private void buttonTrain_Click(object sender, EventArgs e)
        {
            if (radioButtonAll.Checked)
            {
                ReadData.readCompressedData();
            }
            else
            {
                ReadData.readSmallCompressedData();
            }
            RadialBasisNet RB = new RadialBasisNet();

            char[]     delimiterChars = { ' ' };
            string[]   words          = textBoxLayers.Text.Split(delimiterChars);
            List <int> layersList     = new List <int>();

            layersList.Add(256);
            for (int i = 0; i < words.Length; i++)
            {
                if (words[i] != "")
                {
                    layersList.Add(Convert.ToInt32(words[i]));
                }
            }
            layersList.Add(3);
            int[]                      layers = layersList.ToArray();
            Neural_Network             NN     = new Neural_Network();
            Tuple <double, double[, ]> t      = NN.train(layers, Convert.ToInt32(textBoxNum_Iteraions.Text), Convert.ToDouble(textBoxEta.Text));
            ConfusionMatrix            CM     = new ConfusionMatrix();

            CM.fillMatrix(t.Item2);
            textBoxAccuracy.Text = t.Item1.ToString() + '%';
            CM.Show();
        }
Ejemplo n.º 2
0
 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");
     }
 }