public float Run(Models ml, int trainTimes)
{
Random rd = new Random();
float[] data = new float[dimensions];
for (int i = 0; i < trainTimes; i++)
{
int rnd = rd.Next(0, N + 1);
for (int j = 0; j < dimensions; j++)
{
data[j] = datas[rnd][j];
}
float val = ml.Calculate(data);
ml.Callback(expect[rnd]);
}
float sum = 0f;
for (int i = 0; i < N + 1; i++)
{
int rnd = i;
for (int j = 0; j < dimensions; j++)
{
data[j] = datas[rnd][j];
}
float val = ml.Calculate(data);
ml.Callback(expect[rnd]);
sum += (val - expect[rnd]) * (val - expect[rnd]);
//lb1.Items.Add(datas[rnd, 0] + " , " + datas[rnd, 1] + " : " + expect[rnd] + " 输出结果: " + val.ToString("0.00") );
}
return(sum / 2 / (N + 1));
}
public void Draw(Models ml, int resolution)
{
Canva1.Children.Clear();
for (int i = 0; i < resolution; i++)
{
for (int j = 0; j < resolution; j++)
{
Rectangle r = new Rectangle
{
Fill = new SolidColorBrush(Color.FromRgb(
(byte)(ml.Calculate(new float[] { i / (float)resolution, j / (float)resolution }) * 254),
144,
159)),
Width = (Canva1.Width / resolution) + 1f,
Height = (Canva1.Height / resolution) + 1f
};
r.SetValue(Canvas.LeftProperty, i * (Canva1.Width / (float)resolution));
r.SetValue(Canvas.TopProperty, (resolution - j) * ((Canva1.Height - 1) / (float)resolution) - Canva1.Height / resolution);
Canva1.Children.Add(r);
}
}
}
