Subsets.cpp

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//===========================================================================
/*!
 * 
 *
 * \brief       Data Subsets
 * 
 * This file is part of the tutorial "Creating and Using Subsets of Data".
 * By itself, it does not do anything particularly useful.
 *
 * \author      T. Glasmachers
 * \date        2014
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/Shark/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
//===========================================================================
 
#include <shark/Data/Dataset.h>
using namespace shark;
 
#include <shark/Data/CVDatasetTools.h>
 
#include <shark/Data/DataView.h>
 
 
int main()
{
    typedef RealVector I;
    typedef unsigned int L;
 
    std::size_t start = 0, end = 42;
 
{
    LabeledData<I,L> dataset;             // our dataset
 
    // create an indexed subset of batches
    std::vector<std::size_t> indices;     // indices of the batches to be contained in the subset
    LabeledData<I,L> subset = dataset.indexedSubset(indices);
 
    unsigned int k = 7;
    LabeledData<I,L> remaining_batches = dataset.splice(k);
 
    LabeledData<I,L> remaining_elements = splitAtElement(dataset, k);
}
 
    ClassificationDataset data;
    // ...
    repartitionByClass(data);
 
    std::size_t class0 = 0, class1 = 1;
    ClassificationDataset subproblem = binarySubProblem(data, class0, class1);
 
{
    DataView<ClassificationDataset> view(data);
 
    // creating a random subset from indices
    std::size_t k = 100;
    std::vector<std::size_t> indices(view.size());
    for (std::size_t i=0; i<view.size(); i++) indices[i] = i;
    for (std::size_t i=0; i<k; i++) std::swap(indices[i], indices[rand() % view.size()]);
    indices.resize(k);
    DataView<ClassificationDataset> subset1 = subset(view, indices);
 
    // same functionality in one line
    DataView<ClassificationDataset> subset2 = randomSubset(view, k);
}
 
{
    std::size_t numberOfPartitions = 5;
    std::vector<std::size_t> indices;
    // Creates partitions of approximately the same size.
    createCVSameSize(data, numberOfPartitions);
 
    // Creates IID drawn partitions of the data set (without replacement).
    createCVIID(data, numberOfPartitions);
 
    // Creates indexed cross-validation sets. For each element the
    // index describes the fold in which the data point acts as a
    // validation example. This function offers maximal control.
    createCVIndexed(data, numberOfPartitions, indices);
 
    createCVSameSizeBalanced(data, numberOfPartitions);
 
}
 
{
    std::size_t numberOfPartitions = 5;
    std::size_t numberOfFolds = 3;
    CVFolds<RegressionDataset> folds;
 
    for (std::size_t i=0; i<numberOfPartitions; i++)
    {
    // as created in the above example
    RegressionDataset training = folds.training(i);
    RegressionDataset validation = folds.validation(i);
    // explicit copy!
    training.makeIndependent();
    // creating a new fold
    CVFolds<RegressionDataset> innerFolds = createCVSameSize(training, numberOfFolds);
    }
}
 
}