# -*- coding: utf-8 -*-

import sys

import math

import tflearn

import chardet

import numpy as np

import struct

seq = []

max_w = 50

float_size = 4

word_vector_dict = {}

def load_vectors(input):

"""从vectors.bin加载词向量，返回一个word_vector_dict的词典，key是词，value是200维的向量

"""

print "begin load vectors"

input_file = open(input, "rb")

# 获取词表数目及向量维度

words_and_size = input_file.readline()

words_and_size = words_and_size.strip()

words = long(words_and_size.split(' ')[0])

size = long(words_and_size.split(' ')[1])

print "words =", words

print "size =", size

for b in range(0, words):

a = 0

word = ''

# 读取一个词

while True:

c = input_file.read(1)

word = word + c

if False == c or c == ' ':

break

if a < max_w and c != '\n':

a = a + 1

word = word.strip()

vector = []

for index in range(0, size):

m = input_file.read(float_size)

(weight,) = struct.unpack('f', m)

vector.append(weight)

# 将词及其对应的向量存到dict中

word_vector_dict[word.decode('utf-8')] = vector

input_file.close()

print "load vectors finish"

def init_seq():

"""读取切好词的文本文件，加载全部词序列

"""

file_object = open('zhenhuanzhuan.segment', 'r')

vocab_dict = {}

while True:

line = file_object.readline()

if line:

for word in line.decode('utf-8').split(' '):

if word_vector_dict.has_key(word):

seq.append(word_vector_dict[word])

else:

break

file_object.close()

def vector_sqrtlen(vector):

len = 0

for item in vector:

len += item * item

len = math.sqrt(len)

return len

def vector_cosine(v1, v2):

if len(v1) != len(v2):

sys.exit(1)

sqrtlen1 = vector_sqrtlen(v1)

sqrtlen2 = vector_sqrtlen(v2)

value = 0

for item1, item2 in zip(v1, v2):

value += item1 * item2

return value / (sqrtlen1*sqrtlen2)

def vector2word(vector):

max_cos = -10000

match_word = ''

for word in word_vector_dict:

v = word_vector_dict[word]

cosine = vector_cosine(vector, v)

if cosine > max_cos:

max_cos = cosine

match_word = word

return (match_word, max_cos)

def main():

load_vectors("./vectors.bin")

init_seq()

xlist = []

ylist = []

test_X = None

#for i in range(len(seq)-100):

for i in range(1000):

sequence = seq[i:i+20]

xlist.append(sequence)

ylist.append(seq[i+20])

if test_X is None:

test_X = np.array(sequence)

(match_word, max_cos) = vector2word(seq[i+20])

print "right answer=", match_word, max_cos

X = np.array(xlist)

Y = np.array(ylist)

net = tflearn.input_data([None, 20, 200])

net = tflearn.lstm(net, 200)

net = tflearn.fully_connected(net, 200, activation='linear')

net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1,

loss='mean_square')

model = tflearn.DNN(net)

model.fit(X, Y, n_epoch=1000, batch_size=1,snapshot_epoch=False,show_metric=True)

model.save("model")

predict = model.predict([test_X])

#print predict

#for v in test_X:

# print vector2word(v)

(match_word, max_cos) = vector2word(predict[0])

print "predict=", match_word, max_cos

main()

#init_seq()

#

#x1 = [1,2,3,4,5]

#x2 = [0,1,2,3]

#y = [2,4,6,8,10]

#X = np.array([x1,x2])

#Y = np.array([y])

#net = tflearn.input_data([None])

##net = tflearn.embedding(net, input_dim=4, output_dim=2)

#net = tflearn.single_unit(net)

##net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,loss='categorical_crossentropy')

#net = tflearn.regression(net, optimizer='sgd', loss='mean_square',

# metric='R2', learning_rate=0.01)

##layer1 = tflearn.fully_connected(net, 2)

#model = tflearn.DNN(net)

#model.fit(x1, y, n_epoch=1000, snapshot_epoch=False,show_metric=True,batch_size=1)

#print model.predict(x1)

##net = tflearn.embedding(net, input_dim=10000, output_dim=16)

#net = tflearn.lstm(net, 16, dropout=0.8)

#net = tflearn.fully_connected(net, 2, activation='softmax')

#net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,loss='categorical_crossentropy')

#model = tflearn.DNN(net, tensorboard_verbose=0)

#model.fit(X, Y, show_metric=True,batch_size=32)

#sys.exit(0)

# 下面都是测试用的，不用看

def test_case1():

x = [1,2,3]

y = [0.01,0.99]

# 多组x作为输入样本

X = np.array(np.repeat([x], 1, axis=0))

# 多组y作为样本的y值

Y = np.array(np.repeat([y], 1, axis=0))

#X = np.array([x1,x2], dtype=np.float32)

#Y = np.array([y1,y2])

# 这里的第二个数对应了x是多少维的向量

net = tflearn.input_data(shape=[None, 3])

#net = tflearn.fully_connected(net, 32)

net = tflearn.fully_connected(net, 2)

# 这里的第二个参数对应了输出的y是多少维的向量

#net = tflearn.fully_connected(net, 2, activation='softmax')

net = tflearn.regression(net)

model = tflearn.DNN(net)

model.fit(X, Y, n_epoch=1000, batch_size=1, show_metric=True, snapshot_epoch=False)

pred = model.predict([x])

print(pred)

def case_linear_regression():

x = [1,2,3,4,5]

y = [2,4,6,8,10]

net = tflearn.input_data([None])

linear = tflearn.single_unit(net)

net = tflearn.regression(linear, optimizer='sgd', loss='mean_square',

metric='R2', learning_rate=0.01)

model = tflearn.DNN(net)

model.fit(x, y, n_epoch=200, snapshot_epoch=False,show_metric=True,batch_size=1)

print model.predict([8,9]) # [15.990408897399902, 17.988374710083008]

print model.get_weights(linear.W) # [ 1.99796414]

print model.get_weights(linear.b) # [ 0.00669619]

#case_linear_regression()

#X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]]

#Y_xor = [[0.], [1.], [1.], [0.]]

# 如何输出每一步的输出值

# You can re-use a new model that share a same session (to use same weights): . Note that you could also save your 'm' model and load it with 'm2', that gives similar results.

## Graph definition

#with tf.Graph().as_default():

# tnorm = tflearn.initializations.uniform(minval=-1.0, maxval=1.0)

# net = tflearn.input_data(shape=[None, 2], name='inputLayer')

# layer1 = tflearn.fully_connected(net, 2, activation='sigmoid', weights_init=tnorm, name='layer1')

# layer2 = tflearn.fully_connected(layer1, 1, activation='softmax', weights_init=tnorm, name='layer2')

# regressor = tflearn.regression(layer2, optimizer='sgd', learning_rate=2., loss='mean_square', name='layer3')

#

# # Training

# m = tflearn.DNN(regressor)

# m.fit(X, Y_xor, n_epoch=100, snapshot_epoch=False)

#

# # Testing

# print("Testing XOR operator")

# print("0 xor 0:", m.predict([[0., 0.]]))

# print("0 xor 1:", m.predict([[0., 1.]]))

# print("1 xor 0:", m.predict([[1., 0.]]))

# print("1 xor 1:", m.predict([[1., 1.]]))

#

# # You can create a new model, that share the same session (to get same weights)

# # Or you can also simply save and load a model

# m2 = tflearn.DNN(layer1, session=m.session)

# print(m2.predict([[0., 0.]]))