Esempio n. 1
0
    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]))
Esempio n. 2
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()