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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,
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 "interfaceMLPClassifier.h"
#include <QPixmap>
#include <QBitmap>
#include <QPainter>
using namespace std;
ClassMLP::ClassMLP()
{
params = new Ui::ParametersMLP();
params->setupUi(widget = new QWidget());
}
ClassMLP::~ClassMLP()
{
delete params;
}
void ClassMLP::SetParams(Classifier *classifier)
{
if(!classifier) return;
float alpha = params->mlpAlphaSpin->value();
float beta = params->mlpBetaSpin->value();
int layers = params->mlpLayerSpin->value();
int neurons = params->mlpNeuronSpin->value();
int activation = params->mlpFunctionCombo->currentIndex()+1; // 1: sigmoid, 2: gaussian
((ClassifierMLP *)classifier)->SetParams(activation, neurons, layers, alpha, beta);
}
fvec ClassMLP::GetParams()
{
float alpha = params->mlpAlphaSpin->value();
float beta = params->mlpBetaSpin->value();
int layers = params->mlpLayerSpin->value();
int neurons = params->mlpNeuronSpin->value();
int activation = params->mlpFunctionCombo->currentIndex()+1; // 1: sigmoid, 2: gaussian
fvec par(5);
par[0] = alpha;
par[1] = beta;
par[2] = layers;
par[3] = neurons;
par[4] = activation;
return par;
}
void ClassMLP::SetParams(Classifier *classifier, fvec parameters)
{
if(!classifier) return;
float alpha = parameters.size() > 0 ? parameters[0] : 1;
float beta = parameters.size() > 1 ? parameters[1] : 1;
int layers = parameters.size() > 2 ? parameters[2] : 1;
int neurons = parameters.size() > 3 ? parameters[3] : 1;
int activation = parameters.size() > 4 ? parameters[4] : 0;
((ClassifierMLP *)classifier)->SetParams(activation, neurons, layers, alpha, beta);
}
void ClassMLP::GetParameterList(std::vector<QString> ¶meterNames,
std::vector<QString> ¶meterTypes,
std::vector< std::vector<QString> > ¶meterValues)
{
parameterNames.clear();
parameterTypes.clear();
parameterValues.clear();
parameterNames.push_back("Alpha");
parameterNames.push_back("Beta");
parameterNames.push_back("Hidden Layers");
parameterNames.push_back("Neurons per Layer");
parameterNames.push_back("Activation Function");
parameterTypes.push_back("Real");
parameterTypes.push_back("Real");
parameterTypes.push_back("Integer");
parameterTypes.push_back("Integer");
parameterTypes.push_back("List");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("0.00000001f");
parameterValues.back().push_back("9999999.f");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("0.00000001f");
parameterValues.back().push_back("9999999.f");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("1");
parameterValues.back().push_back("999999");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("1");
parameterValues.back().push_back("999999");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("Hyperbolic Tangent");
parameterValues.back().push_back("Gaussian");
}
QString ClassMLP::GetAlgoString()
{
float alpha = params->mlpAlphaSpin->value();
float beta = params->mlpBetaSpin->value();
int layers = params->mlpLayerSpin->value();
int neurons = params->mlpNeuronSpin->value();
int activation = params->mlpFunctionCombo->currentIndex()+1; // 1: sigmoid, 2: gaussian
QString algo = QString("MLP %1 %2 %3 %4 %5").arg(neurons).arg(layers).arg(activation==1 ? "S" : "G").arg(alpha).arg(beta);
return algo;
}
Classifier *ClassMLP::GetClassifier()
{
ClassifierMLP *classifier = new ClassifierMLP();
SetParams(classifier);
return classifier;
}
void ClassMLP::SaveOptions(QSettings &settings)
{
settings.setValue("mlpNeuron", params->mlpNeuronSpin->value());
settings.setValue("mlpAlpha", params->mlpAlphaSpin->value());
settings.setValue("mlpBeta", params->mlpBetaSpin->value());
settings.setValue("mlpLayer", params->mlpLayerSpin->value());
settings.setValue("mlpFunction", params->mlpFunctionCombo->currentIndex());
}
bool ClassMLP::LoadOptions(QSettings &settings)
{
if(settings.contains("mlpNeuron")) params->mlpNeuronSpin->setValue(settings.value("mlpNeuron").toFloat());
if(settings.contains("mlpAlpha")) params->mlpAlphaSpin->setValue(settings.value("mlpAlpha").toFloat());
if(settings.contains("mlpBeta")) params->mlpBetaSpin->setValue(settings.value("mlpBeta").toFloat());
if(settings.contains("mlpLayer")) params->mlpLayerSpin->setValue(settings.value("mlpLayer").toFloat());
if(settings.contains("mlpFunction")) params->mlpFunctionCombo->setCurrentIndex(settings.value("mlpFunction").toInt());
return true;
}
void ClassMLP::SaveParams(QTextStream &file)
{
file << "classificationOptions" << ":" << "mlpNeuron" << " " << params->mlpNeuronSpin->value() << "\n";
file << "classificationOptions" << ":" << "mlpAlpha" << " " << params->mlpAlphaSpin->value() << "\n";
file << "classificationOptions" << ":" << "mlpBeta" << " " << params->mlpBetaSpin->value() << "\n";
file << "classificationOptions" << ":" << "mlpLayer" << " " << params->mlpLayerSpin->value() << "\n";
file << "classificationOptions" << ":" << "mlpFunction" << " " << params->mlpFunctionCombo->currentIndex() << "\n";
}
bool ClassMLP::LoadParams(QString name, float value)
{
if(name.endsWith("mlpNeuron")) params->mlpNeuronSpin->setValue((int)value);
if(name.endsWith("mlpAlpha")) params->mlpAlphaSpin->setValue(value);
if(name.endsWith("mlpBeta")) params->mlpBetaSpin->setValue(value);
if(name.endsWith("mlpLayer")) params->mlpLayerSpin->setValue((int)value);
if(name.endsWith("mlpFunction")) params->mlpFunctionCombo->setCurrentIndex((int)value);
return true;
}
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