/// <summary>
/// Fit the model according to the given training data.
/// </summary>
/// <param name="regressor">A <see cref="IRegressor"/> instance.</param>
/// <param name="x">
/// Matrix with dimensions [nSamples, nFeatures].
/// Training vectors, where nSamples is the number of samples
/// and nFeatures is the number of features.</param>
/// <param name="y">Vector with dimensions [nSamples]. Target values.</param>
/// <param name="sampleWeight">Individual weights for each sample. Vector with dimensions [nSamples].</param>
public static void Fit(
this IRegressor regressor,
Matrix <double> x,
Vector <double> y,
Vector <double> sampleWeight = null)
{
regressor.Fit(x, y.ToColumnMatrix(), sampleWeight);
}
/// <summary>
/// Fit the model according to the given training data.
/// </summary>
/// <param name="regressor">A <see cref="IRegressor"/> instance.</param>
/// <param name="x">
/// Array with dimensions [nSamples, nFeatures].
/// Training vectors, where nSamples is the number of samples
/// and nFeatures is the number of features.</param>
/// <param name="y">Array with dimensions [nSamples, nTargets]. Target values.</param>
/// <param name="sampleWeight">Individual weights for each sample. Array with dimensions [nSamples].</param>
public static void Fit(this IRegressor regressor, double[,] x, double[,] y, double[] sampleWeight = null)
{
regressor.Fit(x.ToDenseMatrix(), y.ToDenseMatrix(), sampleWeight.ToDenseVector());
}
/// <summary>
/// Predict target values for samples in x.
/// </summary>
/// <param name="regressor">A <see cref="IRegressor"/> instance.</param>
/// <param name="x">Array with dimensions [nSamples, nFeatures].</param>
/// <returns>Returns predicted values. Array with dimensions [nSamples].</returns>
public static double[] PredictSingle(this IRegressor regressor, double[,] x)
{
return(regressor.Predict(DenseMatrix.OfArray(x)).Column(0).ToArray());
}
/// <summary>
/// Predict target values for sample x.
/// </summary>
/// <param name="regressor">A <see cref="IRegressor"/> instance.</param>
/// <param name="x">Array of length [nFeatures].</param>
/// <returns>Returns predicted values. Array of length [nTargets].</returns>
public static double[] Predict(this IRegressor regressor, double[] x)
{
return(regressor.Predict(x.ToDenseVector().ToRowMatrix()).Row(0).ToArray());
}
/// <summary>
/// <para>
/// Returns the coefficient of determination R^2 of the prediction.
/// </para>
/// <para>
/// The coefficient R^2 is defined as (1 - u/v), where u is the regression
/// sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual
/// sum of squares ((y_true - y_true.mean()) ** 2).sum().
/// Best possible score is 1.0, lower values are worse.
/// </para>
/// </summary>
/// <param name="regressor">A <see cref="IRegressor"/> instance.</param>
/// <param name="x">Feature matrix. Matrix with dimensions [nSamples, nFeatures].</param>
/// <param name="y">True values for <paramref name="x"/>. Vector with dimensions [nSamples].</param>
/// <returns> R^2 of Predict(X) wrt. y.</returns>
public static double Score(this IRegressor regressor, Matrix <double> x, Vector <double> y)
{
return(Metrics.Metrics.R2Score(y.ToColumnMatrix(), regressor.Predict(x)));
}
/// <summary>
/// <para>
/// Returns the coefficient of determination R^2 of the prediction.
/// </para>
/// <para>
/// The coefficient R^2 is defined as (1 - u/v), where u is the regression
/// sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual
/// sum of squares ((y_true - y_true.mean()) ** 2).sum().
/// Best possible score is 1.0, lower values are worse.
/// </para>
/// </summary>
/// <param name="regressor">A <see cref="IRegressor"/> instance.</param>
/// <param name="x">Feature matrix. Matrix with dimensions [nSamples, nFeatures].</param>
/// <param name="y">True values for <paramref name="x"/>. Matrix with dimensions [nSamples, nTargets].</param>
/// <returns> R^2 of Predict(x) wrt. y.</returns>
public static double Score(this IRegressor regressor, Matrix <double> x, Matrix <double> y)
{
return(Metrics.Metrics.R2Score(y, regressor.Predict(x)));
}
