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
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,
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
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,
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)
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]]
exp.add_connection("input_conn",
connection.IndexConnection(input_ca, g_ca_bin, [width - 1]))
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))
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,
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,
[width-1]))
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",
connection.WeightedConnection(g_ca_memory,
output_layer_real_state,
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')
indices = [[i, (i + 1) % width] for i in range(width)]
values = [1.5] * width
dense_shape = [width, width]
g_esn_real_conn = conn_custom.create_custom_sparse_matrix(
'g_ca_bin_conn', indices, values, dense_shape)
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)
G = nx.DiGraph()
G.add_edges_from(weight_matrix[0])
pos_dict = {}
for i in range(width):
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 [51]]
exp.add_connection(
"input_conn",
connection.IndexConnection(input_ca, g_ca_bin, np.arange(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))
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,
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)]
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)
import evodynamic.connection as connection
import evodynamic.cells.activation as act
import networkx as nx
width = 16
input_size = width // 10
exp = experiment.Experiment()
input_rbn = exp.add_input(tf.float64, [input_size], "input_rbn")
g_rbn = exp.add_group_cells(name="g_rbn", amount=width)
g_rbn_bin = g_rbn.add_binary_state(state_name='g_rbn_bin')
g_rbn_bin_conn = rbn.create_conn_matrix('g_ca_bin_conn',width)
exp.add_connection("input_conn", connection.IndexConnection(input_rbn,g_rbn_bin,np.arange(input_size)))
fargs_list = [(a,) for a in [110]]
exp.add_connection("g_rbn_conn",
connection.WeightedConnection(g_rbn_bin,
g_rbn_bin,act.rule_binary_ca_1d_width3_func,
g_rbn_bin_conn,fargs_list=fargs_list))
exp.initialize_cells()
weight_matrix = exp.session.run(exp.get_connection("g_rbn_conn").w)
G = nx.DiGraph()
G.add_edges_from(weight_matrix[0])
pos_dict = {}