/// <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 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)
{
int classes = output.DistinctCount();
this.forest = new RandomForest(Trees, classes);
Run(inputs, output);
return(this.forest);
}
/// <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>
/// <inheritdoc />
/// </summary>
public override void Train()
{
var inputs = data.GetSelectedInput(features);
var outputs = data.GetExpectedClassificationOutput();
var DecisionVariables = new List <DecisionVariable>();
for (int i = 0; i < inputs[0].Length; i++)
{
DecisionVariables.Add(DecisionVariable.Continuous(i.ToString(), new DoubleRange(0.0, 1.0)));
}
var teacher = new RandomForestLearning(DecisionVariables.ToArray())
{
NumberOfTrees = 20
};
forest = teacher.Learn(inputs, outputs);
Save();
}
protected override void Load()
{
forest = Load <Accord.MachineLearning.DecisionTrees.RandomForest>(this.ToString());
}
/// <summary>
/// Creates a new decision forest learning algorithm.
/// </summary>
///
public RandomForestLearning(RandomForest forest)
{
this.SampleRatio = 0.632;
this.NumberOfTrees = forest.Trees.Length;
this.forest = forest;
}
/// <summary>
/// Creates a new decision forest learning algorithm.
/// </summary>
///
public RandomForestLearning(RandomForest forest)
{
this.SampleRatio = 0.632;
this.NumberOfTrees = forest.Trees.Length;
this.forest = forest;
}
