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/*********************************************************************
FLAME Implementation in MLDemos
Copyright (C) Pierre-Antoine Sondag (pasondag@gmail.com) 2012
Based on the standard implementation of FLAME data clustering algorithm.
Copyright (C) 2007, Fu Limin (phoolimin@gmail.com).
All rights reserved.
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,
version 3 as published by the Free Software Foundation.
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
Lesser 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 "interfaceFlameCluster.h"
#include "drawUtils.h"
#include <QPixmap>
#include <QBitmap>
#include <QPainter>
using namespace std;
ClustFlame::ClustFlame()
{
// we initialize the hyperparameter widget
params = new Ui::ParametersFlame();
params->setupUi(widget = new QWidget());
}
ClustFlame::~ClustFlame()
{
delete params;
}
void ClustFlame::SetParams(Clusterer *clusterer)
{
SetParams(clusterer, GetParams());
}
fvec ClustFlame::GetParams()
{
int knnParameter = (int)(params->knnBox->value());
int knnMetric = (int)(params->metricBox->currentIndex());
int maxIterationsParameter = (int)(params->maxIterationBox->value());
bool isSeveralClasses = (bool)(params->isSeveralClassesBox->isChecked());
float thresholdParameter = (float)(params->thresholdBox->value());
int i=0;
fvec par(5);
par[i++] = knnParameter;
par[i++] = knnMetric;
par[i++] = maxIterationsParameter;
par[i++] = isSeveralClasses;
par[i++] = thresholdParameter;
return par;
}
void ClustFlame::SetParams(Clusterer *clusterer, fvec parameters)
{
if(!clusterer) return;
ClustererFlame * myFlame = dynamic_cast<ClustererFlame *>(clusterer);
if(!myFlame) return;
int i=0;
int knnParameter = parameters.size() > i ? parameters[i] : 0; i++;
int knnMetric = parameters.size() > i ? parameters[i] : 0; i++;
int maxIterationsParameter = parameters.size() > i ? parameters[i] : 0; i++;
bool isSeveralClasses = parameters.size() > i ? parameters[i] : 0; i++;
float thresholdParameter = parameters.size() > i ? parameters[i] : 0; i++;
myFlame->SetParams(
knnParameter, knnMetric,
maxIterationsParameter,
isSeveralClasses, thresholdParameter);
}
void ClustFlame::GetParameterList(std::vector<QString> ¶meterNames,
std::vector<QString> ¶meterTypes,
std::vector< std::vector<QString> > ¶meterValues)
{
parameterNames.clear();
parameterTypes.clear();
parameterValues.clear();
parameterNames.push_back("KNN Parameter");
parameterNames.push_back("KNN Metric");
parameterNames.push_back("Max Iterations");
parameterNames.push_back("Multiple Assignment");
parameterNames.push_back("Multi-Assign. Thresh.");
parameterTypes.push_back("Integer");
parameterTypes.push_back("List");
parameterTypes.push_back("Integer");
parameterTypes.push_back("List");
parameterTypes.push_back("Real");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("1");
parameterValues.back().push_back("99999");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("Euclidean");
parameterValues.back().push_back("Cosine");
parameterValues.back().push_back("Pearson");
parameterValues.back().push_back("UC Pearson");
parameterValues.back().push_back("SQ Pearson");
parameterValues.back().push_back("Dot Product");
parameterValues.back().push_back("Covariance");
parameterValues.back().push_back("Manhattan");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("1");
parameterValues.back().push_back("9999999");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("False");
parameterValues.back().push_back("True");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("0.f");
parameterValues.back().push_back("1.f");
}
Clusterer *ClustFlame::GetClusterer() {
// we instanciate the algorithm object
ClustererFlame *clusterer = new ClustererFlame();
// we set its parameters
SetParams(clusterer);
// we return it to the main program
return clusterer;
}
void ClustFlame::DrawInfo(Canvas *canvas, QPainter &painter, Clusterer *clusterer) {
if(!canvas || !clusterer) return;
painter.setRenderHint(QPainter::Antialiasing);
ClustererFlame * myFlame = dynamic_cast<ClustererFlame*>(clusterer);
if(!myFlame) return;
// Make the painter paint nicely (work well with forms, not so much with text).
painter.setRenderHint(QPainter::Antialiasing);
// Set the brush and pen, in our case no brush (hollow circle) and a thick red edge.
painter.setBrush(Qt::NoBrush);
painter.setPen(QPen(Qt::red, 4));
// Draw a circle arround each support vector.
vector<fvec> supportVectors = myFlame->GetSupports();
int radius = 7;
for (int i = 0; i < supportVectors.size(); i++) {
/* Convert the sample coordinates from dataspace
(N-dimensional in R) to canvas' coordinates. */
QPointF pointInCanvas = canvas->toCanvasCoords(supportVectors[i]);
painter.setPen(QPen(Qt::black, 4));
painter.drawEllipse(pointInCanvas, radius, radius);
painter.setPen(Qt::white);
painter.drawEllipse(pointInCanvas, radius, radius);
}
}
void ClustFlame::DrawModel(Canvas *canvas, QPainter &painter, Clusterer *clusterer) {
// we start by making the painter paint things nicely
painter.setRenderHint(QPainter::Antialiasing);
// for every point in the current dataset
FOR(i, canvas->data->GetSamples().size())
{
// we get the sample
fvec sample = canvas->data->GetSample(i);
// and we test it using the clusterer
fvec res = clusterer->Test(sample);
// we get the point in canvas coordinates (2D pixel by pixel) corresponding to the sample (N-dimensional in R)
QPointF point = canvas->toCanvasCoords(sample);
// we combine together the contribution of each cluster to color the sample properly
float r=0,g=0,b=0;
if(res.size() > 1)
{
FOR(j, res.size())
{
r += SampleColor[(j+1)%SampleColorCnt].red()*res[j];
g += SampleColor[(j+1)%SampleColorCnt].green()*res[j];
b += SampleColor[(j+1)%SampleColorCnt].blue()*res[j];
}
}
// for one-class clustering
else if(res.size())
{
r = (1-res[0])*255 + res[0]* 255;
g = (1-res[0])*255;
b = (1-res[0])*255;
}
// set the color of the brush and a black edge
painter.setBrush(QColor(r,g,b));
painter.setPen(Qt::black);
// and draw the sample itself
painter.drawEllipse(point,5,5);
}
}
void ClustFlame::SaveOptions(QSettings &settings) {
// we save to the system registry each parameter value
settings.setValue("knn", params->knnBox->value());
settings.setValue("knnMetric", params->metricBox->currentIndex());
settings.setValue("maxIteration", params->maxIterationBox->value());
settings.setValue("isSeveralClasses", params->isSeveralClassesBox->isChecked());
settings.setValue("thresholdClasses", params->thresholdBox->value());
}
bool ClustFlame::LoadOptions(QSettings &settings) {
// we load the parameters from the registry so that when we launch the program we keep all values
if(settings.contains("knn")) params->knnBox->setValue(settings.value("knn").toInt());
if(settings.contains("knnMetric")) params->metricBox->setCurrentIndex(settings.value("metric").toInt());
if(settings.contains("maxIteration")) params->maxIterationBox->setValue(settings.value("maxIteration").toInt());
if(settings.contains("isSeveralClasses")) params->isSeveralClassesBox->setChecked(settings.value("isSeveralClasses").toBool());
if(settings.contains("thresholdClasses")) params->thresholdBox->setValue(settings.value("thresholdClasses").toFloat());
return true;
}
void ClustFlame::SaveParams(QTextStream &file) {
// same as above but for files/string saving
file << "clusterOptions" << ":" << "knn" << " " << params->knnBox->value() << "\n";
file << "clusterOptions" << ":" << "knnMetric" << " " << params->metricBox->currentIndex()<< "\n";
file << "clusterOptions" << ":" << "maxIteration" << " " << params->maxIterationBox->value() << "\n";
file << "clusterOptions" << ":" << "isSeveralClasses" << " " << params->isSeveralClassesBox->isChecked() << "\n";
file << "clusterOptions" << ":" << "thresholdClasses" << " " << params->thresholdBox->value() << "\n";
}
bool ClustFlame::LoadParams(QString name, float value) {
// same as above but for files/string saving
if(name.endsWith("knn")) params->knnBox->setValue((int)value);
if(name.endsWith("knnMetric")) params->metricBox->setCurrentIndex((int)value);
if(name.endsWith("maxIteration"))params->maxIterationBox->setValue((int)value);
if(name.endsWith("isSeveralClasses")) params->isSeveralClassesBox->setChecked((int)value);
if(name.endsWith("thresholdClasses")) params->thresholdBox->setValue((int)value);
return true;
}
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