_id stringlengths 2 7 | title stringlengths 3 140 | partition stringclasses 3
values | text stringlengths 73 34.1k | language stringclasses 1
value | meta_information dict |
|---|---|---|---|---|---|
q23000 | ALTaskTextViewerPanel.setText | train | public void setText(Preview preview) {
Point p = this.scrollPaneTable.getViewport().getViewPosition();
previewTableModel.setPreview(preview);
SwingUtilities.invokeLater(
new Runnable(){
boolean structureChanged = previewTableModel.structureChanged();
public void run(){
if(!scrollPaneTable.isVisib... | java | {
"resource": ""
} |
q23001 | ALTaskTextViewerPanel.readCollection | train | public ParsedPreview readCollection(PreviewCollection<Preview> pc) {
ParsedPreview pp = new ParsedPreview();
List<Preview> sps = pc.getPreviews();
if (sps.size() > 0 && sps.get(0) instanceof PreviewCollection) {
// members are PreviewCollections again
// NOTE: this assumes that all elements in sps are of ... | java | {
"resource": ""
} |
q23002 | ALTaskTextViewerPanel.read | train | private ParsedPreview read(Preview p) {
// find measure columns
String[] measureNames = p.getMeasurementNames();
int numMeasures = p.getMeasurementNameCount();
int processFrequencyColumn = -1;
int accuracyColumn = -1;
int kappaColumn = -1;
int kappaTempColumn = -1;
int ramColumn = -1;
int timeColumn... | java | {
"resource": ""
} |
q23003 | DoTask.isJavaVersionOK | train | public static boolean isJavaVersionOK() {
boolean isJavaVersionOK = true;
String versionStr = System.getProperty("java.version");
String[] parts;
double version;
if (versionStr.contains(".")) {
parts = versionStr.split("\\.");
}
else {
parts = new String[]{v... | java | {
"resource": ""
} |
q23004 | AccuracyWeightedEnsemble.computeCandidateWeight | train | protected double computeCandidateWeight(Classifier candidate, Instances chunk, int numFolds) {
double candidateWeight = 0.0;
Random random = new Random(1);
Instances randData = new Instances(chunk);
randData.randomize(random);
if (randData.classAttribute().isNominal()) {
... | java | {
"resource": ""
} |
q23005 | AccuracyWeightedEnsemble.computeWeight | train | protected double computeWeight(Classifier learner, Instances chunk) {
double mse_i = 0;
double mse_r = 0;
double f_ci;
double voteSum;
for (int i = 0; i < chunk.numInstances(); i++) {
try {
voteSum = 0;
for (double element : learner.g... | java | {
"resource": ""
} |
q23006 | AccuracyWeightedEnsemble.getVotesForInstance | train | public double[] getVotesForInstance(Instance inst) {
DoubleVector combinedVote = new DoubleVector();
if (this.trainingWeightSeenByModel > 0.0) {
for (int i = 0; i < this.ensemble.length; i++) {
if (this.ensembleWeights[i] > 0.0) {
DoubleVector vote = new ... | java | {
"resource": ""
} |
q23007 | AccuracyWeightedEnsemble.removePoorestModelBytes | train | protected int removePoorestModelBytes() {
int poorestIndex = Utils.minIndex(this.ensembleWeights);
int byteSize = this.ensemble[poorestIndex].measureByteSize();
discardModel(poorestIndex);
return byteSize;
} | java | {
"resource": ""
} |
q23008 | EMTopDownTreeBuilder.splitDataSetUsingEM | train | private DataSet[] splitDataSetUsingEM(DataSet dataSet, int nrOfPartitions) throws Exception {
if (dataSet.size() <= 1) throw new Exception("EMsplit needs at least 2 objects!");
EMProjectedClustering myEM = new EMProjectedClustering();
// iterate several times and take best solution
int nrOfIterations = 1;
... | java | {
"resource": ""
} |
q23009 | MTRandom.setSeed | train | private final void setSeed(int seed) {
// Annoying runtime check for initialisation of internal data
// caused by java.util.Random invoking setSeed() during init.
// This is unavoidable because no fields in our instance will
// have been initialised at this point, not even if the code
// were placed at the d... | java | {
"resource": ""
} |
q23010 | MTRandom.setSeed | train | public final synchronized void setSeed(int[] buf) {
int length = buf.length;
if (length == 0) throw new IllegalArgumentException("Seed buffer may not be empty");
// ---- Begin Mersenne Twister Algorithm ----
int i = 1, j = 0, k = (N > length ? N : length);
setSeed(MAGIC_SEED);
for (; k > 0; k--) {
mt[i] ... | java | {
"resource": ""
} |
q23011 | AutoClassDiscovery.initCache | train | protected static synchronized void initCache() {
if (m_Cache == null) {
m_Cache = new ClassCache();
// failed to locate any classes on the classpath, maybe inside Weka?
// try loading fixed list of classes
if (m_Cache.isEmpty()) {
InputStream input... | java | {
"resource": ""
} |
q23012 | AutoClassDiscovery.getAllClassNames | train | public static List<String> getAllClassNames() {
List<String> result = new ArrayList<>();
Iterator<String> pkgs = m_Cache.packages();
while (pkgs.hasNext()) {
String pkg = pkgs.next();
if (pkg.startsWith("moa")) {
Set<String> classnames = m_Cache.getClassna... | java | {
"resource": ""
} |
q23013 | AutoClassDiscovery.main | train | public static void main(String[] args) throws Exception {
initCache();
List<String> allClassnames = getAllClassNames();
PrintStream out = System.out;
if (args.length > 0)
out = new PrintStream(new File(args[0]));
Collections.sort(allClassnames);
for (String cl... | java | {
"resource": ""
} |
q23014 | MTree.add | train | public void add(DATA data) {
if(root == null) {
root = new RootLeafNode(data);
try {
root.addData(data, 0);
} catch (SplitNodeReplacement e) {
throw new RuntimeException("Should never happen!");
}
} else {
double distance = distanceFunction.calculate(data, root.data);
try {
root.addDat... | java | {
"resource": ""
} |
q23015 | MTree.remove | train | public boolean remove(DATA data) {
if(root == null) {
return false;
}
double distanceToRoot = distanceFunction.calculate(data, root.data);
try {
root.removeData(data, distanceToRoot);
} catch(RootNodeReplacement e) {
@SuppressWarnings("unchecked")
Node newRoot = (Node) e.newRoot;
root = newR... | java | {
"resource": ""
} |
q23016 | MTree.getNearestByRange | train | public Query getNearestByRange(DATA queryData, double range) {
return getNearest(queryData, range, Integer.MAX_VALUE);
} | java | {
"resource": ""
} |
q23017 | MTree.getNearestByLimit | train | public Query getNearestByLimit(DATA queryData, int limit) {
return getNearest(queryData, Double.POSITIVE_INFINITY, limit);
} | java | {
"resource": ""
} |
q23018 | MTree.getNearest | train | public Query getNearest(DATA queryData) {
return new Query(queryData, Double.POSITIVE_INFINITY, Integer.MAX_VALUE);
} | java | {
"resource": ""
} |
q23019 | WithKmeans.distance | train | private static double distance(double[] pointA, double [] pointB) {
double distance = 0.0;
for (int i = 0; i < pointA.length; i++) {
double d = pointA[i] - pointB[i];
distance += d * d;
}
return Math.sqrt(distance);
} | java | {
"resource": ""
} |
q23020 | WithKmeans.cleanUpKMeans | train | protected static Clustering cleanUpKMeans(Clustering kMeansResult, ArrayList<CFCluster> microclusters) {
/* Convert k-means result to CFClusters */
int k = kMeansResult.size();
CFCluster[] converted = new CFCluster[k];
for (CFCluster mc : microclusters) {
// Find closest kMeans cluster
double minDi... | java | {
"resource": ""
} |
q23021 | Entry.clear | train | protected void clear() {
this.data.clear();
this.buffer.clear();
this.child = null;
this.timestamp = Entry.defaultTimestamp;
} | java | {
"resource": ""
} |
q23022 | Entry.makeOlder | train | protected void makeOlder(long currentTime, double negLambda) {
// assert (currentTime > this.timestamp) : "currentTime : "
// + currentTime + ", this.timestamp: " + this.timestamp;
long diff = currentTime - this.timestamp;
this.buffer.makeOlder(diff, negLambda);
this.data.... | java | {
"resource": ""
} |
q23023 | HSTrees.resetLearningImpl | train | @Override
public void resetLearningImpl()
{
this.windowSize = this.windowSizeOption.getValue();
this.numTrees = this.numTreesOption.getValue();
this.maxDepth = this.maxDepthOption.getValue();
this.sizeLimit = this.sizeLimitOption.getValue();
this.numInstances = 0;
this.forest = new HSTreeNode[numTrees];
... | java | {
"resource": ""
} |
q23024 | HSTrees.trainOnInstanceImpl | train | @Override
public void trainOnInstanceImpl(Instance inst)
{
// If this is the first instance, then initialize the forest.
if(this.numInstances == 0)
{
this.buildForest(inst);
}
// Update the mass profile of every HSTree in the forest
for(int i = 0 ; i < this.numTrees ; i++)
{
forest[i].updateMas... | java | {
"resource": ""
} |
q23025 | HSTrees.buildForest | train | private void buildForest(Instance inst)
{
this.dimensions = inst.numAttributes();
double[]max = new double[dimensions];
double[]min = new double[dimensions];
double sq;
for (int i = 0 ; i < this.numTrees ; i++)
{
for(int j = 0 ; j < this.dimensions ; j++)
{
sq = this.classifierRandom.nextDoubl... | java | {
"resource": ""
} |
q23026 | HSTrees.getVotesForInstance | train | @Override
public double[] getVotesForInstance(Instance inst)
{
double[] votes = {0.5, 0.5};
if(!referenceWindow)
{
votes[1] = this.getAnomalyScore(inst) + 0.5 - this.anomalyThreshold;
votes[0] = 1.0 - votes[1];
}
return votes;
} | java | {
"resource": ""
} |
q23027 | HSTrees.getAnomalyScore | train | public double getAnomalyScore(Instance inst)
{
if(this.referenceWindow)
return 0.5;
else
{
double accumulatedScore = 0.0;
int massLimit = (int) (Math.ceil(this.sizeLimit*this.windowSize));
double maxScore = this.windowSize * Math.pow(2.0, this.maxDepth);
for(int i = 0 ; i < this.numTrees ; i++)
... | java | {
"resource": ""
} |
q23028 | HSTrees.initialize | train | @Override
public void initialize(Collection<Instance> trainingPoints)
{
Iterator<Instance> trgPtsIterator = trainingPoints.iterator();
if(trgPtsIterator.hasNext() && this.numInstances == 0)
{
Instance inst = trgPtsIterator.next();
this.buildForest(inst);
this.trainOnInstance(inst);
}
while(tr... | java | {
"resource": ""
} |
q23029 | FIMTDDNumericAttributeClassObserver.searchForBestSplitOption | train | protected AttributeSplitSuggestion searchForBestSplitOption(Node currentNode, AttributeSplitSuggestion currentBestOption, SplitCriterion criterion, int attIndex) {
// Return null if the current node is null or we have finished looking through all the possible splits
if (currentNode == null || countRight... | java | {
"resource": ""
} |
q23030 | FIMTDDNumericAttributeClassObserver.removeBadSplits | train | public void removeBadSplits(SplitCriterion criterion, double lastCheckRatio, double lastCheckSDR, double lastCheckE) {
removeBadSplitNodes(criterion, this.root, lastCheckRatio, lastCheckSDR, lastCheckE);
} | java | {
"resource": ""
} |
q23031 | FIMTDDNumericAttributeClassObserver.removeBadSplitNodes | train | private boolean removeBadSplitNodes(SplitCriterion criterion, Node currentNode, double lastCheckRatio, double lastCheckSDR, double lastCheckE) {
boolean isBad = false;
if (currentNode == null) {
return true;
}
if (currentNode.left != null) {
isBad = removeBadSpl... | java | {
"resource": ""
} |
q23032 | NearestNeighbourDescription.resetLearningImpl | train | @Override
public void resetLearningImpl()
{
this.nbhdSize = this.neighbourhoodSizeOption.getValue();
//this.k = this.kOption.getValue(); //NOT IMPLEMENTED//
//this.m = this.mOption.getValue(); //NOT IMPLEMENTED//
this.tau = this.thresholdOption.getValue();
this.neighbourhood = new FixedLengthList<Inst... | java | {
"resource": ""
} |
q23033 | NearestNeighbourDescription.getVotesForInstance | train | @Override
public double[] getVotesForInstance(Instance inst)
{
double[] votes = {0.5, 0.5};
if(this.neighbourhood.size() > 2)
{
votes[1] = Math.pow(2.0, -1.0 * this.getAnomalyScore(inst) / this.tau);
votes[0] = 1.0 - votes[1];
}
return votes;
} | java | {
"resource": ""
} |
q23034 | NearestNeighbourDescription.getAnomalyScore | train | public double getAnomalyScore(Instance inst)
{
if(this.neighbourhood.size() < 2)
return 1.0;
Instance nearestNeighbour = getNearestNeighbour(inst, this.neighbourhood, false);
Instance nnNearestNeighbour = getNearestNeighbour(nearestNeighbour, this.neighbourhood, true);
double indicatorArgument = dista... | java | {
"resource": ""
} |
q23035 | NearestNeighbourDescription.getNearestNeighbour | train | private Instance getNearestNeighbour(Instance inst, List<Instance> neighbourhood2, boolean inNbhd)
{
double dist = Double.MAX_VALUE;
Instance nearestNeighbour = null;
for(Instance candidateNN : neighbourhood2)
{
// If inst is in neighbourhood2 and an identical instance is found, then it is no longer requ... | java | {
"resource": ""
} |
q23036 | NearestNeighbourDescription.distance | train | private double distance(Instance inst1, Instance inst2)
{
double dist = 0.0;
for(int i = 0 ; i < inst1.numAttributes() ; i++)
{
dist += Math.pow((inst1.value(i) - inst2.value(i)), 2.0);
}
return Math.sqrt(dist);
} | java | {
"resource": ""
} |
q23037 | Point.costOfPointToCenter | train | public double costOfPointToCenter(Point centre){
if(this.weight == 0.0){
return 0.0;
}
//stores the distance between p and centre
double distance = 0.0;
//loop counter
for(int l=0; l<this.dimension; l++){
//Centroid coordinate of the point
double centroidCoordinatePoint;
if(this.weight != 0.0)... | java | {
"resource": ""
} |
q23038 | MOAUtils.fromOption | train | public static MOAObject fromOption(ClassOption option) {
return MOAUtils.fromCommandLine(option.getRequiredType(), option.getValueAsCLIString());
} | java | {
"resource": ""
} |
q23039 | MOAUtils.toCommandLine | train | public static String toCommandLine(MOAObject obj) {
String result = obj.getClass().getName();
if (obj instanceof AbstractOptionHandler)
result += " " + ((AbstractOptionHandler) obj).getOptions().getAsCLIString();
return result.trim();
} | java | {
"resource": ""
} |
q23040 | ArffLoader.readDenseInstanceSparse | train | private Instance readDenseInstanceSparse() {
//Returns a dense instance
Instance instance = newDenseInstance(this.instanceInformation.numAttributes());
//System.out.println(this.instanceInformation.numAttributes());
int numAttribute;
try {
//while (streamTokenizer.tty... | java | {
"resource": ""
} |
q23041 | MakeObject.main | train | public static void main(String[] args) {
try {
System.err.println();
System.err.println(Globals.getWorkbenchInfoString());
System.err.println();
if (args.length < 2) {
System.err.println("usage: java " + MakeObject.class.getName()
... | java | {
"resource": ""
} |
q23042 | CFCluster.addVectors | train | public static void addVectors(double[] a1, double[] a2) {
assert (a1 != null);
assert (a2 != null);
assert (a1.length == a2.length) : "Adding two arrays of different "
+ "length";
for (int i = 0; i < a1.length; i++) {
a1[i] += a2[i];
}
} | java | {
"resource": ""
} |
q23043 | ALPreviewPanel.refresh | train | private void refresh() {
if (this.previewedThread != null) {
if (this.previewedThread.isComplete()) {
setLatestPreview();
disableRefresh();
} else {
this.previewedThread.getPreview(ALPreviewPanel.this);
}
}
} | java | {
"resource": ""
} |
q23044 | ALPreviewPanel.setTaskThreadToPreview | train | public void setTaskThreadToPreview(ALTaskThread thread) {
this.previewedThread = thread;
setLatestPreview();
if (thread == null) {
disableRefresh();
} else if (!thread.isComplete()) {
enableRefresh();
}
} | java | {
"resource": ""
} |
q23045 | ALPreviewPanel.getColorCodings | train | private Color[] getColorCodings(ALTaskThread thread) {
if (thread == null) {
return null;
}
ALMainTask task = (ALMainTask) thread.getTask();
List<ALTaskThread> subtaskThreads = task.getSubtaskThreads();
if (subtaskThreads.size() == 0) {
// no hierarchical thread, e... | java | {
"resource": ""
} |
q23046 | ALPreviewPanel.disableRefresh | train | private void disableRefresh() {
this.refreshButton.setEnabled(false);
this.autoRefreshLabel.setEnabled(false);
this.autoRefreshComboBox.setEnabled(false);
this.autoRefreshTimer.stop();
} | java | {
"resource": ""
} |
q23047 | ALPreviewPanel.enableRefresh | train | private void enableRefresh() {
this.refreshButton.setEnabled(true);
this.autoRefreshLabel.setEnabled(true);
this.autoRefreshComboBox.setEnabled(true);
updateAutoRefreshTimer();
} | java | {
"resource": ""
} |
q23048 | ReLUFilter.filterInstance | train | public Instance filterInstance(Instance x) {
if(dataset==null){
initialize(x);
}
double z_[] = new double[H+1];
int d = x.numAttributes() - 1; // suppose one class attribute (at the end)
for(int k = 0; k < H; k++) {
// for each hidden unit ...
double a_k = 0.; // k-th activation (dot p... | java | {
"resource": ""
} |
q23049 | TreeCoreset.treeNodeSplitCost | train | double treeNodeSplitCost(treeNode node, Point centreA, Point centreB){
//loop counter variable
int i;
//stores the cost
double sum = 0.0;
for(i=0; i<node.n; i++){
//loop counter variable
int l;
//stores the distance between p and centreA
double distanceA = 0.0;
for(l=0;l<node.points[i]... | java | {
"resource": ""
} |
q23050 | TreeCoreset.treeNodeCostOfPoint | train | double treeNodeCostOfPoint(treeNode node, Point p){
if(p.weight == 0.0){
return 0.0;
}
//stores the distance between centre and p
double distance = 0.0;
//loop counter variable
int l;
for(l=0;l<p.dimension;l++){
//centroid coordinate of the point
double centroidCoordinatePoint;
if(p.weigh... | java | {
"resource": ""
} |
q23051 | TreeCoreset.isLeaf | train | boolean isLeaf(treeNode node){
if(node.lc == null && node.rc == null){
return true;
} else {
return false;
}
} | java | {
"resource": ""
} |
q23052 | TreeCoreset.determineClosestCentre | train | Point determineClosestCentre(Point p, Point centreA, Point centreB){
//loop counter variable
int l;
//stores the distance between p and centreA
double distanceA = 0.0;
for(l=0;l<p.dimension;l++){
//centroid coordinate of the point
double centroidCoordinatePoint;
if(p.weight != 0.0){
centroi... | java | {
"resource": ""
} |
q23053 | TreeCoreset.treeFinished | train | boolean treeFinished(treeNode root){
return (root.parent == null && root.lc == null && root.rc == null);
} | java | {
"resource": ""
} |
q23054 | TreeCoreset.freeTree | train | void freeTree(treeNode root){
while(!treeFinished(root)){
if(root.lc == null && root.rc == null){
root = root.parent;
} else if(root.lc == null && root.rc != null){
//Schau ob rc ein Blatt ist
if(isLeaf(root.rc)){
//Gebe rechtes Kind frei
root.rc.free();
root.rc = null;
} else {
... | java | {
"resource": ""
} |
q23055 | Autoencoder.initializeNetwork | train | private void initializeNetwork()
{
this.hiddenLayerSize = this.hiddenLayerOption.getValue();
this.learningRate = this.learningRateOption.getValue();
this.threshold = this.thresholdOption.getValue();
double[][] randomWeightsOne = new double[this.hiddenLayerSize][this.numAttributes];
double[][] randomWeightsTw... | java | {
"resource": ""
} |
q23056 | Autoencoder.trainOnInstanceImpl | train | @Override
public void trainOnInstanceImpl(Instance inst)
{
//Initialize
if(this.reset)
{
this.numAttributes = inst.numAttributes()-1;
this.initializeNetwork();
}
this.backpropagation(inst);
} | java | {
"resource": ""
} |
q23057 | Autoencoder.firstLayer | train | private RealMatrix firstLayer(RealMatrix input)
{
RealMatrix hidden = (this.weightsOne.multiply(input)).scalarAdd(this.biasOne);
double[] tempValues = new double[this.hiddenLayerSize];
// Logistic function used for hidden layer activation
for(int i = 0 ; i < this.hiddenLayerSize ; i++)
{
tempValues[i] ... | java | {
"resource": ""
} |
q23058 | Autoencoder.secondLayer | train | private RealMatrix secondLayer(RealMatrix hidden)
{
RealMatrix output = (this.weightsTwo.multiply(hidden)).scalarAdd(this.biasTwo);
double[] tempValues = new double[this.numAttributes];
// Logistic function used for output layer activation
for(int i = 0 ; i < this.numAttributes ; i++)
{
tempValues[i] =... | java | {
"resource": ""
} |
q23059 | Autoencoder.backpropagation | train | private void backpropagation(Instance inst)
{
double [] attributeValues = new double[this.numAttributes];
for(int i = 0 ; i < this.numAttributes ; i++)
{
attributeValues[i] = inst.value(i);
}
RealMatrix input = new Array2DRowRealMatrix(attributeValues);
RealMatrix hidden = firstLayer(input);
Rea... | java | {
"resource": ""
} |
q23060 | Autoencoder.getVotesForInstance | train | @Override
public double[] getVotesForInstance(Instance inst)
{
double[] votes = new double[2];
if (this.reset == false)
{
double error = this.getAnomalyScore(inst);
// Exponential function to convert the error [0, +inf) into a vote [1,0].
votes[0] = Math.pow(2.0, -1.0 * (error / this.threshold));
... | java | {
"resource": ""
} |
q23061 | Autoencoder.getAnomalyScore | train | public double getAnomalyScore(Instance inst)
{
double error = 0.0;
if(!this.reset)
{
double [] attributeValues = new double[inst.numAttributes()-1];
for(int i = 0 ; i < attributeValues.length ; i++)
{
attributeValues[i] = inst.value(i);
}
RealMatrix input = new Array2DRowRealMatrix(attri... | java | {
"resource": ""
} |
q23062 | Autoencoder.initialize | train | @Override
public void initialize(Collection<Instance> trainingPoints)
{
Iterator<Instance> trgPtsIterator = trainingPoints.iterator();
if(trgPtsIterator.hasNext() && this.reset)
{
Instance inst = (Instance)trgPtsIterator.next();
this.numAttributes = inst.numAttributes()-1;
this.initializeNetwork();
... | java | {
"resource": ""
} |
q23063 | AttributesInformation.setAttributes | train | public void setAttributes(Attribute[] v) {
this.attributes = v;
this.numberAttributes=v.length;
this.indexValues = new int[numberAttributes];
for (int i = 0; i < numberAttributes; i++) {
this.indexValues[i]=i;
}
} | java | {
"resource": ""
} |
q23064 | AttributesInformation.locateIndex | train | public int locateIndex(int index) {
int min = 0;
int max = this.indexValues.length - 1;
if (max == -1) {
return -1;
}
// Binary search
while ((this.indexValues[min] <= index) && (this.indexValues[max] >= index)) {
int current = (max + min) / 2;
... | java | {
"resource": ""
} |
q23065 | MetaMainTask.setIsLastSubtaskOnLevel | train | public void setIsLastSubtaskOnLevel(
boolean[] parentIsLastSubtaskList, boolean isLastSubtask)
{
this.isLastSubtaskOnLevel =
new boolean[parentIsLastSubtaskList.length + 1];
for (int i = 0; i < parentIsLastSubtaskList.length; i++) {
this.isLastSubtaskOnLevel[i] = parentIsLastSubtaskList[i];
}
thi... | java | {
"resource": ""
} |
q23066 | RankingGraph.fontSelection | train | public void fontSelection() {
FontChooserPanel panel = new FontChooserPanel(textFont);
int result
= JOptionPane.showConfirmDialog(
this, panel, "Font Selection",
JOptionPane.OK_CANCEL_OPTION, JOptionPane.PLAIN_MESSAGE
... | java | {
"resource": ""
} |
q23067 | Options.splitParameterFromRemainingOptions | train | protected static String[] splitParameterFromRemainingOptions(
String cliString) {
String[] paramSplit = new String[2];
cliString = cliString.trim();
if (cliString.startsWith("\"") || cliString.startsWith("'")) {
int endQuoteIndex = cliString.indexOf(cliString.charAt(0), 1... | java | {
"resource": ""
} |
q23068 | ClusTree.updateToTop | train | private void updateToTop(Node toUpdate) {
while(toUpdate!=null){
for (Entry e: toUpdate.getEntries())
e.recalculateData();
if (toUpdate.getEntries()[0].getParentEntry()==null)
break;
toUpdate=toUpdate.getEntries()[0].getParentEntry().getNode();
}
} | java | {
"resource": ""
} |
q23069 | ClusTree.insertHereWithSplit | train | private Entry insertHereWithSplit(Entry toInsert, Node insertNode,
long timestamp) {
//Handle root split
if (insertNode.getEntries()[0].getParentEntry()==null){
root.makeOlder(timestamp, negLambda);
Entry irrelevantEntry = insertNode.getIrrelevantEntry(this.weightThreshold);
i... | java | {
"resource": ""
} |
q23070 | ClusTree.findBestLeafNode | train | private Node findBestLeafNode(ClusKernel newPoint) {
double minDist = Double.MAX_VALUE;
Node bestFit = null;
for (Node e: collectLeafNodes(root)){
if (newPoint.calcDistance(e.nearestEntry(newPoint).getData())<minDist){
bestFit = e;
minDist = newPoint.calcDistance(e.nearestEntry(newPoi... | java | {
"resource": ""
} |
q23071 | ClusTree.calculateBestMergeInNode | train | private BestMergeInNode calculateBestMergeInNode(Node node) {
assert (node.numFreeEntries() == 0);
Entry[] entries = node.getEntries();
int toMerge1 = -1;
int toMerge2 = -1;
double distanceBetweenMergeEntries = Double.NaN;
double minDistance = Double.MAX_VALUE;
... | java | {
"resource": ""
} |
q23072 | EvaluateClustering.setMeasures | train | protected void setMeasures(boolean[] measures)
{
this.generalEvalOption.setValue(measures[0]);
this.f1Option.setValue(measures[1]);
this.entropyOption.setValue(measures[2]);
this.cmmOption.setValue(measures[3]);
this.ssqOption.setValue(measures[4]);
this.separationOption.setValu... | java | {
"resource": ""
} |
q23073 | GridCluster.isConnected | train | public boolean isConnected()
{
this.visited = new HashMap<DensityGrid, Boolean>();
Iterator<DensityGrid> initIter = this.grids.keySet().iterator();
DensityGrid dg;
if (initIter.hasNext())
{
dg = initIter.next();
visited.put(dg, this.grids.get(dg));
boolean changesMade;
do{
changesMade ... | java | {
"resource": ""
} |
q23074 | GridCluster.getInclusionProbability | train | @Override
public double getInclusionProbability(Instance instance) {
Iterator<Map.Entry<DensityGrid, Boolean>> gridIter = grids.entrySet().iterator();
while(gridIter.hasNext())
{
Map.Entry<DensityGrid, Boolean> grid = gridIter.next();
DensityGrid dg = grid.getKey();
if(dg.getInclusionProbability(inst... | java | {
"resource": ""
} |
q23075 | ClusteringFeature.add | train | public void add(int numPoints, double[] sumPoints, double sumSquaredPoints) {
assert (this.sumPoints.length == sumPoints.length);
this.numPoints += numPoints;
super.setWeight(this.numPoints);
for (int i = 0; i < this.sumPoints.length; i++) {
this.sumPoints[i] += sumPoints[i];
}
this.sumSquaredLength += s... | java | {
"resource": ""
} |
q23076 | ClusteringFeature.merge | train | public void merge(ClusteringFeature x) {
assert (this.sumPoints.length == x.sumPoints.length);
this.numPoints += x.numPoints;
super.setWeight(this.numPoints);
for (int i = 0; i < this.sumPoints.length; i++) {
this.sumPoints[i] += x.sumPoints[i];
}
this.sumSquaredLength += x.sumSquaredLength;
} | java | {
"resource": ""
} |
q23077 | ClusteringFeature.toCluster | train | public Cluster toCluster() {
double[] output = new double[this.sumPoints.length];
System.arraycopy(this.sumPoints, 0, output, 0, this.sumPoints.length);
for (int i = 0; i < output.length; i++) {
output[i] /= this.numPoints;
}
return new SphereCluster(output, getThreshold(), this.numPoints);
} | java | {
"resource": ""
} |
q23078 | ClusteringFeature.toClusterCenter | train | public double[] toClusterCenter() {
double[] output = new double[this.sumPoints.length + 1];
System.arraycopy(this.sumPoints, 0, output, 1, this.sumPoints.length);
output[0] = this.numPoints;
for (int i = 1; i < output.length; i++) {
output[i] /= this.numPoints;
}
return output;
} | java | {
"resource": ""
} |
q23079 | ClusteringFeature.printClusterCenter | train | public void printClusterCenter(Writer stream) throws IOException {
stream.write(String.valueOf(this.numPoints));
for (int j = 0; j < this.sumPoints.length; j++) {
stream.write(' ');
stream.write(String.valueOf(this.sumPoints[j] / this.numPoints));
}
stream.write(System.getProperty("line.separator"));
} | java | {
"resource": ""
} |
q23080 | ClusteringFeature.calcKMeansCosts | train | public double calcKMeansCosts(double[] center) {
assert (this.sumPoints.length == center.length);
return this.sumSquaredLength - 2
* Metric.dotProduct(this.sumPoints, center) + this.numPoints
* Metric.dotProduct(center);
} | java | {
"resource": ""
} |
q23081 | ClusteringFeature.calcKMeansCosts | train | public double calcKMeansCosts(double[] center, double[] point) {
assert (this.sumPoints.length == center.length &&
this.sumPoints.length == point.length);
return (this.sumSquaredLength + Metric.distanceSquared(point)) - 2
* Metric.dotProductWithAddition(this.sumPoints, point, center)
+ (this.numPoints +... | java | {
"resource": ""
} |
q23082 | ClusteringFeature.calcKMeansCosts | train | public double calcKMeansCosts(double[] center, ClusteringFeature points) {
assert (this.sumPoints.length == center.length &&
this.sumPoints.length == points.sumPoints.length);
return (this.sumSquaredLength + points.sumSquaredLength)
- 2 * Metric.dotProductWithAddition(this.sumPoints,
points.sumPoints,... | java | {
"resource": ""
} |
q23083 | OnlineAccuracyUpdatedEnsemble.computeWeight | train | protected double computeWeight(int i, Instance example) {
int d = this.windowSize;
int t = this.processedInstances - this.ensemble[i].birthday;
double e_it = 0;
double mse_it = 0;
double voteSum = 0;
try{
double[] votes = this.ensemble[i].clas... | java | {
"resource": ""
} |
q23084 | OnlineAccuracyUpdatedEnsemble.getPoorestClassifierIndex | train | private int getPoorestClassifierIndex() {
int minIndex = 0;
for (int i = 1; i < this.weights.length; i++) {
if(this.weights[i][0] < this.weights[minIndex][0]){
minIndex = i;
}
}
return minIndex;
} | java | {
"resource": ""
} |
q23085 | InstanceImpl.classIndex | train | @Override
public int classIndex() {
int classIndex = instanceHeader.classIndex();
// return ? classIndex : 0;
if(classIndex == Integer.MAX_VALUE)
if(this.instanceHeader.instanceInformation.range!=null)
classIndex=instanceHeader.instanceInformation.range.getStart();
... | java | {
"resource": ""
} |
q23086 | InstanceImpl.setDataset | train | @Override
public void setDataset(Instances dataset) {
if(dataset instanceof InstancesHeader) {
this.instanceHeader = (InstancesHeader) dataset;
}else {
this.instanceHeader = new InstancesHeader(dataset);
}
} | java | {
"resource": ""
} |
q23087 | InstanceImpl.addSparseValues | train | @Override
public void addSparseValues(int[] indexValues, double[] attributeValues, int numberAttributes) {
this.instanceData = new SparseInstanceData(attributeValues, indexValues, numberAttributes); //???
} | java | {
"resource": ""
} |
q23088 | DACC.initVariables | train | protected void initVariables(){
int ensembleSize = (int)this.memberCountOption.getValue();
this.ensemble = new Classifier[ensembleSize];
this.ensembleAges = new double[ensembleSize];
this.ensembleWindows = new int[ensembleSize][(int)this.evaluationSizeOption.getValue()];
} | java | {
"resource": ""
} |
q23089 | DACC.trainAndClassify | train | protected void trainAndClassify(Instance inst){
nbInstances++;
boolean mature = true;
boolean unmature = true;
for (int i = 0; i < getNbActiveClassifiers(); i++) {
// check if all adaptive learners are mature
if (this.ensembleAges[i] < this.maturit... | java | {
"resource": ""
} |
q23090 | DACC.discardModel | train | public void discardModel(int index) {
this.ensemble[index].resetLearning();
this.ensembleWeights[index].val = 0;
this.ensembleAges[index] = 0;
this.ensembleWindows[index]=new int[(int)this.evaluationSizeOption.getValue()];
} | java | {
"resource": ""
} |
q23091 | DACC.updateEvaluationWindow | train | protected double updateEvaluationWindow(int index,int val){
int[] newEnsembleWindows = new int[this.ensembleWindows[index].length];
int wsize = (int)Math.min(this.evaluationSizeOption.getValue(),this.ensembleAges[index]+1);
int sum = 0;
for (int i = 0; i < wsize-1 ; i++){
... | java | {
"resource": ""
} |
q23092 | DACC.getMAXIndexes | train | protected ArrayList<Integer> getMAXIndexes(){
ArrayList<Integer> maxWIndex=new ArrayList<Integer>();
Pair[] newEnsembleWeights = new Pair[getNbActiveClassifiers()];
System.arraycopy(ensembleWeights, 0, newEnsembleWeights, 0, newEnsembleWeights.length);
Arrays.sort(newEnsembleWeights);... | java | {
"resource": ""
} |
q23093 | ImageChart.exportIMG | train | public void exportIMG(String path, String type) throws IOException {
switch (type) {
case "JPG":
try {
ChartUtilities.saveChartAsJPEG(new File(path + File.separator + name + ".jpg"), chart, width, height);
} catch (IOException e) {
... | java | {
"resource": ""
} |
q23094 | MeasureOverview.setActionListener | train | public void setActionListener(ActionListener listener) {
for (int i = 0; i < this.radioButtons.length; i++) {
this.radioButtons[i].addActionListener(listener);
}
} | java | {
"resource": ""
} |
q23095 | MeasureOverview.update | train | public void update(MeasureCollection[] measures, String variedParamName, double[] variedParamValues) {
this.measures = measures;
this.variedParamName = variedParamName;
this.variedParamValues = variedParamValues;
update();
updateParamBox();
} | java | {
"resource": ""
} |
q23096 | MeasureOverview.update | train | public void update() {
if (this.measures == null || this.measures.length == 0) {
// no measures to show -> empty entries
for (int i = 0; i < this.currentValues.length; i++) {
this.currentValues[i].setText("-");
this.meanValues[i].setText("-");
... | java | {
"resource": ""
} |
q23097 | MeasureOverview.updateParamBox | train | private void updateParamBox() {
if (this.variedParamValues == null || this.variedParamValues.length == 0) {
// no varied parameter -> set to empty box
this.paramBox.removeAllItems();
this.paramBox.setEnabled(false);
} else if (this.paramBox.getItemCount() != this.vari... | java | {
"resource": ""
} |
q23098 | AbstractAMRules.getModelMeasurementsImpl | train | @Override
protected Measurement[] getModelMeasurementsImpl() {
return new Measurement[]{
new Measurement("anomaly detections", this.numAnomaliesDetected),
new Measurement("change detections", this.numChangesDetected),
new Measurement("rules (number)", this.ruleSet.size()+1)};
} | java | {
"resource": ""
} |
q23099 | AbstractAMRules.getModelDescription | train | @Override
public void getModelDescription(StringBuilder out, int indent) {
indent=0;
if(!this.unorderedRulesOption.isSet()){
StringUtils.appendIndented(out, indent, "Method Ordered");
StringUtils.appendNewline(out);
}else{
StringUtils.appendIndented(out, indent, "Method Unordered");
StringUtils.appen... | java | {
"resource": ""
} |
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