/// <summary>Generate Linear Regression 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)
{
// create initial theta
Vector theta = Vector.Ones(x.Cols + 1);
Matrix copy = x.Copy();
// normalise features
for (int i = 0; i < copy.Cols; i++)
{
var j = FeatureNormalizer.FeatureScale(copy[i, VectorType.Col]);
for (int k = 0; k < copy.Rows; k++)
{
copy[k, i] = j[k];
}
}
// add intercept term
copy = copy.Insert(Vector.Ones(copy.Rows), 0, VectorType.Col);
// run gradient descent
var run = GradientDescent.Run(theta, copy, y, this.MaxIterations, this.LearningRate, new LinearCostFunction(), this.Lambda, new Regularization());
// once converged create model and apply theta
LinearRegressionModel model = new LinearRegressionModel(x.Mean(VectorType.Row), x.StdDev(VectorType.Row))
{
Descriptor = this.Descriptor,
Theta = run.Item2
};
return(model);
}
/// <summary>
/// TODO The normalise.
/// </summary>
/// <param name="y">
/// TODO The y.
/// </param>
/// <returns>
/// </returns>
private Vector Normalise(Vector y)
{
for (var i = 0; i < y.Length; i++)
{
y[i] = FeatureNormalizer.FeatureScale(y[i], this.FeatureAverages[i], this.FeatureStandardDeviations[i]);
}
return(y.Insert(0, 1.0d));
}
