/* file: log_reg_model_builder.cpp */
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#include "daal.h"
#include "service.h"

using namespace std;
using namespace daal;
using namespace daal::algorithms::logistic_regression;

/* Input data set parameters */
const string trainedModelFileName           = "../data/batch/logreg_trained_model.csv";
const string testDatasetFileName            = "../data/batch/logreg_test.csv";

const size_t nFeatures           = 6;  /* Number of features in training and testing data sets */
const size_t nClasses            = 5;  /* Number of classes */

ModelPtr buildModel();
void testModel(ModelPtr&);

int main(int argc, char *argv[])
    checkArguments(argc, argv, 2, &trainedModelFileName, &testDatasetFileName);

    ModelPtr builtModel = buildModel();

    return 0;

ModelPtr buildModel()
    /* Initialize FileDataSource<CSVFeatureManager> to retrieve the beta data from a .csv file */
    FileDataSource<CSVFeatureManager> modelSource(trainedModelFileName,

    /* Create Numeric Table for beta coefficients */
    NumericTablePtr beta(new HomogenNumericTable<>(nFeatures + 1, 0, NumericTable::doNotAllocate));
    /* Get beta from trained model */

    /* Retrieve  pointer to the beginning of beta */
    BlockDescriptor<> blockResult;
    beta->getBlockOfRows(0, nClasses, readOnly, blockResult);
    /* Define the size of beta */
    size_t numberOfBetas = (beta->getNumberOfRows())*(beta->getNumberOfColumns());

    /* Initialize iterators for beta array with itrecepts */
    float* first = blockResult.getBlockPtr();
    float* last = first + numberOfBetas;

    /* Create model builder with true intercept flag */
    ModelBuilder<> modelBuilder(nFeatures, nClasses);

    /* Set beta */
    modelBuilder.setBeta(first, last);

    printNumericTable(modelBuilder.getModel()->getBeta(), "Logistic Regression coefficients of built model:");

    return modelBuilder.getModel();

void testModel(ModelPtr& inputModel)
    /* Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file */
    FileDataSource<CSVFeatureManager> testDataSource(testDatasetFileName,

    /* Create Numeric Tables for testing data and ground truth values */
    NumericTablePtr testData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
    NumericTablePtr testGroundTruth(new HomogenNumericTable<>(1, 0, NumericTable::doNotAllocate));
    NumericTablePtr mergedData(new MergedNumericTable(testData, testGroundTruth));

    /* Load the data from the data file */

    /* Create an algorithm object to predict values of logistic regression */
    prediction::Batch<> algorithm(nClasses);

    /* Pass a testing data set and the trained model to the algorithm */
    algorithm.input.set(algorithms::classifier::prediction::data, testData);
    algorithm.input.set(algorithms::classifier::prediction::model, inputModel);

    /* Predict values of logistic regression */

    /* Retrieve the algorithm results */
    NumericTablePtr prediction = algorithm.getResult()->get(algorithms::classifier::prediction::prediction);
    printNumericTable(prediction, "Logistic Regression prediction results: (first 10 rows):", 10);
    printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10);
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