/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
///
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
///
/// <returns>A model that has learned how to produce <paramref name="y"/> given <paramref name="x"/>.</returns>
///
public DecisionTree Learn(double[][] x, int[] y, double[] weights = null)
{
if (Model == null)
{
init(DecisionTreeHelper.Create(x, y, this.Attributes));
}
this.run(x, y, weights);
return(Model);
}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
///
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
///
/// <returns>A model that has learned how to produce <paramref name="y"/> given <paramref name="x"/>.</returns>
///
public DecisionTree Learn(int?[][] x, int[] y, double[] weights = null)
{
if (Model == null)
{
init(DecisionTreeHelper.Create(x, y, this.Attributes));
}
this.run(x.Apply((xi, i, j) => xi.HasValue ? (double)xi : Double.NaN), y, weights);
return(Model);
}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
///
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
///
/// <returns>A model that has learned how to produce <paramref name="y"/> given <paramref name="x"/>.</returns>
///
public DecisionTree Learn(int[][] x, int[] y, double[] weights = null)
{
if (weights != null)
{
throw new ArgumentException(Accord.Properties.Resources.NotSupportedWeights, "weights");
}
if (Model == null)
{
init(DecisionTreeHelper.Create(x, y, this.Attributes));
}
this.run(x.ToDouble(), y, weights);
return(Model);
}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
///
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
///
/// <returns>A model that has learned how to produce <paramref name="y"/> given <paramref name="x"/>.</returns>
///
public DecisionTree Learn(int?[][] x, int[] y, double[] weights = null)
{
if (weights != null)
{
throw new ArgumentException(Accord.Properties.Resources.NotSupportedWeights, "weights");
}
if (Model == null)
{
init(DecisionTreeHelper.Create(x, y, this.Attributes));
}
this.run(x.Apply((xi, i, j) => xi.HasValue ? (double)xi : Double.NaN), y);
return(Model);
}
