C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

svm_two_class_csr_batch.cpp

/* file: svm_two_class_csr_batch.cpp */
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/*
! Content:
! C++ example of two-class support vector machine (SVM) classification
!
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
/* Input data set parameters */
string trainDatasetFileName = "../data/batch/svm_two_class_train_csr.csv";
string trainLabelsFileName = "../data/batch/svm_two_class_train_labels.csv";
string testDatasetFileName = "../data/batch/svm_two_class_test_csr.csv";
string testLabelsFileName = "../data/batch/svm_two_class_test_labels.csv";
/* Parameters for the SVM kernel function */
kernel_function::KernelIfacePtr kernel(
new kernel_function::linear::Batch<float, kernel_function::linear::fastCSR>());
/* Model object for the SVM algorithm */
svm::training::ResultPtr trainingResult;
classifier::prediction::ResultPtr predictionResult;
void trainModel();
void testModel();
void printResults();
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 4, &trainDatasetFileName, &trainLabelsFileName, &testDatasetFileName, &testLabelsFileName);
trainModel();
testModel();
printResults();
return 0;
}
void trainModel()
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> trainLabelsDataSource(trainLabelsFileName,
DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create numeric table for training data */
CSRNumericTablePtr trainData(createSparseTable<float>(trainDatasetFileName));
/* Retrieve the data from the input file */
trainLabelsDataSource.loadDataBlock();
/* Create an algorithm object to train the SVM model */
svm::training::Batch<> algorithm;
algorithm.parameter.kernel = kernel;
algorithm.parameter.cacheSize = 40000000;
/* Pass a training data set and dependent values to the algorithm */
algorithm.input.set(classifier::training::data, trainData);
algorithm.input.set(classifier::training::labels, trainLabelsDataSource.getNumericTable());
/* Build the SVM model */
algorithm.compute();
/* Retrieve the algorithm results */
trainingResult = algorithm.getResult();
}
void testModel()
{
/* Create Numeric Tables for testing data */
NumericTablePtr testData(createSparseTable<float>(testDatasetFileName));
/* Create an algorithm object to predict SVM values */
svm::prediction::Batch<> algorithm;
algorithm.parameter.kernel = kernel;
/* Pass a testing data set and the trained model to the algorithm */
algorithm.input.set(classifier::prediction::data, testData);
algorithm.input.set(classifier::prediction::model,
trainingResult->get(classifier::training::model));
/* Predict SVM values */
algorithm.compute();
/* Retrieve the algorithm results */
predictionResult = algorithm.getResult();
}
void printResults()
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file */
FileDataSource<CSVFeatureManager> testLabelsDataSource(testLabelsFileName,
DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Retrieve the data from input file */
testLabelsDataSource.loadDataBlock();
NumericTablePtr testGroundTruth = testLabelsDataSource.getNumericTable();
printNumericTables<int, float>(testGroundTruth,
predictionResult->get(classifier::prediction::prediction),
"Ground truth\t", "Classification results",
"SVM classification results (first 20 observations):", 20);
}

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