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/*********************************************************************
MLDemos: A User-Friendly visualization toolkit for machine learning
Copyright (C) 2010 Basilio Noris
Contact: mldemos@b4silio.com
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library 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
Library General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free
Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*********************************************************************/
#include "public.h"
#include "basicMath.h"
#include "maximizeGradient.h"
#include <QDebug>
using namespace std;
MaximizeGradient::MaximizeGradient()
{
data = 0;
dim = 2;
maximum.resize(dim);
FOR(d,dim) maximum[d] = rand()/(float)RAND_MAX;
strength = 0.1;
unmoving = 0;
adaptive = true;
}
MaximizeGradient::~MaximizeGradient()
{
KILL(data);
}
void MaximizeGradient::SetParams(float strength, bool adaptive)
{
this->strength = strength;
this->adaptive = adaptive;
}
void MaximizeGradient::Draw(QPainter &painter)
{
painter.setPen(QPen(Qt::black, 1.5));
painter.setBrush(Qt::NoBrush);
FOR(i, visited.size())
{
QPointF point(visited[i][0]*w, visited[i][1]*h);
painter.drawEllipse(point, 3, 3);
}
painter.setPen(QPen(Qt::black, 1.5));
FOR(i, history.size()-1 )
{
QPointF point(history[i][0]*w, history[i][1]*h);
QPointF pointNext(history[i+1][0]*w, history[i+1][1]*h);
painter.setBrush(Qt::NoBrush);
painter.drawLine(point, pointNext);
painter.setBrush(Qt::white);
painter.drawEllipse(point, 4, 4);
}
// we draw the current maximum
QPointF point(history[history.size()-1][0]*w, history[history.size()-1][1]*h);
painter.setBrush(QColor(255*(1-historyValue[history.size()-1]), 255, 255*(1-historyValue[history.size()-1])));
painter.drawEllipse(point, 5, 5);
}
void MaximizeGradient::Train(float *dataMap, fVec size, fvec startingPoint)
{
w = size.x;
h = size.y;
if(data) delete [] data;
data = new float[w*h];
memcpy(data, dataMap, w*h*sizeof(float));
bConverged = false;
if(!startingPoint.size())
{
startingPoint.resize(dim);
FOR(d, dim) startingPoint[d] = drand48();
}
unmoving = 0;
maximum = startingPoint;
float value = GetValue(startingPoint);
maximumValue = value;
history.push_back(maximum);
HistoryValue().push_back(value);
evaluations = 0;
//qDebug() << "Starting maximization at " << maximum[0] << " " << maximum[1];
}
fvec MaximizeGradient::Test( const fvec &sample)
{
if(bConverged) return maximum;
fvec newSample;
newSample = sample;
if(!sample.size()) newSample = maximum;
int xIndex = newSample[0]*w;
int yIndex = newSample[1]*h;
float delta = 0.003;
float value = GetValue(newSample);
evaluations++;
// we compute the values of the gradient in the 9 directions around
float values[8];
fVec v[8];
int searchType = 1;
switch(searchType)
{
case 2: // 8 directions
values[0] = GetValue(newSample + fVec(-delta ,-delta));
values[2] = GetValue(newSample + fVec(delta ,-delta));
values[5] = GetValue(newSample + fVec(-delta ,+delta));
values[7] = GetValue(newSample + fVec(delta ,+delta));
v[0] = fVec(-1,-1)*(values[0]-value);
v[2] = fVec( 1,-1)*(values[2]-value);
v[5] = fVec(-1, 1)*(values[5]-value);
v[7] = fVec( 1, 1)*(values[7]-value);
evaluations += 4;
case 1: // 4 directions
values[1] = GetValue(newSample + fVec(0 ,-delta));
values[3] = GetValue(newSample + fVec(-delta ,0));
v[1] = fVec( 0,-1)*(values[1]-value);
v[3] = fVec(-1, 0)*(values[3]-value);
evaluations += 2;
case 0: // 2 directions
values[4] = GetValue(newSample + fVec(delta ,0));
values[6] = GetValue(newSample + fVec(0 ,+delta));
v[4] = fVec( 1, 0)*(values[4]-value);
v[6] = fVec( 0, 1)*(values[6]-value);
evaluations += 2;
}
fVec gradient;
FOR(i, 8) gradient += v[i];
gradient /= (float)(1<<(searchType+1));
if(!adaptive) // we just use the strength as factor for moving
{
gradient.normalize();
gradient *= strength*0.1;
}
else // we use the actual value of gradient to decide how much to move
{
gradient *= strength/delta*0.1;
}
fvec oldSample = newSample;
newSample += gradient;
if(oldSample[0]*w == newSample[0]*w && oldSample[1]*h == newSample[1]*h) // we're not moving anymore!
{
unmoving++;
if(unmoving > 10)
{
bConverged = true;
return newSample;
}
}
else unmoving=0;
visited.push_back(newSample);
value = GetValue(newSample);
if(value > maximumValue)
{
maximum = newSample;
maximumValue = value;
history.push_back(maximum);
historyValue.push_back(value);
}
return newSample;
}
fvec MaximizeGradient::Test(const fVec &sample)
{
return Test((fvec)sample);
}
const char *MaximizeGradient::GetInfoString()
{
char *text = new char[1024];
sprintf(text, "Gradient Ascent");
return text;
}
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