public DecisionTreeDNANode(int p_num_inputs, Range <float> p_threshold_range, int p_num_outputs, Range <float> p_outputs_range)
{
m_input_index = RandomCalc.Rand(new Range <int>(0, p_num_inputs - 1));
m_min_input = RandomCalc.Rand(p_threshold_range);
m_max_input = RandomCalc.Rand(new Range <float>(m_min_input, p_threshold_range.Max));
m_output_values = ArrayCalc.functionInitialize(p_num_outputs, () => { return(RandomCalc.Rand(p_outputs_range)); });
}
//Constructor
public DecisionTreeNode(int p_input_index, float p_min_input, float p_max_input, float[] p_output_values)
{
m_input_index = p_input_index;
m_min_input = p_min_input;
m_max_input = p_max_input > p_min_input ? p_max_input : p_min_input;
m_output_values = ArrayCalc.shallowClone(p_output_values);
}
//Express
public DecisionTree express(T controller)
{
return(new DecisionTree(
recExpress(Root),
ArrayCalc.map(m_inputs, (DInputFactory <T> p_fact) => { return p_fact(controller); }),
ArrayCalc.map(m_outputs, (DOutputFactory <T> p_fact) => { return p_fact(controller); })
));
}
//Private constructor for cloning
private NeuralNetDNA(int p_id, DInputFactory <T>[] p_inputs, DOutputFactory <T>[] p_outputs, Range <float> p_mutation_multiplier)
{
m_id = p_id;
m_inputs = ArrayCalc.shallowClone(p_inputs);
m_outputs = ArrayCalc.shallowClone(p_outputs);
m_mutation_multiplier = p_mutation_multiplier;
}
static void Main(string[] args)
{
ArrayCalc calc = new ArrayCalc();
double[] n = { 1.0, 2.0, 3.3, 5.5, 6.3, -4.5, 12.0 };
Console.WriteLine(calc.Sum(n));
Console.WriteLine(calc.AVG(n));
Console.WriteLine(calc.Max(n));
Console.WriteLine(calc.Min(n));
}
public Gene mutate()
{
Gene clone = this.Clone();
for (int i = 0; i < m_mutation_iterations; i++)
{
int index = ArrayCalc.randomIndex(clone.m_genes);
clone.m_genes[index] += RandomCalc.Rand(m_mutation_range);
}
return(clone);
}
public DecisionNetDNA(int p_id, DInputFactory <T>[] p_inputs, DOutputFactory <T>[] p_outputs, Matrix <float> p_weights, Range <float> p_mutation_multiplier)
{
if (!MatrixCalc.isSize(p_weights, p_inputs.Length, p_outputs.Length))
{
Debug.LogError("DecisionNetDNA requires Matrix input size inputs by outputs");
}
m_id = p_id;
m_inputs = ArrayCalc.shallowClone(p_inputs);
m_outputs = ArrayCalc.shallowClone(p_outputs);
m_weights = MatrixCalc.shallowClone(MatrixCalc.columnNormalize(p_weights));
m_mutation_multiplier = p_mutation_multiplier;
}
public void MaxTest()
{
ArrayCalc calc = new ArrayCalc();
double[] n = { 1.0, 2.0, 3.3, 5.5, 6.3, -4.5, 12.0 };
double expected = 12.0;
// act
double actual = calc.Max(n);
//assert
Assert.AreEqual(expected, actual);
}
public DecisionTreeDNANode crossover(DecisionTreeDNANode p_crossover_object)
{
int cross_input_index = BoolCalc.random() ? m_input_index : p_crossover_object.m_input_index;
float cross_min_input = BoolCalc.random() ? m_min_input : p_crossover_object.m_min_input;
float cross_max_input = BoolCalc.random() ? m_max_input : p_crossover_object.m_max_input;
if (cross_max_input < cross_min_input)
{
cross_max_input = cross_min_input;
}
float[] cross_output_values = ArrayCalc.crossover(m_output_values, p_crossover_object.m_output_values);
return(new DecisionTreeDNANode(cross_input_index, cross_min_input, cross_max_input, cross_output_values));
}
public DecisionTreeDNANode mutate(int p_num_inputs, Range <float> p_mutation_mult)
{
int mut_input_index = RandomCalc.Rand(new Range <int>(0, p_num_inputs - 1));
float mut_min_input = m_min_input * RandomCalc.Rand(p_mutation_mult);
float mut_max_input = m_max_input * RandomCalc.Rand(p_mutation_mult);
if (mut_max_input < mut_min_input)
{
mut_max_input = mut_min_input;
}
float[] mut_output_values = ArrayCalc.shallowClone(m_output_values);
for (int i = 0; i < mut_output_values.Length; i++)
{
mut_output_values[i] *= RandomCalc.Rand(p_mutation_mult);
}
return(new DecisionTreeDNANode(mut_input_index, mut_min_input, mut_max_input, mut_output_values));
}
public NeuralNetDNA(int p_id, DInputFactory <T>[] p_inputs, DOutputFactory <T>[] p_outputs, int p_hidden_number, int p_hidden_size,
Range <float> p_weight_range, Range <float> p_mutation_multiplier)
{
m_id = p_id;
m_inputs = ArrayCalc.shallowClone(p_inputs);
m_outputs = ArrayCalc.shallowClone(p_outputs);
m_weights = new Matrix <float> [p_hidden_number + 1];
m_weights[0] = Matrix <float> .Build.Dense(m_inputs.Length, p_hidden_size, (i, j) => { return(RandomCalc.Rand(p_weight_range)); });
for (int i = 1; i < m_weights.Length - 1; i++)
{
m_weights[i] = Matrix <float> .Build.Dense(p_hidden_size, p_hidden_size, (x, y) => { return(RandomCalc.Rand(p_weight_range)); });
}
m_weights[m_weights.Length - 1] = Matrix <float> .Build.Dense(p_hidden_size, m_outputs.Length, (i, j) => { return(RandomCalc.Rand(p_weight_range)); });
m_mutation_multiplier = p_mutation_multiplier;
}
//Mutation
public DecisionTreeDNA <T> mutate()
{
return(new DecisionTreeDNA <T>(recMutate(Root), ArrayCalc.shallowClone(m_inputs), ArrayCalc.shallowClone(m_outputs), m_mutation_mult));
}
//Crossover
public DecisionTreeDNA <T> crossover(DecisionTreeDNA <T> p_crossover_object)
{
return(new DecisionTreeDNA <T>(recCrossover(Root, p_crossover_object.Root), ArrayCalc.shallowClone(m_inputs), ArrayCalc.shallowClone(m_outputs), m_mutation_mult));
}