public void receiveLearnedMatrix(Matrix <float> p_matrix)
{
if (!MatrixCalc.isSameSize(p_matrix, m_weights))
{
Debug.LogError("Learned matrix must be same size as non-learned matrix");
}
m_weights = MatrixCalc.shallowClone(p_matrix);
}
public DecisionNet(DInput[] p_inputs, DOutput[] p_outputs, Matrix <float> p_weights)
{
if (!MatrixCalc.isSize(p_weights, p_inputs.Length, p_outputs.Length))
{
Debug.LogError("Constructing DecisionNet with wrong sized matrix");
}
m_inputs = p_inputs;
m_outputs = p_outputs;
m_weights = MatrixCalc.shallowClone(p_weights);
}
public NeuralNet(DInput[] p_inputs, DOutput[] p_outputs, Matrix <float>[] p_weights)
{
m_inputs = p_inputs;
m_outputs = p_outputs;
m_weights = new Matrix <float> [p_weights.Length];
for (int i = 0; i < p_weights.Length; i++)
{
m_weights[i] = MatrixCalc.shallowClone(p_weights[i]);
}
}
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 NeuralNetDNA <T> Clone()
{
NeuralNetDNA <T> clone = new NeuralNetDNA <T>(m_id, m_inputs, m_outputs, m_mutation_multiplier);
clone.m_weights = new Matrix <float> [m_weights.Length];
for (int i = 0; i < m_weights.Length; i++)
{
clone.m_weights[i] = MatrixCalc.shallowClone(m_weights[i]);
}
return(clone);
}