def decode(self, DNA):
# First 26 bits are encoding of global dopamine parameters, so we treat
# them separately.
dop = b.b_to_int(DNA[0:10]) / 100.
dopdr = b.b_to_int(DNA[10:26]) / 655360.
self.DNA = DNA[26:]
frames_id = self._find_frames()
self._decode_frame(frames_id)
return self.Cells, dop, dopdr
def _interpret_frame(self, frame):
"""First 8 bits will be the cell id, next 8 bits will be dendrite len,
further 8 bits will be dendrite passive current(int8). Everything after
that will be target id, weight and delay triple - id have 8bits,
weight have 16 divided to two parts: 1 bit for weight sign and 15 bits
for value multiplied by 10 000 bit and delay 4. This triplet will
have 28 bit"""
i0 = 0
i1 = self.id_len
cell_id = frame[i0:i1]
cell_id = b.b_to_int(cell_id)
i0 = i1
i1 += self.coord_len
x = frame[i0:i1]
x = b.b_to_int(x)
# i0 = i1
# i1 += self.coord_len
# y = frame[i0:i1]
# y = b.b_to_int(y)
i0 = i1
i1 += 8 # dend len
dend_len = frame[i0:i1]
dend_len = b.b_to_int(dend_len) + 1 # it cannot be 0
i0 = i1
i1 += 8
dend_pas = frame[i0:i1]
dend_pas = b.b_to_int(dend_pas) - 127
# Scan for (index, weight, delay) triples
iwd_len = self.id_len + 1 + self.w_len + self.d_len # 1 is w_type
iwd_triples = []
i = i1
while i < len(frame)-(iwd_len-1):
iwd_frame = frame[i: i+iwd_len]
iwd_triples.append(self._decode_iwd_triple(iwd_frame))
i+=iwd_len
cell_dict = {"x": x, "dend_len": dend_len, "dend_pas": dend_pas,
"connections": iwd_triples}
# Overwrite existing settings or create new
self.Cells[cell_id] = cell_dict
def _decode_iwd_triple(self, iwd_frame):
i0 = 0
i1 = self.id_len
c_id = iwd_frame[i0:i1]
c_id = b.b_to_int(c_id)
i0 = i1
i1 += 1
w_type = int(iwd_frame[i0:i1])
i0 = i1
i1 += self.w_len
w = iwd_frame[i0:i1]
# Weights can only be positive
w = float(b.b_to_int(w)) / 10000.
i0 = i1
i1 += self.d_len
d = iwd_frame[i0:i1]
d = b.b_to_int(d)
return c_id, (w_type, w), d
def genetic2(self, i_length, generations=50, pop_size=10):
generations = int(generations)
pop_size = int(pop_size)
i_length = int(i_length)
import binstr, random
t = self
logger.info("Started vernünftigen genetischen Algorithmus")
def ind_to_params(ind):
params = [None for i in range(i_length)]
for i in range(i_length):
params[i] = ind[8 * i:8 * (i + 1)]
return params
def params_to_ind(params):
ind = ''
for i in params:
ind += binstr.b_bin_to_gray(binstr.int_to_b(i))
return ind
def individuum():
return params_to_ind([random.randint(0, 256) for i in range(i_length)])
population = [individuum() for i in range(pop_size)]
def mutate(ind):
if len(ind) < 2:
raise TypeError("Individuum too short")
for i in range(len(ind)):
if random.random() < 1 / len(ind):
if ind[i] == '0':
replace = '1'
else:
replace = '0'
ind = ind[:i] + replace + ind[i + 1:]
def crossover(ind1, ind2):
point1, point2 = 0, 255
while point1 == 0:
point1 = random.randint(0, 255)
while point2 == 255:
point2 = random.randint(0, 255)
if point1 > point2: # make sure point1 <= point2
point1, point2 = point2, point1
child0 = ind1[:point1] + ind2[point1:point2] + ind1[point2:]
child1 = ind2[:point1] + ind1[point1:point2] + ind2[point2:]
return (child0, child1)
def select():
cnt = 2
sum_fit = sum([fitness(ind) for ind in population])
pop = population[:]
random.shuffle(pop)
pairs = []
for i in range(cnt):
n = random.uniform(0, sum_fit)
m = random.uniform(0, sum_fit)
tmp_sum = 0
first, second = None, None
for ind in pop:
tmp_sum += fitness(ind)
if tmp_sum >= n:
first = ind
break
tmp_sum = 0
for ind in pop:
tmp_sum += fitness(ind)
if tmp_sum >= m:
second = ind
break
if first is None or second is None:
print("FAILED")
raise RuntimeError("Ooops")
pairs.append((first, second))
print(str(pairs))
return pairs
def fitness(ind):
params = ind_to_params(ind)
params = [int(x) for x in params]
return t.fitness(*params)
for i in range(generations):
logger.debug("Generation {i}".format(i=i))
parents = select()
children = []
for p in parents:
ind1, ind2 = p
children.extend(crossover(ind1, ind2))
n = len(population)
for child in children:
if child not in population:
population.append(child)
#print(str(len(population)))
while len(population) > n: # O(|children|)
population.remove(min(population, key=fitness))
for i, ind in enumerate(population):
new_ind = (ind + '.')[:-1]
mutate(new_ind)
if fitness(new_ind) >= fitness(ind) and new_ind not in population:
population[i] = new_ind
if (algkill[t.msg.id] is not None):
break
best = max(population, key=fitness)
bestfit = fitness(best)
best = ind_to_params(best)
best = [binstr.b_to_int(i) for i in best]
return (best, bestfit)
def daq_readOut(self):
data = None
fifo_count = 0
attempts = 10
while fifo_count == 0 and attempts > 0:
attempts -= 1
message = "r 0x44A10014 1" # word count of data fifo
data = self.udp.udp_client(message, self.UDP_IP, self.UDP_PORT)
if data != None:
data_list = data.split(" ")
fifo_count = int(data_list[2], 16)
time.sleep(1)
print "FIFOCNT ", fifo_count
if data == None or fifo_count == 0:
print "Warning: Did not receive data. Stop readout."
return
if fifo_count == 0:
print "Warning: found 0 FIFO counts. Stop readout."
return
if fifo_count % 2 != 0:
print "Warning: Lost one count in fifo reading."
fifo_count -= 1
peeks_per_cycle = 10
cycles = fifo_count / peeks_per_cycle
remainder = fifo_count % peeks_per_cycle
for cycle in reversed(xrange(1+cycles)):
peeks = peeks_per_cycle if cycle > 0 else remainder
message = "k 0x44A10010 %s" % (peeks)
data = self.udp.udp_client(message, self.UDP_IP, self.UDP_PORT)
data_list = data.split()
for iword in xrange(2, peeks+1, 2):
word0 = int(data_list[iword], 16)
word1 = int(data_list[iword+1], 16)
if not word0 > 0:
print "Out of order or no data."
continue
word0 = word0 >> 2 # get rid of first 2 bits (threshold)
addr = (word0 & 63) + 1 # get channel number as on GUI
word0 = word0 >> 6 # get rid of address
amp = word0 & 1023 # get amplitude
word0 = word0 >> 10 # ?
timing = word0 & 255 #
word0 = word0 >> 8 # we will later check for vmm number
vmm = word0 & 7 # get vmm number
bcid_gray = int(word1 & 4095)
bcid_bin = binstr.b_gray_to_bin(binstr.int_to_b(bcid_gray, 16))
bcid_int = binstr.b_to_int(bcid_bin)
word1 = word1 >> 12 # later we will get the turn number
word1 = word1 >> 4 # 4 bits of zeros?
immfe = int(word1 & 255) # do we need to convert this?
to_print = "word0 = %s word1 = %s addr = %s amp = %s time = %s bcid = %s vmm = %s mmfe = %s"
print to_print % (data_list[iword], data_list[iword+1],
str(addr), str(amp), str(timing),
str(bcid_int), str(vmm), str(immfe))
with open('mmfe8Test.dat', 'a') as myfile:
myfile.write(str(int(addr))+'\t'+ str(int(amp))+'\t'+ str(int(timing))+'\t'+ str(bcid_int) +'\t'+ str(vmm) +'\n')
def genetic2(self, i_length, generations=50, pop_size=10):
generations = int(generations)
pop_size = int(pop_size)
i_length = int(i_length)
import binstr, random
t = self
logger.info("Started vernünftigen genetischen Algorithmus")
def ind_to_params(ind):
params = [None for i in range(i_length)]
for i in range(i_length):
params[i] = ind[8*i:8*(i+1)]
return params
def params_to_ind(params):
ind = ''
for i in params:
ind+=binstr.b_bin_to_gray(binstr.int_to_b(i))
return ind
def individuum():
return params_to_ind([random.randint(0,256) for i in range(i_length)])
population = [individuum() for i in range(pop_size)]
def mutate(ind):
if len(ind) < 2:
raise TypeError("Individuum too short")
for i in range(len(ind)):
if random.random() < 1/len(ind):
if ind[i] == '0':
replace = '1'
else:
replace = '0'
ind = ind[:i] + replace + ind[i+1:]
def crossover(ind1, ind2):
point1, point2 = 0, 255
while point1 == 0:
point1 = random.randint(0,255)
while point2 == 255:
point2 = random.randint(0,255)
if point1 > point2: # make sure point1 <= point2
point1, point2 = point2, point1
child0 = ind1[:point1] + ind2[point1:point2] + ind1[point2:]
child1 = ind2[:point1] + ind1[point1:point2] + ind2[point2:]
return (child0,child1)
def select():
cnt = 2
sum_fit = sum([fitness(ind) for ind in population])
pop = population[:]
random.shuffle(pop)
pairs = []
for i in range(cnt):
n = random.uniform(0, sum_fit)
m = random.uniform(0, sum_fit)
tmp_sum = 0
first, second = None, None
for ind in pop:
tmp_sum += fitness(ind)
if tmp_sum >= n:
first = ind
break
tmp_sum = 0
for ind in pop:
tmp_sum += fitness(ind)
if tmp_sum >= m:
second = ind
break
if first is None or second is None:
print("FAILED")
raise RuntimeError("Ooops")
pairs.append((first, second))
print(str(pairs))
return pairs
def fitness(ind):
params = ind_to_params(ind)
params = [int(x) for x in params]
return t.fitness(*params)
for i in range(generations):
logger.debug("Generation {i}".format(i=i))
parents = select()
children = []
for p in parents:
ind1, ind2 = p
children.extend(crossover(ind1, ind2))
n = len(population)
for child in children:
if child not in population:
population.append(child)
#print(str(len(population)))
while len(population) > n: # O(|children|)
population.remove(min(population, key=fitness))
for i, ind in enumerate(population):
new_ind = (ind+'.')[:-1]
mutate(new_ind)
if fitness(new_ind) >= fitness(ind) and new_ind not in population:
population[i] = new_ind
if(algkill[t.msg.id] is not None):
break
best = max(population, key=fitness)
bestfit = fitness(best)
best = ind_to_params(best)
best = [binstr.b_to_int(i) for i in best]
return (best,bestfit)
