def evaluate_genome(genome=[110], show_result = False):
print(genome)
gen_rule = [(r,) for r in genome]
exp = experiment.Experiment()
g_ca = exp.add_group_cells(name="g_ca", amount=width)
neighbors, center_idx = ca.create_pattern_neighbors_ca1d(3)
g_ca_bin = g_ca.add_binary_state(state_name='g_ca_bin')
g_ca_bin_conn = ca.create_conn_matrix_ca1d('g_ca_bin_conn',width,\
neighbors=neighbors,\
center_idx=center_idx,
is_wrapped_ca=True)
exp.add_connection("g_ca_conn",
connection.WeightedConnection(g_ca_bin,g_ca_bin,
act.rule_binary_ca_1d_width3_func,
g_ca_bin_conn, fargs_list=gen_rule))
exp.add_monitor("g_ca", "g_ca_bin", timesteps)
exp.initialize_cells()
start = time.time()
exp.run(timesteps=timesteps)
print("Execution time:", time.time()-start)
exp.close()
fitness, val_dict = evaluate_result(exp.get_monitor("g_ca", "g_ca_bin")[:,:,0])
return fitness, val_dict
def set_up_evodynamics(self):
fargs_list = [(a, ) for a in [self.ca_rule]]
exp = experiment.Experiment(input_start=0,
input_delay=self.reservoir_height,
training_delay=5)
g_ca = exp.add_group_cells(name="g_ca", amount=self.ca_width)
neighbors, center_idx = ca.create_pattern_neighbors_ca1d(3)
g_ca_bin = g_ca.add_binary_state(state_name='g_ca_bin', init="zeros")
g_ca_bin_conn = ca.create_conn_matrix_ca1d('g_ca_bin_conn',
self.ca_width,
neighbors=neighbors,
center_idx=center_idx)
input_connection = exp.add_input(tf.float64, [self.ca_width],
"input_connection")
exp.add_connection(
"input_conn",
connection.IndexConnection(input_connection, g_ca_bin,
np.arange(self.ca_width)))
exp.add_connection(
"g_ca_conn",
connection.WeightedConnection(g_ca_bin,
g_ca_bin,
act.rule_binary_ca_1d_width3_func,
g_ca_bin_conn,
fargs_list=fargs_list))
# exp.add_monitor("g_ca", "g_ca_bin")
exp.initialize_cells()
# assign to self rather then return touple
return exp, input_connection
def evaluate_genome(genome=6 * [0.5], filename=None):
print(genome)
# gen_rule = [(genome,)]
exp = experiment.Experiment()
g_ca = exp.add_group_cells(name="g_ca", amount=width)
g_ca_bin = g_ca.add_binary_state(state_name='g_ca_bin')
mean_pos = gene2mean(genome[0])
std_pos = 0.2 * mean_pos * genome[1]
mean_neg = gene2mean(genome[2])
std_neg = 0.2 * mean_neg * genome[3]
g_ca_bin_conn = random_net.create_esn_matrix('g_ca_bin_conn', width,\
mean_pos=mean_pos, std_pos=std_pos,\
mean_neg=mean_neg, std_neg=std_neg,\
pos_neg_prop=genome[4],\
sparsity=genome[5], is_sparse=genome[5]<0.9)
exp.add_connection(
"g_ca_conn",
connection.WeightedConnection(g_ca_bin, g_ca_bin,
act.stochastic_sigmoid, g_ca_bin_conn))
exp.add_monitor("g_ca", "g_ca_bin", timesteps)
exp.initialize_cells()
start = time.time()
exp.run(timesteps=timesteps)
#ca_result .append()
print("Execution time:", time.time() - start)
exp.close()
fitness, val_dict = evaluate_result(
exp.get_monitor("g_ca", "g_ca_bin")[:, :, 0])
if isinstance(filename, str):
if ".csv" in filename:
with open(filename, "a+", newline="") as f:
wr = csv.writer(f, delimiter=";")
if os.stat(filename).st_size == 0:
wr.writerow(["genome", "fitness", "norm_ksdist_res", "norm_coef_res", "norm_unique_states",\
"norm_avalanche_pdf_size", "norm_linscore_res", "norm_R_res"])
wr.writerow([str(list(genome)), val_dict["fitness"], val_dict["norm_ksdist_res"],\
val_dict["norm_coef_res"], val_dict["norm_unique_states"],\
val_dict["norm_avalanche_pdf_size"],val_dict["norm_linscore_res"],\
val_dict["norm_R_res"]])
return fitness, val_dict
def evaluate_genome(genome=8*[0.5], filename=None):
print(genome)
gen_rule = [(genome,)]
exp = experiment.Experiment()
g_ca = exp.add_group_cells(name="g_ca", amount=width)
neighbors, center_idx = ca.create_pattern_neighbors_ca1d(3)
g_ca_bin = g_ca.add_binary_state(state_name='g_ca_bin')
g_ca_bin_conn = ca.create_conn_matrix_ca1d('g_ca_bin_conn',width,\
neighbors=neighbors,\
center_idx=center_idx,
is_wrapped_ca=True)
exp.add_connection("g_ca_conn",
connection.WeightedConnection(g_ca_bin,g_ca_bin,
act.rule_binary_sca_1d_width3_func,
g_ca_bin_conn, fargs_list=gen_rule))
exp.add_monitor("g_ca", "g_ca_bin", timesteps)
exp.initialize_cells()
start = time.time()
exp.run(timesteps=timesteps)
print("Execution time:", time.time()-start)
exp.close()
fitness, val_dict = evaluate_result(exp.get_monitor("g_ca", "g_ca_bin")[:,:,0])
if isinstance(filename, str):
if ".csv" in filename:
with open(filename, "a+", newline="") as f:
wr = csv.writer(f, delimiter=";")
if os.stat(filename).st_size == 0:
wr.writerow(["genome", "fitness", "norm_ksdist_res", "norm_coef_res", "norm_unique_states",\
"norm_avalanche_pdf_size", "norm_linscore_res", "norm_R_res"])
wr.writerow([str(list(genome)), val_dict["fitness"], val_dict["norm_ksdist_res"],\
val_dict["norm_coef_res"], val_dict["norm_unique_states"],\
val_dict["norm_avalanche_pdf_size"],val_dict["norm_linscore_res"],\
val_dict["norm_R_res"]])
return fitness, val_dict
input_esn = exp.add_input(tf.float64, [input_size], "input_esn")
g_esn = exp.add_group_cells(name="g_esn", amount=width)
g_esn_real = g_esn.add_real_state(state_name='g_esn_bin')
# Generate custom connection matrix
conn_matrix = np.random.normal(loc=0.0, scale=0.4, size=(width, width))
conn_matrix[np.round(conn_matrix) == 0.0] = 0.0
g_esn_real_conn = conn_custom.create_custom_matrix('g_ca_bin_conn',conn_matrix)
exp.add_connection("input_conn", connection.IndexConnection(input_esn,g_esn_real,np.arange(input_size)))
exp.add_connection("g_esn_conn",
connection.WeightedConnection(g_esn_real,
g_esn_real,act.sigmoid,
g_esn_real_conn))
exp.initialize_cells()
weight_matrix = exp.session.run(exp.get_connection("g_esn_conn").w)
def adjacency2indices_values(adjacency_matrix):
indices = np.argwhere(adjacency_matrix != 0)
values = adjacency_matrix[np.where(adjacency_matrix != 0)]
return indices, values
indices, values = adjacency2indices_values(weight_matrix)
G = nx.DiGraph()
import evodynamic.connection as connection
width = 200
height = 150
exp = experiment.Experiment()
g_ca = exp.add_group_cells(name="g_ca", amount=width * height)
neighbors, center_idx = ca.create_count_neighbors_ca2d(3, 3)
g_ca_bin = g_ca.add_binary_state(state_name='g_ca_bin')
g_ca_bin_conn = ca.create_conn_matrix_ca2d('g_ca_bin_conn',width,height,\
neighbors=neighbors,\
center_idx=center_idx)
exp.add_connection(
"g_ca_conn",
connection.WeightedConnection(g_ca_bin, g_ca_bin, act.game_of_life_func,
g_ca_bin_conn))
exp.initialize_cells()
# Animation
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig = plt.figure()
idx_anim = 0
im = plt.imshow(exp.get_group_cells_state("g_ca", "g_ca_bin")[:, 0].reshape(
(height, width)),
animated=True)
plt.title('Step: 0')
g_ca_bin = g_ca.add_binary_state(state_name='g_ca_bin', init="zeros")
g_ca_bin_conn = ca.create_conn_matrix_ca1d('g_ca_bin_conn',width,\
neighbors=neighbors,\
center_idx=center_idx)
fargs_list = [(a, ) for a in [110]]
exp.add_connection(
"input_conn",
connection.IndexConnection(input_ca, g_ca_bin, np.arange(input_size)))
exp.add_connection(
"g_ca_conn",
connection.WeightedConnection(g_ca_bin,
g_ca_bin,
act.rule_binary_ca_1d_width3_func,
g_ca_bin_conn,
fargs_list=fargs_list))
g_ca_memory = exp.add_state_memory(g_ca_bin, memory_size)
output_layer = exp.add_group_cells(name="output_layer",
amount=output_layer_size)
output_layer_real_state = output_layer.add_real_state(
state_name='output_layer_real_state', stddev=0)
ca_output_conn = randon_conn.create_xavier_connection("ca_output_conn",
width * memory_size,
output_layer_size)
exp.add_trainable_connection(
"output_conn",
neighbors=neighbors,\
center_idx=center_idx)
output_layer = exp.add_group_cells(name="output_layer", amount=height)
output_layer_real_state = output_layer.add_real_state(
state_name='output_layer_real_state', stddev=0)
ca_output_conn = randon_conn.create_xavier_connection("ca_output_conn",
width * height, height)
exp.add_connection(
"input_conn",
connection.IndexConnection(input_ca, g_ca_bin, np.arange(input_size)))
exp.add_connection(
"g_ca_conn",
connection.WeightedConnection(g_ca_bin, g_ca_bin, act.game_of_life_func,
g_ca_bin_conn))
exp.add_trainable_connection(
"output_conn",
connection.WeightedConnection(g_ca_bin, output_layer_real_state,
act.sigmoid, ca_output_conn))
c_loss = tf.losses.mean_squared_error(
labels=desired_output,
predictions=exp.trainable_connections["output_conn"].output)
exp.set_training(c_loss, 0.1)
exp.initialize_cells()
# Animation
import matplotlib.pyplot as plt
g_ca_bin = g_ca.add_binary_state(state_name='g_ca_bin')
g_ca_bin_conn = ca.create_conn_matrix_ca1d('g_ca_bin_conn',width,\
neighbors=neighbors,\
center_idx=center_idx)
fargs_list = [(a, ) for a in [170]]
exp.add_connection(
"input_conn",
connection.IndexConnection(input_ca, g_ca_bin, np.arange(input_size)))
exp.add_connection(
"g_ca_conn",
connection.WeightedConnection(g_ca_bin,
g_ca_bin,
act.rule_binary_ca_1d_width3_func,
g_ca_bin_conn,
fargs_list=fargs_list))
exp.initialize_cells()
# Animation
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig = plt.figure()
idx_anim = 0
im_ca = np.zeros((height_fig, width))
im_ca[0] = exp.get_group_cells_state("g_ca", "g_ca_bin")[:, 0]
g_esn_real = g_esn.add_real_state(state_name='g_esn_real')
exp.add_connection(
"input_conn",
connection.IndexConnection(input_esn, g_esn_real, np.arange(width)))
indices = [[i, i] for i in range(width)]
values = [1] * width
dense_shape = [width, width]
g_esn_real_conn = conn_custom.create_custom_sparse_matrix(
'g_esn_real_conn', indices, values, dense_shape)
exp.add_connection(
"g_esn_conn",
connection.WeightedConnection(g_esn_real, g_esn_real, act.relu,
g_esn_real_conn))
output_layer = exp.add_group_cells(name="output_layer",
amount=output_layer_size)
output_layer_real_state = output_layer.add_real_state(
state_name='output_layer_real_state', stddev=0)
esn_output_conn = conn_random.create_xavier_connection("esn_output_conn",
width,
output_layer_size)
exp.add_trainable_connection(
"output_conn",
connection.WeightedConnection(g_esn_real, output_layer_real_state,
act.sigmoid, esn_output_conn))
c_loss = tf.reduce_mean(
g_ca_bin_conn = random_net.create_esn_matrix('g_ca_bin_conn', width,\
mean_pos=mean_pos, std_pos=std_pos,\
mean_neg=mean_neg, std_neg=std_neg,\
pos_neg_prop=genome[4],\
sparsity=genome[5], is_sparse=genome[5]<0.9)
print("mean_pos", mean_pos)
print("std_pos", std_pos)
print("mean_neg", mean_neg)
print("std_neg", std_neg)
print("pos_neg_prop", genome[4])
print("sparsity", genome[5])
exp.add_connection("g_ca_conn",
connection.WeightedConnection(g_ca_bin,g_ca_bin,
act.stochastic_sigmoid,
g_ca_bin_conn))
exp.add_monitor("g_ca", "g_ca_bin", timesteps)
exp.initialize_cells()
start = time.time()
exp.run(timesteps=timesteps)
ca_result = exp.get_monitor("g_ca", "g_ca_bin")[:,:,0]
print("Execution time:", time.time()-start)
exp.close()
width = 100
input_size = width // 10
exp = experiment.Experiment()
input_esn = exp.add_input(tf.float64, [input_size], "input_esn")
g_esn = exp.add_group_cells(name="g_esn", amount=width)
g_esn_real = g_esn.add_real_state(state_name='g_esn_bin')
g_esn_real_conn = conn_random.create_gaussian_matrix('g_ca_bin_conn',width, sparsity=0.95, is_sparse=True)
exp.add_connection("input_conn", connection.IndexConnection(input_esn,g_esn_real,np.arange(input_size)))
exp.add_connection("g_esn_conn",
connection.WeightedConnection(g_esn_real,
g_esn_real,act.stochastic_sigmoid,
g_esn_real_conn))
exp.initialize_cells()
weight_matrix = exp.session.run(exp.get_connection("g_esn_conn").w)
G = nx.DiGraph()
G.add_edges_from(weight_matrix[0])
pos_dict = {}
for i in range(width):
if i < input_size:
pos_dict[i] = (0,i)
pos = nx.spring_layout(G,pos=pos_dict, fixed=pos_dict.keys())
