func TestNeuronRemoveBias(t *testing.T) {
neuron := &ng.Neuron{
ActivationFunction: ng.EncodableSigmoid(),
NodeId: ng.NewNeuronId("neuron", 0.25),
Bias: 10,
}
neuron.Init()
NeuronRemoveBias(neuron)
assert.True(t, neuron.Bias == 0)
}
func TestNeuronMutateActivation(t *testing.T) {
ng.SeedRandom()
neuron := &ng.Neuron{
ActivationFunction: ng.EncodableSigmoid(),
NodeId: ng.NewNeuronId("neuron", 0.25),
Bias: 10,
}
NeuronMutateActivation(neuron)
assert.True(t, neuron.ActivationFunction != nil)
assert.True(t, neuron.ActivationFunction.Name != ng.EncodableSigmoid().Name)
}
func TestNeuronAddBias(t *testing.T) {
// basic case where there is no bias
neuron := &ng.Neuron{
ActivationFunction: ng.EncodableSigmoid(),
NodeId: ng.NewNeuronId("neuron", 0.25),
}
neuron.Init()
NeuronAddBias(neuron)
assert.True(t, neuron.Bias != 0)
// make sure it treats 0 bias as not having a bias
neuron = &ng.Neuron{
ActivationFunction: ng.EncodableSigmoid(),
NodeId: ng.NewNeuronId("neuron", 0.25),
Bias: 0,
}
neuron.Init()
NeuronAddBias(neuron)
assert.True(t, neuron.Bias != 0)
// make sure it doesn't add a bias if there is an existing one
neuron = &ng.Neuron{
ActivationFunction: ng.EncodableSigmoid(),
NodeId: ng.NewNeuronId("neuron", 0.25),
Bias: 10,
}
neuron.Init()
NeuronAddBias(neuron)
assert.True(t, neuron.Bias == 10)
}
func SingleNeuronCortex(uuid string) *ng.Cortex {
sensor := &ng.Sensor{
NodeId: ng.NewSensorId("sensor", 0.0),
VectorLength: 1,
}
sensor.Init()
neuron := &ng.Neuron{
ActivationFunction: ng.EncodableIdentity(),
NodeId: ng.NewNeuronId("neuron", 0.15),
Bias: 1,
}
neuron.Init()
actuator := &ng.Actuator{
NodeId: ng.NewActuatorId("actuator", 0.5),
VectorLength: 1,
}
actuator.Init()
sensor.ConnectOutbound(neuron)
neuron.ConnectInboundWeighted(sensor, []float64{1})
neuron.ConnectOutbound(actuator)
actuator.ConnectInbound(neuron)
nodeId := ng.NewCortexId(uuid)
cortex := &ng.Cortex{
NodeId: nodeId,
}
cortex.SetSensors([]*ng.Sensor{sensor})
cortex.SetNeurons([]*ng.Neuron{neuron})
cortex.SetActuators([]*ng.Actuator{actuator})
return cortex
}
func BasicCortex() *ng.Cortex {
sensor := &ng.Sensor{
NodeId: ng.NewSensorId("sensor", 0.0),
VectorLength: 2,
}
sensor.Init()
hiddenNeuron1 := &ng.Neuron{
ActivationFunction: ng.EncodableSigmoid(),
NodeId: ng.NewNeuronId("hidden-neuron1", 0.15),
Bias: -30,
}
hiddenNeuron1.Init()
hiddenNeuron2 := &ng.Neuron{
ActivationFunction: ng.EncodableSigmoid(),
NodeId: ng.NewNeuronId("hidden-neuron2", 0.25),
Bias: 10,
}
hiddenNeuron2.Init()
hiddenNeuron3 := &ng.Neuron{
ActivationFunction: ng.EncodableSigmoid(),
NodeId: ng.NewNeuronId("hidden-neuron3", 0.35),
Bias: 10,
}
hiddenNeuron3.Init()
outputNeuron := &ng.Neuron{
ActivationFunction: ng.EncodableSigmoid(),
NodeId: ng.NewNeuronId("output-neuron", 0.45),
Bias: -10,
}
outputNeuron.Init()
actuator := &ng.Actuator{
NodeId: ng.NewActuatorId("actuator", 0.5),
VectorLength: 1,
}
actuator.Init()
sensor.ConnectOutbound(hiddenNeuron1)
hiddenNeuron1.ConnectInboundWeighted(sensor, []float64{20, 20})
hiddenNeuron1.ConnectOutbound(hiddenNeuron2)
hiddenNeuron2.ConnectInboundWeighted(hiddenNeuron1, []float64{1})
hiddenNeuron2.ConnectOutbound(hiddenNeuron3)
hiddenNeuron3.ConnectInboundWeighted(hiddenNeuron2, []float64{1})
hiddenNeuron3.ConnectOutbound(outputNeuron)
outputNeuron.ConnectInboundWeighted(hiddenNeuron3, []float64{1})
outputNeuron.ConnectOutbound(actuator)
actuator.ConnectInbound(outputNeuron)
nodeId := ng.NewCortexId("test-cortex")
cortex := &ng.Cortex{
NodeId: nodeId,
}
cortex.SetSensors([]*ng.Sensor{sensor})
cortex.SetNeurons([]*ng.Neuron{hiddenNeuron1, hiddenNeuron2, hiddenNeuron3, outputNeuron})
cortex.SetActuators([]*ng.Actuator{actuator})
return cortex
}
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)
}