Perceptron.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/classifier/Perceptron.h>
#include <shogun/labels/Labels.h>
#include <shogun/labels/BinaryLabels.h>
#include <shogun/mathematics/Math.h>

using namespace shogun;

CPerceptron::CPerceptron()
: CLinearMachine(), learn_rate(0.1), max_iter(1000)
{
}

CPerceptron::CPerceptron(CDotFeatures* traindat, CLabels* trainlab)
: CLinearMachine(), learn_rate(.1), max_iter(1000)
{
    set_features(traindat);
    set_labels(trainlab);
}

CPerceptron::~CPerceptron()
{
}

bool CPerceptron::train_machine(CFeatures* data)
{
    ASSERT(m_labels);
    ASSERT(m_labels->get_label_type() == LT_BINARY);

    if (data)
    {
        if (!data->has_property(FP_DOT))
            SG_ERROR("Specified features are not of type CDotFeatures\n");
        set_features((CDotFeatures*) data);
    }

    ASSERT(features);
    bool converged=false;
    int32_t iter=0;
    SGVector<int32_t> train_labels=((CBinaryLabels*) m_labels)->get_int_labels();
    int32_t num_feat=features->get_dim_feature_space();
    int32_t num_vec=features->get_num_vectors();

    ASSERT(num_vec==train_labels.vlen);
    w=SGVector<float64_t>(num_feat);
    float64_t* output=SG_MALLOC(float64_t, num_vec);

    //start with uniform w, bias=0
    bias=0;
    for (int32_t i=0; i<num_feat; i++)
        w.vector[i]=1.0/num_feat;

    //loop till we either get everything classified right or reach max_iter
    while (!converged && iter<max_iter)
    {
        converged=true;
        for (int32_t i=0; i<num_vec; i++)
        {
            output[i]=apply_one(i);

            if (CMath::sign<float64_t>(output[i]) != train_labels.vector[i])
            {
                converged=false;
                bias+=learn_rate*train_labels.vector[i];
                features->add_to_dense_vec(learn_rate*train_labels.vector[i], i, w.vector, w.vlen);
            }
        }

        iter++;
    }

    if (converged)
        SG_INFO("Perceptron algorithm converged after %d iterations.\n", iter);
    else
        SG_WARNING("Perceptron algorithm did not converge after %d iterations.\n", max_iter);

    SG_FREE(output);

    return converged;
}