예제 #1
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 def test_init_from_existing_problem(self):
     inner_prob = self.model.problem
     self.assertEqual(len(self.model.variables), glp_get_num_cols(inner_prob))
     self.assertEqual(len(self.model.constraints), glp_get_num_rows(inner_prob))
     self.assertEqual(self.model.variables.keys(),
                      [glp_get_col_name(inner_prob, i) for i in range(1, glp_get_num_cols(inner_prob) + 1)])
     self.assertEqual(self.model.constraints.keys(),
                      [glp_get_row_name(inner_prob, j) for j in range(1, glp_get_num_rows(inner_prob) + 1)])
예제 #2
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def solve_with_glpsol(glp_prob):
    """Solve glpk problem with glpsol commandline solver. Mainly for testing purposes.

    # Examples
    # --------

    # >>> problem = glp_create_prob()
    # ... glp_read_lp(problem, None, "../tests/data/model.lp")
    # ... solution = solve_with_glpsol(problem)
    # ... print 'asdf'
    # 'asdf'
    # >>> print solution
    # 0.839784

    # Returns
    # -------
    # dict
    #     A dictionary containing the objective value (key ='objval')
    #     and variable primals.
    """
    from swiglpk import glp_get_row_name, glp_get_col_name, glp_write_lp, glp_get_num_rows, glp_get_num_cols

    row_ids = [glp_get_row_name(glp_prob, i) for i in range(1, glp_get_num_rows(glp_prob) + 1)]

    col_ids = [glp_get_col_name(glp_prob, i) for i in range(1, glp_get_num_cols(glp_prob) + 1)]

    with tempfile.NamedTemporaryFile(suffix=".lp", delete=True) as tmp_file:
        tmp_file_name = tmp_file.name
        glp_write_lp(glp_prob, None, tmp_file_name)
        cmd = ['glpsol', '--lp', tmp_file_name, '-w', tmp_file_name + '.sol', '--log', '/dev/null']
        term = check_output(cmd)
        log.info(term)

    try:
        with open(tmp_file_name + '.sol') as sol_handle:
            # print sol_handle.read()
            solution = dict()
            for i, line in enumerate(sol_handle.readlines()):
                if i <= 1 or line == '\n':
                    pass
                elif i <= len(row_ids):
                    solution[row_ids[i - 2]] = line.strip().split(' ')
                elif i <= len(row_ids) + len(col_ids) + 1:
                    solution[col_ids[i - 2 - len(row_ids)]] = line.strip().split(' ')
                else:
                    print(i)
                    print(line)
                    raise Exception("Argggh!")
    finally:
        os.remove(tmp_file_name + ".sol")
    return solution
예제 #3
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def solve_with_glpsol(glp_prob):
    """Solve glpk problem with glpsol commandline solver. Mainly for testing purposes.

    # Examples
    # --------

    # >>> problem = glp_create_prob()
    # ... glp_read_lp(problem, None, "../tests/data/model.lp")
    # ... solution = solve_with_glpsol(problem)
    # ... print 'asdf'
    # 'asdf'
    # >>> print solution
    # 0.839784

    # Returns
    # -------
    # dict
    #     A dictionary containing the objective value (key ='objval')
    #     and variable primals.
    """
    from swiglpk import glp_get_row_name, glp_get_col_name, glp_write_lp, glp_get_num_rows, glp_get_num_cols

    row_ids = [glp_get_row_name(glp_prob, i) for i in range(1, glp_get_num_rows(glp_prob) + 1)]

    col_ids = [glp_get_col_name(glp_prob, i) for i in range(1, glp_get_num_cols(glp_prob) + 1)]

    with tempfile.NamedTemporaryFile(suffix=".lp", delete=True) as tmp_file:
        tmp_file_name = tmp_file.name
        glp_write_lp(glp_prob, None, tmp_file_name)
        cmd = ['glpsol', '--lp', tmp_file_name, '-w', tmp_file_name + '.sol', '--log', '/dev/null']
        term = check_output(cmd)
        log.info(term)

    try:
        with open(tmp_file_name + '.sol') as sol_handle:
            # print sol_handle.read()
            solution = dict()
            for i, line in enumerate(sol_handle.readlines()):
                if i <= 1 or line == '\n':
                    pass
                elif i <= len(row_ids):
                    solution[row_ids[i - 2]] = line.strip().split(' ')
                elif i <= len(row_ids) + len(col_ids) + 1:
                    solution[col_ids[i - 2 - len(row_ids)]] = line.strip().split(' ')
                else:
                    print(i)
                    print(line)
                    raise Exception("Argggh!")
    finally:
        os.remove(tmp_file_name + ".sol")
    return solution
 def test_init_from_existing_problem(self):
     inner_prob = self.model.problem
     self.assertEqual(len(self.model.variables),
                      glp_get_num_cols(inner_prob))
     self.assertEqual(len(self.model.constraints),
                      glp_get_num_rows(inner_prob))
     self.assertEqual(self.model.variables.keys(), [
         glp_get_col_name(inner_prob, i)
         for i in range(1,
                        glp_get_num_cols(inner_prob) + 1)
     ])
     self.assertEqual(self.model.constraints.keys(), [
         glp_get_row_name(inner_prob, j)
         for j in range(1,
                        glp_get_num_rows(inner_prob) + 1)
     ])
예제 #5
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    def _initialize_model_from_problem(self, problem):
        try:
            self.problem = problem
            glp_create_index(self.problem)
        except TypeError:
            raise TypeError("Provided problem is not a valid GLPK model.")
        row_num = glp_get_num_rows(self.problem)
        col_num = glp_get_num_cols(self.problem)
        for i in range(1, col_num + 1):
            var = Variable(
                glp_get_col_name(self.problem, i),
                lb=glp_get_col_lb(self.problem, i),
                ub=glp_get_col_ub(self.problem, i),
                problem=self,
                type=_GLPK_VTYPE_TO_VTYPE[
                    glp_get_col_kind(self.problem, i)]
            )
            # This avoids adding the variable to the glpk problem
            super(Model, self)._add_variables([var])
        variables = self.variables

        for j in range(1, row_num + 1):
            ia = intArray(col_num + 1)
            da = doubleArray(col_num + 1)
            nnz = glp_get_mat_row(self.problem, j, ia, da)
            constraint_variables = [variables[ia[i] - 1] for i in range(1, nnz + 1)]

            # Since constraint expressions are lazily retrieved from the solver they don't have to be built here
            # lhs = _unevaluated_Add(*[da[i] * constraint_variables[i - 1]
            #                         for i in range(1, nnz + 1)])
            lhs = 0

            glpk_row_type = glp_get_row_type(self.problem, j)
            if glpk_row_type == GLP_FX:
                row_lb = glp_get_row_lb(self.problem, j)
                row_ub = row_lb
            elif glpk_row_type == GLP_LO:
                row_lb = glp_get_row_lb(self.problem, j)
                row_ub = None
            elif glpk_row_type == GLP_UP:
                row_lb = None
                row_ub = glp_get_row_ub(self.problem, j)
            elif glpk_row_type == GLP_DB:
                row_lb = glp_get_row_lb(self.problem, j)
                row_ub = glp_get_row_ub(self.problem, j)
            elif glpk_row_type == GLP_FR:
                row_lb = None
                row_ub = None
            else:
                raise Exception(
                    "Currently, optlang does not support glpk row type %s"
                    % str(glpk_row_type)
                )
                log.exception()
            if isinstance(lhs, int):
                lhs = symbolics.Integer(lhs)
            elif isinstance(lhs, float):
                lhs = symbolics.Real(lhs)
            constraint_id = glp_get_row_name(self.problem, j)
            for variable in constraint_variables:
                try:
                    self._variables_to_constraints_mapping[variable.name].add(constraint_id)
                except KeyError:
                    self._variables_to_constraints_mapping[variable.name] = set([constraint_id])

            super(Model, self)._add_constraints(
                [Constraint(lhs, lb=row_lb, ub=row_ub, name=constraint_id, problem=self, sloppy=True)],
                sloppy=True
            )

        term_generator = (
            (glp_get_obj_coef(self.problem, index), variables[index - 1])
            for index in range(1, glp_get_num_cols(problem) + 1)
        )
        self._objective = Objective(
            symbolics.add(
                [symbolics.mul((symbolics.Real(term[0]), term[1])) for term in term_generator if
                 term[0] != 0.]
            ),
            problem=self,
            direction={GLP_MIN: 'min', GLP_MAX: 'max'}[glp_get_obj_dir(self.problem)])
        glp_scale_prob(self.problem, GLP_SF_AUTO)
예제 #6
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def get_rates(problem: SwigPyObject) -> Iterable[Tuple[str, float]]:
    for i in range(1, 1 + lp.glp_get_num_rows(problem)):
        yield (
            lp.glp_get_row_name(problem, i),
            lp.glp_mip_row_val(problem, i) / 100,
        )
예제 #7
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    def __init__(self, problem=None, *args, **kwargs):

        super(Model, self).__init__(*args, **kwargs)

        self.configuration = Configuration()

        if problem is None:
            self.problem = glp_create_prob()
            glp_create_index(self.problem)
            if self.name is not None:
                glp_set_prob_name(self.problem, str(self.name))

        else:
            try:
                self.problem = problem
                glp_create_index(self.problem)
            except TypeError:
                raise TypeError("Provided problem is not a valid GLPK model.")
            row_num = glp_get_num_rows(self.problem)
            col_num = glp_get_num_cols(self.problem)
            for i in range(1, col_num + 1):
                var = Variable(
                    glp_get_col_name(self.problem, i),
                    lb=glp_get_col_lb(self.problem, i),
                    ub=glp_get_col_ub(self.problem, i),
                    problem=self,
                    type=_GLPK_VTYPE_TO_VTYPE[
                        glp_get_col_kind(self.problem, i)]
                )
                # This avoids adding the variable to the glpk problem
                super(Model, self)._add_variables([var])
            variables = self.variables

            for j in range(1, row_num + 1):
                ia = intArray(col_num + 1)
                da = doubleArray(col_num + 1)
                nnz = glp_get_mat_row(self.problem, j, ia, da)
                constraint_variables = [variables[ia[i] - 1] for i in range(1, nnz + 1)]

                # Since constraint expressions are lazily retrieved from the solver they don't have to be built here
                # lhs = _unevaluated_Add(*[da[i] * constraint_variables[i - 1]
                #                         for i in range(1, nnz + 1)])
                lhs = 0

                glpk_row_type = glp_get_row_type(self.problem, j)
                if glpk_row_type == GLP_FX:
                    row_lb = glp_get_row_lb(self.problem, j)
                    row_ub = row_lb
                elif glpk_row_type == GLP_LO:
                    row_lb = glp_get_row_lb(self.problem, j)
                    row_ub = None
                elif glpk_row_type == GLP_UP:
                    row_lb = None
                    row_ub = glp_get_row_ub(self.problem, j)
                elif glpk_row_type == GLP_DB:
                    row_lb = glp_get_row_lb(self.problem, j)
                    row_ub = glp_get_row_ub(self.problem, j)
                elif glpk_row_type == GLP_FR:
                    row_lb = None
                    row_ub = None
                else:
                    raise Exception(
                        "Currently, optlang does not support glpk row type %s"
                        % str(glpk_row_type)
                    )
                    log.exception()
                if isinstance(lhs, int):
                    lhs = sympy.Integer(lhs)
                elif isinstance(lhs, float):
                    lhs = sympy.RealNumber(lhs)
                constraint_id = glp_get_row_name(self.problem, j)
                for variable in constraint_variables:
                    try:
                        self._variables_to_constraints_mapping[variable.name].add(constraint_id)
                    except KeyError:
                        self._variables_to_constraints_mapping[variable.name] = set([constraint_id])

                super(Model, self)._add_constraints(
                    [Constraint(lhs, lb=row_lb, ub=row_ub, name=constraint_id, problem=self, sloppy=True)],
                    sloppy=True
                )

            term_generator = (
                (glp_get_obj_coef(self.problem, index), variables[index - 1])
                for index in range(1, glp_get_num_cols(problem) + 1)
            )
            self._objective = Objective(
                _unevaluated_Add(
                    *[_unevaluated_Mul(sympy.RealNumber(term[0]), term[1]) for term in term_generator if
                      term[0] != 0.]),
                problem=self,
                direction={GLP_MIN: 'min', GLP_MAX: 'max'}[glp_get_obj_dir(self.problem)])
        glp_scale_prob(self.problem, GLP_SF_AUTO)