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=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
height_fig = 200
input_size = width // 2
output_layer_size = width
memory_size = 5
exp = experiment.Experiment(input_start=5,
input_delay=14,
training_start=6,
training_delay=0)
input_ca = exp.add_input(tf.float64, [input_size], "input_ca")
desired_output = exp.add_desired_output(tf.float64, [output_layer_size],
"desired_output")
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', 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,
