OnlineLinearMachine.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 <shogun/machine/OnlineLinearMachine.h>
#include <shogun/base/Parameter.h>
#include <shogun/labels/RegressionLabels.h>
#include <shogun/mathematics/Math.h>

using namespace shogun;

COnlineLinearMachine::COnlineLinearMachine()
: CMachine(), w_dim(0), w(NULL), bias(0), features(NULL)
{
    m_parameters->add_vector(&w, &w_dim, "w", "Parameter vector w.");
    SG_ADD(&bias, "bias", "Bias b.", MS_NOT_AVAILABLE);
    SG_ADD((CSGObject**) &features, "features",
        "Feature object.", MS_NOT_AVAILABLE);
}

COnlineLinearMachine::~COnlineLinearMachine()
{
    // It is possible that a derived class may have already
    // called SG_FREE() on the weight vector
    if (w != NULL)
        SG_FREE(w);
    SG_UNREF(features);
}

CBinaryLabels* COnlineLinearMachine::apply_binary(CFeatures* data)
{
    SGVector<float64_t> outputs = apply_get_outputs(data);
    return new CBinaryLabels(outputs);
}

CRegressionLabels* COnlineLinearMachine::apply_regression(CFeatures* data)
{
    SGVector<float64_t> outputs = apply_get_outputs(data);
    return new CRegressionLabels(outputs);
}

SGVector<float64_t> COnlineLinearMachine::apply_get_outputs(CFeatures* data)
{
    if (data)
    {
        if (!data->has_property(FP_STREAMING_DOT))
            SG_ERROR("Specified features are not of type CStreamingDotFeatures\n")

        set_features((CStreamingDotFeatures*) data);
    }

    ASSERT(features)
    ASSERT(features->has_property(FP_STREAMING_DOT))

    DynArray<float64_t>* labels_dynarray=new DynArray<float64_t>();
    int32_t num_labels=0;

    features->start_parser();
    while (features->get_next_example())
    {
        float64_t current_lab=features->dense_dot(w, w_dim) + bias;

        labels_dynarray->append_element(current_lab);
        num_labels++;

        features->release_example();
    }
    features->end_parser();

    SGVector<float64_t> labels_array(num_labels);
    for (int32_t i=0; i<num_labels; i++)
        labels_array.vector[i]=(*labels_dynarray)[i];

    delete labels_dynarray;
    return labels_array;
}

float32_t COnlineLinearMachine::apply_one(float32_t* vec, int32_t len)
{
        return CMath::dot(vec, w, len)+bias;
}

float32_t COnlineLinearMachine::apply_to_current_example()
{
        return features->dense_dot(w, w_dim)+bias;
}

bool COnlineLinearMachine::train_machine(CFeatures *data)
{
    if (data)
    {
        if (!data->has_property(FP_STREAMING_DOT))
            SG_ERROR("Specified features are not of type CStreamingDotFeatures\n")
        set_features((CStreamingDotFeatures*) data);
    }
    start_train();
    features->start_parser();
    while (features->get_next_example())
    {
        train_example(features, features->get_label());
        features->release_example();
    }

    features->end_parser();
    stop_train();

    return true;
}