"""
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.
"""

# Relax and round example for talk.
from __future__ import division
from cvxpy import *
import numpy

# def bool_vars(prob):
#     return [var for var in prob.variables() if var.boolean]

def cvx_relax(prob):
    new_constr = []
    for var in prob.variables():
        if getattr(var, 'boolean', False):
            new_constr += [0 <= var, var <= 1]
    return Problem(prob.objective,
                   prob.constraints + new_constr)

def round_and_fix(prob):
    prob.solve()
    new_constr = []
    for var in prob.variables():
        if getattr(var, 'boolean', False):
            new_constr += [var == numpy.round(var.value)]
    return Problem(prob.objective,
                   prob.constraints + new_constr)

def branch_and_bound(n, A, B, c):
    from Queue import PriorityQueue
    x = Variable(n)
    z = Variable(n)
    L = Parameter(n)
    U = Parameter(n)
    prob = Problem(Minimize(sum_squares(A*x + B*z - c)),
                   [L <= z, z <= U])
    visited = 0
    best_z = None
    f_best = numpy.inf
    nodes = PriorityQueue()
    nodes.put((numpy.inf, 0, numpy.zeros(n), numpy.ones(n), 0))
    while not nodes.empty():
        visited += 1
        # Evaluate the node with the lowest lower bound.
        _, _, L_val, U_val, idx = nodes.get()
        L.value = L_val
        U.value = U_val
        lower_bound = prob.solve()
        z_star = numpy.round(z.value)
        upper_bound = Problem(prob.objective, [z == z_star]).solve()
        f_best = min(f_best, upper_bound)
        if upper_bound == f_best:
            best_z = z_star
        # Add new nodes if not at a leaf and the branch cannot be pruned.
        if idx < n and lower_bound < f_best:
            for i in [0, 1]:
                L_val[idx] = U_val[idx] = i
                nodes.put((lower_bound, i, L_val.copy(), U_val.copy(), idx + 1))

    #print("Nodes visited: %s out of %s" % (visited, 2**(n+1)-1))
    return f_best, best_z

# def round_and_fix2(prob, thresh):
#     prob.solve()
#     new_constr = []
#     for var in bool_vars(prob):
#         new_constr += [var == (var.value > thresh)]
#     return Problem(prob.objective, prob.constraints + new_constr)

# def round_and_fix3(prob, thresh):
#     prob.solve()
#     new_constr = []
#     for var in bool_vars(prob):
#         print var.value
#         new_constr += [(var.value > 1 - thresh ) <= var,
#                        var <= ~(var.value <= thresh)]
#     return Problem(prob.objective, prob.constraints + new_constr)

numpy.random.seed(1)

# Min sum_squares(A*x + B*z - c)
# z boolean.
def example(n, get_vals=False):
    print ("n = %d #################" % n)
    m = 2*n
    A = numpy.matrix(numpy.random.randn(m, n))
    B = numpy.matrix(numpy.random.randn(m, n))
    sltn = (numpy.random.randn(n, 1),
            numpy.random.randint(2, size=(n, 1)))
    noise = numpy.random.normal(size=(m, 1))
    c = A.dot(sltn[0]) + B.dot(sltn[1]) + noise

    x = Variable(n)
    #x.boolean = False
    z = Variable(n)
    z.boolean = True

    obj = sum_squares(A*x + B*z - c)
    prob = Problem(Minimize(obj))
    relaxation = cvx_relax(prob)
    print ("relaxation", relaxation.solve())
    rel_z = z.value
    rounded = round_and_fix(relaxation)
    rounded.solve()
    print ("relax and round", rounded.value)
    truth, true_z = branch_and_bound(n, A, B, c)
    print ("true optimum", truth)
    if get_vals:
        return (rel_z, z.value, true_z)
    return (relaxation.value, rounded.value, truth)

# Plot relaxation z_star.
import matplotlib.pyplot as plt
n = 20
vals = range(1, n+1)
relaxed, rounded, truth = map(numpy.asarray, example(n, True))
plt.figure(figsize=(6,4))
plt.plot(vals, relaxed, 'ro')
plt.axhline(y=0.5,color='k',ls='dashed')
plt.xlabel(r'$i$')
plt.ylabel(r'$z^\mathrm{rel}_i$')
plt.show()

# Plot optimal values.
import matplotlib.pyplot as plt
relaxed = []
rounded = []
truth = []
vals = range(1, 36)
for n in vals:
    results = example(n)
    results = list(map(lambda x: numpy.around(x, 3), results))
    relaxed.append(results[0])
    rounded.append(results[1])
    truth.append(results[2])

plt.figure(figsize=(6,4))
plt.plot(vals, rounded, vals, truth, vals, relaxed)
plt.xlabel("n")
plt.ylabel("Objective value")
plt.legend(["Relax and round value", "Global optimum", "Lower bound"], loc=2)
plt.show()

# m = 10
# n = 8
# nnz = 5
# A = numpy.random.randn(m, n)
# solution = numpy.random.randint(2, size=(n, 1))
# b = A.dot(solution)
# x = Variable(n)
# y = Variable(n)
# x.boolean = False
# y.boolean = True
# U = 100
# L = -100
# obj = sum_squares(A*x - b)
# constraints = [L*y <= x, x <= U*y,
#                sum(y) <= nnz]
# prob = Problem(Minimize(obj), constraints)
# relaxation = cvx_relax(prob)
# print relaxation.solve()
# rounded = relaxation
# K = 4
# for i in range(K+1):
#     rounded = round_and_fix3(rounded, i/(2*K))
# print rounded.solve()
# print numpy.around(x.value, 2)
# print numpy.around(y.value, 2)

# # Warehouse operation.
# #  http://web.mit.edu/15.053/www/AMP-Chapter-09.pdf
# # cost per unit from warehouse i to customer j
# # cost for warehouse being used
# # fixed customer demand
# m = 100 # number of customers.
# n = 50 # number of warehouses.

# numpy.random.seed(1)
# C = numpy.random.random((n, m))
# f = numpy.random.random((n, 1))
# d = numpy.random.random((m, 1))
# X = Variable(n, m)
# y = Variable(n)

# # Annotate variables.
# X.boolean = False
# y.boolean = True
# demand = [sum(X[:, j]) == d[j] for j in range(m)]
# valid = [sum(X[i, :]) <= y[i]*d.sum() for i in range(n)]
# obj = sum(multiply(C, X)) + f.T*y
# prob = Problem(Minimize(obj),
#                [X >= 0, sum(y) >= 3*n/4] + demand + valid)

# relaxation = cvx_relax(prob)
# print relaxation.solve()
# rounded = round_and_fix(relaxation)
# # rounded = relaxation
# # K = 4
# # for i in range(K):
# #     print i
# #     rounded = round_and_fix3(rounded, i/(2*K))
# #     print y.value.sum()
# print rounded.solve()
# print rounded.status
# print y.value.sum()
# # print numpy.around(X.value, 2)
# # print numpy.around(y.value, 2)
