List of usage examples for weka.classifiers.functions SMO sparseWeights
public double[][][] sparseWeights()
From source file:etc.aloe.cscw2013.SMOFeatureWeighting.java
License:Open Source License
@Override public List<Entry<String, Double>> getFeatureWeights(ExampleSet trainingExamples, Model model) { WekaModel wekaModel = (WekaModel) model; Classifier classifier = wekaModel.getClassifier(); Instances dataFormat = trainingExamples.getInstances(); SMO smo = getSMO(classifier); double[] sparseWeights = smo.sparseWeights()[0][1]; int[] sparseIndices = smo.sparseIndices()[0][1]; Map<String, Double> weights = new HashMap<String, Double>(); for (int i = 0; i < sparseWeights.length; i++) { int index = sparseIndices[i]; double weight = sparseWeights[i]; String name = dataFormat.attribute(index).name(); weights.put(name, weight);//from ww w . j a v a 2 s . c o m } List<Map.Entry<String, Double>> entries = new ArrayList<Map.Entry<String, Double>>(weights.entrySet()); Collections.sort(entries, new Comparator<Map.Entry<String, Double>>() { @Override public int compare(Map.Entry<String, Double> o1, Map.Entry<String, Double> o2) { return o1.getKey().compareTo(o2.getKey()); } }); return entries; }
From source file:KFST.featureSelection.embedded.SVMBasedMethods.MSVM_RFE.java
License:Open Source License
/** * generates binary classifiers (SVM by applying k-fold cross validation * resampling strategy) using input data and based on selected feature * subset.//from w w w . ja va2 s . c o m * * @param selectedFeature an array of indices of the selected feature subset * * @return an array of the weights of features */ protected double[][] buildSVM_KFoldCrossValidation(int[] selectedFeature) { double[][] weights = new double[numRun * kFoldValue][selectedFeature.length]; int classifier = 0; for (int i = 0; i < numRun; i++) { double[][] copyTrainSet = ArraysFunc.copyDoubleArray2D(trainSet); //shuffles the train set MathFunc.randomize(copyTrainSet); int numSampleInFold = copyTrainSet.length / kFoldValue; int remainder = copyTrainSet.length % kFoldValue; int indexStart = 0; for (int k = 0; k < kFoldValue; k++) { int indexEnd = indexStart + numSampleInFold; if (k < remainder) { indexEnd++; } double[][] subTrainSet = ArraysFunc.copyDoubleArray2D(copyTrainSet, indexStart, indexEnd); String nameDataCSV = TEMP_PATH + "dataCSV[" + i + "-" + k + "].csv"; String nameDataARFF = TEMP_PATH + "dataARFF[" + i + "-" + k + "].arff"; FileFunc.createCSVFile(subTrainSet, selectedFeature, nameDataCSV, nameFeatures, classLabel); FileFunc.convertCSVtoARFF(nameDataCSV, nameDataARFF, TEMP_PATH, selectedFeature.length, numFeatures, nameFeatures, numClass, classLabel); try { BufferedReader readerTrain = new BufferedReader(new FileReader(nameDataARFF)); Instances dataTrain = new Instances(readerTrain); readerTrain.close(); dataTrain.setClassIndex(dataTrain.numAttributes() - 1); SMO svm = new SMO(); svm.setC(parameterC); svm.setKernel(WekaSVMKernel.parse(kernelType)); svm.buildClassifier(dataTrain); double[] weightsSparse = svm.sparseWeights()[0][1]; int[] indicesSparse = svm.sparseIndices()[0][1]; for (int m = 0; m < weightsSparse.length; m++) { weights[classifier][indicesSparse[m]] = weightsSparse[m]; } } catch (Exception ex) { Logger.getLogger(MSVM_RFE.class.getName()).log(Level.SEVERE, null, ex); } indexStart = indexEnd; classifier++; } } return weights; }
From source file:KFST.featureSelection.embedded.SVMBasedMethods.SVMBasedMethods.java
License:Open Source License
/** * generates binary classifiers (SVM) using input data and based on selected * feature subset, and finally returns the weights of features. * One-Versus-One strategy is used to construct classifiers in multiclass * classification.// w w w.j av a2 s .c om * * @param selectedFeature an array of indices of the selected feature subset * * @return an array of the weights of features */ protected double[][][] buildSVM_OneAgainstOne(int[] selectedFeature) { String nameDataCSV = TEMP_PATH + "dataCSV.csv"; String nameDataARFF = TEMP_PATH + "dataARFF.arff"; double[][][] weights = new double[numClass][numClass][selectedFeature.length]; FileFunc.createCSVFile(trainSet, selectedFeature, nameDataCSV, nameFeatures, classLabel); FileFunc.convertCSVtoARFF(nameDataCSV, nameDataARFF, TEMP_PATH, selectedFeature.length, numFeatures, nameFeatures, numClass, classLabel); try { BufferedReader readerTrain = new BufferedReader(new FileReader(nameDataARFF)); Instances dataTrain = new Instances(readerTrain); readerTrain.close(); dataTrain.setClassIndex(dataTrain.numAttributes() - 1); SMO svm = new SMO(); svm.setC(parameterC); svm.setKernel(WekaSVMKernel.parse(kernelType)); svm.buildClassifier(dataTrain); for (int i = 0; i < numClass; i++) { for (int j = i + 1; j < numClass; j++) { double[] weightsSparse = svm.sparseWeights()[i][j]; int[] indicesSparse = svm.sparseIndices()[i][j]; for (int k = 0; k < weightsSparse.length; k++) { weights[i][j][indicesSparse[k]] = weightsSparse[k]; } } } } catch (Exception ex) { Logger.getLogger(SVMBasedMethods.class.getName()).log(Level.SEVERE, null, ex); } return weights; }
From source file:KFST.featureSelection.embedded.SVMBasedMethods.SVMBasedMethods.java
License:Open Source License
/** * generates binary classifiers (SVM) using input data and based on selected * feature subset, and finally returns the weights of features. * One-Versus-All strategy is used to construct classifiers in multiclass * classification.//from ww w . j a va 2 s .co m * * @param selectedFeature an array of indices of the selected feature subset * * @return an array of the weights of features */ protected double[][] buildSVM_OneAgainstRest(int[] selectedFeature) { double[][] weights = new double[numClass][selectedFeature.length]; String[] tempClassLabel = new String[] { "c1", "c2" }; for (int indexClass = 0; indexClass < numClass; indexClass++) { double[][] copyTrainSet = ArraysFunc.copyDoubleArray2D(trainSet); String nameDataCSV = TEMP_PATH + "dataCSV" + indexClass + ".csv"; String nameDataARFF = TEMP_PATH + "dataARFF" + indexClass + ".arff"; for (double[] dataRow : copyTrainSet) { if (dataRow[numFeatures] == classLabelInTrainSet[indexClass]) { dataRow[numFeatures] = 0; } else { dataRow[numFeatures] = 1; } } FileFunc.createCSVFile(copyTrainSet, selectedFeature, nameDataCSV, nameFeatures, tempClassLabel); FileFunc.convertCSVtoARFF(nameDataCSV, nameDataARFF, TEMP_PATH, selectedFeature.length, numFeatures, nameFeatures, tempClassLabel.length, tempClassLabel); try { BufferedReader readerTrain = new BufferedReader(new FileReader(nameDataARFF)); Instances dataTrain = new Instances(readerTrain); readerTrain.close(); dataTrain.setClassIndex(dataTrain.numAttributes() - 1); SMO svm = new SMO(); svm.setC(parameterC); svm.setKernel(WekaSVMKernel.parse(kernelType)); svm.buildClassifier(dataTrain); double[] weightsSparse = svm.sparseWeights()[0][1]; int[] indicesSparse = svm.sparseIndices()[0][1]; for (int k = 0; k < weightsSparse.length; k++) { weights[indexClass][indicesSparse[k]] = weightsSparse[k]; } } catch (Exception ex) { Logger.getLogger(SVMBasedMethods.class.getName()).log(Level.SEVERE, null, ex); } } return weights; }