@Override public RegressionModel encodeModel(Schema schema){ List<? extends Number> coef = getCoef(); List<? extends Number> intercept = getIntercept(); return RegressionModelUtil.createRegression(schema.getFeatures(), ValueUtil.asDoubles(coef), ValueUtil.asDouble(Iterables.getOnlyElement(intercept)), null, schema); }
@Override public SupportVectorMachineModel encodeModel(Schema schema){ int[] shape = getSupportVectorsShape(); int numberOfVectors = shape[0]; int numberOfFeatures = shape[1]; List<Integer> support = getSupport(); List<? extends Number> supportVectors = getSupportVectors(); List<? extends Number> dualCoef = getDualCoef(); List<? extends Number> intercept = getIntercept(); SupportVectorMachineModel supportVectorMachineModel = LibSVMUtil.createRegression(new CMatrix<>(ValueUtil.asDoubles(supportVectors), numberOfVectors, numberOfFeatures), SupportVectorMachineUtil.formatIds(support), ValueUtil.asDouble(Iterables.getOnlyElement(intercept)), ValueUtil.asDoubles(dualCoef), schema) .setKernel(SupportVectorMachineUtil.createKernel(getKernel(), getDegree(), getGamma(), getCoef0())); return supportVectorMachineModel; }
@Override public Model encodeModel(Schema schema){ int[] shape = getCoefShape(); int numberOfClasses = shape[0]; int numberOfFeatures = shape[1]; boolean hasProbabilityDistribution = hasProbabilityDistribution(); List<? extends Number> coef = getCoef(); List<? extends Number> intercepts = getIntercept(); CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel(); List<? extends Feature> features = schema.getFeatures(); if(numberOfClasses == 1){ ClassifierUtil.checkSize(2, categoricalLabel); return RegressionModelUtil.createBinaryLogisticClassification(features, ValueUtil.asDoubles(CMatrixUtil.getRow(coef, numberOfClasses, numberOfFeatures, 0)), ValueUtil.asDouble(intercepts.get(0)), RegressionModel.NormalizationMethod.LOGIT, hasProbabilityDistribution, schema); } else if(numberOfClasses >= 3){ ClassifierUtil.checkSize(numberOfClasses, categoricalLabel); Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), Collections.emptyList()); List<RegressionModel> regressionModels = new ArrayList<>(); for(int i = 0, rows = categoricalLabel.size(); i < rows; i++){ RegressionModel regressionModel = RegressionModelUtil.createRegression(features, ValueUtil.asDoubles(CMatrixUtil.getRow(coef, numberOfClasses, numberOfFeatures, i)), ValueUtil.asDouble(intercepts.get(i)), RegressionModel.NormalizationMethod.LOGIT, segmentSchema) .setOutput(ModelUtil.createPredictedOutput(FieldName.create("decisionFunction(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.DOUBLE)); regressionModels.add(regressionModel); } return MiningModelUtil.createClassification(regressionModels, RegressionModel.NormalizationMethod.SIMPLEMAX, hasProbabilityDistribution, schema); } else { throw new IllegalArgumentException(); } }
@Override public SupportVectorMachineModel encodeModel(Schema schema){ int[] shape = getSupportVectorsShape(); int numberOfVectors = shape[0]; int numberOfFeatures = shape[1]; List<Integer> support = getSupport(); List<? extends Number> supportVectors = getSupportVectors(); List<Integer> supportSizes = getSupportSizes(); List<? extends Number> dualCoef = getDualCoef(); List<? extends Number> intercept = getIntercept(); SupportVectorMachineModel supportVectorMachineModel = LibSVMUtil.createClassification(new CMatrix<>(ValueUtil.asDoubles(supportVectors), numberOfVectors, numberOfFeatures), supportSizes, SupportVectorMachineUtil.formatIds(support), ValueUtil.asDoubles(intercept), ValueUtil.asDoubles(dualCoef), schema) .setKernel(SupportVectorMachineUtil.createKernel(getKernel(), getDegree(), getGamma(), getCoef0())); List<SupportVectorMachine> supportVectorMachines = supportVectorMachineModel.getSupportVectorMachines(); for(SupportVectorMachine supportVectorMachine : supportVectorMachines){ String category = supportVectorMachine.getTargetCategory(); // LibSVM: (decisionFunction > 0 ? first : second) // PMML: (decisionFunction < 0 ? first : second) supportVectorMachine.setTargetCategory(supportVectorMachine.getAlternateTargetCategory()); supportVectorMachine.setAlternateTargetCategory(category); } return supportVectorMachineModel; }
RegressionModel firstRegressionModel = RegressionModelUtil.createRegression(features, ValueUtil.asDoubles(CMatrixUtil.getRow(coef, 1, numberOfFeatures, 0)), ValueUtil.asDouble(intercepts.get(0)), null, segmentSchema) .setOutput(ModelUtil.createPredictedOutput(FieldName.create("decisionFunction"), OpType.CONTINUOUS, DataType.DOUBLE)); RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(features, ValueUtil.asDoubles(CMatrixUtil.getRow(coef, numberOfClasses, numberOfFeatures, i)), ValueUtil.asDouble(intercepts.get(i))) .setTargetCategory(categoricalLabel.getValue(i));