from logistic_regression import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
if __name__ == '__main__':
raw_data = pd.read_csv('train_binary.csv', header=0)
data = raw_data.values
imgs = data[0::, 1::]
labels = data[::, 0]
test_time = 10
p = Perceptron()
lr = LogisticRegression()
# writer = writer_csv_file('result.csv')
# with open('result.csv', 'wb', encoding='utf-8') as f:
# writer = csv.writer(f,)
csvfile = codecs.open('result.csv', 'w+', encoding='utf-8')
writer = csv.writer(csvfile)
for time in range(test_time):
print('iterater time %d' % time)
train_features, test_features, train_labels, test_labels = train_test_split(
imgs, labels, test_size=0.33, random_state=23323)
from logistic_regression import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
if __name__ == '__main__':
raw_data = pd.read_csv('../data/train_binary.csv', header=0)
data = raw_data.values
imgs = data[0::, 1::]
labels = data[::, 0]
test_time = 10
p = Perceptron()
lr = LogisticRegression()
writer = csv.writer(file('result.csv', 'wb'))
for time in xrange(test_time):
print 'iterater time %d' % time
train_features, test_features, train_labels, test_labels = train_test_split(
imgs, labels, test_size=0.33, random_state=23323)
p.train(train_features, train_labels)
lr.train(train_features, train_labels)
p_predict = p.predict(test_features)
lr_predict = lr.predict(test_features)