How to compute similarity(percentage) between two matrix/arrays. or find the closest array/matrix to a given array, on the basis of how similar their data value
I have this table with these values I have this code in java import java.util.ArrayList; import java.util.HashMap; import java.util.List; import java.util.Map;
I was planning to use PTV Developer API to calculate a distance matrix using something more sophisticated than a Manhattan distance. However looking at the Rou
I'm trying to decompose a 9x9 matrix into 9 3x3 matrices. I've already used the command reshape, but the matrices it returns are the ones as transforming the co
I seem to have a logical misconception, regarding the matrix of svg elements, specifically the parts e and f, which I hope someone can help me clarify. Basing o
I understand that eigenvectors are only defined up to a multiplicative constant. As far as I see all numpy algorithms (e.g. linalg.eig, linalg.eigh, linalg.svd)
I would like to create a vector of the same matrix in numpy (so as an array). Let's say the matrix is: w = np.array([[1,2], [3,4], [
I was posting a question on similar subject, and encountered another more important question. When I apply SVD to a matrix 'A' (code below) the output I get is
What do I want to do? I'm working on a project on dynamic matrix multiplication. I want to input from the user that on how many matrices, he/she wants to perfor
i have an 2-d array(matrix) 3x3 like [[1, 2, 3],[4, 5, 6],[7, 8, 9]] and i need to replace 2 rows where is max and min value so its looks like: [[7, 8, 9],[4, 5
The task is to write a function that returns all its neighbors for an element. Diagonal elements are not considered neighbors. In general, the problem is solved
This question is probably more related to math than svg itself. Inside a main svg, got multiples transformed svg, (from different events), by a viewBox attrib
I am looking to implement a constraint on an optimization on all diagonals of a matrix using CVXPY. The diag function in CVXPY only returns the main diagonal.
In the image A-L is the longest path, but L-M is the heaviest The heaviest path of the graph is the path with the most edges connected to it, for this current c
I want to sort a binary matrix so that its columns and rows are both in lexicographical order by switching rows and columns. In other words, I need a double-lex
I'm looking for rows in B which are close to any of the rows in A example: eps = 0.1 A = [[1.22, 1.33], [1.45, 1.66]] B = [[1.25, 1.34], [1.77, 1.66], [1.44, 1.
Suppose, I have these values for a container of height 200 and width 300: scaleX = 0.9198 scaleY = 0.9198 skewX = -0.3923 skewY = 0.3923 translateX = 150 transl
What is the Pythonic way to get a list of diagonal elements in a matrix passing through entry (i,j)? For e.g., given a matrix like: [1 2 3 4 5] [6 7 8
In the following code I would like to assign a values to elements of a Mat variable in a loop. I get the runtime error below. pair<Mat, Mat> meshgrid(vect
I have already make many animations using Segment Scale Compensate, I need to import these animations to Unity, unfortunately, while SSC is enabled, animations
github-mantle