#!/usr/bin/env python

"""
Perform a series of trials given uniform distributions for the cost
coefficients. Output the number of times each solution winds up being
the optimal one.
"""

import os
import random
import site
import sys

# Basically, add '../src' to our path.
# Needed for the imports that follow.
site.addsitedir(os.path.dirname(os.path.abspath(sys.argv[0])) + '/../../src')

import LinearProgramming
import probability.Distribution

# How many linear programs will we solve?
NUM_TRIALS = 100000


lp = LinearProgramming.LinearProgram()

# We will say that the cost coefficients have the following means:
#
# c1 : 5
# c2 : 100
# c3 : 30
# c4 : 10
# c5 : 20
# c6 : 300

dist1 = probability.Distribution.Uniform(0, 10)
dist2 = probability.Distribution.Uniform(75, 125)
dist3 = probability.Distribution.Uniform(20, 40)
dist4 = probability.Distribution.Uniform(0, 20)
dist5 = probability.Distribution.Uniform(10, 30)
dist6 = probability.Distribution.Uniform(200, 400)

# We'll store all of the solution vectors and the number of times
# that they have occurred in this hash.
solutions = {}

for idx in range(0, NUM_TRIALS):
    c1 = dist1.sample()
    c2 = dist2.sample()
    c3 = dist3.sample()
    c4 = dist4.sample()
    c5 = dist5.sample()
    c6 = dist6.sample()

    lp.objective_coefficients = [c1, c2, c3, c4, c5, c6]
    
    lp.constraint_matrix = [ [1,1,1,0,0,0],
                             [0,0,0,1,1,1],
                             [1,0,0,1,0,0],
                             [0,1,0,0,1,0],
                             [0,0,1,0,0,1] ]

    lp.inequalities = ([LinearProgramming.LE] * 2) + ([LinearProgramming.EQ] * 3)
    
    lp.rhs = [ 500, 600, 400, 300, 200 ]

    [v,x,duals] = lp.solve()

    # Implementation detail: lists can't serve as the key in a hash,
    # so we just use the string representation of x.
    solution_string = str(x)
    
    if solution_string in solutions:
        solutions[solution_string] += 1
    else:
        solutions[solution_string] = 1


print "Solution Vector : Count"
print "-----------------------"

for (key, value) in solutions.iteritems():
    print key, ":", value