def sign_for_intf(self, interface):
     """Test sign for a given interface.
     """
     mat = interface.const_to_matrix([[1,2,3,4],[3,4,5,6]])
     self.assertEquals(intf.sign(mat), Sign.POSITIVE)
     self.assertEquals(intf.sign(-mat), Sign.NEGATIVE)
     self.assertEquals(intf.sign(0*mat), Sign.ZERO)
     mat = interface.const_to_matrix([[-1,2,3,4],[3,4,5,6]])
     self.assertEquals(intf.sign(mat), Sign.UNKNOWN)
Exemple #2
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 def sign_for_intf(self, interface):
     """Test sign for a given interface.
     """
     mat = interface.const_to_matrix([[1, 2, 3, 4], [3, 4, 5, 6]])
     self.assertEquals(intf.sign(mat), Sign.POSITIVE)
     self.assertEquals(intf.sign(-mat), Sign.NEGATIVE)
     self.assertEquals(intf.sign(0 * mat), Sign.ZERO)
     mat = interface.const_to_matrix([[-1, 2, 3, 4], [3, 4, 5, 6]])
     self.assertEquals(intf.sign(mat), Sign.UNKNOWN)
Exemple #3
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 def sign_for_intf(self, interface):
     """Test sign for a given interface.
     """
     mat = interface.const_to_matrix([[1, 2, 3, 4], [3, 4, 5, 6]])
     self.assertEqual(intf.sign(mat), (True, False))  # Positive.
     self.assertEqual(intf.sign(-mat), (False, True))  # Negative.
     self.assertEqual(intf.sign(0*mat), (True, True))  # Zero.
     mat = interface.const_to_matrix([[-1, 2, 3, 4], [3, 4, 5, 6]])
     self.assertEqual(intf.sign(mat), (False, False))  # Unknown.
Exemple #4
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 def sign_for_intf(self, interface):
     """Test sign for a given interface.
     """
     mat = interface.const_to_matrix([[1, 2, 3, 4], [3, 4, 5, 6]])
     self.assertEqual(intf.sign(mat), (True, False))  # Positive.
     self.assertEqual(intf.sign(-mat), (False, True))  # Negative.
     self.assertEqual(intf.sign(0*mat), (True, True))  # Zero.
     mat = interface.const_to_matrix([[-1, 2, 3, 4], [3, 4, 5, 6]])
     self.assertEqual(intf.sign(mat), (False, False))  # Unknown.
Exemple #5
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    def _validate_value(self, val):
        """Check that the value satisfies the leaf's symbolic attributes.

        Parameters
        ----------
        val : numeric type
            The value assigned.

        Returns
        -------
        numeric type
            The value converted to the proper matrix type.
        """
        if val is not None:
            # Convert val to the proper matrix type.
            val = intf.DEFAULT_INTF.const_to_matrix(val)
            size = intf.size(val)
            if size != self.size:
                raise ValueError("Invalid dimensions (%s, %s) for %s value." %
                                 (size[0], size[1], self.__class__.__name__))
            # All signs are valid if sign is unknown.
            # Otherwise value sign must match declared sign.
            pos_val, neg_val = intf.sign(val)
            if self.is_positive() and not pos_val or \
               self.is_negative() and not neg_val:
                raise ValueError("Invalid sign for %s value." %
                                 self.__class__.__name__)
            # Round to correct sign.
            elif self.is_positive():
                val = np.maximum(val, 0)
            elif self.is_negative():
                val = np.minimum(val, 0)
        return val
Exemple #6
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    def _validate_value(self, val):
        """Check that the value satisfies the leaf's symbolic attributes.

        Parameters
        ----------
        val : numeric type
            The value assigned.

        Returns
        -------
        numeric type
            The value converted to the proper matrix type.
        """
        if val is not None:
            # Convert val to the proper matrix type.
            val = intf.DEFAULT_INTF.const_to_matrix(val)
            size = intf.size(val)
            if size != self.size:
                raise ValueError(
                    "Invalid dimensions (%s, %s) for %s value." %
                    (size[0], size[1], self.__class__.__name__)
                )
            # All signs are valid if sign is unknown.
            # Otherwise value sign must match declared sign.
            pos_val, neg_val = intf.sign(val)
            if self.is_positive() and not pos_val or \
               self.is_negative() and not neg_val:
                raise ValueError(
                    "Invalid sign for %s value." % self.__class__.__name__
                )
        return val
Exemple #7
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    def _validate_value(self, val):
        """Check that the value satisfies the parameter's symbolic attributes.

        Parameters
        ----------
        val : numeric type
            The value assigned.

        Returns
        -------
        numeric type
            The value converted to the proper matrix type.
        """
        # Convert val to the proper matrix type.
        val = intf.DEFAULT_INTERFACE.const_to_matrix(val)
        size = intf.size(val)
        if size != self.size:
            raise ValueError("Invalid dimensions (%s, %s) for %s value." %
                             (size[0], size[1], self.__class__.__name__))
        # All signs are valid if sign is unknown.
        # Otherwise value sign must match declared sign.
        sign = intf.sign(val)
        if self.is_positive() and not sign.is_positive() or \
           self.is_negative() and not sign.is_negative():
            raise ValueError("Invalid sign for %s value." %
                             self.__class__.__name__)
        return val
Exemple #8
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 def _compute_attr(self):
     """Compute the attributes of the constant related to complex/real, sign.
     """
     # Set DCP attributes.
     is_real, is_imag = intf.is_complex(self.value)
     if self.is_complex():
         is_nonneg = is_nonpos = False
     else:
         is_nonneg, is_nonpos = intf.sign(self.value)
     self._imag = (is_imag and not is_real)
     self._nonpos = is_nonpos
     self._nonneg = is_nonneg
Exemple #9
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    def presolve(objective, constr_map):
        """Eliminates unnecessary constraints and short circuits the solver
        if possible.

        Parameters
        ----------
        objective : LinOp
            The canonicalized objective.
        constr_map : dict
            A map of constraint type to a list of constraints.

        Returns
        -------
        bool
            Is the problem infeasible?
        """
        # Remove redundant constraints.
        for key, constraints in constr_map.items():
            ids = set()
            uniq_constr = []
            for c in constraints:
                if c.constr_id not in ids:
                    uniq_constr.append(c)
                    ids.add(c.constr_id)
            constr_map[key] = uniq_constr

        # If there are no constraints, the problem is unbounded
        # if any of the coefficients are non-zero.
        # If all the coefficients are zero then return the constant term
        # and set all variables to 0.
        if not any(constr_map.values()):
            str(objective)  # TODO

        # Remove constraints with no variables or parameters.
        for key in [s.EQ, s.LEQ]:
            new_constraints = []
            for constr in constr_map[key]:
                vars_ = lu.get_expr_vars(constr.expr)
                if len(vars_) == 0 and not lu.get_expr_params(constr.expr):
                    V, I, J, coeff = canonInterface.get_problem_matrix(
                        [constr])
                    is_pos, is_neg = intf.sign(coeff)
                    # For equality constraint, coeff must be zero.
                    # For inequality (i.e. <= 0) constraint,
                    # coeff must be negative.
                    if key == s.EQ and not (is_pos and is_neg) or \
                            key == s.LEQ and not is_neg:
                        return s.INFEASIBLE
                else:
                    new_constraints.append(constr)
            constr_map[key] = new_constraints

        return None
Exemple #10
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    def presolve(objective, constr_map, check_params=False):
        """Eliminates unnecessary constraints and short circuits the solver
        if possible.

        Parameters
        ----------
        objective : LinOp
            The canonicalized objective.
        constr_map : dict
            A map of constraint type to a list of constraints.
        check_params : bool, optional
            Should constraints with parameters be evaluated?

        Returns
        -------
        bool
            Is the problem infeasible?
        """
        # Remove redundant constraints.
        for key, constraints in constr_map.items():
            uniq_constr = unique(constraints,
                                 key=lambda c: c.constr_id)
            constr_map[key] = list(uniq_constr)

        # If there are no constraints, the problem is unbounded
        # if any of the coefficients are non-zero.
        # If all the coefficients are zero then return the constant term
        # and set all variables to 0.
        if not any(constr_map.values()):
            str(objective) # TODO

        # Remove constraints with no variables or parameters.
        for key in [s.EQ, s.LEQ]:
            new_constraints = []
            for constr in constr_map[key]:
                vars_ = lu.get_expr_vars(constr.expr)
                if len(vars_) == 0 and not lu.get_expr_params(constr.expr):
                    coeff = op2mat.get_constant_coeff(constr.expr)
                    sign = intf.sign(coeff)
                    # For equality constraint, coeff must be zero.
                    # For inequality (i.e. <= 0) constraint,
                    # coeff must be negative.
                    if key is s.EQ and not sign.is_zero() or \
                        key is s.LEQ and not sign.is_negative():
                        return s.INFEASIBLE
                else:
                    new_constraints.append(constr)
            constr_map[key] = new_constraints

        return None
Exemple #11
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    def presolve(objective, constr_map, check_params=False):
        """Eliminates unnecessary constraints and short circuits the solver
        if possible.

        Parameters
        ----------
        objective : LinOp
            The canonicalized objective.
        constr_map : dict
            A map of constraint type to a list of constraints.
        check_params : bool, optional
            Should constraints with parameters be evaluated?

        Returns
        -------
        bool
            Is the problem infeasible?
        """
        # Remove redundant constraints.
        for key, constraints in constr_map.items():
            uniq_constr = unique(constraints, key=lambda c: c.constr_id)
            constr_map[key] = list(uniq_constr)

        # If there are no constraints, the problem is unbounded
        # if any of the coefficients are non-zero.
        # If all the coefficients are zero then return the constant term
        # and set all variables to 0.
        if not any(constr_map.values()):
            str(objective)  # TODO

        # Remove constraints with no variables or parameters.
        for key in [s.EQ, s.LEQ]:
            new_constraints = []
            for constr in constr_map[key]:
                vars_ = lu.get_expr_vars(constr.expr)
                if len(vars_) == 0 and not lu.get_expr_params(constr.expr):
                    coeff = op2mat.get_constant_coeff(constr.expr)
                    sign = intf.sign(coeff)
                    # For equality constraint, coeff must be zero.
                    # For inequality (i.e. <= 0) constraint,
                    # coeff must be negative.
                    if key is s.EQ and not sign.is_zero() or \
                        key is s.LEQ and not sign.is_negative():
                        return s.INFEASIBLE
                else:
                    new_constraints.append(constr)
            constr_map[key] = new_constraints

        return None
Exemple #12
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 def __init__(self, value):
     # TODO HACK.
     # A fix for c.T*x where c is a 1D array.
     self.is_1D_array = False
     # Keep sparse matrices sparse.
     if intf.is_sparse(value):
         self._value = intf.DEFAULT_SPARSE_INTF.const_to_matrix(value)
         self._sparse = True
     else:
         if isinstance(value, np.ndarray) and len(value.shape) == 1:
             self.is_1D_array = True
         self._value = intf.DEFAULT_INTF.const_to_matrix(value)
         self._sparse = False
     # Set DCP attributes.
     self._size = intf.size(self.value)
     self._is_pos, self._is_neg = intf.sign(self.value)
     super(Constant, self).__init__()
Exemple #13
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 def __init__(self, value):
     # TODO HACK.
     # A fix for c.T*x where c is a 1D array.
     self.is_1D_array = False
     # Keep sparse matrices sparse.
     if intf.is_sparse(value):
         self._value = intf.DEFAULT_SPARSE_INTF.const_to_matrix(value)
         self._sparse = True
     else:
         if isinstance(value, np.ndarray) and len(value.shape) == 1:
             self.is_1D_array = True
         self._value = intf.DEFAULT_INTF.const_to_matrix(value)
         self._sparse = False
     # Set DCP attributes.
     self._size = intf.size(self.value)
     self._is_pos, self._is_neg = intf.sign(self.value)
     super(Constant, self).__init__()
Exemple #14
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 def init_dcp_attr(self):
     shape = u.Shape(*intf.size(self.value))
     sign = intf.sign(self.value)
     self._dcp_attr = u.DCPAttr(sign, u.Curvature.CONSTANT, shape)
Exemple #15
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 def init_dcp_attr(self):
     shape = u.Shape(*intf.size(self.value))
     sign = intf.sign(self.value)
     self._dcp_attr = u.DCPAttr(sign, u.Curvature.CONSTANT, shape)