public void CyclicOutput()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 1, 1.0),
new WeightedDirectedConnection <double>(1, 1, 1.0)
};
// Create graph.
var digraph = WeightedDirectedGraphBuilder <double> .Create(connList, 1, 1);
// Create neural net
var actFn = new LogisticFunction();
var net = new CyclicNeuralNet(digraph, actFn.Fn, 1, false);
// Activate and test.
const double input = 0.1;
double inputVal = input;
net.InputVector[0] = inputVal;
for (int i = 0; i < 10; i++)
{
net.Activate();
double outputExpected = actFn.Fn(inputVal);
Assert.AreEqual(outputExpected, net.OutputVector[0]);
inputVal = input + outputExpected;
}
}
public void SingleInput_WeightZero()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 1, 0.0)
};
// Create graph.
var digraph = WeightedDirectedGraphBuilder <double> .Create(connList, 1, 1);
// Create neural net
var actFn = new LogisticFunction();
var net = new CyclicNeuralNet(digraph, actFn.Fn, 2, false);
// Note. The single connection weight is zero, so the input value has no affect.
// Activate and test.
net.InputVector[0] = 100.0;
net.Activate();
Assert.AreEqual(0.5, net.OutputVector[0]);
// Activate and test.
net.InputVector[0] = 0;
net.Activate();
Assert.AreEqual(0.5, net.OutputVector[0]);
// Activate and test.
net.InputVector[0] = -100;
net.Activate();
Assert.AreEqual(0.5, net.OutputVector[0]);
}
public void MultipleInputsOutputs()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 5, 1.0),
new WeightedDirectedConnection <double>(1, 3, 1.0),
new WeightedDirectedConnection <double>(2, 4, 1.0)
};
// Create graph.
var digraph = WeightedDirectedGraphBuilder <double> .Create(connList, 3, 3);
// Create neural net
var actFn = new LogisticFunction();
var net = new CyclicNeuralNet(digraph, actFn.Fn, 1, false);
// Activate and test.
net.InputVector[0] = 1.0;
net.InputVector[1] = 2.0;
net.InputVector[2] = 3.0;
net.Activate();
Assert.AreEqual(actFn.Fn(2.0), net.OutputVector[0]);
Assert.AreEqual(actFn.Fn(3.0), net.OutputVector[1]);
Assert.AreEqual(actFn.Fn(1.0), net.OutputVector[2]);
}
/// <summary>
/// Decode a genome into a working neural network.
/// </summary>
/// <param name="genome">The genome to decode.</param>
public IPhenome <double> Decode(
NeatGenome <double> genome)
{
// Note. In principle an acyclic net can be decoded to a cyclic network (but not the other way around), but standard sharpneat behaviour is not to support this.
Debug.Assert(!genome.MetaNeatGenome.IsAcyclic);
// Create a working neural net.
var neuralNet = new CyclicNeuralNet(
genome.DirectedGraph,
genome.ConnectionGenes._weightArr,
genome.MetaNeatGenome.ActivationFn.Fn,
_activationCount, _boundedOutput);
return(neuralNet);
}
public void SingleInput_WeightOne()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 1, 1.0)
};
// Create graph.
var digraph = WeightedDirectedGraphBuilder <double> .Create(connList, 1, 1);
// Create neural net
var actFn = new LogisticFunction();
var net = new CyclicNeuralNet(digraph, actFn.Fn, 1, false);
// Activate and test.
net.InputVector[0] = 0.0;
for (int i = 0; i < 10; i++)
{
net.Activate();
Assert.AreEqual(0.5, net.OutputVector[0]);
}
// Activate and test.
net.InputVector[0] = 1.0;
for (int i = 0; i < 10; i++)
{
net.Activate();
Assert.AreEqual(actFn.Fn(1), net.OutputVector[0]);
}
// Activate and test.
net.InputVector[0] = 10.0;
for (int i = 0; i < 10; i++)
{
net.Activate();
Assert.AreEqual(actFn.Fn(10), net.OutputVector[0]);
}
}
public void ComplexCyclic()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 1, -2.0),
new WeightedDirectedConnection <double>(0, 2, 1.0),
new WeightedDirectedConnection <double>(1, 2, 1.0),
new WeightedDirectedConnection <double>(2, 1, 1.0)
};
// Create graph.
var digraph = WeightedDirectedGraphBuilder <double> .Create(connList, 1, 1);
// Create neural net
var actFn = new LogisticFunction();
var net = new CyclicNeuralNet(digraph, actFn.Fn, 1, false);
// Simulate network in C# and compare calculated outputs with actual network outputs.
double[] preArr = new double[3];
double[] postArr = new double[3];
postArr[0] = 3.0;
net.InputVector[0] = 3.0;
for (int i = 0; i < 10; i++)
{
preArr[1] = postArr[0] * -2.0 + postArr[2];
preArr[2] = postArr[0] + postArr[1];
postArr[1] = actFn.Fn(preArr[1]);
postArr[2] = actFn.Fn(preArr[2]);
net.Activate();
Assert.AreEqual(postArr[1], net.OutputVector[0]);
}
}
