/* file: sgd_custom_obj_func_dense_batch.cpp */
/*******************************************************************************
* Copyright 2014 Intel Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*     http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/

/*
!  Content:
!    C++ example of the Stochastic gradient descent algorithm with custom
!    objective function
!******************************************************************************/

/**
 * <a name="DAAL-EXAMPLE-CPP-SGD_CUSTOM_OBJ_FUNC_DENSE_BATCH"></a>
 * \example sgd_custom_obj_func_dense_batch.cpp
 */

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

/* Custom objective function declaration */
#include "custom_obj_func.h"

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

std::string datasetFileName = "../data/batch/custom.csv";

const size_t nIterations = 1000;
const size_t nFeatures = 4;
const float learningRate = 0.01f;
const double accuracyThreshold = 0.02;

float initialPoint[nFeatures + 1] = { 1, 1, 1, 1, 1 };

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 data and values for dependent variable */
    daal::services::Status s;
    NumericTablePtr data =
        HomogenNumericTable<>::create(nFeatures, 0, NumericTable::doNotAllocate, &s);
    checkStatus(s);
    NumericTablePtr dependentVariables =
        HomogenNumericTable<>::create(1, 0, NumericTable::doNotAllocate, &s);
    checkStatus(s);
    NumericTablePtr mergedData = MergedNumericTable::create(data, dependentVariables, &s);
    checkStatus(s);

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

    size_t nVectors = data->getNumberOfRows();

    services::SharedPtr<new_objective_function::Batch<> > customObjectiveFunction(
        new new_objective_function::Batch<>(nVectors));
    customObjectiveFunction->input.set(new_objective_function::data, data);
    customObjectiveFunction->input.set(new_objective_function::dependentVariables,
                                       dependentVariables);

    /* Create objects to compute the Stochastic gradient descent result using the default method */
    optimization_solver::sgd::Batch<> sgdAlgorithm(customObjectiveFunction);

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

    /* Compute the Stochastic gradient descent result */
    s = sgdAlgorithm.compute();
    checkStatus(s);

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

    return 0;
}
