idx int64 0 41.2k | question stringlengths 74 4.21k | target stringlengths 5 888 |
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21,500 | 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 ; } | Calculates the distance between the argument instance and its nearest neighbour as well as the distance between that nearest neighbour and its own nearest neighbour . The ratio of these distances is compared to the threshold value tau and converted into a vote score . |
21,501 | 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 indicatorArgum... | Returns the anomaly score for an argument instance based on the distance from it to its nearest neighbour compared to the distance from its nearest neighbour to the neighbour s nearest neighbour . |
21,502 | private Instance getNearestNeighbour ( Instance inst , List < Instance > neighbourhood2 , boolean inNbhd ) { double dist = Double . MAX_VALUE ; Instance nearestNeighbour = null ; for ( Instance candidateNN : neighbourhood2 ) { if ( inNbhd && ( distance ( inst , candidateNN ) == 0 ) ) { inNbhd = false ; } else { if ( di... | Searches the neighbourhood in order to find the argument instance s nearest neighbour . |
21,503 | 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 ) ; } | Calculates the Euclidean distance between two instances . |
21,504 | public double costOfPointToCenter ( Point centre ) { if ( this . weight == 0.0 ) { return 0.0 ; } double distance = 0.0 ; for ( int l = 0 ; l < this . dimension ; l ++ ) { double centroidCoordinatePoint ; if ( this . weight != 0.0 ) { centroidCoordinatePoint = this . coordinates [ l ] / this . weight ; } else { centroi... | Computes the cost of this point with centre centre |
21,505 | public static MOAObject fromOption ( ClassOption option ) { return MOAUtils . fromCommandLine ( option . getRequiredType ( ) , option . getValueAsCLIString ( ) ) ; } | Creates a MOA object from the specified class option . |
21,506 | public static String toCommandLine ( MOAObject obj ) { String result = obj . getClass ( ) . getName ( ) ; if ( obj instanceof AbstractOptionHandler ) result += " " + ( ( AbstractOptionHandler ) obj ) . getOptions ( ) . getAsCLIString ( ) ; return result . trim ( ) ; } | Returs the commandline for the given object . If the object is not derived from AbstractOptionHandler then only the classname . Otherwise the classname and the options are returned . |
21,507 | private Instance readDenseInstanceSparse ( ) { Instance instance = newDenseInstance ( this . instanceInformation . numAttributes ( ) ) ; int numAttribute ; try { streamTokenizer . nextToken ( ) ; while ( streamTokenizer . ttype != StreamTokenizer . TT_EOL && streamTokenizer . ttype != StreamTokenizer . TT_EOF ) { while... | Reads an instance sparse and returns a dense one . |
21,508 | 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 ( ) + " outputfile.moa \"<object name> <option... | Main method for writing an object to a file from the command line . |
21,509 | 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 ] ; } } | Adds the second array to the first array element by element . The arrays must have the same length . |
21,510 | private void refresh ( ) { if ( this . previewedThread != null ) { if ( this . previewedThread . isComplete ( ) ) { setLatestPreview ( ) ; disableRefresh ( ) ; } else { this . previewedThread . getPreview ( ALPreviewPanel . this ) ; } } } | Refreshes the preview . |
21,511 | public void setTaskThreadToPreview ( ALTaskThread thread ) { this . previewedThread = thread ; setLatestPreview ( ) ; if ( thread == null ) { disableRefresh ( ) ; } else if ( ! thread . isComplete ( ) ) { enableRefresh ( ) ; } } | Sets the TaskThread that will be previewed . |
21,512 | private Color [ ] getColorCodings ( ALTaskThread thread ) { if ( thread == null ) { return null ; } ALMainTask task = ( ALMainTask ) thread . getTask ( ) ; List < ALTaskThread > subtaskThreads = task . getSubtaskThreads ( ) ; if ( subtaskThreads . size ( ) == 0 ) { return new Color [ ] { task . getColorCoding ( ) } ; }... | Reads the color codings of the subtasks . |
21,513 | private void disableRefresh ( ) { this . refreshButton . setEnabled ( false ) ; this . autoRefreshLabel . setEnabled ( false ) ; this . autoRefreshComboBox . setEnabled ( false ) ; this . autoRefreshTimer . stop ( ) ; } | Disables refreshing . |
21,514 | private void enableRefresh ( ) { this . refreshButton . setEnabled ( true ) ; this . autoRefreshLabel . setEnabled ( true ) ; this . autoRefreshComboBox . setEnabled ( true ) ; updateAutoRefreshTimer ( ) ; } | Enables refreshing . |
21,515 | public Instance filterInstance ( Instance x ) { if ( dataset == null ) { initialize ( x ) ; } double z_ [ ] = new double [ H + 1 ] ; int d = x . numAttributes ( ) - 1 ; for ( int k = 0 ; k < H ; k ++ ) { double a_k = 0. ; for ( int j = 0 ; j < d ; j ++ ) { a_k += ( x . value ( j ) * W [ k ] [ j ] ) ; } z_ [ k ] = ( a_k... | Filter an instance . Assume that the instance has a single class label as the final attribute . Note that this may not always be the case! |
21,516 | double treeNodeSplitCost ( treeNode node , Point centreA , Point centreB ) { int i ; double sum = 0.0 ; for ( i = 0 ; i < node . n ; i ++ ) { int l ; double distanceA = 0.0 ; for ( l = 0 ; l < node . points [ i ] . dimension ; l ++ ) { double centroidCoordinatePoint ; if ( node . points [ i ] . weight != 0.0 ) { centro... | computes the hypothetical cost if the node would be split with new centers centreA centreB |
21,517 | double treeNodeCostOfPoint ( treeNode node , Point p ) { if ( p . weight == 0.0 ) { return 0.0 ; } double distance = 0.0 ; int l ; for ( l = 0 ; l < p . dimension ; l ++ ) { double centroidCoordinatePoint ; if ( p . weight != 0.0 ) { centroidCoordinatePoint = p . coordinates [ l ] / p . weight ; } else { centroidCoordi... | computes the cost of point p with the centre of treenode node |
21,518 | boolean isLeaf ( treeNode node ) { if ( node . lc == null && node . rc == null ) { return true ; } else { return false ; } } | tests if a node is a leaf |
21,519 | Point determineClosestCentre ( Point p , Point centreA , Point centreB ) { int l ; double distanceA = 0.0 ; for ( l = 0 ; l < p . dimension ; l ++ ) { double centroidCoordinatePoint ; if ( p . weight != 0.0 ) { centroidCoordinatePoint = p . coordinates [ l ] / p . weight ; } else { centroidCoordinatePoint = p . coordin... | returns the next centre |
21,520 | boolean treeFinished ( treeNode root ) { return ( root . parent == null && root . lc == null && root . rc == null ) ; } | Checks if the storage is completly freed |
21,521 | 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 ) { if ( isLeaf ( root . rc ) ) { root . rc . free ( ) ; root . rc = null ; } else { root = root . rc ; } } else if ( root . lc ... | frees a tree of its storage |
21,522 | 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 . numAttribut... | Initializes the autoencoder network . |
21,523 | public void trainOnInstanceImpl ( Instance inst ) { if ( this . reset ) { this . numAttributes = inst . numAttributes ( ) - 1 ; this . initializeNetwork ( ) ; } this . backpropagation ( inst ) ; } | Uses backpropagation to update the weights in the autoencoder . |
21,524 | private RealMatrix firstLayer ( RealMatrix input ) { RealMatrix hidden = ( this . weightsOne . multiply ( input ) ) . scalarAdd ( this . biasOne ) ; double [ ] tempValues = new double [ this . hiddenLayerSize ] ; for ( int i = 0 ; i < this . hiddenLayerSize ; i ++ ) { tempValues [ i ] = 1.0 / ( 1.0 + Math . pow ( Math ... | Performs the requisite calculations between the input layer and the hidden layer . |
21,525 | private RealMatrix secondLayer ( RealMatrix hidden ) { RealMatrix output = ( this . weightsTwo . multiply ( hidden ) ) . scalarAdd ( this . biasTwo ) ; double [ ] tempValues = new double [ this . numAttributes ] ; for ( int i = 0 ; i < this . numAttributes ; i ++ ) { tempValues [ i ] = 1.0 / ( 1.0 + Math . pow ( Math .... | Performs the requisite calculations between the hidden layer and the output layer . |
21,526 | 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 ( inpu... | Performs backpropagation based on a training instance . |
21,527 | public double [ ] getVotesForInstance ( Instance inst ) { double [ ] votes = new double [ 2 ] ; if ( this . reset == false ) { double error = this . getAnomalyScore ( inst ) ; votes [ 0 ] = Math . pow ( 2.0 , - 1.0 * ( error / this . threshold ) ) ; votes [ 1 ] = 1.0 - votes [ 0 ] ; } return votes ; } | Calculates the error between the autoencoder s reconstruction of the input and the argument instances . This error is converted to vote scores . |
21,528 | 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 (... | Returns the squared error between the input value and the reconstructed value as the anomaly score for the argument instance . |
21,529 | 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 . initializ... | Initializes the Autoencoder classifier on the argument trainingPoints . |
21,530 | 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 ; } } | Sets the attribute information . |
21,531 | public int locateIndex ( int index ) { int min = 0 ; int max = this . indexValues . length - 1 ; if ( max == - 1 ) { return - 1 ; } while ( ( this . indexValues [ min ] <= index ) && ( this . indexValues [ max ] >= index ) ) { int current = ( max + min ) / 2 ; if ( this . indexValues [ current ] > index ) { max = curre... | Locates the greatest index that is not greater than the given index . |
21,532 | 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 ] ;... | Set the list of booleans indicating if the current branch in the subtask tree is the last one on its respective level . |
21,533 | public void fontSelection ( ) { FontChooserPanel panel = new FontChooserPanel ( textFont ) ; int result = JOptionPane . showConfirmDialog ( this , panel , "Font Selection" , JOptionPane . OK_CANCEL_OPTION , JOptionPane . PLAIN_MESSAGE ) ; if ( result == JOptionPane . OK_OPTION ) { textFont = panel . getSelectedFont ( )... | Allow to select the text font . |
21,534 | 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 ) ; if ( e... | Internal method that splits a string into two parts - the parameter for the current option and the remaining options . |
21,535 | 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 ( ) ; } } | recalculates data for all entries that lie on the path from the root to the Entry toUpdate . |
21,536 | private Entry insertHereWithSplit ( Entry toInsert , Node insertNode , long timestamp ) { if ( insertNode . getEntries ( ) [ 0 ] . getParentEntry ( ) == null ) { root . makeOlder ( timestamp , negLambda ) ; Entry irrelevantEntry = insertNode . getIrrelevantEntry ( this . weightThreshold ) ; int numFreeEntries = insertN... | Method called by insertBreadthFirst . |
21,537 | 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 (... | This method calculates the distances between the new point and each Entry in a leaf node . It returns the node that contains the entry with the smallest distance to the new point . |
21,538 | 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 ; for ( int i = 0 ; i < entries .... | Calculates the best merge possible between two nodes in a node . This means that the pair with the smallest distance is found . |
21,539 | 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 . sep... | Given an array summarizing selected measures set the appropriate flag options |
21,540 | 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 ... | Tests a grid cluster for connectedness according to Definition 3 . 4 Grid Group from Chen and Tu 2007 . |
21,541 | 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 . getInclus... | Iterates through the DensityGrids in the cluster and calculates the inclusion probability for each . |
21,542 | 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 ] ... | Adds a point to the ClusteringFeature . |
21,543 | 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 +... | Merges the ClusteringFeature with an other ClusteringFeature . |
21,544 | 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 ( ) ... | Creates a Cluster of the ClusteringFeature . |
21,545 | 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... | Creates the cluster center of the ClusteringFeature . |
21,546 | 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 ( Syst... | Writes the cluster center to a given stream . |
21,547 | 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 ) ; } | Calculates the k - means costs of the ClusteringFeature too a center . |
21,548 | 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 , cen... | Calculates the k - means costs of the ClusteringFeature and a point too a center . |
21,549 | 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 ,... | Calculates the k - means costs of the ClusteringFeature and another ClusteringFeature too a center . |
21,550 | 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 ] . classifier . getVotesForInstance ( example ) ; for ... | Computes the weight of a learner before training a given example . |
21,551 | 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 ; } | Finds the index of the classifier with the smallest weight . |
21,552 | public int classIndex ( ) { int classIndex = instanceHeader . classIndex ( ) ; if ( classIndex == Integer . MAX_VALUE ) if ( this . instanceHeader . instanceInformation . range != null ) classIndex = instanceHeader . instanceInformation . range . getStart ( ) ; else classIndex = 0 ; return classIndex ; } | Class index . |
21,553 | public void setDataset ( Instances dataset ) { if ( dataset instanceof InstancesHeader ) { this . instanceHeader = ( InstancesHeader ) dataset ; } else { this . instanceHeader = new InstancesHeader ( dataset ) ; } } | Sets the dataset . |
21,554 | public void addSparseValues ( int [ ] indexValues , double [ ] attributeValues , int numberAttributes ) { this . instanceData = new SparseInstanceData ( attributeValues , indexValues , numberAttributes ) ; } | Adds the sparse values . |
21,555 | 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 ( ) ] ; } | Initializes the method variables |
21,556 | protected void trainAndClassify ( Instance inst ) { nbInstances ++ ; boolean mature = true ; boolean unmature = true ; for ( int i = 0 ; i < getNbActiveClassifiers ( ) ; i ++ ) { if ( this . ensembleAges [ i ] < this . maturityOption . getValue ( ) && i < getNbAdaptiveClassifiers ( ) ) mature = false ; if ( this . ense... | Receives a training instance from the stream and updates the adaptive classifiers accordingly |
21,557 | 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 ( ) ] ; } | Resets a classifier in the ensemble |
21,558 | 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 ++ ... | Updates the evaluation window of a classifier and returns the updated weight value . |
21,559 | 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 ( newEnsembleWei... | Returns the classifiers that vote for the final prediction when the MAX combination function is selected |
21,560 | 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 ) { } break ; case "PNG" : try { ChartUtilities . saveChartAsPNG ( ne... | Export the image to formats JPG PNG SVG and EPS . |
21,561 | public void setActionListener ( ActionListener listener ) { for ( int i = 0 ; i < this . radioButtons . length ; i ++ ) { this . radioButtons [ i ] . addActionListener ( listener ) ; } } | Sets the ActionListener for the radio buttons . |
21,562 | public void update ( MeasureCollection [ ] measures , String variedParamName , double [ ] variedParamValues ) { this . measures = measures ; this . variedParamName = variedParamName ; this . variedParamValues = variedParamValues ; update ( ) ; updateParamBox ( ) ; } | Updates the measure overview by assigning new measure collections and varied parameter properties . If no measures are currently to display reset the display to hyphens . Otherwise display the last measured and mean values . Updates the parameter combo box if needed . |
21,563 | public void update ( ) { if ( this . measures == null || this . measures . length == 0 ) { for ( int i = 0 ; i < this . currentValues . length ; i ++ ) { this . currentValues [ i ] . setText ( "-" ) ; this . meanValues [ i ] . setText ( "-" ) ; } return ; } DecimalFormat d = new DecimalFormat ( "0.00" ) ; MeasureCollec... | Updates the measure overview . If no measures are currently to display reset the display to hyphens . Otherwise display the last measured and mean values . |
21,564 | private void updateParamBox ( ) { if ( this . variedParamValues == null || this . variedParamValues . length == 0 ) { this . paramBox . removeAllItems ( ) ; this . paramBox . setEnabled ( false ) ; } else if ( this . paramBox . getItemCount ( ) != this . variedParamValues . length ) { this . paramBox . removeAllItems (... | Updates the parameter combo box . If there is no varied parameter empty and disable the box . Otherwise display the available parameters . |
21,565 | 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 ) } ; } | print GUI evaluate model |
21,566 | 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" ) ; Stri... | print GUI learn model |
21,567 | protected void debug ( String string , int level ) { if ( VerbosityOption . getValue ( ) >= level ) { System . out . println ( string ) ; } } | Print to console |
21,568 | public Vote getVotes ( Instance instance ) { ErrorWeightedVote errorWeightedVote = newErrorWeightedVote ( ) ; debug ( "Test" , 3 ) ; int numberOfRulesCovering = 0 ; VerboseToConsole ( instance ) ; for ( Rule rule : ruleSet ) { if ( rule . isCovering ( instance ) == true ) { numberOfRulesCovering ++ ; double [ ] vote = ... | getVotes extension of the instance method getVotesForInstance in moa . classifier . java returns the prediction of the instance . Called in WeightedRandomRules |
21,569 | 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 ; } | Checks whteher the agent is present . |
21,570 | private String createScript ( File resultFile ) { String newLine = System . getProperty ( "line.separator" ) ; int sourceFileIdx = 0 ; String script = "set term " + terminalOptions ( Terminal . valueOf ( outputTypeOption . getChosenLabel ( ) ) ) + newLine ; script += "set output '" + resultFile . getAbsolutePath ( ) + ... | Creates the content of the gnuplot script . |
21,571 | 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 ) ; } } } | calculate initial connectivities |
21,572 | 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 ; while ( changedConnection ) { if ( debug ) {... | Calculate connections between clusters and merge clusters accordingly as long as connections exceed threshold |
21,573 | public double getNoiseSeparability ( ) { if ( noise . isEmpty ( ) ) return 1 ; double connectivity = 0 ; for ( int p : noise ) { CMMPoint npoint = cmmpoints . get ( p ) ; double maxConnection = 0 ; for ( int c = 0 ; c < gt0Clusters . size ( ) ; c ++ ) { double connection = getConnectionValue ( npoint , c ) ; if ( conne... | Calculates how well noise is separable from the given clusters Small values indicate bad separability values close to 1 indicate good separability |
21,574 | 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 ) . getInclusionProbability ( cmdp ) >= 1 ) { ... | Calculates the relative number of errors being caused by the underlying cluster model |
21,575 | 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 ) ; } | Calculates Euclidian distance |
21,576 | public String getParameterString ( ) { String para = "" ; para += "k=" + knnNeighbourhood + ";" ; if ( useExpConnectivity ) { para += "lambdaConnX=" + lambdaConnX + ";" ; para += "lambdaConn=" + lamdaConn + ";" ; para += "lambdaConnRef=" + lambdaConnRefXValue + ";" ; } para += "m=" + clusterConnectionMaxPoints + ";" ; ... | String with main CMM parameters |
21,577 | public Color [ ] generateColors ( int numColors ) { Color [ ] colors = new Color [ numColors ] ; Random rand = new Random ( 0 ) ; for ( int i = 0 ; i < numColors ; ++ i ) { float hueRatio = i / ( float ) numColors ; float saturationRatio = rand . nextFloat ( ) ; float brightnessRatio = rand . nextFloat ( ) ; float hue ... | Generate numColors unique colors which should be easily distinguishable . |
21,578 | 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 ) ; } } | Update the mass profile of this node . |
21,579 | 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 { ... | If this node is a leaf node or it has a mass profile of less than sizeLimit this returns the anomaly score for the argument instance . Otherwise this node determines which of its subordinate nodes the argument instance belongs to and asks it provide the anomaly score . |
21,580 | protected void printNode ( ) { System . out . println ( this . depth + ", " + this . splitAttribute + ", " + this . splitValue + ", " + this . r ) ; if ( this . internalNode ) { this . right . printNode ( ) ; this . left . printNode ( ) ; } } | Prints this node to string and if it is an internal node prints its children nodes as well . Useful for debugging the entire tree structure . |
21,581 | private void initialClustering ( ) { updateGridListDensity ( ) ; Iterator < Map . Entry < DensityGrid , CharacteristicVector > > glIter = this . grid_list . entrySet ( ) . iterator ( ) ; HashMap < DensityGrid , CharacteristicVector > newGL = new HashMap < DensityGrid , CharacteristicVector > ( ) ; while ( glIter . hasN... | Implements the procedure given in Figure 3 of Chen and Tu 2007 |
21,582 | private HashMap < DensityGrid , CharacteristicVector > adjustForSparseGrid ( DensityGrid dg , CharacteristicVector cv , int dgClass ) { HashMap < DensityGrid , CharacteristicVector > glNew = new HashMap < DensityGrid , CharacteristicVector > ( ) ; if ( dgClass != NO_CLASS ) { GridCluster gc = this . cluster_list . get ... | Adjusts the clustering of a sparse density grid . Implements lines 5 and 6 from Figure 4 of Chen and Tu 2007 . |
21,583 | private HashMap < DensityGrid , CharacteristicVector > adjustForTransitionalGrid ( DensityGrid dg , CharacteristicVector cv , int dgClass ) { GridCluster ch ; double hChosenSize = 0.0 ; DensityGrid dgH ; int hClass = NO_CLASS ; int hChosenClass = NO_CLASS ; Iterator < DensityGrid > dgNeighbourhood = dg . getNeighbours ... | Adjusts the clustering of a transitional density grid . Implements lines 20 and 21 from Figure 4 of Chen and Tu 2007 . |
21,584 | private void cleanClusters ( ) { Iterator < GridCluster > clusIter = this . cluster_list . iterator ( ) ; ArrayList < GridCluster > toRem = new ArrayList < GridCluster > ( ) ; while ( clusIter . hasNext ( ) ) { GridCluster c = clusIter . next ( ) ; if ( c . getWeight ( ) == 0 ) toRem . add ( c ) ; } if ( ! toRem . isEm... | Iterates through cluster_list to ensure that all empty clusters have been removed and that all cluster IDs match the cluster s index in cluster_list . |
21,585 | private void removeSporadic ( ) { Iterator < Map . Entry < DensityGrid , CharacteristicVector > > glIter = this . grid_list . entrySet ( ) . iterator ( ) ; HashMap < DensityGrid , CharacteristicVector > newGL = new HashMap < DensityGrid , CharacteristicVector > ( ) ; ArrayList < DensityGrid > remGL = new ArrayList < De... | Implements the procedure described in section 4 . 2 of Chen and Tu 2007 |
21,586 | private boolean checkIfSporadic ( CharacteristicVector cv ) { if ( cv . getCurrGridDensity ( this . getCurrTime ( ) , this . getDecayFactor ( ) ) < densityThresholdFunction ( cv . getDensityTimeStamp ( ) , this . cl , this . getDecayFactor ( ) , this . N ) ) { if ( cv . getRemoveTime ( ) == - 1 || this . getCurrTime ( ... | Determines whether a sparse density grid is sporadic using rules S1 and S2 of Chen and Tu 2007 |
21,587 | 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 ) ) ; } | Implements the function pi given in Definition 4 . 1 of Chen and Tu 2007 |
21,588 | private void mergeClusters ( int smallClus , int bigClus ) { for ( Map . Entry < DensityGrid , CharacteristicVector > grid : grid_list . entrySet ( ) ) { DensityGrid dg = grid . getKey ( ) ; CharacteristicVector cv = grid . getValue ( ) ; if ( cv . getLabel ( ) == smallClus ) { cv . setLabel ( bigClus ) ; this . grid_l... | Reassign all grids belonging in the small cluster to the big cluster Merge the GridCluster objects representing each cluster |
21,589 | 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 . getDecayFa... | Iterates through grid_list and updates the density for each density grid therein . Also marks each density grid as unvisited for this call to adjustClustering . |
21,590 | 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 ( )... | Iterates through grid_list and prints out each density grid therein as a string . |
21,591 | 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 ( ) + "}" ) ; } } | Iterates through cluster_list and prints out each grid cluster therein as a string . |
21,592 | public void addObject ( DataSet dataSet ) throws Exception { DataObject [ ] dataObjects = dataSet . getDataObjectArray ( ) ; for ( int i = 0 ; i < dataObjects . length ; i ++ ) { this . addObject ( dataObjects [ i ] ) ; } } | Adds all objects in the given data set |
21,593 | 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 ( ) ; } | Counts the number of classes that are present in the data set . !!! It does not check whether all classes are contained !!! |
21,594 | public DataSet [ ] getDataSetsPerClass ( ) throws Exception { DataSet [ ] dataSetsPerClass = new DataSet [ this . getNrOfClasses ( ) ] ; for ( int i = 0 ; i < dataSetsPerClass . length ; i ++ ) { dataSetsPerClass [ i ] = new DataSet ( this . nrOfDimensions ) ; } for ( DataObject currentObject : dataList ) { dataSetsPer... | Separates the objects in this data set according to their class label |
21,595 | public double [ ] getVariances ( ) { double N = this . size ( ) ; double [ ] LS = new double [ this . getNrOfDimensions ( ) ] ; double [ ] SS = new double [ this . getNrOfDimensions ( ) ] ; double [ ] tmpFeatures ; double [ ] variances = new double [ this . getNrOfDimensions ( ) ] ; for ( DataObject dataObject : dataLi... | Calculates the variance of this data set for each dimension |
21,596 | public double [ ] toDoubleArray ( ) { double [ ] array = new double [ numAttributes ( ) ] ; for ( int i = 0 ; i < numValues ( ) ; i ++ ) { array [ index ( i ) ] = valueSparse ( i ) ; } return array ; } | To double array . |
21,597 | private double calcC1 ( int objectId ) { int nrOfPreviousResults = previousOScoreResultList . get ( objectId ) . size ( ) ; if ( nrOfPreviousResults == 0 ) { return 0.0 ; } int count = 1 ; double difSum_k = Math . abs ( lastOScoreResult . get ( objectId ) - previousOScoreResultList . get ( objectId ) . get ( nrOfPrevio... | Calculates the Confidence on the basis of the standard deviation of previous OScore results |
21,598 | public void invertedSumariesPerMeasure ( String path ) { int cont = 0 ; int algorithmSize = this . streams . get ( 0 ) . algorithm . size ( ) ; int streamSize = this . streams . size ( ) ; int measureSize = this . streams . get ( 0 ) . algorithm . get ( 0 ) . measures . size ( ) ; while ( cont != measureSize ) { String... | Generates a latex summary in which the rows are the datasets and the columns the algorithms . |
21,599 | public void generateCSV ( ) { int cont = 0 ; int algorithmSize = this . streams . get ( 0 ) . algorithm . size ( ) ; int streamSize = this . streams . size ( ) ; int measureSize = this . streams . get ( 0 ) . algorithm . get ( 0 ) . measures . size ( ) ; while ( cont != measureSize ) { String output = "" ; output += "A... | Generate a csv file for the statistical analysis . |
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