/// <summary>
/// Construct a simulated annleaing trainer for a feedforward neural network.
/// </summary>
/// <param name="network"> The neural network to be trained. </param>
/// <param name="startTemp"> The starting temperature. </param>
/// <param name="stopTemp"> The ending temperature. </param>
/// <param name="cycles"> The number of cycles in a training iteration. </param>
//JAVA TO C# CONVERTER WARNING: 'final' parameters are not allowed in .NET:
//ORIGINAL LINE: public SimulatedAnnealingLearning(final org.neuroph.core.NeuralNetwork network, final double startTemp, final double stopTemp, final int cycles)
public SimulatedAnnealingLearning(NeuralNetwork network, double startTemp, double stopTemp, int cycles)
{
NeuralNetwork = network;
this.temperature = startTemp;
this.startTemperature = startTemp;
this.stopTemperature = stopTemp;
this.cycles = cycles;
this.weights = new double[NeuralNetworkCODEC.determineArraySize(network)];
this.bestWeights = new double[NeuralNetworkCODEC.determineArraySize(network)];
NeuralNetworkCODEC.network2array(network, this.weights);
NeuralNetworkCODEC.network2array(network, this.bestWeights);
}
/// <summary>
/// Randomize the weights and thresholds. This function does most of the work
/// of the class. Each call to this class will randomize the data according
/// to the current temperature. The higher the temperature the more
/// randomness. </summary>
/// <param name="randomChance"> </param>
public virtual void randomize(double randomChance)
{
for (int i = 0; i < this.weights.Length; i++)
{
if (new Random(1).NextDouble() < randomChance)
{
double add = 0.5 - (new Random(2).NextDouble());
add /= this.startTemperature;
add *= this.temperature;
this.weights[i] = this.weights[i] + add;
}
}
NeuralNetworkCODEC.array2network(this.weights, Network);
}
public virtual void doLearningEpoch(DataSet trainingSet, double randomChance)
{
Array.Copy(this.weights, 0, this.bestWeights, 0, this.weights.Length);
double bestError = determineError(trainingSet);
this.temperature = this.startTemperature;
for (int i = 0; i < this.cycles; i++)
{
randomize(randomChance);
double currentError = determineError(trainingSet);
if (currentError < bestError)
{
Array.Copy(this.weights, 0, this.bestWeights, 0, this.weights.Length);
bestError = currentError;
}
else
{
Array.Copy(this.bestWeights, 0, this.weights, 0, this.weights.Length);
}
NeuralNetworkCODEC.array2network(this.bestWeights, Network);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double ratio = Math.exp(Math.log(this.stopTemperature / this.startTemperature) / (this.cycles - 1));
double ratio = Math.Exp(Math.Log(this.stopTemperature / this.startTemperature) / (this.cycles - 1));
this.temperature *= ratio;
}
// the following line is probably wrong (when is reset() called?), but the result might not be used for anything
this.previousEpochError = ErrorFunction.TotalError;
// moved stopping condition to separate method hasReachedStopCondition()
// so it can be overriden / customized in subclasses
if (hasReachedStopCondition())
{
stopLearning();
}
}
