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));
}
public PsoParticle(NeuralNetwork network, ParticleProperties props, Random rnd)
{
this.bestAccuracy = 0.0;
this.Network = network;
var data = network.Data;
this.props = props;
this.rnd = rnd;
var numvelocities = /*weights: */(data.Props.NumInput * data.Props.NumHidden) + (data.Props.NumHidden * data.Props.NumOutput) /*bias: */ + data.Props.NumHidden + data.Props.NumOutput;
this.velocities = new double[numvelocities];
Array.Clear(this.velocities, 0, numvelocities);
}
public PsoParticle(NeuralNetwork network, ParticleProperties props, Random rnd)
{
this.bestAccuracy = 0.0;
this.Network = network;
var data = network.Data;
this.props = props;
this.rnd = rnd;
var numvelocities = /*weights: */ (data.Props.NumInput * data.Props.NumHidden) + (data.Props.NumHidden * data.Props.NumOutput) /*bias: */ + data.Props.NumHidden + data.Props.NumOutput;
this.velocities = new double[numvelocities];
Array.Clear(this.velocities, 0, numvelocities);
}
