def num_neurons(function_file): file = abstractfile.open(function_file, 'r') header = file.readline() if 'version' in header: header = file.readline() file.close() entries = header.split() return int(entries[2])
def num_neurons(function_file):
file = abstractfile.open(function_file, 'r')
header = file.readline()
if 'version' in header:
header = file.readline()
file.close()
entries = header.split()
return int(entries[2])
def num_synapses(function_file): file = abstractfile.open(function_file, 'r') header = file.readline() if 'version' in header: header = file.readline() index = 5 else: index = 4 file.close() entries = header.split() return int(entries[index])
def num_synapses(function_file):
file = abstractfile.open(function_file, 'r')
header = file.readline()
if 'version' in header:
header = file.readline()
index = 5
else:
index = 4
file.close()
entries = header.split()
return int(entries[index])
def getRawValue( self, geneName ):
index = self.schema.getIndex( geneName )
genomePath = os.path.join( self.schema.path_run, 'genome/agents/genome_%d.txt' % self.agentNumber )
f = abstractfile.open( genomePath )
for i in range(index+1):
line = f.readline()
f.close()
return int( line )
def getRawValue(self, geneName):
index = self.schema.getIndex(geneName)
genomePath = os.path.join(
self.schema.path_run,
'genome/agents/genome_%d.txt' % self.agentNumber)
f = abstractfile.open(genomePath)
for i in range(index + 1):
line = f.readline()
f.close()
return int(line)
def read_anatomy(anatomy_file):
ga = []
gp = []
def __num_input_neurons(header):
start = header.rfind('-') + 1
if start > 1: # new-style anatomy file that has the input neuron info
return int(header[start:]) + 1 # +1 because the value in the header is a 0-based index, but we need a count
else: # old-style anatomy file that doesn't have input neuron info
# /pwd/run_df8_F30_complexity_0_partial/brain/Recent/0/../../anatomy/brainAnatomy_1_death.txt
brain_dir = anatomy_file[:anatomy_file.find('Recent')]
agent_id = anatomy_file.split('_')[-2]
function_filename = brain_dir + 'function/brainFunction_' + agent_id + '.txt'
# don't bother with abstractfile since this is an old format
function_file = open(function_filename, 'r')
function_header = function_file.readline()
function_file.close()
return int(function_header.split(' ')[-2])
file = abstractfile.open(anatomy_file, 'r')
lines = file.readlines()
file.close()
header = lines[0].rstrip() # get rid of the newline
num_input_neurons = __num_input_neurons(header)
lines.pop(0) # drop the header line
for i in range(len(lines)-1): # -1 to leave out the bias unit
l = lines[i].split()
l.remove(';')
row_a = []
row_p = []
for j in range(len(l)-1): # -1 to leave out the bias links
if j == i:
w = float(l[j])
if w != 0.0:
print 'self-connection in', anatomy_file, 'i = j =', i, 'w =', w
w = 0.0
else:
w = abs(float(l[j]))
row_a.append(w)
if j >= num_input_neurons:
row_p.append(w)
ga.append(row_a) # must agree with neuron-sets and graph-types in common_metric.py
if i >= num_input_neurons:
gp.append(row_p) # must agree with neuron-sets and graph-types in common_metric.py
for i in range(len(ga)):
for j in range(i):
temp = ga[i][j]
ga[i][j] = ga[j][i]
ga[j][i] = temp
for i in range(len(gp)):
for j in range(i):
temp = gp[i][j]
gp[i][j] = gp[j][i]
gp[j][i] = temp
return ga, gp, header
def read_anatomy(anatomy_file):
ga = []
gp = []
def __num_input_neurons(header):
start = header.rfind('-') + 1
if start > 1: # new-style anatomy file that has the input neuron info
return int(header[start:]) + 1 # +1 because the value in the header is a 0-based index, but we need a count
else: # old-style anatomy file that doesn't have input neuron info
# /pwd/run_df8_F30_complexity_0_partial/brain/Recent/0/../../anatomy/brainAnatomy_1_death.txt
brain_dir = anatomy_file[:anatomy_file.find('Recent')]
agent_id = anatomy_file.split('_')[-2]
function_filename = brain_dir + 'function/brainFunction_' + agent_id + '.txt'
# don't bother with abstractfile since this is an old format
function_file = open(function_filename, 'r')
function_header = function_file.readline()
function_file.close()
return int(function_header.split(' ')[-2])
def __max_weight(header):
start = header.find('maxWeight')
start += header[start:].find('=') + 1
stop = start + header[start:].find(' ')
max_weight = float(header[start:stop])
return max_weight
file = abstractfile.open(anatomy_file, 'r')
lines = file.readlines()
file.close()
header = lines[0].rstrip() # get rid of the newline
num_input_neurons = __num_input_neurons(header)
max_weight = __max_weight(header)
lines.pop(0) # drop the header line
for i in range(len(lines)-1): # -1 to leave out the bias unit
l = lines[i].split()
l.remove(';')
row_a = []
row_p = []
for j in range(len(l)-1): # -1 to leave out the bias links
if j == i:
w = float(l[j])
if w != 0.0:
print 'self-connection in', anatomy_file, 'i = j =', i, 'w =', w
w = 0.0
else:
w = abs(float(l[j]))
row_a.append(w)
if j >= num_input_neurons:
row_p.append(w)
ga.append(row_a) # must agree with neuron-sets and graph-types in common_metric.py
if i >= num_input_neurons:
gp.append(row_p) # must agree with neuron-sets and graph-types in common_metric.py
# Transpose the matrixes so g[i][j] describes a connection from i to j (not j to i, as stored)
for i in range(len(ga)):
for j in range(i):
temp = ga[i][j]
ga[i][j] = ga[j][i]
ga[j][i] = temp
for i in range(len(gp)):
for j in range(i):
temp = gp[i][j]
gp[i][j] = gp[j][i]
gp[j][i] = temp
return ga, gp, max_weight, header
