#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
string trainDatasetFileName = "../data/batch/svm_multi_class_train_csr.csv";
string trainLabelsFileName = "../data/batch/svm_multi_class_train_labels.csv";
string testDatasetFileName = "../data/batch/svm_multi_class_test_csr.csv";
string testLabelsFileName = "../data/batch/svm_multi_class_test_labels.csv";
const size_t nClasses = 5;
services::SharedPtr<svm::training::Batch<> > training(new svm::training::Batch<>());
services::SharedPtr<svm::prediction::Batch<> > prediction(new svm::prediction::Batch<>());
multi_class_classifier::training::ResultPtr trainingResult;
classifier::prediction::ResultPtr predictionResult;
kernel_function::KernelIfacePtr kernel(
new kernel_function::linear::Batch<float, kernel_function::linear::fastCSR>());
NumericTablePtr testGroundTruth;
void trainModel();
void testModel();
void printResults();
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 4, &trainDatasetFileName, &trainLabelsFileName, &testDatasetFileName, &testLabelsFileName);
training->parameter.cacheSize = 100000000;
training->parameter.kernel = kernel;
prediction->parameter.kernel = kernel;
trainModel();
testModel();
printResults();
return 0;
}
void trainModel()
{
FileDataSource<CSVFeatureManager> trainLabelsDataSource(trainLabelsFileName,
DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
CSRNumericTablePtr trainData(createSparseTable<float>(trainDatasetFileName));
trainLabelsDataSource.loadDataBlock();
multi_class_classifier::training::Batch<> algorithm(nClasses);
algorithm.parameter.training = training;
algorithm.parameter.prediction = prediction;
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));
multi_class_classifier::prediction::Batch<> algorithm(nClasses);
algorithm.parameter.training = training;
algorithm.parameter.prediction = prediction;
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();
testGroundTruth = testLabelsDataSource.getNumericTable();
printNumericTables<int, int>(testGroundTruth,
predictionResult->get(classifier::prediction::prediction),
"Ground truth", "Classification results",
"Multi-class SVM classification sample program results (first 20 observations):", 20);
}