def createSHCchain(self):
#Construct the following specifically interconnected actors into an actor molecule.
#1. A function actor that takes two sensory inputs (e.g. accelerometer z and accelerometer y)
# and outputs the product of them as a message.
actor1 = actorClass(name="actor",
count=0,
inputs=[10, 10],
outputs=None,
function="product",
typeA=1) #Species input vector and the function.
#ARGUMENTS: name, number,data inputs, function type
#2. A SHC actor that reads that message as a fitness input and outputs a parameter vector as a message.
actor2 = actorClass(name="actor",
count=1,
inputs=[189],
outputs=[6],
messageInputs=[1],
function=None,
typeA=3)
#ARGUMENTS: name, number, data inputs, message inputs, 3 = optimization actor.
#3. A message to motor function that reads the above message and outputs a motor command
#Make into an actor array
actors = [actor1, actor2]
return actors
pass
def copyMotorPNode(self, a):
#print "Mutating motorP atom actor, STRUCTURALLY, i.e. add, an atom, upstream, downtream, in new branch."
#Create new downstream motorP atom with some probability.
if random.random() < 0.05:
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.messageDelays is not None:
amd = list(a.messageDelays)
else:
amd = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
if a.parameters is not None:
par = list(a.parameters)
else:
par = None
#Lateral mutation
nm = None
if random.random() < 0.5:
nm = actorClass(
copyA=True,
atomA=a,
typeA=a.type,
nameA=a.atomKind,
count=self.totalCount,
sensors=se,
messages=am,
messageDelays=amd,
motors=mo,
function=a.function,
parameters=par)
else:
#Downstream mutation
nm = actorClass(
copyA=True,
atomA=a,
typeA=a.type,
nameA=a.atomKind,
count=self.totalCount,
sensors=se,
messages=list([a.id]),
messageDelays=[1],
motors=mo,
function=a.function,
parameters=par)
self.totalCount = self.totalCount + 1
nm.mutate()
return nm
def copyMotorPNode(self, a):
#print "Mutating motorP atom actor, STRUCTURALLY, i.e. add, an atom, upstream, downtream, in new branch."
#Create new downstream motorP atom with some probability.
if random.random() < 0.08:
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.messageDelays is not None:
amd = list(a.messageDelays)
else:
amd = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
if a.parameters is not None:
par = list(a.parameters)
else:
par = None
#Lateral mutation
nm = None
if random.random() < 0.5:
nm = actorClass(copyA=True,
atomA=a,
typeA=a.type,
nameA=a.atomKind,
count=self.totalCount,
sensors=se,
messages=am,
messageDelays=amd,
motors=mo,
function=a.function,
parameters=par)
else:
#Downstream mutation
nm = actorClass(copyA=True,
atomA=a,
typeA=a.type,
nameA=a.atomKind,
count=self.totalCount,
sensors=se,
messages=list([a.id]),
messageDelays=[1],
motors=mo,
function=a.function,
parameters=par)
self.totalCount = self.totalCount + 1
nm.mutate()
return nm
def __init__(self,name, type):
ALModule.__init__(self,name)
self.isRunning=True
self.mm = ALProxy("memoryManager")
#Create a molecule consisting of three atoms
#a. An atom taking a sensory input and writing a function of it to its ALMemory location
actor0 = actorClass(typeA = "sensory", nameA = "actor",count = 0, sensors = [10,10],messages = None, motors = None, function = "sum", parameters = None)
#b. An atom taking an ALMemory location of the first atom and doing SHC with it, and a downstream atom.
actor1 = actorClass(typeA = "shc", nameA = "actor",count = 1,sensors = None, messages = [0], motors = None, function = "sum", parameters = [0,0])
#c. An atom that takes an ALMemory location and converts it into a motor action (as specified by its parameters)
actor2 = actorClass(typeA = "motor", nameA = "actor",count = 2, sensors = None, messages = [1] , motors = [6], function = None, parameters = None)
self.actors = [actor0, actor1, actor2]
self.molecules = []
def replicateAtoms(self):
for act in self.actors:
if act.id is 0:
print "REPLICATING *************"
cop = actorClass(
typeA=act.type,
nameA=act.atomKind,
count=len(self.actors),
sensors=act.sensors,
messages=act.messages,
motors=act.motors,
function=act.function,
parameters=act.parameters,
)
# Check that the downstream atoms get input from this new copied atom.
# 1.Go through all the atoms checking if their input list includes the act atom.
for others in self.actors:
if others is not act:
if others.messages is not None:
for i in others.messages:
if i is act.id:
others.messages.append(
cop.id
) # Get input from offspring actor to the downstream act that also got input from the offspring's parent.
self.actors.append(cop)
def copyMotorPNode(self, a):
#print "Mutating motorP atom actor, STRUCTURALLY, i.e. add, an atom, upstream, downtream, in new branch."
#Create new downstream motorP atom with some probability.
if random.random() < 0.1:
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
nm = actorClass(typeA=a.type,
nameA=a.atomKind,
count=self.totalCount,
sensors=se,
messages=am,
motors=mo,
function=a.function,
parameters=a.parameters)
self.totalCount = self.totalCount + 1
nm.mutate()
return nm
def __init__(self,name):
ALModule.__init__(self,name)
self.isRunning=True
self.POP_SIZE = 10
#Create a population of random actor objects.
self.actors = [actorClass("actor", count) for count in xrange(self.POP_SIZE)]
def loadGenomes():
print "Loading genomes"
pkl_file = open('actordata.pkl' + str(89), 'rb')
data1 = pickle.load(pkl_file)
# pprint.pprint(data1[0][3]) #Prints the pickled data of the first actor.
#Input this data into the relavent data structures.
actorPopulation.molecules = data1[0][0]
print actorPopulation.molecules
actorPopulation.moleculeFitness = data1[0][1]
actorPopulation.totalCount = data1[0][2]
for index, i in enumerate(data1[0][3]):
## print data1[0][3][index][0]
## print data1[0][3][index][1]
## print data1[0][3][index][2]
## print data1[0][3][index][3]
## print data1[0][3][index][4]
## print data1[0][3][index][5]
## print data1[0][3][index][6]
## print data1[0][3][index][7]
actorPopulation.actors[data1[0][3][index][2]] = actorClass(
typeA=data1[0][3][index][0],
nameA=data1[0][3][index][1],
count=data1[0][3][index][2],
sensors=data1[0][3][index][3],
messages=data1[0][3][index][4],
motors=data1[0][3][index][5],
function=data1[0][3][index][6],
parameters=data1[0][3][index][7])
#[self.type, self.atomKind, self.id, self.sensors, self.messages, self.motors, self.function, self.parameters, ]
pkl_file.close()
def loadGenomes():
print "Loading genomes"
pkl_file = open("actordata.pkl" + str(89), "rb")
data1 = pickle.load(pkl_file)
# pprint.pprint(data1[0][3]) #Prints the pickled data of the first actor.
# Input this data into the relavent data structures.
actorPopulation.molecules = data1[0][0]
print actorPopulation.molecules
actorPopulation.moleculeFitness = data1[0][1]
actorPopulation.totalCount = data1[0][2]
for index, i in enumerate(data1[0][3]):
## print data1[0][3][index][0]
## print data1[0][3][index][1]
## print data1[0][3][index][2]
## print data1[0][3][index][3]
## print data1[0][3][index][4]
## print data1[0][3][index][5]
## print data1[0][3][index][6]
## print data1[0][3][index][7]
actorPopulation.actors[data1[0][3][index][2]] = actorClass(
typeA=data1[0][3][index][0],
nameA=data1[0][3][index][1],
count=data1[0][3][index][2],
sensors=data1[0][3][index][3],
messages=data1[0][3][index][4],
motors=data1[0][3][index][5],
function=data1[0][3][index][6],
parameters=data1[0][3][index][7],
)
# [self.type, self.atomKind, self.id, self.sensors, self.messages, self.motors, self.function, self.parameters, ]
pkl_file.close()
def loadGenomes(self, i):
print "Loading genomes"
pkl_file = open('actordata.pkl' + str(i), 'rb')
data1 = pickle.load(pkl_file)
# pprint.pprint(data1[0][3]) #Prints the pickled data of the first actor.
#Input this data into the relavent data structures.
self.molecules = data1[0][0]
print self.molecules
self.moleculeFitness = data1[0][1]
print self.moleculeFitness
self.totalCount = data1[0][2]
print self.totalCount
for index, i in enumerate(data1[0][3]):
## print data1[0][3][index][0]
## print data1[0][3][index][1]
## print data1[0][3][index][2]
## print data1[0][3][index][3]
## print data1[0][3][index][4]
## print data1[0][3][index][5]
## print data1[0][3][index][6]
## print data1[0][3][index][7]
## print data1[0][3][index][8]
if data1[0][3][index][4] is not None:
am = list(data1[0][3][index][4])
else:
am = None
if data1[0][3][index][5] is not None:
amd = list(data1[0][3][index][5])
else:
amd = None
if data1[0][3][index][3] is not None:
se = list(data1[0][3][index][3])
else:
se = None
if data1[0][3][index][6] is not None:
mo = list(data1[0][3][index][6])
else:
mo = None
if data1[0][3][index][8] is not None:
pa = list(data1[0][3][index][8])
else:
pa = None
self.actors[data1[0][3][index][2]] = actorClass(
copyA=True,
atomA=data1[0][3][index],
typeA=data1[0][3][index][0],
nameA=data1[0][3][index][1],
count=data1[0][3][index][2],
sensors=se,
messages=am,
messageDelays=amd,
motors=mo,
function=data1[0][3][index][7],
parameters=pa
) #[self.type, self.atomKind, self.id, self.sensors, self.messages, self.motors, self.function, self.parameters, ]
pkl_file.close()
def loadGenomes(self, i):
print "Loading genomes"
pkl_file = open('actordata.pkl' + str(i), 'rb')
data1 = pickle.load(pkl_file)
# pprint.pprint(data1[0][3]) #Prints the pickled data of the first actor.
#Input this data into the relavent data structures.
self.molecules = data1[0][0]
print self.molecules
self.moleculeFitness = data1[0][1]
print self.moleculeFitness
self.totalCount = data1[0][2]
print self.totalCount
for index, i in enumerate(data1[0][3]):
## print data1[0][3][index][0]
## print data1[0][3][index][1]
## print data1[0][3][index][2]
## print data1[0][3][index][3]
## print data1[0][3][index][4]
## print data1[0][3][index][5]
## print data1[0][3][index][6]
## print data1[0][3][index][7]
## print data1[0][3][index][8]
if data1[0][3][index][4] is not None:
am = list(data1[0][3][index][4])
else:
am = None
if data1[0][3][index][5] is not None:
amd = list(data1[0][3][index][5])
else:
amd = None
if data1[0][3][index][3] is not None:
se = list(data1[0][3][index][3])
else:
se = None
if data1[0][3][index][6] is not None:
mo = list(data1[0][3][index][6])
else:
mo = None
if data1[0][3][index][8] is not None:
pa = list(data1[0][3][index][8])
else:
pa = None
self.actors[data1[0][3][index][2]] = actorClass(
copyA=True,
atomA=data1[0][3][index],
typeA=data1[0][3][index][0],
nameA=data1[0][3][index][1],
count=data1[0][3][index][2],
sensors=se,
messages=am,
messageDelays=amd,
motors=mo,
function=data1[0][3][index][7],
parameters=pa
) #[self.type, self.atomKind, self.id, self.sensors, self.messages, self.motors, self.function, self.parameters, ]
pkl_file.close()
def __init__(self,name, kind, from_file):
ALModule.__init__(self,name)
self.isRunning=True
self.MAX_POP_SIZE = 10
self.mm = ALProxy("memoryManager")
self.games = ALProxy("gamePopulation")
self.totalCount = 0
self.actors = {}
#Create a molecule consisting of three atoms
#a. An atom taking a sensory input and writing a function of it to its ALMemory location
if from_file is False and kind is 1:
actor0 = actorClass(typeA = "sensory", nameA = "actor",count = self.totalCount, sensors = [141,142,143,144,145,146,147,140,139,138],messages = None, motors = None, function = "sum", parameters = None)
self.totalCount = self.totalCount + 1
#b. An atom taking an ALMemory location of the first atom and doing SHC with it, and a downstream atom.
actor1 = actorClass(typeA = "shc", nameA = "actor",count = self.totalCount,sensors = None, messages = [0], motors = None, function = "sum", parameters = [0,0,0,0,0,0,0,0,0,0])
self.totalCount = self.totalCount + 1
#c. An atom that takes an ALMemory location and converts it into a motor action (as specified by its parameters)
actor2 = actorClass(typeA = "motor", nameA = "actor",count = self.totalCount, sensors = None, messages = [1] , motors = [self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor()], function = None, parameters = None)
self.totalCount = self.totalCount + 1
self.actors = {0:actor0, 1:actor1, 2:actor2}
if from_file is False and kind is 2:
for i in range(self.MAX_POP_SIZE):
self.actors[self.totalCount] = actorClass(typeA = "sensory", nameA = "actor",count = self.totalCount, sensors = [self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor()],messages = None, motors = None, function = "sum", parameters = None)
self.totalCount = self.totalCount + 1
#b. An atom taking an ALMemory location of the first atom and doing SHC with it, and a downstream atom.
self.actors[self.totalCount] = actorClass(typeA = "shc", nameA = "actor",count = self.totalCount,sensors = None, messages = [self.totalCount-1], motors = None, function = "sum", parameters = [0,0,0,0,0,0,0,0,0,0])
self.totalCount = self.totalCount + 1
#c. An atom that takes an ALMemory location and converts it into a motor action (as specified by its parameters)
self.actors[self.totalCount] = actorClass(typeA = "motor", nameA = "actor",count = self.totalCount, sensors = None, messages = [self.totalCount-1] , motors = [self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor()], function = None, parameters = None)
self.totalCount = self.totalCount + 1
#Compositional Motor-Sequence Molecule Initialization.
if from_file is False and kind is 3:
for i in range(self.MAX_POP_SIZE):
self.actors[self.totalCount] = actorClass(typeA = "sensory", nameA = "actor",count = self.totalCount, sensors = [188],messages = [ self.totalCount], motors = None, function = "position", parameters = None)
self.totalCount = self.totalCount + 1
self.actors[self.totalCount] = actorClass(typeA = "motorP", nameA = "actor",count = self.totalCount, sensors = None, messages = [self.totalCount-1] , motors = [self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor()], function = "sequence", parameters = [[0], [0.2, 0.2, 0.2], [1, 1, 1]])
self.totalCount = self.totalCount + 1
self.actors[self.totalCount] = actorClass(typeA = "motorP", nameA = "actor",count = self.totalCount, sensors = None, messages = [self.totalCount-1], motors = [self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor()], function = "sequence", parameters = [[5], [-0.1, -0.2, -0.3], [1, 1, 1]])
self.totalCount = self.totalCount + 1
self.molecules = []
self.moleculeFitness = []
self.f = open('fitnessfile','w')
self.fitnessHistory = []
plt.ion()
def __init__(self,name, kind, from_file):
ALModule.__init__(self,name)
self.isRunning=True
self.MAX_POP_SIZE = 10
self.mm = ALProxy("memoryManager")
self.games = ALProxy("gamePopulation")
self.totalCount = 0
self.actors = {}
#Create a molecule consisting of three atoms
#a. An atom taking a sensory input and writing a function of it to its ALMemory location
if from_file is False and kind is 1:
actor0 = actorClass(typeA = "sensory", nameA = "actor",count = self.totalCount, sensors = [141,142,143,144,145,146,147,140,139,138],messages = None, motors = None, function = "sum", parameters = None)
self.totalCount = self.totalCount + 1
#b. An atom taking an ALMemory location of the first atom and doing SHC with it, and a downstream atom.
actor1 = actorClass(typeA = "shc", nameA = "actor",count = self.totalCount,sensors = None, messages = [0], motors = None, function = "sum", parameters = [0,0,0,0,0,0,0,0,0,0])
self.totalCount = self.totalCount + 1
#c. An atom that takes an ALMemory location and converts it into a motor action (as specified by its parameters)
actor2 = actorClass(typeA = "motor", nameA = "actor",count = self.totalCount, sensors = None, messages = [1] , motors = [self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor()], function = None, parameters = None)
self.totalCount = self.totalCount + 1
self.actors = {0:actor0, 1:actor1, 2:actor2}
if from_file is False and kind is 2:
for i in range(self.MAX_POP_SIZE):
self.actors[self.totalCount] = actorClass(typeA = "sensory", nameA = "actor",count = self.totalCount, sensors = [self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor(),self.mm.getRandomSensor()],messages = None, motors = None, function = "sum", parameters = None)
self.totalCount = self.totalCount + 1
#b. An atom taking an ALMemory location of the first atom and doing SHC with it, and a downstream atom.
self.actors[self.totalCount] = actorClass(typeA = "shc", nameA = "actor",count = self.totalCount,sensors = None, messages = [self.totalCount-1], motors = None, function = "sum", parameters = [0,0,0,0,0,0,0,0,0,0])
self.totalCount = self.totalCount + 1
#c. An atom that takes an ALMemory location and converts it into a motor action (as specified by its parameters)
self.actors[self.totalCount] = actorClass(typeA = "motor", nameA = "actor",count = self.totalCount, sensors = None, messages = [self.totalCount-1] , motors = [self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor()], function = None, parameters = None)
self.totalCount = self.totalCount + 1
#Compositional Motor-Sequence Molecule Initialization.
if from_file is False and kind is 3:
for i in range(self.MAX_POP_SIZE):
self.actors[self.totalCount] = actorClass(typeA = "sensory", nameA = "actor",count = self.totalCount, sensors = [143],messages = [ self.totalCount], motors = None, function = "position", parameters = None)
self.totalCount = self.totalCount + 1
self.actors[self.totalCount] = actorClass(typeA = "motorP", nameA = "actor",count = self.totalCount, sensors = None, messages = [self.totalCount-1] , motors = [self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor()], function = "sequence", parameters = [[random.randint(0,3)], [2*(random.random()-0.5),2*(random.random()-0.5),2*(random.random()-0.5)], [1, 1, 1]])
self.totalCount = self.totalCount + 1
self.actors[self.totalCount] = actorClass(typeA = "motorP", nameA = "actor",count = self.totalCount, sensors = None, messages = [self.totalCount-1], motors = [self.mm.getRandomMotor(),self.mm.getRandomMotor(),self.mm.getRandomMotor()], function = "sequence", parameters = [[random.randint(0,3)], [2*(random.random()-0.5), 2*(random.random()-0.5), 2*(random.random()-0.5)], [1, 1, 1]])
self.totalCount = self.totalCount + 1
self.molecules = []
self.moleculeFitness = []
self.f = open('fitnessfile','w')
self.fitnessHistory = []
plt.ion()
def __init__(self, name, type):
ALModule.__init__(self, name)
self.isRunning = True
if type is 0:
self.POP_SIZE = 10
# Create a population of random actor objects.
self.actors = [actorClass("actor", count) for count in xrange(self.POP_SIZE)]
if type is 1: # HAND DESIGNED (BUT EVOLVABLE) SHC OPTIMIZATION ACTOR MOLECULE
self.POP_SIZE = 3
self.actors = self.createSHCchain()
def __init__(self,name, type):
ALModule.__init__(self,name)
self.isRunning=True
self.mm = ALProxy("memoryManager")
self.games = ALProxy("gamePopulation")
self.totalCount = 0
#Create a molecule consisting of three atoms
#a. An atom taking a sensory input and writing a function of it to its ALMemory location
actor0 = actorClass(typeA = "sensory", nameA = "actor",count = self.totalCount, sensors = [10,10],messages = None, motors = None, function = "sum", parameters = None)
self.totalCount = self.totalCount + 1
#b. An atom taking an ALMemory location of the first atom and doing SHC with it, and a downstream atom.
actor1 = actorClass(typeA = "shc", nameA = "actor",count = self.totalCount,sensors = None, messages = [0], motors = None, function = "sum", parameters = [0,0])
self.totalCount = self.totalCount + 1
#c. An atom that takes an ALMemory location and converts it into a motor action (as specified by its parameters)
actor2 = actorClass(typeA = "motor", nameA = "actor",count = self.totalCount, sensors = None, messages = [1] , motors = [6], function = None, parameters = None)
self.totalCount = self.totalCount + 1
self.actors = {0:actor0, 1:actor1, 2:actor2}
self.molecules = []
self.moleculeFitness = []
self.MAX_POP_SIZE = 20
def microbeOverwrite(self, winner, looser):
newMol = []
topographicMap = {}
parentMolecule = self.molecules[winner]
for index, i in enumerate(parentMolecule):
a = self.actors[i]
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
newMol.append(actorClass(typeA = a.type, nameA = a.atomKind, count = self.totalCount, sensors = se, messages = am, motors = mo, function = a.function, parameters = a.parameters))
self.totalCount = self.totalCount + 1
newMol[len(newMol)-1].mutate()
topographicMap[str(a.id)] = newMol[len(newMol)-1].id
#Remove the looser molecule and corresponding atoms!
print self.molecules
for index, i in enumerate(self.molecules[looser]):
print "poping from actor dict " + str(i)
print self.actors[i].id
if self.actors.has_key(i):
del self.actors[i] #self.actors.pop(self.molecules[looser][index])
print "looser = " + str(looser)
del self.molecules[looser] #self.molecules.pop(looser)
del self.moleculeFitness[looser]
for index, i in enumerate(newMol):
self.actors[newMol[index].id] = newMol[index]
#Copy connectivity.
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(self.actors[topographicMap[str(i)]].messages):
self.actors[topographicMap[str(i)]].messages[ind] = topographicMap[str(self.actors[i].messages[ind])]
#Update game messages.
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(self.actors[topographicMap[str(i)]].messages):
#Check all games observing old message and make them also view the new message
self.games.updateGameMessages(self.actors[i].messages[ind], topographicMap[str(self.actors[i].messages[ind])])
def createSHCchain(self):
# Construct the following specifically interconnected actors into an actor molecule.
# 1. A function actor that takes two sensory inputs (e.g. accelerometer z and accelerometer y)
# and outputs the product of them as a message.
actor1 = actorClass(
name="actor", count=0, inputs=[141, 142], function="product", typeA=1
) # Species input vector and the function.
# ARGUMENTS: name, number,data inputs, function type
# 2. A SHC actor that reads that message as a fitness input and outputs a parameter vector as a message.
actor2 = actorClass(name="actor", count=1, inputs=[189], messageInputs=[1], function=None, typeA=3)
# ARGUMENTS: name, number, data inputs, message inputs, 3 = optimization actor.
# 3. A message to motor function that reads the above message and outputs a motor command
# Make into an actor array
actors = [actor1, actor2]
return actors
pass
def __init__(self, name, type):
ALModule.__init__(self, name)
self.isRunning = True
if type is 0:
self.POP_SIZE = 10
#Create a population of random actor objects.
self.actors = [
actorClass("actor", count) for count in xrange(self.POP_SIZE)
]
if type is 1: #HAND DESIGNED (BUT EVOLVABLE) SHC OPTIMIZATION ACTOR MOLECULE
self.POP_SIZE = 2
self.actors = self.createSHCchain()
def copyMotorPNode(self, a):
#print "Mutating motorP atom actor, STRUCTURALLY, i.e. add, an atom, upstream, downtream, in new branch."
#Create new downstream motorP atom with some probability.
if random.random() < 0.01:
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
nm = actorClass(typeA = a.type, nameA = a.atomKind, count = self.totalCount, sensors = se, messages = am, motors = mo, function = a.function, parameters = a.parameters)
self.totalCount = self.totalCount + 1
nm.mutate()
return nm
def replicateAtoms(self):
for act in self.actors:
if act.id is 0:
print "REPLICATING *************"
cop = actorClass(typeA=act.type,
nameA=act.atomKind,
count=len(self.actors),
sensors=act.sensors,
messages=act.messages,
motors=act.motors,
function=act.function,
parameters=act.parameters)
#Check that the downstream atoms get input from this new copied atom.
#1.Go through all the atoms checking if their input list includes the act atom.
for others in self.actors:
if others is not act:
if others.messages is not None:
for i in others.messages:
if i is act.id:
others.messages.append(
cop.id
) #Get input from offspring actor to the downstream act that also got input from the offspring's parent.
self.actors.append(cop)
def microbeOverwrite(self, winner, looser):
newMol = []
topographicMap = {}
parentMolecule = self.molecules[winner]
for index, i in enumerate(parentMolecule):
a = self.actors[i]
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
newMol.append(actorClass(typeA = a.type, nameA = a.atomKind, count = self.totalCount, sensors = se, messages = am, motors = mo, function = a.function, parameters = a.parameters))
self.totalCount = self.totalCount + 1
newMol[len(newMol)-1].mutate()
topographicMap[str(a.id)] = newMol[len(newMol)-1].id
#Remove the looser molecule and corresponding atoms!
for index, i in enumerate(self.molecules[looser]):
# print "poping from actor dict " + str(i)
#print self.actors[i].id
if self.actors.has_key(i):
del self.actors[i] #self.actors.pop(self.molecules[looser][index])
#print "looser = " + str(looser)
#Remove game pointers to these atoms
self.games.removeGameMessages(self.molecules[looser])
del self.molecules[looser] #self.molecules.pop(looser)
del self.moleculeFitness[looser]
#Add the newMol atoms to the actors dictionary.
for index, i in enumerate(newMol):
self.actors[newMol[index].id] = newMol[index]
# print "new mol = " + str(newMol[index].id)
#Copy connectivity.
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(self.actors[topographicMap[str(i)]].messages):
self.actors[topographicMap[str(i)]].messages[ind] = topographicMap[str(self.actors[i].messages[ind])]
#Update game messages.
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(self.actors[topographicMap[str(i)]].messages):
#Check all games observing old message and make them also view the new message
self.games.updateGameMessages(self.actors[i].messages[ind], topographicMap[str(self.actors[i].messages[ind])])
#Structurally mutate the molecule
#If there is a motorP atom, then we permit a range of viable random variants.
extraMol = []
for index, i in enumerate(newMol):
if newMol[index].type == "motorP":
nm = self.copyMotorPNode(newMol[index])
if nm is not None:
extraMol.append(nm)
#Add the extramol atoms to the actors dictionary.
for index, i in enumerate(extraMol):
self.actors[extraMol[index].id] = extraMol[index]
# print "Extra mol = " + str(extraMol[index].id)
#Add a random connection within the new molecule.
newMol.extend(extraMol)#First make the new molecule.
#for index, i in enumerate(newMol):
# print "combined new mol = " + str(newMol[index].id)
if random.random() < 0.1:
r1 = random.randint(0,len(newMol)-1)
while newMol[r1].type != "motorP":
r1 = random.randint(0,len(newMol)-1)
r2 = random.randint(0,len(newMol)-1)
while newMol[r2].type != "motorP":
r2 = random.randint(0,len(newMol)-1)
self.addLink(newMol[r1], newMol[r2])
#Delete a node randomly in the new molecule.
deleted = 0
del_val = -1
for index, i in enumerate(newMol):
if random.random() < 0.05 and deleted == 0 and len(newMol) > 3:
#Delete a node in newMol.
del_val = i.id
if self.actors.has_key(i.id) and newMol[index].type == "motorP":
del self.actors[i.id]
del newMol[index]
self.games.removeGameMessages([i.id])
print "deleted actor " + str(i.id)
deleted = 1
if deleted == 1:
for index2, p in enumerate(newMol):
for index3, j in enumerate(self.actors[newMol[index2].id].messages):
if self.actors[newMol[index2].id].messages[index3] == del_val:
del self.actors[newMol[index2].id].messages[index3]
print "removed input message "
def microbeOverwrite(self, winner, looser):
newMol = []
topographicMap = {}
parentMolecule = self.molecules[winner]
for index, i in enumerate(parentMolecule):
a = self.actors[i]
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.messageDelays is not None:
amd = list(a.messageDelays)
else:
amd = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
if a.parameters is not None:
par = list(a.parameters)
else:
par = None
newMol.append(
actorClass(copyA=True,
atomA=a,
typeA=a.type,
nameA=a.atomKind,
count=self.totalCount,
sensors=se,
messages=am,
messageDelays=amd,
motors=mo,
function=a.function,
parameters=par))
self.totalCount = self.totalCount + 1
newMol[len(newMol) - 1].mutate()
topographicMap[str(a.id)] = newMol[len(newMol) - 1].id
#Remove the looser molecule and corresponding atoms!
for index, i in enumerate(self.molecules[looser]):
# print "poping from actor dict " + str(i)
if self.actors.has_key(i):
del self.actors[
i] #self.actors.pop(self.molecules[looser][index])
#print "looser = " + str(looser)
#Remove game pointers to these atoms
# self.games.removeGameMessages(self.molecules[looser])
del self.molecules[looser] #self.molecules.pop(looser)
del self.moleculeFitness[looser]
del self.moleculeNiche[looser]
#Add the newMol atoms to the actors dictionary.
for index, i in enumerate(newMol):
self.actors[newMol[index].id] = newMol[index]
# print "new mol = " + str(newMol[index].id)
#Copy connectivity.
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(
self.actors[topographicMap[str(i)]].messages):
self.actors[topographicMap[str(
i)]].messages[ind] = topographicMap[str(
self.actors[i].messages[ind])]
#Update game messages.
# for i in parentMolecule:
# if self.actors[topographicMap[str(i)]].messages is not None:
# for ind, j in enumerate(self.actors[topographicMap[str(i)]].messages):
# #Check all games observing old message and make them also view the new message
# self.games.updateGameMessages(self.actors[i].messages[ind], topographicMap[str(self.actors[i].messages[ind])])
#Structurally mutate the molecule
#If there is a motorP atom, then we permit a range of viable random variants.
extraMol = []
done = 0
for index, i in enumerate(newMol):
if (newMol[index].type == "motorP"
or newMol[index].type == "motorIzh") and done == 0:
nm = self.copyMotorPNode(newMol[index])
done = 1
if nm is not None:
extraMol.append(nm)
#Add the extramol atoms to the actors dictionary.
for index, i in enumerate(extraMol):
self.actors[extraMol[index].id] = extraMol[index]
# print "Extra mol = " + str(extraMol[index].id)
#Add a random connection within the new molecule.
newMol.extend(extraMol) #First make the new molecule.
#for index, i in enumerate(newMol):
# print "combined new mol = " + str(newMol[index].id)
# if random.random() < 1:
# r1 = random.randint(0,len(newMol)-1)
# while newMol[r1].type != "motorP" and newMol[r1].type != "motorIzh" :
# r1 = random.randint(0,len(newMol)-1)
# r2 = random.randint(0,len(newMol)-1)
# while newMol[r2].type != "motorP" and newMol[r2].type != "motorIzh" :
# r2 = random.randint(0,len(newMol)-1)
# self.addLink(newMol[r1], newMol[r2])
#Delete a node randomly in the new molecule.
deleted = 0
for index, i in enumerate(newMol):
del_val = -1
if random.random() < 0.05 and deleted == 0 and len(
newMol) >= 3 and (newMol[index].type == "motorP"
or newMol[index].type == "motorIzh"):
print "Length new mol before deletion = " + str(len(newMol))
#Go through the molecules data structure removing this atom id.
for index4, q in enumerate(self.molecules):
for index5, q2 in enumerate(self.molecules[index4]):
if q2 == i.id:
# del self.molecules[index3][index4]
del self.molecules[index4][index5]
#Delete a node in newMol.
del_val = i.id
if self.actors.has_key(i.id):
del self.actors[i.id]
del newMol[index]
# self.games.removeGameMessages([i.id])
print "deleted actor " + str(i.id)
deleted = 1
print "Length new mol after deletion = " + str(len(newMol))
if deleted == 1:
for index2, p in enumerate(newMol):
for index3, j in enumerate(
self.actors[newMol[index2].id].messages):
if self.actors[
newMol[index2].id].messages[index3] == del_val:
del self.actors[newMol[index2].id].messages[index3]
del self.actors[
newMol[index2].id].messageDelays[index3]
print " removed input message "
def microbeOverwrite(self, winner, looser):
newMol = []
topographicMap = {}
parentMolecule = self.molecules[winner]
for index, i in enumerate(parentMolecule):
a = self.actors[i]
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.messageDelays is not None:
amd = list(a.messageDelays)
else:
amd = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
newMol.append(actorClass(typeA = a.type, nameA = a.atomKind, count = self.totalCount, sensors = se, messages = am, messageDelays = amd, motors = mo, function = a.function, parameters = a.parameters))
self.totalCount = self.totalCount + 1
newMol[len(newMol)-1].mutate()
topographicMap[str(a.id)] = newMol[len(newMol)-1].id
#Remove the looser molecule and corresponding atoms!
for index, i in enumerate(self.molecules[looser]):
# print "poping from actor dict " + str(i)
#print self.actors[i].id
if self.actors.has_key(i):
del self.actors[i] #self.actors.pop(self.molecules[looser][index])
#print "looser = " + str(looser)
#Remove game pointers to these atoms
# self.games.removeGameMessages(self.molecules[looser])
del self.molecules[looser] #self.molecules.pop(looser)
del self.moleculeFitness[looser]
#Add the newMol atoms to the actors dictionary.
for index, i in enumerate(newMol):
self.actors[newMol[index].id] = newMol[index]
# print "new mol = " + str(newMol[index].id)
#Copy connectivity.
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(self.actors[topographicMap[str(i)]].messages):
self.actors[topographicMap[str(i)]].messages[ind] = topographicMap[str(self.actors[i].messages[ind])]
#Update game messages.
# for i in parentMolecule:
# if self.actors[topographicMap[str(i)]].messages is not None:
# for ind, j in enumerate(self.actors[topographicMap[str(i)]].messages):
# #Check all games observing old message and make them also view the new message
# self.games.updateGameMessages(self.actors[i].messages[ind], topographicMap[str(self.actors[i].messages[ind])])
#Structurally mutate the molecule
#If there is a motorP atom, then we permit a range of viable random variants.
extraMol = []
done = 0
for index, i in enumerate(newMol):
if newMol[index].type == "motorP" and done == 0:
nm = self.copyMotorPNode(newMol[index])
done = 1
if nm is not None:
extraMol.append(nm)
#Add the extramol atoms to the actors dictionary.
for index, i in enumerate(extraMol):
self.actors[extraMol[index].id] = extraMol[index]
# print "Extra mol = " + str(extraMol[index].id)
#Add a random connection within the new molecule.
newMol.extend(extraMol)#First make the new molecule.
#for index, i in enumerate(newMol):
# print "combined new mol = " + str(newMol[index].id)
## if random.random() < 1:
## r1 = random.randint(0,len(newMol)-1)
## while newMol[r1].type != "motorP":
## r1 = random.randint(0,len(newMol)-1)
## r2 = random.randint(0,len(newMol)-1)
## while newMol[r2].type != "motorP":
## r2 = random.randint(0,len(newMol)-1)
## self.addLink(newMol[r1], newMol[r2])
#Delete a node randomly in the new molecule.
deleted = 0
for index, i in enumerate(newMol):
del_val = -1
if random.random() < 0.05 and deleted == 0 and len(newMol) >= 3 and newMol[index].type == "motorP":
print "Length new mol before deletion = " + str(len(newMol))
#Go through the molecules data structure removing this atom id.
for index4,q in enumerate(self.molecules):
for index5, q2 in enumerate(self.molecules[index4]):
if q2 == i.id:
# del self.molecules[index3][index4]
del self.molecules[index4][index5]
#Delete a node in newMol.
del_val = i.id
if self.actors.has_key(i.id):
del self.actors[i.id]
del newMol[index]
# self.games.removeGameMessages([i.id])
print "deleted actor " + str(i.id)
deleted = 1
print "Length new mol after deletion = " + str(len(newMol))
if deleted == 1:
for index2, p in enumerate(newMol):
for index3, j in enumerate(self.actors[newMol[index2].id].messages):
if self.actors[newMol[index2].id].messages[index3] == del_val:
del self.actors[newMol[index2].id].messages[index3]
# del self.actors[newMol[index2].id].messageDelays[index3]
print " removed input message "
def replicateMolecules(self):
#print "REPLICATING *************"
# print "REPLICATING *************"
#Take a molecule from self.molecules and replicate it.
newMol = []
topographicMap = {} #Dictionary linking parent key to offspring value
if len(self.molecules) > 0:
#Choose which parent molecule to replicate based on its fitness.
#Choose two molecules randomly (Use microbial selection)
r1 = random.randint(0, len(self.molecules) - 1)
r2 = random.randint(0, len(self.molecules) - 1)
if len(self.molecules) > 2:
while r1 is r2:
r2 = random.randint(0, len(self.molecules) - 1)
fit1 = sum(self.moleculeFitness[r1])
fit2 = sum(self.moleculeFitness[r2])
print "Fitness r1 = " + str(fit1)
print "Fitness r2 = " + str(fit2)
winner = None
looser = None
if fit1 > fit2:
winner = r1
looser = r2
else:
winner = r2
looser = r1
parentMolecule = self.molecules[winner]
#parentMolecule = self.molecules[len(self.molecules)-1]
for index, i in enumerate(parentMolecule):
a = self.actors[i]
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
newMol.append(
actorClass(typeA=a.type,
nameA=a.atomKind,
count=self.totalCount,
sensors=se,
messages=am,
motors=mo,
function=a.function,
parameters=a.parameters))
self.totalCount = self.totalCount + 1
newMol[len(newMol) - 1].mutate()
topographicMap[str(a.id)] = newMol[len(newMol) - 1].id
if len(self.molecules) > self.MAX_POP_SIZE:
#Remove the looser molecule and corresponding atoms!
for index, i in enumerate(self.molecules[looser]):
#print "poping from actor dict " + str(i)
#print self.actors[i].id
if self.actors.has_key(i):
del self.actors[
i] #self.actors.pop(self.molecules[looser][index])
#print "deleting self.molecules[looser] "
del self.molecules[looser] #self.molecules.pop(looser)
del self.moleculeFitness[looser]
#self.actors.extend(newMol)
for index, i in enumerate(newMol):
self.actors[newMol[index].id] = newMol[index]
#for k,act in self.actors.iteritems():
# print "actor " + str(act.id) + "messages = " + str(act.messages)
#Copy connectivity.
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(
self.actors[topographicMap[str(i)]].messages):
self.actors[topographicMap[str(
i)]].messages[ind] = topographicMap[str(
self.actors[i].messages[ind])]
#Update game messages.
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(
self.actors[topographicMap[str(i)]].messages):
#Check all games observing old message and make them also view the new message
self.games.updateGameMessages(
self.actors[i].messages[ind],
topographicMap[str(self.actors[i].messages[ind])])
#for k,act in self.actors.iteritems():
# print "actor " + str(act.id) + "messages = " + str(act.messages)
#Go through the parent molecule
# for index, i in enumerate(parentMolecule):
# if self.actors[topographicMap[str(i)]].messages is not None:
# for index2, j in enumerate(self.actors[topographicMap[str(i)]].messages):
# j = topographicMap[str(self.actors[i].messages[index2])]
##
## #Check the connectivity of the copied molecule
## print "Offspring molecule connectivity"
## for index, i in enumerate(newMol):
## print "atom" + str(i.id) + "gets input from atoms:"
## if i.messages is not None:
## for index2, j in enumerate(i.messages):
## print "atom " + str(j)
##
##
## #Check the connectivity of the parent molecule
## print "Parent molecule connectivity POST-COPY "
## for index, i in enumerate(parentMolecule):
## print "atom" + str(self.actors[i].id) + "gets input from atoms:"
## if self.actors[i].messages is not None:
## for index2, j in enumerate(self.actors[i].messages):
## print "atom " + str(j)
##
# for i in newMol:
# print i.id
pass
def replicateMolecules(self):
#print "REPLICATING *************"
# print "REPLICATING *************"
#Take a molecule from self.molecules and replicate it.
newMol = []
topographicMap = {} #Dictionary linking parent key to offspring value
if len(self.molecules) > 0:
#Choose which parent molecule to replicate based on its fitness.
#Choose two molecules randomly (Use microbial selection)
r1 = random.randint(0,len(self.molecules)-1)
r2 = random.randint(0,len(self.molecules)-1)
if len(self.molecules) > 2:
while r1 is r2:
r2 = random.randint(0,len(self.molecules)-1)
fit1 = sum(self.moleculeFitness[r1])
fit2 = sum(self.moleculeFitness[r2])
print "Fitness r1 = " + str(fit1)
print "Fitness r2 = " + str(fit2)
winner = None
looser = None
if fit1 > fit2:
winner = r1
looser = r2
else:
winner = r2
looser = r1
parentMolecule = self.molecules[winner]
#parentMolecule = self.molecules[len(self.molecules)-1]
for index, i in enumerate(parentMolecule):
a = self.actors[i]
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
newMol.append(actorClass(typeA = a.type, nameA = a.atomKind, count = self.totalCount, sensors = se, messages = am, motors = mo, function = a.function, parameters = a.parameters))
self.totalCount = self.totalCount + 1
newMol[len(newMol)-1].mutate()
topographicMap[str(a.id)] = newMol[len(newMol)-1].id
if len(self.molecules) > self.MAX_POP_SIZE:
#Remove the looser molecule and corresponding atoms!
for index, i in enumerate(self.molecules[looser]):
#print "poping from actor dict " + str(i)
#print self.actors[i].id
if self.actors.has_key(i):
del self.actors[i] #self.actors.pop(self.molecules[looser][index])
#print "deleting self.molecules[looser] "
del self.molecules[looser] #self.molecules.pop(looser)
del self.moleculeFitness[looser]
#self.actors.extend(newMol)
for index, i in enumerate(newMol):
self.actors[newMol[index].id] = newMol[index]
#for k,act in self.actors.iteritems():
# print "actor " + str(act.id) + "messages = " + str(act.messages)
#Copy connectivity.
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(self.actors[topographicMap[str(i)]].messages):
self.actors[topographicMap[str(i)]].messages[ind] = topographicMap[str(self.actors[i].messages[ind])]
#Update game messages.
# for i in parentMolecule:
# if self.actors[topographicMap[str(i)]].messages is not None:
# for ind, j in enumerate(self.actors[topographicMap[str(i)]].messages):
# #Check all games observing old message and make them also view the new message
# self.games.updateGameMessages(self.actors[i].messages[ind], topographicMap[str(self.actors[i].messages[ind])])
#for k,act in self.actors.iteritems():
# print "actor " + str(act.id) + "messages = " + str(act.messages)
#Go through the parent molecule
# for index, i in enumerate(parentMolecule):
# if self.actors[topographicMap[str(i)]].messages is not None:
# for index2, j in enumerate(self.actors[topographicMap[str(i)]].messages):
# j = topographicMap[str(self.actors[i].messages[index2])]
##
## #Check the connectivity of the copied molecule
## print "Offspring molecule connectivity"
## for index, i in enumerate(newMol):
## print "atom" + str(i.id) + "gets input from atoms:"
## if i.messages is not None:
## for index2, j in enumerate(i.messages):
## print "atom " + str(j)
##
##
## #Check the connectivity of the parent molecule
## print "Parent molecule connectivity POST-COPY "
## for index, i in enumerate(parentMolecule):
## print "atom" + str(self.actors[i].id) + "gets input from atoms:"
## if self.actors[i].messages is not None:
## for index2, j in enumerate(self.actors[i].messages):
## print "atom " + str(j)
##
# for i in newMol:
# print i.id
pass
def microbeOverwrite(self, winner, looser):
newMol = []
topographicMap = {}
parentMolecule = self.molecules[winner]
for index, i in enumerate(parentMolecule):
a = self.actors[i]
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
newMol.append(
actorClass(typeA=a.type,
nameA=a.atomKind,
count=self.totalCount,
sensors=se,
messages=am,
motors=mo,
function=a.function,
parameters=a.parameters))
self.totalCount = self.totalCount + 1
newMol[len(newMol) - 1].mutate()
topographicMap[str(a.id)] = newMol[len(newMol) - 1].id
#Remove the looser molecule and corresponding atoms!
for index, i in enumerate(self.molecules[looser]):
print "poping from actor dict " + str(i)
print self.actors[i].id
if self.actors.has_key(i):
del self.actors[
i] #self.actors.pop(self.molecules[looser][index])
print "looser = " + str(looser)
del self.molecules[looser] #self.molecules.pop(looser)
del self.moleculeFitness[looser]
for index, i in enumerate(newMol):
self.actors[newMol[index].id] = newMol[index]
#Copy connectivity.
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(
self.actors[topographicMap[str(i)]].messages):
self.actors[topographicMap[str(
i)]].messages[ind] = topographicMap[str(
self.actors[i].messages[ind])]
#Update game messages.
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(
self.actors[topographicMap[str(i)]].messages):
#Check all games observing old message and make them also view the new message
self.games.updateGameMessages(
self.actors[i].messages[ind],
topographicMap[str(self.actors[i].messages[ind])])
def replicateMolecules(self):
print "REPLICATING *************"
print "REPLICATING *************"
#Take a molecule from self.molecules and replicate it.
newMol = []
topographicMap = {} #Dictionary linking parent key to offspring value
if len(self.molecules) > 0:
#print self.molecules[len(self.molecules)-1]
parentMolecule = self.molecules[len(self.molecules)-1]
#Check the connectivity of the parent molecule
## print "Parent molecule connectivity PRE-COPY "
## for index, i in enumerate(parentMolecule):
## print "atom" + str(self.actors[i].id) + "gets input from atoms:"
## if self.actors[i].messages is not None:
## for index2, j in enumerate(self.actors[i].messages):
## print "atom " + str(j)
##
for index, i in enumerate(parentMolecule):
a = self.actors[i]
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
newMol.append(actorClass(typeA = a.type, nameA = a.atomKind, count = len(self.actors)+index, sensors = se, messages = am, motors = mo, function = a.function, parameters = a.parameters))
topographicMap[str(a.id)] = newMol[len(newMol)-1].id
self.actors.extend(newMol)
for act in self.actors:
print "actor " + str(act.id) + "messages = " + str(act.messages)
#self.actors[4].messages[0] = 10
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(self.actors[topographicMap[str(i)]].messages):
self.actors[topographicMap[str(i)]].messages[ind] = topographicMap[str(self.actors[i].messages[ind])]
for act in self.actors:
print "actor " + str(act.id) + "messages = " + str(act.messages)
#Go through the parent molecule
# for index, i in enumerate(parentMolecule):
# if self.actors[topographicMap[str(i)]].messages is not None:
# for index2, j in enumerate(self.actors[topographicMap[str(i)]].messages):
# j = topographicMap[str(self.actors[i].messages[index2])]
#Check the connectivity of the copied molecule
print "Offspring molecule connectivity"
for index, i in enumerate(newMol):
print "atom" + str(i.id) + "gets input from atoms:"
if i.messages is not None:
for index2, j in enumerate(i.messages):
print "atom " + str(j)
#Check the connectivity of the parent molecule
print "Parent molecule connectivity POST-COPY "
for index, i in enumerate(parentMolecule):
print "atom" + str(self.actors[i].id) + "gets input from atoms:"
if self.actors[i].messages is not None:
for index2, j in enumerate(self.actors[i].messages):
print "atom " + str(j)
# for i in newMol:
# print i.id
pass
def replicateMolecules(self):
print "REPLICATING *************"
print "REPLICATING *************"
#Take a molecule from self.molecules and replicate it.
newMol = []
topographicMap = {} #Dictionary linking parent key to offspring value
if len(self.molecules) > 0:
#print self.molecules[len(self.molecules)-1]
parentMolecule = self.molecules[len(self.molecules) - 1]
#Check the connectivity of the parent molecule
## print "Parent molecule connectivity PRE-COPY "
## for index, i in enumerate(parentMolecule):
## print "atom" + str(self.actors[i].id) + "gets input from atoms:"
## if self.actors[i].messages is not None:
## for index2, j in enumerate(self.actors[i].messages):
## print "atom " + str(j)
##
for index, i in enumerate(parentMolecule):
a = self.actors[i]
if a.messages is not None:
am = list(a.messages)
else:
am = None
if a.sensors is not None:
se = list(a.sensors)
else:
se = None
if a.motors is not None:
mo = list(a.motors)
else:
mo = None
newMol.append(
actorClass(typeA=a.type,
nameA=a.atomKind,
count=len(self.actors) + index,
sensors=se,
messages=am,
motors=mo,
function=a.function,
parameters=a.parameters))
topographicMap[str(a.id)] = newMol[len(newMol) - 1].id
self.actors.extend(newMol)
for act in self.actors:
print "actor " + str(act.id) + "messages = " + str(
act.messages)
#self.actors[4].messages[0] = 10
for i in parentMolecule:
if self.actors[topographicMap[str(i)]].messages is not None:
for ind, j in enumerate(
self.actors[topographicMap[str(i)]].messages):
self.actors[topographicMap[str(
i)]].messages[ind] = topographicMap[str(
self.actors[i].messages[ind])]
for act in self.actors:
print "actor " + str(act.id) + "messages = " + str(
act.messages)
#Go through the parent molecule
# for index, i in enumerate(parentMolecule):
# if self.actors[topographicMap[str(i)]].messages is not None:
# for index2, j in enumerate(self.actors[topographicMap[str(i)]].messages):
# j = topographicMap[str(self.actors[i].messages[index2])]
#Check the connectivity of the copied molecule
print "Offspring molecule connectivity"
for index, i in enumerate(newMol):
print "atom" + str(i.id) + "gets input from atoms:"
if i.messages is not None:
for index2, j in enumerate(i.messages):
print "atom " + str(j)
#Check the connectivity of the parent molecule
print "Parent molecule connectivity POST-COPY "
for index, i in enumerate(parentMolecule):
print "atom" + str(
self.actors[i].id) + "gets input from atoms:"
if self.actors[i].messages is not None:
for index2, j in enumerate(self.actors[i].messages):
print "atom " + str(j)
# for i in newMol:
# print i.id
pass