Source code for cvxpy.atoms.affine.sum
"""
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.
"""
import builtins
from functools import wraps
from typing import List, Optional, Tuple
import numpy as np
import cvxpy.interface as intf
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.axis_atom import AxisAtom
from cvxpy.constraints.constraint import Constraint
class Sum(AxisAtom, AffAtom):
"""Sum the entries of an expression.
Parameters
----------
expr : Expression
The expression to sum the entries of.
axis : int
The axis along which to sum.
keepdims : bool
Whether to drop dimensions after summing.
"""
def __init__(self, expr, axis: Optional[int] = None, keepdims: bool = False) -> None:
super(Sum, self).__init__(expr, axis=axis, keepdims=keepdims)
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 False
def numeric(self, values):
"""Sums the entries of value.
"""
if intf.is_sparse(values[0]):
result = np.sum(values[0], axis=self.axis)
if not self.keepdims and self.axis is not None:
result = result.A.flatten()
else:
result = np.sum(values[0], axis=self.axis, keepdims=self.keepdims)
return result
def graph_implementation(
self, arg_objs, shape: Tuple[int, ...], data=None
) -> Tuple[lo.LinOp, List[Constraint]]:
"""Sum the linear expression's entries.
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)
"""
axis = data[0]
keepdims = data[1]
if axis is None:
obj = lu.sum_entries(arg_objs[0], shape=shape)
elif axis == 1:
if keepdims:
const_shape = (arg_objs[0].shape[1], 1)
else:
const_shape = (arg_objs[0].shape[1],)
ones = lu.create_const(np.ones(const_shape), const_shape)
obj = lu.rmul_expr(arg_objs[0], ones, shape)
else: # axis == 0
if keepdims:
const_shape = (1, arg_objs[0].shape[0])
else:
const_shape = (arg_objs[0].shape[0],)
ones = lu.create_const(np.ones(const_shape), const_shape)
obj = lu.mul_expr(ones, arg_objs[0], shape)
return (obj, [])
[docs]@wraps(Sum)
def sum(expr, axis: Optional[int] = None, keepdims: bool = False):
"""Wrapper for Sum class.
"""
if isinstance(expr, list):
return builtins.sum(expr)
else:
return Sum(expr, axis, keepdims)