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

SVMMultiClassDenseBatch.java

/* file: SVMMultiClassDenseBatch.java */
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/*
// Content:
// Java example of multi-class support vector machine (SVM) classification
//
// The program trains multi-class SVM model on a supplied training data set
// in dense format and then performs classification of previously unseen
// data.
*/
package com.intel.daal.examples.svm;
import com.intel.daal.algorithms.classifier.prediction.ModelInputId;
import com.intel.daal.algorithms.classifier.prediction.NumericTableInputId;
import com.intel.daal.algorithms.classifier.prediction.PredictionResult;
import com.intel.daal.algorithms.classifier.prediction.PredictionResultId;
import com.intel.daal.algorithms.classifier.training.InputId;
import com.intel.daal.algorithms.classifier.training.TrainingResultId;
import com.intel.daal.algorithms.multi_class_classifier.Model;
import com.intel.daal.algorithms.multi_class_classifier.prediction.*;
import com.intel.daal.algorithms.multi_class_classifier.training.*;
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 SVMMultiClassDenseBatch {
/* Input data set parameters */
private static final String trainDatasetFileName = "../data/batch/svm_multi_class_train_dense.csv";
private static final String testDatasetFileName = "../data/batch/svm_multi_class_test_dense.csv";
private static final int nFeatures = 20;
private static final int nClasses = 5;
private static TrainingResult trainingResult;
private static PredictionResult predictionResult;
private static NumericTable testGroundTruth;
private static com.intel.daal.algorithms.svm.training.TrainingBatch twoClassTraining;
private static com.intel.daal.algorithms.svm.prediction.PredictionBatch twoClassPrediction;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
trainModel();
testModel();
printResults();
context.dispose();
}
private static void trainModel() {
twoClassTraining = new com.intel.daal.algorithms.svm.training.TrainingBatch(
context, Float.class, com.intel.daal.algorithms.svm.training.TrainingMethod.boser);
twoClassPrediction = new com.intel.daal.algorithms.svm.prediction.PredictionBatch(
context, Float.class, com.intel.daal.algorithms.svm.prediction.PredictionMethod.defaultDense);
/* Retrieve the data from input data sets */
FileDataSource trainDataSource = new FileDataSource(context, trainDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for training data and labels */
NumericTable trainData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
NumericTable trainGroundTruth = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(trainData);
mergedData.addNumericTable(trainGroundTruth);
/* Retrieve the data from an input file */
trainDataSource.loadDataBlock(mergedData);
/* Create an algorithm to train the multi-class SVM model */
TrainingBatch algorithm = new TrainingBatch(context, Float.class, TrainingMethod.oneAgainstOne, nClasses);
/* Set parameters for the multi-class SVM algorithm */
algorithm.parameter.setTraining(twoClassTraining);
algorithm.parameter.setPrediction(twoClassPrediction);
/* Pass a training data set and dependent values to the algorithm */
algorithm.input.set(InputId.data, trainData);
algorithm.input.set(InputId.labels, trainGroundTruth);
/* Train the multi-class SVM model */
trainingResult = algorithm.compute();
}
private static void testModel() {
FileDataSource testDataSource = new FileDataSource(context, testDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for testing data and 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);
/* Retrieve the data from an input file */
testDataSource.loadDataBlock(mergedData);
/* Create a numeric table to store the prediction results */
PredictionBatch algorithm = new PredictionBatch(context, Float.class, PredictionMethod.multiClassClassifierWu, nClasses);
algorithm.parameter.setTraining(twoClassTraining);
algorithm.parameter.setPrediction(twoClassPrediction);
Model model = trainingResult.get(TrainingResultId.model);
/* Pass a testing data set and the trained model to the algorithm */
algorithm.input.set(NumericTableInputId.data, testData);
algorithm.input.set(ModelInputId.model, model);
/* Compute the prediction results */
predictionResult = algorithm.compute();
}
private static void printResults() {
NumericTable predictionResults = predictionResult.get(PredictionResultId.prediction);
Service.printClassificationResult(testGroundTruth, predictionResults, "Ground truth", "Classification results",
"Multi-class SVM classification sample program results (first 20 observations):", 20);
System.out.println("");
}
}

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