matrix | 易学教程

Building and updating a sparse matrix in python using scipy

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问题 I'm trying to build and update a sparse matrix as I read data from file. The matrix is of size 100000X40000 What is the most efficient way of updating multiple entries of the sparse matrix? specifically I need to increment each entry by 1. Let's say I have row indices [2, 236, 246, 389, 1691] and column indices [117, 3, 34, 2757, 74, 1635, 52] so all the following entries must be incremented by one: (2,117) (2,3) (2,34) (2,2757) ... (236,117) (236,3) (236, 34) (236,2757) ... and so on. I'm

Building and updating a sparse matrix in python using scipy

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Building and updating a sparse matrix in python using scipy

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LeetCode（1277）：统计全为 1 的正方形子矩阵 Count Square Submatrices with All Ones（Java）

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2019.12.29 LeetCode 从零单刷个人笔记整理（持续更新） github： https://github.com/ChopinXBP/LeetCode-Babel 原地动态规划。遍历每一个位置ij时，将该位的数值置为从该位起左边的1的总个数，例如对于矩阵 [0,1,1,1], [1,1,1,1], [0,1,1,1] 遍历结束后效果为： [0,1,2,3], [1,2,3,4], [0,1,2,3] 为了统计正方形矩阵的个数，可以假定当前位置ij为正方形的右下角点，其值为可能的最大边长maxlen = matrix[i][j]。沿着同一列j向上遍历，遍历行数即为当前正方形的边长curlen，当遍历过程的最小边长minlen >= curlen时，可以构成当前正方形。传送门：统计全为 1 的正方形子矩阵 Given a m * n matrix of ones and zeros, return how many square submatrices have all ones. 给你一个 m * n 的矩阵，矩阵中的元素不是 0 就是 1，请你统计并返回其中完全由 1 组成的正方形子矩阵的个数。示例 1：输入：matrix = [ [0,1,1,1], [1,1,1,1], [0,1,1,1] ] 输出：15 解释：边长为 1 的正方形有 10 个。

OpenCV Homography, Transform a point, what is this code doing?

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问题 I'm working with a homography calculated by OpenCV. I currently use this homography to transform points using the function below. This function performs the task I require however I have no clue how it actually works. Can anyone explain, line by line exactly, the logic/theory behind the last 3 lines of code, I understand that this transforms the point x,y but I'm unclear as to why this works: Why are Z , px and py calculated in this way, what do the elements in h correspond to? Your comments

OpenCV Homography, Transform a point, what is this code doing?

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OpenCV Homography, Transform a point, what is this code doing?

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Efficient 4x4 matrix inverse (affine transform)

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问题 I was hoping someone can point out an efficient formula for 4x4 affine matrix transform. Currently my code uses cofactor expansion and it allocates a temporary array for each cofactor. It's easy to read, but it's slower than it should be. Note, this isn't homework and I know how to work it out manually using 4x4 co-factor expansion, it's just a pain and not really an interesting problem for me. Also I've googled and came up with a few sites that give you the formula already (http://www

Achieving batch matrix multiply using tensordot

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问题 I'm trying to achieve the same behaviour as np.matmul parallel matrix multiplication using just tensordot,dot and reshaping etc. The library I am translating this to using does not have a matmul that supports parallel multiplication, only dot and tensordot. Additionally I want to avoid iterating over the first dimension, and want to do this using a set of matrix multiplications and reshaping (want as much of it to run using BLAS/GPU as i have large numbers of small matrices to calculate in

How to pass matrix by reference or get the return value of function

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问题 I have a 1 x 118 matrix called current_load that I need to update periodically. This matrix resides in the main workspace of Matlab (as shown in the code bellow). current_loads = zeros(1, 118); for col=1:118 current_loads(1,col)=10; %// Initially give all nodes a current load of 10 end recursive_remove(current_loads); %calling function This matrix will be passed to a function call recursive_remove ( shown bellow ). function updater = recursive_remove( current_load ) current_load(1,3) = 2.6; %