/// <summary>
/// Runs one iteration of the learning algorithm with the
/// specified input training observation and corresponding
/// output label.
/// </summary>
///
/// <param name="observations">The training observations.</param>
/// <param name="outputs">The observation's labels.</param>
///
/// <returns>The error in the last iteration.</returns>
///
public double Run(T[][] observations, int[] outputs)
{
convergence.Clear();
do
{
RunEpoch(observations, outputs);
}
while (!convergence.HasConverged);
return convergence.NewValue;
}
private double run(T[][] observations, int[] outputs)
{
convergence.Clear();
do
{
RunEpoch(observations, outputs);
if (Token.IsCancellationRequested)
break;
}
while (!convergence.HasConverged);
return convergence.NewValue;
}
/// <summary>
/// Runs the learning algorithm.
/// </summary>
///
protected override double InnerRun(T[][] observations, int[] outputs)
{
init();
convergence.Clear();
do
{
RunEpoch(observations, outputs);
if (Token.IsCancellationRequested)
break;
}
while (!convergence.HasConverged);
return convergence.NewValue;
}
/// <summary>
/// Resets the step size.
/// </summary>
///
public void Reset()
{
convergence.Clear();
stepSize = 0;
}