/* file: impl_als_csr_batch.cpp */
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!  Content:
!    C++ example of the implicit alternating least squares (ALS) algorithm in
!    the batch processing mode.
!    The program trains the implicit ALS model on a training data set.

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

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

/* Input data set parameters */
string trainDatasetFileName = "../data/batch/implicit_als_csr.csv";

typedef float algorithmFPType;   /* Algorithm floating-point type */

/* Algorithm parameters */
const size_t nFactors = 2;

NumericTablePtr dataTable;
ModelPtr initialModel;
training::ResultPtr trainingResult;

void initializeModel();
void trainModel();
void testModel();

int main(int argc, char *argv[])
    checkArguments(argc, argv, 1, &trainDatasetFileName);




    return 0;

void initializeModel()
    /* Read trainDatasetFileName from a file and create a numeric table to store the input data */
    dataTable = NumericTablePtr(createSparseTable<float>(trainDatasetFileName));

    /* Create an algorithm object to initialize the implicit ALS model with the default method */
    training::init::Batch<algorithmFPType, training::init::fastCSR> initAlgorithm;
    initAlgorithm.parameter.nFactors = nFactors;

    /* Pass a training data set and dependent values to the algorithm */
    initAlgorithm.input.set(training::init::data, dataTable);

    /* Initialize the implicit ALS model */

    initialModel = initAlgorithm.getResult()->get(training::init::model);

void trainModel()
    /* Create an algorithm object to train the implicit ALS model with the default method */
    training::Batch<algorithmFPType, training::fastCSR> algorithm;

    /* Pass a training data set and dependent values to the algorithm */
    algorithm.input.set(training::data, dataTable);
    algorithm.input.set(training::inputModel, initialModel);

    algorithm.parameter.nFactors = nFactors;

    /* Build the implicit ALS model */

    /* Retrieve the algorithm results */
    trainingResult = algorithm.getResult();

void testModel()
    /* Create an algorithm object to predict recommendations of the implicit ALS model */
    prediction::ratings::Batch<> algorithm;
    algorithm.parameter.nFactors = nFactors;

    algorithm.input.set(prediction::ratings::model, trainingResult->get(training::model));


    NumericTablePtr predictedRatings = algorithm.getResult()->get(prediction::ratings::prediction);

    printNumericTable(predictedRatings, "Predicted ratings:");
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