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EMMixtureModel.cpp
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EMMixtureModel.cpp
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/*
* Copyright (c) The Shogun Machine Learning Toolbox
* Written (w) 2014 Parijat Mazumdar
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
* 2. 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.
*
* 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 OWNER 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 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* The views and conclusions contained in the software and documentation are those
* of the authors and should not be interpreted as representing official policies,
* either expressed or implied, of the Shogun Development Team.
*/
#include <shogun/distributions/EMMixtureModel.h>
#include <shogun/distributions/Distribution.h>
#include <shogun/mathematics/Math.h>
using namespace shogun;
CEMMixtureModel::CEMMixtureModel()
: CEMBase <MixModelData>()
{ }
CEMMixtureModel::~CEMMixtureModel()
{ }
float64_t CEMMixtureModel::expectation_step()
{
float64_t log_likelihood=0;
// for each data point
for (int32_t i=0;i<data.alpha.num_rows;i++)
{
SGVector<float64_t> alpha_ij(data.alpha.num_cols);
// for each component
for (int32_t j=0;j<data.alpha.num_cols;j++)
{
CDistribution* jth_component=data.components->get_element(j)->as<CDistribution>();
alpha_ij[j]=CMath::log(data.weights[j])+jth_component->get_log_likelihood_example(i);
SG_UNREF(jth_component);
};
float64_t normalize=CMath::log_sum_exp(alpha_ij);
log_likelihood+=normalize;
// fill row of alpha
for (int32_t j=0;j<data.alpha.num_cols;j++)
data.alpha(i,j)=CMath::exp(alpha_ij[j]-normalize);
}
return log_likelihood;
}
void CEMMixtureModel::maximization_step()
{
// for each component
float64_t* alpha_j=NULL;
float64_t sum_weights=0;
for (int32_t j=0;j<data.alpha.num_cols;j++)
{
CDistribution* jth_component=data.components->get_element(j)->as<CDistribution>();
// update mean covariance of components
alpha_j=data.alpha.matrix+j*data.alpha.num_rows;
float64_t weight_j=jth_component->update_params_em(alpha_j,data.alpha.num_rows);
// update weights
sum_weights+=weight_j;
data.weights[j]=weight_j;
SG_UNREF(jth_component);
}
// update weights - normalization
for (int32_t j=0;j<data.alpha.num_cols;j++)
data.weights[j]/=sum_weights;
}