IEnumerator LoadTrainingSet()
{
string path = ann.GetPath("trainingData");
string line;
if (File.Exists(path))
{
int lineCount = File.ReadAllLines(path).Length;
StreamReader tdf = File.OpenText(path);
List <double> calcOutputs = new List <double>();
List <double> inputs = new List <double>();
List <double> outputs = new List <double>();
for (int i = 0; i < epochs; i++)
{
// set file pointer to beginning of file
sse = 0;
tdf.BaseStream.Position = 0;
string currentWeights = ann.PrintWeights();
while ((line = tdf.ReadLine()) != null)
{
string[] data = line.Split(',');
// if nothing to be learned ignore this line
float thisError = 0;
if (System.Convert.ToDouble(data[5]) != 0 && System.Convert.ToDouble(data[6]) != 0)
{
inputs.Clear();
outputs.Clear();
inputs.Add(System.Convert.ToDouble(data[0]));
inputs.Add(System.Convert.ToDouble(data[1]));
inputs.Add(System.Convert.ToDouble(data[2]));
inputs.Add(System.Convert.ToDouble(data[3]));
inputs.Add(System.Convert.ToDouble(data[4]));
double out1 = Utils.Map(0, 1, -1, 1, System.Convert.ToSingle(data[5]));
double out2 = Utils.Map(0, 1, -1, 1, System.Convert.ToSingle(data[6]));
outputs.Add(out1);
outputs.Add(out2);
calcOutputs = ann.Train(inputs, outputs);
thisError = ((Mathf.Pow((float)(outputs[0] - calcOutputs[0]), 2) +
Mathf.Pow((float)(outputs[1] - calcOutputs[1]), 2))) / 2.0f;
}
sse += thisError;
}
trainingProgress = ((float)i / (float)epochs) * 100;
sse /= lineCount;
if (lastSSE < sse) // if sse isnt better reload old one and decrease alpha
{
ann.LoadWeights(currentWeights);
ann.alpha = Mathf.Clamp((float)ann.alpha - 0.001f, 0.01f, 0.9f);
}
else // increase alpha
{
ann.alpha = Mathf.Clamp((float)ann.alpha + 0.001f, 0.01f, 0.9f);
lastSSE = sse;
}
yield return(null);
}
tdf.Close();
}
trainingDone = true;
ann.SaveWeightsToFile();
}
