def populate(self, hidden_neurons):
"""
Fills the cortex with neurons and establishes connections
Cortexes have a layer of hidden neurons, and a layer of output
neurons to start with, but everything is set up such that
more complex interconnections may evolve with future work.
"""
HiddenLayer = []
OutLayer = []
for i in range(hidden_neurons):
HiddenLayer.append("n_{0}".format(i))
for i in range(self.size_output):
OutLayer.append("n_{0}".format(i + hidden_neurons))
self.hidden_neurons = [ neuron(name) for name in HiddenLayer]
self.output_neurons = [ neuron(name) for name in OutLayer]
for neur in self.output_neurons:
for child in self.hidden_neurons:
neur.add_child(child)
child.add_parent(neur)
self.neurons = self.hidden_neurons + self.output_neurons
self.size = len(self.neurons)
self.size_hidden = len(self.hidden_neurons)
self.size_output = len(self.output_neurons)
return
def import_state(self,filepath):
"""
Imports an already trained cortex thats been saved before
this function demonstrates my lack of understanding of
python regular expressions.....i think
Check out the "export_state" funtction for insight on how this
should be formatted.
"""
print("="*50)
print("Importing cortex state from '{0}'".format(filepath))
print("="*50 + '\n')
self.imported_from = filepath
self.neurons = []
self.output_neurons = []
self.hidden_neurons = []
with open(filepath,'r+') as f:
# grabs cortex level information from the top first
cortex_header = next(f).replace('\n','').replace(' ','').split(';')
cortex_line = next(f).replace('\n','').replace(' ','')
cortex_line = cortex_line.replace("[","").replace("]","")
cortex_info = cortex_line.split(';')
for i,field in enumerate(cortex_info):
if ',' in field:
cortex_info[i] = cortex_info[i].replace("'",'').split(',')
for i,attribute in enumerate(cortex_header):
setattr(self, attribute, cortex_info[i])
print("imported attribute '{0}' as {1}".format(attribute,cortex_info[i]))
# correct formats that shouldn't be strings
self.size = int(self.size)
self.size_hidden = int(self.size_hidden)
self.size_output = int(self.size_output)
self.accuracy = float(self.accuracy)
self.min_acc = float(self.min_acc)
self.col_accuracy = map(float,self.col_accuracy)
self.max_err = float(self.max_err)
self.crit_instability = float(self.crit_instability)
# moves on to grab neuron information
neuron_header = next(f).replace('\n','').replace(' ','').split(';')
for line in f:
line = line.replace('\n','').replace(' ','').replace("[","").replace("]","")
info = line.split(';')
# handles neuron info one row at a time
for i,field in enumerate(info):
if ',' in field:
info[i] = info[i].split(',')
if 'True' in field:
info[i] = True
elif 'False' in field:
info[i] = False
neur = neuron(info[0])
for i,attribute in enumerate(neuron_header):
setattr(neur,attribute,info[i])
neur.weights = map(float,neur.weights)
neur.bias = float(neur.bias)
neur.f_age = int(neur.f_age)
neur.r_age = int(neur.r_age)
neur.size = int(neur.size)
neur.dec = int(neur.dec)
neur.delta = float(neur.delta)
neur.del_delta = float(neur.del_delta)
neur.hasparents = bool(neur.hasparents)
neur.haschildren= bool(neur.haschildren)
self.neurons.append(neur)
print("imported member neuron with name '{0}'".format(neur.name))
for neur in self.neurons:
# presently, each neurons children and parents lists
# are just strings. turn these into object lists.
if neur.hasparents:
if not isinstance(neur.parents,list):#handles single parent lists
neur.parents = [neur.parents]
for i,parent in enumerate(neur.parents):
neur.parents[i] = self.find_object(parent)
self.hidden_neurons.append(neur)
else:
neur.parents = []
if neur.haschildren:
if not isinstance(neur.children,list):# handles single child lists
neur.children = [neur.children]
for i,child in enumerate(neur.children):
neur.children[i] = self.find_object(child)
if not neur.hasparents:
self.output_neurons.append(neur)
else:
neur.children = []
f.close()
print("="*50 + '\n')
return