private void bControllerReset_Click(object sender, EventArgs e)
{
int inputCount;
int outputCount;
var structure = ParseStructureBox(out inputCount, out outputCount);
Tuple <double, double, double, double> Norm = Tuple.Create(10.0, 0.0, 3.0, 0.0);
List <double[]> input = new List <double[]>();
List <double[]> output = new List <double[]>();
var t = Data.ReadData(FilePath, inputCount, outputCount, Norm, rbMNIST.Checked);
input = t.Item1;
output = t.Item2;
PerceptronNetwork network = new MLP.PerceptronNetwork(structure);
NetworkController = new MLP.Network_Control(network, output, input, Double.Parse(tbLearningRate.Text), false, Norm, inputCount, outputCount);
cartesianChart1.Series[0].Values.Clear();
cartesianChart2.Series[0].Values.Clear();
cartesianChart3.Series[0].Values.Clear();
if (!cbdisabledrawing.Checked)
{
var Image = NetworkImage.Draw(NetworkController.Network, NetworkController.Network.Layers.Count);
IMG_NetworkTopography.Image = Image;
}
}
public Form1()
{
FilePath = "IRIS_Train.csv";
Tuple <double, double, double, double> Norm = Tuple.Create(10.0, 0.0, 3.0, 0.0);
List <double[]> input = new List <double[]>();
List <double[]> output = new List <double[]>();
var t = Data.ReadData("IRIS.csv", 4, 1, Norm, false);
input = t.Item1;
output = t.Item2;
PerceptronNetwork network = new MLP.PerceptronNetwork(new int[] { 4, 5, 5, 1 });
NetworkController = new MLP.Network_Control(network, output, input, 0.02, false, Norm, 4, 1);
InitializeComponent();
//Set image to the starting network's image
if (!cbdisabledrawing.Checked)
{
var Image = NetworkImage.Draw(network, network.Layers.Count);
IMG_NetworkTopography.Image = Image;
IMG_NetworkTopography.SizeMode = PictureBoxSizeMode.Zoom;
}
rtbConsole.Text = "";
NetworkController.Paused = true;
}
private void rbMNIST_CheckedChanged(object sender, EventArgs e)
{
//btnbatch.Enabled = false;
Tuple <double, double, double, double> Norm = Tuple.Create(255.0, 0.0, 9.0, 0.0);
List <double[]> input = new List <double[]>();
List <double[]> output = new List <double[]>();
var t = Data.ReadData("mnist_test.csv", 785, 1, Norm, true);
input = t.Item1;
output = t.Item2;
//https://www.kaggle.com/donfuzius/vectordigits
PerceptronNetwork network = new MLP.PerceptronNetwork(new int[] { 784, 784, 1 });
NetworkController = new MLP.Network_Control(network, output, input, double.Parse(tbLearningRate.Text), false, Norm, 784, 1);
tbStructure.Text = "784 784 1";
/*
* Tuple<double, double, double, double> Norm = Tuple.Create(255.0, 0.0, 9.0, 0.0);
* List<double[]> input = new List<double[]>();
* List<double[]> output = new List<double[]>();
* var t = Data.ReadData(path, 784, 1, Norm);
* input = t.Item1;
* output = t.Item2;
*
* int errors = 0;
* for (int i = 0; i < input.Count; i++)
* {
* for (int j = 0; j < output[i].Length; j++)
* {
* var expected = Data.inverseNormalize(output[i][j], Norm.Item4, Norm.Item3);
*
* var a = NetworkController.Network.Activate(input[i]); //Get the result from the given outputs
* var bbb = Data.inverseNormalize(a[j], Norm.Item4, Norm.Item3); //Inverse normalize the result to fit it between output values
*
* if (Math.Round(bbb) != expected) { errors++; }//Error?
* rtbConsole.Text += "Expected: " + expected + " Got :" + Math.Round(bbb) + "\n";
* rtbConsole.SelectionStart = rtbConsole.Text.Length;
* rtbConsole.ScrollToCaret();
*
* }
*
*
* }
* rtbConsole.Text += "Total Number of Errors: " + errors + "/" + input.Count*output.Count + "\n";
*
*/
}
/// <summary>
/// Draws selection boundry of the two given features, decrepit
/// </summary>
/// <param name="p"></param>
/// <param name="feature1"></param>
/// <param name="feature2"></param>
/// <param name="otherInputsStatic"></param>
/// <param name="possibleOutputCount"></param>
/// <param name="minOutputValue"></param>
/// <param name="maxOutputValue"></param>
public static void DrawBoundryMap_IRIS(Network_Control p, double[] feature1, double[] feature2, double[] otherInputsStatic, int possibleOutputCount, double minOutputValue, double maxOutputValue)
{
int sizeMod = 20;
int width = feature1.Length * sizeMod;
int height = feature2.Length * sizeMod;
Bitmap m = new Bitmap(width, height);
Graphics g = Graphics.FromImage(m);
Random rnd = new Random();
/*var a = NetworkController.Network.Activate(input[i]);
* var bbb = Data.inverseNormalize(a[0], 0.0, 3.0);*/
List <int[]> GradientColors = new List <int[]>();
for (int i = 0; i < possibleOutputCount; i++)
{
GradientColors.Add(new int[] { rnd.Next(0, 255), rnd.Next(0, 255), rnd.Next(0, 255) });
}
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
var sx = (int)Math.Floor((double)(y / sizeMod));
var sy = (int)Math.Floor((double)(x / sizeMod));
var a = p.Network.Activate(new double[] { feature1[sx], feature2[sy] }.Concat(otherInputsStatic).ToArray());
var bbb = Data.inverseNormalize(a[0], minOutputValue, maxOutputValue);
for (int k = 0; k < GradientColors.Count; k++)
{
if (Math.Round(bbb) == k)
{
//var c = Color.FromArgb((int)Math.Abs(Math.Floor(GradientColors[k][0] * multiplier)), Math.Abs((int)Math.Floor(GradientColors[k][1] * multiplier)), Math.Abs((int)Math.Floor(GradientColors[k][2] * multiplier)));
Pen pen = new Pen(Color.FromArgb(GradientColors[k][0], GradientColors[k][1], GradientColors[k][2]), 1);
g.DrawEllipse(pen, x, y, 1, 1);
}
}
}
}
m.Save("bound.png", ImageFormat.Png);
}
private void rbIris_CheckedChanged(object sender, EventArgs e)
{
//btnbatch.Enabled = true;
Tuple <double, double, double, double> Norm = Tuple.Create(10.0, 0.0, 3.0, 0.0);
List <double[]> input = new List <double[]>();
List <double[]> output = new List <double[]>();
var t = Data.ReadData("IRIS_Train.csv", 4, 1, Norm, false);
input = t.Item1;
output = t.Item2;
PerceptronNetwork network = new MLP.PerceptronNetwork(new int[] { 4, 5, 5, 1 });
NetworkController = new MLP.Network_Control(network, output, input, double.Parse(tbLearningRate.Text), false, Norm, 4, 1);
tbStructure.Text = "4 5 5 1";
}