id int32 0 165k | repo stringlengths 7 58 | path stringlengths 12 218 | func_name stringlengths 3 140 | original_string stringlengths 73 34.1k | language stringclasses 1
value | code stringlengths 73 34.1k | code_tokens list | docstring stringlengths 3 16k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 105 339 |
|---|---|---|---|---|---|---|---|---|---|---|---|
28,900 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/HSTrees.java | HSTrees.initialize | @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 | @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... | [
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28,901 | Waikato/moa | moa/src/main/java/moa/classifiers/core/attributeclassobservers/FIMTDDNumericAttributeClassObserver.java | FIMTDDNumericAttributeClassObserver.searchForBestSplitOption | 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 | 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
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28,902 | Waikato/moa | moa/src/main/java/moa/classifiers/core/attributeclassobservers/FIMTDDNumericAttributeClassObserver.java | FIMTDDNumericAttributeClassObserver.removeBadSplits | public void removeBadSplits(SplitCriterion criterion, double lastCheckRatio, double lastCheckSDR, double lastCheckE) {
removeBadSplitNodes(criterion, this.root, lastCheckRatio, lastCheckSDR, lastCheckE);
} | java | public void removeBadSplits(SplitCriterion criterion, double lastCheckRatio, double lastCheckSDR, double lastCheckE) {
removeBadSplitNodes(criterion, this.root, lastCheckRatio, lastCheckSDR, lastCheckE);
} | [
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28,903 | Waikato/moa | moa/src/main/java/moa/classifiers/core/attributeclassobservers/FIMTDDNumericAttributeClassObserver.java | FIMTDDNumericAttributeClassObserver.removeBadSplitNodes | 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 | 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... | [
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28,904 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/NearestNeighbourDescription.java | NearestNeighbourDescription.resetLearningImpl | @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 | @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... | [
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28,905 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/NearestNeighbourDescription.java | NearestNeighbourDescription.getVotesForInstance | @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 | @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;
} | [
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28,906 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/NearestNeighbourDescription.java | NearestNeighbourDescription.getAnomalyScore | 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 | 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... | [
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28,907 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/NearestNeighbourDescription.java | NearestNeighbourDescription.getNearestNeighbour | 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 | private Instance getNearestNeighbour(Instance inst, List<Instance> neighbourhood2, boolean inNbhd)
{
double dist = Double.MAX_VALUE;
Instance nearestNeighbour = null;
for(Instance candidateNN : neighbourhood2)
{
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28,908 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/NearestNeighbourDescription.java | NearestNeighbourDescription.distance | 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 | 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);
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28,909 | Waikato/moa | moa/src/main/java/moa/clusterers/streamkm/Point.java | Point.costOfPointToCenter | 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 | 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;
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28,910 | Waikato/moa | moa/src/main/java/weka/core/MOAUtils.java | MOAUtils.fromOption | public static MOAObject fromOption(ClassOption option) {
return MOAUtils.fromCommandLine(option.getRequiredType(), option.getValueAsCLIString());
} | java | public static MOAObject fromOption(ClassOption option) {
return MOAUtils.fromCommandLine(option.getRequiredType(), option.getValueAsCLIString());
} | [
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28,911 | Waikato/moa | moa/src/main/java/weka/core/MOAUtils.java | MOAUtils.toCommandLine | public static String toCommandLine(MOAObject obj) {
String result = obj.getClass().getName();
if (obj instanceof AbstractOptionHandler)
result += " " + ((AbstractOptionHandler) obj).getOptions().getAsCLIString();
return result.trim();
} | java | public static String toCommandLine(MOAObject obj) {
String result = obj.getClass().getName();
if (obj instanceof AbstractOptionHandler)
result += " " + ((AbstractOptionHandler) obj).getOptions().getAsCLIString();
return result.trim();
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28,912 | Waikato/moa | moa/src/main/java/com/yahoo/labs/samoa/instances/ArffLoader.java | ArffLoader.readDenseInstanceSparse | 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 | 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... | [
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28,913 | Waikato/moa | moa/src/main/java/moa/MakeObject.java | MakeObject.main | 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 | 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()
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28,914 | Waikato/moa | moa/src/main/java/moa/cluster/CFCluster.java | CFCluster.addVectors | 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 | 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];
}
} | [
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28,915 | Waikato/moa | moa/src/main/java/moa/gui/active/ALPreviewPanel.java | ALPreviewPanel.refresh | private void refresh() {
if (this.previewedThread != null) {
if (this.previewedThread.isComplete()) {
setLatestPreview();
disableRefresh();
} else {
this.previewedThread.getPreview(ALPreviewPanel.this);
}
}
} | java | private void refresh() {
if (this.previewedThread != null) {
if (this.previewedThread.isComplete()) {
setLatestPreview();
disableRefresh();
} else {
this.previewedThread.getPreview(ALPreviewPanel.this);
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}
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28,916 | Waikato/moa | moa/src/main/java/moa/gui/active/ALPreviewPanel.java | ALPreviewPanel.setTaskThreadToPreview | public void setTaskThreadToPreview(ALTaskThread thread) {
this.previewedThread = thread;
setLatestPreview();
if (thread == null) {
disableRefresh();
} else if (!thread.isComplete()) {
enableRefresh();
}
} | java | public void setTaskThreadToPreview(ALTaskThread thread) {
this.previewedThread = thread;
setLatestPreview();
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disableRefresh();
} else if (!thread.isComplete()) {
enableRefresh();
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28,917 | Waikato/moa | moa/src/main/java/moa/gui/active/ALPreviewPanel.java | ALPreviewPanel.getColorCodings | 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 | private Color[] getColorCodings(ALTaskThread thread) {
if (thread == null) {
return null;
}
ALMainTask task = (ALMainTask) thread.getTask();
List<ALTaskThread> subtaskThreads = task.getSubtaskThreads();
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28,918 | Waikato/moa | moa/src/main/java/moa/gui/active/ALPreviewPanel.java | ALPreviewPanel.disableRefresh | private void disableRefresh() {
this.refreshButton.setEnabled(false);
this.autoRefreshLabel.setEnabled(false);
this.autoRefreshComboBox.setEnabled(false);
this.autoRefreshTimer.stop();
} | java | private void disableRefresh() {
this.refreshButton.setEnabled(false);
this.autoRefreshLabel.setEnabled(false);
this.autoRefreshComboBox.setEnabled(false);
this.autoRefreshTimer.stop();
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28,919 | Waikato/moa | moa/src/main/java/moa/gui/active/ALPreviewPanel.java | ALPreviewPanel.enableRefresh | private void enableRefresh() {
this.refreshButton.setEnabled(true);
this.autoRefreshLabel.setEnabled(true);
this.autoRefreshComboBox.setEnabled(true);
updateAutoRefreshTimer();
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28,920 | Waikato/moa | moa/src/main/java/moa/streams/filters/ReLUFilter.java | ReLUFilter.filterInstance | 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 | 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... | [
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Assume that the instance has a single class label, as the final attribute. Note that this may not always be the case!
@param x input instance
@return output instance | [
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28,921 | Waikato/moa | moa/src/main/java/moa/clusterers/streamkm/TreeCoreset.java | TreeCoreset.treeNodeSplitCost | 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 | 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]... | [
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28,922 | Waikato/moa | moa/src/main/java/moa/clusterers/streamkm/TreeCoreset.java | TreeCoreset.treeNodeCostOfPoint | 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 | 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... | [
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28,923 | Waikato/moa | moa/src/main/java/moa/clusterers/streamkm/TreeCoreset.java | TreeCoreset.isLeaf | boolean isLeaf(treeNode node){
if(node.lc == null && node.rc == null){
return true;
} else {
return false;
}
} | java | boolean isLeaf(treeNode node){
if(node.lc == null && node.rc == null){
return true;
} else {
return false;
}
} | [
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28,924 | Waikato/moa | moa/src/main/java/moa/clusterers/streamkm/TreeCoreset.java | TreeCoreset.determineClosestCentre | 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 | 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... | [
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28,925 | Waikato/moa | moa/src/main/java/moa/clusterers/streamkm/TreeCoreset.java | TreeCoreset.treeFinished | boolean treeFinished(treeNode root){
return (root.parent == null && root.lc == null && root.rc == null);
} | java | boolean treeFinished(treeNode root){
return (root.parent == null && root.lc == null && root.rc == null);
} | [
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28,926 | Waikato/moa | moa/src/main/java/moa/clusterers/streamkm/TreeCoreset.java | TreeCoreset.freeTree | 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 | 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 {
... | [
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28,927 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/Autoencoder.java | Autoencoder.initializeNetwork | 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 | 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... | [
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28,928 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/Autoencoder.java | Autoencoder.trainOnInstanceImpl | @Override
public void trainOnInstanceImpl(Instance inst)
{
//Initialize
if(this.reset)
{
this.numAttributes = inst.numAttributes()-1;
this.initializeNetwork();
}
this.backpropagation(inst);
} | java | @Override
public void trainOnInstanceImpl(Instance inst)
{
//Initialize
if(this.reset)
{
this.numAttributes = inst.numAttributes()-1;
this.initializeNetwork();
}
this.backpropagation(inst);
} | [
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] | 395982e5100bfe75a3a4d26115462ce2cc74cbb0 | https://github.com/Waikato/moa/blob/395982e5100bfe75a3a4d26115462ce2cc74cbb0/moa/src/main/java/moa/classifiers/oneclass/Autoencoder.java#L154-L165 |
28,929 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/Autoencoder.java | Autoencoder.firstLayer | 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 | 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] ... | [
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28,930 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/Autoencoder.java | Autoencoder.secondLayer | 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 | 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++)
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28,931 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/Autoencoder.java | Autoencoder.backpropagation | 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 | 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... | [
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28,932 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/Autoencoder.java | Autoencoder.getVotesForInstance | @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 | @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));
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28,933 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/Autoencoder.java | Autoencoder.getAnomalyScore | 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 | 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... | [
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28,934 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/Autoencoder.java | Autoencoder.initialize | @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 | @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();
... | [
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28,935 | Waikato/moa | moa/src/main/java/com/yahoo/labs/samoa/instances/AttributesInformation.java | AttributesInformation.setAttributes | 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 | 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;
}
} | [
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] | 395982e5100bfe75a3a4d26115462ce2cc74cbb0 | https://github.com/Waikato/moa/blob/395982e5100bfe75a3a4d26115462ce2cc74cbb0/moa/src/main/java/com/yahoo/labs/samoa/instances/AttributesInformation.java#L113-L120 |
28,936 | Waikato/moa | moa/src/main/java/com/yahoo/labs/samoa/instances/AttributesInformation.java | AttributesInformation.locateIndex | 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 | 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;
... | [
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28,937 | Waikato/moa | moa/src/main/java/moa/tasks/meta/MetaMainTask.java | MetaMainTask.setIsLastSubtaskOnLevel | 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 | 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... | [
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28,938 | Waikato/moa | moa/src/main/java/moa/gui/experimentertab/RankingGraph.java | RankingGraph.fontSelection | public void fontSelection() {
FontChooserPanel panel = new FontChooserPanel(textFont);
int result
= JOptionPane.showConfirmDialog(
this, panel, "Font Selection",
JOptionPane.OK_CANCEL_OPTION, JOptionPane.PLAIN_MESSAGE
... | java | public void fontSelection() {
FontChooserPanel panel = new FontChooserPanel(textFont);
int result
= JOptionPane.showConfirmDialog(
this, panel, "Font Selection",
JOptionPane.OK_CANCEL_OPTION, JOptionPane.PLAIN_MESSAGE
... | [
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28,939 | Waikato/moa | moa/src/main/java/com/github/javacliparser/Options.java | Options.splitParameterFromRemainingOptions | 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 | 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... | [
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@param cliString
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28,940 | Waikato/moa | moa/src/main/java/moa/clusterers/clustree/ClusTree.java | ClusTree.updateToTop | 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 | 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();
}
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28,941 | Waikato/moa | moa/src/main/java/moa/clusterers/clustree/ClusTree.java | ClusTree.insertHereWithSplit | 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 | 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);
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28,942 | Waikato/moa | moa/src/main/java/moa/clusterers/clustree/ClusTree.java | ClusTree.findBestLeafNode | 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 | 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... | [
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28,943 | Waikato/moa | moa/src/main/java/moa/clusterers/clustree/ClusTree.java | ClusTree.calculateBestMergeInNode | 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 | 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;
... | [
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28,944 | Waikato/moa | moa/src/main/java/moa/tasks/EvaluateClustering.java | EvaluateClustering.setMeasures | 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 | 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... | [
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28,945 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/GridCluster.java | GridCluster.isConnected | 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 | 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 ... | [
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28,946 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/GridCluster.java | GridCluster.getInclusionProbability | @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 | @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... | [
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28,947 | Waikato/moa | moa/src/main/java/moa/clusterers/kmeanspm/ClusteringFeature.java | ClusteringFeature.add | 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 | 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... | [
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@param numPoints
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@param sumPoints
the sum of points to add
@param sumSquaredPoints
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28,948 | Waikato/moa | moa/src/main/java/moa/clusterers/kmeanspm/ClusteringFeature.java | ClusteringFeature.merge | 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 | 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;
} | [
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28,949 | Waikato/moa | moa/src/main/java/moa/clusterers/kmeanspm/ClusteringFeature.java | ClusteringFeature.toCluster | 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 | 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);
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28,950 | Waikato/moa | moa/src/main/java/moa/clusterers/kmeanspm/ClusteringFeature.java | ClusteringFeature.toClusterCenter | 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 | 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;
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return output;
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28,951 | Waikato/moa | moa/src/main/java/moa/clusterers/kmeanspm/ClusteringFeature.java | ClusteringFeature.printClusterCenter | 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 | 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));
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stream.write(System.getProperty("line.separator"));
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28,952 | Waikato/moa | moa/src/main/java/moa/clusterers/kmeanspm/ClusteringFeature.java | ClusteringFeature.calcKMeansCosts | 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 | 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);
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28,953 | Waikato/moa | moa/src/main/java/moa/clusterers/kmeanspm/ClusteringFeature.java | ClusteringFeature.calcKMeansCosts | 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 | 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
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28,954 | Waikato/moa | moa/src/main/java/moa/clusterers/kmeanspm/ClusteringFeature.java | ClusteringFeature.calcKMeansCosts | 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 | public double calcKMeansCosts(double[] center, ClusteringFeature points) {
assert (this.sumPoints.length == center.length &&
this.sumPoints.length == points.sumPoints.length);
return (this.sumSquaredLength + points.sumSquaredLength)
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28,955 | Waikato/moa | moa/src/main/java/moa/classifiers/meta/OnlineAccuracyUpdatedEnsemble.java | OnlineAccuracyUpdatedEnsemble.computeWeight | 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 | 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;
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28,956 | Waikato/moa | moa/src/main/java/moa/classifiers/meta/OnlineAccuracyUpdatedEnsemble.java | OnlineAccuracyUpdatedEnsemble.getPoorestClassifierIndex | 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 | 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;
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28,957 | Waikato/moa | moa/src/main/java/com/yahoo/labs/samoa/instances/InstanceImpl.java | InstanceImpl.classIndex | @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 | @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();
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28,958 | Waikato/moa | moa/src/main/java/com/yahoo/labs/samoa/instances/InstanceImpl.java | InstanceImpl.setDataset | @Override
public void setDataset(Instances dataset) {
if(dataset instanceof InstancesHeader) {
this.instanceHeader = (InstancesHeader) dataset;
}else {
this.instanceHeader = new InstancesHeader(dataset);
}
} | java | @Override
public void setDataset(Instances dataset) {
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}else {
this.instanceHeader = new InstancesHeader(dataset);
}
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28,959 | Waikato/moa | moa/src/main/java/com/yahoo/labs/samoa/instances/InstanceImpl.java | InstanceImpl.addSparseValues | @Override
public void addSparseValues(int[] indexValues, double[] attributeValues, int numberAttributes) {
this.instanceData = new SparseInstanceData(attributeValues, indexValues, numberAttributes); //???
} | java | @Override
public void addSparseValues(int[] indexValues, double[] attributeValues, int numberAttributes) {
this.instanceData = new SparseInstanceData(attributeValues, indexValues, numberAttributes); //???
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28,960 | Waikato/moa | moa/src/main/java/moa/classifiers/meta/DACC.java | DACC.initVariables | 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 | 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()];
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28,961 | Waikato/moa | moa/src/main/java/moa/classifiers/meta/DACC.java | DACC.trainAndClassify | 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 | 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
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28,962 | Waikato/moa | moa/src/main/java/moa/classifiers/meta/DACC.java | DACC.discardModel | 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 | 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()];
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28,963 | Waikato/moa | moa/src/main/java/moa/classifiers/meta/DACC.java | DACC.updateEvaluationWindow | 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 | 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++){
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28,964 | Waikato/moa | moa/src/main/java/moa/classifiers/meta/DACC.java | DACC.getMAXIndexes | 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 | protected ArrayList<Integer> getMAXIndexes(){
ArrayList<Integer> maxWIndex=new ArrayList<Integer>();
Pair[] newEnsembleWeights = new Pair[getNbActiveClassifiers()];
System.arraycopy(ensembleWeights, 0, newEnsembleWeights, 0, newEnsembleWeights.length);
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28,965 | Waikato/moa | moa/src/main/java/moa/gui/experimentertab/ImageChart.java | ImageChart.exportIMG | 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 | 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) {
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28,966 | Waikato/moa | moa/src/main/java/moa/gui/active/MeasureOverview.java | MeasureOverview.setActionListener | public void setActionListener(ActionListener listener) {
for (int i = 0; i < this.radioButtons.length; i++) {
this.radioButtons[i].addActionListener(listener);
}
} | java | public void setActionListener(ActionListener listener) {
for (int i = 0; i < this.radioButtons.length; i++) {
this.radioButtons[i].addActionListener(listener);
}
} | [
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28,967 | Waikato/moa | moa/src/main/java/moa/gui/active/MeasureOverview.java | MeasureOverview.update | public void update(MeasureCollection[] measures, String variedParamName, double[] variedParamValues) {
this.measures = measures;
this.variedParamName = variedParamName;
this.variedParamValues = variedParamValues;
update();
updateParamBox();
} | java | public void update(MeasureCollection[] measures, String variedParamName, double[] variedParamValues) {
this.measures = measures;
this.variedParamName = variedParamName;
this.variedParamValues = variedParamValues;
update();
updateParamBox();
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28,968 | Waikato/moa | moa/src/main/java/moa/gui/active/MeasureOverview.java | MeasureOverview.update | 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 | public void update() {
if (this.measures == null || this.measures.length == 0) {
// no measures to show -> empty entries
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28,969 | Waikato/moa | moa/src/main/java/moa/gui/active/MeasureOverview.java | MeasureOverview.updateParamBox | 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 | 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);
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28,970 | Waikato/moa | moa/src/main/java/moa/classifiers/rules/AbstractAMRules.java | AbstractAMRules.getModelMeasurementsImpl | @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 | @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)};
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28,971 | Waikato/moa | moa/src/main/java/moa/classifiers/rules/AbstractAMRules.java | AbstractAMRules.getModelDescription | @Override
public void getModelDescription(StringBuilder out, int indent) {
indent=0;
if(!this.unorderedRulesOption.isSet()){
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StringUtils.appendNewline(out);
}else{
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StringUtils.appen... | java | @Override
public void getModelDescription(StringBuilder out, int indent) {
indent=0;
if(!this.unorderedRulesOption.isSet()){
StringUtils.appendIndented(out, indent, "Method Ordered");
StringUtils.appendNewline(out);
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28,972 | Waikato/moa | moa/src/main/java/moa/classifiers/rules/AbstractAMRules.java | AbstractAMRules.debug | protected void debug(String string, int level) {
if (VerbosityOption.getValue()>=level){
System.out.println(string);
}
} | java | protected void debug(String string, int level) {
if (VerbosityOption.getValue()>=level){
System.out.println(string);
}
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28,973 | Waikato/moa | moa/src/main/java/moa/classifiers/rules/AbstractAMRules.java | AbstractAMRules.getVotes | public Vote getVotes(Instance instance) {
ErrorWeightedVote errorWeightedVote=newErrorWeightedVote();
//DoubleVector combinedVote = new DoubleVector();
debug("Test",3);
int numberOfRulesCovering = 0;
VerboseToConsole(instance); // Verbose to console Dataset name.
for (Rule rule : ruleSet) {
if (rule... | java | public Vote getVotes(Instance instance) {
ErrorWeightedVote errorWeightedVote=newErrorWeightedVote();
//DoubleVector combinedVote = new DoubleVector();
debug("Test",3);
int numberOfRulesCovering = 0;
VerboseToConsole(instance); // Verbose to console Dataset name.
for (Rule rule : ruleSet) {
if (rule... | [
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28,974 | Waikato/moa | moa/src/main/java/moa/core/SizeOf.java | SizeOf.isPresent | protected static synchronized boolean isPresent() {
if (m_Present == null) {
try {
SizeOfAgent.fullSizeOf(new Integer(1));
m_Present = true;
} catch (Throwable t) {
m_Present = false;
}
}
return m_Present;
} | java | protected static synchronized boolean isPresent() {
if (m_Present == null) {
try {
SizeOfAgent.fullSizeOf(new Integer(1));
m_Present = true;
} catch (Throwable t) {
m_Present = false;
}
}
return m_Present;
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28,975 | Waikato/moa | moa/src/main/java/moa/tasks/Plot.java | Plot.createScript | private String createScript(File resultFile) {
String newLine = System.getProperty("line.separator");
int sourceFileIdx = 0;
// terminal options;
String script = "set term "
+ terminalOptions(Terminal.valueOf(outputTypeOption
.getChosenLabel())) + newLine;
script += "set output '" + resultFile.getAbsolutePat... | java | private String createScript(File resultFile) {
String newLine = System.getProperty("line.separator");
int sourceFileIdx = 0;
// terminal options;
String script = "set term "
+ terminalOptions(Terminal.valueOf(outputTypeOption
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28,976 | Waikato/moa | moa/src/main/java/moa/evaluation/CMM_GTAnalysis.java | CMM_GTAnalysis.calculateGTPointQualities | private void calculateGTPointQualities(){
for (int p = 0; p < numPoints; p++) {
CMMPoint cmdp = cmmpoints.get(p);
if(!cmdp.isNoise()){
cmdp.connectivity = getConnectionValue(cmdp, cmdp.workclass());
cmdp.p.setMeasureValue("Connectivity", cmdp.connectivity)... | java | private void calculateGTPointQualities(){
for (int p = 0; p < numPoints; p++) {
CMMPoint cmdp = cmmpoints.get(p);
if(!cmdp.isNoise()){
cmdp.connectivity = getConnectionValue(cmdp, cmdp.workclass());
cmdp.p.setMeasureValue("Connectivity", cmdp.connectivity)... | [
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28,977 | Waikato/moa | moa/src/main/java/moa/evaluation/CMM_GTAnalysis.java | CMM_GTAnalysis.calculateGTClusterConnections | private void calculateGTClusterConnections(){
for (int c0 = 0; c0 < gt0Clusters.size(); c0++) {
for (int c1 = 0; c1 < gt0Clusters.size(); c1++) {
gt0Clusters.get(c0).calculateClusterConnection(c1, true);
}
}
boolean changedConnection = true;
w... | java | private void calculateGTClusterConnections(){
for (int c0 = 0; c0 < gt0Clusters.size(); c0++) {
for (int c1 = 0; c1 < gt0Clusters.size(); c1++) {
gt0Clusters.get(c0).calculateClusterConnection(c1, true);
}
}
boolean changedConnection = true;
w... | [
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28,978 | Waikato/moa | moa/src/main/java/moa/evaluation/CMM_GTAnalysis.java | CMM_GTAnalysis.getNoiseSeparability | public double getNoiseSeparability(){
if(noise.isEmpty())
return 1;
double connectivity = 0;
for(int p : noise){
CMMPoint npoint = cmmpoints.get(p);
double maxConnection = 0;
//TODO: some kind of pruning possible. what about weighting?
... | java | public double getNoiseSeparability(){
if(noise.isEmpty())
return 1;
double connectivity = 0;
for(int p : noise){
CMMPoint npoint = cmmpoints.get(p);
double maxConnection = 0;
//TODO: some kind of pruning possible. what about weighting?
... | [
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Small values indicate bad separability, values close to 1 indicate good separability
@return index of noise separability | [
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28,979 | Waikato/moa | moa/src/main/java/moa/evaluation/CMM_GTAnalysis.java | CMM_GTAnalysis.getModelQuality | public double getModelQuality(){
for(int p = 0; p < numPoints; p++){
CMMPoint cmdp = cmmpoints.get(p);
for(int hc = 0; hc < numGTClusters;hc++){
if(gtClustering.get(hc).getGroundTruth() != cmdp.trueClass){
if(gtClustering.get(hc).getInclusionProbabilit... | java | public double getModelQuality(){
for(int p = 0; p < numPoints; p++){
CMMPoint cmdp = cmmpoints.get(p);
for(int hc = 0; hc < numGTClusters;hc++){
if(gtClustering.get(hc).getGroundTruth() != cmdp.trueClass){
if(gtClustering.get(hc).getInclusionProbabilit... | [
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28,980 | Waikato/moa | moa/src/main/java/moa/evaluation/CMM_GTAnalysis.java | CMM_GTAnalysis.distance | private double distance(Instance inst1, double[] inst2){
double distance = 0.0;
for (int i = 0; i < numDims; i++) {
double d = inst1.value(i) - inst2[i];
distance += d * d;
}
return Math.sqrt(distance);
} | java | private double distance(Instance inst1, double[] inst2){
double distance = 0.0;
for (int i = 0; i < numDims; i++) {
double d = inst1.value(i) - inst2[i];
distance += d * d;
}
return Math.sqrt(distance);
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28,981 | Waikato/moa | moa/src/main/java/moa/evaluation/CMM_GTAnalysis.java | CMM_GTAnalysis.getParameterString | public String getParameterString(){
String para = "";
para+="k="+knnNeighbourhood+";";
if(useExpConnectivity){
para+="lambdaConnX="+lambdaConnX+";";
para+="lambdaConn="+lamdaConn+";";
para+="lambdaConnRef="+lambdaConnRefXValue+";";
}
para+="m="+clusterC... | java | public String getParameterString(){
String para = "";
para+="k="+knnNeighbourhood+";";
if(useExpConnectivity){
para+="lambdaConnX="+lambdaConnX+";";
para+="lambdaConn="+lamdaConn+";";
para+="lambdaConnRef="+lambdaConnRefXValue+";";
}
para+="m="+clusterC... | [
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@return main CMM parameter | [
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28,982 | Waikato/moa | moa/src/main/java/moa/gui/colorGenerator/HSVColorGenerator.java | HSVColorGenerator.generateColors | @Override
public Color[] generateColors(int numColors) {
Color[] colors = new Color[numColors];
// fix the seed to always get the same colors for the same numColors parameter and ranges for hue, saturation and brightness
Random rand = new Random(0);
for(int i = 0; i < numColors; ++i)
{
float hueRatio = i/... | java | @Override
public Color[] generateColors(int numColors) {
Color[] colors = new Color[numColors];
// fix the seed to always get the same colors for the same numColors parameter and ranges for hue, saturation and brightness
Random rand = new Random(0);
for(int i = 0; i < numColors; ++i)
{
float hueRatio = i/... | [
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28,983 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/HSTreeNode.java | HSTreeNode.updateMass | public void updateMass(Instance inst, boolean referenceWindow)
{
if(referenceWindow)
r++;
else
l++;
if(internalNode)
{
if(inst.value(this.splitAttribute) > this.splitValue)
right.updateMass(inst, referenceWindow);
else
left.updateMass(inst, referenceWindow);
}
} | java | public void updateMass(Instance inst, boolean referenceWindow)
{
if(referenceWindow)
r++;
else
l++;
if(internalNode)
{
if(inst.value(this.splitAttribute) > this.splitValue)
right.updateMass(inst, referenceWindow);
else
left.updateMass(inst, referenceWindow);
}
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28,984 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/HSTreeNode.java | HSTreeNode.score | public double score(Instance inst, int sizeLimit)
{
double anomalyScore = 0.0;
if(this.internalNode && this.r > sizeLimit)
{
if(inst.value(this.splitAttribute) > this.splitValue)
anomalyScore = right.score(inst, sizeLimit);
else
anomalyScore = left.score(inst, sizeLimit);
}
else
{
anoma... | java | public double score(Instance inst, int sizeLimit)
{
double anomalyScore = 0.0;
if(this.internalNode && this.r > sizeLimit)
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anoma... | [
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28,985 | Waikato/moa | moa/src/main/java/moa/classifiers/oneclass/HSTreeNode.java | HSTreeNode.printNode | protected void printNode()
{
System.out.println(this.depth+", "+this.splitAttribute+", "+this.splitValue+", "+this.r);
if(this.internalNode)
{
this.right.printNode();
this.left.printNode();
}
} | java | protected void printNode()
{
System.out.println(this.depth+", "+this.splitAttribute+", "+this.splitValue+", "+this.r);
if(this.internalNode)
{
this.right.printNode();
this.left.printNode();
}
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28,986 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/Dstream.java | Dstream.initialClustering | private void initialClustering() {
//System.out.println("INITIAL CLUSTERING CALLED");
//printDStreamState();
// 1. Update the density of all grids in grid_list
updateGridListDensity();
//printGridList();
// 2. Assign each dense grid to a distinct cluster
// and
// 3. Label all other grids as NO_CL... | java | private void initialClustering() {
//System.out.println("INITIAL CLUSTERING CALLED");
//printDStreamState();
// 1. Update the density of all grids in grid_list
updateGridListDensity();
//printGridList();
// 2. Assign each dense grid to a distinct cluster
// and
// 3. Label all other grids as NO_CL... | [
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28,987 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/Dstream.java | Dstream.adjustForSparseGrid | private HashMap<DensityGrid, CharacteristicVector> adjustForSparseGrid(DensityGrid dg, CharacteristicVector cv, int dgClass)
{
HashMap<DensityGrid, CharacteristicVector> glNew = new HashMap<DensityGrid, CharacteristicVector>();
//System.out.print("Density grid "+dg.toString()+" is adjusted as a sparse grid at time... | java | private HashMap<DensityGrid, CharacteristicVector> adjustForSparseGrid(DensityGrid dg, CharacteristicVector cv, int dgClass)
{
HashMap<DensityGrid, CharacteristicVector> glNew = new HashMap<DensityGrid, CharacteristicVector>();
//System.out.print("Density grid "+dg.toString()+" is adjusted as a sparse grid at time... | [
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28,988 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/Dstream.java | Dstream.adjustForTransitionalGrid | private HashMap<DensityGrid, CharacteristicVector> adjustForTransitionalGrid(DensityGrid dg, CharacteristicVector cv, int dgClass)
{
//System.out.print("Density grid "+dg.toString()+" is adjusted as a transitional grid at time "+this.getCurrTime()+". ");
// Among all neighbours of dg, find the grid h whose clus... | java | private HashMap<DensityGrid, CharacteristicVector> adjustForTransitionalGrid(DensityGrid dg, CharacteristicVector cv, int dgClass)
{
//System.out.print("Density grid "+dg.toString()+" is adjusted as a transitional grid at time "+this.getCurrTime()+". ");
// Among all neighbours of dg, find the grid h whose clus... | [
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28,989 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/Dstream.java | Dstream.cleanClusters | private void cleanClusters()
{
//System.out.println("Clean Clusters");
Iterator<GridCluster> clusIter = this.cluster_list.iterator();
ArrayList<GridCluster> toRem = new ArrayList<GridCluster>();
// Check to see if there are any empty clusters
while(clusIter.hasNext())
{
GridCluster c = clusIter.next();... | java | private void cleanClusters()
{
//System.out.println("Clean Clusters");
Iterator<GridCluster> clusIter = this.cluster_list.iterator();
ArrayList<GridCluster> toRem = new ArrayList<GridCluster>();
// Check to see if there are any empty clusters
while(clusIter.hasNext())
{
GridCluster c = clusIter.next();... | [
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28,990 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/Dstream.java | Dstream.removeSporadic | private void removeSporadic() {
//System.out.println("REMOVE SPORADIC CALLED");
// 1. For each grid g in grid_list
// a. If g is sporadic
// i. If currTime - tg > gap, delete g from grid_list
// ii. Else if (S1 && S2), mark as sporadic
// iii. Else, mark as normal
// b. Else
// ... | java | private void removeSporadic() {
//System.out.println("REMOVE SPORADIC CALLED");
// 1. For each grid g in grid_list
// a. If g is sporadic
// i. If currTime - tg > gap, delete g from grid_list
// ii. Else if (S1 && S2), mark as sporadic
// iii. Else, mark as normal
// b. Else
// ... | [
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28,991 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/Dstream.java | Dstream.checkIfSporadic | private boolean checkIfSporadic(CharacteristicVector cv)
{
// Check S1
if(cv.getCurrGridDensity(this.getCurrTime(), this.getDecayFactor()) < densityThresholdFunction(cv.getDensityTimeStamp(), this.cl, this.getDecayFactor(), this.N))
{
// Check S2
if(cv.getRemoveTime() == -1 || this.getCurrTime() >= ((1 + t... | java | private boolean checkIfSporadic(CharacteristicVector cv)
{
// Check S1
if(cv.getCurrGridDensity(this.getCurrTime(), this.getDecayFactor()) < densityThresholdFunction(cv.getDensityTimeStamp(), this.cl, this.getDecayFactor(), this.N))
{
// Check S2
if(cv.getRemoveTime() == -1 || this.getCurrTime() >= ((1 + t... | [
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28,992 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/Dstream.java | Dstream.densityThresholdFunction | private double densityThresholdFunction(int tg, double cl, double decayFactor, int N)
{
return (cl * (1.0 - Math.pow(decayFactor, (this.getCurrTime()-tg+1.0))))/(N * (1.0 - decayFactor));
} | java | private double densityThresholdFunction(int tg, double cl, double decayFactor, int N)
{
return (cl * (1.0 - Math.pow(decayFactor, (this.getCurrTime()-tg+1.0))))/(N * (1.0 - decayFactor));
} | [
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28,993 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/Dstream.java | Dstream.mergeClusters | private void mergeClusters (int smallClus, int bigClus)
{
//System.out.println("Merge clusters "+smallClus+" and "+bigClus+".");
// Iterate through the density grids in grid_list to find those which are in highClass
for (Map.Entry<DensityGrid, CharacteristicVector> grid : grid_list.entrySet())
{
DensityGr... | java | private void mergeClusters (int smallClus, int bigClus)
{
//System.out.println("Merge clusters "+smallClus+" and "+bigClus+".");
// Iterate through the density grids in grid_list to find those which are in highClass
for (Map.Entry<DensityGrid, CharacteristicVector> grid : grid_list.entrySet())
{
DensityGr... | [
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28,994 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/Dstream.java | Dstream.updateGridListDensity | private void updateGridListDensity()
{
for (Map.Entry<DensityGrid, CharacteristicVector> grid : grid_list.entrySet())
{
DensityGrid dg = grid.getKey();
CharacteristicVector cvOfG = grid.getValue();
dg.setVisited(false);
cvOfG.updateGridDensity(this.getCurrTime(), this.getDecayFactor(), this.getDL(), t... | java | private void updateGridListDensity()
{
for (Map.Entry<DensityGrid, CharacteristicVector> grid : grid_list.entrySet())
{
DensityGrid dg = grid.getKey();
CharacteristicVector cvOfG = grid.getValue();
dg.setVisited(false);
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28,995 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/Dstream.java | Dstream.printGridList | public void printGridList()
{
System.out.println("Grid List. Size "+this.grid_list.size()+".");
for (Map.Entry<DensityGrid, CharacteristicVector> grid : grid_list.entrySet())
{
DensityGrid dg = grid.getKey();
CharacteristicVector cv = grid.getValue();
if (cv.getAttribute() != SPARSE)
{
double... | java | public void printGridList()
{
System.out.println("Grid List. Size "+this.grid_list.size()+".");
for (Map.Entry<DensityGrid, CharacteristicVector> grid : grid_list.entrySet())
{
DensityGrid dg = grid.getKey();
CharacteristicVector cv = grid.getValue();
if (cv.getAttribute() != SPARSE)
{
double... | [
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28,996 | Waikato/moa | moa/src/main/java/moa/clusterers/dstream/Dstream.java | Dstream.printGridClusters | public void printGridClusters()
{
System.out.println("List of Clusters. Total "+this.cluster_list.size()+".");
for(GridCluster gc : this.cluster_list)
{
System.out.println(gc.getClusterLabel()+": "+gc.getWeight()+" {"+gc.toString()+"}");
}
} | java | public void printGridClusters()
{
System.out.println("List of Clusters. Total "+this.cluster_list.size()+".");
for(GridCluster gc : this.cluster_list)
{
System.out.println(gc.getClusterLabel()+": "+gc.getWeight()+" {"+gc.toString()+"}");
}
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] | 395982e5100bfe75a3a4d26115462ce2cc74cbb0 | https://github.com/Waikato/moa/blob/395982e5100bfe75a3a4d26115462ce2cc74cbb0/moa/src/main/java/moa/clusterers/dstream/Dstream.java#L1401-L1408 |
28,997 | Waikato/moa | moa/src/main/java/moa/clusterers/outliers/AnyOut/util/DataSet.java | DataSet.addObject | public void addObject(DataSet dataSet) throws Exception {
DataObject[] dataObjects = dataSet.getDataObjectArray();
for (int i = 0; i < dataObjects.length; i++) {
this.addObject(dataObjects[i]);
}
} | java | public void addObject(DataSet dataSet) throws Exception {
DataObject[] dataObjects = dataSet.getDataObjectArray();
for (int i = 0; i < dataObjects.length; i++) {
this.addObject(dataObjects[i]);
}
} | [
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@see addObject(DataObject newData)
@param dataSet
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] | 395982e5100bfe75a3a4d26115462ce2cc74cbb0 | https://github.com/Waikato/moa/blob/395982e5100bfe75a3a4d26115462ce2cc74cbb0/moa/src/main/java/moa/clusterers/outliers/AnyOut/util/DataSet.java#L95-L100 |
28,998 | Waikato/moa | moa/src/main/java/moa/clusterers/outliers/AnyOut/util/DataSet.java | DataSet.getNrOfClasses | public int getNrOfClasses() {
HashMap<Integer, Integer> classes = new HashMap<Integer, Integer>();
for (DataObject currentObject : dataList) {
if (!classes.containsKey(currentObject.getClassLabel()))
classes.put(currentObject.getClassLabel(), 1);
}
return classes.size();
} | java | public int getNrOfClasses() {
HashMap<Integer, Integer> classes = new HashMap<Integer, Integer>();
for (DataObject currentObject : dataList) {
if (!classes.containsKey(currentObject.getClassLabel()))
classes.put(currentObject.getClassLabel(), 1);
}
return classes.size();
} | [
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!!! It does not check whether all classes are contained !!!
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28,999 | Waikato/moa | moa/src/main/java/moa/clusterers/outliers/AnyOut/util/DataSet.java | DataSet.getDataSetsPerClass | public DataSet[] getDataSetsPerClass() throws Exception {
DataSet[] dataSetsPerClass = new DataSet[this.getNrOfClasses()];
// create a new data set for each class
for (int i = 0; i < dataSetsPerClass.length; i++) {
dataSetsPerClass[i] = new DataSet(this.nrOfDimensions);
}
// fill the data... | java | public DataSet[] getDataSetsPerClass() throws Exception {
DataSet[] dataSetsPerClass = new DataSet[this.getNrOfClasses()];
// create a new data set for each class
for (int i = 0; i < dataSetsPerClass.length; i++) {
dataSetsPerClass[i] = new DataSet(this.nrOfDimensions);
}
// fill the data... | [
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