/// <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; }