vectorization

问题 I have a numpy array like this import numpy as np ar = np.array([1, 2, 3, 4]) and I want to create an array that looks like this: array([[4, 1, 2, 3], [3, 4, 1, 2], [2, 3, 4, 1], [1, 2, 3, 4]]) Thereby, each row corresponds to ar which is shifted by the row index + 1. A straightforward implementation could look like this: ar_roll = np.tile(ar, ar.shape[0]).reshape(ar.shape[0], ar.shape[0]) for indi, ri in enumerate(ar_roll): ar_roll[indi, :] = np.roll(ri, indi + 1) which gives me the desired

问题 In a follow-up to some previous questions on converting RGB to RGBA, and ARGB to BGR, I would like to speed up a RGB to BGRA conversion with SSE . Assume a 32-bit machine, and would like to use intrinsics . I'm having difficulty aligning both source and destination buffers to work with 128-bit registers, and seek for other savvy vectorization solutions. The routine to be vectorized is as follows... void RGB8ToBGRX8(int w, const void *in, void *out) { int i; int width = w; const unsigned char

问题 Given a list a containing vectors of unequal length and a vector b containing some elements from the vectors in a , I want to get a vector of equal length to b containing the index in a where the element in b matches (this is a bad explanation I know)... The following code does the job: a <- list(1:3, 4:5, 6:9) b <- c(2, 3, 5, 8) sapply(b, function(x, list) which(unlist(lapply(list, function(y, z) z %in% y, z=x))), list=a) [1] 1 1 2 3 Replacing the sapply with a for loop achieves the same of

问题 Searching for an idea how to avoid using loop in my Matlab code, I found following comments under one question on SE: The statement "for loops are slow in Matlab" is no longer generally true since Matlab...euhm, R2008a? and Have you tried to benchmark a for loop vs what you already have? sometimes it is faster than vectorized code... So I would like to ask, is there commonly used way to test the speed of a process in Matlab ? Can user see somewhere how much time the process takes or the only

问题 I want to calculate the average geographical distance between a number of houses per province. Suppose I have the following data. df1 <- data.frame(province = c(1, 1, 1, 2, 2, 2), house = c(1, 2, 3, 4, 5, 6), lat = c(-76.6, -76.5, -76.4, -75.4, -80.9, -85.7), lon = c(39.2, 39.1, 39.3, 60.8, 53.3, 40.2)) Using the geosphere library I can find the distance between two houses. For instance: library(geosphere) distm(c(df1$lon[1], df1$lat[1]), c(df1$lon[2], df1$lat[2]), fun = distHaversine) #11429

问题 For each row in the input table, I need to generate multiple rows by separating the date range based on monthly. (please refer to the below sample output). There is a simple iterative approach to convert row by row, but it is very slow on large dataframes. Could anyone suggest a vectorized approach, such as using apply(), map() etc. to achieve the objective? The output table is a new table. Input: ID, START_DATE, END_DATE 1, 2010-12-08, 2011-03-01 2, 2010-12-10, 2011-01-12 3, 2010-12-16, 2011

问题 I have a (N,3) array of numpy values: >>> vals = numpy.array([[1,2,3],[4,5,6],[7,8,7],[0,4,5],[2,2,1],[0,0,0],[5,4,3]]) >>> vals array([[1, 2, 3], [4, 5, 6], [7, 8, 7], [0, 4, 5], [2, 2, 1], [0, 0, 0], [5, 4, 3]]) I'd like to remove rows from the array that have a duplicate value. For example, the result for the above array should be: >>> duplicates_removed array([[1, 2, 3], [4, 5, 6], [0, 4, 5], [5, 4, 3]]) I'm not sure how to do this efficiently with numpy without looping (the array could

问题 Suppose I have the following two variables: start_idx = [1 4 7]; end_idx = [2 6 15]; I want to efficiently (no for loop if possible) generate a single row which consists of the colon operator being applied between corresponding elements of start_idx and end_idx . For this example, this would result in: result = [1:2 4:6 7:15]; Therefore: results = [1 2 4 5 6 7 8 9 10 11 12 13 14 15]; The method to do this should be usable inside Simulink's MATLAB Function block. Thank you very much! 回答1: Here

问题 The recent questions regarding the use of require versus :: raised the question about which programming styles are used when programming in R, and what their advantages/disadvantages are. Browsing through the source code or browsing on the net, you see a lot of different styles displayed. The main trends in my code : heavy vectorization I play a lot with the indices (and nested indices), which results in rather obscure code sometimes but is generally a lot faster than other solutions. eg: x[x

问题 I have a pandas Series which presently looks like this: 14 [Yellow, Pizza, Restaurants] ... 160920 [Automotive, Auto Parts & Supplies] 160921 [Lighting Fixtures & Equipment, Home Services] 160922 [Food, Pizza, Candy Stores] 160923 [Hair Removal, Nail Salons, Beauty & Spas] 160924 [Hair Removal, Nail Salons, Beauty & Spas] And I want to radically reshape it into a dataframe that looks something like this... Yellow Automotive Pizza 14 1 0 1 … 160920 0 1 0 160921 0 0 0 160922 0 0 1 160923 0 0 0