idx int64 0 41.2k | question stringlengths 74 4.21k | target stringlengths 5 888 |
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21,600 | public ArrayList < DensityGrid > getNeighbours ( ) { ArrayList < DensityGrid > neighbours = new ArrayList < DensityGrid > ( ) ; DensityGrid h ; int [ ] hCoord = this . getCoordinates ( ) ; for ( int i = 0 ; i < this . dimensions ; i ++ ) { hCoord [ i ] = hCoord [ i ] - 1 ; h = new DensityGrid ( hCoord ) ; neighbours . ... | Generates an Array List of neighbours for this density grid by varying each coordinate by one in either direction . Does not test whether the generated neighbours are valid as DensityGrid is not aware of the number of partitions in each dimension . |
21,601 | public double getInclusionProbability ( Instance instance ) { for ( int i = 0 ; i < this . dimensions ; i ++ ) { if ( ( int ) instance . value ( i ) != this . coordinates [ i ] ) return 0.0 ; } return 1.0 ; } | Provides the probability of the argument instance belonging to the density grid in question . |
21,602 | public void readBuffer ( List < String > algPath , List < String > algNames , List < Measure > measures ) { BufferedReader buffer = null ; for ( int i = 0 ; i < algPath . size ( ) ; i ++ ) { try { buffer = new BufferedReader ( new FileReader ( algPath . get ( i ) ) ) ; } catch ( FileNotFoundException ex ) { Logger . ge... | Read each algorithm file . |
21,603 | protected void fireClusterChange ( long timestamp , String type , String message ) { if ( listeners != null && ! listeners . isEmpty ( ) ) { ClusterEvent event = new ClusterEvent ( this , timestamp , type , message ) ; Vector targets ; synchronized ( this ) { targets = ( Vector ) listeners . clone ( ) ; } Enumeration e... | Fire a ClusterChangeEvent to all registered listeners |
21,604 | protected void notifyChangeListeners ( ) { ChangeEvent e = new ChangeEvent ( this ) ; for ( ChangeListener l : changeListeners ) { l . stateChanged ( e ) ; } } | Notifies all registered change listeners that the options have changed . |
21,605 | public void setGraph ( MeasureCollection [ ] measures , MeasureCollection [ ] stds , double [ ] variedParamValues , Color [ ] colors ) { this . variedParamValues = variedParamValues ; super . setGraph ( measures , stds , colors ) ; } | Draws a scatter graph based on the varied parameter and the measures . |
21,606 | private void scatter ( Graphics g , int i ) { int height = getHeight ( ) ; int width = getWidth ( ) ; int x = ( int ) ( ( ( this . variedParamValues [ i ] - this . lower_x_value ) / ( this . upper_x_value - this . lower_x_value ) ) * width ) ; double value = this . measures [ i ] . getLastValue ( this . measureSelected... | Paint a dot onto the panel . |
21,607 | public static MOAObject copy ( MOAObject obj ) { try { return ( MOAObject ) SerializeUtils . copyObject ( obj ) ; } catch ( Exception e ) { throw new RuntimeException ( "Object copy failed." , e ) ; } } | This method produces a copy of an object . |
21,608 | public static String get ( String property , String defaultValue ) { return PROPERTIES . getProperty ( property , defaultValue ) ; } | returns the value for the specified property if non - existent then the default value . |
21,609 | public static String [ ] getTabs ( ) { String [ ] result ; String tabs ; tabs = get ( "Tabs" , "moa.gui.ClassificationTabPanel,moa.gui.RegressionTabPanel,moa.gui.MultiLabelTabPanel,moa.gui.MultiTargetTabPanel,moa.gui.clustertab.ClusteringTabPanel,moa.gui.outliertab.OutlierTabPanel,moa.gui.ConceptDriftTabPanel,moa.gui.A... | returns an array with the classnames of all the additional panels to display as tabs in the GUI . |
21,610 | public static int getFrameWidth ( ) { int result ; String str ; str = get ( "FrameWidth" , "1200" ) ; try { result = Integer . parseInt ( str ) ; } catch ( Exception e ) { result = 1200 ; } return result ; } | Returns the width for the frame . |
21,611 | public static void main ( String [ ] args ) { Enumeration names ; String name ; Vector sorted ; System . out . println ( "\nMOA defaults:" ) ; names = PROPERTIES . propertyNames ( ) ; sorted = new Vector ( ) ; while ( names . hasMoreElements ( ) ) { sorted . add ( names . nextElement ( ) ) ; } Collections . sort ( sort... | only for testing - prints the content of the props file . |
21,612 | public static double inverseError ( double x ) { double z = Math . sqrt ( Math . PI ) * x ; double res = ( z ) / 2 ; double z2 = z * z ; double zProd = z * z2 ; res += ( 1.0 / 24 ) * zProd ; zProd *= z2 ; res += ( 7.0 / 960 ) * zProd ; zProd *= z2 ; res += ( 127 * zProd ) / 80640 ; zProd *= z2 ; res += ( 4369 * zProd )... | Approximates the inverse error function . Clustream needs this . |
21,613 | protected void bicoUpdate ( double [ ] x ) { assert ( ! this . bufferPhase && this . numDimensions == x . length ) ; ClusteringTreeNode r = this . root ; int i = 1 ; while ( true ) { ClusteringTreeNode y = r . nearestChild ( x ) ; if ( r . hasNoChildren ( ) || y == null || Metric . distanceSquared ( x , y . getCenter (... | Inserts a new point into the ClusteringFeature tree . |
21,614 | protected void rebuild ( ) { while ( this . rootCount > this . maxNumClusterFeatures ) { this . T *= 2.0 ; this . root . setThreshold ( calcRSquared ( 1 ) ) ; Queue < ClusteringTreeNode > Q = new LinkedList < ClusteringTreeNode > ( ) ; Q . addAll ( this . root . getChildren ( ) ) ; this . root . clearChildren ( ) ; thi... | If the number of ClusteringTreeNodes exceeds the maximum bound the global threshold T will be doubled and the tree will be rebuild with the new threshold . |
21,615 | protected void bicoCFUpdate ( ClusteringTreeNode x ) { ClusteringTreeNode r = this . root ; int i = 1 ; while ( true ) { ClusteringTreeNode y = r . nearestChild ( x . getCenter ( ) ) ; if ( r . hasNoChildren ( ) || y == null || Metric . distanceSquared ( x . getCenter ( ) , y . getCenter ( ) ) > calcRSquared ( i ) ) { ... | Inserts a ClusteringTreeNode into the ClusteringFeature tree . |
21,616 | public static void combSort11 ( double arrayToSort [ ] , int linkedArray [ ] ) { int switches , j , top , gap ; double hold1 ; int hold2 ; gap = arrayToSort . length ; do { gap = ( int ) ( gap / 1.3 ) ; switch ( gap ) { case 0 : gap = 1 ; break ; case 9 : case 10 : gap = 11 ; break ; default : break ; } switches = 0 ; ... | sorts the two given arrays . |
21,617 | public static void quickSort ( double [ ] arrayToSort , double [ ] linkedArray , int left , int right ) { if ( left < right ) { int middle = partition ( arrayToSort , linkedArray , left , right ) ; quickSort ( arrayToSort , linkedArray , left , middle ) ; quickSort ( arrayToSort , linkedArray , middle + 1 , right ) ; }... | performs quicksort . |
21,618 | public Instances kNearestNeighbours ( Instance target , int k ) throws Exception { checkMissing ( target ) ; MyHeap heap = new MyHeap ( k ) ; findNearestNeighbours ( target , m_Root , k , heap , 0.0 ) ; Instances neighbours = new Instances ( m_Instances , ( heap . size ( ) + heap . noOfKthNearest ( ) ) ) ; m_DistanceLi... | Returns the k nearest neighbours of the supplied instance . > ; k neighbours are returned if there are more than one neighbours at the kth boundary . |
21,619 | public void update ( Instance instance ) throws Exception { if ( m_Instances == null ) throw new Exception ( "No instances supplied yet. Have to call " + "setInstances(instances) with a set of Instances " + "first." ) ; addInstanceInfo ( instance ) ; addInstanceToTree ( instance , m_Root ) ; } | Adds one instance to the KDTree . This updates the KDTree structure to take into account the newly added training instance . |
21,620 | protected void checkMissing ( Instances instances ) throws Exception { for ( int i = 0 ; i < instances . numInstances ( ) ; i ++ ) { Instance ins = instances . instance ( i ) ; for ( int j = 0 ; j < ins . numValues ( ) ; j ++ ) { if ( ins . index ( j ) != ins . classIndex ( ) ) if ( ins . isMissingSparse ( j ) ) { thro... | Checks if there is any instance with missing values . Throws an exception if there is as KDTree does not handle missing values . |
21,621 | protected void checkMissing ( Instance ins ) throws Exception { for ( int j = 0 ; j < ins . numValues ( ) ; j ++ ) { if ( ins . index ( j ) != ins . classIndex ( ) ) if ( ins . isMissingSparse ( j ) ) { throw new Exception ( "ERROR: KDTree can not deal with missing " + "values. Please run ReplaceMissingValues filter " ... | Checks if there is any missing value in the given instance . |
21,622 | public Enumeration enumerateMeasures ( ) { Vector < String > newVector = new Vector < String > ( ) ; newVector . addElement ( "measureTreeSize" ) ; newVector . addElement ( "measureNumLeaves" ) ; newVector . addElement ( "measureMaxDepth" ) ; return newVector . elements ( ) ; } | Returns an enumeration of the additional measure names . |
21,623 | public double getMeasure ( String additionalMeasureName ) { if ( additionalMeasureName . compareToIgnoreCase ( "measureMaxDepth" ) == 0 ) { return measureMaxDepth ( ) ; } else if ( additionalMeasureName . compareToIgnoreCase ( "measureTreeSize" ) == 0 ) { return measureTreeSize ( ) ; } else if ( additionalMeasureName .... | Returns the value of the named measure . |
21,624 | public void centerInstances ( Instances centers , int [ ] assignments , double pc ) throws Exception { int [ ] centList = new int [ centers . numInstances ( ) ] ; for ( int i = 0 ; i < centers . numInstances ( ) ; i ++ ) centList [ i ] = i ; determineAssignments ( m_Root , centers , centList , assignments , pc ) ; } | Assigns instances to centers using KDTree . |
21,625 | protected void determineAssignments ( KDTreeNode node , Instances centers , int [ ] candidates , int [ ] assignments , double pc ) throws Exception { int [ ] owners = refineOwners ( node , centers , candidates ) ; if ( owners . length == 1 ) { for ( int i = node . m_Start ; i <= node . m_End ; i ++ ) { assignments [ m_... | Assigns instances to the current centers called candidates . |
21,626 | protected int [ ] refineOwners ( KDTreeNode node , Instances centers , int [ ] candidates ) throws Exception { int [ ] owners = new int [ candidates . length ] ; double minDistance = Double . POSITIVE_INFINITY ; int ownerIndex = - 1 ; Instance owner ; int numCand = candidates . length ; double [ ] distance = new double... | Refines the ownerlist . |
21,627 | protected double distanceToHrect ( KDTreeNode node , Instance x ) throws Exception { double distance = 0.0 ; Instance closestPoint = ( Instance ) x . copy ( ) ; boolean inside ; inside = clipToInsideHrect ( node , closestPoint ) ; if ( ! inside ) distance = m_EuclideanDistance . distance ( closestPoint , x ) ; return d... | Returns the distance between a point and an hyperrectangle . |
21,628 | protected boolean clipToInsideHrect ( KDTreeNode node , Instance x ) { boolean inside = true ; for ( int i = 0 ; i < m_Instances . numAttributes ( ) ; i ++ ) { if ( x . value ( i ) < node . m_NodeRanges [ i ] [ MIN ] ) { x . setValue ( i , node . m_NodeRanges [ i ] [ MIN ] ) ; inside = false ; } else if ( x . value ( i... | Finds the closest point in the hyper rectangle to a given point . Change the given point to this closest point by clipping of at all the dimensions to be clipped of . If the point is inside the rectangle it stays unchanged . The return value is true if the point was not changed so the the return value is true if the po... |
21,629 | public void assignSubToCenters ( KDTreeNode node , Instances centers , int [ ] centList , int [ ] assignments ) throws Exception { int numCent = centList . length ; if ( assignments == null ) { assignments = new int [ m_Instances . numInstances ( ) ] ; for ( int i = 0 ; i < assignments . length ; i ++ ) { assignments [... | Assigns instances of this node to center . Center to be assign to is decided by the distance function . |
21,630 | public void setDistanceFunction ( DistanceFunction df ) throws Exception { if ( ! ( df instanceof EuclideanDistance ) ) throw new Exception ( "KDTree currently only works with " + "EuclideanDistanceFunction." ) ; m_DistanceFunction = m_EuclideanDistance = ( EuclideanDistance ) df ; } | sets the distance function to use for nearest neighbour search . |
21,631 | public int count ( ) { int count = ( clusteringFeature != null ) ? 1 : 0 ; for ( ClusteringTreeNode child : children ) { count += child . count ( ) ; } return count ; } | Counts the elements in tree with this node as the root . |
21,632 | public Clustering addToClustering ( Clustering clustering ) { if ( center != null && getClusteringFeature ( ) != null ) { clustering . add ( getClusteringFeature ( ) . toCluster ( ) ) ; } for ( ClusteringTreeNode child : children ) { child . addToClustering ( clustering ) ; } return clustering ; } | Adds all ClusterFeatures of the tree with this node as the root to a Clustering . |
21,633 | public List < double [ ] > addToClusteringCenters ( List < double [ ] > clustering ) { if ( center != null && getClusteringFeature ( ) != null ) { clustering . add ( getClusteringFeature ( ) . toClusterCenter ( ) ) ; } for ( ClusteringTreeNode child : children ) { child . addToClusteringCenters ( clustering ) ; } retur... | Adds all clustering centers of the ClusterFeatures of the tree with this node as the root to a List of points . |
21,634 | public void printClusteringCenters ( Writer stream ) throws IOException { if ( center != null && getClusteringFeature ( ) != null ) { getClusteringFeature ( ) . printClusterCenter ( stream ) ; } for ( ClusteringTreeNode child : children ) { child . printClusteringCenters ( stream ) ; } } | Writes all clustering centers of the ClusterFeatures of the tree with this node as the root to a given stream . |
21,635 | public ClusteringTreeNode nearestChild ( double [ ] pointA ) { assert ( this . center . length == pointA . length ) ; double minDistance = Double . POSITIVE_INFINITY ; ClusteringTreeNode min = null ; for ( ClusteringTreeNode node : this . getChildren ( ) ) { double d = Metric . distance ( pointA , node . getCenter ( ) ... | Searches for the nearest child node by comparing each representation . |
21,636 | public boolean addChild ( ClusteringTreeNode e ) { assert ( this . center . length == e . center . length ) ; return this . children . add ( e ) ; } | Adds a child node . |
21,637 | private ClassifierKS getPreviousClassifier ( Classifier classifier , List < Instance > instances ) { ExecutorService threadPool = Executors . newFixedThreadPool ( this . threadSizeOption . getValue ( ) ) ; int SIZE = this . classifiers . size ( ) ; Map < Integer , Future < Double > > futures = new HashMap < > ( ) ; for... | Searches for the classifier best suited for actual data . All statistical tests are performed in parallel . |
21,638 | protected void makeOlder ( long timeDifference , double negLambda ) { if ( timeDifference == 0 ) { return ; } assert ( negLambda < 0 ) ; assert ( timeDifference > 0 ) ; double weightFactor = Math . pow ( 2.0 , negLambda * timeDifference ) ; this . N *= weightFactor ; for ( int i = 0 ; i < LS . length ; i ++ ) { LS [ i ... | Make this cluster older . This means multiplying weighted N LS and SS with a weight factor given by the time difference and the parameter negLambda . |
21,639 | protected void overwriteOldCluster ( ClusKernel other ) { this . totalN = other . totalN ; this . N = other . N ; assert ( LS . length == other . LS . length ) ; System . arraycopy ( other . LS , 0 , LS , 0 , LS . length ) ; assert ( SS . length == other . SS . length ) ; System . arraycopy ( other . SS , 0 , SS , 0 , ... | Overwrites the LS SS and weightedN in this cluster to the values of the given cluster but adds N and classCount of the given cluster to this one . This function is useful when the weight of an entry becomes to small and we want to forget the information of the old points . |
21,640 | public double [ ] getVotesForInstance ( Instance inst ) { double vSTM [ ] ; double vLTM [ ] ; double vCM [ ] ; double v [ ] ; double distancesSTM [ ] ; double distancesLTM [ ] ; int predClassSTM = 0 ; int predClassLTM = 0 ; int predClassCM = 0 ; try { if ( this . stm . numInstances ( ) > 0 ) { distancesSTM = get1ToNDis... | Predicts the label of a given sample by using the STM LTM and the CM . |
21,641 | private void clusterDown ( ) { int classIndex = this . ltm . classIndex ( ) ; for ( int c = 0 ; c <= this . maxClassValue ; c ++ ) { List < double [ ] > classSamples = new ArrayList < > ( ) ; for ( int i = this . ltm . numInstances ( ) - 1 ; i > - 1 ; i -- ) { if ( this . ltm . get ( i ) . classValue ( ) == c ) { class... | Performs classwise kMeans ++ clustering for given samples with corresponding labels . The number of samples is halved per class . |
21,642 | private void memorySizeCheck ( ) { if ( this . stm . numInstances ( ) + this . ltm . numInstances ( ) > this . maxSTMSize + this . maxLTMSize ) { if ( this . ltm . numInstances ( ) > this . maxLTMSize ) { this . clusterDown ( ) ; } else { int numShifts = this . maxLTMSize - this . ltm . numInstances ( ) + 1 ; for ( int... | Makes sure that the STM and LTM combined doe not surpass the maximum size . |
21,643 | private void clean ( Instances cleanAgainst , Instances toClean , boolean onlyLast ) { if ( cleanAgainst . numInstances ( ) > this . kOption . getValue ( ) && toClean . numInstances ( ) > 0 ) { if ( onlyLast ) { cleanSingle ( cleanAgainst , ( cleanAgainst . numInstances ( ) - 1 ) , toClean ) ; } else { for ( int i = 0 ... | Removes distance - based all instances from the input samples that contradict those in the STM . |
21,644 | private double [ ] getDistanceWeightedVotes ( double distances [ ] , int [ ] nnIndices , Instances instances ) { double v [ ] = new double [ this . maxClassValue + 1 ] ; for ( int nnIdx : nnIndices ) { v [ ( int ) instances . instance ( nnIdx ) . classValue ( ) ] += 1. / Math . max ( distances [ nnIdx ] , 0.000000001 )... | Returns the distance weighted votes . |
21,645 | private int getClassFromVotes ( double votes [ ] ) { double maxVote = - 1 ; int maxVoteClass = - 1 ; for ( int i = 0 ; i < votes . length ; i ++ ) { if ( votes [ i ] > maxVote ) { maxVote = votes [ i ] ; maxVoteClass = i ; } } return maxVoteClass ; } | Returns the class with maximum vote . |
21,646 | private double getDistance ( Instance sample , Instance sample2 ) { double sum = 0 ; for ( int i = 0 ; i < sample . numInputAttributes ( ) ; i ++ ) { double diff = sample . valueInputAttribute ( i ) - sample2 . valueInputAttribute ( i ) ; sum += diff * diff ; } return Math . sqrt ( sum ) ; } | Returns the Euclidean distance . |
21,647 | private double [ ] get1ToNDistances ( Instance sample , Instances samples ) { double distances [ ] = new double [ samples . numInstances ( ) ] ; for ( int i = 0 ; i < samples . numInstances ( ) ; i ++ ) { distances [ i ] = this . getDistance ( sample , samples . get ( i ) ) ; } return distances ; } | Returns the Euclidean distance between one sample and a collection of samples in an 1D - array . |
21,648 | private void adaptHistories ( int numberOfDeletions ) { for ( int i = 0 ; i < numberOfDeletions ; i ++ ) { SortedSet < Integer > keys = new TreeSet < > ( this . predictionHistories . keySet ( ) ) ; this . predictionHistories . remove ( keys . first ( ) ) ; keys = new TreeSet < > ( this . predictionHistories . keySet ( ... | Removes predictions of the largest window size and shifts the remaining ones accordingly . |
21,649 | private double getHistoryErrorRate ( List < Integer > predHistory ) { double sumCorrect = 0 ; for ( Integer e : predHistory ) { sumCorrect += e ; } return 1. - ( sumCorrect / predHistory . size ( ) ) ; } | Calculates the achieved error rate of a history . |
21,650 | public static double distanceSquared ( double [ ] pointA ) { double distance = 0.0 ; for ( int i = 0 ; i < pointA . length ; i ++ ) { distance += pointA [ i ] * pointA [ i ] ; } return distance ; } | Calculates the squared Euclidean length of a point . |
21,651 | public static double distanceSquared ( double [ ] pointA , double [ ] pointB , int offsetB ) { assert ( pointA . length == pointB . length + offsetB ) ; double distance = 0.0 ; for ( int i = 0 ; i < pointA . length ; i ++ ) { double d = pointA [ i ] - pointB [ i + offsetB ] ; distance += d * d ; } return distance ; } | Calculates the squared Euclidean distance of two points . Starts at dimension offset + 1 of pointB . |
21,652 | public static double distance ( double [ ] pointA , double [ ] pointB , int offsetB ) { return Math . sqrt ( distanceSquared ( pointA , pointB , offsetB ) ) ; } | Calculates the Euclidean distance of two points . Starts at dimension offset + 1 of pointB . |
21,653 | public static double distanceWithDivisionSquared ( double [ ] pointA , double dA ) { double distance = 0.0 ; for ( int i = 0 ; i < pointA . length ; i ++ ) { double d = pointA [ i ] / dA ; distance += d * d ; } return distance ; } | Calculates the squared Euclidean length of a point divided by a scalar . |
21,654 | public static double distanceWithDivision ( double [ ] pointA , double dA , double [ ] pointB ) { return Math . sqrt ( distanceWithDivisionSquared ( pointA , dA , pointB ) ) ; } | Calculates the Euclidean distance of the first point divided by a scalar and another second point . |
21,655 | public static double dotProduct ( double [ ] pointA ) { double product = 0.0 ; for ( int i = 0 ; i < pointA . length ; i ++ ) { product += pointA [ i ] * pointA [ i ] ; } return product ; } | Calculates the dot product of the point with itself . |
21,656 | public static double dotProductWithAddition ( double [ ] pointA1 , double [ ] pointA2 , double [ ] pointB ) { assert ( pointA1 . length == pointA2 . length && pointA1 . length == pointB . length ) ; double product = 0.0 ; for ( int i = 0 ; i < pointA1 . length ; i ++ ) { product += ( pointA1 [ i ] + pointA2 [ i ] ) * p... | Calculates the dot product of the addition of the first and the second point with the third point . |
21,657 | public static double dotProductWithAddition ( double [ ] pointA1 , double [ ] pointA2 , double [ ] pointB1 , double [ ] pointB2 ) { assert ( pointA1 . length == pointA2 . length && pointB1 . length == pointB2 . length && pointA1 . length == pointB1 . length ) ; double product = 0.0 ; for ( int i = 0 ; i < pointA1 . len... | Calculates the dot product of the addition of the first and the second point with the addition of the third and the fourth point . |
21,658 | public Instance samoaInstance ( weka . core . Instance inst ) { Instance samoaInstance ; if ( inst instanceof weka . core . SparseInstance ) { double [ ] attributeValues = new double [ inst . numValues ( ) ] ; int [ ] indexValues = new int [ inst . numValues ( ) ] ; for ( int i = 0 ; i < inst . numValues ( ) ; i ++ ) {... | Samoa instance from weka instance . |
21,659 | public Instances samoaInstances ( weka . core . Instances instances ) { Instances samoaInstances = samoaInstancesInformation ( instances ) ; this . samoaInstanceInformation = samoaInstances ; for ( int i = 0 ; i < instances . numInstances ( ) ; i ++ ) { samoaInstances . add ( samoaInstance ( instances . instance ( i ) ... | Samoa instances from weka instances . |
21,660 | public Instances samoaInstancesInformation ( weka . core . Instances instances ) { Instances samoaInstances ; List < Attribute > attInfo = new ArrayList < Attribute > ( ) ; for ( int i = 0 ; i < instances . numAttributes ( ) ; i ++ ) { attInfo . add ( samoaAttribute ( i , instances . attribute ( i ) ) ) ; } samoaInstan... | Samoa instances information . |
21,661 | protected Attribute samoaAttribute ( int index , weka . core . Attribute attribute ) { Attribute samoaAttribute ; if ( attribute . isNominal ( ) ) { Enumeration enu = attribute . enumerateValues ( ) ; List < String > attributeValues = new ArrayList < String > ( ) ; while ( enu . hasMoreElements ( ) ) { attributeValues ... | Get Samoa attribute from a weka attribute . |
21,662 | public void doSaveAs ( ) throws IOException { JFileChooser fileChooser = new JFileChooser ( ) ; ExtensionFileFilter filterPNG = new ExtensionFileFilter ( "PNG Image Files" , ".png" ) ; fileChooser . addChoosableFileFilter ( filterPNG ) ; ExtensionFileFilter filterJPG = new ExtensionFileFilter ( "JPG Image Files" , ".jp... | Method for save the images . |
21,663 | protected BigDecimal round ( double val ) { BigDecimal value = new BigDecimal ( val ) ; if ( val != 0.0 ) { value = value . setScale ( 3 , BigDecimal . ROUND_DOWN ) ; } return value ; } | Round an number |
21,664 | public void initializeRuleStatistics ( RuleClassification rl , Predicates pred , Instance inst ) { rl . predicateSet . add ( pred ) ; rl . obserClassDistrib = new DoubleVector ( ) ; rl . observers = new AutoExpandVector < AttributeClassObserver > ( ) ; rl . observersGauss = new AutoExpandVector < AttributeClassObserver... | This function initializes the statistics of a rule |
21,665 | public void updateRuleAttribStatistics ( Instance inst , RuleClassification rl , int ruleIndex ) { rl . instancesSeen ++ ; if ( rl . squaredAttributeStatisticsSupervised . size ( ) == 0 && rl . attributeStatisticsSupervised . size ( ) == 0 ) { for ( int s = 0 ; s < inst . numAttributes ( ) - 1 ; s ++ ) { ArrayList < Do... | Update rule statistics |
21,666 | public void createRule ( Instance inst ) { int remainder = ( int ) Double . MAX_VALUE ; int numInstanciaObservers = ( int ) this . observedClassDistribution . sumOfValues ( ) ; if ( numInstanciaObservers != 0 && this . gracePeriodOption . getValue ( ) != 0 ) { remainder = ( numInstanciaObservers ) % ( this . gracePerio... | This function creates a rule |
21,667 | public void theBestAttributes ( Instance instance , AutoExpandVector < AttributeClassObserver > observersParameter ) { for ( int z = 0 ; z < instance . numAttributes ( ) - 1 ; z ++ ) { if ( ! instance . isMissing ( z ) ) { int instAttIndex = modelAttIndexToInstanceAttIndex ( z , instance ) ; ArrayList < Double > attrib... | This function gives the best value of entropy for each attribute |
21,668 | public void mainFindBestValEntropy ( Node root ) { if ( root != null ) { DoubleVector parentClassCL = new DoubleVector ( ) ; DoubleVector classCountL = root . classCountsLeft ; DoubleVector classCountR = root . classCountsRight ; double numInst = root . classCountsLeft . sumOfValues ( ) + root . classCountsRight . sumO... | Best value of entropy |
21,669 | public void findBestValEntropyNominalAtt ( AutoExpandVector < DoubleVector > attrib , int attNumValues ) { ArrayList < ArrayList < Double > > distClassValue = new ArrayList < ArrayList < Double > > ( ) ; for ( int z = 0 ; z < attrib . size ( ) ; z ++ ) { distClassValue . add ( new ArrayList < Double > ( ) ) ; } for ( i... | Find best value of entropy for nominal attributes |
21,670 | public boolean checkBestAttrib ( double n , AutoExpandVector < AttributeClassObserver > observerss , DoubleVector observedClassDistribution ) { double h0 = entropy ( observedClassDistribution ) ; boolean isTheBest = false ; double [ ] entropyValues = getBestSecondBestEntropy ( this . saveBestGlobalEntropy ) ; double be... | Check if the best attribute is really the best |
21,671 | protected double [ ] getBestSecondBestEntropy ( DoubleVector entropy ) { double [ ] entropyValues = new double [ 2 ] ; double best = Double . MAX_VALUE ; double secondBest = Double . MAX_VALUE ; for ( int i = 0 ; i < entropy . numValues ( ) ; i ++ ) { if ( entropy . getValue ( i ) < best ) { secondBest = best ; best = ... | Get best and second best attributes |
21,672 | protected double getRuleMajorityClassIndex ( RuleClassification r ) { double maxvalue = 0.0 ; int posMaxValue = 0 ; for ( int i = 0 ; i < r . obserClassDistrib . numValues ( ) ; i ++ ) { if ( r . obserClassDistrib . getValue ( i ) > maxvalue ) { maxvalue = r . obserClassDistrib . getValue ( i ) ; posMaxValue = i ; } } ... | Get rule majority class index |
21,673 | protected double [ ] oberversDistribProb ( Instance inst , DoubleVector classDistrib ) { double [ ] votes = new double [ this . numClass ] ; double sum = classDistrib . sumOfValues ( ) ; for ( int z = 0 ; z < this . numClass ; z ++ ) { votes [ z ] = classDistrib . getValue ( z ) / sum ; } return votes ; } | Get observers class distribution probability |
21,674 | protected double [ ] weightedMax ( Instance inst ) { int countFired = 0 ; boolean fired = false ; double highest = 0.0 ; double [ ] votes = new double [ this . numClass ] ; ArrayList < Double > ruleSetVotes = new ArrayList < Double > ( ) ; ArrayList < ArrayList < Double > > majorityProb = new ArrayList < ArrayList < Do... | Get the votes using weighted Max |
21,675 | protected double [ ] weightedSum ( Instance inst ) { boolean fired = false ; int countFired = 0 ; double [ ] votes = new double [ this . numClass ] ; ArrayList < Double > weightSum = new ArrayList < Double > ( ) ; ArrayList < ArrayList < Double > > majorityProb = new ArrayList < ArrayList < Double > > ( ) ; for ( int j... | Get the votes using weighted Sum |
21,676 | protected void closeDialog ( ) { if ( m_CustomEditor instanceof Container ) { Dialog dlg = PropertyDialog . getParentDialog ( ( Container ) m_CustomEditor ) ; if ( dlg != null ) dlg . setVisible ( false ) ; } } | Closes the dialog . |
21,677 | protected Component createCustomEditor ( ) { JPanel panel ; panel = new JPanel ( new BorderLayout ( ) ) ; panel . setBorder ( BorderFactory . createEmptyBorder ( 5 , 5 , 5 , 5 ) ) ; m_EditComponent = ( ClassOptionEditComponent ) getEditComponent ( ( ClassOption ) getValue ( ) ) ; m_EditComponent . addChangeListener ( n... | Creates the custom editor . |
21,678 | public void paintValue ( Graphics gfx , Rectangle box ) { FontMetrics fm ; int vpad ; String val ; fm = gfx . getFontMetrics ( ) ; vpad = ( box . height - fm . getHeight ( ) ) / 2 ; val = ( ( ClassOption ) getValue ( ) ) . getValueAsCLIString ( ) ; gfx . drawString ( val , 2 , fm . getHeight ( ) + vpad ) ; } | Paints a representation of the current Object . |
21,679 | private double prediction ( Instance inst ) { if ( this . initialisePerceptron ) { return 0 ; } else { double [ ] normalizedInstance = normalizedInstance ( inst ) ; double normalizedPrediction = prediction ( normalizedInstance ) ; return denormalizedPrediction ( normalizedPrediction ) ; } } | Output the prediction made by this perceptron on the given instance |
21,680 | void insertPoint ( Point p ) { int cursize = this . buckets [ 0 ] . cursize ; if ( cursize >= this . maxBucketsize ) { int curbucket = 0 ; int nextbucket = 1 ; if ( this . buckets [ nextbucket ] . cursize == 0 ) { int i ; for ( i = 0 ; i < this . maxBucketsize ; i ++ ) { this . buckets [ nextbucket ] . points [ i ] = t... | inserts a single point into the bucketmanager |
21,681 | protected void processChunk ( ) { Classifier addedClassifier = null ; double mse_r = this . computeMseR ( ) ; double candidateClassifierWeight = 1.0 / ( mse_r + Double . MIN_VALUE ) ; for ( int i = 0 ; i < this . learners . length ; i ++ ) { this . weights [ i ] [ 0 ] = 1.0 / ( mse_r + this . computeMse ( this . learne... | Processes a chunk of instances . This method is called after collecting a chunk of examples . |
21,682 | protected double computeMse ( Classifier learner , Instances chunk ) { double mse_i = 0 ; double f_ci ; double voteSum ; for ( int i = 0 ; i < chunk . numInstances ( ) ; i ++ ) { try { voteSum = 0 ; for ( double element : learner . getVotesForInstance ( chunk . instance ( i ) ) ) { voteSum += element ; } if ( voteSum >... | Computes the MSE of a learner for a given chunk of examples . |
21,683 | private void trainOnChunk ( Classifier classifierToTrain ) { for ( int num = 0 ; num < this . chunkSizeOption . getValue ( ) ; num ++ ) { classifierToTrain . trainOnInstance ( this . currentChunk . instance ( num ) ) ; } } | Trains a component classifier on the most recent chunk of data . |
21,684 | public Cluster get ( int index ) { if ( index < clusters . size ( ) ) { return clusters . get ( index ) ; } return null ; } | get a cluster from the clustering |
21,685 | public FoundNode [ ] filterInstanceToLeaves ( Instance inst , SplitNode parent , int parentBranch , boolean updateSplitterCounts ) { List < FoundNode > nodes = new LinkedList < FoundNode > ( ) ; ( ( NewNode ) this . treeRoot ) . filterInstanceToLeaves ( inst , parent , parentBranch , nodes , updateSplitterCounts ) ; re... | New for options vote |
21,686 | public void setRange ( String range ) { String single = range . trim ( ) ; int hyphenIndex = range . indexOf ( '-' ) ; if ( hyphenIndex > 0 ) { this . start = rangeSingle ( range . substring ( 0 , hyphenIndex ) ) ; this . end = rangeSingle ( range . substring ( hyphenIndex + 1 ) ) ; } else { int number = rangeSingle ( ... | Sets the range from a string representation . |
21,687 | protected int rangeSingle ( String singleSelection ) { String single = singleSelection . trim ( ) ; if ( single . toLowerCase ( ) . equals ( "first" ) ) { return 0 ; } if ( single . toLowerCase ( ) . equals ( "last" ) || single . toLowerCase ( ) . equals ( "-1" ) ) { return - 1 ; } int index = Integer . parseInt ( sing... | Translates a single string selection into it s internal 0 - based equivalent . |
21,688 | public static < T extends Comparable < T > > Pair < T > minMax ( Iterable < T > items ) { Iterator < T > iterator = items . iterator ( ) ; if ( ! iterator . hasNext ( ) ) { return null ; } T min = iterator . next ( ) ; T max = min ; while ( iterator . hasNext ( ) ) { T item = iterator . next ( ) ; if ( item . compareTo... | Identifies the minimum and maximum elements from an iterable according to the natural ordering of the elements . |
21,689 | public static < T > List < T > randomSample ( Collection < T > collection , int n ) { List < T > list = new ArrayList < T > ( collection ) ; List < T > sample = new ArrayList < T > ( n ) ; Random random = new Random ( ) ; while ( n > 0 && ! list . isEmpty ( ) ) { int index = random . nextInt ( list . size ( ) ) ; sampl... | Randomly chooses elements from the collection . |
21,690 | private boolean updateMinMaxValues ( ) { double min_y_value_new = min_y_value ; double max_y_value_new = max_y_value ; double max_x_value_new = max_x_value ; if ( measure0 != null && measure1 != null ) { min_y_value_new = Math . min ( measure0 . getMinValue ( measureSelected ) , measure1 . getMinValue ( measureSelected... | returns true when values have changed |
21,691 | private void addEvents ( ) { if ( clusterEvents != null && clusterEvents . size ( ) > eventCounter ) { ClusterEvent ev = clusterEvents . get ( eventCounter ) ; eventCounter ++ ; JLabel eventMarker = new JLabel ( ev . getType ( ) . substring ( 0 , 1 ) ) ; eventMarker . setPreferredSize ( new Dimension ( 20 , y_offset_to... | check if there are any new events in the event list and add them to the plot |
21,692 | public void addResult ( Example < Instance > example , double [ ] classVotes ) { Prediction p = new MultiLabelPrediction ( 1 ) ; p . setVotes ( classVotes ) ; addResult ( example , p ) ; } | only for one output |
21,693 | public void setGraph ( MeasureCollection [ ] measures , MeasureCollection [ ] measureStds , double [ ] variedParamValues , Color [ ] colors ) { this . measures = measures ; this . variedParamValues = variedParamValues ; ( ( GraphScatter ) this . plotPanel ) . setGraph ( measures , measureStds , variedParamValues , colo... | Sets the scatter graph . |
21,694 | public double [ ] getVotesForInstance ( Instance instance ) { CNode host = m_cobwebTree ; CNode temp = null ; determineNumberOfClusters ( ) ; if ( this . m_numberOfClusters < 1 ) { return ( new double [ 0 ] ) ; } double [ ] ret = new double [ this . m_numberOfClusters ] ; do { if ( host . m_children == null ) { temp = ... | Classifies a given instance . |
21,695 | protected void determineNumberOfClusters ( ) { if ( ! m_numberOfClustersDetermined && ( m_cobwebTree != null ) ) { int [ ] numClusts = new int [ 1 ] ; numClusts [ 0 ] = 0 ; m_cobwebTree . assignClusterNums ( numClusts ) ; m_numberOfClusters = numClusts [ 0 ] ; m_numberOfClustersDetermined = true ; } } | determines the number of clusters if necessary |
21,696 | public String graph ( ) { StringBuffer text = new StringBuffer ( ) ; text . append ( "digraph CobwebTree {\n" ) ; m_cobwebTree . graphTree ( text ) ; text . append ( "}\n" ) ; return text . toString ( ) ; } | Generates the graph string of the Cobweb tree |
21,697 | public double sqDifference ( int index , double val1 , double val2 ) { double val = difference ( index , val1 , val2 ) ; return val * val ; } | Returns the squared difference of two values of an attribute . |
21,698 | public int closestPoint ( Instance instance , Instances allPoints , int [ ] pointList ) throws Exception { double minDist = Integer . MAX_VALUE ; int bestPoint = 0 ; for ( int i = 0 ; i < pointList . length ; i ++ ) { double dist = distance ( instance , allPoints . instance ( pointList [ i ] ) , Double . POSITIVE_INFIN... | Returns the index of the closest point to the current instance . Index is index in Instances object that is the second parameter . |
21,699 | public void openConfig ( String path ) { Properties properties = new Properties ( ) ; try { properties . load ( new FileInputStream ( path ) ) ; } catch ( IOException ex ) { JOptionPane . showMessageDialog ( this , "Problems reading the properties file" , "Error" , JOptionPane . ERROR_MESSAGE ) ; } this . jTextFieldPro... | Opens a previously saved configuration |
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