import sys

import random_data

import debug

import glpk

import etri

# Lambda

lbda = 0.76

# Weights

weights = {'g1': 0.02, 'g2': 0.05, 'g3': 0.06, 'g4': 0.06, 'g5': 0.07, 'g6':0.09, 'g7':0.09, 'g8':0.11, 'g9': 0.13, 'g10': 0.15, 'g11': 0.17}

# Criteria

criteria = weights.keys()

# Actions

pt = {

}

alternatives = pt.keys()

# Reference actions

b1 = {'g1':1, 'g2':2, 'g3': 1, 'g4':1, 'g5':1, 'g6':1, 'g7':2, 'g8':4, 'g9':1, 'g10':1, 'g11':1}

b2 = {'g1':2, 'g2':2, 'g3': 2, 'g4':2, 'g5':3, 'g6':1, 'g7':3, 'g8':4, 'g9':2, 'g10':3, 'g11':4}

b3 = {'g1':2, 'g2':3, 'g3': 3, 'g4':2, 'g5':3, 'g6':1, 'g7':3, 'g8':5, 'g9':3, 'g10':3, 'g11':5}

# Indifference, Preference and Veto

q = {'g1':0, 'g2':0, 'g3': 0, 'g4':0, 'g5':0, 'g6':0, 'g7':0, 'g8':0, 'g9':0, 'g10':0, 'g11':0}

p = {'g1':1, 'g2':1, 'g3': 1, 'g4':1, 'g5':1, 'g6':1, 'g7':1, 'g8':1, 'g9':1, 'g10':2, 'g11':1}

#v = {'g11':4}

v = {}

prof1 = { 'refs': b1, 'q': q, 'p': p, 'v': v }

prof2 = { 'refs': b2, 'q': q, 'p': p, 'v': v }

prof3 = { 'refs': b3, 'q': q, 'p': p, 'v': v }

profiles = [ prof1, prof2, prof3 ]

# Categories

categories = [(i+1) for i in range(len(profiles)+1)]

categories_rank = {}

for i, cat in enumerate(categories):

categories_rank[cat] = i+1

learning_alts = pt.keys()[0:50]

# ELECTRE TRI model

model = etri.electre_tri(pt, profiles, weights, lbda)

pessimist = model.pessimist()

optimist = model.optimist()

#debug.print_performance_table_with_assignements(pt, alternatives, criteria, pessimist)

infile = glpk.create_input_file(learning_alts, criteria, pt, categories, categories_rank, pessimist)

(status, output) = glpk.solve_normalized(infile.name)

infile.close()

if status:

sys.exit("gklp returned status %d" % status)

(iweights, iprofiles, ilbda, icompat) = glpk.parse_output(output, learning_alts, criteria)

if iweights == None:

sys.exit("Invalid weights");

if iprofiles == None:

sys.exit("Invalid profiles")

if ilbda == None:

sys.exit("Invalid lambda")

if icompat == None:

sys.exit("Invalid compat");

# Apply ELECTRE TRI model

modeli = etri.electre_tri(pt, iprofiles, iweights, ilbda)

ipessimist = model.pessimist()

debug.print_lambda(lbda, ilbda)

debug.print_weights(weights, criteria, iweights)

debug.print_profiles(profiles, criteria, iprofiles)

debug.print_performance_table_with_assignements(pt, alternatives, criteria, pessimist, ipessimist, icompat)