hopfere.blogg.se - Boa vs python size

It is sometimes referred to as a "Combat Magnum". 357 Magnum caliber revolver formerly manufactured by Colt's Manufacturing Company of Hartford, Connecticut.

reticulatus, is among the longest snakes known.""" ) document3 = tb ( """The Colt Python is a. Python (2004), both also made-for-TV films.""" ) document2 = tb ( """Python, from the Greek word (πύθων/πύθωνας), is a genus of nonvenomous pythons found in Africa and Asia. Python was followed by two sequels: Python II (2002) and Boa vs. It was filmed in Los Angeles, California and Malibu, California. It includes the classic final girl scenario evident in films like Friday the 13th. The film concerns a genetically engineered snake, a python, that escapes and unleashes itself on a small town. The film features several cult favorite actors, including William Zabka of The Karate Kid fame, Wil Wheaton, Casper Van Dien, Jenny McCarthy, Keith Coogan, Robert Englund (best known for his role as Freddy Krueger in the A Nightmare on Elm Street series of films), Dana Barron, David Bowe, and Sean Whalen. Now to test it out on some real documents taken from Wikipedia.ĭocument1 = tb ( """Python is a 2000 made-for-TV horror movie directed by Richard Clabaugh.

tfidf(word, blob, bloblist) computes the TF-IDF score.

Add 1 to the divisor to prevent division by zero. We take the ratio of the total number of documents to the number of documents containing word, then take the log of that. The more common a word is, the lower its idf.

idf(word, bloblist) computes "inverse document frequency" which measures how common a word is among all documents in bloblist.

A generator expression is passed to the sum() function.

n_containing(word, bloblist) returns the number of documents containing word.

We use TextBlob for breaking up the text into words and getting the word counts.

tf(word, blob) computes "term frequency" which is the number of times a word appears in a document blob, normalized by dividing by the total number of words in blob.log ( len ( bloblist ) / ( 1 + n_containing ( word, bloblist ))) def tfidf ( word, blob, bloblist ): return tf ( word, blob ) * idf ( word, bloblist ) words ) def idf ( word, bloblist ): return math. words ) def n_containing ( word, bloblist ): return sum ( 1 for blob in bloblist if word in blob. Import math from textblob import TextBlob as tb def tf ( word, blob ): return blob.