exit()
if not test:
print("Empty input")
if parsed_args.o:
print(test)
f = Feature()
sess = tf.Session()
W = tf.Variable(tf.random_uniform([300000, 1], -1.0, 1.0), name="Weigh")
b = tf.Variable(tf.zeros([1]), name="Bias")
saver = tf.train.Saver()
saver.restore(sess, 'my-model-20000')
x = f.features_for_tensorflow(test)
y = tf.add(tf.matmul(W, x, transpose_a=True), b)
comment = sess.run(y)
# output
if parsed_args.l:
print(clf.predict([f.features(test)]))
print(clf.predict_proba([f.features(test)]))
print(comment[0][0])
else:
print("Result:")
if clf.predict([f.features(test)]):
print("The content is politics-related")
else:
print("The content is not politics-related")
print("Probability of the content being politics-related: "+str(clf.predict_proba([f.features(test)])))
print("Predicted rating (推數減噓數): "+str(comment[0][0]))
# coding=utf-8
import numpy as np
from feature import Counter, Feature
import sqlite3
db = sqlite3.connect('ptt.db')
cur = db.execute('select * from articles')
f = Feature()
a = 0
for i in cur:
a += 1
try:
f.features(i[5])
except:
print(i[5])
print(f.size())
f.store()