from machine_learning.prototypes.sgp import sgp from machine_learning.utils.evaluate import get_acertion_tax from machine_learning.utils.database_loader import load_database import sys if __name__ == '__main__': database = 'glass' if len(sys.argv) > 1: database = sys.argv[2] test = load_database('databases/'+ database + '.test') training = load_database('databases/'+ database + '.train') prototypes = sgp(training) print 'tamanho da base original %d' % (len(training)) print 'tamanho da base pos-sqp %d' % (len(prototypes)) print 'taxa de acerto' print get_acertion_tax(1, test, prototypes)
args = sys.argv optlist, args = getopt.getopt(sys.argv[1:],'d:a:w:e:l:', ['generate-prototypes','help']) optlist = dict(optlist) if optlist.has_key('--help'): print 'help, I need somebody, help, not just anybody, help' exit(0) database = optlist.get('-d') alpha = float(optlist.get('-a')) window = float(optlist.get('-w')) epsilon = float(optlist.get('-e')) limit = float(optlist.get('-l')) training = load_database('databases/' + database + ".train") test = load_database('databases/' + database + ".test") range_vector = get_range_vector([e[:-1] for e in training]) prototypes = [] if not optlist.has_key('--generate-prototypes'): prototypes = load_database('databases' + database + '.lqvprototypes') if (len(prototypes) == 0): prototypes = generate_prototypes(training) f = open('databases/' + database + '.lvqprototypes','w') for e in prototypes: for a in e: f.write(str(a) + '\t') f.write('\n')