vectorization

问题 Assume I have a 1d array, what I want is to sample with a moving window and within the window divide each element by the first element. For example if I have [2, 5, 8, 9, 6] and a window size of 3, the result will be [[1, 2.5, 4], [1, 1.6, 1.8], [1, 1.125, 0.75]]. What I'm doing now is basically a for loop import numpy as np arr = np.array([2., 5., 8., 9., 6.]) window_size = 3 for i in range(len(arr) - window_size + 1): result.append(arr[i : i + window_size] / arr[i]) etc. When the array is

问题 I have some code that I would like to run fast, so I was hoping I could persuade gcc (g++) to vectorise some of my inner loops. My compiler flags include -O3 -msse2 -ffast-math -ftree-vectorize -ftree-vectorizer-verbose=5 but gcc fails to vectorize the most important loops, giving me the following not-really-very-verbose-at-all messages: Not vectorized: complicated access pattern. and Not vectorized: unsupported use in stmt. My questions are (1) what exactly do these mean? (How complicated

问题 The recipe section of itertools docs begins with this text: The extended tools offer the same high performance as the underlying toolset. The superior memory performance is kept by processing elements one at a time rather than bringing the whole iterable into memory all at once. Code volume is kept small by linking the tools together in a functional style which helps eliminate temporary variables. High speed is retained by preferring “vectorized” building blocks over the use of for-loops and

问题 Suppose I have a long data vector y, plus some indices into it. I want to extract a short snippet or window around every index. For example, suppose I want to construct a matrix containing 64 samples before and 64 samples after every value that is below three. This is trivial to do in a for-loop: WIN_SIZE = 64; % Sample data with padding data = [nan(WIN_SIZE,1); randn(1e6,1); nan(WIN_SIZE,1)]; % Sample events, could be anything index = find(data < 3); snippets = nan(length(index), 2*WIN_SIZE

问题 What's a clever (i.e., not a loop) way to get the length of each spell of missing values in a vector? My ideal output is a vector that is the same length, in which each missing value is replaced by the length of the spell of missing values of which it was a part, and all other values are 0's. So, for input like: x <- c(2,6,1,2,NA,NA,NA,3,4,NA,NA) I'd like output like: y <- c(0,0,0,0,3,3,3,0,0,2,2) 回答1: One simple option using rle : m <- rle(is.na(x)) > rep(ifelse(m$values,m$lengths,0),times =

问题 I'm looking for the most efficient way of finding the intersection of two different-sized matrices. Each matrix has three variables (columns) and a varying number of observations (rows). For example, matrix A: a = np.matrix('1 5 1003; 2 4 1002; 4 3 1008; 8 1 2005') b = np.matrix('7 9 1006; 4 4 1007; 7 7 1050; 8 2 2003'; 9 9 3000; 7 7 1000') If I set the tolerance for each column as col1 = 1 , col2 = 2 , and col3 = 10 , I would want a function such that it would output the indices in a and b

问题 Given two arrays, say arr = array([10, 24, 24, 24, 1, 21, 1, 21, 0, 0], dtype=int32) rep = array([3, 2, 2, 0, 0, 0, 0, 0, 0, 0], dtype=int32) np.repeat(arr, rep) returns array([10, 10, 10, 24, 24, 24, 24], dtype=int32) Is there any way to replicate this functionality for a set of 2D arrays? That is given arr = array([[10, 24, 24, 24, 1, 21, 1, 21, 0, 0], [10, 24, 24, 1, 21, 1, 21, 32, 0, 0]], dtype=int32) rep = array([[3, 2, 2, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 0, 0, 0, 0, 0, 0, 0]], dtype

问题 Let's assume I have the following 9 x 5 matrix: myArray = [ 54.7 8.1 81.7 55.0 22.5 29.6 92.9 79.4 62.2 17.0 74.4 77.5 64.4 58.7 22.7 18.8 48.6 37.8 20.7 43.5 68.6 43.5 81.1 30.1 31.1 18.3 44.6 53.2 47.0 92.3 36.8 30.6 35.0 23.0 43.0 62.5 50.8 93.9 84.4 18.4 78.0 51.0 87.5 19.4 90.4 ]; I have 11 "subsets" of this matrix and I need to run a function (let's say max ) on each of these subsets. The subsets can be identified with the following matirx of logicals (identified column-wise, not row

问题 I have a list of matrices L , where each item M is a x*n matrix ( x is a variable, n is a constant). I want to compute the sum of M'*M for all items in L ( M' is the transpose of M ) as the following Python code does: for M in L: res += np.dot(M.T, M) Actually I want to implement this in Theano (which doesn't support variable length multidimensional arrays), and I don't want to pad all matrices to the same size because that will waste too much space (some of the matrices can be very large).

问题 I have a relatively large matrix NxN (N~20,000) and a Nx1 vector identifying the indices that must be grouped together. I want to sum together parts of the matrix, which in principle can have a different number of elements and non-adjacent elements. I quickly wrote a double for-loop that works correctly but of course it is inefficient. The profiler identified these loops as one of the bottlenecks in my code. I tried to find a smart vectorization method to solve the problem. I explored the