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Example one shows how narrative text can be interspersed with testable examples in a docstring. In the second example, more features of doctest are shown, together with their explanation. Example three is set up to run all doctests in a file when the file is run, but when imported as a module, the tests will not be run.
The key insight in this algorithm is that if the end of the pattern is compared to the text, then jumps along the text can be made rather than checking every character of the text. The reason that this works is that in lining up the pattern against the text, the last character of the pattern is compared to the character in the text.
A simple and inefficient way to see where one string occurs inside another is to check at each index, one by one. First, we see if there is a copy of the needle starting at the first character of the haystack; if not, we look to see if there's a copy of the needle starting at the second character of the haystack, and so forth.
The bag-of-words model (BoW) is a model of text which uses a representation of text that is based on an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity.
The algorithm attempts to find the same word, but in all its word endings. A fuzzy search will match a different word. Words (but not phrases) accept approximate string matching or "fuzzy search". A tilde ~ character is appended for this "sounds like" search. The other word must differ by no more than two letters. Not the first two letters.
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus.
Just Words is a word game for one or two players where you scores points by making new words using singularly lettered tiles on a board, bringing you the classic SCRABBLE experience, but with a twist!
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]