public static DecisionTree Create(int[][] x, int[] y, IList <DecisionVariable> attributes)
{
if (attributes == null || attributes.Count == 0)
{
attributes = DecisionVariable.FromData(x);
}
return(Create(y, attributes));
}
/// <summary>
/// Learns a new Random Forest with the given data.
/// </summary>
///
/// <param name="inputs">The input points.</param>
/// <param name="output">The class label for each point.</param>
///
/// <returns>A <see cref="RandomForest"/> object that learned
/// how to assign class labels to input points.</returns>
///
public RandomForest Learn(double[][] inputs, int[] output)
{
if (forest == null)
{
int classes = output.Max() + 1;
this.forest = new RandomForest(NumberOfTrees, classes);
var variables = DecisionVariable.FromData(inputs);
for (int i = 0; i < forest.Trees.Length; i++)
{
forest.Trees[i] = new DecisionTree(variables, classes);
}
}
run(inputs, output);
return(this.forest);
}
/// <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 RandomForest Learn(double[][] x, int[] y, double[] weights = null)
{
if (weights != null)
{
throw new ArgumentException(Accord.Properties.Resources.NotSupportedWeights, "weights");
}
if (forest == null)
{
if (this.attributes == null)
{
this.attributes = DecisionVariable.FromData(x);
}
this.forest = CreateTree(y);
}
run(x, y);
return(this.forest);
}
/// <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.</param>
///
/// <returns>A model that has learned how to produce <paramref name="y"/> given <paramref name="x"/>.</returns>
///
public RandomForest Learn(double[][] x, int[] y, double[] weights = null)
{
if (forest == null)
{
int classes = y.Max() + 1;
this.forest = new RandomForest(NumberOfTrees, classes);
if (this.attributes == null)
{
this.attributes = DecisionVariable.FromData(x);
}
for (int i = 0; i < forest.Trees.Length; i++)
{
forest.Trees[i] = new DecisionTree(attributes, classes);
}
}
run(x, y);
return(this.forest);
}
/// <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 RandomForest Learn(double[][] x, int[] y, double[] weights = null)
{
if (weights != null)
{
throw new ArgumentException(Accord.Properties.Resources.NotSupportedWeights, "weights");
}
if (forest == null)
{
int classes = y.Max() + 1;
this.forest = new RandomForest(NumberOfTrees, classes);
if (this.attributes == null)
{
this.attributes = DecisionVariable.FromData(x);
}
for (int i = 0; i < forest.Trees.Length; i++)
{
forest.Trees[i] = new DecisionTree(attributes, classes);
}
}
run(x, y);
return(this.forest);
}