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"""!
@brief Examples of usage and demonstration of abilities of Pulse Coupled Neural Network.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
from pyclustering.utils import draw_dynamics;
from pyclustering.utils import read_image, rgb2gray, draw_image_mask_segments;
from pyclustering.nnet.pcnn import pcnn_network, pcnn_parameters;
from pyclustering.nnet import *;
from pyclustering.samples.definitions import IMAGE_SIMPLE_SAMPLES;
from pyclustering.nnet.pcnn import pcnn_visualizer
def template_dynamic_pcnn(num_osc, steps, stimulus = None, params = None, conn_type = conn_type.NONE, separate_representation = True, ccore_flag = True):
net = pcnn_network(num_osc, params, conn_type, ccore = ccore_flag);
dynamic = net.simulate(steps, stimulus);
ensembles = dynamic.allocate_sync_ensembles();
print("Number of objects:", len(ensembles), "\nEnsembles:", ensembles);
pcnn_visualizer.show_output_dynamic(dynamic);
return ensembles;
def one_neuron_unstimulated():
template_dynamic_pcnn(1, 100, [0]);
def one_neuron_stimulated():
template_dynamic_pcnn(1, 100, [1]);
def nine_neurons_stimulated_one_sync():
"Just dynamic demonstration"
params = pcnn_parameters();
template_dynamic_pcnn(9, 100, [1.0] * 9, params, conn_type.GRID_FOUR);
def nine_neurons_mix_stimulated():
"Just dynamic demonstration"
template_dynamic_pcnn(9, 100, [1, 1, 1,
0, 0, 0,
1, 1, 1], None, conn_type.GRID_FOUR);
def twenty_five_neurons_mix_stimulated():
"Object allocation"
"If M = 0 then only object will be allocated"
params = pcnn_parameters();
params.AF = 0.1;
params.AL = 0.0;
params.AT = 0.7;
params.VF = 1.0;
params.VL = 1.0;
params.VT = 10.0;
params.M = 0.0;
template_dynamic_pcnn(25, 100, [0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 1, 1, 0, 0,
0, 1, 1, 0, 0,
0, 0, 0, 0, 0], params, conn_type.GRID_FOUR, False);
def hundred_neurons_mix_stimulated():
"Allocate several clusters: the first contains borders (indexes of oscillators) and the second objects (indexes of oscillators)"
params = pcnn_parameters();
params.AF = 0.1;
params.AL = 0.1;
params.AT = 0.8;
params.VF = 1.0;
params.VL = 1.0;
params.VT = 20.0;
params.W = 1.0;
params.M = 1.0;
template_dynamic_pcnn(100, 50, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0], params, conn_type.GRID_EIGHT, False);
def segmentation_double_t():
image = read_image(IMAGE_SIMPLE_SAMPLES.IMAGE_SIMPLE10);
image = rgb2gray(image);
for pixel_index in range(len(image)):
if (image[pixel_index] < 128):
image[pixel_index] = 1;
else:
image[pixel_index] = 0;
params = pcnn_parameters();
params.AF = 0.1;
params.AL = 0.1;
params.AT = 0.8;
params.VF = 1.0;
params.VL = 1.0;
params.VT = 20.0;
params.W = 1.0;
params.M = 1.0;
ensembles = template_dynamic_pcnn(32 * 32, 28, image, params, conn_type.GRID_EIGHT, False);
draw_image_mask_segments(IMAGE_SIMPLE_SAMPLES.IMAGE_SIMPLE10, ensembles);
one_neuron_unstimulated();
one_neuron_stimulated();
nine_neurons_stimulated_one_sync();
nine_neurons_mix_stimulated();
twenty_five_neurons_mix_stimulated();
hundred_neurons_mix_stimulated();
segmentation_double_t();
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