查看NLTK软件包：http：//www.nltk.org，它具有您需要的一切
对于cosine_similarity：
def cosine_distance(u, v): """ Returns the cosine of the angle between vectors v and u. This is equal to u.v / |u||v|. """ return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v)))
对于ngrams：
def ngrams(sequence, n, pad_left=False, pad_right=False, pad_symbol=None): """ A utility that produces a sequence of ngrams from a sequence of items. For example: >>> ngrams([1,2,3,4,5], 3) [(1, 2, 3), (2, 3, 4), (3, 4, 5)] Use ingram for an iterator version of this function. Set pad_left or pad_right to true in order to get additional ngrams: >>> ngrams([1,2,3,4,5], 2, pad_right=True) [(1, 2), (2, 3), (3, 4), (4, 5), (5, None)] @param sequence: the source data to be converted into ngrams @type sequence: C{sequence} or C{iterator} @param n: the degree of the ngrams @type n: C{int} @param pad_left: whether the ngrams should be left-padded @type pad_left: C{boolean} @param pad_right: whether the ngrams should be right-padded @type pad_right: C{boolean} @param pad_symbol: the symbol to use for padding (default is None) @type pad_symbol: C{any} @return: The ngrams @rtype: C{list} of C{tuple}s """ if pad_left: sequence = chain((pad_symbol,) * (n-1), sequence) if pad_right: sequence = chain(sequence, (pad_symbol,) * (n-1)) sequence = list(sequence) count = max(0, len(sequence) - n + 1) return [tuple(sequence[i:i+n]) for i in range(count)]对于tf-idf，您将必须首先计算分布，我正在使用Lucene来做到这一点，但您可能会对NLTK做类似的事情，请使用FreqDist：
http://nltk.googlepre.com/svn/trunk/doc/book/ch01.html#frequency_distribution_index_term
如果您喜欢pylucene，这将告诉您如何上下班tf.idf
# reader = lucene.IndexReader(FSDirectory.open(index_loc)) docs = reader.numDocs() for i in xrange(docs): tfv = reader.getTermFreqVector(i, fieldname) if tfv: rec = {} terms = tfv.getTerms() frequencies = tfv.getTermFrequencies() for (t,f,x) in zip(terms,frequencies,xrange(maxtokensperdoc)): df= searcher.docFreq(Term(fieldname, t)) # number of docs with the given term tmap.setdefault(t, len(tmap)) rec[t] = sim.tf(f) * sim.idf(df, max_doc) #compute TF.IDF # and normalize the values using cosine normalization if cosine_normalization: denom = sum([x**2 for x in rec.values()])**0.5 for k,v in rec.items(): rec[k] = v / denom
