目的
将gensim输出的格式转化为numpy array格式，支持作为scikit-learn，tensorflow的输入实施
使用nltk库的停用词和网上收集的资料整合成一份新的停用词表，用来过滤文档中的停用词，也去除了数字和特殊的标点符号，最后将所有字母转化为小写形式。
以下是原文：
Subject: Re: Candida(yeast) Bloom, Fact or Fiction
From: [email protected] (Pat Churchill)
Organization: Actrix Networks
Lines: 17
I am currently in the throes of a hay fever attack. SO who certainly
never reads Usenet, let alone Sci.med, said quite spontaneously "
There are a lot of mushrooms and toadstools out on the lawn at the
moment. Sure that‘s not your problem?"
Well, who knows? Or maybe it‘s the sourdough bread I bake?
After reading learned, semi-learned, possibly ignorant and downright
ludicrous stuff in this thread, I am about ready to believe anything :-)
If the hayfever gets any worse, maybe I will cook those toadstools...
--
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The floggings will continue until morale improves
[email protected] Pat Churchill, Wellington New Zealand
::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
下面是分词后的结果：
[‘subject‘, ‘bloom‘, ‘fiction‘, ‘pat‘, ‘organization‘, ‘networks‘, ‘lines‘, ‘hay‘, ‘fever‘, ‘attack‘, ‘reads‘, ‘usenet‘, ‘lot‘, ‘lawn‘, ‘moment‘, ‘bread‘, ‘reading‘, ‘learned‘, ‘ignorant‘, ‘stuff‘, ‘thread‘, ‘ready‘, ‘worse‘, ‘cook‘, ‘continue‘, ‘pat‘, ‘wellington‘, ‘zealand‘]
使用gensim工具包转化为词袋子模型如下：
[(17, 1.0), (23, 1.0), (32, 1.0), (381, 1.0), (536, 1.0), (768, 1.0), (776, 1.0), (877, 1.0), (950, 1.0), (1152, 1.0), (1195, 1.0), (1389, 1.0), (1548, 1.0), (1577, 1.0), (1682, 2.0), (1861, 1.0), (2041, 1.0), (3098, 1.0), (3551, 1.0), (3886, 1.0), (5041, 1.0), (5148, 1.0), (5149, 1.0), (8494, 1.0), (8534, 1.0), (9972, 1.0), (11608, 1.0)]
上述gensim转换的格式不能直接作为scikit-learn，tensorflow的输入，需要使用
Gensim包的工具函数进行转换, 转换后变为：
[ 0. 0. 0. ..., 0. 0. 0.]
···
custom_train_matrix = gensim.matutils.corpus2dense(custom_train_corpus, num_terms=len_custom_dict).T # 关键方法为corpus2dense
custom_test_matrix = gensim.matutils.corpus2dense(custom_test_corpus, num_terms=len_custom_dict).T
···
上述输出的格式已经是numpy arrary格式了，可以作为scikit-learn，tensorflow的输入了。我们使用tf-idf技术，提高重要单词的比重，降低常见单词的比重，使用sckit-learn包转换上述输出如下：
(0, 11608) 0.179650698812
(0, 9972) 0.235827148023
(0, 8534) 0.208306524508
...................
(0, 381) 0.119518580927
(0, 32) 0.034904390394
(0, 23) 0.0374215245774
(0, 17) 0.0349485731726
上述输入已经可以作为scikit-learn的输入数据了，如果要作为tensorflow的输入数据，还需要将其转化为numpy array格式
···
custom_train_matrix = custom_train_matrix.toarray() # 将稀疏矩阵转化为numpy array
custom_test_matrix = custom_test_matrix.toarray() # 将稀疏矩阵转化为numpy array
···