/home/marsyas/marsyas/src/marsyas/ZeroRClassifier.cpp

/*
** Copyright (C) 1998-2006 George Tzanetakis <gtzan@cs.princeton.edu>
**
** 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 2 of the License, or
** (at your option) any later version.
**
** This program is distributed in the hope that it will be useful,
** but WITHOUT ANY WARRANTY; without even the implied warranty of
** MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
** GNU General Public License for more details.
**
** You should have received a copy of the GNU General Public License
** along with this program; if not, write to the Free Software
** Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
*/

#include "ZeroRClassifier.h"

using namespace std;
using namespace Marsyas;

ZeroRClassifier::ZeroRClassifier(mrs_string name):MarSystem("ZeroRClassifier",name)
{
    addControls();
}

ZeroRClassifier::~ZeroRClassifier()
{
}

MarSystem*
ZeroRClassifier::clone() const
{
    return new ZeroRClassifier(*this);
}

void
ZeroRClassifier::addControls()
{
    addctrl("mrs_string/mode", "train");
    addctrl("mrs_natural/nClasses", 1);
    setctrlState("mrs_natural/nClasses", true);
    addctrl("mrs_bool/done", false);
    addctrl("mrs_natural/prediction", 0);
}

void
ZeroRClassifier::myUpdate(MarControlPtr sender)
{
    (void) sender;
    MRSDIAG("ZeroRClassifier.cpp - ZeroRClassifier:myUpdate");

    setctrl("mrs_natural/onSamples", getctrl("mrs_natural/inSamples"));
    setctrl("mrs_natural/onObservations", (mrs_natural)2);
    setctrl("mrs_real/osrate", getctrl("mrs_real/israte"));

    mrs_natural nlabels = getctrl("mrs_natural/nClasses")->to<mrs_natural>();

    if ((mrs_natural)labelSizes_.getSize() != nlabels)
        labelSizes_.create(nlabels);
    mrs_string mode = getctrl("mrs_string/mode")->to<mrs_string>();
    if (mode == "predict")
    {

    }
}

void
ZeroRClassifier::myProcess(realvec& in, realvec& out)
{
    mrs_string mode = getctrl("mrs_string/mode")->to<mrs_string>();
    mrs_natural nlabels = getctrl("mrs_natural/nClasses")->to<mrs_natural>();
    mrs_natural l, t;
    mrs_natural prediction = 0;

    mrs_real label;

    if ((prev_mode_ == "predict") && (mode == "train"))
    {
        labelSizes_.setval(0.0);
    }

    if (mode == "train")
    {
        for (t=0; t < inSamples_; t++)
        {
            label = in(inObservations_-1, t);
            if(label >= 0)
            {
                labelSizes_((int)label) = labelSizes_((int)label) + 1;
            }
            out(0,t) = label;
            out(1,t) = label;
        }
    }

    if ((prev_mode_ == "train") && (mode == "predict"))
    {
        int max = -1;
        for (l=0; l < nlabels; l++)
        {
            if (labelSizes_(l) > max)
            {
                prediction = l;
                max = (int)labelSizes_(l);
            }
        }
        updControl("mrs_natural/prediction", prediction);
    }

    if (mode == "predict")
    {
        for (t=0; t < inSamples_; t++)
        {
            label = in(inObservations_-1, t);
            prediction = getctrl("mrs_natural/prediction")->to<mrs_natural>();
            out(0,t) = (mrs_real)prediction;
            out(1,t) = label;
        }
    }
    prev_mode_ = mode;
}

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