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Metaphone is a phonetic algorithm, published by Lawrence Philips in 1990, for indexing words by their English pronunciation. [1] It fundamentally improves on the Soundex algorithm by using information about variations and inconsistencies in English spelling and pronunciation to produce a more accurate encoding, which does a better job of matching words and names which sound similar.
In all versions of Python, boolean operators treat zero values or empty values such as "", 0, None, 0.0, [], and {} as false, while in general treating non-empty, non-zero values as true. The boolean values True and False were added to the language in Python 2.2.1 as constants (subclassed from 1 and 0 ) and were changed to be full blown ...
The following is a list of the 172 most common word duplicates (number after word is count of occurrences) extracted from a search of all English Wikipedia articles existing on 21 February 2006. Most punctuation was automatically removed and so the count is unlikely to be 100% accurate.
By allowing a false positive rate for the duplicates, the communication volume can be reduced further as the PEs don't have to send elements with duplicated hashes at all and instead any element with a duplicated hash can simply be marked as a duplicate. As a result, the false positive rate for duplicate detection is the same as the false ...
Commonly provided are dup, to duplicate the element atop the stack, exch (or swap), to exchange elements atop the stack (the first becomes second and the second becomes first), roll, to cyclically permute elements in the stack or on part of the stack, pop (or drop), to discard the element atop the stack (push is implicit), and others. These ...
Then the duplicates will be listed, and must be manually merged. There will often be false positives; web.archive.org URLs, in particular, are a nuisance as they contain the original URLs, which show as duplicates. The optional part of Step 2 eliminates the archive URLs, but unfortunately the list of duplicates includes the archived pages.
In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1] [2] can be applied to image classification or retrieval, by treating image features as words. In document classification , a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary.
The constants 1 and 0 are sometimes used to represent the Boolean values true and false in programming languages without a Boolean type, such as older versions of C. Most modern programming languages provide a boolean or bool primitive type and so the use of 0 and 1 is ill-advised. This can be more confusing since 0 sometimes means programmatic ...