Source code for cvxpy.expressions.constants.parameter

"""
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 cvxpy import settings as s
from cvxpy.expressions.leaf import Leaf
import cvxpy.lin_ops.lin_utils as lu


[docs]class Parameter(Leaf): """Parameters in optimization problems. Parameters are constant expressions whose value may be specified after problem creation. The only way to modify a problem after its creation is through parameters. For example, you might choose to declare the hyper-parameters of a machine learning model to be Parameter objects; more generally, Parameters are useful for computing trade-off curves. """ PARAM_COUNT = 0 def __init__(self, shape=(), name=None, value=None, **kwargs): self.id = lu.get_id() if name is None: self._name = "%s%d" % (s.PARAM_PREFIX, self.id) else: self._name = name # Initialize with value if provided. self._value = None super(Parameter, self).__init__(shape, value, **kwargs) def get_data(self): """Returns info needed to reconstruct the expression besides the args. """ return [self.shape, self._name, self.value, self.attributes] def name(self): return self._name def is_constant(self): return True # Getter and setter for parameter value. @property def value(self): """NumPy.ndarray or None: The numeric value of the parameter. """ return self._value @value.setter def value(self, val): self._value = self._validate_value(val) @property def grad(self): """Gives the (sub/super)gradient of the expression w.r.t. each variable. Matrix expressions are vectorized, so the gradient is a matrix. Returns: A map of variable to SciPy CSC sparse matrix or None. """ return {} def parameters(self): """Returns itself as a parameter. """ return [self] def canonicalize(self): """Returns the graph implementation of the object. Returns: A tuple of (affine expression, [constraints]). """ obj = lu.create_param(self, self.shape) return (obj, []) def __repr__(self): """String to recreate the object. """ attr_str = self._get_attr_str() if len(attr_str) > 0: return "Parameter(%s%s)" % (self.shape, attr_str) else: return "Parameter(%s)" % (self.shape,)