/// <summary>Generate model based on a set of examples.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix x, Vector y)
{
// because I said so...
if (this.MaxIterations == -1)
{
this.MaxIterations = x.Rows * 1000;
}
var network = Network.Default(this.Descriptor, x, y, this.Activation);
var model = new NeuralNetworkModel {
Descriptor = this.Descriptor, Network = network
};
this.OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));
for (int i = 0; i < this.MaxIterations; i++)
{
int idx = i % x.Rows;
network.Forward(x[idx, VectorType.Row]);
//OnModelChanged(this, ModelEventArgs.Make(model, "Forward"));
network.Back(y[idx], this.LearningRate);
var output = String.Format("Run ({0}/{1})", i, this.MaxIterations);
this.OnModelChanged(this, ModelEventArgs.Make(model, output));
}
return(model);
}
/// <summary>Generate model based on a set of examples.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix x, Vector y)
{
// because I said so...
if (MaxIterations == -1)
{
MaxIterations = x.Rows * 1000;
}
var network = Network.Default(Descriptor, x, y, Activation);
for (int i = 0; i < MaxIterations; i++)
{
int idx = i % x.Rows;
network.Forward(x[idx, VectorType.Row]);
network.Back(y[idx], LearningRate);
}
return(new NeuralNetworkModel {
Descriptor = Descriptor, Network = network
});
}
