def main():
parser = ld.parse_arguments()
print "#### LEAVE ONE OUT "
print "# KNN Classifier", parser.k
# stopwords
stopwords = None
if parser.stopwords_path:
stopwords = ld.load_stopwords(parser.stopwords_path)
# loading the necessary data
(vocabulary, neigh_classes) = ld.load_train(parser.train_path, stopwords)
# transforming each item to a v-dimensional space
train = space.train_transform(vocabulary, parser.train_path)
print "# Classifying"
acc = 0
for x in xrange(len(train)):
dist_heap = []
item = train[x]
for i in xrange(len(train)):
# skipping the own element
if x == i:
continue
point = train[i]
distance = spd.euclidean(item, point)
tup = (distance, i)
heapq.heappush(dist_heap, tup)
# return the highest k similar points
top_k = heapq.nsmallest(parser.k, dist_heap)
# classifing
classification = np.zeros(2)
for (_, idi) in top_k:
classe = neigh_classes[idi]
classification[int(classe)] += 1
out_class = 0
if classification[0] >= classification[1]:
out_class = 0
else:
out_class = 1
print x, " -> ", out_class, neigh_classes[x]
# increment the acc
if out_class == neigh_classes[x]:
acc += 1
print "# Acurácia para ", parser.k, ": ",
print acc, float(acc) / float(len(train))
def main():
print "# KNN Classifier"
parser = ld.parse_arguments()
# priting args
print '\t-k = ' + str(parser.k)
print '\t-d = ' + parser.distance
stopwords = None
if parser.stopwords_path:
stopwords = ld.load_stopwords(parser.stopwords_path)
voc = load_vocabulary(parser.train_path, stopwords)
answers = load_answers(parser.train_path)
train = transform(voc, parser.train_path)
test = transform(voc, parser.test_path)
# output file
out_path = '../results/' + parser.distance + '_' + str(parser.k)
out_path += '.txt'
out_file = open(out_path, 'w')
for point in test:
neighbors = []
for i in xrange(len(train)):
neigh = train[i]
distance = 0.0
if parser.distance == 'cosine':
distance = spd.cosine(neigh, point)
elif parser.distance == 'jaccard':
distance = spd.jaccard(neigh, point)
elif parser.distance == 'euclidean':
distance = spd.euclidean(neigh, point)
elif parser.distance == 'dice':
distance = spd.dice(neigh, point)
elif parser.distance == 'correlation':
distance = spd.correlation(neigh, point)
elif parser.distance == 'manhattan':
distance = spd.cityblock(neigh, point)
else:
print >> stderr, "ERRO! - Distância informada inválida."
exit()
tup = (distance, i)
heapq.heappush(neighbors, tup)
# return the highest k similar points
top_k = heapq.nsmallest(parser.k, neighbors)
# classifing
classification = np.zeros(2)
for (_, idi) in top_k:
classe = answers[idi]
classification[int(classe)] += 1
# outputing classification
if(classification[0] >= classification[1]):
print >> out_file, '0'
print '0'
else:
print >> out_file, '1'
print '1'
# outputing the results'
print
print "# Resultados salvos no arquivo: " + out_path
out_file.close()
result.result("../data/imdb_test", out_path)
def main():
print "# KNN Classifier"
parser = ld.parse_arguments()
stopwords = None
if parser.stopwords_path:
stopwords = ld.load_stopwords(parser.stopwords_path)
# priting args
print '\t-k = ' + str(parser.k)
print '\t-d = ' + parser.distance
# loading the necessary data
(vocabulary, neigh_classes) = ld.load_train(parser.train_path, stopwords)
print "# Tamanho do vocabulário:", len(vocabulary)
# transforming each item to a v-dimensional space
(train, test) = space.transform(vocabulary, parser.train_path,
parser.test_path)
# output file
out_path = parser.distance + '_' + str(parser.k)
out_path += '.txt'
out_file = open(out_path, 'w')
# knn classification
print "# Classifying", len(train) * parser.percentage
for item in test:
dist_heap = []
# calculates the distance to every point in the training set
for i in xrange(int(len(train) * parser.percentage)):
point = train[i]
distance = 0.0
if parser.distance == 'cosine':
distance = spd.cosine(item, point)
elif parser.distance == 'jaccard':
distance = spd.jaccard(item, point)
elif parser.distance == 'euclidean':
distance = spd.euclidean(item, point)
elif parser.distance == 'hamming':
distance = spd.hamming(item, point)
elif parser.distance == 'correlation':
distance = spd.correlation(item, point)
elif parser.distance == 'manhattan':
distance = spd.cityblock(item, point)
else:
print >> stderr, "ERRO! - Distância informada inválida."
exit()
tup = (distance, i)
heapq.heappush(dist_heap, tup)
# return the highest k similar points
top_k = heapq.nsmallest(parser.k, dist_heap)
# classifing
classification = np.zeros(2)
for (_, idi) in top_k:
classe = neigh_classes[idi]
classification[int(classe)] += 1
# DEBUG
print classification,
# outputing classification
if(classification[0] >= classification[1]):
print >> out_file, '0'
print '0'
else:
print >> out_file, '1'
print '1'
print
print "# Resultados salvos no arquivo: " + out_path
out_file.close()
result.result("../data/imdb_test", out_path)
