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')
