def generate(n_centers, low, high, dimensions=None):
r"""
Factory method to build uniformly spaced gaussian radial basis functions
with a 25\% overlap.
Args:
n_centers (list): list of the number of radial basis functions to be
used for each dimension.
low (np.ndarray): lowest value for each dimension;
high (np.ndarray): highest value for each dimension;
dimensions (list, None): list of the dimensions of the input to be
considered by the feature. The number of dimensions must match
the number of elements in ``n_centers`` and ``low``.
Returns:
The list of the generated radial basis functions.
"""
n_features = len(low)
assert len(n_centers) == n_features
assert len(low) == len(high)
assert dimensions is None or n_features == len(dimensions)
grid, b = uniform_grid(n_centers, low, high)
basis = list()
for i in range(len(grid)):
v = grid[i, :]
bf = GaussianRBF(v, b, dimensions)
basis.append(bf)
return basis
def generate(n_centers, ranges):
"""
Factory method that generates the list of dictionaries to build the
tensors representing a set of uniformly spaced Gaussian radial basis
functions with a 25\% overlap.
Args:
n_centers (list): list of the number of radial basis functions to be
used for each dimension.
ranges (list): list of two-elements lists specifying the range of
each state variable.
Returns:
The list of dictionaries as described above.
"""
n_features = len(ranges)
assert len(n_centers) == n_features
assert len(ranges[0]) == 2
grid, scale = uniform_grid(n_centers, ranges)
tensor_list = list()
for i in range(len(grid)):
mu = grid[i, :]
tensor_list.append(PyTorchGaussianRBF(mu, scale))
return tensor_list
def generate(n_centers, low, high, dimensions=None):
"""
Factory method that generates the list of dictionaries to build the
tensors representing a set of uniformly spaced Gaussian radial basis
functions with a 25\% overlap.
Args:
n_centers (list): list of the number of radial basis functions to be
used for each dimension;
low (np.ndarray): lowest value for each dimension;
high (np.ndarray): highest value for each dimension;
dimensions (list, None): list of the dimensions of the input to be
considered by the feature. The number of dimensions must match
the number of elements in ``n_centers`` and ``low``.
Returns:
The list of dictionaries as described above.
"""
n_features = len(low)
assert len(n_centers) == n_features
assert len(low) == len(high)
assert dimensions is None or n_features == len(dimensions)
grid, scale = uniform_grid(n_centers, low, high)
tensor_list = list()
for i in range(len(grid)):
mu = grid[i, :]
tensor_list.append(PyTorchGaussianRBF(mu, scale, dimensions))
return tensor_list
def generate(n_centers, ranges, dimensions=None):
"""
Factory method to build uniformly spaced gaussian radial basis functions
with a 25\% overlap.
Args:
n_centers (list): list of the number of radial basis functions to be
used for each dimension.
ranges (list): list of two-elements lists specifying the range of
each state variable;
dimensions (list, None): list of the dimensions of the input to be
considered by the feature. The number of dimensions must match
the number of elements in ``n_centers`` and ``ranges``.
Returns:
The list of the generated radial basis functions.
"""
n_features = len(ranges)
assert len(n_centers) == n_features
assert len(ranges[0]) == 2
assert dimensions is None or n_features == len(dimensions)
grid, b = uniform_grid(n_centers, ranges)
basis = list()
for i in range(len(grid)):
v = grid[i, :]
bf = GaussianRBF(v, b, dimensions)
basis.append(bf)
return basis
def generate(n_centers, ranges):
"""
Factory method that generates the list of dictionaries to build the tensors
representing a set of uniformly spaced Gaussian radial basis functions with
a 25\% overlap.
Args:
n_centers (list): list of the number of radial basis functions to be
used for each dimension.
ranges (list): list of two-elements lists specifying the range of
each state variable.
Returns:
The list of dictionaries as described above.
"""
n_features = len(ranges)
assert len(n_centers) == n_features
assert len(ranges[0]) == 2
grid, b = uniform_grid(n_centers, ranges)
tensor_list = list()
for i in xrange(len(grid)):
v = grid[i, :]
bf = {'type': tensors.gaussian_tensor, 'params': [v, b]}
tensor_list.append(bf)
return tensor_list
