/// <summary>
/// Copy output training data from an array.
/// </summary>
/// <param name="vector">The target array.</param>
/// <param name="config">The configuration instance.</param>
public override void SyncTrainingFrom(double[] vector, BasicConfig config)
{
for (int i = 0; i < _outputTraining.Length && i < vector.Length; i++)
{
_outputTraining[i] = config.ConvertOutput(vector[i]);
}
}
/// <summary>
/// Instanciates a new pattern.
/// </summary>
/// <param name="title">The name/title of the pattern.</param>
/// <param name="input">The input pattern data (gets copied).</param>
/// <param name="numberOfClasses">The count of output neurons.</param>
/// <param name="classification">The position of the favoured output neuron in the output layer.</param>
/// <param name="config">The network configuration instance (needed to generate an appropriate training vector)</param>
public DoublePattern(string title, double[] input, int numberOfClasses, int classification, BasicConfig config)
: base(title, classification)
{
_inputPattern = new double[input.Length];
input.CopyTo(_inputPattern, 0);
_outputTraining = new double[numberOfClasses];
for (int i = 0; i < numberOfClasses; i++)
{
OutputTraining[i] = config.ConvertOutput(i == classification);
}
}
/// <summary>
/// Create a boolean pattern based on this pattern.
/// </summary>
/// <param name="config">The configuration instance.</param>
public BooleanPattern ToBooleanPattern(BasicConfig config)
{
bool[] input = new bool[_inputPattern.Length];
bool[] output = new bool[_outputTraining.Length];
for (int i = 0; i < input.Length; i++)
{
input[i] = config.ConvertInput(_inputPattern[i]);
}
for (int i = 0; i < output.Length; i++)
{
output[i] = config.ConvertOutput(_outputTraining[i]);
}
return(new BooleanPattern(Title, input, output, Classification));
}
/// <summary>
/// Create a double pattern based on this pattern.
/// </summary>
/// <param name="config">The configuration instance.</param>
public DoublePattern ToDoublePattern(BasicConfig config)
{
double[] input = new double[_inputPattern.Length];
double[] output = new double[_outputTraining.Length];
for (int i = 0; i < input.Length; i++)
{
input[i] = config.ConvertInput(_inputPattern[i]);
}
for (int i = 0; i < output.Length; i++)
{
output[i] = config.ConvertOutput(_outputTraining[i]);
}
return(new DoublePattern(Title, input, output, Classification));
}
