static INeuralNetwork BuildPsoNetwork()
{
var props = new NetworkProperties {
InitWeightMin = -0.1,
InitWeightMax = 0.1,
NumHidden = Globals.gNumHidden, // orig 2
NumInput = Globals.gNumInput, // orig 4
NumOutput = Globals.gNumOutput // orig 3
};
var particleProps = new ParticleProperties {
MaxVDelta = 2.0,
MinVDelta = -2.0,
V = 3.0,
VSelf = 2.0,
VSocial = 2.0
};
var netProps = new PsoNetworkProperties {
DesiredAccuracy = 0.98,
Iterations = Globals.gMaxIterations, // orig 1000,
NumNetworks = 4,
ParticleProps = particleProps
};
return new PsoNetwork(netProps, props, new Random(0));
}
// ReSharper restore InconsistentNaming
public NetworkData(NetworkProperties props)
{
this.Props = props.Clone();
//weights
this.ihWeights = MakeMatrix(this.Props.NumInput, this.Props.NumHidden);
this.hBiases = new double[this.Props.NumHidden];
this.hoWeights = MakeMatrix(this.Props.NumHidden, this.Props.NumOutput);
this.oBiases = new double[this.Props.NumOutput];
//input and output node values
this.hOutputs = new double[this.Props.NumHidden];
this.inputs = new double[this.Props.NumInput];
this.outputs = new double[this.Props.NumOutput];
}
// ReSharper restore InconsistentNaming
public NetworkData(NetworkProperties props)
{
this.Props = props.Clone();
//weights
this.ihWeights = MakeMatrix(this.Props.NumInput, this.Props.NumHidden);
this.hBiases = new double[this.Props.NumHidden];
this.hoWeights = MakeMatrix(this.Props.NumHidden, this.Props.NumOutput);
this.oBiases = new double[this.Props.NumOutput];
//input and output node values
this.hOutputs = new double[this.Props.NumHidden];
this.inputs = new double[this.Props.NumInput];
this.outputs = new double[this.Props.NumOutput];
}
public BackPropNetwork(NetworkProperties props, BackPropProperties backProps, Random rnd)
{
this.rnd = rnd; // for Shuffle()
this.backProps = backProps;
this.Network = new NeuralNetwork(props, rnd);
// back-prop related arrays below
this.hGrads = new double[props.NumHidden];
this.oGrads = new double[props.NumOutput];
this.ihPrevWeightsDelta = NetworkData.MakeMatrix(props.NumInput, props.NumHidden);
this.hPrevBiasesDelta = new double[props.NumHidden];
this.hoPrevWeightsDelta = NetworkData.MakeMatrix(props.NumHidden, props.NumOutput);
this.oPrevBiasesDelta = new double[props.NumOutput];
}
public BackPropNetwork(NetworkProperties props, BackPropProperties backProps, Random rnd)
{
this.rnd = rnd; // for Shuffle()
this.backProps = backProps;
this.Network = new NeuralNetwork(props, rnd);
// back-prop related arrays below
this.hGrads = new double[props.NumHidden];
this.oGrads = new double[props.NumOutput];
this.ihPrevWeightsDelta = NetworkData.MakeMatrix(props.NumInput, props.NumHidden);
this.hPrevBiasesDelta = new double[props.NumHidden];
this.hoPrevWeightsDelta = NetworkData.MakeMatrix(props.NumHidden, props.NumOutput);
this.oPrevBiasesDelta = new double[props.NumOutput];
}
static INeuralNetwork BuildBackPropNetwork()
{
var props = new NetworkProperties {
InitWeightMin = -0.1,
InitWeightMax = 0.1,
NumHidden = Globals.gNumHidden, // orig 2
NumInput = Globals.gNumInput, // orig 4
NumOutput = Globals.gNumOutput // orig 3
};
var backProps = new BackPropProperties
{
learnRate = 0.05,
maxEprochs = Globals.gMaxIterations, // orig 2000,
momentum = 0.00,
weightDecay = 0.000,
mseStopCondition = 0.020
};
return new BackPropNetwork(props, backProps, new Random(0));
}
static INeuralNetwork BuildBackPropNetwork()
{
var props = new NetworkProperties {
InitWeightMin = -0.1,
InitWeightMax = 0.1,
NumHidden = 2,
NumInput = 4,
NumOutput = 3
};
var backProps = new BackPropProperties
{
learnRate = 0.05,
maxEprochs = 2000,
momentum = 0.00,
weightDecay = 0.000,
mseStopCondition = 0.020
};
return new BackPropNetwork(props, backProps, new Random(0));
}
public PsoNetwork(PsoNetworkProperties netProps, NetworkProperties props, Random rng)
{
this.PsoProps = netProps;
this.NetworkProps = props;
this.rng = rng;
}
public PsoNetwork(PsoNetworkProperties netProps, NetworkProperties props, Random rng)
{
this.PsoProps = netProps;
this.NetworkProps = props;
this.rng = rng;
}
public NeuralNetwork(NetworkProperties props, Random rng)
{
this.Data = new NetworkData(props);
this.Data.InitializeWeights(rng, props.InitWeightMin, props.InitWeightMax);
}
public NeuralNetwork(NetworkProperties props, Random rng)
{
this.Data = new NetworkData(props);
this.Data.InitializeWeights(rng, props.InitWeightMin, props.InitWeightMax);
}