/// <summary>
/// Uses gradient descent to calculate the "theta" vector.
/// On each iteration gets calculated as per
///
/// theta = theta - (alpha/m)*(X*theta - Y)'*X
///
/// </summary>
private void GradientDescent(Input input)
{
var monitor = new CostFunctionMonitor(input);
_theta = new DenseMatrix(1, input.FeaturesCount);
var multiplier = (Settings.LearningRate / input.SamplesCount);
for (int i = 0; i < Settings.MaxIterations; i++) {
_theta -= multiplier * ((input.X * _theta.Transpose() - input.Y).Transpose() * input.X);
if (monitor.IsConverged(_theta)) {
break;
}
}
}
/// <summary>
/// Uses gradient descent to calculate the "theta" vector.
/// On each iteration gets calculated as per
///
/// theta = theta - (alpha/m)*(X*theta - Y)'*X
///
/// </summary>
private void GradientDescent(Input input)
{
var monitor = new CostFunctionMonitor(input);
_theta = new DenseMatrix(1, input.FeaturesCount);
var multiplier = (Settings.LearningRate / input.SamplesCount);
for (int i = 0; i < Settings.MaxIterations; i++)
{
_theta -= multiplier * ((input.X * _theta.Transpose() - input.Y).Transpose() * input.X);
if (monitor.IsConverged(_theta))
{
break;
}
}
}
