def topoMutate(self, p, innov,gen):
"""Randomly alter topology of individual
Note: This operator forces precisely ONE topological change
Args:
child - (Ind) - individual to be mutated
.conns - (np_array) - connection genes
[5 X nUniqueGenes]
[0,:] == Innovation Number (unique Id)
[1,:] == Source Node Id
[2,:] == Destination Node Id
[3,:] == Weight Value
[4,:] == Enabled?
.nodes - (np_array) - node genes
[3 X nUniqueGenes]
[0,:] == Node Id
[1,:] == Type (1=input, 2=output 3=hidden 4=bias)
[2,:] == Activation function (as int)
innov - (np_array) - innovation record
[5 X nUniqueGenes]
[0,:] == Innovation Number
[1,:] == Source
[2,:] == Destination
[3,:] == New Node?
[4,:] == Generation evolved
Returns:
child - (Ind) - newly created individual
innov - (np_array) - innovation record
"""
# Readability
nConn = np.shape(self.conn)[1]
connG = np.copy(self.conn)
nodeG = np.copy(self.node)
# Choose topological mutation
topoRoulette = np.array((p['prob_addConn'], p['prob_addNode'], \
p['prob_enable'] , p['prob_mutAct']))
spin = np.random.rand()*np.sum(topoRoulette)
slot = topoRoulette[0]
choice = topoRoulette.size
for i in range(1,topoRoulette.size):
if spin < slot:
choice = i
break
else:
slot += topoRoulette[i]
# Add Connection
if choice is 1:
connG, innov = self.mutAddConn(connG, nodeG, innov, gen, p)
# Add Node
elif choice is 2:
connG, nodeG, innov = self.mutAddNode(connG, nodeG, innov, gen, p)
# Enable Connection
elif choice is 3:
disabled = np.where(connG[4,:] == 0)[0]
if len(disabled) > 0:
enable = np.random.randint(len(disabled))
connG[4,disabled[enable]] = 1
# Mutate Activation
elif choice is 4:
start = 1+self.nInput + self.nOutput
end = nodeG.shape[1]
if start != end:
mutNode = np.random.randint(start,end)
newActPool = listXor([int(nodeG[2,mutNode])], list(p['ann_actRange']))
nodeG[2,mutNode] = int(newActPool[np.random.randint(len(newActPool))])
child = WannInd(connG, nodeG)
child.birth = gen
return child, innov
def topoMutate(self, p, innov, gen):
"""Randomly alter topology of individual
Note: This operator forces precisely ONE topological change
Args:
child - (Ind) - individual to be mutated
.conns - (np_array) - connection genes
[5 X nUniqueGenes]
[0,:] == Innovation Number (unique Id)
[1,:] == Source Node Id
[2,:] == Destination Node Id
[3,:] == Weight Value
[4,:] == Enabled?
.nodes - (np_array) - node genes
[3 X nUniqueGenes]
[0,:] == Node Id
[1,:] == Type (1=input, 2=output 3=hidden 4=bias)
[2,:] == Activation function (as int)
innov - (np_array) - innovation record
[5 X nUniqueGenes]
[0,:] == Innovation Number
[1,:] == Source
[2,:] == Destination
[3,:] == New Node?
[4,:] == Generation evolved
Returns:
child - (Ind) - newly created individual
innov - (np_array) - innovation record
"""
# Readability
nConn = np.shape(self.conn)[1]
connG = np.copy(self.conn)
nodeG = np.copy(self.node)
# Choose topological mutation
topoRoulette = np.array((p['prob_addConn'], p['prob_addNode'], \
p['prob_enable'] , p['prob_mutAct']))
spin = np.random.rand() * np.sum(topoRoulette)
slot = topoRoulette[0]
choice = topoRoulette.size
for i in range(1, topoRoulette.size):
if spin < slot:
choice = i
break
else:
slot += topoRoulette[i]
# Add Connection
if choice is 1:
connG, innov = self.mutAddConn(connG, nodeG, innov, gen, p)
# Add Node
# FIXED
elif choice is 2:
connG, nodeG, innov = self.mutAddNode(connG, nodeG, innov, gen, p)
# Enable Connection
elif choice is 3:
disabled = np.where(connG[4, :] == 0)[0]
if len(disabled) > 0:
enable = np.random.randint(len(disabled))
connG[4, disabled[enable]] = 1
# Mutate Activation
elif choice is 4:
start = 1 + self.nInput + self.nOutput
end = nodeG.shape[1]
if start != end:
mutNode = np.random.randint(start, end)
newActPool = listXor([int(nodeG[2, mutNode])],
list(p['ann_actRange']))
nodeG[2, mutNode] = int(newActPool[np.random.randint(
len(newActPool))])
#print(p['ann_actRange'])
#print("activation before modify in wann_ind.py: ", nodeG[2:])
"""
case 1 -- Linear
case 2 -- Unsigned Step Function
case 3 -- Sin
case 4 -- Gausian with mean 0 and sigma 1
case 5 -- Hyperbolic Tangent [tanh] (signed)
case 6 -- Sigmoid unsigned [1 / (1 + exp(-x))]
case 7 -- Inverse
case 8 -- Absolute Value
case 9 -- Relu
case 10 -- Cosine
case 11 -- Squared
"""
NO_THIS_A = 5
lst = []
for nG in nodeG[2:]:
lst2 = []
#print(nG)
for a in nG:
if a == NO_THIS_A:
lst2.append(1)
else:
lst2.append(a)
lst.append(lst2)
nodeG[2:] = lst
#print("activation after modify in wann_ind.py: ", nodeG[2:])
child = WannInd(connG, nodeG)
child.birth = gen
return child, innov