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mainwindow.cpp
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/*
Copyright (C) 2012 Martin Klapetek <martin.klapetek@gmail.com>
Based on C++ implementation by Chesnokov Yuriy, Copyright (C) 2008
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 3 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, see <http://www.gnu.org/licenses/>.
*/
#include "mainwindow.h"
#include "ui_mainwindow.h"
#include "network.h"
#include "neuron.h"
#include "race-client.h"
#include <QGraphicsEllipseItem>
#include <QDebug>
#include <QPointF>
#include <QFileDialog>
#include <QFile>
#include <QMessageBox>
#include <QInputDialog>
#include <QTimer>
#include <QList>
#include <math.h>
MainWindow::MainWindow(QWidget *parent) :
QMainWindow(parent),
ui(new Ui::MainWindow)
{
ui->setupUi(this);
m_netForRecognition = false;
qDebug() << "Ready";
//connect UI stuff
connect(ui->trainButton, SIGNAL(clicked()),
this, SLOT(train()));
connect(ui->openFileButton, SIGNAL(clicked()),
this, SLOT(openFileDialog()));
connect(ui->runButton, SIGNAL(clicked()),
this, SLOT(run()));
connect(ui->lrEdit, SIGNAL(editingFinished()),
this, SLOT(netParamsChanged()));
connect(ui->momentumEdit, SIGNAL(editingFinished()),
this, SLOT(netParamsChanged()));
connect(ui->maxErrorEdit, SIGNAL(editingFinished()),
this, SLOT(netParamsChanged()));
connect(ui->connectButton, SIGNAL(clicked()),
this, SLOT(connectToServer()));
connect(ui->resetButton, SIGNAL(clicked()),
this, SLOT(reset()));
connect(ui->display, SIGNAL(cellClicked(int,int)),
this, SLOT(toggleDisplaySegment(int,int)));
connect(ui->recognizeButton, SIGNAL(clicked(bool)),
this, SLOT(recognizeNumber()));
connect(ui->oneButton, SIGNAL(clicked(bool)),
this, SLOT(paintNumber()));
connect(ui->twoButton, SIGNAL(clicked(bool)),
this, SLOT(paintNumber()));
connect(ui->threeButton, SIGNAL(clicked(bool)),
this, SLOT(paintNumber()));
connect(ui->fourButton, SIGNAL(clicked(bool)),
this, SLOT(paintNumber()));
connect(ui->fiveButton, SIGNAL(clicked(bool)),
this, SLOT(paintNumber()));
connect(ui->sixButton, SIGNAL(clicked(bool)),
this, SLOT(paintNumber()));
connect(ui->sevenButton, SIGNAL(clicked(bool)),
this, SLOT(paintNumber()));
connect(ui->eightButton, SIGNAL(clicked(bool)),
this, SLOT(paintNumber()));
connect(ui->nineButton, SIGNAL(clicked(bool)),
this, SLOT(paintNumber()));
connect(ui->zeroButton, SIGNAL(clicked(bool)),
this, SLOT(paintNumber()));
ui->runButton->setDisabled(true);
ui->trainButton->setDisabled(true);
}
MainWindow::~MainWindow()
{
delete ui;
}
void MainWindow::changeEvent(QEvent *e)
{
QMainWindow::changeEvent(e);
switch (e->type()) {
case QEvent::LanguageChange:
ui->retranslateUi(this);
break;
default:
break;
}
}
void MainWindow::train()
{
int i = 0;
QList<float> *netOutputs;
bool trained = false;
while (!trained && i < ui->iterationsEdit->text().toInt()) {
qDebug() << "Epoch" << i;
trained = m_network.data()->trainSet(m_normTrainingSet, m_expectedOutputs);
i++;
ui->iterationsCountEdit->setText(QString::number(i));
if (i % 2 == 0) {
for (int k = 0; k < m_normTrainingSet.size(); k++) {
netOutputs = m_network.data()->run(m_normTrainingSet.at(k));
QString nout;
for (int j = 0; j < netOutputs->size(); j++) {
nout.append(QString::number(netOutputs->at(j)));
if (j != netOutputs->size() - 1) {
nout.append("; ");
}
}
ui->trainTableWidget->setItem(k, 2, new QTableWidgetItem(nout));
QTimer::singleShot(0, ui->trainTableWidget, SLOT(resizeColumnsToContents()));
}
}
}
qDebug() << "Trained after" << i;
ui->trainFirstLabel->hide();
ui->runButton->setEnabled(true);
}
void MainWindow::reset()
{
m_iteration = 0;
m_trainingSet.clear();
m_normTrainingSet.clear();
m_expectedOutputs.clear();
m_runSet.clear();
m_normRunSet.clear();
m_inputsList.clear();
if (!m_network.isNull()) {
delete m_network.data();
// m_network = 0;
}
m_neuronsInLayersList.clear();
m_outputsList.clear();
ui->iterationsCountEdit->setText(QString("0"));
ui->trainFirstLabel->show();
ui->runButton->setDisabled(true);
ui->trainButton->setDisabled(true);
ui->trainTableWidget->clearContents();
ui->runSetTableWidget->clearContents();
ui->trainTableWidget->setRowCount(0);
ui->runSetTableWidget->setRowCount(0);
}
void MainWindow::openFileDialog()
{
QString fileName = QFileDialog::getOpenFileName(this,
tr("Open File"), QString(), tr("Text files (*.txt)"));
loadFile(fileName);
}
void MainWindow::loadFile(QString filename)
{
reset();
QString lineString;
bool layers = false;
bool inputs = false;
bool neuronsInLayers = false;
bool outputs = false;
bool lr = false;
bool momentum = false;
bool trainingSet = false;
bool runSet = false;
bool trainCountRead = false;
bool runCountRead = false;
int inputsRead = 0;
int trainRead = 0;
int trainToRead = 999;
int runRead = 0;
int runToRead = 999;
int layersCount = 0;
int inputNumber = 0;
QFile file(filename);
if (!file.open(QIODevice::ReadOnly | QIODevice::Text))
return;
while (!file.atEnd()) {
QByteArray line = file.readLine();
qDebug() << "Read line:" << line;
if(line.contains("#") || line.isEmpty() || line == "\n")
continue;
if(layers == false) {
QString sline(line.trimmed());
layersCount = sline.toInt();
//add input layer
layersCount++;
qDebug() << "Layers Count:" << layersCount;
ui->layerCountEdit->setText(QString::number(layersCount));
layers = true;
continue;
}
if(layers && inputs == false)
{
qDebug() << "* Inputs while";
QString sline(line.trimmed());
inputNumber = sline.toInt();
qDebug() << "Inputs to read:" << inputNumber;
while(inputsRead < inputNumber)
{
line = file.readLine();
if(line.contains("#") || line.isEmpty() || line == "\n")
continue;
qDebug() << "Appending line with inputs";
m_inputsList.append(QString(line.simplified()).split(" "));
inputsRead++;
}
inputs = true;
continue;
}
if(layers && inputs && neuronsInLayers == false)
{
qDebug() << "* Neurons in layers while";
// line = file.readLine();
if(line.contains("#") || line.isEmpty() || line == "\n")
continue;
qDebug() << "Read in Neurons in layers while:" << line;
foreach(const QString &n, QString(line.simplified()).split(" ")) {
m_neuronsInLayersList.append(n.toInt());
}
//ui->layerCountEdit->setText(QString::number(m_neuronsInLayersList.size()));
neuronsInLayers = true;
// continue;
}
if(layers && inputs && neuronsInLayers && outputs == false)
{
qDebug() << "* Outputs while";
int outputsRead = 0;
int outputNumber = m_neuronsInLayersList.last();
while(outputsRead < outputNumber)
{
line = file.readLine();
if(line.contains("#") || line.isEmpty() || line == "\n")
continue;
qDebug() << "Outputs read:" << line;
m_outputsList.append(QString(line.simplified()));
outputsRead++;
}
outputs = true;
// continue;
}
if(layers && inputs && neuronsInLayers && outputs && lr == false)
{
qDebug() << "* LR while";
while(lr == false)
{
line = file.readLine();
qDebug() << "Read in LR while:" << line;
if(line.contains("#") || line.isEmpty() || line == "\n")
continue;
ui->lrEdit->setText(QString(line.simplified()));
lr = true;
}
}
if(layers && inputs && neuronsInLayers && outputs && lr && momentum == false)
{
qDebug() << "* Momentum while";
while(momentum == false)
{
line = file.readLine();
qDebug() << "Read in momentum while:" << line;
if(line.contains("#") || line.isEmpty() || line == "\n")
continue;
ui->momentumEdit->setText(QString(line.simplified()));
momentum = true;
}
}
if(layers && inputs && neuronsInLayers && outputs && lr && momentum && trainingSet == false)
{
qDebug() << "* TS while";
// QString sline(line.trimmed());
// trainToRead = sline.toInt();
while(trainRead < trainToRead)
{
line = file.readLine();
line = line.trimmed();
qDebug() << "Read in TS while:" << line;
if(line.contains("#") || line.isEmpty() || line == "\n")
continue;
if(trainCountRead == false)
{
trainToRead = line.toInt();
qDebug() << "Train data to read:" << trainToRead;
trainCountRead = true;
continue;
}
QString sline(line.simplified());
QStringList numbers = sline.split(" ");
qDebug() << numbers.size() << numbers;
QList<float> l;
for(int j = 0; j < numbers.size() - m_outputsList.size(); j++)
{
l.append(numbers.at(j).toFloat());
}
m_trainingSet.append(l);
QList<float> o;
for(int j = numbers.size() - m_outputsList.size(); j < numbers.size(); j++) {
o.append(numbers.at(j).toFloat());
}
m_expectedOutputs.append(o);
trainRead++;
}
trainingSet = true;
}
if(layers && inputs && neuronsInLayers && outputs && lr && momentum && trainingSet && runSet == false)
{
while(runRead < runToRead)
{
line = file.readLine();
line = line.trimmed();
qDebug() << "Read in RS while:" << line;
if(line.contains("#") || line.isEmpty() || line == "\n")
continue;
if(runCountRead == false)
{
runToRead = line.toInt();
qDebug() << "Run data to read:" << runToRead;
runCountRead = true;
continue;
}
QString sline(line);
QStringList numbers = sline.split(" ");
QList<float> l;
for(int j = 0; j < numbers.size(); j++)
{
l.append(numbers.at(j).toFloat());
}
m_runSet.append(l);
runRead++;
}
runSet = true;
}
}
m_neuronsInLayersList.prepend(m_inputsList.size());
//normalize training set
float x;
QList<float> n;
for (int i = 0; i < m_trainingSet.size(); i++) {
n.clear();
for (int j = 0; j < m_inputsList.size(); j++) {
x = (m_trainingSet.at(i).at(j) - m_inputsList.at(j).at(1).toFloat()) / (m_inputsList.at(j).at(2).toFloat() - m_inputsList.at(j).at(1).toFloat());
n.append(x);
}
m_normTrainingSet.append(n);
}
//normalize run set
QList<float> r;
for (int i = 0; i < m_runSet.size(); i++) {
r.clear();
for (int j = 0; j < m_inputsList.size(); j++) {
x = (m_runSet.at(i).at(j) - m_inputsList.at(j).at(1).toFloat()) / (m_inputsList.at(j).at(2).toFloat() - m_inputsList.at(j).at(1).toFloat());
r.append(x);
}
m_normRunSet.append(r);
}
qDebug() << "##### Summary #####";
qDebug() << "Inputs list:" << m_inputsList;
qDebug() << "Layers:" << layersCount;
qDebug() << "Neurons in layer:" << m_neuronsInLayersList;
qDebug() << "Outputs list:" << m_outputsList;
qDebug() << "Training set:" << m_trainingSet;
qDebug() << "Norm. training set:" << m_normTrainingSet;
qDebug() << "Expected outputs:" << m_expectedOutputs;
qDebug() << "Run set:" << m_runSet;
qDebug() << "Norm. run set:" << m_normRunSet;
qDebug() << "Learning:" << ui->lrEdit->text();
qDebug() << "Momentum:" << ui->momentumEdit->text();
m_network = new Network(m_neuronsInLayersList, ui->momentumEdit->text().toFloat(), ui->lrEdit->text().toFloat(), ui->maxErrorEdit->text().toFloat());
m_netForRecognition = false;
for (int i = 0; i < m_trainingSet.size(); i++) {
QString tset, eout;
for (int j = 0; j < m_trainingSet.at(i).size(); j++) {
tset.append(QString::number(m_trainingSet.at(i).at(j)));
if (j != m_trainingSet.at(i).size() - 1) {
tset.append("; ");
}
}
for (int j = 0; j < m_expectedOutputs.at(i).size(); j++) {
eout.append(QString::number(m_expectedOutputs.at(i).at(j)));
if (j != m_expectedOutputs.at(i).size() - 1) {
eout.append("; ");
}
}
ui->trainTableWidget->setRowCount(ui->trainTableWidget->rowCount() + 1);
ui->trainTableWidget->setItem(i, 0, new QTableWidgetItem(tset));
ui->trainTableWidget->setItem(i, 1, new QTableWidgetItem(eout));
}
for (int i = 0; i < m_runSet.size(); i++) {
QString rset;
for (int j = 0; j < m_runSet.at(i).size(); j++) {
rset.append(QString::number(m_runSet.at(i).at(j)));
if (j != m_runSet.at(i).size() - 1) {
rset.append("; ");
}
}
ui->runSetTableWidget->setRowCount(ui->runSetTableWidget->rowCount() + 1);
ui->runSetTableWidget->setItem(i, 0, new QTableWidgetItem(rset));
}
ui->trainButton->setEnabled(true);
ui->tabWidget->setCurrentIndex(0);
}
void MainWindow::run()
{
QList<float> *result;
for (int i = 0; i < m_normRunSet.size(); i++) {
result = m_network.data()->run(m_normRunSet.at(i));
qDebug() << "Net output for" << m_normRunSet.at(i) << ":" << *result;
QString rout;
for (int j = 0; j < result->size(); j++) {
rout.append(QString::number(result->at(j)));
if (j != result->size() - 1) {
rout.append("; ");
}
}
ui->runSetTableWidget->setItem(i, 1, new QTableWidgetItem(rout));
}
// QMessageBox::information(this, "Running set completed", perceptronOutput, QMessageBox::Ok);
}
void MainWindow::netParamsChanged()
{
if (!m_network.isNull()) {
m_network.data()->setLearningRate(ui->lrEdit->text().toFloat());
m_network.data()->setMomentum(ui->momentumEdit->text().toFloat());
m_network.data()->setMaxError(ui->maxErrorEdit->text().toFloat());
}
}
void MainWindow::connectToServer()
{
bool ok;
QString text = QInputDialog::getText(this,
tr("Enter server:port"),
tr("Connect to:"),
QLineEdit::Normal,
QString("127.0.0.1:9477"), &ok);
if (ok) {
QStringList s = text.split(":");
m_raceClient = new RaceClient(s.at(0), s.at(1).toInt(), this);
connect(m_raceClient, SIGNAL(stateChanged(RaceClient::ClientState)),
this, SLOT(handleRaceStateChange(RaceClient::ClientState)));
// ui->tabWidget->setCurrentIndex(2);
}
}
void MainWindow::handleRaceStateChange(RaceClient::ClientState state)
{
// qDebug() << "Got state change" << state;
switch (state) {
case RaceClient::Connecting:
ui->raceStateLabel->setText("Connecting...");
break;
case RaceClient::ConnectingToRace:
ui->raceStateLabel->setText("Connecting to race...");
ui->serverHostLabel->setText(m_raceClient->host());
break;
case RaceClient::GettingRaceList:
ui->raceStateLabel->setText("Getting race list...");
ui->serverHostLabel->setText(m_raceClient->host());
break;
case RaceClient::Racing:
ui->raceStateLabel->setText("Race in progress");
break;
case RaceClient::Disconnected:
ui->raceStateLabel->setText("Disconnected");
ui->serverHostLabel->setText("Not connected");
break;
case RaceClient::Error:
ui->raceStateLabel->setText("Error");
ui->serverHostLabel->setText("Not connected");
break;
}
}
void MainWindow::toggleDisplaySegment(int row, int column)
{
if (ui->display->item(row, column)->backgroundColor() == Qt::black) {
ui->display->item(row, column)->setBackgroundColor(Qt::white);
} else {
ui->display->item(row, column)->setBackgroundColor(Qt::black);
}
}
void MainWindow::recognizeNumber()
{
if (!m_netForRecognition) {
if (!m_network.isNull()) {
delete m_network.data();
}
//init network with 4 layers, alpha=0.8, learningRate=0.1 and maxError=0.05
m_network = new Network(QList<int>() << 25 << 18 << 8 << 4, 0.8f, 0.1f, 0.05f);
m_netForRecognition = true;
}
bool train = false;
if (train) {
QList<QList<float> > trainSet;
//prepare the train set
trainSet << (QList<float>() << 0 << 0 << 0 << 1 << 0
<< 0 << 0 << 1 << 1 << 0
<< 0 << 1 << 0 << 1 << 0
<< 0 << 0 << 0 << 1 << 0
<< 0 << 0 << 0 << 1 << 0)
<< (QList<float>() << 0 << 0 << 1 << 1 << 0
<< 0 << 1 << 0 << 0 << 1
<< 0 << 0 << 0 << 1 << 0
<< 0 << 0 << 1 << 0 << 0
<< 0 << 1 << 1 << 1 << 1)
<< (QList<float>() << 0 << 1 << 1 << 1 << 0
<< 0 << 0 << 0 << 1 << 0
<< 0 << 1 << 1 << 1 << 0
<< 0 << 0 << 0 << 1 << 0
<< 0 << 1 << 1 << 1 << 0)
<< (QList<float>() << 0 << 1 << 0 << 1 << 0
<< 0 << 1 << 0 << 1 << 0
<< 0 << 1 << 1 << 1 << 0
<< 0 << 0 << 0 << 1 << 0
<< 0 << 0 << 0 << 1 << 0)
<< (QList<float>() << 0 << 1 << 1 << 1 << 0
<< 0 << 1 << 0 << 0 << 0
<< 0 << 1 << 1 << 1 << 0
<< 0 << 0 << 0 << 1 << 0
<< 0 << 1 << 1 << 1 << 0)
<< (QList<float>() << 0 << 1 << 1 << 1 << 0
<< 0 << 1 << 0 << 0 << 0
<< 0 << 1 << 1 << 1 << 0
<< 0 << 1 << 0 << 1 << 0
<< 0 << 1 << 1 << 1 << 0)
<< (QList<float>() << 0 << 1 << 1 << 1 << 0
<< 0 << 0 << 0 << 1 << 0
<< 0 << 0 << 0 << 1 << 0
<< 0 << 0 << 0 << 1 << 0
<< 0 << 0 << 0 << 1 << 0)
<< (QList<float>() << 0 << 1 << 1 << 1 << 0
<< 0 << 1 << 0 << 1 << 0
<< 0 << 1 << 1 << 1 << 0
<< 0 << 1 << 0 << 1 << 0
<< 0 << 1 << 1 << 1 << 0)
<< (QList<float>() << 0 << 1 << 1 << 1 << 0
<< 0 << 1 << 0 << 1 << 0
<< 0 << 1 << 1 << 1 << 0
<< 0 << 0 << 0 << 1 << 0
<< 0 << 1 << 1 << 1 << 0)
<< (QList<float>() << 0 << 1 << 1 << 1 << 0
<< 0 << 1 << 0 << 1 << 0
<< 0 << 1 << 0 << 1 << 0
<< 0 << 1 << 0 << 1 << 0
<< 0 << 1 << 1 << 1 << 0);
QList<QList<float> > outputs;
//expected outputs for the trainset
outputs << (QList<float>() << 0 << 0 << 0 << 1 )
<< (QList<float>() << 0 << 0 << 1 << 0 )
<< (QList<float>() << 0 << 0 << 1 << 1 )
<< (QList<float>() << 0 << 1 << 0 << 0 )
<< (QList<float>() << 0 << 1 << 0 << 1 )
<< (QList<float>() << 0 << 1 << 1 << 0 )
<< (QList<float>() << 0 << 1 << 1 << 1 )
<< (QList<float>() << 1 << 0 << 0 << 0 )
<< (QList<float>() << 1 << 0 << 0 << 1 )
<< (QList<float>() << 0 << 0 << 0 << 0);
bool trained = false;
int i = 0;
while (!trained && i < 1000000) {
qDebug() << "Epoch" << i;
trained = m_network.data()->trainSet(trainSet, outputs);
i++;
}
qDebug() << "Trained after" << i;
m_network.data()->printWeights();
}
else if (!m_network.data()->isTrained()) {
QList<QList<QList<float> > > weights;
//these are already learnt weights,
//we can plug them in directly
weights << (QList<QList<float> >() //Layer 1
<< (QList<float>() //======= Neuron 0
<< -0.638249
<< 5.1979e+34
<< -1.20019
<< -1.6819
<< -1.09797
<< 0.924777
<< 0.657582
<< 1.43451
<< 0.858619
<< -1.06778
<< 5.1979e+34
<< 0.690148
<< 1.82082
<< 8.44057
<< -0.887892
<< 0.268565
<< 8.39364
<< 0.963709
<< 7.91213
<< -1.00071
<< 0.0830466
<< 0.295485
<< 8.28963
<< 1.14708
<< -0.124517
<< 0.304544
)
<< (QList<float>() //======= Neuron 1
<< 0.29967
<< 0.870814
<< 0.765024
<< 0.679504
<< 0.069199
<< 0.240901
<< 0.110031
<< 0.726196
<< 0.539977
<< 0.126177
<< 0.650974
<< 0.197559
<< 0.0849223
<< 0.765929
<< 5.1979e+34
<< 0.879934
<< 0.456076
<< 0.846467
<< 0.699583
<< 0.68227
<< 0.115032
<< 0.593228
<< 0.293917
<< 0.527159
<< 0.706633
<< 0.376964
)
<< (QList<float>() //======= Neuron 2
<< 0.459642
<< 0.139617
<< 0.141882
<< 0.944169
<< 0.0812357
<< 7.97222
<< 0.683023
<< 0.0548349
<< -0.180621
<< 0.29584
<< 0.450087
<< 0.261754
<< 0.0199526
<< 0.792234
<< 0.0250054
<< 0.129392
<< 0.187624
<< 0.551229
<< 8.39532
<< 0.0685427
<< 0.352788
<< 0.351397
<< 0.314208
<< 0.0884204
<< -0.329294
<< 0.392975
)
<< (QList<float>() //======= Neuron 3
<< 0.627829
<< 0.327585
<< 0.952842
<< 0.402285
<< 0.686778
<< 0.742779
<< 0.49185
<< 0.864678
<< 0.500939
<< 0.903072
<< 0.352692
<< 0.238011
<< 0.0900838
<< 0.502744
<< 0.972462
<< 0.595284
<< 0.762793
<< 0.46233
<< 0.588605
<< 0.129226
<< 0.598043
<< 0.772971
<< 0.640006
<< 0.5098
<< 0.186489
<< 0.979626
)
<< (QList<float>() //======= Neuron 4
<< 0.814555
<< 0.482391
<< 0.0466217
<< 0.918806
<< 0.845161
<< 0.67434
<< 0.206014
<< 0.776563
<< -0.0222101
<< 0.872025
<< 0.53864
<< 0.518424
<< 0.722334
<< 0.0767335
<< 0.424393
<< 0.136975
<< 0.331279
<< 0.600645
<< 0.638374
<< 0.290789
<< 0.194916
<< 0.400807
<< 0.795931
<< 0.786033
<< 0.521326
<< 0.388687
)
<< (QList<float>() //======= Neuron 5
<< 0.413598
<< 0.175111
<< 0.811081
<< 0.654055
<< 0.0115422
<< 0.726331
<< 0.240075
<< 0.147409
<< 0.570754
<< 0.0102338
<< 0.818822
<< 0.810774
<< 0.868903
<< 0.870152
<< 0.58166
<< 0.449223
<< 0.399556
<< 0.512367
<< 0.506754
<< 0.750066
<< 0.645971
<< 0.87377
<< 0.412466
<< 0.242805
<< 0.0553436
<< 0.612802
)
<< (QList<float>() //======= Neuron 6
<< 0.569417
<< 0.985444
<< 0.356942
<< 0.194374
<< 0.258291
<< 0.982039
<< 0.411436
<< 0.182654
<< 0.701165
<< 0.464978
<< 0.976098
<< 0.979798
<< 0.642279
<< 0.540673
<< -0.0202861
<< 0.597694
<< 0.397084
<< -0.030039
<< 0.473526
<< 0.00808778
<< 0.455406
<< 0.878382
<< 0.595643
<< 0.966126
<< 0.616998
<< 0.268087
)
<< (QList<float>() //======= Neuron 7
<< 0.753884
<< 0.185996
<< 0.645607
<< -0.00577245
<< 0.716753
<< 0.242165
<< 0.0523934
<< 0.058172
<< 0.452261
<< 0.445495
<< 0.0655982
<< 0.889925
<< 0.638352
<< 1.01554
<< 0.334988
<< 0.662814
<< 0.964899
<< 0.129958
<< 0.0693398
<< 0.121968
<< 0.793669
<< 0.646209
<< -0.110862
<< 0.0952327
<< 0.646846
<< 0.341836
)
<< (QList<float>() //======= Neuron 8
<< 0.100053
<< 0.392045
<< 0.495312
<< 0.879876
<< 0.61502
<< 0.391291
<< 0.0692459
<< 0.213325
<< 0.42099
<< 0.839641
<< 0.476256
<< 0.510634
<< 0.115516
<< 0.932432
<< 0.886265
<< 0.4125
<< 0.833385
<< 0.606765
<< 0.408887
<< 0.224176
<< 0.280755
<< 0.362501
<< 0.489256
<< 0.536698
<< 0.371874
<< 0.287155
)
<< (QList<float>() //======= Neuron 9
<< 0.170275
<< 0.488962
<< 0.613596
<< 1.05904
<< 0.00491128
<< 0.699241
<< 0.332762
<< 0.48596
<< 0.518941
<< 0.951626
<< 0.914193
<< 0.651736
<< 0.147743
<< 0.378435
<< 0.550325
<< 0.68597
<< 0.890478
<< 0.704469
<< 0.622018
<< 0.828893
<< 0.135139
<< 0.462814
<< 0.48916
<< 0.576806
<< 0.743847
<< 0.71844
)
<< (QList<float>() //======= Neuron 10
<< -0.200217
<< 0.23124
<< -3.28007
<< 0.35858
<< -0.588327
<< 0.431818
<< 0.806845
<< -3.66991
<< 1.17144