private void ButtonRecognize_Click(object sender, EventArgs e)
{
var bitmap = (Bitmap)pictureBox1.Image;
var array = FromImageToArray(bitmap);
var result = new StochasticGradient().NeuronFunc(array, _weights);
MessageBox.Show(result.ToString());
}
private void ButtonLearn_Click(object sender, EventArgs e)
{
Abort.Abort = false;
if (_samples == null)
{
var dict = GetLearningSamples();
_samples = dict.Keys.ToArray();
foreach (var input in _samples)
{
for (var i = 0; i < input.Length; i++)
{
input[i] = input[i] > 0 ? 1 : 0;
}
}
_answers = dict.Values.ToArray();
}
var eta = double.Parse(etaTextBox.Text, CultureInfo.InvariantCulture);
var stab = double.Parse(stabilityTextBox.Text, CultureInfo.InvariantCulture);
var wstab = double.Parse(wstabTextBox.Text, CultureInfo.InvariantCulture);
_weights = new double[_samples[0].Length];
thread = new Thread(() =>
{
var SG = new StochasticGradient {
Abort = Abort
};
SG.IterationChange += (o, args) => iterNew = SG.Iterations.ToString();
SG.DQChanged += (o, args) => dqNew = SG.D_Q.ToString("e2");
SG.VectorChanged += (o, args) => vectDelta = SG.VectDelta.ToString("e2");
_weights = SG.SG(_samples, _answers, eta, 1.0 / _samples.Length, stab, wstab);
if (!SG.Abort.Abort)
{
isSetted = true;
}
})
{
IsBackground = true, Priority = ThreadPriority.Highest
};
thread.Start();
}