public static DoubleVectorList Load(BinaryReader r)
{
DoubleVectorList nnvl = new DoubleVectorList();
int count = r.ReadInt32();
for (int i = 0; i < count; i++)
{
nnvl.Add(DoubleVector.Load(r));
}
return(nnvl);
}
/// <summary>
/// Train the network on a single input vector. Returns the sum of the mean square error of the sets.
/// </summary>
/// <param name="inputs">The input vectors to train on</param>
/// <param name="targets">The target output vectors</param>
/// <param name="iterations">The number of iterations to train on these inputs</param>
public double Train(DoubleVectorList inputs, DoubleVectorList targets, int iterations)
{
// Make sure all input vectors have a matching output vector
if (inputs.Count != targets.Count)
{
throw new Exception("You must provide a desired output vector for each input vector!");
}
double error = 0.0;
int progress;
Random r = new Random();
for (int i = 0; i < iterations; i++)
{
// The error for this iteration
error = 0.0;
// Create a list of indexes and mix it up so that we don't train on the sets sequentially
List <int> indices = new List <int>();
for (int n = 0; n < inputs.Count; n++)
{
indices.Insert(r.Next(n), n);
}
// Train for one iteration on this set
for (int s = 0; s < inputs.Count; s++)
{
error += Train(inputs[indices[s]], targets[indices[s]], 1);
// Report Progress
progress = (int)(((double)i * 100 / (double)iterations) + (((double)s * 100) / (double)inputs.Count) / (double)iterations);
if (OnTrainingProgressChange != null)
{
OnTrainingProgressChange(this, new OnTrainingProgressChangeEventArgs(progress, (error / (double)s) * (double)inputs.Count));
}
}
// Lower the learning rate
learningRate *= _learningRateDescent;
}
// Return error of most recent iteration
return(error);
}