我只是想在nltk中做非常簡單的無監督HMM訓練。NLTK中的無監督HMM訓練
考慮：
import nltk
trainer = nltk.tag.hmm.HiddenMarkovModelTrainer()
from nltk.corpus import gutenberg
emma = gutenberg.words('austen-emma.txt')
m = trainer.train_unsupervised(emma)
ValueError: A Uniform probability distribution must have at least one sample.
我能找到使用nltk.tag.hmm.HiddenMarkovModelTrainer.train_unsupervised的例子嗎？
顯然，nltk要求我們手動指定一組觀察到的符號和狀態，並且還要求未標記的序列的形式爲[ [(symb,tag),(symb,tag),...], [(symb,tag),(symb,tag),...], ...]。
因此，我們必須
s = """"Your humble writer knows a little bit about a lot of things, but despite writing a fair amount about text processing (a book, for example), linguistic processing is a relatively novel area for me. Forgive me if I stumble through my explanations of the quite remarkable Natural Language Toolkit (NLTK), a wonderful tool for teaching, and working in, computational linguistics using Python. Computational linguistics, moreover, is closely related to the fields of artificial intelligence, language/speech recognition, translation, and grammar checking.\nWhat NLTK includes\nIt is natural to think of NLTK as a stacked series of layers that build on each other. Readers familiar with lexing and parsing of artificial languages (like, say, Python) will not have too much of a leap to understand the similar -- but deeper -- layers involved in natural language modeling.\nGlossary of terms\nCorpora: Collections of related texts. For example, the works of Shakespeare might, collectively, by called a corpus; the works of several authors, corpora.\nHistogram: The statistic distribution of the frequency of different words, letters, or other items within a data set.\nSyntagmatic: The study of syntagma; namely, the statistical relations in the contiguous occurrence of letters, words, or phrases in corpora.\nContext-free grammar: Type-2 in Noam Chomsky's hierarchy of the four types of formal grammars. See Resources for a thorough description.\nWhile NLTK comes with a number of corpora that have been pre-processed (often manually) to various degrees, conceptually each layer relies on the processing in the adjacent lower layer. Tokenization comes first; then words are tagged; then groups of words are parsed into grammatical elements, like noun phrases or sentences (according to one of several techniques, each with advantages and drawbacks); and finally sentences or other grammatical units can be classified. Along the way, NLTK gives you the ability to generate statistics about occurrences of various elements, and draw graphs that represent either the processing itself, or statistical aggregates in results.\nIn this article, you'll see some relatively fleshed-out examples from the lower-level capabilities, but most of the higher-level capabilities will be simply described abstractly. Let's now take the first steps past text processing, narrowly construed. """
sentences = s.split('.')[:-1]
seq = [map(lambda x:(x,''), ss.split(' ')) for ss in sentences]
symbols = list(set([ss[0] for sss in seq for ss in sss]))
states = range(5)
trainer = nltk.tag.hmm.HiddenMarkovModelTrainer(states=states,symbols=symbols)
m = trainer.train_unsupervised(seq)
m.random_sample(random.Random(),10)
我認爲這是在NLTK這個bug：
http://code.google.com/p/nltk/issues/detail?id=681
但是錯誤消息「等概率分佈必須有至少一個樣本「。與你從錯誤中獲得的不同。