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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 "interfaceTreesClassifier.h"
#include <QPixmap>
#include <QBitmap>
#include <QPainter>
#include <QDebug>
using namespace std;
ClassTrees::ClassTrees()
: displayLabel(0)
{
params = new Ui::ParametersTrees();
params->setupUi(widget = new QWidget());
connect(params->displayButton, SIGNAL(clicked()), this, SLOT(DisplayTrees()));
}
ClassTrees::~ClassTrees()
{
delete params;
DEL(displayLabel);
}
void ClassTrees::DisplayTrees()
{
if(!displayLabel)
{
displayLabel = new QLabel();
displayLabel->setScaledContents(true);
}
if(!treePixmap.isNull())
{
displayLabel->setPixmap(treePixmap);
displayLabel->setGeometry(displayLabel->geometry().x(), displayLabel->geometry().y(),
treePixmap.width(), treePixmap.height());
displayLabel->show();
}
}
void ClassTrees::SetParams(Classifier *classifier)
{
if(!classifier) return;
ClassifierTrees *trees = dynamic_cast<ClassifierTrees *>(classifier);
if(!trees) return;
bool bBalanceClasses= params->balanceClassesCheck->isChecked();
int minSampleCount = params->sampleCountSpin->value();
int maxDepth = params->maxDepthSpin->value();
int maxTrees = params->maxTreesSpin->value();
float accuracyTolerance = params->accuracySpin->value();
trees->SetParams(bBalanceClasses, minSampleCount, maxDepth, maxTrees, accuracyTolerance);
}
fvec ClassTrees::GetParams()
{
bool bBalanceClasses= params->balanceClassesCheck->isChecked();
int minSampleCount = params->sampleCountSpin->value();
int maxDepth = params->maxDepthSpin->value();
int maxTrees = params->maxTreesSpin->value();
float accuracyTolerance = params->accuracySpin->value();
fvec par(5);
par[0] = maxTrees;
par[1] = maxDepth;
par[2] = minSampleCount;
par[3] = bBalanceClasses;
par[4] = accuracyTolerance;
return par;
}
void ClassTrees::SetParams(Classifier *classifier, fvec parameters)
{
if(!classifier) return;
int maxTrees = parameters.size() > 0 ? parameters[0] : 1;
int maxDepth = parameters.size() > 1 ? parameters[1] : 1;
int minSampleCount = parameters.size() > 2 ? parameters[2] : 1;
bool bBalanceClasses = parameters.size() > 3 ? parameters[3] : false;
float accuracyTolerance = parameters.size() > 4 ? parameters[4] : 10;
ClassifierTrees *trees = dynamic_cast<ClassifierTrees *>(classifier);
if(!trees) return;
trees->SetParams(bBalanceClasses, minSampleCount, maxDepth, maxTrees, accuracyTolerance);
}
void ClassTrees::GetParameterList(std::vector<QString> ¶meterNames,
std::vector<QString> ¶meterTypes,
std::vector< std::vector<QString> > ¶meterValues)
{
parameterNames.clear();
parameterTypes.clear();
parameterValues.clear();
parameterNames.push_back("Maximum Trees");
parameterNames.push_back("Maximum Depth");
parameterNames.push_back("Minimum Samples per Node");
parameterNames.push_back("Balance Classes");
parameterNames.push_back("Accuracy Tolerance");
parameterTypes.push_back("Integer");
parameterTypes.push_back("Integer");
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("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("1");
parameterValues.back().push_back("999999");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("False");
parameterValues.back().push_back("Trees");
parameterValues.push_back(vector<QString>());
parameterValues.back().push_back("0.00000001f");
parameterValues.back().push_back("9999999.f");
}
QString ClassTrees::GetAlgoString()
{
bool bBalanceClasses= params->balanceClassesCheck->isChecked();
int minSampleCount = params->sampleCountSpin->value();
int maxDepth = params->maxDepthSpin->value();
int maxTrees = params->maxTreesSpin->value();
float accuracyTolerance = params->accuracySpin->value();
QString algo = QString("RForest: T%1").arg(maxTrees);
algo += QString(" D:%1").arg(maxDepth);
algo += QString(" S:%1").arg(minSampleCount);
algo += QString(" A:%1").arg(accuracyTolerance);
algo += QString(" %1").arg(bBalanceClasses ? "Bal" : "Unbal");
return algo;
}
Classifier *ClassTrees::GetClassifier()
{
ClassifierTrees *classifier = new ClassifierTrees();
SetParams(classifier);
params->importanceList->clear();
return classifier;
}
void ClassTrees::DrawInfo(Canvas *canvas, QPainter &painter, Classifier *classifier)
{
if(!classifier || !canvas) return;
painter.setRenderHint(QPainter::Antialiasing, true);
bool bUseMinMax = false;
bUseMinMax = true;
ClassifierTrees *trees = dynamic_cast<ClassifierTrees*>(classifier);
if(!trees) return;
treePixmap = trees->treePixmap;
if(params->displayButton->isChecked()) DisplayTrees();
fvec importance = trees->GetImportance();
params->importanceList->clear();
FOR(i, importance.size())
{
params->importanceList->addItem(QString("Dim %1: %2%").arg(i+1).arg(importance[i]*100, 0, 'f', 1));
}
}
void ClassTrees::SaveOptions(QSettings &settings)
{
settings.setValue("balanceClasses", params->balanceClassesCheck->isChecked());
settings.setValue("sampleCount", params->sampleCountSpin->value());
settings.setValue("maxDepth", params->maxDepthSpin->value());
settings.setValue("maxTrees", params->maxTreesSpin->value());
settings.setValue("accuracy", params->accuracySpin->value());
}
bool ClassTrees::LoadOptions(QSettings &settings)
{
if(settings.contains("balanceClasses")) params->balanceClassesCheck->setChecked(settings.value("balanceClasses").toBool());
if(settings.contains("sampleCount")) params->sampleCountSpin->setValue(settings.value("sampleCount").toInt());
if(settings.contains("maxDepth")) params->maxDepthSpin->setValue(settings.value("maxDepth").toInt());
if(settings.contains("maxTrees")) params->maxTreesSpin->setValue(settings.value("maxTrees").toInt());
if(settings.contains("accuracy")) params->accuracySpin->setValue(settings.value("accuracy").toFloat());
return true;
}
void ClassTrees::SaveParams(QTextStream &file)
{
file << "classificationOptions" << ":" << "balanceClasses" << " " << params->balanceClassesCheck->isChecked() << "\n";
file << "classificationOptions" << ":" << "sampleCount" << " " << params->sampleCountSpin->value() << "\n";
file << "classificationOptions" << ":" << "maxDepth" << " " << params->maxDepthSpin->value() << "\n";
file << "classificationOptions" << ":" << "maxTrees" << " " << params->maxTreesSpin->value() << "\n";
file << "classificationOptions" << ":" << "accuracy" << " " << params->accuracySpin->value() << "\n";
}
bool ClassTrees::LoadParams(QString name, float value)
{
if(name.endsWith("balanceClasses")) params->balanceClassesCheck->setChecked((bool)value);
if(name.endsWith("sampleCount")) params->sampleCountSpin->setValue((int)value);
if(name.endsWith("maxDepth")) params->maxDepthSpin->setValue((int)value);
if(name.endsWith("maxTrees")) params->maxTreesSpin->setValue((int)value);
if(name.endsWith("accuracy")) params->accuracySpin->setValue((float)value);
return true;
}
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