List of usage examples for weka.core Instance weight
public double weight();
From source file:moa.classifiers.meta.LeveragingBag.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { int numClasses = inst.numClasses(); //Output Codes if (this.initMatrixCodes == true) { this.matrixCodes = new int[this.ensemble.length][inst.numClasses()]; for (int i = 0; i < this.ensemble.length; i++) { int numberOnes; int numberZeros; do { // until we have the same number of zeros and ones numberOnes = 0;/* w w w. j a va 2 s . c om*/ numberZeros = 0; for (int j = 0; j < numClasses; j++) { int result = 0; if (j == 1 && numClasses == 2) { result = 1 - this.matrixCodes[i][0]; } else { result = (this.classifierRandom.nextBoolean() ? 1 : 0); } this.matrixCodes[i][j] = result; if (result == 1) { numberOnes++; } else { numberZeros++; } } } while ((numberOnes - numberZeros) * (numberOnes - numberZeros) > (this.ensemble.length % 2)); } this.initMatrixCodes = false; } boolean Change = false; Instance weightedInst = (Instance) inst.copy(); double w = this.weightShrinkOption.getValue(); //Train ensemble of classifiers for (int i = 0; i < this.ensemble.length; i++) { double k = 0.0; switch (this.leveraginBagAlgorithmOption.getChosenIndex()) { case 0: //LeveragingBag k = MiscUtils.poisson(w, this.classifierRandom); break; case 1: //LeveragingBagME double error = this.ADError[i].getEstimation(); k = !this.ensemble[i].correctlyClassifies(weightedInst) ? 1.0 : (this.classifierRandom.nextDouble() < (error / (1.0 - error)) ? 1.0 : 0.0); break; case 2: //LeveragingBagHalf w = 1.0; k = this.classifierRandom.nextBoolean() ? 0.0 : w; break; case 3: //LeveragingBagWT w = 1.0; k = 1.0 + MiscUtils.poisson(w, this.classifierRandom); break; case 4: //LeveragingSubag w = 1.0; k = MiscUtils.poisson(1, this.classifierRandom); k = (k > 0) ? w : 0; break; } if (k > 0) { if (this.outputCodesOption.isSet()) { weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]); } weightedInst.setWeight(inst.weight() * k); this.ensemble[i].trainOnInstance(weightedInst); } boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst); double ErrEstim = this.ADError[i].getEstimation(); if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1)) { if (this.ADError[i].getEstimation() > ErrEstim) { Change = true; } } } if (Change) { numberOfChangesDetected++; double max = 0.0; int imax = -1; for (int i = 0; i < this.ensemble.length; i++) { if (max < this.ADError[i].getEstimation()) { max = this.ADError[i].getEstimation(); imax = i; } } if (imax != -1) { this.ensemble[imax].resetLearning(); //this.ensemble[imax].trainOnInstance(inst); this.ADError[imax] = new ADWIN((double) this.deltaAdwinOption.getValue()); } } }
From source file:moa.classifiers.meta.OCBoost.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { double d = 1.0; int[] m = new int[this.ensemble.length]; for (int j = 0; j < this.ensemble.length; j++) { int j0 = 0; //max(0,j-K) pipos[j] = 1.0;/*from ww w .j a va 2 s. c o m*/ pineg[j] = 1.0; m[j] = -1; if (this.ensemble[j].correctlyClassifies(inst) == true) { m[j] = 1; } for (int k = j0; k <= j - 1; k++) { pipos[j] *= wpos[j][k] / wpos[j][j] * Math.exp(-alphainc[k]) + (1.0 - wpos[j][k] / wpos[j][j]) * Math.exp(alphainc[k]); pineg[j] *= wneg[j][k] / wneg[j][j] * Math.exp(-alphainc[k]) + (1.0 - wneg[j][k] / wneg[j][j]) * Math.exp(alphainc[k]); } for (int k = 0; k <= j; k++) { wpos[j][k] = wpos[j][k] * pipos[j] + d * (m[k] == 1 ? 1 : 0) * (m[j] == 1 ? 1 : 0); wneg[j][k] = wneg[j][k] * pineg[j] + d * (m[k] == -1 ? 1 : 0) * (m[j] == -1 ? 1 : 0); } alphainc[j] = -alpha[j]; alpha[j] = 0.5 * Math.log(wpos[j][j] / wneg[j][j]); alphainc[j] += alpha[j]; d = d * Math.exp(-alpha[j] * m[j]); if (d > 0.0) { Instance weightedInst = (Instance) inst.copy(); weightedInst.setWeight(inst.weight() * d); this.ensemble[j].trainOnInstance(weightedInst); } } }
From source file:moa.classifiers.meta.OzaBag.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { for (int i = 0; i < this.ensemble.length; i++) { int k = MiscUtils.poisson(1.0, this.classifierRandom); if (k > 0) { Instance weightedInst = (Instance) inst.copy(); weightedInst.setWeight(inst.weight() * k); this.ensemble[i].trainOnInstance(weightedInst); }//from w w w . ja v a 2s . c o m } }
From source file:moa.classifiers.meta.OzaBagAdwin.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { boolean Change = false; for (int i = 0; i < this.ensemble.length; i++) { int k = MiscUtils.poisson(1.0, this.classifierRandom); if (k > 0) { Instance weightedInst = (Instance) inst.copy(); weightedInst.setWeight(inst.weight() * k); this.ensemble[i].trainOnInstance(weightedInst); }//from www . ja v a 2s . c om boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(inst); double ErrEstim = this.ADError[i].getEstimation(); if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1)) { if (this.ADError[i].getEstimation() > ErrEstim) { Change = true; } } } if (Change) { double max = 0.0; int imax = -1; for (int i = 0; i < this.ensemble.length; i++) { if (max < this.ADError[i].getEstimation()) { max = this.ADError[i].getEstimation(); imax = i; } } if (imax != -1) { this.ensemble[imax].resetLearning(); //this.ensemble[imax].trainOnInstance(inst); this.ADError[imax] = new ADWIN(); } } }
From source file:moa.classifiers.meta.OzaBagASHT.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { int trueClass = (int) inst.classValue(); for (int i = 0; i < this.ensemble.length; i++) { int k = MiscUtils.poisson(1.0, this.classifierRandom); if (k > 0) { Instance weightedInst = (Instance) inst.copy(); weightedInst.setWeight(inst.weight() * k); if (Utils.maxIndex(this.ensemble[i].getVotesForInstance(inst)) == trueClass) { this.error[i] += alpha * (0.0 - this.error[i]); //EWMA } else { this.error[i] += alpha * (1.0 - this.error[i]); //EWMA }//from w w w . j a v a 2s . c o m this.ensemble[i].trainOnInstance(weightedInst); } } }
From source file:moa.classifiers.meta.OzaBagLambda.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { for (int i = 0; i < this.ensemble.length; i++) { int k = MiscUtils.poisson(this.lambdaOption.getValue(), this.classifierRandom); if (!m_debug) { if (k > 0) { Instance weightedInst = (Instance) inst.copy(); weightedInst.setWeight(inst.weight() * k); this.ensemble[i].trainOnInstance(weightedInst); }/* ww w . ja va 2s . c om*/ } if (m_debug) { System.out.println(inst.weight() * k); } } }
From source file:moa.classifiers.meta.OzaBoost.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { double lambda_d = 1.0; for (int i = 0; i < this.ensemble.length; i++) { double k = this.pureBoostOption.isSet() ? lambda_d : MiscUtils.poisson(lambda_d, this.classifierRandom); if (k > 0.0) { Instance weightedInst = (Instance) inst.copy(); weightedInst.setWeight(inst.weight() * k); this.ensemble[i].trainOnInstance(weightedInst); }//ww w . j a v a2 s . co m if (this.ensemble[i].correctlyClassifies(inst)) { this.scms[i] += lambda_d; lambda_d *= this.trainingWeightSeenByModel / (2 * this.scms[i]); } else { this.swms[i] += lambda_d; lambda_d *= this.trainingWeightSeenByModel / (2 * this.swms[i]); } } }
From source file:moa.classifiers.meta.OzaBoostAdwin.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { int numClasses = inst.numClasses(); // Set log (k-1) and (k-1) for SAMME Method if (this.sammeOption.isSet()) { this.Km1 = numClasses - 1; this.logKm1 = Math.log(this.Km1); this.initKm1 = false; }/*www . j ava2 s . c o m*/ //Output Codes if (this.initMatrixCodes == true) { this.matrixCodes = new int[this.ensemble.length][inst.numClasses()]; for (int i = 0; i < this.ensemble.length; i++) { int numberOnes; int numberZeros; do { // until we have the same number of zeros and ones numberOnes = 0; numberZeros = 0; for (int j = 0; j < numClasses; j++) { int result = 0; if (j == 1 && numClasses == 2) { result = 1 - this.matrixCodes[i][0]; } else { result = (this.classifierRandom.nextBoolean() ? 1 : 0); } this.matrixCodes[i][j] = result; if (result == 1) { numberOnes++; } else { numberZeros++; } } } while ((numberOnes - numberZeros) * (numberOnes - numberZeros) > (this.ensemble.length % 2)); } this.initMatrixCodes = false; } boolean Change = false; double lambda_d = 1.0; Instance weightedInst = (Instance) inst.copy(); for (int i = 0; i < this.ensemble.length; i++) { double k = this.pureBoostOption.isSet() ? lambda_d : MiscUtils.poisson(lambda_d * this.Km1, this.classifierRandom); if (k > 0.0) { if (this.outputCodesOption.isSet()) { weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]); } weightedInst.setWeight(inst.weight() * k); this.ensemble[i].trainOnInstance(weightedInst); } boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst); if (correctlyClassifies) { this.scms[i] += lambda_d; lambda_d *= this.trainingWeightSeenByModel / (2 * this.scms[i]); } else { this.swms[i] += lambda_d; lambda_d *= this.trainingWeightSeenByModel / (2 * this.swms[i]); } double ErrEstim = this.ADError[i].getEstimation(); if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1)) { if (this.ADError[i].getEstimation() > ErrEstim) { Change = true; } } } if (Change) { numberOfChangesDetected++; double max = 0.0; int imax = -1; for (int i = 0; i < this.ensemble.length; i++) { if (max < this.ADError[i].getEstimation()) { max = this.ADError[i].getEstimation(); imax = i; } } if (imax != -1) { this.ensemble[imax].resetLearning(); //this.ensemble[imax].trainOnInstance(inst); this.ADError[imax] = new ADWIN((double) this.deltaAdwinOption.getValue()); this.scms[imax] = 0; this.swms[imax] = 0; } } }
From source file:moa.classifiers.meta.PAME.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { // get the prediction vector back double[] ht = this.getPredictions(inst); double yt = inst.classValue(); if (inst.classIndex() == 0) { this.rareCount += 1.0; }/*from w w w . ja v a 2 s . c om*/ this.count += 1.0; // convert to a positive / negative classification scenario if (yt == 0) { yt = 1.0; } else { yt = -1.0; } /* * update expert weights */ if (this.updateMethodOption.getChosenIndex() == PAME1) { pame1_weights(ht, yt); } else if (this.updateMethodOption.getChosenIndex() == PAME2) { pame2_weights(ht, yt); } else if (this.updateMethodOption.getChosenIndex() == PAME3) { pame3_weights(ht, yt); } /* * we are going to use an online bagging / boosting strategy to update the * experts. In the end our objective with the weight formulation is a bit * more of a decision theoretic approach. */ for (int i = 0; i < this.ensemble.length; i++) { // sample from a Poisson probability distribution as implemented in // online bagging and boosting] double w; if (this.overSampleOption.isSet() && inst.classIndex() == 0) { w = 1.0 / (this.rareCount / this.count); if (this.logTransformOption.isSet()) { w = Math.log(w); } } else if (this.underSampleOption.isSet() && inst.classIndex() != 0) { w = 1.0 - this.rareCount / this.count; } else { w = 1.0; } int k = MiscUtils.poisson(w, this.classifierRandom); // update the expert accordingly if (k > 0) { // this works by updating the expert k-times with the same example. // thus is k = 4. the expert is trained updated on the same example // 4 times in a row. pretty easy. Instance weightedInst = (Instance) inst.copy(); weightedInst.setWeight(inst.weight() * k); // set the # of training times this.ensemble[i].trainOnInstance(weightedInst); // update expert } } this.n_negativeWeights = 0; for (int i = 0; i < this.weights.length; i++) { if (this.weights[i] < 0.0) this.n_negativeWeights++; } }
From source file:moa.classifiers.meta.PAMEAdwin.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { // get the prediction vector back double[] ht = this.getPredictions(inst); double yt = inst.classValue(); if (inst.classIndex() == 0) { this.rareCount += 1.0; }/*from w w w . ja v a 2s .com*/ this.count += 1.0; // convert to a positive / negative classification scenario if (yt == 0) { //System.out.println("Y is positive" + yt); yt = 1.0; } else { //System.out.println("Y is negative" + yt); yt = -1.0; } /* * update expert weights */ if (this.updateMethodOption.getChosenIndex() == PAME1) { pame1_weights(ht, yt); } else if (this.updateMethodOption.getChosenIndex() == PAME2) { pame2_weights(ht, yt); } else if (this.updateMethodOption.getChosenIndex() == PAME3) { pame3_weights(ht, yt); } else if (this.updateMethodOption.getChosenIndex() == PAME23) { pame23_weights(ht, yt); } /* * we are going to use an online bagging / boosting strategy to update the * experts. In the end our objective with the weight formulation is a bit * more of a decision theoretic approach. */ boolean Change = false; for (int i = 0; i < this.ensemble.length; i++) { // sample from a Poisson probability distribution as implemented in // online bagging and boosting double w; if (this.overSampleOption.isSet() && inst.classIndex() == 0) { w = 1.0 / (this.rareCount / this.count); if (this.logTransformOption.isSet()) { w = Math.log(w); } } else if (this.underSampleOption.isSet() && inst.classIndex() != 0) { w = 1.0 - this.rareCount / this.count; } else { w = 1.0; } int k = MiscUtils.poisson(w, this.classifierRandom); // update the expert accordingly if (k > 0) { // this works by updating the expert k-times with the same example. // thus is k = 4. the expert is trained updated on the same example // 4 times in a row. pretty easy. Instance weightedInst = (Instance) inst.copy(); weightedInst.setWeight(inst.weight() * k); // set the # of training times this.ensemble[i].trainOnInstance(weightedInst); // update expert } boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(inst); double ErrEstim = this.ADError[i].getEstimation(); if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1)) { if (this.ADError[i].getEstimation() > ErrEstim) { Change = true; } } } /* * if change was detected, remove the worst expert from the ensemble of * classifiers. */ if (Change) { double max = 0.0; int imax = -1; for (int i = 0; i < this.ensemble.length; i++) { if (max < this.ADError[i].getEstimation()) { max = this.ADError[i].getEstimation(); imax = i; } } if (imax != -1) { this.ensemble[imax].resetLearning(); //this.ensemble[imax].trainOnInstance(inst); this.ADError[imax] = new ADWIN(); } } this.n_negativeWeights = 0; for (int i = 0; i < this.weights.length; i++) { if (this.weights[i] < 0.0) this.n_negativeWeights++; } }