def main(argv=None):
if argv is None:
argv = sys.argv
# Parse command line
(nalternatives, ncriteria, nprofiles, nlearning, seed, error) = parse_cmdline(argv)
print "Input parameters"
print "================"
print "Nalternatives:", nalternatives
print "Ncriteria:", ncriteria
print "Nprofiles:", nprofiles
print "Nlearning:", nlearning
print "Seed:", seed
# Create a model
(alternatives, criteria, palternatives) = create_model(nalternatives, ncriteria, nprofiles)
(pt, profiles, weights, lbda) = generate_random_data(seed, alternatives, criteria, palternatives)
model = etri.electre_tri(pt, profiles, weights, lbda)
affectations = model.pessimist()
# Add errors in learning alternatives
learning_alts = [ "a%d" % (i+1) for i in range(nlearning) ]
add_errors_in_learning_alts(affectations, learning_alts, nprofiles, error)
profs = []
for profile in profiles:
profs.append(profile['refs'])
# Infer ELECTRE Tri parameters
(iweights, iprofiles, ilbda, icompat, info) = etri_infer_parameters(learning_alts, criteria, pt, affectations, nprofiles, "models/etri_bm_weights_compat.mod", profiles=profs)
# Apply ELECTRE Tri model with infered parameters
modeli = etri.electre_tri(pt, iprofiles, iweights, ilbda)
iaffectations = modeli.pessimist()
# Print result
print "Output"
print "======"
print "Time used:", info[0]
print "Memory used:", info[1]
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, affectations, iaffectations, icompat)
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)