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Python3 支持 #2

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Python3 支持 #2

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9b30373
python 3 support; matplotlib.finance module is deprecated in 2.0, so …
SmirkCao Sep 13, 2018
468e299
add .py file
SmirkCao Sep 13, 2018
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6 changes: 3 additions & 3 deletions Dice_01.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@
c = np.array([i])
x_next[i] = dice_hmm.predict(X, i)

print "state: ", Z
print "logprob: ", logprob
print "prob of x_next: ", x_next
print("state: ", Z)
print("logprob: ", logprob)
print("prob of x_next: ", x_next)

15 changes: 7 additions & 8 deletions Stock_03.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,27 +5,26 @@
import datetime

import numpy as np
import pandas as pd
from matplotlib import cm, pyplot as plt
from matplotlib.dates import YearLocator, MonthLocator
from matplotlib.finance import quotes_historical_yahoo_ochl

from hmm import GaussianHMM
from sklearn.preprocessing import scale


###############################################################################
# 导入Yahoo金融数据
quotes = quotes_historical_yahoo_ochl(
"INTC", datetime.date(1995, 1, 1), datetime.date(2012, 1, 6))

dates = np.array([q[0] for q in quotes], dtype=int) # 日期列
close_v = np.array([q[2] for q in quotes]) # 收盘价
volume = np.array([q[5] for q in quotes])[1:] # 交易数
quotes = pd.read_csv('data/yahoofinance-INTC-19950101-20040412.csv')

dates = quotes.index.values
close_v = quotes[["Close"]].values.flatten()
volume = quotes[["Volume"]].values.flatten()
# diff：out[n] = a[n+1] - a[n] 得到价格变化
diff = np.diff(close_v)
dates = dates[1:]
close_v = close_v[1:]
volume = volume[1:]

# scale归一化处理：均值为0和方差为1
# 将价格和交易数组成输入数据
Expand All @@ -39,7 +38,7 @@
hidden_states = model.decode(X)

# 打印参数
print "Transition matrix: ", model.transmat_prob
print("Transition matrix: ", model.transmat_prob)
print("Means and vars of each hidden state")
for i in range(model.n_state):
print("{0}th hidden state".format(i))
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22 changes: 11 additions & 11 deletions Wordseg_02.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ def getList(input_str):

# 预处理词典：RenMinData.txt_utf8
def precess_data():
ifp = file("RenMinData.txt_utf8")
ifp = open("RenMinData.txt_utf8")
line_num = 0
word_dic = {}
word_ind = 0
Expand All @@ -38,18 +38,18 @@ def precess_data():
for line in ifp:
line_num += 1
if line_num % 10000 == 0:
print line_num
print(line_num)

line = line.strip()
if not line:continue
line = line.decode("utf-8","ignore")
# line = line.decode("utf-8","ignore")

word_list = []
for i in range(len(line)):
if line[i] == " ":continue
word_list.append(line[i])
# 建立单词表
if not word_dic.has_key(line[i]):
if not word_dic.__contains__(line[i]):
word_dic[line[i]] = word_ind
word_ind += 1
line_seq.append(word_list)
Expand All @@ -71,7 +71,7 @@ def precess_data():
def word_trans(wordline, word_dic):
word_inc = []
line = wordline.strip()
line = line.decode("utf-8", "ignore")
# line = line.decode("utf-8", "ignore")
for n in range(len(line)):
word_inc.append([word_dic[line[n]]])

Expand All @@ -81,8 +81,8 @@ def word_trans(wordline, word_dic):
wordseg_hmm = hmm.DiscreteHMM(4,len(word_dic),5)
wordseg_hmm.train_batch(X,Z)

print "startprob_prior: ", wordseg_hmm.start_prob
print "transmit: ", wordseg_hmm.transmat_prob
print("startprob_prior: ", wordseg_hmm.start_prob)
print("transmit: ", wordseg_hmm.transmat_prob)

sentence_1 = "我要回家吃饭"
sentence_2 = "中国人民从此站起来了"
Expand All @@ -94,8 +94,8 @@ def word_trans(wordline, word_dic):
Z_3 = wordseg_hmm.decode(word_trans(sentence_3,word_dic))
Z_4 = wordseg_hmm.decode(word_trans(sentence_4,word_dic))

print u"我要回家吃饭: ", Z_1
print u"中国人民从此站起来了: ", Z_2
print u"经党中央研究决定: ", Z_3
print u"江主席发表重要讲话: ", Z_4
print(u"我要回家吃饭: ", Z_1)
print(u"中国人民从此站起来了: ", Z_2)
print(u"经党中央研究决定: ", Z_3)
print(u"江主席发表重要讲话: ", Z_4)

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