private void AddPatternBut_Click(object sender, EventArgs e)
{
Image imgPattern;
if (ofd.ShowDialog() == DialogResult.OK)
{
imgPattern = Image.FromFile(ofd.FileName);
if (imgPattern.Width != imageDim || imgPattern.Height != imageDim)
{
MessageBox.Show("Image size must be " + imageDim.ToString() + "x" + imageDim.ToString() + " pixels", "Wrong image size", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
else
{
int[,] patternPixels;
int p = 0;
int midColor = Math.Abs((int)(Color.Black.ToArgb() / 2));
Bitmap b = new Bitmap(imgPattern);
patternPixels = new int[imageDim, imageDim];
List<Neuron> pattern = new List<Neuron>(imageDim * imageDim);
for (int i = 0; i < imageDim; i++)
for (int j = 0; j < imageDim; j++)
{
Neuron n = new Neuron();
p = Math.Abs(b.GetPixel(i, j).ToArgb());
if (p < midColor)
{
b.SetPixel(i, j, Color.White);
n.State = NeuronStates.AlongField;
}
else
{
b.SetPixel(i, j, Color.Black);
n.State = NeuronStates.AgainstField;
}
pattern.Add(n);
}
NN.AddPattern(pattern);
ImageMagnifier.ImageMagnifier im = new ImageMagnifier.ImageMagnifier();
im.ImageToMagnify = b;
im.MagnificationCoefficient = 8;
im.Location = new System.Drawing.Point(1, ((NN.M - 1) * (imageDim + 2) * im.MagnificationCoefficient));
im.Size = new System.Drawing.Size(imageDim * im.MagnificationCoefficient, imageDim * im.MagnificationCoefficient);
im.TabIndex = 0;
im.Click += new EventHandler(im_Click);
panelStoredImages.Controls.Add(im);
butRunDynamics.Enabled = true;
UpdatePropertiesPB();
}
}
}
/// <summary>
/// Builder.
/// Initializes a new instance of the <seealso cref="NeuralNetwork"/> class
/// </summary>
/// <param name="n">Number of neurons</param>
public NeuralNetwork(int n)
{
this.NeuronsCount = n;
Neurons = new List<Neuron>(n);
for (int i = 0; i< n; i++)
{
Neuron neuron = new Neuron();
neuron.State = 0;
Neurons.Add(neuron);
}
Matrix = new int[n, n];
PatternsCount = 0;
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
{
Matrix[i, j] = 0;
}
}
/// <summary>
/// Initializes a new instance of the <seealso cref="NeuralNetwork"/> class
/// </summary>
/// <param name="n">Number of neurons</param>
public NeuralNetwork(int n)
{
this.n = n;
neurons = new List<Neuron>(n);
for (int i = 0; i< n; i++)
{
Neuron neuron = new Neuron();
neuron.State = 0;
neurons.Add(neuron);
}
T = new int[n, n];
m = 0;
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
{
T[i, j] = 0;
}
}
private void RunDynamicsBut_Click(object sender, EventArgs e)
{
if (!patternSelected)
{
MessageBox.Show("You must select initial pattern.", "Neural Network Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
else
{
List<HopfieldNeuralNetwork.Neuron> initialState = new List<HopfieldNeuralNetwork.Neuron>(NN.N);
for (int i = 0; i < imageDim; i++)
for (int j=0;j<imageDim;j++)
{
Neuron neuron = new Neuron();
if (imNNState.pixels[i, j] == Color.Black.ToArgb()) neuron.State = NeuronStates.AgainstField;
else if (imNNState.pixels[i, j] == Color.White.ToArgb()) neuron.State = NeuronStates.AlongField;
initialState.Add(neuron);
}
NN.Run(initialState);
lblEnergy.Text = NN.Energy.ToString();
patternSelected = false;
}
}
/// <summary>
/// Adds a random pattern to interconnection matrix
/// </summary>
public void AddRandomPattern()
{
List<Neuron> randomPattern = new List<HopfieldNeuralNetwork.Neuron>(NeuronsCount);
Random rnd = new Random();
for (int i = 0; i < NeuronsCount; i++)
{
Neuron neuron = new Neuron();
randomPattern.Add(neuron);
int bit;
bit = rnd.Next(2);
if (bit == 0) randomPattern[i].State = NeuronStates.AlongField;
else
if (bit == 1) randomPattern[i].State = NeuronStates.AgainstField;
}
for (int i = 0; i < NeuronsCount; i++)
for (int j = 0; j < NeuronsCount; j++)
{
if (i == j) Matrix[i, j] = 0;
else Matrix[i, j] += randomPattern[i].State * randomPattern[j].State;
}
PatternsCount++;
}
/// <summary>
/// Realiza el pasaje del patron grafico a su interpretacion valida para la NNA de Hopfield
/// </summary>
/// <param name="pattern">Patron grafico</param>
/// <returns>Patron neuronal valido para hopfield</returns>
private List<Neuron> patternToNeurons(Pattern pattern)
{
var result = new List<Neuron>();
for(int i = 0; i < pattern.Count; i++)
{
var neuron = new Neuron();
neuron.State = pattern[i] == 1 ? NeuronStates.AgainstField : NeuronStates.AlongField;
result.Add(neuron);
}
return result;
}
