public void SingleInput_WeightOne()
{
var connList = new List <WeightedDirectedConnection <double> >();
connList.Add(new WeightedDirectedConnection <double>(0, 1, 1.0));
// Create graph.
var digraph = WeightedAcyclicDirectedGraphBuilder <double> .Create(connList, 1, 1);
// Create neural net
var actFn = new LogisticFunction();
var net = new AcyclicNeuralNet(digraph, actFn.Fn, false);
// Activate and test.
net.InputVector[0] = 0.0;
net.Activate();
Assert.AreEqual(0.5, net.OutputVector[0]);
// Activate and test.
net.InputVector[0] = 1.0;
net.Activate();
Assert.AreEqual(actFn.Fn(1), net.OutputVector[0]);
// Activate and test.
net.InputVector[0] = 10.0;
net.Activate();
Assert.AreEqual(actFn.Fn(10.0), net.OutputVector[0]);
}
public void TwoInputs_WeightHalf()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 2, 0.5),
new WeightedDirectedConnection <double>(1, 2, 0.5)
};
// Create graph.
var digraph = WeightedAcyclicDirectedGraphBuilder <double> .Create(connList, 2, 1);
// Create neural net
var actFn = new LogisticFunction();
var net = new AcyclicNeuralNet(digraph, actFn.Fn, false);
// Activate and test.
net.InputVector[0] = 0.0;
net.InputVector[1] = 0.0;
net.Activate();
Assert.AreEqual(0.5, net.OutputVector[0]);
// Activate and test.
net.InputVector[0] = 1.0;
net.InputVector[1] = 2.0;
net.Activate();
Assert.AreEqual(actFn.Fn(1.5), net.OutputVector[0]);
// Activate and test.
net.InputVector[0] = 10.0;
net.InputVector[1] = 20.0;
net.Activate();
Assert.AreEqual(actFn.Fn(15.0), 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 = WeightedAcyclicDirectedGraphBuilder <double> .Create(connList, 3, 3);
// Create neural net
var actFn = new LogisticFunction();
var net = new AcyclicNeuralNet(digraph, actFn.Fn, 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]);
}
public void SingleInput_WeightZero()
{
var connList = new List <WeightedDirectedConnection <double> >();
connList.Add(new WeightedDirectedConnection <double>(0, 1, 0.0));
// Create graph.
var digraph = WeightedAcyclicDirectedGraphBuilder <double> .Create(connList, 1, 1);
// Create neural net
var actFn = new LogisticFunction();
var net = new AcyclicNeuralNet(digraph, actFn.Fn, 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 Complex_WeightOne()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 4, 1.0),
new WeightedDirectedConnection <double>(1, 4, 1.0),
new WeightedDirectedConnection <double>(1, 5, 1.0),
new WeightedDirectedConnection <double>(3, 4, 1.0),
new WeightedDirectedConnection <double>(4, 2, 0.9),
new WeightedDirectedConnection <double>(5, 3, 1.0)
};
// Create graph.
var digraph = WeightedAcyclicDirectedGraphBuilder <double> .Create(connList, 2, 2);
// Create neural net
var actFn = new LogisticFunction();
var net = new AcyclicNeuralNet(digraph, actFn.Fn, false);
// Activate and test.
net.InputVector[0] = 0.5;
net.InputVector[1] = 0.25;
net.Activate();
double output1 = actFn.Fn(actFn.Fn(0.25));
Assert.AreEqual(output1, net.OutputVector[1]);
double output0 = actFn.Fn(actFn.Fn(output1 + 0.5 + 0.25) * 0.9);
Assert.AreEqual(output0, net.OutputVector[0]);
}
public void HiddenNode()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 3, 0.5),
new WeightedDirectedConnection <double>(1, 3, 0.5),
new WeightedDirectedConnection <double>(3, 2, 2.0)
};
// Create graph.
var digraph = WeightedAcyclicDirectedGraphBuilder <double> .Create(connList, 2, 1);
// Create neural net
var actFn = new LogisticFunction();
var net = new AcyclicNeuralNet(digraph, actFn.Fn, false);
// Activate and test.
net.InputVector[0] = 0.0;
net.InputVector[1] = 0.0;
net.Activate();
Assert.AreEqual(actFn.Fn(1.0), net.OutputVector[0]);
// Activate and test.
net.InputVector[0] = 0.5;
net.InputVector[1] = 0.25;
net.Activate();
Assert.AreEqual(actFn.Fn(actFn.Fn(0.375) * 2.0), net.OutputVector[0]);
}
/// <summary>
/// Decode a genome into a working neural network.
/// </summary>
/// <param name="genome">The genome to decode.</param>
/// <param name="boundedOutput">Indicates whether the output nodes should be bounded to the interval [0,1]</param>
public IPhenome <double> Decode(
NeatGenome <double> genome)
{
Debug.Assert(genome?.MetaNeatGenome?.IsAcyclic == true);
Debug.Assert(null != genome?.ConnectionGenes);
Debug.Assert(genome.ConnectionGenes.Length == genome?.ConnectionIndexMap?.Length);
Debug.Assert(genome.DirectedGraph is AcyclicDirectedGraph);
// Create neural net weight array.
// Note. We cannot use the genome's weight array directly here (as is done in NeatGenomeDecoder,
// i.e. for cyclic graphs) because the genome connections and digraph connections have a
// different order.
double[] neuralNetWeightArr = CreateNeuralNetWeightArray(genome);
// Create a working neural net.
var neuralNet = new AcyclicNeuralNet(
(AcyclicDirectedGraph)genome.DirectedGraph,
neuralNetWeightArr,
genome.MetaNeatGenome.ActivationFn.Fn,
_boundedOutput);
return(neuralNet);
}
