def template_dynamic_legion(num_osc,
steps,
time,
conn_type,
stimulus,
params=None,
separate_repr=True,
ccore_flag=True):
net = legion_network(num_osc, params, conn_type, ccore=ccore_flag)
print("Created")
dynamic = net.simulate(steps, time, stimulus, solution=solve_type.RK4)
print("Simulated")
draw_dynamics(dynamic.time,
dynamic.output,
x_title="Time",
y_title="x(t)",
separate=separate_repr)
draw_dynamics(dynamic.time,
dynamic.inhibitor,
x_title="Time",
y_title="z(t)")
ensembles = dynamic.allocate_sync_ensembles(0.1)
print(ensembles)
def template_segmentation_image(image_file, parameters, steps, time, ccore_flag = True):
image = read_image(image_file);
stimulus = rgb2gray(image);
for pixel_index in range(len(stimulus)):
if (stimulus[pixel_index] < 235): stimulus[pixel_index] = 1;
else: stimulus[pixel_index] = 0;
if (parameters is None):
parameters = legion_parameters();
net = legion_network(len(stimulus), parameters, conn_type.GRID_FOUR, ccore = ccore_flag);
output_dynamic = net.simulate(steps, time, stimulus);
ensembles = output_dynamic.allocate_sync_ensembles();
draw_image_mask_segments(image_file, ensembles);
# draw_dynamics(output_dynamic.time, output_dynamic.output, x_title = "Time", y_title = "x(t)", separate = ensembles);
# just for checking correctness of results - let's use classical algorithm
dbscan_instance = dbscan(image, 3, 4, True);
dbscan_instance.process();
trustable_clusters = dbscan_instance.get_clusters();
draw_dynamics(output_dynamic.time, output_dynamic.output, x_title = "Time", y_title = "x(t)", separate = trustable_clusters);
def template_clustering(file, map_size, trust_order, sync_order = 0.999, show_dyn = False, show_layer1 = False, show_layer2 = False, show_clusters = True):
# Read sample
sample = read_sample(file);
# Create network
network = syncsom(sample, map_size[0], map_size[1]);
# Run processing
(ticks, (dyn_time, dyn_phase)) = timedcall(network.process, trust_order, show_dyn, sync_order);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
# Show dynamic of the last layer.
if (show_dyn == True):
draw_dynamics(dyn_time, dyn_phase, x_title = "Time", y_title = "Phase", y_lim = [0, 2 * 3.14]);
if (show_clusters == True):
clusters = network.get_som_clusters();
draw_clusters(network.som_layer.weights, clusters);
# Show network stuff.
if (show_layer1 == True):
network.show_som_layer();
if (show_layer2 == True):
network.show_sync_layer();
if (show_clusters == True):
clusters = network.get_clusters();
draw_clusters(sample, clusters);
def template_graph_coloring(filename,
alpha,
eps,
steps,
time,
title=None,
tolerance=0.1,
threshold_steps=10):
if (title is None): title = filename
graph = read_graph(filename)
network = hysteresisgcolor(graph.data, alpha, eps)
output_dynamic = network.simulate(steps, time)
draw_dynamics(output_dynamic.time,
output_dynamic.output,
x_title="Time",
y_title="State")
clusters = output_dynamic.allocate_clusters(tolerance, threshold_steps)
for index in range(0, len(clusters)):
print("Color #", index, ": ", clusters[index])
coloring_map = output_dynamic.allocate_map_coloring(
tolerance, threshold_steps)
draw_graph(graph, coloring_map)
def template_clustering(file, map_size, radius, sync_order = 0.999, show_dyn = False, show_layer1 = False, show_layer2 = False, show_clusters = True):
# Read sample
sample = read_sample(file);
# Create network
network = syncsom(sample, map_size[0], map_size[1], radius);
# Run processing
(ticks, (dyn_time, dyn_phase)) = timedcall(network.process, show_dyn, sync_order);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
# Show dynamic of the last layer.
if (show_dyn == True):
draw_dynamics(dyn_time, dyn_phase, x_title = "Time", y_title = "Phase", y_lim = [0, 3.14]);
if (show_clusters == True):
clusters = network.get_som_clusters();
visualizer = cluster_visualizer();
visualizer.append_clusters(clusters, network.som_layer.weights);
visualizer.show();
# Show network stuff.
if (show_layer1 == True):
network.show_som_layer();
if (show_layer2 == True):
network.show_sync_layer();
if (show_clusters == True):
clusters = network.get_clusters();
visualizer = cluster_visualizer();
visualizer.append_clusters(clusters, sample);
visualizer.show();
def template_segmentation_image(image_file, parameters, steps, time, ccore_flag = True):
image = read_image(image_file);
stimulus = rgb2gray(image);
for pixel_index in range(len(stimulus)):
if (stimulus[pixel_index] < 235): stimulus[pixel_index] = 1;
else: stimulus[pixel_index] = 0;
if (parameters is None):
parameters = legion_parameters();
net = legion_network(len(stimulus), parameters, conn_type.GRID_FOUR, ccore = ccore_flag);
output_dynamic = net.simulate(steps, time, stimulus);
ensembles = output_dynamic.allocate_sync_ensembles();
draw_image_mask_segments(image_file, ensembles);
# draw_dynamics(output_dynamic.time, output_dynamic.output, x_title = "Time", y_title = "x(t)", separate = ensembles);
# just for checking correctness of results - let's use classical algorithm
dbscan_instance = dbscan(image, 3, 4, True);
dbscan_instance.process();
trustable_clusters = dbscan_instance.get_clusters();
draw_dynamics(output_dynamic.time, output_dynamic.output, x_title = "Time", y_title = "x(t)", separate = trustable_clusters);
def show_output_dynamic(hysteresis_output_dynamic):
"""!
@brief Shows output dynamic (output of each oscillator) during simulation.
@param[in] hysteresis_output_dynamic (hysteresis_dynamic): Output dynamic of the hysteresis oscillatory network.
"""
draw_dynamics(hysteresis_output_dynamic.time, hysteresis_output_dynamic.output, x_title = "Time", y_title = "x(t)");
def show_output_dynamic(hysteresis_output_dynamic):
"""!
@brief Shows output dynamic (output of each oscillator) during simulation.
@param[in] hysteresis_output_dynamic (hysteresis_dynamic): Output dynamic of the hysteresis oscillatory network.
"""
draw_dynamics(hysteresis_output_dynamic.time, hysteresis_output_dynamic.output, x_title = "Time", y_title = "x(t)");
def show_output_dynamic(sync_output_dynamic):
"""!
@brief Shows output dynamic (output of each oscillator) during simulation.
@param[in] sync_output_dynamic (sync_dynamic): Output dynamic of the Sync network.
"""
draw_dynamics(sync_output_dynamic.time, sync_output_dynamic.output, x_title = "t", y_title = "phase", y_lim = [0, 2 * 3.14]);
def show_output_dynamic(pcnn_output_dynamic, separate_representation = False):
"""!
@brief Shows output dynamic (output of each oscillator) during simulation.
@param[in] pcnn_output_dynamic (pcnn_dynamic): Output dynamic of the pulse-coupled neural network.
@param[in] separate_representation (list): Consists of lists of oscillators where each such list consists of oscillator indexes that will be shown on separated stage.
"""
draw_dynamics(pcnn_output_dynamic.time, pcnn_output_dynamic.output, x_title = "t", y_title = "y(t)", separate = separate_representation);
def show_output_dynamic(pcnn_output_dynamic, separate_representation = False):
"""!
@brief Shows output dynamic (output of each oscillator) during simulation.
@param[in] pcnn_output_dynamic (pcnn_dynamic): Output dynamic of the pulse-coupled neural network.
@param[in] separate_representation (list): Consists of lists of oscillators where each such list consists of oscillator indexes that will be shown on separated stage.
"""
draw_dynamics(pcnn_output_dynamic.time, pcnn_output_dynamic.output, x_title = "t", y_title = "y(t)", separate = separate_representation)
def template_dynamic(num_osc, own_weight = -3, neigh_weight = -1, initial_states = None, initial_outputs = None, steps = 1000, time = 10):
network = hysteresis_network(num_osc, own_weight, neigh_weight);
if (initial_states is not None):
network.states = initial_states;
if (initial_outputs is not None):
network.outputs = initial_outputs;
(t, x) = network.simulate(steps, time);
draw_dynamics(t, x, x_title = "Time", y_title = "x(t)");
def template_dynamic_legion(num_osc, steps, time, conn_type, stimulus, params = None, separate_repr = True, ccore_flag = True):
net = legion_network(num_osc, params, conn_type, ccore = ccore_flag);
print("Created");
dynamic = net.simulate(steps, time, stimulus, solution = solve_type.RK4);
print("Simulated");
draw_dynamics(dynamic.time, dynamic.output, x_title = "Time", y_title = "x(t)", separate = separate_repr);
draw_dynamics(dynamic.time, dynamic.inhibitor, x_title = "Time", y_title = "z(t)");
ensembles = dynamic.allocate_sync_ensembles(0.1);
print(ensembles);
def template_segmentation_image(source, map_som_size = [5, 5], average_neighbors = 5, sync_order = 0.998, show_dyn = False, show_som_map = False):
data = read_image(source);
network = syncsom(data, map_som_size[0], map_som_size[1]);
(ticks, (dyn_time, dyn_phase)) = timedcall(network.process, average_neighbors, show_dyn, sync_order);
print("Sample: ", source, "\t\tExecution time: ", ticks, "\t\tWinners: ", network.som_layer.get_winner_number(), "\n");
if (show_dyn is True):
draw_dynamics(dyn_time, dyn_phase);
clusters = network.get_clusters();
draw_image_mask_segments(source, clusters);
def template_segmentation_image(source, map_som_size = [5, 5], radius = 128.0, sync_order = 0.998, show_dyn = False, show_som_map = False):
data = read_image(source);
network = syncsom(data, map_som_size[0], map_som_size[1], 1.0);
(ticks, (dyn_time, dyn_phase)) = timedcall(network.process, show_dyn, sync_order);
print("Sample: ", source, "\t\tExecution time: ", ticks, "\t\tWinners: ", network.som_layer.get_winner_number(), "\n");
if (show_dyn is True):
draw_dynamics(dyn_time, dyn_phase);
clusters = network.get_clusters();
draw_image_mask_segments(source, clusters);
def show_output_dynamic(cnn_output_dynamic):
"""!
@brief Shows output dynamic (output of each neuron) during simulation.
@param[in] cnn_output_dynamic (cnn_dynamic): Output dynamic of the chaotic neural network.
@see show_dynamic_matrix
@see show_observation_matrix
"""
draw_dynamics(cnn_output_dynamic.time, cnn_output_dynamic.output, x_title = "t", y_title = "x");
def template_graph_coloring(filename, alpha, eps, steps, time, title = None, tolerance = 0.1):
if (title is None): title = filename;
graph = read_graph(filename);
network = hysteresisgcolor(graph.data, alpha, eps);
(t, dyn) = network.simulate(steps, time);
draw_dynamics(t, dyn, x_title = "Time", y_title = "State");
clusters = network.get_clusters(tolerance);
for index in range(0, len(clusters)):
print("Color #", index, ": ", clusters[index]);
coloring_map = network.get_map_coloring(tolerance);
draw_graph(graph, coloring_map);
def template_graph_coloring(filename, alpha, eps, steps, time, title = None, tolerance = 0.1, threshold_steps = 10):
if (title is None): title = filename;
graph = read_graph(filename);
network = hysteresisgcolor(graph.data, alpha, eps);
output_dynamic = network.simulate(steps, time);
draw_dynamics(output_dynamic.time, output_dynamic.output, x_title = "Time", y_title = "State");
clusters = output_dynamic.allocate_clusters(tolerance, threshold_steps);
for index in range(0, len(clusters)):
print("Color #", index, ": ", clusters[index]);
coloring_map = output_dynamic.allocate_map_coloring(tolerance, threshold_steps);
draw_graph(graph, coloring_map);
def template_dynamic_hhn(num_osc,
steps,
time,
stimulus=None,
params=None,
separate_representation=False):
net = hhn_network(num_osc, stimulus, params)
(t, dyn) = net.simulate(steps, time)
draw_dynamics(t,
dyn,
x_title="Time",
y_title="V",
separate=separate_representation)
def template_dynamic(num_osc,
own_weight=-3,
neigh_weight=-1,
initial_states=None,
initial_outputs=None,
steps=1000,
time=10):
network = hysteresis_network(num_osc, own_weight, neigh_weight)
if (initial_states is not None):
network.states = initial_states
if (initial_outputs is not None):
network.outputs = initial_outputs
(t, x) = network.simulate(steps, time)
draw_dynamics(t, x, x_title="Time", y_title="x(t)")
def template_graph_coloring(filename,
alpha,
eps,
steps,
time,
title=None,
tolerance=0.1):
if (title is None): title = filename
graph = read_graph(filename)
network = hysteresisgcolor(graph.data, alpha, eps)
(t, dyn) = network.simulate(steps, time)
draw_dynamics(t, dyn, x_title="Time", y_title="State")
clusters = network.get_clusters(tolerance)
for index in range(0, len(clusters)):
print("Color #", index, ": ", clusters[index])
coloring_map = network.get_map_coloring(tolerance)
draw_graph(graph, coloring_map)
def show_output_dynamic(sync_output_dynamic):
"""!
@brief Shows output dynamic (output of each oscillator) during simulation.
@param[in] sync_output_dynamic (sync_dynamic): Output dynamic of the Sync network.
@see show_output_dynamics
"""
figure, _ = draw_dynamics(sync_output_dynamic.time, sync_output_dynamic.output, x_title="t", y_title="phase", y_lim=[0, 2 * pi])
plt.close(figure)
def template_dynamic_hhn(num_osc, steps, time, stimulus=None, params=None, separate_representation=False):
net = hhn_network(num_osc, stimulus, params)
(t, dyn) = net.simulate(steps, time)
draw_dynamics(t, dyn, x_title="Time", y_title="V", separate=separate_representation)