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 = WeightedDirectedGraphAcyclicBuilder <double> .Create(connList, 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 NeuralNet.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
Complex_WeightOne_Inner(vnet, actFn);
}
public void DepthNodeReorderTest()
{
// Define graph connections.
var connList = new LightweightList <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 4, 0.0),
new WeightedDirectedConnection <double>(4, 5, 1.0),
new WeightedDirectedConnection <double>(5, 2, 2.0),
new WeightedDirectedConnection <double>(1, 2, 3.0),
new WeightedDirectedConnection <double>(2, 3, 4.0)
};
// Create graph.
var connSpan = connList.AsSpan();
connSpan.Sort(WeightedDirectedConnectionComparer <double> .Default);
var digraph = WeightedDirectedGraphAcyclicBuilder <double> .Create(connSpan, 2, 2);
// The nodes should have IDs allocated based on depth, i.e. the layer they are in.
// And connections should be ordered by source node ID.
var connListExpected = new LightweightList <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 2, 0.0),
new WeightedDirectedConnection <double>(1, 4, 3.0),
new WeightedDirectedConnection <double>(2, 3, 1.0),
new WeightedDirectedConnection <double>(3, 4, 2.0),
new WeightedDirectedConnection <double>(4, 5, 4.0)
};
// Compare actual and expected connections.
var connSpanExpected = connListExpected.AsSpan();
CompareConnectionLists(connSpanExpected, digraph.ConnectionIdArrays, digraph.WeightArray);
// Test layer info.
LayerInfo[] layerArrExpected = new LayerInfo[5];
layerArrExpected[0] = new LayerInfo(2, 2);
layerArrExpected[1] = new LayerInfo(3, 3);
layerArrExpected[2] = new LayerInfo(4, 4);
layerArrExpected[3] = new LayerInfo(5, 5);
layerArrExpected[4] = new LayerInfo(6, 5);
Assert.Equal(5, digraph.LayerArray.Length);
// Check the node count.
Assert.Equal(6, digraph.TotalNodeCount);
}
public void SingleInput_WeightOne()
{
var connList = new List <WeightedDirectedConnection <double> >();
connList.Add(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 NeuralNet.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
SingleInput_WeightOne_Inner(vnet, actFn);
}
public void SimpleAcyclic()
{
// Simple acyclic graph.
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 3, 0.0),
new WeightedDirectedConnection <double>(1, 3, 1.0),
new WeightedDirectedConnection <double>(2, 3, 2.0),
new WeightedDirectedConnection <double>(2, 4, 3.0)
};
// Create graph.
var digraph = WeightedDirectedGraphAcyclicBuilder <double> .Create(connList, 3, 2);
// The graph should be unchanged from the input connections.
CompareConnectionLists(connList, digraph.ConnectionIdArrays, digraph.WeightArray);
// Check the node count.
Assert.Equal(5, digraph.TotalNodeCount);
}
public void SingleInput_WeightZero()
{
var connList = new LightweightList <WeightedDirectedConnection <double> > {
new WeightedDirectedConnection <double>(0, 1, 0.0)
};
var connSpan = connList.AsSpan();
// Create graph.
var digraph = WeightedDirectedGraphAcyclicBuilder <double> .Create(connSpan, 1, 1);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetAcyclic(digraph, actFn.Fn);
SingleInput_WeightZero_Inner(net);
// Create vectorized neural net and run tests.
var vnet = new NeuralNets.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
SingleInput_WeightZero_Inner(vnet);
}
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 NeuralNet.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 NeuralNet.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
MultipleInputsOutputs_Inner(vnet, actFn);
}
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 = WeightedDirectedGraphAcyclicBuilder <double> .Create(connList, 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);
}