def __upload_dump_to_ccore(self, state_dump):
self.__upload_common_part(state_dump)
self.__ccore_som_pointer = wrapper.som_create(self._rows, self._cols,
self._conn_type,
self._params)
wrapper.som_load(self.__ccore_som_pointer, state_dump['weights'],
state_dump['award'], state_dump['capture_objects'])
def __init__(self, rows, cols, conn_type=type_conn.grid_eight, parameters=None, ccore=True):
"""!
@brief Constructor of self-organized map.
@param[in] rows (uint): Number of neurons in the column (number of rows).
@param[in] cols (uint): Number of neurons in the row (number of columns).
@param[in] conn_type (type_conn): Type of connection between oscillators in the network (grid four, grid eight, honeycomb, function neighbour).
@param[in] parameters (som_parameters): Other specific parameters.
@param[in] ccore (bool): If True simulation is performed by CCORE library (C++ implementation of pyclustering).
"""
# some of these parameters are required despite core implementation, for example, for network visualization.
self._cols = cols
self._rows = rows
self._size = cols * rows
self._conn_type = conn_type
self._data = None
self._neighbors = None
self._local_radius = 0.0
self._learn_rate = 0.0
self.__ccore_som_pointer = None
self._params = parameters or som_parameters()
if self._params.init_radius is None:
self._params.init_radius = self.__initialize_initial_radius(rows, cols)
if (ccore is True) and ccore_library.workable():
self.__ccore_som_pointer = wrapper.som_create(rows, cols, conn_type, self._params)
else:
# location
self._location = self.__initialize_locations(rows, cols)
# default weights
self._weights = [[0.0]] * self._size
# awards
self._award = [0] * self._size
# captured objects
self._capture_objects = [[] for i in range(self._size)]
# distances - calculate and store them only during training
self._sqrt_distances = None
# connections
if conn_type != type_conn.func_neighbor:
self._create_connections(conn_type)
def __init__(self, rows, cols, conn_type = type_conn.grid_eight, parameters = None, ccore = False):
"""!
@brief Constructor of self-organized map.
@param[in] rows (uint): Number of neurons in the column (number of rows).
@param[in] cols (uint): Number of neurons in the row (number of columns).
@param[in] conn_type (type_conn): Type of connection between oscillators in the network (grid four, grid eight, honeycomb, function neighbour).
@param[in] parameters (som_parameters): Other specific parameters.
@param[in] ccore (bool): If True simulation is performed by CCORE library (C++ implementation of pyclustering).
"""
# some of these parameters are required despite core implementation, for example, for network demonstration.
self._cols = cols;
self._rows = rows;
self._size = cols * rows;
self._conn_type = conn_type;
if (parameters is not None):
self._params = parameters;
else:
self._params = som_parameters();
if (self._params.init_radius is None):
if ((cols + rows) / 4.0 > 1.0):
self._params.init_radius = 2.0;
elif ( (cols > 1) and (rows > 1) ):
self._params.init_radius = 1.5;
else:
self._params.init_radius = 1.0;
if (ccore is True):
self.__ccore_som_pointer = wrapper.som_create(rows, cols, conn_type, self._params);
else:
# location
self._location = list();
for i in range(self._rows):
for j in range(self._cols):
self._location.append([float(i), float(j)]);
# awards
self._award = [0] * self._size;
self._capture_objects = [ [] for i in range(self._size) ];
# distances
self._sqrt_distances = [ [ [] for i in range(self._size) ] for j in range(self._size) ];
for i in range(self._size):
for j in range(i, self._size, 1):
dist = euclidean_distance_sqrt(self._location[i], self._location[j]);
self._sqrt_distances[i][j] = dist;
self._sqrt_distances[j][i] = dist;
# connections
if (conn_type != type_conn.func_neighbor):
self._create_connections(conn_type);
def __init__(self,
rows,
cols,
conn_type=type_conn.grid_eight,
parameters=None,
ccore=False):
"""!
@brief Constructor of self-organized map.
@param[in] rows (uint): Number of neurons in the column (number of rows).
@param[in] cols (uint): Number of neurons in the row (number of columns).
@param[in] conn_type (type_conn): Type of connection between oscillators in the network (grid four, grid eight, honeycomb, function neighbour).
@param[in] parameters (som_parameters): Other specific parameters.
@param[in] ccore (bool): If True simulation is performed by CCORE library (C++ implementation of pyclustering).
"""
# some of these parameters are required despite core implementation, for example, for network demonstration.
self._cols = cols
self._rows = rows
self._size = cols * rows
self._conn_type = conn_type
if (parameters is not None):
self._params = parameters
else:
self._params = som_parameters()
if (self._params.init_radius is None):
if ((cols + rows) / 4.0 > 1.0):
self._params.init_radius = 2.0
elif ((cols > 1) and (rows > 1)):
self._params.init_radius = 1.5
else:
self._params.init_radius = 1.0
if (ccore is True):
self.__ccore_som_pointer = wrapper.som_create(
rows, cols, conn_type, self._params)
else:
# location
self._location = list()
for i in range(self._rows):
for j in range(self._cols):
self._location.append([float(i), float(j)])
# awards
self._award = [0] * self._size
self._capture_objects = [[] for i in range(self._size)]
# distances
self._sqrt_distances = [[[] for i in range(self._size)]
for j in range(self._size)]
for i in range(self._size):
for j in range(i, self._size, 1):
dist = euclidean_distance_sqrt(self._location[i],
self._location[j])
self._sqrt_distances[i][j] = dist
self._sqrt_distances[j][i] = dist
# connections
if (conn_type != type_conn.func_neighbor):
self._create_connections(conn_type)
def __init__(self, rows, cols, conn_type = type_conn.grid_eight, parameters = None, ccore = True):
"""!
@brief Constructor of self-organized map.
@param[in] rows (uint): Number of neurons in the column (number of rows).
@param[in] cols (uint): Number of neurons in the row (number of columns).
@param[in] conn_type (type_conn): Type of connection between oscillators in the network (grid four, grid eight, honeycomb, function neighbour).
@param[in] parameters (som_parameters): Other specific parameters.
@param[in] ccore (bool): If True simulation is performed by CCORE library (C++ implementation of pyclustering).
"""
# some of these parameters are required despite core implementation, for example, for network demonstration.
self._cols = cols
self._rows = rows
self._size = cols * rows
self._conn_type = conn_type
self._data = None
self._neighbors = None
self._local_radius = 0.0
self._learn_rate = 0.0
self.__ccore_som_pointer = None
if parameters is not None:
self._params = parameters
else:
self._params = som_parameters()
if self._params.init_radius is None:
self._params.init_radius = self.__initialize_initial_radius(rows, cols)
if (ccore is True) and ccore_library.workable():
self.__ccore_som_pointer = wrapper.som_create(rows, cols, conn_type, self._params)
else:
# location
self._location = self.__initialize_locations(rows, cols)
# default weights
self._weights = [ [0.0] ] * self._size
# awards
self._award = [0] * self._size
# captured objects
self._capture_objects = [ [] for i in range(self._size) ]
# distances - calculate and store them only during training
self._sqrt_distances = None
# connections
if conn_type != type_conn.func_neighbor:
self._create_connections(conn_type)
def __upload_dump_to_ccore(self, state_dump):
self.__upload_common_part(state_dump)
self.__ccore_som_pointer = wrapper.som_create(self._rows, self._cols, self._conn_type, self._params)
wrapper.som_load(self.__ccore_som_pointer, state_dump['weights'], state_dump['award'], state_dump['capture_objects'])
def __init__(self, rows, cols, data, epochs, conn_type = type_conn.grid_eight, parameters = None, ccore = False):
"""!
@brief Constructor of self-organized map.
@param[in] rows (uint): Number of neurons in the column (number of rows).
@param[in] cols (uint): Number of neurons in the row (number of columns).
@param[in] data (list): Input data - list of points where each point is represented by list of features, for example coordinates.
@param[in] epochs (uint): Number of epochs for training.
@param[in] conn_type (type_conn): Type of connection between oscillators in the network (grid four, grid eight, honeycomb, function neighbour).
@param[in] parameters (som_parameters): Other specific parameters.
@param[in] ccore (bool): If True simulation is performed by CCORE library (C++ implementation of pyclustering).
"""
# some of these parameters are required despite core implementation, for example, for network demonstration.
self._cols = cols;
self._rows = rows;
self._data = data;
self._size = cols * rows;
self._epochs = epochs;
self._conn_type = conn_type;
if (parameters is not None):
self._params = parameters;
else:
self._params = som_parameters();
if (self._params.init_radius is None):
if ((cols + rows) / 4.0 > 1.0):
self._params.init_radius = 2.0;
elif ( (cols > 1) and (rows > 1) ):
self._params.init_radius = 1.5;
else:
self._params.init_radius = 1.0;
if (ccore is True):
self.__ccore_som_pointer = wrapper.som_create(data, rows, cols, epochs, conn_type, self._params);
else:
# location
self._location = list();
for i in range(self._rows):
for j in range(self._cols):
self._location.append([float(i), float(j)]);
# awards
self._award = [0] * self._size;
self._capture_objects = [ [] for i in range(self._size) ];
# distances
self._sqrt_distances = [ [ [] for i in range(self._size) ] for j in range(self._size) ];
for i in range(self._size):
for j in range(i, self._size, 1):
dist = euclidean_distance_sqrt(self._location[i], self._location[j]);
self._sqrt_distances[i][j] = dist;
self._sqrt_distances[j][i] = dist;
# connections
if (conn_type != type_conn.func_neighbor):
self._create_connections(conn_type);
# weights
self._create_initial_weights(self._params.init_type);
