def template_self_organization(file,
rows,
cols,
time,
structure,
init_type=None,
init_radius=None,
init_rate=None,
umatrix=False,
pmatrix=False,
awards=False):
parameters = som_parameters()
if (init_type is not None):
parameters.init_type = init_type
if (init_radius is not None):
parameters.init_radius = init_radius
if (init_rate is not None):
parameters.init_learn_rate = init_rate
sample = read_sample(file)
network = som(rows, cols, structure, parameters, True)
network.train(sample, time)
network.show_network(False, dataset=False)
if (umatrix is True):
network.show_distance_matrix()
if (pmatrix is True):
network.show_density_matrix()
if (awards is True):
network.show_winner_matrix()
def templateTestAwardNeurons(file, rows, cols, time, expected_result, autostop, ccore_flag, parameters = None, **kwargs):
store_load = kwargs.get('store_load', False)
types = [type_conn.func_neighbor, type_conn.grid_eight, type_conn.grid_four, type_conn.honeycomb]
sample = read_sample(file)
if (parameters is None):
parameters = som_parameters()
for stucture in types:
network = som(rows, cols, stucture, parameters, ccore = ccore_flag)
if store_load:
dump_network = pickle.dumps(network)
network = pickle.loads(dump_network)
network.train(sample, time, autostop)
winners = network.get_winner_number()
assert winners == len(expected_result)
if sorted(network.awards) != expected_result:
network.show_network(awards = True)
assert sorted(network.awards) == expected_result
total_capture_points = 0
for points in network.capture_objects:
total_capture_points += len(points)
assert total_capture_points == sum(expected_result)
del network
def templateTestAwardNeurons(file, rows, cols, time, expected_result, autostop, ccore_flag, parameters = None, **kwargs):
store_load = kwargs.get('store_load', False)
types = [type_conn.func_neighbor, type_conn.grid_eight, type_conn.grid_four, type_conn.honeycomb]
sample = read_sample(file)
if (parameters is None):
parameters = som_parameters()
for stucture in types:
network = som(rows, cols, stucture, parameters, ccore = ccore_flag)
if store_load:
dump_network = pickle.dumps(network)
network = pickle.loads(dump_network)
network.train(sample, time, autostop)
winners = network.get_winner_number()
assert winners == len(expected_result)
if sorted(network.awards) != expected_result:
network.show_network(awards = True)
assert sorted(network.awards) == expected_result
total_capture_points = 0
for points in network.capture_objects:
total_capture_points += len(points)
assert total_capture_points == sum(expected_result)
del network
def templateTestAwardNeurons(self,
file,
rows,
cols,
time,
expected_result,
autostop=False,
ccore_flag=False,
parameters=None):
types = [
type_conn.func_neighbor, type_conn.grid_eight, type_conn.grid_four,
type_conn.honeycomb
]
sample = read_sample(file)
if (parameters is None):
parameters = som_parameters()
for stucture in types:
network = som(rows, cols, stucture, parameters, ccore=ccore_flag)
network.train(sample, time, autostop)
if (sorted(network.awards) != expected_result):
network.show_network(awards=True)
print(sorted(network.awards))
assert sorted(network.awards) == expected_result
total_capture_points = 0
for points in network.capture_objects:
total_capture_points += len(points)
assert total_capture_points == sum(expected_result)
del network
def testSomVisualizationByCore(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4)
parameters = som_parameters()
network = som(5, 5, type_conn.grid_eight, parameters, ccore=True)
network.train(sample, 100, True)
network.show_network()
network.show_winner_matrix()
network.show_distance_matrix()
network.show_density_matrix()
def testSomVisualizationByCore(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4)
parameters = som_parameters()
network = som(5, 5, type_conn.grid_eight, parameters, ccore = True)
network.train(sample, 100, True)
network.show_network()
network.show_winner_matrix()
network.show_distance_matrix()
network.show_density_matrix()
def random_state(rows, cols, connections, random_state, ccore_flag):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)
params = som_parameters()
params.random_state = random_state
network_1 = som(rows, cols, connections, ccore=ccore_flag)
steps_1 = network_1.train(sample, 100, True)
network_2 = som(rows, cols, connections, ccore=ccore_flag)
steps_2 = network_2.train(sample, 100, True)
assert steps_1 == steps_2
assert network_1.weights == network_2.weights
assert network_1.capture_objects == network_2.capture_objects
assert network_1.awards == network_2.awards
def testDoubleTrain(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
parameters = som_parameters();
network = som(2, 2, type_conn.grid_eight, parameters, ccore = False);
network.train(sample, 100, False);
network.train(sample, 100, False);
assert sum(network.awards) == len(sample);
total_capture_points = 0;
for points in network.capture_objects:
total_capture_points += len(points);
assert total_capture_points == len(sample);
def testDoubleTrain(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
parameters = som_parameters();
network = som(2, 2, type_conn.grid_eight, parameters, ccore = False);
network.train(sample, 100, False);
network.train(sample, 100, False);
assert sum(network.awards) == len(sample);
total_capture_points = 0;
for points in network.capture_objects:
total_capture_points += len(points);
assert total_capture_points == len(sample);
def process(self):
"""!
@brief Performs cluster analysis by competition between neurons in self-organized map.
@return (somsc) Returns itself (SOM Simple Clustering instance).
@see get_clusters()
"""
params = som_parameters()
params.random_state = self.__random_state
self.__network = som(1, self.__amount_clusters, type_conn.grid_four,
params, self.__ccore)
self.__network.train(self.__data_pointer, self.__epouch, True)
return self
def template_self_organization(file, rows, cols, time, structure, init_type = None, init_radius = None, init_rate = None, umatrix = False, pmatrix = False, awards = False):
parameters = som_parameters();
if (init_type is not None):
parameters.init_type = init_type;
if (init_radius is not None):
parameters.init_radius = init_radius;
if (init_rate is not None):
parameters.init_learn_rate = init_rate;
sample = read_sample(file);
network = som(rows, cols, structure, parameters, True);
network.train(sample, time);
network.show_network(False, dataset = False);
if (umatrix is True):
network.show_distance_matrix();
if (pmatrix is True):
network.show_density_matrix();
if (awards is True):
network.show_winner_matrix();
def templateTestAwardNeurons(self, file, rows, cols, time, expected_result, autostop = False, ccore_flag = False, parameters = None):
types = [type_conn.func_neighbor, type_conn.grid_eight, type_conn.grid_four, type_conn.honeycomb];
sample = read_sample(file);
if (parameters is None):
parameters = som_parameters();
for stucture in types:
network = som(rows, cols, stucture, parameters, ccore = ccore_flag);
network.train(sample, time, autostop);
if (sorted(network.awards) != expected_result):
network.show_network(awards = True);
print(sorted(network.awards));
assert sorted(network.awards) == expected_result;
total_capture_points = 0;
for points in network.capture_objects:
total_capture_points += len(points);
assert total_capture_points == sum(expected_result);
del network;
x = round(x)
y = round(y)
## 3. 좌표의 BGR 값 받아오기
bgr_val.append([img_org.item(y, x, 0),
img_org.item(y, x, 1),
img_org.item(y, x, 2)])
print('selected_pixel_bgr :\n', bgr_val)
## 5. SOM
sample = bgr_val
# create SOM parameters
parameters = som_parameters() # initialization of initial neuron weights, radius, rateOfLearning, autostop
# create self-organized feature map with size 7x7
rows = 6 # five rows
cols = 6 # five columns
structure = type_conn.honeycomb # each neuron has max. four neighbors.
network = som(rows, cols, structure, parameters)
# train network on sample during 100 epochs.
network.train(sample, 50)
# simulate trained network using randomly modified point from input dataset.
index_point = random.randint(0, len(sample) - 1)
point = sample[index_point] # obtain randomly point from data
point[0] += random.random() * 0.2 # change randomly X-coordinate
def testOneDimensionSampleSimple7Cluster(self):
parameters = som_parameters();
parameters.init_type = type_init.random_surface;
self.templateTestAwardNeurons(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, 2, 1, 100, [10, 10], True, False, parameters);
def testOneDimensionSampleSimple7ClusterByCore(self):
parameters = som_parameters()
parameters.init_type = type_init.random_surface
SomTestTemplates.templateTestAwardNeurons(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, 2, 1, 100, [10, 10], True, True, parameters)
def testOneDimensionSampleSimple7Cluster(self):
parameters = som_parameters();
parameters.init_type = type_init.random_surface;
SomTestTemplates.templateTestAwardNeurons(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, 2, 1, 100, [10, 10], True, False, parameters);
def testOneDimensionSampleSimple7ClusterByCore(self):
parameters = som_parameters()
parameters.init_type = type_init.random_surface
self.templateTestAwardNeurons(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, 2, 1, 100,
[10, 10], True, True, parameters)
from pyclustering.nnet.som import som, type_conn, type_init, som_parameters
from pyclustering.samples.definitions import FCPS_SAMPLES
import csv
# read sample 'Lsun' from file
# sample = read_sample(FCPS_SAMPLES.SAMPLE_LSUN)
# with open('C:/flashwoman/Object-detection/testfiles_sey/books_coord.csv', 'r') as f:
# reader = csv.reader(f)
# sample = list(reader)
#
# print(len(sample))
# required data_shape : [[2.0, 3.0], [0.387577, 0.268546], [0.17678, 0.582963], [3.277701, 0.814082], ...]
# create SOM parameters
parameters = som_parameters()
# create self-organized feature map with size 7x7
rows = 10 # five rows
cols = 10 # five columns
structure = type_conn.grid_four # .grid_four : each neuron has max. four neighbors. / .grid_eight
network = som(rows, cols, structure, parameters)
# train network on 'coord' sample during 100 epochs.
network.train(sample, 100)
# simulate trained network using randomly modified point from input dataset.
index_point = random.randint(0, len(sample) - 1)
point = sample[index_point] # obtain randomly point from data
point[0] += random.random() * 0.2 # change randomly X-coordinate
point[1] += random.random() * 0.2 # change randomly Y-coordinate
