Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

SVMTwoClassModelBuilder.java

/* file: SVMTwoClassModelBuilder.java */
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/*
// Content:
// Java example of two-class support vector machine (SVM) classification model builder
//
// The program builds support vector machine using model builder and
// computes classification for the test data.
*/
package com.intel.daal.examples.svm;
import java.nio.FloatBuffer;
import com.intel.daal.algorithms.classifier.prediction.*;
import com.intel.daal.algorithms.classifier.training.InputId;
import com.intel.daal.algorithms.svm.Model;
import com.intel.daal.algorithms.svm.ModelBuilder;
import com.intel.daal.algorithms.svm.prediction.PredictionBatch;
import com.intel.daal.algorithms.svm.prediction.PredictionMethod;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.data_management.data.MergedNumericTable;
import com.intel.daal.data_management.data_source.DataSource;
import com.intel.daal.data_management.data_source.FileDataSource;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
class SVMTwoClassModelBuilder {
/* Input data set parameters */
private static final String trainedModelsFileName = "../data/batch/svm_two_class_trained_model.csv";
private static final String testDatasetFileName = "../data/batch/svm_two_class_test_dense.csv";
private static final long nFeatures = 20;
private static final float bias = -0.562F;
static Model model;
static NumericTable results;
static NumericTable testGroundTruth;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
buildModelFromTraining();
testModel();
printResults();
context.dispose();
}
public static void buildModelFromTraining() {
/* Initialize FileDataSource to retrieve trained model from a .csv file */
FileDataSource trainDataSource = new FileDataSource(context, trainedModelsFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for supportVectors and classification coefficients */
NumericTable supportVectors = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
NumericTable classificationCoefficients = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(supportVectors);
mergedData.addNumericTable(classificationCoefficients);
trainDataSource.loadDataBlock(mergedData);
long nSV = supportVectors.getNumberOfRows();
ModelBuilder modelBuilder = new ModelBuilder(context, Float.class, nFeatures, nSV);
/* Write numbers in model */
FloatBuffer bufferSupportVectors = FloatBuffer.allocate(0);
bufferSupportVectors = supportVectors.getBlockOfRows(0, nSV, bufferSupportVectors);
modelBuilder.setSupportVectors(bufferSupportVectors, nSV*nFeatures);
/* Set classification coefficients */
FloatBuffer bufferClassCoef = FloatBuffer.allocate(0);
bufferClassCoef = classificationCoefficients.getBlockOfRows(0, nSV, bufferClassCoef);
modelBuilder.setClassificationCoefficients(bufferClassCoef, nSV);
/* Set bias */
modelBuilder.setBias(bias);
model = modelBuilder.getModel();
}
private static void testModel() {
/* Initialize FileDataSource to retrieve the test data from a .csv file */
FileDataSource testDataSource = new FileDataSource(context, testDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for testing data and ground truth labels */
NumericTable testData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
testGroundTruth = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(testData);
mergedData.addNumericTable(testGroundTruth);
/* Load the data from the input file */
testDataSource.loadDataBlock(mergedData);
/* Create an algorithm object to predict SVM values */
PredictionBatch svmPredict = new PredictionBatch(context, Float.class, PredictionMethod.defaultDense);
svmPredict.parameter.setKernel(new com.intel.daal.algorithms.kernel_function.linear.Batch(context, Float.class));
svmPredict.input.set(NumericTableInputId.data, testData);
svmPredict.input.set(ModelInputId.model, model);
/* Compute prediction results */
PredictionResult predictionResult = svmPredict.compute();
results = predictionResult.get(PredictionResultId.prediction);
}
private static void printResults() {
Service.printClassificationResult(testGroundTruth, results, "Ground truth", "Classification results",
"SVM classification sample program results (first 20 observations):", 20);
}
}

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