ReducedVarianceModelBuilder.hxx

/*
 * This file is part of the statismo library.
 *
 * Author: Marcel Luethi (marcel.luethi@unibas.ch)
 *
 * Copyright (c) 2011 University of Basel
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *
 * Redistributions of source code must retain the above copyright notice,
 * this list of conditions and the following disclaimer.
 *
 * Redistributions in binary form must reproduce the above copyright
 * notice, this list of conditions and the following disclaimer in the
 * documentation and/or other materials provided with the distribution.
 *
 * Neither the name of the project's author nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
 * HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
 * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
 * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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 * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 */

#ifndef __ReducedVarianceModelBuilder_hxx
#define __ReducedVarianceModelBuilder_hxx

#include "ReducedVarianceModelBuilder.h"

#include <iostream>

#include <Eigen/SVD>

#include "CommonTypes.h"
#include "Exceptions.h"

namespace statismo {

template <typename T>
ReducedVarianceModelBuilder<T>::ReducedVarianceModelBuilder()
    : Superclass() {
}

template <typename T>
typename ReducedVarianceModelBuilder<T>::StatisticalModelType*
ReducedVarianceModelBuilder<T>::BuildNewModelWithLeadingComponents(
    const StatisticalModelType* inputModel,
    unsigned numberOfPrincipalComponents) const

{
    StatisticalModelType* reducedModel = StatisticalModelType::Create(
            inputModel->GetRepresenter(),
            inputModel->GetMeanVector(),
            inputModel->GetOrthonormalPCABasisMatrix().leftCols(numberOfPrincipalComponents),
            inputModel->GetPCAVarianceVector().topRows(numberOfPrincipalComponents),
            inputModel->GetNoiseVariance());

    // Write the parameters used to build the models into the builderInfo
    typename ModelInfo::BuilderInfoList builderInfoList = inputModel->GetModelInfo().GetBuilderInfoList();

    BuilderInfo::ParameterInfoList bi;
    bi.push_back(BuilderInfo::KeyValuePair("NumberOfPincipalComponents ", Utils::toString(numberOfPrincipalComponents)));

    BuilderInfo::DataInfoList di;

    BuilderInfo builderInfo("ReducedVarianceModelBuilder", di, bi);
    builderInfoList.push_back(builderInfo);

    // If the scores matrix is not set, or if we have for some reasons not as many score entries as the number of principal components,
    // we simply work with what is there.
    unsigned numComponentsForScores = std::min(static_cast<unsigned>(inputModel->GetModelInfo().GetScoresMatrix().rows()), numberOfPrincipalComponents);

    ModelInfo info(inputModel->GetModelInfo().GetScoresMatrix().topRows(numComponentsForScores), builderInfoList);
    reducedModel->SetModelInfo(info);

    return reducedModel;

}



template <typename T>
typename ReducedVarianceModelBuilder<T>::StatisticalModelType*
ReducedVarianceModelBuilder<T>::BuildNewModelWithVariance(
    const StatisticalModelType* inputModel,
    double totalVariance) const {

    VectorType pcaVariance = inputModel->GetPCAVarianceVector();
    double modelVariance = pcaVariance.sum();

    //count the number of modes required for the model
    double cumulatedVariance = 0;
    unsigned numComponentsToReachPrescribedVariance = 0;
    for (unsigned i = 0; i < pcaVariance.size(); i++) {
        cumulatedVariance += pcaVariance(i);
        numComponentsToReachPrescribedVariance++;
        if (cumulatedVariance / modelVariance >= totalVariance)
            break;
    }
    return BuildNewModelWithLeadingComponents(inputModel, numComponentsToReachPrescribedVariance);
}

template <typename T>
typename ReducedVarianceModelBuilder<T>::StatisticalModelType*
ReducedVarianceModelBuilder<T>::BuildNewModelFromModel(
    const StatisticalModelType* inputModel,
    double totalVariance) const {

    return BuildNewModelWithVariance(inputModel, totalVariance);
}

} // namespace statismo

#endif