"""
Copyright 2013 Steven Diamond
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import annotations
import numbers
from typing import List, Tuple
import numpy as np
import cvxpy.lin_ops.lin_op as lo
import cvxpy.lin_ops.lin_utils as lu
from cvxpy.atoms.affine.affine_atom import AffAtom
from cvxpy.atoms.affine.hstack import hstack
from cvxpy.constraints.constraint import Constraint
from cvxpy.expressions.expression import Expression
from cvxpy.utilities.shape import size_from_shape
[docs]class reshape(AffAtom):
"""Reshapes the expression.
Vectorizes the expression then unvectorizes it into the new shape.
The entries are reshaped and stored in column-major order, also known
as Fortran order.
Parameters
----------
expr : Expression
The expression to promote.
shape : tuple or int
The shape to promote to.
order : F(ortran) or C
"""
def __init__(self, expr, shape: int | Tuple[int, ...], order: str = 'F') -> None:
if isinstance(shape, numbers.Integral):
shape = (int(shape),)
if len(shape) > 2:
raise ValueError("Expressions of dimension greater than 2 "
"are not supported.")
if any(d == -1 for d in shape):
shape = self._infer_shape(shape, expr.size)
self._shape = tuple(shape)
assert order in ['F', 'C']
self.order = order
super(reshape, self).__init__(expr)
@staticmethod
def _infer_shape(shape: Tuple[int, ...], size: int) -> Tuple[int, ...]:
assert shape.count(-1) == 1, "Only one dimension can be -1."
if len(shape) == 1:
shape = (size,)
else:
unspecified_index = shape.index(-1)
specified = shape[1 - unspecified_index]
assert specified >= 0, "Specified dimension must be nonnegative."
unspecified, remainder = divmod(size, shape[1 - unspecified_index])
if remainder != 0:
raise ValueError(
f"Cannot reshape expression of size {size} into shape {shape}."
)
shape = tuple(unspecified if d == -1 else specified for d in shape)
return shape
def is_atom_log_log_convex(self) -> bool:
"""Is the atom log-log convex?
"""
return True
def is_atom_log_log_concave(self) -> bool:
"""Is the atom log-log concave?
"""
return True
@AffAtom.numpy_numeric
def numeric(self, values):
"""Reshape the value.
"""
return np.reshape(values[0], self.shape, self.order)
def validate_arguments(self) -> None:
"""Checks that the new shape has the same number of entries as the old.
"""
old_len = self.args[0].size
new_len = size_from_shape(self._shape)
if not old_len == new_len:
raise ValueError(
"Invalid reshape dimensions %s." % (self._shape,)
)
def shape_from_args(self) -> Tuple[int, ...]:
"""Returns the shape from the rows, cols arguments.
"""
return self._shape
def get_data(self):
"""Returns info needed to reconstruct the expression besides the args.
"""
return [self._shape, self.order]
def graph_implementation(
self, arg_objs, shape: Tuple[int, ...], data=None
) -> Tuple[lo.LinOp, List[Constraint]]:
"""Reshape
Parameters
----------
arg_objs : list
LinExpr for each argument.
shape : tuple
The shape of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
arg = arg_objs[0]
if data[1] == 'F':
return (lu.reshape(arg, shape), [])
else: # 'C':
arg = lu.transpose(arg)
if len(shape) <= 1:
return (lu.reshape(arg, shape), [])
else:
result = lu.reshape(arg, (shape[1], shape[0]))
return (lu.transpose(result), [])
def deep_flatten(x):
# base cases
if isinstance(x, Expression):
if len(x.shape) == 1:
return x
else:
return x.flatten()
elif isinstance(x, np.ndarray) or isinstance(x, (int, float)):
x = Expression.cast_to_const(x)
return x.flatten()
# recursion
if isinstance(x, list):
y = []
for x0 in x:
x1 = deep_flatten(x0)
y.append(x1)
y = hstack(y)
return y
msg = 'The input to deep_flatten must be an Expression, a NumPy array, an int'\
+ ' or float, or a nested list thereof. Received input of type %s' % type(x)
raise ValueError(msg)