#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
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";
kernel_function::KernelIfacePtr kernel(
new kernel_function::linear::Batch<float, kernel_function::linear::fastCSR>());
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()
{
FileDataSource<CSVFeatureManager> trainLabelsDataSource(trainLabelsFileName,
DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
CSRNumericTablePtr trainData(createSparseTable<float>(trainDatasetFileName));
trainLabelsDataSource.loadDataBlock();
svm::training::Batch<> algorithm;
algorithm.parameter.kernel = kernel;
algorithm.parameter.cacheSize = 40000000;
algorithm.input.set(classifier::training::data, trainData);
algorithm.input.set(classifier::training::labels, trainLabelsDataSource.getNumericTable());
algorithm.compute();
trainingResult = algorithm.getResult();
}
void testModel()
{
NumericTablePtr testData(createSparseTable<float>(testDatasetFileName));
svm::prediction::Batch<> algorithm;
algorithm.parameter.kernel = kernel;
algorithm.input.set(classifier::prediction::data, testData);
algorithm.input.set(classifier::prediction::model,
trainingResult->get(classifier::training::model));
algorithm.compute();
predictionResult = algorithm.getResult();
}
void printResults()
{
FileDataSource<CSVFeatureManager> testLabelsDataSource(testLabelsFileName,
DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
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);
}