/// <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);
}
public Backend()
{
//Prepare the default configuration set.
_node = new ConfigNode();
_config = new BasicConfig(_node);
_config.BiasNeuronEnable.Value = true;
_config.LearningRate.Value = 0.3;
}
void IConfigurable.Rebind(ConfigNode node)
{
_node = node;
if (_config == null)
{
_config = new BasicConfig(node);
}
else
{
_config.Node = node;
}
}
double[] outTT = new double[16]; // X=True Y=True
#endregion Fields
#region Constructors
public Backend()
{
//Prepare the default configuration set.
node = new ConfigNode();
config = new BasicConfig(node);
config.BiasNeuronEnable.Value = true;
config.WeightDecayEnable.Value = true;
config.FlatspotEliminationEnable.Value = true;
config.BiasNeuronOutput.Value = 0.9d;
config.LearningRate.Value = 0.3;
config.WeightDecay.Value = 0.005;
InitOutputs(1d,-1d);
}
public TrainingConfig(ConfigNode config)
{
_config = config;
_gaussian = new NormalGenerator(0.0, 0.5);
_basic = new BasicConfig(config);
_autoTrainingEpochs = new ConfigItem<int>(_config, "AutoTrainingEpochs", 400);
_autoTrainingAttempts = new ConfigItem<int>(_config, "AutoTrainingAttempts", 1);
_autoTrainingPercentSuccessful = new ConfigItem<double>(_config, "AutoTrainingPercentSuccessful", 1.0);
_shuffleSwapProbability = new ConfigItem<double>(_config, "ShuffleSwapProbability", 0.05);
_shuffleNoiseSigma = new ConfigItem<double>(_config, "ShuffleNoiseSigma", 0.5);
_shuffleEn = new ConfigItem<bool>(_config, "ShuffleEnable", false);
}
void IConfigurable.Rebind(ConfigNode node)
{
_node = node;
if(_config == null)
_config = new BasicConfig(node);
else
_config.Node = node;
}
/// <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);
}
/// <summary>
/// Copy output training data to an array.
/// </summary>
/// <param name="vector">The target array.</param>
/// <param name="config">The configuration instance.</param>
public override void SyncTrainingTo(double[] vector, BasicConfig config)
{
for(int i = 0; i < _outputTraining.Length && i < vector.Length; i++)
vector[i] = config.ConvertOutput(_outputTraining[i]);
}
/// <summary>
/// Copy input pattern data to an array.
/// </summary>
/// <param name="vector">The target array.</param>
/// <param name="config">The configuration instance.</param>
public override void SyncInputTo(double[] vector, BasicConfig config)
{
for(int i = 0; i < _inputPattern.Length && i < vector.Length; i++)
vector[i] = config.ConvertInput(_inputPattern[i]);
}
/// <summary> /// Copy output training data from an array. /// </summary> /// <param name="vector">The target array.</param> /// <param name="config">The configuration instance.</param> public abstract void SyncTrainingFrom(double[] vector, BasicConfig config);
/// <summary> /// Copy input pattern data to an array. /// </summary> /// <param name="vector">The target array.</param> /// <param name="config">The configuration instance.</param> public abstract void SyncInputTo(double[] vector, BasicConfig config);
/// <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>
/// Copy output training data to an array.
/// </summary>
/// <param name="vector">The target array.</param>
/// <param name="config">The configuration instance.</param>
public override void SyncTrainingTo(double[] vector, BasicConfig config)
{
_outputTraining.CopyTo(vector, 0);
}
/// <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)
{
vector.CopyTo(_outputTraining, 0);
}
/// <summary>
/// Copy input pattern data to an array.
/// </summary>
/// <param name="vector">The target array.</param>
/// <param name="config">The configuration instance.</param>
public override void SyncInputTo(double[] vector, BasicConfig config)
{
_inputPattern.CopyTo(vector, 0);
}
/// <summary>
/// Copy input pattern data from an array.
/// </summary>
/// <param name="vector">The target array.</param>
/// <param name="config">The configuration instance.</param>
public override void SyncInputFrom(double[] vector, BasicConfig config)
{
vector.CopyTo(_inputPattern, 0);
}
