public NetworkHelper(IList<Minutia> features, SOMParams somParams)
{
var trainingFeaturesCount = features.Count;
var inputs = new double[trainingFeaturesCount][];
var featureCharacteristicsCount = somParams.CharacteristicsCount;
for (var i = 0; i < trainingFeaturesCount; i++)
{
var feature = features[i];
inputs[i] = new double[featureCharacteristicsCount];
inputs[i][0] = feature.Angle * somParams.AngleMultiplier;
inputs[i][1] = (double)feature.MinutiaType * somParams.TypeMultiplier;
inputs[i][2] = feature.X * somParams.CoordsMultiplier;
inputs[i][3] = feature.Y * somParams.CoordsMultiplier;
inputs[i][4] = feature.GetHashCode()*somParams.HashCodeMultiplier;
}
_networkSize = CreateRectangleFromFeatures(trainingFeaturesCount);
_som = new DistanceNetwork(featureCharacteristicsCount, trainingFeaturesCount);
_teacher = new SOMLearning(_som, _networkSize[0], _networkSize[1]);
_inputs = inputs;
_learningRadius = somParams.LearningRadius;
_fixedLearningRate = somParams.LearningRate / 10;
_driftingLearningRate = _fixedLearningRate * 9;
}
/// <summary>
/// Crea la red neuronal artificial.
/// </summary>
public override void CreateANN()
{
base.CreateANN();
// Crea un diccionario vacio
this.learnedKanjis = new Dictionary<string, Kanji>();
// Crea la red
somNN = new DistanceNetwork(InputSize, OutputSize);
}
public NetworkHelper(double[][] features, SOMParams somParams)
{
var trainingFeaturesCount = features.GetLength(0);
var featureCharacteristicsCount = somParams.CharacteristicsCount;
_networkSize = CreateRectangleFromFeatures(trainingFeaturesCount);
_som = new DistanceNetwork(featureCharacteristicsCount, trainingFeaturesCount);
_teacher = new SOMLearning(_som, _networkSize[0], _networkSize[1]);
_inputs = features;
_learningRadius = somParams.LearningRadius;
_fixedLearningRate = somParams.LearningRate / 10;
_driftingLearningRate = _fixedLearningRate * 9;
}
// Constructor
public MainForm()
{
InitializeComponent();
// Create network
network = new DistanceNetwork(3, 100 * 100);
// Create map bitmap
mapBitmap = new Bitmap(200, 200, PixelFormat.Format24bppRgb);
//
RandomizeNetwork();
UpdateSettings();
}
// Constructor
public MainForm( )
{
//
// Required for Windows Form Designer support
//
InitializeComponent( );
// Create network
network = new DistanceNetwork( 3, 100 * 100 );
// Create map bitmap
mapBitmap = new Bitmap( 200, 200, PixelFormat.Format24bppRgb );
//
RandomizeNetwork( );
UpdateSettings( );
}
static void Main()
{
network = new DistanceNetwork(2, task.NetworkWidth * task.NetworkHeight);
for (int x=0;x<task.NetworkWidth;x++)
for (int y = 0; y < task.NetworkHeight; y++)
{
var n = network.Layers[0].Neurons[x * task.NetworkHeight + y];
n.Weights[0] = rnd.NextDouble() * 0.2 + 0.4;
n.Weights[1] = rnd.NextDouble() * 0.2 + 0.4;
}
learning = new SOMLearning(network, task.NetworkWidth, task.NetworkHeight);
learning.LearningRadius = task.LearningRadius;
learning.LearningRate = task.LearningRate;
Inputs = task.GenerateInputs().ToArray();
pointsPanel = new MyUserControl() { Dock= DockStyle.Fill};
pointsPanel.Paint += DrawPoints;
networkPanel = new MyUserControl() { Dock = DockStyle.Fill };
networkPanel.Paint += DrawNetwork;
networkGraphControl = new MyUserControl { Dock = DockStyle.Fill };
networkGraphControl.Paint += DrawGraph;
var pauseButton = new Button { Text = "Pause/Resume" };
pauseButton.Click+=PauseResume;
var table = new TableLayoutPanel() { Dock = DockStyle.Fill, RowCount=2, ColumnCount=2 };
table.Controls.Add(pointsPanel, 0, 0);
table.Controls.Add(networkPanel, 0, 1);
table.Controls.Add(networkGraphControl, 1, 0);
table.Controls.Add(pauseButton,1,1);
// table.Controls.Add(pause, 1, 1);
table.RowStyles.Add(new RowStyle(SizeType.Percent, 50f));
table.RowStyles.Add(new RowStyle(SizeType.Percent, 50f));
table.ColumnStyles.Add(new ColumnStyle(SizeType.Percent, 50f));
table.ColumnStyles.Add(new ColumnStyle(SizeType.Percent, 50f));
form = new Form()
{
ClientSize = new Size(600, 600),
Controls =
{
table
}
};
timer = new System.Windows.Forms.Timer();
timer.Tick += (sender, args) => { Learning(); form.Invalidate(true); };
timer.Interval = 100;
timer.Start();
Application.Run(form);
}
// Update map
private void UpdateMap( DistanceNetwork network )
{
// get first layer
Layer layer = network[0];
// lock
Monitor.Enter( this );
// run through all neurons
for ( int i = 0, n = layer.NeuronsCount; i < n; i++ )
{
Neuron neuron = layer[i];
int x = i % networkSize;
int y = i / networkSize;
map[y, x, 0] = (int) neuron[0];
map[y, x, 1] = (int) neuron[1];
map[y, x, 2] = 0;
}
// collect active neurons
for ( int i = 0; i < pointsCount; i++ )
{
network.Compute( trainingSet[i] );
int w = network.GetWinner( );
map[w / networkSize, w % networkSize, 2] = 1;
}
// unlock
Monitor.Exit( this );
//
mapPanel.Invalidate( );
}
// Worker thread
void SearchSolution( )
{
// set random generators range
Neuron.RandRange = new DoubleRange( 0, Math.Max( pointsPanel.ClientRectangle.Width, pointsPanel.ClientRectangle.Height ) );
// create network
DistanceNetwork network = new DistanceNetwork( 2, networkSize * networkSize );
// create learning algorithm
SOMLearning trainer = new SOMLearning( network, networkSize, networkSize );
// create map
map = new int[networkSize, networkSize, 3];
double fixedLearningRate = learningRate / 10;
double driftingLearningRate = fixedLearningRate * 9;
// iterations
int i = 0;
RefreshDelegate refreshIterationsBox = delegate () {
currentIterationBox.Text = i.ToString( );
};
RefreshDelegate enableControls = delegate () {
EnableControls( true );
};
// loop
while ( !needToStop )
{
trainer.LearningRate = driftingLearningRate * ( iterations - i ) / iterations + fixedLearningRate;
trainer.LearningRadius = (double) learningRadius * ( iterations - i ) / iterations;
// run training epoch
trainer.RunEpoch( trainingSet );
// update map
UpdateMap( network );
// increase current iteration
i++;
// set current iteration's info
this.Invoke(refreshIterationsBox);
// stop ?
if ( i >= iterations )
break;
}
// enable settings controls
this.Invoke(enableControls);
}
// Worker thread
void SearchSolution()
{
// set random generators range
Neuron.RandRange = new DoubleRange(0, 1000);
// create network
DistanceNetwork network = new DistanceNetwork(2, neurons);
// create learning algorithm
ElasticNetworkLearning trainer = new ElasticNetworkLearning(network);
double fixedLearningRate = learningRate / 20;
double driftingLearningRate = fixedLearningRate * 19;
// path
double[,] path = new double[neurons + 1, 2];
// input
double[] input = new double[2];
// iterations
int i = 0;
// loop
while (!needToStop)
{
// update learning speed & radius
trainer.LearningRate = driftingLearningRate * (iterations - i) / iterations + fixedLearningRate;
trainer.LearningRadius = learningRadius * (iterations - i) / iterations;
// set network input
int currentCity = rand.Next(citiesCount);
input[0] = map[currentCity, 0];
input[1] = map[currentCity, 1];
// run one training iteration
trainer.Run(input);
// show current path
for (int j = 0; j < neurons; j++)
{
path[j, 0] = network[0][j][0];
path[j, 1] = network[0][j][1];
}
path[neurons, 0] = network[0][0][0];
path[neurons, 1] = network[0][0][1];
chart.UpdateDataSeries("path", path);
// increase current iteration
i++;
// set current iteration's info
UpdateTextbox(currentIterationBox, i.ToString());
//currentIterationBox.Text = i.ToString();
// stop ?
if (i >= iterations)
break;
}
// enable settings controls
EnableControls(true);
}
void KohonenTeach()
{
Neuron.RandRange = new Range(0.0f, 1.0f);
kohonenNetwork = new DistanceNetwork(liczba_wejsc, liczba_neuronow_pion * liczba_neuronow_poziom);
kohonenNetwork.Randomize();
SOMLearning teacher = new SOMLearning(kohonenNetwork);
double driftingLearningRate = this.wps_zmiany_wsp;
double fixedLearningRate = this.pocz_wart_wsp_nauki;
double learningRadius = this.pocz_rozmiar_sasiedz;
double driftingLearningRadius = this.wsp_zmian_rozm_sasiedz;
int iteration = 1;
while (!needToStop)
{
teacher.LearningRate = driftingLearningRate * (numberOfCycles - iteration) / numberOfCycles + fixedLearningRate;
teacher.LearningRadius = (double)learningRadius * (numberOfCycles - iteration) / numberOfCycles;
teacher.RunEpoch(trainingKData);
SetIterationsCount(iteration++);
if (iteration > numberOfCycles) break;
}
teachingDone = true;
needToStop = false;
UpdateLabels();
}
// On "Start" button click
private void startButton_Click(object sender, System.EventArgs e)
{
// get network size
try
{
networkSize = Math.Max( 5, Math.Min( 50, int.Parse( sizeBox.Text ) ) );
}
catch
{
networkSize = 15;
}
// get iterations count
try
{
iterations = Math.Max( 10, Math.Min( 1000000, int.Parse( iterationsBox.Text ) ) );
}
catch
{
iterations = 500;
}
// get learning rate
try
{
learningRate = Math.Max( 0.00001, Math.Min( 1.0, double.Parse( rateBox.Text ) ) );
}
catch
{
learningRate = 0.3;
}
// get radius
try
{
learningRadius = Math.Max( 1, Math.Min( 30, int.Parse( radiusBox.Text ) ) );
}
catch
{
learningRadius = 3;
}
// update settings controls
UpdateSettings( );
// disable all settings controls except "Stop" button
EnableControls( false );
// generate training set
for ( int i = 0; i < pointsCount; i++ )
{
// create new training sample
trainingSet[i] = new double[2] { points[i, 0], points[i, 1] };
}
//// run worker thread
//needToStop = false;
//workerThread = new Thread( new ThreadStart( SearchSolution ) );
//workerThread.Start( );
Neuron.RandRange = new DoubleRange(0, Math.Max(pointsPanel.ClientRectangle.Width, pointsPanel.ClientRectangle.Height));
// create network
network = new DistanceNetwork(2, networkSize * networkSize);
// create learning algorithm
trainer = new SOMLearning(network, networkSize, networkSize);
// create map
map = new int[networkSize, networkSize, 3];
fixedLearningRate = learningRate / 10;
driftingLearningRate = fixedLearningRate * 9;
// iterations
_i = 0;
}
