C++ API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

lbfgs_cr_entr_loss_dense_batch.cpp

/* file: lbfgs_cr_entr_loss_dense_batch.cpp */
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* Copyright 2014-2019 Intel Corporation.
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/*
! Content:
! C++ example of the limited memory Broyden-Fletcher-Goldfarb-Shanno
! algorithm with cross entropy loss function
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
using namespace daal::data_management;
const string datasetFileName = "../data/batch/logreg_train.csv";
const size_t nFeatures = 6; /* Number of features in training and testing data sets */
const size_t nClasses = 5; /* Number of classes */
const size_t nIterations = 1000;
const float stepLength = 1.0e-4f;
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &datasetFileName);
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> dataSource(datasetFileName,
DataSource::notAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create Numeric Tables for input data and dependent variables */
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 input file */
dataSource.loadDataBlock(mergedData.get());
services::SharedPtr<optimization_solver::cross_entropy_loss::Batch<> > func = optimization_solver::cross_entropy_loss::Batch<>::create(nClasses, data->getNumberOfRows());
func->input.set(optimization_solver::cross_entropy_loss::data, data);
func->input.set(optimization_solver::cross_entropy_loss::dependentVariables, dependentVariables);
/* Create objects to compute LBFGS result using the default method */
optimization_solver::lbfgs::Batch<> algorithm(func);
algorithm.parameter.nIterations = nIterations;
algorithm.parameter.stepLengthSequence =
NumericTablePtr(new HomogenNumericTable<>(1, 1, NumericTableIface::doAllocate, stepLength));
const size_t nParameters = nClasses * (nFeatures + 1);
DAAL_DATA_TYPE initialPoint[nParameters];
for(size_t i = 0; i < nParameters; ++i)
initialPoint[i] = 0.001f;
/* Set input objects for LBFGS algorithm */
algorithm.input.set(optimization_solver::iterative_solver::inputArgument, HomogenNumericTable<>::create(initialPoint, 1, nParameters));
/* Compute LBFGS result */
algorithm.compute();
DAAL_DATA_TYPE expectedPoint[nParameters] = { -2.277f, 2.836f, 14.985f, 0.511f, 7.510f, -2.831f, -5.814f, -0.033f, 13.227f, -24.447f, 3.730f,
10.394f, -10.461f, -0.766f, 0.077f, 1.558f, -1.133f, 2.884f, -3.825f, 7.699f, 2.421f, -0.135f, -6.996f, 1.785f, -2.294f, -9.819f, 1.692f,
-0.725f, 0.069f, -8.41f, 1.458f, -3.306f, -4.719f, 5.507f, -1.642f };
NumericTablePtr expectedCoefficients = HomogenNumericTable<>::create(expectedPoint, 1, nParameters);
/* Print computed LBFGS results */
printNumericTable(expectedCoefficients,
"Expected coefficients:");
printNumericTable(algorithm.getResult()->get(optimization_solver::iterative_solver::minimum),
"Resulting coefficients:");
printNumericTable(algorithm.getResult()->get(optimization_solver::iterative_solver::nIterations),
"Number of iterations performed:");
return 0;
}

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