public void Complex_WeightOne()
{
var connList = new LightweightList <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)
};
var connSpan = connList.AsSpan();
// Create graph.
var digraph = WeightedDirectedGraphAcyclicBuilder <double> .Create(connSpan, 2, 2);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetAcyclic(digraph, actFn.Fn);
Complex_WeightOne_Inner(net, actFn);
// Create vectorized neural net and run tests.
var vnet = new NeuralNets.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
Complex_WeightOne_Inner(vnet, actFn);
}
public void SingleInput_WeightOne()
{
var connList = new List <WeightedDirectedConnection <double> > {
new WeightedDirectedConnection <double>(0, 1, 1.0)
};
// Create graph.
var digraph = WeightedDirectedGraphAcyclicBuilder <double> .Create(connList, 1, 1);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetAcyclic(digraph, actFn.Fn);
SingleInput_WeightOne_Inner(net, actFn);
// Create vectorized neural net and run tests.
var vnet = new NeuralNets.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
SingleInput_WeightOne_Inner(vnet, actFn);
}
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 = WeightedDirectedGraphAcyclicBuilder <double> .Create(connList, 2, 1);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetAcyclic(digraph, actFn.Fn);
TwoInputs_WeightHalf_Inner(net, actFn);
// Create vectorized neural net and run tests.
var vnet = new NeuralNets.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
TwoInputs_WeightHalf_Inner(vnet, actFn);
}
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 = WeightedDirectedGraphAcyclicBuilder <double> .Create(connList, 3, 3);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetAcyclic(digraph, actFn.Fn);
MultipleInputsOutputs_Inner(net, actFn);
// Create vectorized neural net and run tests.
var vnet = new NeuralNets.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
MultipleInputsOutputs_Inner(vnet, actFn);
}
public void HiddenNode()
{
var connList = new LightweightList <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 3, 0.5),
new WeightedDirectedConnection <double>(1, 3, 0.5),
new WeightedDirectedConnection <double>(3, 2, 2.0)
};
var connSpan = connList.AsSpan();
// Create graph.
var digraph = WeightedDirectedGraphAcyclicBuilder <double> .Create(connSpan, 2, 1);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetAcyclic(digraph, actFn.Fn);
HiddenNode_Inner(net, actFn);
// Create vectorized neural net and run tests.
var vnet = new NeuralNets.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
HiddenNode_Inner(vnet, actFn);
}