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_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 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]); }
public void DepthNodeReorderTest() { // Define graph connections. var connList = new List <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. connList.Sort(WeightedDirectedConnectionComparer <double> .Default); var digraph = WeightedAcyclicDirectedGraphBuilder <double> .Create(connList, 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 List <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. CompareConnectionLists(connListExpected, 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.AreEqual(5, digraph.LayerArray.Length); // Check the node count. Assert.AreEqual(6, digraph.TotalNodeCount); }
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 = WeightedAcyclicDirectedGraphBuilder <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.AreEqual(5, digraph.TotalNodeCount); }