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For a square N×N matrix A n,m = A(n,m), in-place transposition is easy because all of the cycles have length 1 (the diagonals A n,n) or length 2 (the upper triangle is swapped with the lower triangle). 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)
The fundamental idea behind array programming is that operations apply at once to an entire set of values. This makes it a high-level programming model as it allows the programmer to think and operate on whole aggregates of data, without having to resort to explicit loops of individual scalar operations.
More generally, there are d! possible orders for a given array, one for each permutation of dimensions (with row-major and column-order just 2 special cases), although the lists of stride values are not necessarily permutations of each other, e.g., in the 2-by-3 example above, the strides are (3,1) for row-major and (1,2) for column-major.
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]
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 ...
even = x (2:: 2); odd = x (:: 2); is how one would use Fortran to create arrays from the even and odd entries of an array. Another common use of vectorized indices is a filtering operation.
An m × n (read as m by n) order matrix is a set of numbers arranged in m rows and n columns. Matrices of the same order can be added by adding the corresponding elements. Two matrices can be multiplied, the condition being that the number of columns of the first matrix is equal to the number of rows of the second matrix.
a = [3, 1, 5, 7] // assign an array to the variable a a [0.. 1] // return the first two elements of a a [.. 1] // return the first two elements of a: the zero can be omitted a [2..] // return the element 3 till last one a [[0, 3]] // return the first and the fourth element of a a [[0, 3]] = [100, 200] // replace the first and the fourth element ...