public void TestRandomizeNeuronOutput()
{
double[] d = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
BasicNetwork network = EncogUtility.SimpleFeedForward(2, 3, 0, 1, false);
NetworkCODEC.ArrayToNetwork(d, network);
PruneSelective prune = new PruneSelective(network);
prune.RandomizeNeuron(100, 100, 2, 0);
Assert.AreEqual("100,100,100,100,0,0,0,0,0,0,0,0,0", network.DumpWeights());
}
public void TestNeuronSignificance()
{
BasicNetwork network = ObtainNetwork();
PruneSelective prune = new PruneSelective(network);
double inputSig = prune.DetermineNeuronSignificance(0, 1);
double hiddenSig = prune.DetermineNeuronSignificance(1, 1);
double outputSig = prune.DetermineNeuronSignificance(2, 1);
Assert.AreEqual(63.0, inputSig, 0.01);
Assert.AreEqual(95.0, hiddenSig, 0.01);
Assert.AreEqual(26.0, outputSig, 0.01);
}
public void TestIncreaseNeuronCountHidden2()
{
BasicNetwork network = EncogUtility.SimpleFeedForward(5, 6, 0, 2, true);
PruneSelective prune = new PruneSelective(network);
prune.ChangeNeuronCount(1, 60);
BasicMLData input = new BasicMLData(5);
BasicNetwork model = EncogUtility.SimpleFeedForward(5, 60, 0, 2, true);
CheckWithModel(model.Structure.Flat, network.Structure.Flat);
model.Compute(input);
network.Compute(input);
}
public void TestPruneNeuronHidden()
{
BasicNetwork network = ObtainNetwork();
PruneSelective prune = new PruneSelective(network);
prune.Prune(1, 1);
Assert.AreEqual(18, network.EncodedArrayLength());
Assert.AreEqual(2, network.GetLayerNeuronCount(1));
Assert.AreEqual("1,3,4,5,7,8,9,11,12,13,15,16,17,18,19,23,24,25", network.DumpWeights());
BasicNetwork model = EncogUtility.SimpleFeedForward(2, 2, 0, 4, false);
CheckWithModel(model.Structure.Flat, network.Structure.Flat);
}
public void TestIncreaseNeuronCountHidden()
{
BasicNetwork network = XOR.CreateTrainedXOR();
Assert.IsTrue(XOR.VerifyXOR(network, 0.10));
PruneSelective prune = new PruneSelective(network);
prune.ChangeNeuronCount(1, 5);
BasicNetwork model = EncogUtility.SimpleFeedForward(2, 5, 0, 1, false);
CheckWithModel(model.Structure.Flat, network.Structure.Flat);
Assert.IsTrue(XOR.VerifyXOR(network, 0.10));
}
public void TestPruneNeuronOutput()
{
BasicNetwork network = ObtainNetwork();
Assert.AreEqual(4, network.OutputCount);
PruneSelective prune = new PruneSelective(network);
prune.Prune(2, 1);
Assert.AreEqual(21, network.EncodedArrayLength());
Assert.AreEqual(3, network.GetLayerNeuronCount(2));
Assert.AreEqual("1,2,3,4,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25", network.DumpWeights());
BasicNetwork model = EncogUtility.SimpleFeedForward(2, 3, 0, 3, false);
CheckWithModel(model.Structure.Flat, network.Structure.Flat);
Assert.AreEqual(3, network.OutputCount);
}
public void TestIncreaseNeuronCountHidden()
{
BasicNetwork network = XOR.CreateTrainedXOR();
Assert.IsTrue(XOR.VerifyXOR(network, 0.10));
PruneSelective prune = new PruneSelective(network);
prune.ChangeNeuronCount(1, 5);
BasicNetwork model = EncogUtility.SimpleFeedForward(2, 5, 0, 1, false);
CheckWithModel(model.Structure.Flat, network.Structure.Flat);
Assert.IsTrue(XOR.VerifyXOR(network, 0.10));
}
public void TestRandomizeNeuronOutput()
{
double[] d = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
BasicNetwork network = EncogUtility.SimpleFeedForward(2, 3, 0, 1, false);
NetworkCODEC.ArrayToNetwork(d, network);
PruneSelective prune = new PruneSelective(network);
prune.RandomizeNeuron(100, 100, 2, 0);
Assert.AreEqual("100,100,100,100,0,0,0,0,0,0,0,0,0", network.DumpWeights());
}
public void TestPruneNeuronOutput()
{
BasicNetwork network = ObtainNetwork();
Assert.AreEqual(4, network.OutputCount);
PruneSelective prune = new PruneSelective(network);
prune.Prune(2, 1);
Assert.AreEqual(21, network.EncodedArrayLength());
Assert.AreEqual(3, network.GetLayerNeuronCount(2));
Assert.AreEqual("1,2,3,4,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25", network.DumpWeights());
BasicNetwork model = EncogUtility.SimpleFeedForward(2, 3, 0, 3, false);
CheckWithModel(model.Structure.Flat, network.Structure.Flat);
Assert.AreEqual(3, network.OutputCount);
}
public void TestPruneNeuronHidden()
{
BasicNetwork network = ObtainNetwork();
PruneSelective prune = new PruneSelective(network);
prune.Prune(1, 1);
Assert.AreEqual(18, network.EncodedArrayLength());
Assert.AreEqual(2, network.GetLayerNeuronCount(1));
Assert.AreEqual("1,3,4,5,7,8,9,11,12,13,15,16,17,18,19,23,24,25", network.DumpWeights());
BasicNetwork model = EncogUtility.SimpleFeedForward(2, 2, 0, 4, false);
CheckWithModel(model.Structure.Flat, network.Structure.Flat);
}
public void TestNeuronSignificance()
{
BasicNetwork network = ObtainNetwork();
PruneSelective prune = new PruneSelective(network);
double inputSig = prune.DetermineNeuronSignificance(0, 1);
double hiddenSig = prune.DetermineNeuronSignificance(1, 1);
double outputSig = prune.DetermineNeuronSignificance(2, 1);
Assert.AreEqual(63.0, inputSig, 0.01);
Assert.AreEqual(95.0, hiddenSig, 0.01);
Assert.AreEqual(26.0, outputSig, 0.01);
}
public void TestIncreaseNeuronCountHidden2()
{
BasicNetwork network = EncogUtility.SimpleFeedForward(5, 6, 0, 2, true);
PruneSelective prune = new PruneSelective(network);
prune.ChangeNeuronCount(1, 60);
BasicMLData input = new BasicMLData(5);
BasicNetwork model = EncogUtility.SimpleFeedForward(5, 60, 0, 2, true);
CheckWithModel(model.Structure.Flat, network.Structure.Flat);
model.Compute(input);
network.Compute(input);
}
