Python pulp.LpMaximize方法代码示例

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpMaximize [as 别名]
def define_lp(fares, product_names):
    """Set up LP.

    Parameters
    ----------
    fares: 2D np array
            contains fares for products, size n_classes*n_products
    products_names: list
            product names (typically relation/class combinations)

    Returns
    -------
    tuple of the form (LP problem, decision variables)
    """
    prob = pulp.LpProblem('network_RM', pulp.LpMaximize)

    # decision variables: available seats per product
    x = pulp.LpVariable.dicts('x', product_names, lowBound=0)

    # objective function
    revenue = pulp.lpSum([x[it]*fares.ravel()[i] for i, it in
                          enumerate(product_names)])
    prob += revenue

    return prob, x 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpMaximize [as 别名]
def __init__(self):
        self.prob = LpProblem('Daily Fantasy Sports', LpMaximize) 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpMaximize [as 别名]
def get_objective_type(self):
        ''' Returns pulp.LpMaximize - we maximize objective function in case
            of input-oriented multiplier model.

            Returns:
                pulp.LpMaximize.
        '''
        return pulp.LpMaximize 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpMaximize [as 别名]
def get_objective_type(self):
        ''' Returns pulp.LpMinimize - we minimize objective function in case
            of input-oriented envelopment model.

            Returns:
                pulp.LpMaximize: type of objective function.
        '''
        return pulp.LpMinimize 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpMaximize [as 别名]
def _create_lp(self):
        ''' See base class.
        '''
        self.model._create_lp()
        self.lp_model_max_slack = self.model.lp_model.deepcopy()

        input_slack_vars = pulp.LpVariable.dicts(
            'input_slack', self.strongly_disposal_input_categories,
            0, None, pulp.LpContinuous)
        output_slack_vars = pulp.LpVariable.dicts(
            'output_slack', self.strongly_disposal_output_categories,
            0, None, pulp.LpContinuous)

        # change objective function
        self.lp_model_max_slack.sense = pulp.LpMaximize
        self.lp_model_max_slack.objective = (
            pulp.lpSum(list(input_slack_vars.values())) +
            pulp.lpSum(list(output_slack_vars.values())))

        # change constraints
        for input_category in self.strongly_disposal_input_categories:
            name = self.model._constraints[input_category]
            self.lp_model_max_slack.constraints[name].addterm(
                input_slack_vars[input_category], 1)
            self.lp_model_max_slack.constraints[name].sense = pulp.LpConstraintEQ

        for output_category in self.strongly_disposal_output_categories:
            name = self.model._constraints[output_category]
            self.lp_model_max_slack.constraints[name].addterm(
                output_slack_vars[output_category], -1)
            self.lp_model_max_slack.constraints[name].sense = pulp.LpConstraintEQ 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpMaximize [as 别名]
def solve_ilp(self, N):
        # build the A matrix: a_ij is 1 if j-th gram appears in the i-th sentence

        A = np.zeros((len(self.sentences_idx), len(self.ref_ngrams_idx)))
        for i in self.sentences_idx:
            sent = self.sentences[i].untokenized_form
            sngrams = list(extract_ngrams2([sent], self.stemmer, self.LANGUAGE, N))
            for j in self.ref_ngrams_idx:
                if self.ref_ngrams[j] in sngrams:
                    A[i][j] = 1

        # Define ILP variable, x_i is 1 if sentence i is selected, z_j is 1 if gram j appears in the created summary
        x = pulp.LpVariable.dicts('sentences', self.sentences_idx, lowBound=0, upBound=1, cat=pulp.LpInteger)
        z = pulp.LpVariable.dicts('grams', self.ref_ngrams_idx, lowBound=0, upBound=1, cat=pulp.LpInteger)

        # Define ILP problem, maximum coverage of grams from the reference summaries
        prob = pulp.LpProblem("ExtractiveUpperBound", pulp.LpMaximize)
        prob += pulp.lpSum(z[j] for j in self.ref_ngrams_idx)

        # Define ILP constraints, length constraint and consistency constraint (impose that z_j is 1 if j
        # appears in the created summary)
        prob += pulp.lpSum(x[i] * self.sentences[i].length for i in self.sentences_idx) <= self.sum_length

        for j in self.ref_ngrams_idx:
            prob += pulp.lpSum(A[i][j] * x[i] for i in self.sentences_idx) >= z[j]

        # Solve ILP problem and post-processing to get the summary
        try:
            print('Solving using CPLEX')
            prob.solve(pulp.CPLEX(msg=0))
        except:
            print('Fall back to GLPK')
            prob.solve(pulp.GLPK(msg=0))
                

        summary_idx = []
        for idx in self.sentences_idx:
            if x[idx].value() == 1.0:
                summary_idx.append(idx)

        return summary_idx 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpMaximize [as 别名]
def get_reaction(self, var, facet=None):
        """
        For a given composition, what is the maximum delta_composition reaction
        on the given facet. If None, returns the whole reaction for the given
        PhaseSpace.

        Examples::

            >>> space = PhaseSpace('Fe2O3-Li2O')
            >>> equilibria = space.hull[0]
            >>> space.get_reaction('Li2O', facet=equilibria)

        """

        if isinstance(var, basestring):
            var = parse_comp(var)

        if facet:
            phases = facet
        else:
            phases = self.stable

        prob = pulp.LpProblem('BalanceReaction', pulp.LpMaximize)
        pvars = pulp.LpVariable.dicts('prod', phases, 0)
        rvars = pulp.LpVariable.dicts('react', phases, 0)
        prob += sum([ p.fraction(var)['var']*pvars[p] for p in phases ])-\
                sum([ p.fraction(var)['var']*rvars[p] for p in phases ]),\
                "Maximize delta comp"
        for celt in self.space:
            prob += sum([ p.fraction(var)[celt]*pvars[p] for p in phases ]) ==\
                    sum([ p.fraction(var)[celt]*rvars[p] for p in phases ]),\
                    'identical %s composition on both sides' % celt
        prob += sum([ rvars[p] for p in phases ]) == 1
        
        if pulp.GUROBI().available():
            prob.solve(pulp.GUROBI(msg=False))
        elif pulp.COIN_CMD().available():
            prob.solve(pulp.COIN_CMD())
        elif pulp.COINMP_DLL().available():
            prob.solve(pulp.COINMP_DLL())
        else:
            prob.solve()

        prods = defaultdict(float,[ (c, pvars[c].varValue) for c in phases
            if pvars[c].varValue > 1e-4 ])
        reacts = defaultdict(float,[ (c, rvars[c].varValue) for c in phases
            if rvars[c].varValue > 1e-4 ])
        n_elt = pulp.value(prob.objective)
        return reacts, prods, n_elt