def rand_init(inputdim, outputdim):
global innov_ctr, z
newchromo = chromosome.Chromosome(0)
newchromo.node_ctr = inputdim + outputdim + 1
innov_ctr = 1 # Warning!! these two lines change(reset) global variables, here might be some error
lisI = [
gene.Node(num_setter, 'I')
for num_setter in range(1, newchromo.node_ctr - outputdim)
]
lisO = [
gene.Node(num_setter, 'O')
for num_setter in range(inputdim + 1, newchromo.node_ctr)
]
newchromo.node_arr = lisI + lisO
for inputt in lisI:
for outputt in lisO:
newchromo.conn_arr.append(
gene.Conn(innov_ctr, (inputt, outputt), z, status=True))
z = z + 1
innov_ctr += 1
newchromo.bias_arr = [
gene.BiasConn(outputt, np.random.random()) for outputt in lisO
]
newchromo.dob = 0
return newchromo
def __init__(self, inputdim, outputdim):
global innov_ctr
self.node_ctr = inputdim + outputdim + 1
# NO MORE # Warning!! these two lines change(reset) global variables, here might be some error
lisI = [
gene.Node(num_setter, 'I')
for num_setter in range(1, self.node_ctr - outputdim)
]
lisO = [
gene.Node(num_setter, 'O')
for num_setter in range(inputdim + 1, self.node_ctr)
]
self.node_arr = lisI + lisO
self.conn_arr = []
p = 1
for inputt in lisI:
for outputt in lisO:
self.conn_arr.append(
gene.Conn(p, (inputt, outputt),
np.random.random(),
status=True))
p += 1
# print(p)
# assert (p == innov_ctr)
self.bias_conn_arr = []
self.bias_conn_arr = [
gene.BiasConn(outputt,
np.random.random() / 1000) for outputt in lisO
]
self.dob = 0
def edge_mutation(self, inputdim, outputdim,
rng): # not tested, might just have some error!
newmatenc = self.convert_to_MatEnc(inputdim, outputdim)
key_list = list(newmatenc.WMatrix.keys())
# key_list.remove('IO')
# print(key_list)
chosen_key = rng.choice(key_list)
while chosen_key not in newmatenc.CMatrix.keys():
chosen_key = rng.choice(key_list)
mat = newmatenc.CMatrix[chosen_key]
# print(chosen_key, mat.shape, list(newmatenc.node_map.items()))
i = rng.randint(0, mat.shape[0] - 1)
if mat.shape[1] > 1:
j = rng.randint(0, mat.shape[1] - 1)
else:
j = 0
split_key1, split_key2 = matenc.split_key(chosen_key)
# print(split_key1, split_key2)
# couple = (newmatenc.node_map[split_key1][i], newmatenc.node_map[split_key2][j])
ctr = 0
# print(mat[i][j])
while mat[i][j] != 0:
i = rng.randint(0, mat.shape[0] - 1)
if mat.shape[1] > 1:
j = rng.randint(0, mat.shape[1] - 1)
else:
j = 0
if ctr > 10:
return
ctr += 1
couple = (newmatenc.node_map[split_key1][i],
newmatenc.node_map[split_key2][j])
mat[i][j] = 1
if not newmatenc.WMatrix[chosen_key][i][j]:
innov_num = normalize_conn_arr_for_this_gen(self, couple)
con_obj = gene.Conn(innov_num, couple, (rng.random() - 0.5) * 2,
True)
self.conn_arr.append(con_obj)
# con_obj.pp()
else:
con_obj = newmatenc.couple_to_conn_map[couple]
con_obj.status = True
def test3():
for_node = [(i, 'I') for i in range(1, 9)]
for_node += [(i, 'O') for i in range(9, 10)]
# for_node += [(i, 'H2') for i in range(6, 8)]
node_ctr = 10
innov_num = 9
dob = 0
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [(inn, (node_lis[inn - 1], node_lis[8]), np.random.random(),
True) for inn in range(1, innov_num)]
conn_lis = [gene.Conn(*p) for p in for_conn]
for_bias = [(4, 0.2)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
indim = 8
outdim = 1
newchromo = Chromosome(indim, outdim)
newchromo.reset_chromo_to_zero()
newchromo.__setattr__('conn_arr', conn_lis)
newchromo.__setattr__('bias_conn_arr', bias_conn_lis)
newchromo.__setattr__('node_arr', node_lis)
newchromo.__setattr__('dob', dob)
newchromo.set_node_ctr(node_ctr)
# newchromo.pp()
def calc_output_directly(inputarr):
lis = []
for arr in inputarr:
output1 = sigmoid(
relu(arr[0] * 0.5) * 0.4 + 0.25 * arr[1] + 0.7 * arr[2] - 0.2)
output2 = sigmoid(arr[0] * 0.25 + arr[1] * 0.5 +
relu(arr[2] * 0.3) * 0.6 - 0.1)
lis.append([output1, output2])
return np.array(lis)
indim = 8
outdim = 1
# num_data = 2
# inputarr = np.random.random((num_data, indim))
neter = Neterr(indim, outdim, 10, np.random)
print(neter.feedforward_ne(newchromo))
# print(calc_output_directly(inputarr))
print("break")
print(neter.feedforward_cm(newchromo))
def test1():
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
for_node += [(i, 'H2') for i in range(6, 8)]
node_ctr = 8
innov_num = 11
dob = 0
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [(1, (1, 4), 0.4, False), (2, (1, 5), 0.25, True),
(3, (2, 4), 0.25, True), (4, (2, 5), 0.5, True),
(5, (3, 4), 0.7, True), (6, (3, 5), 0.6, False),
(7, (1, 6), 0.5, True), (8, (6, 4), 0.4, True),
(9, (3, 7), 0.3, True), (10, (7, 5), 0.6, True)]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newchromo = Chromosome(dob, node_lis, conn_lis, bias_conn_lis)
newchromo.set_node_ctr(node_ctr)
# newchromo.pp()
def calc_output_directly(inputarr):
lis = []
for arr in inputarr:
output1 = sigmoid(
relu(arr[0] * 0.5) * 0.4 + 0.25 * arr[1] + 0.7 * arr[2] - 0.2)
output2 = sigmoid(arr[0] * 0.25 + arr[1] * 0.5 +
relu(arr[2] * 0.3) * 0.6 - 0.1)
lis.append([output1, output2])
return np.array(lis)
inputarr = np.array([[3, 2, 1], [4, 1, 2]])
indim = 3
outdim = 2
np.random.seed(4)
num_data = 2
# inputarr = np.random.random((num_data, indim))
neter = Neterr(indim, outdim, inputarr, 10, np.random)
print(neter.feedforward_ne(newchromo4))
print(calc_output_directly(inputarr))
print(neter.feedforward_cm(newchromo4))
def test2():
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
st = '2212211'
for_node += [(i + 6, 'H' + st[i]) for i in range(len(st))]
node_ctr = 13
innov_num = 25
dob = 0
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [
(1, (1, 4), 0.3, True),
(2, (1, 5), 0.25, False),
(3, (2, 4), 0.25, False),
(4, (2, 5), 0.5, False),
(5, (3, 4), 0.7, False),
(6, (3, 5), 0.5, True),
(7, (1, 6), 0.2, True),
(8, (6, 4), 0.1, True),
(9, (2, 7), 0.1, True),
(10, (7, 4), 0.15, True),
(11, (1, 8), 0.5, True),
(12, (8, 6), 0.7, True),
(13, (1, 9), 0.3, False),
(14, (9, 5), 1.0, True),
(15, (3, 10), 0.33, True),
(16, (10, 5), 0.77, True),
(17, (1, 11), 0.25, True),
(18, (11, 9), 0.15, True),
(19, (2, 12), 0.6, True),
(20, (12, 7), 0.4, True),
(21, (3, 12), 0.8, True),
(22, (2, 9), 0.9, True),
(23, (12, 4), 0.75, True),
(24, (11, 5), 0.25, True),
]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newchromo = chromosome.Chromosome(dob, node_lis, conn_lis, bias_conn_lis)
newchromo.set_node_ctr(node_ctr)
# newchromo.pp()
def calc_output_directly(inputarr):
lis = []
for arr in inputarr:
x1 = arr[0]
x2 = arr[1]
x3 = arr[2]
output1 = sigmoid(
0.3 * x1 + 0.1 * relu(0.7 * relu(0.5 * x1) + 0.2 * x1) +
0.15 * relu(0.1 * x2 + 0.4 * relu(0.6 * x2 + 0.8 * x3)) +
0.75 * relu(0.6 * x2 + 0.8 * x3) - 0.2)
#output2 = sigmoid(arr[0] * 0.25 + arr[1] * 0.5 + relu(arr[2] * 0.3) * 0.6 - 0.1)
output2 = sigmoid(0.5 * x3 +
1 * relu(0.15 * relu(0.25 * x1) + 0.9 * x2) +
0.25 * relu(0.25 * x1) + 0.77 * relu(0.33 * x3) -
0.1)
lis.append([output1, output2])
return np.array(lis)
inputarr = np.array([[0.0, 2, 1], [0.8, 1, 2]])
indim = 3
outdim = 2
#np.random
rng = np.random
num_data = 2
# inputarr = np.random.random((num_data, indim))
#neter = Neterr(indim, outdim, inputarr, 10, np.random)
ka = np.random.randint(0, 2, (num_data, ))
"""
targetarr = np.zeros((num_data,outdim)).astype(dtype = 'float32')
for i in range(num_data):
targetarr[i,ka[i]] = 1
print("target is ", targetarr)
"""
targetarr = ka.astype('int32')
print(targetarr.dtype)
inputarr = inputarr.astype('float32')
print("input type", inputarr.dtype)
print(targetarr)
x = tf.placeholder(shape=[None, indim], dtype=tf.float32)
y = tf.placeholder(shape=[
None,
], dtype=tf.int32)
newmat_enc = newchromo.convert_to_MatEnc(indim, outdim)
newnet = DeepNet(x, indim, outdim, newmat_enc)
cost = newnet.negative_log_likelihood(y)
learning_rate = 0.05
optmzr = tf.train.GradientDescentOptimizer(learning_rate).minimize(
cost, var_list=newnet.params)
#cost = newnet.errors(y)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print("initially")
print(sess.run(newnet.wei_mat_var_map['IH2']))
print(sess.run(newnet.con_mat_var_map['IH2']))
print(
sess.run([optmzr, newnet.y_pred, cost],
feed_dict={
x: inputarr,
y: targetarr
}))
print(sess.run(newnet.wei_mat_var_map['IH2']))
print(
sess.run([optmzr, newnet.bias_var, cost],
feed_dict={
x: inputarr,
y: targetarr
}))
print(sess.run(newnet.con_mat_var_map['IH2']))
print(
sess.run([optmzr, newnet.bias_var,
newnet.errors(y)],
feed_dict={
x: inputarr,
y: targetarr
}))
def node_mutation(self, inputdim, outputdim, rng):
# global innov_ctr
type = 0
newmatenc = self.convert_to_MatEnc(inputdim, outputdim)
key_list = ['IH2', 'H1O', 'IO']
stlis = ['H1', 'H2', 'H2']
# prob_list = [0.1, 0.3, 0.6]
prndm = rng.random()
if prndm > 0.4:
ind = 2
elif prndm > 0.1:
ind = 1
elif prndm > 0:
ind = 0
chosen_key = key_list[ind]
while chosen_key not in newmatenc.CMatrix.keys():
prndm = rng.random()
if prndm > 0.4:
ind = 2
elif prndm > 0.1:
ind = 1
elif prndm > 0:
ind = 0
chosen_key = key_list[ind]
# key_list.remove('IO')
# print(key_list)
# chosen_key = (key_list)
mat = newmatenc.CMatrix[chosen_key]
# print(chosen_key, mat.shape, list(newmatenc.node_map.items()))
i = rng.randint(0, mat.shape[0] - 1)
if mat.shape[1] > 1:
j = rng.randint(0, mat.shape[1] - 1)
else:
j = 0
split_key1, split_key2 = matenc.split_key(chosen_key)
# (split_key1, split_key2)
ctr = 0
if not newmatenc.WMatrix[chosen_key][i][j] and not type:
while mat[i][j] == 0:
i = rng.randint(0, mat.shape[0] - 1)
if mat.shape[1] > 1:
j = rng.randint(0, mat.shape[1] - 1)
else:
j = 0
if ctr > 10:
return
ctr += 1
couple = (newmatenc.node_map[split_key1][i],
newmatenc.node_map[split_key2][j])
con_obj = newmatenc.couple_to_conn_map[couple]
con_obj.status = False
newnode = gene.Node(self.node_ctr, stlis[ind])
self.node_ctr += 1
innov_num = normalize_conn_arr_for_this_gen(self,
(con_obj.source, newnode))
new_conn1 = gene.Conn(innov_num, (con_obj.source, newnode), 1.0, True)
innov_num = normalize_conn_arr_for_this_gen(
self, (newnode, con_obj.destination))
new_conn2 = gene.Conn(innov_num, (newnode, con_obj.destination),
con_obj.weight, True)
self.node_arr.append(newnode)
self.conn_arr.append(new_conn1)
self.conn_arr.append(new_conn2)
def test_for_cluster():
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
for_node += [(i, 'H2') for i in range(6, 8)]
node_ctr = 8
indim = 3
outdim = 2
innov_num = 11
dob = 0
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [(1, (1, 4), 0.4, False), (2, (1, 5), 0.25, True),
(3, (2, 4), 0.25, True), (4, (2, 5), 0.5, True),
(5, (3, 4), 0.7, True), (6, (3, 5), 0.6, False),
(7, (1, 6), 0.5, True), (8, (6, 4), 0.4, True),
(9, (3, 7), 0.3, True), (10, (7, 5), 0.6, True)]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newchromo1 = Chromosome(indim, outdim)
newchromo1.__setattr__('conn_arr', conn_lis)
newchromo1.__setattr__('bias_conn_arr', bias_conn_lis)
newchromo1.__setattr__('node_arr', node_lis)
newchromo1.__setattr__('dob', dob)
newchromo1.set_node_ctr(node_ctr)
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
st = '2212211'
for_node += [(i + 6, 'H' + st[i]) for i in range(len(st))]
node_ctr = 13
innov_num = 25
dob = 0
indim = 8
outdim = 2
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [
(1, (1, 4), 0.3, True),
(2, (1, 5), 0.25, False),
(3, (2, 4), 0.25, False),
(4, (2, 5), 0.5, False),
(5, (3, 4), 0.7, False),
(6, (3, 5), 0.5, True),
(7, (1, 6), 0.2, True),
(8, (6, 4), 0.1, True),
(9, (2, 7), 0.1, True),
(10, (7, 4), 0.15, True),
(11, (1, 8), 0.5, True),
(12, (8, 6), 0.7, True),
(13, (1, 9), 0.3, False),
(14, (9, 5), 1.0, True),
(15, (3, 10), 0.33, True),
(16, (10, 5), 0.77, True),
(17, (1, 11), 0.25, True),
(18, (11, 9), 0.15, True),
(19, (2, 12), 0.6, True),
(20, (12, 7), 0.4, True),
(21, (3, 12), 0.8, True),
(22, (2, 9), 0.9, True),
(23, (12, 4), 0.75, True),
(24, (11, 5), 0.25, True),
]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newchromo2 = Chromosome(indim, outdim)
newchromo2.__setattr__('conn_arr', conn_lis)
newchromo2.__setattr__('bias_conn_arr', bias_conn_lis)
newchromo2.__setattr__('node_arr', node_lis)
newchromo2.__setattr__('dob', dob)
newchromo2.set_node_ctr(node_ctr)
# newchromo.pp()
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
st = '221221'
for_node += [(i + 6, 'H' + st[i]) for i in range(len(st))]
node_ctr = 12
dob = 0
indim = 3
outdim = 2
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [(1, (1, 4), 0.3, True), (2, (1, 5), 0.25, False),
(3, (2, 4), 0.25, False), (4, (2, 5), 0.5, False),
(5, (3, 4), 0.7, False), (6, (3, 5), 0.5, True),
(7, (1, 6), 0.2, True), (8, (6, 4), 0.1, True),
(9, (2, 7), 0.1, True), (10, (7, 4), 0.15, True),
(11, (1, 8), 0.5, True), (12, (8, 6), 0.7, True),
(13, (1, 9), 0.3, False), (14, (9, 5), 1.0, True),
(15, (3, 10), 0.33, True), (16, (10, 5), 0.77, True),
(19, (2, 11), 0.6, True), (20, (11, 7), 0.4, True),
(22, (3, 11), 0.75, True)]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newchromo3 = Chromosome(indim, outdim)
newchromo3.__setattr__('conn_arr', conn_lis)
newchromo3.__setattr__('bias_conn_arr', bias_conn_lis)
newchromo3.__setattr__('node_arr', node_lis)
newchromo3.__setattr__('dob', dob)
newchromo3.set_node_ctr(node_ctr)
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
st = '221221'
for_node += [(i + 6, 'H' + st[i]) for i in range(len(st))]
node_ctr = 12
innov_num = 21
dob = 0
indim = 3
outdim = 2
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [(1, (1, 4), 0.3, True), (2, (1, 5), 0.25, False),
(3, (2, 4), 0.25, False), (4, (2, 5), 0.5, False),
(5, (3, 4), 0.7, False), (6, (3, 5), 0.5, True),
(7, (1, 6), 0.2, True), (8, (6, 4), 0.1, True),
(9, (2, 7), 0.1, True), (10, (7, 4), 0.15, True),
(11, (1, 8), 0.5, True), (12, (8, 6), 0.7, True),
(13, (1, 9), 0.3, False), (14, (9, 5), 1.0, True),
(15, (3, 10), 0.33, True), (16, (10, 5), 0.77, True),
(17, (1, 11), 0.25, True), (18, (11, 9), 0.15, True),
(21, (2, 9), 0.65, True)]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newchromo4 = Chromosome(indim, outdim)
newchromo4.__setattr__('conn_arr', conn_lis)
newchromo4.__setattr__('bias_conn_arr', bias_conn_lis)
newchromo4.__setattr__('node_arr', node_lis)
newchromo4.__setattr__('dob', dob)
newchromo4.set_node_ctr(node_ctr)
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
st = '2212211'
for_node += [(i + 6, 'H' + st[i]) for i in range(len(st))]
node_ctr = 13
innov_num = 25
dob = 0
indim = 3
outdim = 2
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [
(1, (1, 4), 0.3, False),
(2, (1, 5), 0.25, False),
(3, (2, 4), 0.25, False),
(4, (2, 5), 0.5, False),
(5, (3, 4), 0.7, False),
(6, (3, 5), 0.5, True),
(7, (1, 6), 0.2, True),
(8, (6, 4), 0.1, True),
(9, (2, 7), 0.1, True),
(10, (7, 4), 0.15, True),
(11, (1, 8), 0.5, True),
(12, (8, 6), 0.7, True),
(13, (1, 9), 0.3, False),
(14, (9, 5), 1.0, True),
(15, (3, 10), 0.33, True),
(16, (10, 5), 0.77, True),
(17, (1, 11), 0.25, True),
(21, (3, 12), 0.8, True),
(22, (2, 9), 0.9, True),
(23, (12, 4), 0.75, True),
(24, (11, 5), 0.25, True),
]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newchromo5 = Chromosome(indim, outdim)
newchromo5.__setattr__('conn_arr', conn_lis)
newchromo5.__setattr__('bias_conn_arr', bias_conn_lis)
newchromo5.__setattr__('node_arr', node_lis)
newchromo5.__setattr__('dob', dob)
newchromo5.set_node_ctr(node_ctr)
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
st = '22122'
for_node += [(i + 6, 'H' + st[i]) for i in range(len(st))]
node_ctr = 11
innov_num = 17
dob = 0
indim = 3
outdim = 2
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [(1, (1, 4), 0.8, True), (2, (1, 5), 0.25, False),
(3, (2, 4), 0.25, False), (4, (2, 5), 0.5, False),
(5, (3, 4), 0.7, False), (6, (3, 5), 0.5, True),
(7, (1, 6), 0.2, True), (8, (6, 4), 0.1, True),
(9, (2, 7), 0.1, True), (10, (7, 4), 0.15, True),
(11, (1, 8), 0.5, True), (12, (8, 6), 0.7, True),
(13, (1, 9), 0.3, False), (14, (9, 5), 1.0, True),
(15, (3, 10), 0.33, True), (16, (10, 5), 0.77, True)]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newchromo6 = Chromosome(indim, outdim)
newchromo6.__setattr__('conn_arr', conn_lis)
newchromo6.__setattr__('bias_conn_arr', bias_conn_lis)
newchromo6.__setattr__('node_arr', node_lis)
newchromo6.__setattr__('dob', dob)
newchromo6.set_node_ctr(node_ctr)
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
for_node += [(i, 'H2') for i in range(6, 8)]
node_ctr = 8
indim = 3
outdim = 2
innov_num = 11
dob = 0
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [(1, (1, 4), 0.4, False), (2, (1, 5), 0.25, True),
(3, (2, 4), 0.25, True), (4, (2, 5), 0.5, True),
(5, (3, 4), 0.7, True), (6, (3, 5), 0.6, False),
(7, (1, 6), 0.5, True), (8, (6, 4), 0.4, True),
(9, (3, 7), 0.3, True)]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newchromo7 = Chromosome(indim, outdim)
newchromo7.__setattr__('conn_arr', conn_lis)
newchromo7.__setattr__('bias_conn_arr', bias_conn_lis)
newchromo7.__setattr__('node_arr', node_lis)
newchromo7.__setattr__('dob', dob)
newchromo7.set_node_ctr(node_ctr)
chromo_list = [
newchromo1, newchromo2, newchromo3, newchromo4, newchromo5, newchromo6,
newchromo7
]
#print([item.pp() for item in chromo_list])
print(cluster.distance(newchromo3, newchromo2))
cluster.give_cluster_head(chromo_list, 2)
inputarr = np.array([[0, 2, 1], [0.8, 1, 2]])
def test2():
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
st = '2212211'
for_node += [(i + 6, 'H' + st[i]) for i in range(len(st))]
node_ctr = 13
innov_num = 25
dob = 0
indim = 8
outdim = 2
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [
(1, (1, 4), 0.3, True),
(2, (1, 5), 0.25, False),
(3, (2, 4), 0.25, False),
(4, (2, 5), 0.5, False),
(5, (3, 4), 0.7, False),
(6, (3, 5), 0.5, True),
(7, (1, 6), 0.2, True),
(8, (6, 4), 0.1, True),
(9, (2, 7), 0.1, True),
(10, (7, 4), 0.15, True),
(11, (1, 8), 0.5, True),
(12, (8, 6), 0.7, True),
(13, (1, 9), 0.3, False),
(14, (9, 5), 1.0, True),
(15, (3, 10), 0.33, True),
(16, (10, 5), 0.77, True),
(17, (1, 11), 0.25, True),
(18, (11, 9), 0.15, True),
(19, (2, 12), 0.6, True),
(20, (12, 7), 0.4, True),
(21, (3, 12), 0.8, True),
(22, (2, 9), 0.9, True),
(23, (12, 4), 0.75, True),
(24, (11, 5), 0.25, True),
]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newchromo = Chromosome(indim, outdim)
newchromo.__setattr__('conn_arr', conn_lis)
newchromo.__setattr__('bias_conn_arr', bias_conn_lis)
newchromo.__setattr__('node_arr', node_lis)
newchromo.__setattr__('dob', dob)
newchromo.set_node_ctr(node_ctr)
def calc_output_directly(inputarr):
lis = []
for arr in inputarr:
x1 = arr[0]
x2 = arr[1]
x3 = arr[2]
output1 = sigmoid(
0.3 * x1 + 0.1 * relu(0.7 * relu(0.5 * x1) + 0.2 * x1) +
0.15 * relu(0.1 * x2 + 0.4 * relu(0.6 * x2 + 0.8 * x3)) +
0.75 * relu(0.6 * x2 + 0.8 * x3) - 0.2)
# output2 = sigmoid(arr[0] * 0.25 + arr[1] * 0.5 + relu(arr[2] * 0.3) * 0.6 - 0.1)
output2 = sigmoid(0.5 * x3 +
1 * relu(0.15 * relu(0.25 * x1) + 0.9 * x2) +
0.25 * relu(0.25 * x1) + 0.77 * relu(0.33 * x3) -
0.1)
lis.append([output1, output2])
return np.array(lis)
inputarr = np.array([[0, 2, 1], [0.8, 1, 2]])
np.random.seed(4)
num_data = 2
# inputarr = np.random.random((num_data, indim))
neter = Neterr(indim, outdim, 10, np.random)
print(neter.feedforward_ne(newchromo))
print(calc_output_directly(inputarr))
tempchromo = newchromo
print(neter.feedforward_cm(newchromo))
if (newchromo == tempchromo):
print("yeah they are equal")
print(neter.feedforward_ne(newchromo, play=1))
print(neter.feedforward_cm(newchromo, play=1))
print(neter.feedforward_cm(newchromo, play=1))
print("done right")
def interchanging_test(chromo):
new_mat_enc = chromo.convert_to_MatEnc(indim, outdim)
newchromo = new_mat_enc.convert_to_chromosome(indim, outdim, dob)
if newchromo.bias_conn_arr != chromo.bias_conn_arr:
print("falied 1")
if newchromo.node_arr != chromo.node_arr:
print("failed 2")
if newchromo.dob != chromo.dob or newchromo.node_ctr != chromo.node_ctr:
print("failed 3", "node_ctr are",
newchromo.node_ctr, chromo.node_ctr, len(newchromo.node_arr),
len(chromo.node_arr))
listup1 = [(con.status, con.weight, con.get_couple(), con.innov_num)
for con in newchromo.conn_arr]
listup2 = [(con.status, con.weight, con.get_couple(), con.innov_num)
for con in chromo.conn_arr]
# print( set(listup1),set(listup2))
if set(listup1) != set(listup2):
print("failed 4")
interchanging_test(newchromo)
def test_for_cros2():
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
st = '221221'
for_node += [(i + 6, 'H' + st[i]) for i in range(len(st))]
node_ctr = 12
innov_num = 21
dob = 0
indim = 3
outdim = 2
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [(1, (1, 4), 0.3, True), (2, (1, 5), 0.25, False),
(3, (2, 4), 0.25, False), (4, (2, 5), 0.5, False),
(5, (3, 4), 0.7, False), (6, (3, 5), 0.5, True),
(7, (1, 6), 0.2, True), (8, (6, 4), 0.1, True),
(9, (2, 7), 0.1, True), (10, (7, 4), 0.15, True),
(11, (1, 8), 0.5, True), (12, (8, 6), 0.7, True),
(13, (1, 9), 0.3, False), (14, (9, 5), 1.0, True),
(15, (3, 10), 0.33, True), (16, (10, 5), 0.77, True),
(17, (1, 11), 0.25, True), (18, (11, 9), 0.15, True),
(21, (2, 9), 0.65, True)]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newchromo = Chromosome(indim, outdim)
newchromo.__setattr__('conn_arr', conn_lis)
newchromo.__setattr__('bias_conn_arr', bias_conn_lis)
newchromo.__setattr__('node_arr', node_lis)
newchromo.__setattr__('dob', dob)
newchromo.set_node_ctr(node_ctr)
inputarr = np.array([[0, 2, 1], [0.8, 1, 2]])
np.random.seed(4)
num_data = 2
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
st = '221221'
for_node += [(i + 6, 'H' + st[i]) for i in range(len(st))]
node_ctr = 12
dob = 0
indim = 3
outdim = 2
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [(1, (1, 4), 0.3, True), (2, (1, 5), 0.25, False),
(3, (2, 4), 0.25, False), (4, (2, 5), 0.5, False),
(5, (3, 4), 0.7, False), (6, (3, 5), 0.5, True),
(7, (1, 6), 0.2, True), (8, (6, 4), 0.1, True),
(9, (2, 7), 0.1, True), (10, (7, 4), 0.15, True),
(11, (1, 8), 0.5, True), (12, (8, 6), 0.7, True),
(13, (1, 9), 0.3, False), (14, (9, 5), 1.0, True),
(15, (3, 10), 0.33, True), (16, (10, 5), 0.77, True),
(19, (2, 11), 0.6, True), (20, (11, 7), 0.4, True),
(22, (3, 11), 0.75, True)]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
newnewchromo = Chromosome(indim, outdim)
newnewchromo.__setattr__('conn_arr', conn_lis)
newnewchromo.__setattr__('bias_conn_arr', bias_conn_lis)
newnewchromo.__setattr__('node_arr', node_lis)
newnewchromo.__setattr__('dob', dob)
newnewchromo.set_node_ctr(node_ctr)
# print("newchromo")
# newchromo.pp()
# print("newnewchromo")
# newnewchromo.pp()
print("final")
gen_no = 1
tup1 = (0.4, 0.5, 0.7, 0.8)
tup2 = (0.6, 0.4, 0.6, 0.7)
# chromosome.crossover(newchromo, newnewchromo, gen_no).pp()
# chromosome.crossoverTest((newchromo, tup1), (newnewchromo, tup2), gen_no).pp()
wt_muta = 0.3
node_muta = 0.1
conn_muta = 0.2
chromosome.do_mutation(wt_muta, conn_muta, node_muta, indim, outdim, 5)
def test_mtbp():
for_node = [(i, 'I') for i in range(1, 4)]
for_node += [(i, 'O') for i in range(4, 6)]
st = '2212211'
for_node += [(i + 6, 'H' + st[i]) for i in range(len(st))]
node_ctr = 13
innov_num = 25
dob = 0
node_lis = [gene.Node(x, y) for x, y in for_node]
for_conn = [
(1, (1, 4), 0.3, True),
(2, (1, 5), 0.25, False),
(3, (2, 4), 0.25, False),
(4, (2, 5), 0.5, False),
(5, (3, 4), 0.7, False),
(6, (3, 5), 0.5, True),
(7, (1, 6), 0.2, True),
(8, (6, 4), 0.1, True),
(9, (2, 7), 0.1, True),
(10, (7, 4), 0.15, True),
(11, (1, 8), 0.5, True),
(12, (8, 6), 0.7, True),
(13, (1, 9), 0.3, False),
(14, (9, 5), 1.0, True),
(15, (3, 10), 0.33, True),
(16, (10, 5), 0.77, True),
(17, (1, 11), 0.25, True),
(18, (11, 9), 0.15, True),
(19, (2, 12), 0.6, True),
(20, (12, 7), 0.4, True),
(21, (3, 12), 0.8, True),
(22, (2, 9), 0.9, True),
(23, (12, 4), 0.75, True),
(24, (11, 5), 0.25, True),
]
conn_lis = [
gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status)
for x, tup, w, status in for_conn
]
for_bias = [(4, 0.2), (5, 0.1)]
bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]
indim = 8
outdim = 2
newchromo = Chromosome(indim, outdim)
newchromo.reset_chromo_to_zero()
newchromo.__setattr__('conn_arr', conn_lis)
newchromo.__setattr__('bias_conn_arr', bias_conn_lis)
newchromo.__setattr__('node_arr', node_lis)
newchromo.__setattr__('dob', dob)
newchromo.set_node_ctr()
# newchromo.pp()
def calc_output_directly(inputarr):
lis = []
for arr in inputarr:
x1 = arr[0]
x2 = arr[1]
x3 = arr[2]
output1 = sigmoid(
0.3 * x1 + 0.1 * relu(0.7 * relu(0.5 * x1) + 0.2 * x1) +
0.15 * relu(0.1 * x2 + 0.4 * relu(0.6 * x2 + 0.8 * x3)) +
0.75 * relu(0.6 * x2 + 0.8 * x3) - 0.2)
# output2 = sigmoid(arr[0] * 0.25 + arr[1] * 0.5 + relu(arr[2] * 0.3) * 0.6 - 0.1)
output2 = sigmoid(0.5 * x3 +
1 * relu(0.15 * relu(0.25 * x1) + 0.9 * x2) +
0.25 * relu(0.25 * x1) + 0.77 * relu(0.33 * x3) -
0.1)
lis.append([output1, output2])
return np.array(lis)
#inputarr = np.array([[0.0, 2, 1], [0.8, 1, 2]])
indim = 8
outdim = 2
# np.random
# rng = np.random
# num_data = 10
# inputarr = np.random.random((num_data, indim))
neter = network.Neterr(indim, outdim, 10, np.random)
# ka = np.random.randint(0, 2, (num_data,))
# print(neter.feedforward_ne(chromo))
"""
targetarr = np.zeros((num_data,outdim)).astype(dtype = 'float32')
for i in range(num_data):
targetarr[i,ka[i]] = 1
print("target is ", targetarr)
"""
#targetarr = ka.astype('int32')
#print(targetarr.dtype)
#inputarr = inputarr.astype('float32')
tempchromo = copy.deepcopy(newchromo)
arr = newchromo.node_arr
newmatenc = tempchromo.convert_to_MatEnc(indim, outdim)
newmatenc = copy.deepcopy(newmatenc)
print(newmatenc.CMatrix['IO'])
newchromo.modify_thru_backprop(indim, outdim, neter.rest_setx,
neter.rest_sety)
if not newchromo.node_arr == arr:
print("failed 1")
if not newchromo.dob == tempchromo.dob and not newchromo.node_ctr == tempchromo.node_ctr:
print("failed 2")
if not len(newchromo.conn_arr) == len(tempchromo.conn_arr):
print("failed 3", len(newchromo.conn_arr), len(tempchromo.conn_arr))
newnewmatenc = newchromo.convert_to_MatEnc(indim, outdim)
for key in newnewmatenc.WMatrix.keys():
if (newnewmatenc.WMatrix[key] == newmatenc.WMatrix[key]).all():
print("failed 5", key)