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import random # this function checks whether or not the array is sorted def is_sorted (random_array): for i in range (1, len (random_array)): if random_array [i] < random_array [i-1]: return False return True # this function repeatedly shuffles the elements of the array until they are sorted def bogo_sort (random_array): while not is_sorted (random_array): random. shuffle (random_array) return ...
Microsoft Excel provides two ranking functions, the Rank.EQ function which assigns competition ranks ("1224") and the Rank.AVG function which assigns fractional ranks ("1 2.5 2.5 4"). The functions have the order argument, [ 1 ] which is by default is set to descending , i.e. the largest number will have a rank 1.
Shuffling can also be implemented by a sorting algorithm, namely by a random sort: assigning a random number to each element of the list and then sorting based on the random numbers. This is generally not done in practice, however, and there is a well-known simple and efficient algorithm for shuffling: the Fisher–Yates shuffle .
In computing, an odd–even sort or odd–even transposition sort (also known as brick sort [1] [self-published source] or parity sort) is a relatively simple sorting algorithm, developed originally for use on parallel processors with local interconnections. It is a comparison sort related to bubble sort, with which it shares many ...
We select a random element q of a random permutation and ask about the expected size of the cycle that contains q. Here the function b ( k ) {\displaystyle b(k)} is equal to k 2 {\displaystyle k^{2}} , because a cycle of length k contributes k elements that are on cycles of length k .
For a random sample as above, with cumulative distribution (), the order statistics for that sample have cumulative distributions as follows [2] (where r specifies which order statistic): () = = [()] [()] The proof of this formula is pure combinatorics: for the th order statistic to be , the number of samples that are > has to be between and .
In mathematics and computer science, the sorting numbers are a sequence of numbers introduced in 1950 by Hugo Steinhaus for the analysis of comparison sort algorithms. These numbers give the worst-case number of comparisons used by both binary insertion sort and merge sort. However, there are other algorithms that use fewer comparisons.
A very simple random sort algorithm was proved by Sunter in 1977. [10] The algorithm simply assigns a random number drawn from uniform distribution ( 0 , 1 ) {\displaystyle (0,1)} as a key to each item, then sorts all items using the key and selects the smallest k {\displaystyle k} items.
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