sgd_mini_dense_batch.cpp

/* file: sgd_mini_dense_batch.cpp */
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/*
!  Content:
!    C++ example of the Stochastic gradient descent algorithm
!******************************************************************************/

#include "daal.h"
#include "service.h"

using namespace std;
using namespace daal;
using namespace daal::algorithms;
using namespace daal::data_management;

string datasetFileName = "../data/batch/mse.csv";

const size_t nFeatures = 3;
const double accuracyThreshold = 0.0000001;
const size_t nIterations = 1000;
const size_t batchSize = 4;
const float  learningRate = 0.5;
float initialPoint[nFeatures + 1] = {8, 2, 1, 4};

int main(int argc, char *argv[])
{
    /* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
    FileDataSource<CSVFeatureManager> dataSource(datasetFileName,
            DataSource::notAllocateNumericTable,
            DataSource::doDictionaryFromContext);

    /* Create Numeric Tables for data and values for dependent variable */
    NumericTablePtr data(new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
    NumericTablePtr dependentVariables(new HomogenNumericTable<>(1, 0, NumericTable::doNotAllocate));
    NumericTablePtr mergedData(new MergedNumericTable(data, dependentVariables));

    /* Retrieve the data from the input file */
    dataSource.loadDataBlock(mergedData.get());

    size_t nVectors = data->getNumberOfRows();

    services::SharedPtr<optimization_solver::mse::Batch<> > mseObjectiveFunction(new optimization_solver::mse::Batch<>(nVectors));
    mseObjectiveFunction->input.set(optimization_solver::mse::data, data);
    mseObjectiveFunction->input.set(optimization_solver::mse::dependentVariables, dependentVariables);

    /* Create objects to compute the Stochastic gradient descent result using the mini-batch method */
    optimization_solver::sgd::Batch<float, optimization_solver::sgd::miniBatch> sgdMiniBatchAlgorithm(mseObjectiveFunction);

    /* Set input objects for the the Stochastic gradient descent algorithm */
    sgdMiniBatchAlgorithm.input.set(optimization_solver::iterative_solver::inputArgument,
                                    NumericTablePtr(new HomogenNumericTable<>(initialPoint, 1, nFeatures + 1)));
    sgdMiniBatchAlgorithm.parameter.learningRateSequence =
        NumericTablePtr(new HomogenNumericTable<>(1, 1, NumericTable::doAllocate, learningRate));
    sgdMiniBatchAlgorithm.parameter.nIterations = nIterations;
    sgdMiniBatchAlgorithm.parameter.batchSize = batchSize;
    sgdMiniBatchAlgorithm.parameter.accuracyThreshold = accuracyThreshold;

    /* Compute the Stochastic gradient descent result */
    sgdMiniBatchAlgorithm.compute();

    /* Print computed the Stochastic gradient descent result */
    printNumericTable(sgdMiniBatchAlgorithm.getResult()->get(optimization_solver::iterative_solver::minimum), "Minimum");
    printNumericTable(sgdMiniBatchAlgorithm.getResult()->get(optimization_solver::iterative_solver::nIterations), "Number of iterations performed:");

    return 0;
}
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