh.updateTurn(maxTurn, gameID, getOwnName(), 0, \"Begin\");\r\n\t\t} else if (maxTurn == 2 && myTurn) {\r\n\t\t\tdbh.updateTurn(max... | [
{
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"text": "private void fillRowWithBeepers() {\n\t\tputBeeper();\n\t\twhile (frontIsClear()) {\n\t\t\tmove();\n\t\t\tputBeeper();\n\t\t}\n\t}",
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9a2f7015e1b7703fe9da501b130f3ae3 | Create an image file name | [
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{
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"score": "0.8093278",
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{
"docid": "e5a4aa90cb439041f4f36081e9e8a194",
"score": "0.5375719",
"text": "public void growTree(World world, int i, int j, int k, Random rand)\n {\n \n \tWorldGenBush worldgenbush = new WorldGenBush();\n \t\n if (!((WorldGenerator)worldgenbush).generate(world, rand, i, j, k))\n... | [
{
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"score": "0.6700441",
"text": "public void expandTree() {\n expandTree(3);\n }",
"title": ""
},
{
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"score": "0.6608092",
"text": "private void grow(){\n\t\n }",
"title": ""
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... |
953d58aff51c95c83fd7e648ff939b29 | DO NOT CHANGE THIS !!! | [
{
"docid": "8dc8f3443d3ecf9ab93fb544598acb57",
"score": "0.0",
"text": "public void initModel(String fileName) throws IOException {\n mVocabulary = buildVocabularyIndex(fileName);\n mBigramCounts = buildCountsArray(fileName, mVocabulary);\n\n }",
"title": ""
}
] | [
{
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"score": "0.70446223",
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{
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"score": "0.66783404",
"text": "@Override\n\t\tpublic void pintate() {\n\t\t\t\n\t\t}",
"title": ""... |
7f69261fc982dbcf55307216b55a5018 | optional uint32 label = 4; may be using PHP | [
{
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"title": ""
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] | [
{
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{
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"score": "0.6486471",
"text": "int getLabel();",
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{
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1de1dc0eeab271899edf2eabb4dbeaee | ====== bean methods ===========// | [
{
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"title": ""
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] | [
{
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"title": ""
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"score": "0.6442215",
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{
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0be6e00dc6d90ab9aa7df63b0f5e3b4a | repeated .PropertyDefProto property = 5; | [
{
"docid": "d083f4851d610d5efd96643068bd205e",
"score": "0.0",
"text": "public Builder removeProperty(int index) {\n if (propertyBuilder_ == null) {\n ensurePropertyIsMutable();\n property_.remove(index);\n onChanged();\n } else {\n propertyBuilder_.remove(index);\n... | [
{
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"text": "protobuf.schema.UserOuterClass.TestUserOrBuilder getProp5OrBuilder();",
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"text": "protobuf.schema.UserOuterClass.TestUser ... |
8d1662e1bd67ac6cf5d95eb1a5e3e19c | / synartisi pou kaleitai otan vrethei paketo sto arxeio eisodou | [
{
"docid": "91874486280af2129542eb104c73513a",
"score": "0.0",
"text": "public void packageHandler(DataToken token){\n\t\t\n\t\t\tif((activePackage=doSearchPackage(retString))==null){\n\t\t\t\tactivePackage = new thePackage(retString);\n\t\t\t\tSystem.out.println(\"packageHandler willing to add: \"+retS... | [
{
"docid": "2e3af6e40acd1a06b3b9966545bbe889",
"score": "0.68976426",
"text": "public void jokaldiaEgin() {\r\n\t\tSystem.out.println(\" \");\r\n\t\tSystem.out.println(\"IA-ren txanda da.\");\r\n\t\tthis.azeriaErabiliDu = false;\r\n\t\tKarta k1 = null,k2 = null,k3 = null,k4 = null, baztertzekoKarta;\r\n... |
518bf5364cda3c6202a1a392996f93d1 | Inflate the menu; this adds items to the action bar if it is present. | [
{
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] | [
{
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"text": "@Override\n\tpublic boolean onCreateOptionsMenu(Menu menu) {\n\t\tMenuInflater mif = getMenuInflater();\n\t\tmif.inflate(R.menu.main_activity_action, menu);\n\t\treturn super.onCreateOptionsMenu(menu);\n\t}",
"title": ""
... |
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{
"docid": "9253c47341b15ebffc651d916c3041e2",
"score": "0.6811344",
"text": "protected void addHeaders(WebResponse rsp) {\n\t\tif (origin != null) {\r\n\t\t\trsp.setHeader(\"Access-Control-Allow-Origin\", origin);\r\n\t\t}\r\n\r\n\t\t// Add the Access-Control-Allow-Methods header\r\n\t\tif (methods != ... | [
{
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"text": "public static void addCorsHeader(HttpServletResponse response){\n response.addHeader(\"Access-Control-Allow-Origin\", \"*\");\n response.addHeader(\"Access-Control-Allow-Methods\", \"POST, GET, OPTIONS, PUT, DEL... |
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{
"docid": "5846c3891602d9f5bff12cf14a053221",
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{
"docid": "696f49f2d765d885d72fd208aed8233e",
"score": "0.70555854",
"text": "private static File tryGetUncompressedFile(final File src) {\n try {\n final File dst = File.createTempFile(PREFIX, SUFFIX);\n dst.deleteOnExit();\n try (\n final InputSt... |
9516e782a9645864f9c495d44e729dca | Genereates a static SocketAddressFactory from a list of SocketAddresses | [
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"title": ""
}
] | [
{
"docid": "16c496208b51f5da69f8bdce71a8c891",
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{
"docid": "fc680577dbe11f435a8490379dd5316f",
"score": "0.0",
"text": "@Override\n\tpublic void onItemClick(AdapterView<?> arg0, View arg1, int arg2, long arg3) {\n\t\tLessonInfo info = list.get(arg2);\n\t\tstartActivity(new Intent(this, AcLessonDetail.class).putExtra(\"info\",\n\t\t\t\tinfo));\n\t}",
... | [
{
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"title": ""
},
{
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"score": "0.68683225",
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4a05c9e724493b09d5c9a5a681f0660b | Getter for a database field. | [
{
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"score": "0.0",
"text": "public java.math.BigDecimal getReferenceNumber() {\r\n return referenceNumber;\r\n }",
"title": ""
}
] | [
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{
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"text": "@Override\n public void onStateChanged(final Session session, final State oldState, final State newState) {\n Log.i(TAG, \"onStateChanged, new state=\" + newState.toString());\n runOnUiThread(new Runnable() {\n ... | [
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{
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"text": "private void channelStateCallback() {\n final Co... |
56ec1d0a88f2af5499b6c17f623379c2 | cleans the line stored in rawLine, stores it in cleanLine, then splits the cleaned line on spaces and stores the result in lineParts | [
{
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"score": "0.77039945",
"text": "private void cleanLine() {\n int comment = rawLine.indexOf(\"//\");\n String intermediate = rawLine;\n if (comment != -1) { // remove any comments, should they exists\n intermediate = rawLine.s... | [
{
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"title": ""
},
{
"docid": "2704e5cf2ab25921129163872db95085",
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0fc991629ed5e1310aa350e3aa968bc1 | Este metodo fue cambiado de nombre desde saveContact a saveOrUpdateContact | [
{
"docid": "1af23ce2bfb4d1397abb9e2a323f6913",
"score": "0.55912703",
"text": "@RequestMapping(value = \"/user/save_contact\")\n public String saveOrUpdateContact(@ModelAttribute(\"command\") Contacto c, Model m, HttpSession session) {\n Integer contactId = (Integer) session.getAttribute(\"aCo... | [
{
"docid": "9b7225fe9000f7cf359bc96cfe44ac60",
"score": "0.6559561",
"text": "@Override\r\n public Contact updateContact(Contact contact, String nom) {\n return null;\r\n }",
"title": ""
},
{
"docid": "8b2fe1798af6623298d2f4c916ef92d3",
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"text": "voi... |
faa3b4275775789a1004de345c472b17 | Method for getting the reference to the previous node of the this node | [
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"score": "0.7474066",
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"text": "public Node getPrev()\n\t\t{\n return prev; // XXX-CHANGED-XXX\n\t\t}",
"title": ""
},
{
"docid": "bda7879a3e933e94abf24d2349357034",
"score": "0.8614535",
"text": "public Node<E> getPreviousNode... |
f15fd5d24dd9b85a2f97052295390419 | insert a new Cloathing | [
{
"docid": "602c3ef7c69f1e12d4e0cafb0121b13c",
"score": "0.0",
"text": "@Override\r\n\tpublic int insert(Equipment equip) throws Exception{\r\n\t\tint idFromProduct = GetMax.getMaxId(\"Select max(id) from PRODUCT\");\r\n\t\tSystem.out.println(\"Id of the product coresponding to equipment= \" + idFromPro... | [
{
"docid": "41282d66e6d20526a0f554613ab8b30e",
"score": "0.6658552",
"text": "void insert(PCB newPCB);",
"title": ""
},
{
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"score": "0.6569509",
"text": "@Override\n\tpublic void insert() {\n\t\t\n\t}",
"title": ""
},
{
"docid":... |
b9d98b8a52fa7d28e8eb841f0b1b4960 | This method was generated by MyBatis Generator. This method returns the value of the database column PDSCI.RES_ASSESS_CFG.RECORD_STATUS | [
{
"docid": "1d87089bf4aad7692fc53bd3ed817d34",
"score": "0.6808533",
"text": "public String getRecordStatus() {\r\n\t\treturn recordStatus;\r\n\t}",
"title": ""
}
] | [
{
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"text": "public Integer getRecordStatus() {\n return recordStatus;\n }",
"title": ""
},
{
"docid": "b560a98b8cd8dc45018cabcb79c5000c",
"score": "0.6776141",
"text": "public String getRecordStatus() {\r\n ... |
9b8b3da58a475ff5b598d0f1aa417c93 | TODO: read settings from configuration file | [
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"docid": "734bc29b98f01c891cb230c812aceeb8",
"score": "0.0",
"text": "@Bean\n public DataSyncClient dataSyncClient() {\n return new DataSyncClient(false, \"127.0.0.1\", 9000, simpleClientChannelHandler, 10);\n }",
"title": ""
}
] | [
{
"docid": "430658526661589e8d30b0c6935f1417",
"score": "0.7414462",
"text": "private void ReadFromConfig() {\r\n\r\n }",
"title": ""
},
{
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"text": "public void loadSettings() {\n \n }",
"title": ""
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... |
7fea8663d8a3c49ec1d2900d54aa8962 | Gets a Transform Group containing the representation for a specific appearance label (e.g. 'cube', 'red_train'). If a TranformGroup is provided, the nodes needed for representation should be added as children, otherwise a new TransformGroup should be created. | [
{
"docid": "1e64f90b6ddac6cf1c751f2977ef07df",
"score": "0.5694656",
"text": "public TransformGroup buildTransformGroupForShape(appearance.Shape shape,\n \t\t\tTransformGroup transformGroup) {\n \t\tif (transformGroup == null)\n \t\t{\n \t\t\ttransformGroup = new TransformGroup();\n \t\t\ttransformGroup... | [
{
"docid": "c0345d82e3da2b30ba23a3652b193df2",
"score": "0.6861609",
"text": "public TransformGroup getTransformGroup();",
"title": ""
},
{
"docid": "8d1d3d5bcb2fb95aaff8dc9660765079",
"score": "0.5258106",
"text": "public Node cloneObject() {\n\n Groups group = new Groups(nul... |
c54a087f920c4abddf55b04e4ecfd430 | This method was generated by MyBatis Generator. This method returns the value of the database column PLT_COMMON_LOG.BUSI_ITEM_15 | [
{
"docid": "800c4356197b65192bc392218852696c",
"score": "0.7282943",
"text": "public String getBUSI_ITEM_15() {\n return BUSI_ITEM_15;\n }",
"title": ""
}
] | [
{
"docid": "ac8a054b5f45d56996eec58c649ad151",
"score": "0.6910445",
"text": "public String getBUSI_ITEM_11() {\n return BUSI_ITEM_11;\n }",
"title": ""
},
{
"docid": "45813ca1f3a0a29b238e6ed135781d6f",
"score": "0.6901248",
"text": "public String getBUSI_ITEM_5() {\n ... |
25197b4cf814864ecd440f23b9149cb3 | TODO Autogenerated method stub | [
{
"docid": "bfd1f434601a08603b9a4dd2e0d1d5eb",
"score": "0.0",
"text": "private void aggiornamentoSalvataggio() {\n\t\tSharedPreferences pref = getSharedPreferences(\"CARPOOLING\", MODE_PRIVATE);\n\n\t\tif(pref.getInt(\"checked\", -1)==1){\n\t\t\tString user = pref.getString(\"username\", \"-1\");\n\t\t... | [
{
"docid": "74a4209c94e08d9f5eb0b60e580f5e04",
"score": "0.68867725",
"text": "@Override\r\n\tpublic void comit() {\n\t\t\r\n\t}",
"title": ""
},
{
"docid": "b645eacb329adc01490ab5013a6c4f7f",
"score": "0.67295986",
"text": "@Override\n\t\t\tpublic void perfom() {\n\n\t\t\t}",
"t... |
3171d309656a640ed0d92a6458dd6038 | Returns the ending date in a nicely formatted string. | [
{
"docid": "d1de3f7cd50124e7bdc889978319ff34",
"score": "0.7190263",
"text": "public String getEndDate() {\n return endDate.format(OutputDateTimeFormat.OUTPUT_DATE_FORMAT);\n }",
"title": ""
}
] | [
{
"docid": "eac6e00e1df16d1019296c75e1ccfb3a",
"score": "0.7730047",
"text": "public String getEndingDate() {\r\n\t\t\r\n\t}",
"title": ""
},
{
"docid": "1d259e4bb81952a1778aa274bd5eb343",
"score": "0.71110296",
"text": "public String getDateEnd() {\n\t\treturn dateEnd.get();\n\t}",
... |
263dcb9838302d75cdd3db372cfbdb5b | Inflate the layout for this fragment | [
{
"docid": "12ae58dd600cc3082a0ff941185f3344",
"score": "0.0",
"text": "@Override\n // TODO: Add layout type here\n public View onCreateView(LayoutInflater inflater, ViewGroup container, Bundle savedInstanceState) {\n view = inflater.inflate(R.layout.fragment_home_2, container, false);\n ... | [
{
"docid": "8b5df47011cc799d795dc7120e1c0c49",
"score": "0.68263656",
"text": "@Override\n public View onCreateView(LayoutInflater inflater, ViewGroup container, Bundle savedInstanceState) {\n return inflater.inflate(R.layout.kuliahfragisidata, container, false);\n }",
"title": ""
},
... |
1cee87926784171f90f6ce67c6444ad2 | Specify the value which is the unique id for this persistent object, normally the values on this property should be assigned by the database (e.g. autoincrement) | [
{
"docid": "67b00d7403e9ef4b388e59f9b79027fb",
"score": "0.0",
"text": "public String getIdPropertyName();",
"title": ""
}
] | [
{
"docid": "ae5af7f9ab9ee1e5e0f874ff36a5c263",
"score": "0.7008709",
"text": "@Override\r\n\tpublic Long getId() {\n\t\treturn asseroomid;\r\n\t}",
"title": ""
},
{
"docid": "313f9932065e8c002b532a882ae481bb",
"score": "0.69761163",
"text": "public void setId(long value) { _id = valu... |
a4cf38c4a32b240da372f794f8c84c11 | Este metodo retorna uma referencia do endereco da imagem do candidato. | [
{
"docid": "d447f5767fec65ef7fff05e9ab0a6bc0",
"score": "0.0",
"text": "public String getCaminhoImagem() {\n\t\treturn caminhoImagem;\n\t}",
"title": ""
}
] | [
{
"docid": "b4d49a72d48c67703f47dbc419249ef8",
"score": "0.5972067",
"text": "String getAvatarUrl();",
"title": ""
},
{
"docid": "b4d49a72d48c67703f47dbc419249ef8",
"score": "0.5972067",
"text": "String getAvatarUrl();",
"title": ""
},
{
"docid": "8964b236a7755e53ab9d6950... |
25197b4cf814864ecd440f23b9149cb3 | TODO Autogenerated method stub | [
{
"docid": "ba18c15a9f07dfaa8f91cb2c523c404c",
"score": "0.0",
"text": "@Override\n\t\t\tpublic void onCheckedChanged(CompoundButton buttonView, boolean isChecked) {\n\t\t\t\tif(isChecked) {\n\t\t\t\t engine = TPSVG_CODE;\n\t\t\t\t webkit.setChecked(false);\n\t\t\t\t svgand2.setChecked(false);\... | [
{
"docid": "b7c706d331e2b507ec0ff8404ad87dc7",
"score": "0.69742316",
"text": "@Override\r\n\t\t\tpublic void crispel() {\n\t\t\t\t\r\n\t\t\t}",
"title": ""
},
{
"docid": "76b3966c8e3f64884c4127d1a3df09a8",
"score": "0.68059677",
"text": "@Override\r\n\t\t\tpublic void maruti() {\n\t... |
13f3cc544934cd9c6dd574a3535153a2 | Will be called just after the interchange process service has been finished. | [
{
"docid": "908e4653c81581ff2d69a6a75b72b9fe",
"score": "0.7751619",
"text": "public void onFinished(InterchangeProcessInfo interchangeProcessInfo);",
"title": ""
}
] | [
{
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"score": "0.6653642",
"text": "@Override\n\tpublic void afterProcessStarted(ProcessStartedEvent event) {\n\t}",
"title": ""
},
{
"docid": "0b24b03befb644dc37bbe644a0c8985b",
"score": "0.64840055",
"text": "private void finish() {\n\n ... |
49616622b94ff5ce0939ccaab2d6fabd | Force synchronization for all subgroups (recursively whole tree) of the group (useful for group structure) | [
{
"docid": "164a1f517aa9a04cbc6c3060336f674b",
"score": "0.78285813",
"text": "void forceAllSubGroupsSynchronization(PerunSession sess, Group group) throws GroupNotExistsException, PrivilegeException;",
"title": ""
}
] | [
{
"docid": "029850d20137343e5590e727670f5b05",
"score": "0.6425509",
"text": "@Override\n public void onSyncGroups(GroupMessage message)\n {\n }",
"title": ""
},
{
"docid": "029850d20137343e5590e727670f5b05",
"score": "0.6425509",
"text": "@Override\n public void onSyncGr... |
4caf0814efa0365bd413416e61953610 | Add user key words. | [
{
"docid": "d1a37f05b94dcb788304e571fddef2ba",
"score": "0.55956715",
"text": "@PostMapping(\"/user/resume/key-words\")\n @Nonnull\n public String keyWordSave(\n @Nonnull @RequestParam(value = \"words\", defaultValue = \"\") final String words,\n @Nonnull final Map<String, Ob... | [
{
"docid": "0867fb26c13d2dd7027a6b28be6a29a3",
"score": "0.6904213",
"text": "private void addWord() {\n System.out.print(\"Enter the word you'd like to add: \");\n \tString input = getUserInput();\n \tDictionaryWord word = editor.find(input);\n \tif (word == null) {\n\t\t\tSystem.out.pr... |
7ecdac505ce54530cb524bf73f0c0db5 | I do not need any action here you might | [
{
"docid": "ab7425585eb8a29ff5530f30923dcc29",
"score": "0.0",
"text": "@Override\n public void onClick(DialogInterface dialog,\n int which) {\n dialog.dismiss();\n ... | [
{
"docid": "33d41636b65afa8267c9085dae3d1a2d",
"score": "0.7078042",
"text": "private void apparence() {\r\n\r\n\t}",
"title": ""
},
{
"docid": "f777356e2cd2fecd871f35f7e556fda8",
"score": "0.6725506",
"text": "public void mo3640e() {\n }",
"title": ""
},
{
"docid"... |
35186183627bf9e26af84c2824f5d7a7 | Get the catalog host id for the corresponding SiteProcedureConnection | [
{
"docid": "4dda9c92a1303e4f691df9fac6a328d8",
"score": "0.6455372",
"text": "public int getCorrespondingHostId();",
"title": ""
}
] | [
{
"docid": "42d4b5abe246a058c81d8b561b223a18",
"score": "0.6192345",
"text": "public String getSiteCatalogImplementor() {\n return mProps.getProperty(PegasusProperties.PEGASUS_SITE_CATALOG_PROPERTY);\n }",
"title": ""
},
{
"docid": "4941e9a578e5fa8bac3afb3b82707873",
"score": "... |
1f51ae370e3b92e2a8910b23e08b3792 | converts a bitmap to a string so it can be sent to the server check if image is null in which case we will return an empty string | [
{
"docid": "a9254f6d3e0fa6494edb66d85d3c2f27",
"score": "0.7904604",
"text": "private String imageToString(Bitmap bitmap) {\n if (bitmap == null)\n return \"\";\n\n ByteArrayOutputStream output = new ByteArrayOutputStream();\n bitmap.compress(Bitmap.CompressFormat.JPEG, 1... | [
{
"docid": "5423b6bc86904ffb6350ecf21db7e2a2",
"score": "0.7902482",
"text": "private String imageToString(Bitmap bitmap){\n if(bitmap==null){\n return \"\";\n }else{\n ByteArrayOutputStream byteArrayOutputStream = new ByteArrayOutputStream();\n bitmap.comp... |
25197b4cf814864ecd440f23b9149cb3 | TODO Autogenerated method stub | [
{
"docid": "a644c96c326dacf1946b41e87cd4e54a",
"score": "0.0",
"text": "@Override\n public DateTime creationTime() {\n return null;\n }",
"title": ""
}
] | [
{
"docid": "1acc57d42c31dee937ac33ea6f2a5b0b",
"score": "0.68399656",
"text": "@Override\r\n\tpublic void comer() {\n\t\t\r\n\t}",
"title": ""
},
{
"docid": "0c69424878be03e50f19e2ca61ef640b",
"score": "0.68002176",
"text": "@Override\r\n\tpublic void leggi() {\n\t\t\r\n\t}",
"ti... |
15a2cc6a98c10ed6166c34977f31461d | Is the provided short VDC id this VDC's id (or empty) | [
{
"docid": "80d85656bf1ea2b0634657fc1e664a07",
"score": "0.7069584",
"text": "public boolean isLocalVdcId(String shortVdcId) {\n return StringUtils.isBlank(shortVdcId) || StringUtils.equals(shortVdcId, VdcUtil.getLocalShortVdcId());\n }",
"title": ""
}
] | [
{
"docid": "bfbfaa155130ccfa690bf2a9373f88da",
"score": "0.66598177",
"text": "public void verifyVdcId(String shortVdcId) {\n // TODO: we may want to also check the connection status of this VDC\n if (StringUtils.isNotBlank(shortVdcId) && (VdcUtil.getVdcUrn(shortVdcId) == null)) {\n ... |
d8369e1a103dc2e98e1a95d9c36d8f9b | Utility function to add an item to the queue | [
{
"docid": "54d2d0985f6d781a35af366064a3ad8d",
"score": "0.64545846",
"text": "public void enqueue(int item)\r\n\t{\r\n\t\t// check for queue overflow\r\n\t\tif (isFull())\r\n\t\t{\r\n\t\t\tSystem.out.println(\"OverFlow\\nProgram Terminated\");\r\n\t\t\tSystem.exit(1);\r\n\t\t}\r\n\r\n\t\tSystem.out.pri... | [
{
"docid": "6637d14699738a7d00a3b0e9b6d53817",
"score": "0.7883371",
"text": "public void enqueueItem(E item);",
"title": ""
},
{
"docid": "a600d22cf7f18c7c62732c030590416d",
"score": "0.76090634",
"text": "public void enqueue(T item);",
"title": ""
},
{
"docid": "837dcd5... |
d17c3194fafa09d66a84dbe1678f2438 | lets print number between 0 10 | [
{
"docid": "67124fc26a05d7b53acb4124529e8f45",
"score": "0.5768819",
"text": "public static void main(String[] args) {\n\t \n\t\tint i ;\n\t\t\n \n\tfor (i = 0; i <= 10; i++ )\n \t\n {System.out.println(i);}\n\t\n\t\t\n\t}",
"title": ""
}
] | [
{
"docid": "83721c8831587b63a83fd64cd8367d12",
"score": "0.69701016",
"text": "private static void printNumUsingForLoop() {\n for (int i = 1; i <= 10; i++) {\n System.out.println(i);\n }\n }",
"title": ""
},
{
"docid": "3119676007c9fa9a876de0026388e0ff",
"scor... |
5c7e4fd1033b58610665b5458d35caca | string GroupAmount = 2; | [
{
"docid": "5bc1cd9e90c0f71e0cfe24844f202032",
"score": "0.6326341",
"text": "public java.lang.String getGroupAmount() {\n java.lang.Object ref = groupAmount_;\n if (!(ref instanceof java.lang.String)) {\n com.google.protobuf.ByteString bs =\n (com.google.protobuf.ByteString)... | [
{
"docid": "22bd0feac5961c06cc6317ff1c8db60f",
"score": "0.68160325",
"text": "String getGroupNum();",
"title": ""
},
{
"docid": "8e652a9be56846dde59b7be9f77d51b4",
"score": "0.66187656",
"text": "public String group(String groupVar);",
"title": ""
},
{
"docid": "50aa3c4a... |
b9515bed24d456a99014f1a4f63c4ee3 | .ratis.common.NotReplicatedExceptionProto notReplicatedException = 4; | [
{
"docid": "9874435106e26fa8cda1f8e839dffa6e",
"score": "0.61805415",
"text": "private org.apache.ratis.shaded.com.google.protobuf.SingleFieldBuilderV3<\n org.apache.ratis.shaded.proto.RaftProtos.NotReplicatedExceptionProto, org.apache.ratis.shaded.proto.RaftProtos.NotReplicatedExceptionProto.B... | [
{
"docid": "469ca4a10a1d277c24dd3a96040139a0",
"score": "0.8460043",
"text": "org.apache.ratis.shaded.proto.RaftProtos.NotReplicatedExceptionProto getNotReplicatedException();",
"title": ""
},
{
"docid": "3a3ebf7c1d80814d256318acd1786921",
"score": "0.82809865",
"text": "org.apache.r... |
8b12597e75dac6903b989ba8526aab07 | Load the serialized data into the JID. | [
{
"docid": "7bcd98c4337b21896134da34b6af2520",
"score": "0.0",
"text": "@Override\n public void load(final ReadableBytes readableBytes) throws Exception {\n super.load(readableBytes);\n _len = _loadLen(readableBytes);\n tuple = null;\n readableBytes.skip(_len);\n }",
... | [
{
"docid": "087a18940bd297a01f0e01560b25e1ad",
"score": "0.64062667",
"text": "public void load() {\r\n\t\tPersistence persistence = new Persistence();\r\n\t\tObject[] result = persistence.load();\r\n\t\tmembers = (ArrayList<Member>) result[0];\r\n\t\tidCounter = (int) result[1];\t\t//Set the idCounter ... |
cb7e675b3a3de751a41eecc625af0122 | please look at below code | [
{
"docid": "36a666cce984f3d428ca772f54ea3c29",
"score": "0.0",
"text": "public boolean equals(Object obj) {\n\t\tif(obj instanceof MyObject) {\n\t\t\tboolean result1 = s.equals(((MyObject)obj).s);\n\t\t\treturn result1;\n\t\t}\n\t\telse {\n\t\t\treturn false;\n\t\t}\n\t}",
"title": ""
}
] | [
{
"docid": "9144a32a2cd5a72f1d1380aa1f1d5f09",
"score": "0.60226935",
"text": "public void mo5774g() {\n }",
"title": ""
},
{
"docid": "80d20df1cc75d8fa96c12c49a757fc43",
"score": "0.59372175",
"text": "@Override\n\tprotected void getExras() {\n\t\t\n\t}",
"title": ""
},... |
470e8b3753c34c31078297fcd76259ee | Persist commit in the database | [
{
"docid": "16c9e7c3133bfb0bb1b6cd1ee6d80097",
"score": "0.0",
"text": "private synchronized ChunkCommitDao persistChunkCommit(ChunkCommitDao chunkCommit)\n\t\t\tthrows InvalidBeanException, SQLException {\n\t\tccdao = new ChunkCommitDao();\n\t\tChunkCommitDao aux = ccdao.get(chunkCommit);\n\t\tif (aux ... | [
{
"docid": "ad9a1ee137c0aaa261fac6299d9bd216",
"score": "0.8198553",
"text": "public void commit() {\n\t//save();\n\t}",
"title": ""
},
{
"docid": "fc70ad0878497831845f4b81f9ab0e64",
"score": "0.8044131",
"text": "public void commit() {}",
"title": ""
},
{
"docid": "428ae... |
22d8b349b4c36bf4249baed6c5f244d5 | region ================================ Campaign create ================================ | [
{
"docid": "8b7a24d4b7795fb580b17a8143cb7099",
"score": "0.66526866",
"text": "AccessCertificationCampaignType createCampaignObject(AccessCertificationDefinitionType definition,\n Task task, OperationResult result) throws SecurityViolationException... | [
{
"docid": "ee43062e7df9848848aee91efb243b6f",
"score": "0.72504014",
"text": "public native void campaignCreate();",
"title": ""
},
{
"docid": "d527aae062126eddf837adb0446ca602",
"score": "0.7204372",
"text": "public MailchimpResponse<CampaignResponse> create(CampaignRequest request... |
f41c1e953058ac65978b6db842caa751 | Test case number: 7 /Coverage entropy=1.0986122886681096 | [
{
"docid": "a92accd8ab0ac362638988f0c9980445",
"score": "0.0",
"text": "@Test(timeout = 4000)\n public void test07() throws Throwable {\n DynamicSelectModel dynamicSelectModel0 = new DynamicSelectModel();\n // Undeclared exception!\n try { \n dynamicSelectModel0.translateValue(\... | [
{
"docid": "0ef0b4c562e56cbcdb375b4e19c4662a",
"score": "0.732844",
"text": "@Test(timeout = 4000)\n public void test077() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate();\n Evaluation evaluation0 = new Evaluat... |
25197b4cf814864ecd440f23b9149cb3 | TODO Autogenerated method stub | [
{
"docid": "64ec1cd2e8837d0bce24303eec6c77e5",
"score": "0.0",
"text": "public static void main(String[] args) {\n\t\tVehicle car1=new Car(\"bike\");\r\n\t\tBufferedReader reader=new BufferedReader(new InputStreamReader(System.in));\r\n\t\tString input = null;\r\n\t\ttry {\r\n\t\t\twhile(input!=\"quit\"... | [
{
"docid": "63a51b9825ac81396e1d7ad0b9f3ed82",
"score": "0.6866318",
"text": "@Override\n\tpublic void 군무() {\n\t\t\n\t}",
"title": ""
},
{
"docid": "15ae679aae74424c73fde647cf4332f6",
"score": "0.68461496",
"text": "@Override\r\n\t\t\tpublic void hissetmek() {\n\r\n\t\t\t}",
"ti... |
60d39b68e0097a160c67382de81d055c | This method was generated by MyBatis Generator. This method sets the value of the database column persons.city | [
{
"docid": "8f9e1ff83c70e12f8c00f3971c32ce26",
"score": "0.6988063",
"text": "public void setCity(String city) {\n this.city = city;\n }",
"title": ""
}
] | [
{
"docid": "d0957d02c4d7e51a775bf0f53575804a",
"score": "0.7239875",
"text": "public void setCity(String city);",
"title": ""
},
{
"docid": "577df40dc05208b7a1a6b693ea45de45",
"score": "0.72193974",
"text": "public void setCity(String city) {\r\n\t this.city=city;\r\n }",
"title"... |