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=[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 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
""" Simple animation of Echo State Network with custom connection matrix"""
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import numpy as np
import evodynamic.experiment as experiment
import evodynamic.connection.custom as conn_custom
import evodynamic.connection as connection
import evodynamic.cells.activation as act
import networkx as nx
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')
# 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,
tf.disable_v2_behavior()
import numpy as np
import evodynamic.experiment as experiment
import evodynamic.connection.cellular_automata as ca
import evodynamic.connection as connection
import evodynamic.connection.random as randon_conn
import evodynamic.cells.activation as act
width = 100
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]]
y_test_one_hot = np.zeros((y_test.max() + 1, y_test.size))
y_test_one_hot[y_test, np.arange(y_test.size)] = 1
y_test = y_test_one_hot
epochs = 10
batch_size = 100
num_batches = int(np.ceil(x_train_num_images / batch_size))
width = 28 * 28
timesteps = 28 * 28
input_size = 1
output_layer_size = 10
image_num_pixels = x_train_image_shape[0] * x_train_image_shape[1]
exp = experiment.Experiment(input_start=0,
input_delay=0,
training_start=timesteps - 1,
training_delay=timesteps - 1,
reset_cells_after_train=False,
batch_size=batch_size)
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 [170]]
""" Cellular automata 1D with input - animation """
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import evodynamic.experiment as experiment
import evodynamic.connection.cellular_automata as ca
import evodynamic.cells.activation as act
import evodynamic.connection as connection
import numpy as np
width = 100
height_fig = 200
timesteps = 400
input_size = width // 2
exp = experiment.Experiment(input_start=5, input_delay=14)
input_ca = exp.add_input(tf.float64, [input_size], "input_ca")
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)
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(
x_train= ((x_train / 255.0) > 0.5).astype(np.int8)
x_train = x_train.reshape(x_train.shape[0],-1)
x_train = np.transpose(x_train)
y_train_one_hot = np.zeros((y_train.max()+1, y_train.size))
y_train_one_hot[y_train,np.arange(y_train.size)] = 1
y_train = y_train_one_hot
width = 100
input_size = 1
output_layer_size = 10
memory_size = 28*28
height_fig = memory_size
exp = experiment.Experiment(input_start=0,input_delay=0,training_start=memory_size,
training_delay=memory_size-1,reset_cells_after_train=True,
reset_memories_after_train=True)
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,
""" Stochastic Cellular automata 1D with input - animation """
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import evodynamic.experiment as experiment
import evodynamic.connection.cellular_automata as ca
import evodynamic.cells.activation as act
import evodynamic.connection as connection
import numpy as np
import matplotlib.pyplot as plt
width = 100
timesteps = 400
input_size = width // 2
exp = experiment.Experiment(input_start=25, input_delay=24)
input_ca = exp.add_input(tf.float64, [input_size], "input_ca")
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)
"""
Best 2
80;4.375510397876782;{'norm_ksdist_res': 0.9613162536023352,
'norm_coef_res': 1.6377095314393233, 'norm_unique_states': 1.0,
'norm_avalanche_pdf_size': 0.9659114597767141, 'norm_linscore_res': 0.8704469695084798,
'norm_R_res': 0.7277920719335422, 'fitness': 4.375510397876782};
[0.39422172047670734, 0.09472197628905793, 0.2394927250526252, 0.4084554505943707, 0.0, 0.7302038703441202, 0.9150343715586952, 1.0]
"""
number_of_resovoirs = 4
resovoir_width = 20
iterations_between_input = 4
input_locations = np.random.randint(resovoir_width, size=number_of_resovoirs)
input_true_locations = []
for i in range(len(input_locations)):
input_true_locations.append((i * resovoir_width) + (input_locations[i]))
width = number_of_resovoirs * resovoir_width
height_fig = 200
timesteps = 200
input_stream = np.ones(5, dtype=int)
exp = experiment.Experiment(input_delay=0, training_delay=5)
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)
input_connection = exp.add_input(tf.float64, [width], "input_connection")
fargs_list = [(a, ) for a in [30]]
exp.add_connection(
"input_conn",
connection.IndexConnection(input_connection, g_ca_bin, np.arange(width)))
y_test_one_hot = np.zeros((y_test.max() + 1, y_test.size))
y_test_one_hot[y_test, np.arange(y_test.size)] = 1
y_test = y_test_one_hot
epochs = 10
batch_size = 100
num_batches = int(np.ceil(x_train_num_images / batch_size))
width = 28 * 28
input_size = 28 * 28
output_layer_size = 10
image_num_pixels = x_train_image_shape[0] * x_train_image_shape[1]
exp = experiment.Experiment(input_start=0,
input_delay=0,
training_start=0,
training_delay=0,
reset_cells_after_train=True,
batch_size=batch_size)
input_esn = exp.add_input(tf.float64, [input_size], "input_esn")
desired_output = exp.add_desired_output(tf.float64, [output_layer_size],
"desired_output")
g_esn = exp.add_group_cells(name="g_esn", amount=width)
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)]
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import evodynamic.experiment as experiment
import evodynamic.connection.cellular_automata as ca
import evodynamic.cells.activation as act
import evodynamic.connection as connection
import numpy as np
import matplotlib.pyplot as plt
width = 100
timesteps = 400
input_size = width // 2
batch_size = 2
exp = experiment.Experiment(input_start=25,
input_delay=24,
batch_size=batch_size)
input_ca = exp.add_input(tf.float64, [input_size], "input_ca")
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)
fargs_list = [(a, ) for a in [204]]
exp.add_connection(
"input_conn",
connection.IndexConnection(input_ca, g_ca_bin, np.arange(input_size)))
x_train = np.transpose(x_train)
y_train_one_hot = np.zeros((y_train.max() + 1, y_train.size))
y_train_one_hot[y_train, np.arange(y_train.size)] = 1
y_train = y_train_one_hot
width = 28 * 28
timesteps = 28 * 28
input_size = 1
output_layer_size = 10
image_num_pixels = x_train_image_shape[0] * x_train_image_shape[1]
height_fig = timesteps
exp = experiment.Experiment(input_start=0,
input_delay=0,
training_start=timesteps - 1,
training_delay=timesteps - 1,
reset_cells_after_train=True)
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 [170]]
""" Cellular automata 1D in batch - simulation plot """
import evodynamic.experiment as experiment
import evodynamic.connection.cellular_automata as ca
import evodynamic.cells.activation as act
import evodynamic.connection as connection
import matplotlib.pyplot as plt
width = 100
timesteps = 400
batch_size = 4
exp = experiment.Experiment(batch_size=batch_size)
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)
fargs_list = [(a, ) for a in [110]]
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")
