func TestNeuronMutateWeights(t *testing.T) {
xnorCortex := ng.XnorCortex()
neuron := xnorCortex.NeuronUUIDMap()["output-neuron"]
assert.True(t, neuron != nil)
neuronCopy := neuron.Copy()
foundModifiedWeight := false
for i := 0; i < 100; i++ {
didMutateWeights, _ := NeuronMutateWeights(neuron)
if didMutateWeights == true {
foundModifiedWeight = verifyWeightsModified(neuron, neuronCopy)
}
if foundModifiedWeight == true {
break
}
}
assert.True(t, foundModifiedWeight == true)
}
func TestAddBias(t *testing.T) {
xnorCortex := ng.XnorCortex()
for _, neuron := range xnorCortex.Neurons {
neuron.Bias = 0.0
}
beforeString := ng.JsonString(xnorCortex)
AddBias(xnorCortex)
afterString := ng.JsonString(xnorCortex)
assert.True(t, beforeString != afterString)
}
func TestNeuronAddInlinkRecurrent(t *testing.T) {
madeNonRecurrentInlink := false
madeRecurrentInlink := false
for i := 0; i < 100; i++ {
xnorCortex := ng.XnorCortex()
neuron := xnorCortex.NeuronUUIDMap()["output-neuron"]
ok, mutateResult := NeuronAddInlinkRecurrent(neuron)
if !ok {
continue
}
inboundConnection := mutateResult.(*ng.InboundConnection)
if neuron.IsInboundConnectionRecurrent(inboundConnection) {
// the first time we make a nonRecurrentInlink,
// test the network out
if madeRecurrentInlink == false {
// make sure the network actually works
examples := ng.XnorTrainingSamples()
fitness := xnorCortex.Fitness(examples)
assert.True(t, fitness >= 0)
}
madeRecurrentInlink = true
} else {
// the first time we make a nonRecurrentInlink,
// test the network out
if madeNonRecurrentInlink == false {
// make sure the network doesn't totally break
examples := ng.XnorTrainingSamples()
fitness := xnorCortex.Fitness(examples)
assert.True(t, fitness >= 0)
}
madeNonRecurrentInlink = true
}
}
assert.True(t, madeNonRecurrentInlink)
assert.True(t, madeRecurrentInlink)
}
func TestPerturbParameters(t *testing.T) {
cortex := ng.XnorCortex()
nnJson, _ := json.Marshal(cortex)
nnJsonString := fmt.Sprintf("%s", nnJson)
saturationBounds := []float64{-100000, 10000}
PerturbParameters(cortex, saturationBounds)
nnJsonAfter, _ := json.Marshal(cortex)
nnJsonStringAfter := fmt.Sprintf("%s", nnJsonAfter)
// the json should be different after we perturb it
assert.NotEquals(t, nnJsonString, nnJsonStringAfter)
}
func TestNeuronAddInlinkNonRecurrent(t *testing.T) {
ng.SeedRandom()
madeNonRecurrentInlink := false
madeRecurrentInlink := false
firstTime := true
// since it's stochastic, repeat the operation many times and make
// sure that it always produces expected behavior
for i := 0; i < 100; i++ {
xnorCortex := ng.XnorCortex()
sensor := xnorCortex.Sensors[0]
neuron := xnorCortex.NeuronUUIDMap()["output-neuron"]
hiddenNeuron1 := xnorCortex.NeuronUUIDMap()["hidden-neuron1"]
targetLayerIndex := hiddenNeuron1.NodeId.LayerIndex
// add a new neuron at the same layer index as the hidden neurons
hiddenNeuron3 := &ng.Neuron{
ActivationFunction: ng.EncodableSigmoid(),
NodeId: ng.NewNeuronId("hidden-neuron3", targetLayerIndex),
Bias: -30,
}
hiddenNeuron3.Init()
xnorCortex.Neurons = append(xnorCortex.Neurons, hiddenNeuron3)
weights := randomWeights(sensor.VectorLength)
sensor.ConnectOutbound(hiddenNeuron3)
hiddenNeuron3.ConnectInboundWeighted(sensor, weights)
ok, mutateResult := NeuronAddInlinkNonRecurrent(neuron)
if !ok {
continue
}
inboundConnection := mutateResult.(*ng.InboundConnection)
if neuron.IsInboundConnectionRecurrent(inboundConnection) {
madeRecurrentInlink = true
} else {
madeNonRecurrentInlink = true
}
if firstTime == true {
// only two possibilities - the hiddenNeuron3 or the
// sensor. if it was the sensor, then the hiddenNeuron3
// is "dangliing" and so lets connect it
if inboundConnection.NodeId.UUID == "sensor" {
weights2 := randomWeights(1)
hiddenNeuron3.ConnectOutbound(neuron)
neuron.ConnectInboundWeighted(hiddenNeuron3, weights2)
}
// run network make sure it runs
examples := ng.XnorTrainingSamples()
fitness := xnorCortex.Fitness(examples)
assert.True(t, fitness >= 0)
firstTime = false
}
}
assert.True(t, madeNonRecurrentInlink)
assert.False(t, madeRecurrentInlink)
}
