def _create_initial_weights(self, init_type):
"""!
@brief Creates initial weights for neurons in line with the specified initialization.
@param[in] init_type (type_init): Type of initialization of initial neuron weights (random, random in center of the input data, random distributed in data, ditributed in line with uniform grid).
"""
dim_info = dimension_info(self._data)
step_x = dim_info.get_center()[0]
if self._rows > 1:
step_x = dim_info.get_width()[0] / (self._rows - 1)
step_y = 0.0
if dim_info.get_dimensions() > 1:
step_y = dim_info.get_center()[1]
if self._cols > 1:
step_y = dim_info.get_width()[1] / (self._cols - 1)
# generate weights (topological coordinates)
random.seed(self._params.random_state)
# Uniform grid.
if init_type == type_init.uniform_grid:
# Predefined weights in line with input data.
self._weights = [[[] for i in range(dim_info.get_dimensions())] for j in range(self._size)]
for i in range(self._size):
location = self._location[i]
for dim in range(dim_info.get_dimensions()):
if dim == 0:
if self._rows > 1:
self._weights[i][dim] = dim_info.get_minimum_coordinate()[dim] + step_x * location[dim]
else:
self._weights[i][dim] = dim_info.get_center()[dim]
elif dim == 1:
if self._cols > 1:
self._weights[i][dim] = dim_info.get_minimum_coordinate()[dim] + step_y * location[dim]
else:
self._weights[i][dim] = dim_info.get_center()[dim]
else:
self._weights[i][dim] = dim_info.get_center()[dim]
elif init_type == type_init.random_surface:
# Random weights at the full surface.
self._weights = [
[random.uniform(dim_info.get_minimum_coordinate()[i], dim_info.get_maximum_coordinate()[i]) for i in
range(dim_info.get_dimensions())] for _ in range(self._size)]
elif init_type == type_init.random_centroid:
# Random weights at the center of input data.
self._weights = [[(random.random() + dim_info.get_center()[i]) for i in range(dim_info.get_dimensions())]
for _ in range(self._size)]
else:
# Random weights of input data.
self._weights = [[random.random() for i in range(dim_info.get_dimensions())] for _ in range(self._size)]
def _create_initial_weights(self, init_type):
"""!
@brief Creates initial weights for neurons in line with the specified initialization.
@param[in] init_type (type_init): Type of initialization of initial neuron weights (random, random in center of the input data, random distributed in data, ditributed in line with uniform grid).
"""
dim_info = dimension_info(self._data)
step_x = dim_info.get_center()[0]
if self._rows > 1: step_x = dim_info.get_width()[0] / (self._rows - 1);
step_y = 0.0
if dim_info.get_dimensions() > 1:
step_y = dim_info.get_center()[1]
if self._cols > 1: step_y = dim_info.get_width()[1] / (self._cols - 1);
# generate weights (topological coordinates)
random.seed()
# Uniform grid.
if init_type == type_init.uniform_grid:
# Predefined weights in line with input data.
self._weights = [ [ [] for i in range(dim_info.get_dimensions()) ] for j in range(self._size)]
for i in range(self._size):
location = self._location[i]
for dim in range(dim_info.get_dimensions()):
if dim == 0:
if self._rows > 1:
self._weights[i][dim] = dim_info.get_minimum_coordinate()[dim] + step_x * location[dim]
else:
self._weights[i][dim] = dim_info.get_center()[dim]
elif dim == 1:
if self._cols > 1:
self._weights[i][dim] = dim_info.get_minimum_coordinate()[dim] + step_y * location[dim]
else:
self._weights[i][dim] = dim_info.get_center()[dim]
else:
self._weights[i][dim] = dim_info.get_center()[dim]
elif init_type == type_init.random_surface:
# Random weights at the full surface.
self._weights = [[random.uniform(dim_info.get_minimum_coordinate()[i], dim_info.get_maximum_coordinate()[i]) for i in range(dim_info.get_dimensions())] for _ in range(self._size)]
elif init_type == type_init.random_centroid:
# Random weights at the center of input data.
self._weights = [[(random.random() + dim_info.get_center()[i]) for i in range(dim_info.get_dimensions())] for _ in range(self._size)]
else:
# Random weights of input data.
self._weights = [[random.random() for i in range(dim_info.get_dimensions())] for _ in range(self._size)]
def testGetDimension(self):
info = dimension_info([[1], [2], [3], [4], [5]])
assert 1 == info.get_dimensions()
info = dimension_info([[1, 2], [3, 4]])
assert 2 == info.get_dimensions()
def testGetDimension(self):
info = dimension_info([ [1], [2], [3], [4], [5] ]);
assert 1 == info.get_dimensions();
info = dimension_info([[1, 2], [3, 4]]);
assert 2 == info.get_dimensions();
