def do_txt_training(self, txt, k):
dataset, labels = self.txt2dataset(txt)
dataset = normalize(dataset)
# 90% for training, 10% for verify
index = int(0.9 * len(labels))
training_set = dataset[:index]
training_lables = labels[:index]
ref_set = dataset[index:]
ref_labels = labels[index:]
# start testing
errno = 0
for (x, label) in itertools.izip(ref_set, ref_labels):
result = classify(x, training_set, training_lables, k)
msg = 'Data: %s, label: %s, result: %s' % (x, label, result)
logging.debug(msg)
if result != label:
errno += 1
return float(errno) / len(ref_labels)
import kNN
import numpy
result_array = ['not at all', 'in small doses', 'in large doses']
dataset, labels = kNN.file_to_array('datingTestSet2.txt')
normalize_dataset, min_array, max_array, range_array = kNN.normalize(dataset)
fly_input = float(input('每年获得的飞行常客里程数 >>>'))
game_input = float(input('玩视频游戏所耗时间百分比 >>>'))
icecream_input = float(input('每周消费的冰淇淋公升数 >>>'))
input_array = numpy.array([fly_input, game_input, icecream_input])
normalize_input_array = (input_array - min_array) / range_array
label = kNN.classify(normalize_dataset, labels, normalize_input_array, 5)
print('label %s' % result_array[label - 1])
each_line = line.split('\t')
to_matrix[index, :] = each_line[0:3]
class_label_vector.append(int(
each_line[-1])) # int("label\n") => label: int
index += 1
return to_matrix, class_label_vector
if __name__ == "__main__":
root_path = os.path.dirname(os.path.abspath(__file__))
data_folder = os.path.join(root_path, "data")
data_file = os.path.join(data_folder, "dating_DataSet.txt")
training_set, class_label_vector = file_to_matrix(data_file)
normalized_training_set, range_values, min_value = kNN.normalize(
training_set)
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.scatter(normalized_training_set[:, 0], normalized_training_set[:, 1],
# np.array(class_label_vector), np.array(class_label_vector))
# plt.show()
# Classifier Test
k = 5
test_ratio = 0.15
data_size = normalized_training_set.shape[0]
test_set_size = int(data_size * test_ratio)
error_count = 0
for i in range(test_set_size):
def classifyBykNN(dataSet, labels, k, predictX):
dataSet = kNN.normalize(dataSet)
return kNN.kNNclassify(dataSet, labels, k, predictX)
classes = Rocchio.train(dataSet, labels)
return Rocchio.predict(classes, predictX)
def classifyByNBC(predictX):
return NBCpredict.predict(predictX)
def classifyBySVM(predictX):
return SVMpredict.predict(predictX)
def classifyByANN(predictX):
return ANNpredict.predict(predictX)
if __name__ == "__main__":
''' Classify the document using the given method '''
ds, labels = loadDataSet(CLASSIFY_FILE, 5400, 967)
fh = open('D:/e.txt', 'r')
x = vectorArticleByTFIDF(fh.read())
fh.close()
ds = kNN.normalize(ds)
c1 = int(classifyBykNN(ds, labels, 10, x))
c2 = int(classifyByRocchio(ds, labels, x))
##print c1;exit()
for c in CLASS_LABEL:
cid = CLASS_LABEL[c][0]
cname = CLASS_LABEL[c][1]
if cid == c1:
print 'Predicted by kNN: %s - %d(%s)' % (c, cid, cname)
if cid == c2:
print 'Predicted by Rocchio: %s - %d(%s)' % (c, cid, cname)
# Change lines into array
featureCount = len(lines[0].split()) - 1
group = np.zeros((len(lines), featureCount))
labels = []
for i in range(len(lines)):
lst = lines[i].split()
group[i] = np.array(lst[:-1])
labels.append(lst[-1])
return (group, labels)
# Get training set
group, labels = createDataSetFromFile('data/dating/training.txt')
group = kNN.normalize(group)
# Try on test set
testGroup, testLabels = createDataSetFromFile('data/dating/test.txt')
testGroup = kNN.normalize(testGroup)
correct = 0
for i in range(testGroup.shape[0]):
res = kNN.classify0(testGroup[i], group, labels, 3)
if res == 'didntLike':
res = '1'
elif res == 'smallDoses':
res = '2'
else:
res = '3'
if res == testLabels[i]:
def test_dataset():
group = numpy.array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
labels = ['A', 'A', 'B', 'B']
x = numpy.array([1, 0.5])
result = classify(x, normalize(group), labels, 3)
logging.info(result)
