PluginEstimate.cpp

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/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 1999-2009 Soeren Sonnenburg
 * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
 */

#include "lib/common.h"
#include "lib/io.h"
#include "features/StringFeatures.h"
#include "features/Labels.h"
#include "distributions/LinearHMM.h"
#include "classifier/PluginEstimate.h"

using namespace shogun;

CPluginEstimate::CPluginEstimate(float64_t pos_pseudo, float64_t neg_pseudo)
: CClassifier(), m_pos_pseudo(1e-10), m_neg_pseudo(1e-10),
    pos_model(NULL), neg_model(NULL), features(NULL)
{
}

CPluginEstimate::~CPluginEstimate()
{
    delete pos_model;
    delete neg_model;

    SG_UNREF(features);
}

bool CPluginEstimate::train(CFeatures* data)
{
    ASSERT(labels);
    if (data)
    {
        if (data->get_feature_class() != C_STRING ||
                data->get_feature_type() != F_WORD)
        {
            SG_ERROR("Features not of class string type word\n");
        }

        set_features((CStringFeatures<uint16_t>*) data);
    }
    ASSERT(features);

    delete pos_model;
    delete neg_model;

    pos_model=new CLinearHMM(features);
    neg_model=new CLinearHMM(features);

    int32_t* pos_indizes=new int32_t[((CStringFeatures<uint16_t>*) features)->get_num_vectors()];
    int32_t* neg_indizes=new int32_t[((CStringFeatures<uint16_t>*) features)->get_num_vectors()];

    ASSERT(labels->get_num_labels()==features->get_num_vectors());

    int32_t pos_idx=0;
    int32_t neg_idx=0;

    for (int32_t i=0; i<labels->get_num_labels(); i++)
    {
        if (labels->get_label(i) > 0)
            pos_indizes[pos_idx++]=i;
        else
            neg_indizes[neg_idx++]=i;
    }

    SG_INFO( "training using pseudos %f and %f\n", m_pos_pseudo, m_neg_pseudo);
    pos_model->train(pos_indizes, pos_idx, m_pos_pseudo);
    neg_model->train(neg_indizes, neg_idx, m_neg_pseudo);

    delete[] pos_indizes;
    delete[] neg_indizes;
    
    return true;
}

CLabels* CPluginEstimate::classify()
{
    ASSERT(features);
    CLabels* result=new CLabels(features->get_num_vectors());
    ASSERT(result->get_num_labels()==features->get_num_vectors());

    for (int32_t vec=0; vec<features->get_num_vectors(); vec++)
        result->set_label(vec, classify_example(vec));

    return result;
}

CLabels* CPluginEstimate::classify(CFeatures* data)
{
    if (!data)
        SG_ERROR("No features specified\n");

    if (data->get_feature_class() != C_STRING ||
            data->get_feature_type() != F_WORD)
    {
        SG_ERROR("Features not of class string type word\n");
    }

    set_features((CStringFeatures<uint16_t>*) data);
    return classify();
}

float64_t CPluginEstimate::classify_example(int32_t vec_idx)
{
    ASSERT(features);

    int32_t len;
    bool free_vec;
    uint16_t* vector=features->get_feature_vector(vec_idx, len, free_vec);

    if ((!pos_model) || (!neg_model))
        SG_ERROR( "model(s) not assigned\n");
      
    float64_t result=pos_model->get_log_likelihood_example(vector, len) - neg_model->get_log_likelihood_example(vector, len);
    features->free_feature_vector(vector, vec_idx, free_vec);
    return result;
}