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)
import numpy as np
if __name__ == '__main__':
database = 'glass' # the name of the database you want to run
components = 3 # the number of components you want to use in PCA
training = load_database('databases/' + database + '.train')
test = load_database('databases/' + database + '.test')
training_feats = [t[:-1] for t in training]
training_class = [t[-1] for t in training]
test_feats = [t[:-1] for t in test]
test_class = [t[-1] for t in test]
P, mn = pca(training_feats, components)
pca_training = proj(P, training_feats, mn).tolist()
pca_training = [a + [b] for (a,b) in zip(pca_training, training_class)]
pca_test = proj(P, test_feats, mn).tolist()
pca_test = [a + [b] for (a,b) in zip(pca_test, test_class)]
print '\tK\tKNN\tPCA'
for k in [1,3,5]:
print '\t%d %.2f %.2f' % (k, get_acertion_tax(k, test, training), get_acertion_tax(k, pca_test, pca_training))
