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NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
In Python NumPy arrays implement the flatten method, [note 1] while in R the desired effect can be achieved via the c() or as.vector() functions. In R , function vec() of package 'ks' allows vectorization and function vech() implemented in both packages 'ks' and 'sn' allows half-vectorization.
An array data structure can be mathematically modeled as an abstract data structure (an abstract array) with two operations get(A, I): the data stored in the element of the array A whose indices are the integer tuple I. set(A, I, V): the array that results by setting the value of that element to V. These operations are required to satisfy the ...
Pseudocode to accomplish this (assuming zero-based array indices) is: for n = 0 to N - 1 for m = n + 1 to N swap A(n,m) with A(m,n) This type of implementation, while simple, can exhibit poor performance due to poor cache-line utilization, especially when N is a power of two (due to cache-line conflicts in a CPU cache with limited associativity).
Support for multi-dimensional arrays may also be provided by external libraries, which may even support arbitrary orderings, where each dimension has a stride value, and row-major or column-major are just two possible resulting interpretations. Row-major order is the default in NumPy [19] (for Python).
The square step: For each diamond in the array, set the midpoint of that diamond to be the average of the four corner points plus a random value. Each random value is multiplied by a scale constant, which decreases with each iteration by a factor of 2 −h, where h is a value between 0.0 and 1.0 (lower values produce rougher terrain). [2]
Flood fill, also called seed fill, is a flooding algorithm that determines and alters the area connected to a given node in a multi-dimensional array with some matching attribute. It is used in the "bucket" fill tool of paint programs to fill connected, similarly colored areas with a different color, and in games such as Go and Minesweeper for ...
Matrix completion is the task of filling in the missing entries of a partially observed matrix, which is equivalent to performing data imputation in statistics. A wide range of datasets are naturally organized in matrix form.