private void buttonClassify_Click(object sender, EventArgs e)
{
if (textBoxPath.Text != null)
{
ReadData.readUserData(textBoxPath.Text, Convert.ToInt32(outputNumText.Text), Convert.ToDouble(textBoxLearningRate.Text));
MessageBox.Show("Data has been compressed in 'Compressed User images.txt'\n PCA weights has also been stored in PCA User weights in release folder. ");
}
}
private void buttonClassify_Click(object sender, EventArgs e)
{
if (image != null)
{
Neural_Network pretrainedNN = new Neural_Network();
image = new Bitmap(image, 50, 50);
double[,] input = ImageProcessing.imageToGrayscale(image);
ReadData.normalizeImage(ref input);
PCA pca = new PCA();
pca.readPretrainedWeights();
input = Utilities.addBias(pca.compress(input));
textBoxClassification.Text = pretrainedNN.classify(input);
}
else
{
MessageBox.Show("Enter picture first");
}
}
private void buttonTrain_Click(object sender, EventArgs e)
{
if (radioButtonAll.Checked)
{
ReadData.readCompressedData();
}
else
{
ReadData.readSmallCompressedData();
}
RadialBasisNet RB = new RadialBasisNet();
Tuple <double, double[, ]> t = RB.train(Convert.ToInt32(textBoxLayers.Text), Convert.ToInt32(textBoxNum_Iteraions.Text), Convert.ToDouble(textBoxEta.Text));
ConfusionMatrix CM = new ConfusionMatrix();
CM.fillMatrix(t.Item2);
textBoxAccuracy.Text = t.Item1.ToString() + '%';
CM.Show();
}
