def frame0_to_1(self, in_): """ Allocating version of :meth`AbstractMapping.frame1_to_0`. If input array is a matrix, mapping operates on rows. If array is a vector, mapping operates on entries. Arguments: in_: Input array with :attr:`AbstractMapping.n_frame0` rows and ``k`` >= ``1`` columns. Returns: :class:`numpy.ndarray`: Array with :attr:`AbstractMapping.n_frame0` rows and same number of columns as input. Input entries are mapped one-to-one or many-to-one *into* output. """ if is_vector(in_): out_ = zeros(self.__n_frame0) elif sparse_or_dense(in_): dim1 = self.__n_frame0 dim2 = in_.shape[1] out_ = zeros((dim1, dim2)) return self.frame1_to_0_inplace(in_, out_)
def frame0_to_1(self, in_): """ Map elements of array ``in_`` to a new array. If input array is a matrix, mapping operates on rows. If array is a vector, mapping operates on entries. A new empty vector or matrix Arguments: in_: Input array with :attr:`AbstractMapping.n_frame0` rows and ``k`` >= ``1`` columns. Returns: :class:`numpy.ndarray`: Array with :attr:`AbstractMapping.n_frame1` rows and ``k`` columns. Input entries are mapped one-to-one or one-to-many *into* output. """ if is_vector(in_): out_ = zeros(self.__n_frame1) elif sparse_or_dense(in_): dim1 = self.__n_frame1 dim2 = in_.shape[1] out_ = zeros((dim1, dim2)) return self.frame0_to_1_inplace(in_, out_)
def frame1_to_0(self, in_): """ Allocating version of :meth`DiscreteMapping.frame1_to_0`. If input array is a matrix, mapping operates on rows. If array is a vector, mapping operates on entries. Arguments: in_: Input array with :attr:`DiscreteMapping.n_frame0` rows and ``k`` >= ``1`` columns. Returns: :class:`numpy.ndarray`: Array with :attr:`DiscreteMapping.n_frame0` rows and same number of columns as input. Input entries are mapped one-to-one or many-to-one *into* output. """ if is_vector(in_): out_ = np.zeros(self.__n_frame0) elif sparse_or_dense(in_): dim1 = self.__n_frame0 dim2 = in_.shape[1] out_ = np.zeros((dim1, dim2)) return self.frame1_to_0_inplace(in_, out_)
def frame0_to_1(self, in_): """ Map elements of array ``in_`` to a new array. If input array is a matrix, mapping operates on rows. If array is a vector, mapping operates on entries. A new empty vector or matrix Arguments: in_: Input array with :attr:`DiscreteMapping.n_frame0` rows and ``k`` >= ``1`` columns. Returns: :class:`numpy.ndarray`: Array with :attr:`DiscreteMapping.n_frame1` rows and ``k`` columns. Input entries are mapped one-to-one or one-to-many *into* output. """ if is_vector(in_): out_ = np.zeros(self.__n_frame1) elif sparse_or_dense(in_): dim1 = self.__n_frame1 dim2 = in_.shape[1] out_ = np.zeros((dim1, dim2)) return self.frame0_to_1_inplace(in_, out_)
def frame0_to_1_inplace(self, in_, out_, clear_output=False):
"""
Map elements of array ``in_`` to elements of array ``out_``.
Procedure::
# let forward_map be the map: SET_0 --> SET_1.
# for each INDEX_0, INDEX_1 in forward_map, do
# out_[INDEX_1] += in_[INDEX_0]
# end for
If arrays are matrices, mapping operates on rows. If arrays are
vectors, mapping operates on entries.
Arguments:
in_: Input array with :attr:`AbstractMapping.n_frame0` rows
and ``k`` >= ``1`` columns.
out_: Output array with :attr:`AbstractMapping.n_frame1`
rows and ``k`` >= 1 columns. Modified in-place.
clear_output (:obj:`bool`, optional): If ``True``, set
output array to ``0`` before adding input values.
Returns:
Vector `out_`, after in-place modification.
Raises:
TypeError: If input and output arrays are not (jointly)
vectors or matrices.
ValueError: If input and output array dimensions are not
compatible with each other or consistent with the
dimensions of the mapping.
"""
vector_processing = is_vector(in_) and is_vector(out_)
matrix_processing = sparse_or_dense(in_) and sparse_or_dense(out_)
if not vector_processing or matrix_processing:
raise TypeError('arguments "in_" and "out_" be numpy or '
'scipy vectors or matrices')
dim_in1 = in_.shape[0]
dim_out1 = out_.shape[0]
if vector_processing:
dim_in2 = 1
dim_out2 = 1
else:
dim_in2 = in_.shape[1]
dim_out2 = out_.shape[1]
if bool(
dim_in_1 != self.n_frame0 or
dim_out1 != self.n_frame1 or
dim_in2 != dim_out2):
raise ValueError('arguments "in_" and "out_" be vectors or '
'matrices of dimensions M x N, and K x N, '
'respectively, with:\nM = {}\nK={}\n'
'Provided:\n input: {}x{}\noutput: {}x{}'
''.format(self.n_frame0, self.n_frame1,
dim_in1, dim_in2, dim_out1, dim_out2))
if clear_output:
out_ *= 0
if vector_processing:
for idx_0, idx_1 in enumerate(self.vec):
out_[idx_1] += in_[idx_0]
if matrix_processing:
for idx_0, idx_1 in enumerate(self.vec):
out_[idx_1, :] += in_[idx_0, :]
return out_
def frame0_to_1_inplace(self, in_, out_, clear_output=False):
"""
Map elements of array ``in_`` to elements of array ``out_``.
Procedure::
# let forward_map be the map: SET_0 --> SET_1.
# for each INDEX_0, INDEX_1 in forward_map, do
# out_[INDEX_1] += in_[INDEX_0]
# end for
If arrays are matrices, mapping operates on rows. If arrays are
vectors, mapping operates on entries.
Arguments:
in_: Input array with :attr:`DiscreteMapping.n_frame0` rows
and ``k`` >= ``1`` columns.
out_: Output array with :attr:`DiscreteMapping.n_frame1`
rows and ``k`` >= 1 columns. Modified in-place.
clear_output (:obj:`bool`, optional): If ``True``, set
output array to ``0`` before adding input values.
Returns:
Vector `out_`, after in-place modification.
Raises:
TypeError: If input and output arrays are not (jointly)
vectors or matrices.
ValueError: If input and output array dimensions are not
compatible with each other or consistent with the
dimensions of the mapping.
"""
vector_processing = is_vector(in_) and is_vector(out_)
matrix_processing = sparse_or_dense(in_) and sparse_or_dense(out_)
if not (vector_processing or matrix_processing):
raise TypeError('arguments "in_" and "out_" be numpy or '
'scipy vectors or matrices')
dim_in1 = in_.shape[0]
dim_out1 = out_.shape[0]
if vector_processing:
dim_in2 = 1
dim_out2 = 1
else:
dim_in2 = in_.shape[1]
dim_out2 = out_.shape[1]
if bool(
dim_in1 != self.n_frame0 or
dim_out1 != self.n_frame1 or
dim_in2 != dim_out2):
raise ValueError('arguments "in_" and "out_" be vectors or '
'matrices of dimensions M x N, and K x N, '
'respectively, with:\nM = {}\nK={}\n'
'Provided:\n input: {}x{}\noutput: {}x{}'
''.format(self.n_frame0, self.n_frame1,
dim_in1, dim_in2, dim_out1, dim_out2))
if clear_output:
out_ *= 0
if vector_processing:
for idx_0, idx_1 in enumerate(self.vec):
out_[idx_1] += in_[idx_0]
else:
for idx_0, idx_1 in enumerate(self.vec):
out_[idx_1, :] += in_[idx_0, :]
# TODO: use efficient slicing when input is sparse. warn when output sparse??
return out_
