August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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非监督学习
：
主题建模与聚类
｜
257
from gensim.models import LdaModel
lda_gensim_para = LdaModel(corpus=bow_gensim_para, id2word=dict_gensim_para,
chunksize=2000, alpha='auto', eta='auto', iterations=400, num_topics=10,
passes=20, eval_every=None, random_state=42)
我们感兴趣的是主题的单词分布：
display_topics_gensim(lda_gensim_para)
输出结果：
texts=gensim_paragraphs,
dictionary=dict_gensim_para,
coherence='c_v')
nmf_gensim_para_coherence_score = nmf_gensim_para_coherence.get_coherence()
print(nmf_gensim_para_coherence_score)
Out:
0.6500661701098243
The score varies with the number of topics. If you want to find the optimal number of
topics, a frequent approach is to run NMF for several ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.