List of usage examples for weka.core Instance isMissing
public boolean isMissing(Attribute att);
From source file:milk.visualize.MIPlot2D.java
License:Open Source License
/** * Renders this component//ww w . ja v a 2s . c o m * @param gx the graphics context */ public void paintComponent(Graphics gx) { //if(!isEnabled()) // return; super.paintComponent(gx); if (plotExemplars != null) { gx.setColor(m_axisColour); // Draw the axis name String xname = plotExemplars.attribute(m_xIndex).name(), yname = plotExemplars.attribute(m_yIndex).name(); gx.drawString(yname, m_XaxisStart + m_labelMetrics.stringWidth("M"), m_YaxisStart + m_labelMetrics.getAscent() / 2 + m_tickSize); gx.drawString(xname, m_XaxisEnd - m_labelMetrics.stringWidth(yname) + m_tickSize, (int) (m_YaxisEnd - m_labelMetrics.getAscent() / 2)); // Draw points Attribute classAtt = plotExemplars.classAttribute(); for (int j = 0; j < m_plots.size(); j++) { PlotData2D temp_plot = (PlotData2D) (m_plots.elementAt(j)); Instances instances = temp_plot.getPlotInstances(); StringTokenizer st = new StringTokenizer( instances.firstInstance().stringValue(plotExemplars.idIndex()), "_"); //////////////////// TLD stuff ///////////////////////////////// /* double[] mu = new double[plotExemplars.numAttributes()], sgm = new double[plotExemplars.numAttributes()]; st.nextToken(); // Squeeze first element int p=0; while(p<mu.length){ if((p==plotExemplars.idIndex()) || (p==plotExemplars.classIndex())) p++; if(p<mu.length){ mu[p] = Double.parseDouble(st.nextToken()); sgm[p] = Double.parseDouble(st.nextToken()); p++; } } Instance ins = instances.firstInstance(); gx.setColor((Color)m_colorList.elementAt((int)ins.classValue())); double mux=mu[m_xIndex], muy=mu[m_yIndex], sgmx=sgm[m_xIndex], sgmy=sgm[m_yIndex]; double xs = convertToPanelX(mux-3*sgmx), xe = convertToPanelX(mux+3*sgmx), xleng = Math.abs(xe-xs); double ys = convertToPanelY(muy+3*sgmy), ye = convertToPanelY(muy-3*sgmy), yleng = Math.abs(ye-ys); // Draw oval gx.drawOval((int)xs,(int)ys,(int)xleng,(int)yleng); // Draw a dot gx.fillOval((int)convertToPanelX(mux)-2, (int)convertToPanelY(muy)-2, 4, 4); */ //////////////////// TLD stuff ///////////////////////////////// //////////////////// instance-based stuff ///////////////////////////////// /* double[] core = new double[plotExemplars.numAttributes()], range=new double[plotExemplars.numAttributes()]; st.nextToken(); // Squeeze first element int p=0; while(p<range.length){ if((p==plotExemplars.idIndex()) || (p==plotExemplars.classIndex())) p++; if(p<range.length) range[p++] = Double.parseDouble(st.nextToken()); } p=0; while(st.hasMoreTokens()){ if((p==plotExemplars.idIndex()) || (p==plotExemplars.classIndex())) p++; core[p++] = Double.parseDouble(st.nextToken()); } Instance ins = instances.firstInstance(); gx.setColor((Color)m_colorList.elementAt((int)ins.classValue())); double rgx=range[m_xIndex], rgy=range[m_yIndex]; double x1 = convertToPanelX(core[m_xIndex]-rgx/2), y1 = convertToPanelY(core[m_yIndex]-rgy/2), x2 = convertToPanelX(core[m_xIndex]+rgx/2), y2 = convertToPanelY(core[m_yIndex]+rgy/2), x = convertToPanelX(core[m_xIndex]), y = convertToPanelY(core[m_yIndex]); // Draw a rectangle gx.drawLine((int)x1, (int)y1, (int)x2, (int)y1); gx.drawLine((int)x1, (int)y1, (int)x1, (int)y2); gx.drawLine((int)x2, (int)y1, (int)x2, (int)y2); gx.drawLine((int)x1, (int)y2, (int)x2, (int)y2); // Draw a dot gx.fillOval((int)x-3, (int)y-3, 6, 6); // Draw string StringBuffer text =new StringBuffer(temp_plot.getPlotName()+":"+instances.numInstances()); gx.drawString(text.toString(), (int)x1, (int)y2+m_labelMetrics.getHeight()); */ //////////////////// instance-based stuff ///////////////////////////////// //////////////////// normal graph ///////////////////////////////// // Paint numbers for (int i = 0; i < instances.numInstances(); i++) { Instance ins = instances.instance(i); if (!ins.isMissing(m_xIndex) && !ins.isMissing(m_yIndex)) { if (classAtt.isNominal()) gx.setColor((Color) m_colorList.elementAt((int) ins.classValue())); else { double r = (ins.classValue() - m_minC) / (m_maxC - m_minC); r = (r * 240) + 15; gx.setColor(new Color((int) r, 150, (int) (255 - r))); } double x = convertToPanelX(ins.value(m_xIndex)); double y = convertToPanelY(ins.value(m_yIndex)); String id = temp_plot.getPlotName(); gx.drawString(id, (int) (x - m_labelMetrics.stringWidth(id) / 2), (int) (y + m_labelMetrics.getHeight() / 2)); } } //////////////////// normal graph ///////////////////////////////// } } //////////////////// TLD stuff ///////////////////////////////// // Draw two Guassian contour with 3 stdDev // (-1, -1) with stdDev 1, 2 // (1, 1) with stdDev 2, 1 /*gx.setColor(Color.black); double mu=-1.5, sigmx, sigmy; // class 0 if(m_xIndex == 1) sigmx = 1; else sigmx = 2; if(m_yIndex == 1) sigmy = 1; else sigmy = 2; double x1 = convertToPanelX(mu-3*sigmx), x2 = convertToPanelX(mu+3*sigmx), xlen = Math.abs(x2-x1); double y1 = convertToPanelY(mu+3*sigmy), y2 = convertToPanelY(mu-3*sigmy), ylen = Math.abs(y2-y1); // Draw heavy oval gx.drawOval((int)x1,(int)y1,(int)xlen,(int)ylen); gx.drawOval((int)x1-1,(int)y1-1,(int)xlen+2,(int)ylen+2); gx.drawOval((int)x1+1,(int)y1+1,(int)xlen-2,(int)ylen-2); // Draw a dot gx.fillOval((int)convertToPanelX(mu)-3, (int)convertToPanelY(mu)-3, 6, 6); mu=1.5; // class 1 if(m_xIndex == 1) sigmx = 1; else sigmx = 2; if(m_yIndex == 1) sigmy = 1; else sigmy = 2; x1 = convertToPanelX(mu-3*sigmx); x2 = convertToPanelX(mu+3*sigmx); xlen = Math.abs(x2-x1); y1 = convertToPanelY(mu+3*sigmy); y2 = convertToPanelY(mu-3*sigmy); ylen = Math.abs(y2-y1); // Draw heavy oval gx.drawOval((int)x1,(int)y1,(int)xlen,(int)ylen); gx.drawOval((int)x1-1,(int)y1-1,(int)xlen+2,(int)ylen+2); gx.drawOval((int)x1+1,(int)y1+1,(int)xlen-2,(int)ylen-2); // Draw a dot gx.fillOval((int)convertToPanelX(mu)-3, (int)convertToPanelY(mu)-3, 6, 6); */ //////////////////// TLD stuff ///////////////////////////////// //////////////////// instance-based stuff ///////////////////////////////// /* // Paint a log-odds line: 1*x0+2*x1=0 double xstart, xend, ystart, yend, xCoeff, yCoeff; if(m_xIndex == 1) xCoeff = 1; else xCoeff = 2; if(m_yIndex == 1) yCoeff = 1; else yCoeff = 2; xstart = m_minX; ystart = -xstart*xCoeff/yCoeff; if(ystart > m_maxY){ ystart = m_maxY; xstart = -ystart*yCoeff/xCoeff; } yend = m_minY; xend = -yend*yCoeff/xCoeff; if(xend > m_maxX){ xend = m_maxX; yend = -xend*xCoeff/yCoeff; } // Draw a heavy line gx.setColor(Color.black); gx.drawLine((int)convertToPanelX(xstart), (int)convertToPanelY(ystart), (int)convertToPanelX(xend), (int)convertToPanelY(yend)); gx.drawLine((int)convertToPanelX(xstart)+1, (int)convertToPanelY(ystart)+1, (int)convertToPanelX(xend)+1, (int)convertToPanelY(yend)+1); gx.drawLine((int)convertToPanelX(xstart)-1, (int)convertToPanelY(ystart)-1, (int)convertToPanelX(xend)-1, (int)convertToPanelY(yend)-1); */ //////////////////// instance-based stuff ///////////////////////////////// }
From source file:moa.classifiers.bayes.NaiveBayes.java
License:Open Source License
public static double[] doNaiveBayesPrediction(Instance inst, DoubleVector observedClassDistribution, AutoExpandVector<AttributeClassObserver> attributeObservers) { double[] votes = new double[observedClassDistribution.numValues()]; double observedClassSum = observedClassDistribution.sumOfValues(); for (int classIndex = 0; classIndex < votes.length; classIndex++) { votes[classIndex] = observedClassDistribution.getValue(classIndex) / observedClassSum; for (int attIndex = 0; attIndex < inst.numAttributes() - 1; attIndex++) { int instAttIndex = modelAttIndexToInstanceAttIndex(attIndex, inst); AttributeClassObserver obs = attributeObservers.get(attIndex); if ((obs != null) && !inst.isMissing(instAttIndex)) { votes[classIndex] *= obs.probabilityOfAttributeValueGivenClass(inst.value(instAttIndex), classIndex);//from w w w .j ava 2s . c o m } } } // TODO: need logic to prevent underflow? return votes; }
From source file:moa.classifiers.bayes.NaiveBayes.java
License:Open Source License
public static double[] doNaiveBayesPredictionLog(Instance inst, DoubleVector observedClassDistribution, AutoExpandVector<AttributeClassObserver> observers, AutoExpandVector<AttributeClassObserver> observers2) { AttributeClassObserver obs;/*w w w .j a v a2s.c o m*/ double[] votes = new double[observedClassDistribution.numValues()]; double observedClassSum = observedClassDistribution.sumOfValues(); for (int classIndex = 0; classIndex < votes.length; classIndex++) { votes[classIndex] = Math.log10(observedClassDistribution.getValue(classIndex) / observedClassSum); for (int attIndex = 0; attIndex < inst.numAttributes() - 1; attIndex++) { int instAttIndex = modelAttIndexToInstanceAttIndex(attIndex, inst); if (inst.attribute(instAttIndex).isNominal()) { obs = observers.get(attIndex); } else { obs = observers2.get(attIndex); } if ((obs != null) && !inst.isMissing(instAttIndex)) { votes[classIndex] += Math .log10(obs.probabilityOfAttributeValueGivenClass(inst.value(instAttIndex), classIndex)); } } } return votes; }
From source file:moa.classifiers.bayes.NaiveBayesMultinomial.java
License:Open Source License
/** * Trains the classifier with the given instance. * * @param instance the new training instance to include in the model *///from ww w .j a v a 2 s . c o m @Override public void trainOnInstanceImpl(Instance inst) { if (this.reset == true) { this.m_numClasses = inst.numClasses(); double laplace = this.laplaceCorrectionOption.getValue(); int numAttributes = inst.numAttributes(); m_probOfClass = new double[m_numClasses]; Arrays.fill(m_probOfClass, laplace); m_classTotals = new double[m_numClasses]; Arrays.fill(m_classTotals, laplace * numAttributes); m_wordTotalForClass = new DoubleVector[m_numClasses]; for (int i = 0; i < m_numClasses; i++) { //Arrays.fill(wordTotal, laplace); m_wordTotalForClass[i] = new DoubleVector(); } this.reset = false; } // Update classifier int classIndex = inst.classIndex(); int classValue = (int) inst.value(classIndex); double w = inst.weight(); m_probOfClass[classValue] += w; m_classTotals[classValue] += w * totalSize(inst); double total = m_classTotals[classValue]; for (int i = 0; i < inst.numValues(); i++) { int index = inst.index(i); if (index != classIndex && !inst.isMissing(i)) { //m_wordTotalForClass[index][classValue] += w * inst.valueSparse(i); double laplaceCorrection = 0.0; if (m_wordTotalForClass[classValue].getValue(index) == 0) { laplaceCorrection = this.laplaceCorrectionOption.getValue(); } m_wordTotalForClass[classValue].addToValue(index, w * inst.valueSparse(i) + laplaceCorrection); } } }
From source file:moa.classifiers.bayes.NaiveBayesMultinomial.java
License:Open Source License
/** * Calculates the class membership probabilities for the given test * instance./*from w ww. j a va 2 s . co m*/ * * @param instance the instance to be classified * @return predicted class probability distribution */ @Override public double[] getVotesForInstance(Instance instance) { if (this.reset == true) { return new double[2]; } double[] probOfClassGivenDoc = new double[m_numClasses]; double totalSize = totalSize(instance); for (int i = 0; i < m_numClasses; i++) { probOfClassGivenDoc[i] = Math.log(m_probOfClass[i]) - totalSize * Math.log(m_classTotals[i]); } for (int i = 0; i < instance.numValues(); i++) { int index = instance.index(i); if (index == instance.classIndex() || instance.isMissing(i)) { continue; } double wordCount = instance.valueSparse(i); for (int c = 0; c < m_numClasses; c++) { double value = m_wordTotalForClass[c].getValue(index); probOfClassGivenDoc[c] += wordCount * Math.log(value == 0 ? this.laplaceCorrectionOption.getValue() : value); } } return Utils.logs2probs(probOfClassGivenDoc); }
From source file:moa.classifiers.bayes.NaiveBayesMultinomial.java
License:Open Source License
public double totalSize(Instance instance) { int classIndex = instance.classIndex(); double total = 0.0; for (int i = 0; i < instance.numValues(); i++) { int index = instance.index(i); if (index == classIndex || instance.isMissing(i)) { continue; }//from www . ja v a2 s .c o m double count = instance.valueSparse(i); if (count >= 0) { total += count; } else { //throw new Exception("Numeric attribute value is not >= 0. " + i + " " + index + " " + // instance.valueSparse(i) + " " + " " + instance); } } return total; }
From source file:moa.classifiers.NaiveBayesMultinomial.java
License:Open Source License
/** * Trains the classifier with the given instance. * * @param instance the new training instance to include in the model *///from w w w . ja v a 2 s. c o m @Override public void trainOnInstanceImpl(Instance inst) { if (this.reset == true) { this.m_numClasses = inst.numClasses(); double laplace = this.laplaceCorrectionOption.getValue(); int numAttributes = inst.numAttributes(); m_probOfClass = new double[m_numClasses]; Arrays.fill(m_probOfClass, laplace); m_classTotals = new double[m_numClasses]; Arrays.fill(m_classTotals, laplace * numAttributes); m_wordTotalForClass = new double[numAttributes][m_numClasses]; for (double[] wordTotal : m_wordTotalForClass) { Arrays.fill(wordTotal, laplace); } this.reset = false; } // Update classifier int classIndex = inst.classIndex(); int classValue = (int) inst.value(classIndex); double w = inst.weight(); m_probOfClass[classValue] += w; m_classTotals[classValue] += w * totalSize(inst); double total = m_classTotals[classValue]; for (int i = 0; i < inst.numValues(); i++) { int index = inst.index(i); if (index != classIndex && !inst.isMissing(i)) { m_wordTotalForClass[index][classValue] += w * inst.valueSparse(i); } } }
From source file:moa.classifiers.NaiveBayesMultinomial.java
License:Open Source License
/** * Calculates the class membership probabilities for the given test * instance.//from w w w . j av a 2 s . c o m * * @param instance the instance to be classified * @return predicted class probability distribution */ @Override public double[] getVotesForInstance(Instance instance) { if (this.reset == true) { return new double[2]; } double[] probOfClassGivenDoc = new double[m_numClasses]; double totalSize = totalSize(instance); for (int i = 0; i < m_numClasses; i++) { probOfClassGivenDoc[i] = Math.log(m_probOfClass[i]) - totalSize * Math.log(m_classTotals[i]); } for (int i = 0; i < instance.numValues(); i++) { int index = instance.index(i); if (index == instance.classIndex() || instance.isMissing(i)) { continue; } double wordCount = instance.valueSparse(i); for (int c = 0; c < m_numClasses; c++) { probOfClassGivenDoc[c] += wordCount * Math.log(m_wordTotalForClass[index][c]); } } return Utils.logs2probs(probOfClassGivenDoc); }
From source file:moa.classifiers.rules.core.conditionaltests.NumericAttributeBinaryRulePredicate.java
License:Apache License
@Override public int branchForInstance(Instance inst) { int instAttIndex = this.attIndex < inst.classIndex() ? this.attIndex : this.attIndex + 1; if (inst.isMissing(instAttIndex)) { return -1; }// w ww .j ava 2 s.c o m double v = inst.value(instAttIndex); int ret = 0; switch (this.operator) { case 0: ret = (v == this.attValue) ? 0 : 1; break; case 1: ret = (v <= this.attValue) ? 0 : 1; break; case 2: ret = (v > this.attValue) ? 0 : 1; } return ret; }
From source file:moa.classifiers.rules.RuleClassifier.java
License:Apache License
@Override public void trainOnInstanceImpl(Instance inst) { int countRuleFiredTrue = 0; boolean ruleFired = false; this.instance = inst; this.numAttributes = instance.numAttributes() - 1; this.numClass = instance.numClasses(); this.numInstance = numInstance + 1; int conta1 = 0; for (int j = 0; j < ruleSet.size(); j++) { if (this.ruleSet.get(j).ruleEvaluate(inst) == true) { countRuleFiredTrue = countRuleFiredTrue + 1; double anomaly = 0.0; if (this.Supervised.isSet()) { anomaly = computeAnomalySupervised(this.ruleSet.get(j), j, inst); // compute anomaly (Supervised method) } else if (this.Unsupervised.isSet()) { anomaly = computeAnomalyUnsupervised(this.ruleSet.get(j), j, inst); // compute anomaly (Unsupervised method) }/*from www. j a v a 2 s. c o m*/ if (anomaly >= this.anomalyProbabilityThresholdOption.getValue()) { conta1 = conta1 + 1; } // System.out.print(numInstance+";"+anomaly+"\n"); try { File dir = new File("SeaAnomaliesUnsupervised.txt"); FileWriter fileWriter = new FileWriter(dir, true); PrintWriter printWriter = new PrintWriter(fileWriter); printWriter.println(numInstance + ";" + anomaly); printWriter.flush(); printWriter.close(); } catch (IOException e) { e.printStackTrace(); } if ((this.ruleSet.get(j).instancesSeen <= this.anomalyNumInstThresholdOption.getValue()) || (anomaly < this.anomalyProbabilityThresholdOption.getValue() && this.anomalyDetectionOption.isSet()) || !this.anomalyDetectionOption.isSet()) { this.ruleSet.get(j).obserClassDistrib.addToValue((int) inst.classValue(), inst.weight()); for (int i = 0; i < inst.numAttributes() - 1; i++) { int instAttIndex = modelAttIndexToInstanceAttIndex(i, inst); if (!inst.isMissing(instAttIndex)) { AttributeClassObserver obs = this.ruleSet.get(j).observers.get(i); // Nominal and binary tree. AttributeClassObserver obsGauss = this.ruleSet.get(j).observersGauss.get(i); // Gaussian. if (obs == null) { obs = inst.attribute(instAttIndex).isNominal() ? newNominalClassObserver() : newNumericClassObserver(); this.ruleSet.get(j).observers.set(i, obs); } if (obsGauss == null) { obsGauss = inst.attribute(instAttIndex).isNumeric() ? newNumericClassObserver2() : null; this.ruleSet.get(j).observersGauss.set(i, obsGauss); } obs.observeAttributeClass(inst.value(instAttIndex), (int) inst.classValue(), inst.weight()); if (inst.attribute(instAttIndex).isNumeric()) { obsGauss.observeAttributeClass(inst.value(instAttIndex), (int) inst.classValue(), inst.weight()); } } } expandeRule(this.ruleSet.get(j), inst, j); // This function expands the rule } if (this.orderedRulesOption.isSet()) { // Ordered rules break; } } } if (countRuleFiredTrue > 0) { ruleFired = true; } else { ruleFired = false; } if (ruleFired == false) { //If none of the rules cover the example update sufficient statistics of the default rule this.observedClassDistribution.addToValue((int) inst.classValue(), inst.weight()); for (int i = 0; i < inst.numAttributes() - 1; i++) { int instAttIndex = modelAttIndexToInstanceAttIndex(i, inst); if (!inst.isMissing(instAttIndex)) { AttributeClassObserver obs = this.attributeObservers.get(i); AttributeClassObserver obsGauss = this.attributeObserversGauss.get(i); if (obs == null) { obs = inst.attribute(instAttIndex).isNominal() ? newNominalClassObserver() : newNumericClassObserver(); this.attributeObservers.set(i, obs); } if (obsGauss == null) { obsGauss = inst.attribute(instAttIndex).isNumeric() ? newNumericClassObserver2() : null; this.attributeObserversGauss.set(i, obsGauss); } obs.observeAttributeClass(inst.value(instAttIndex), (int) inst.classValue(), inst.weight()); if (inst.attribute(instAttIndex).isNumeric()) { obsGauss.observeAttributeClass(inst.value(instAttIndex), (int) inst.classValue(), inst.weight()); } } } createRule(inst); //This function creates a rule } }