vectorization

来源： https://stackoverflow.com/questions/53450282/data-alignment-to-enable-vectorization-efficient-cache-access

来源： https://stackoverflow.com/questions/57976816/numpy-way-to-generate-linear-operation-matrix-from-a-convolution-kernel

来源： https://stackoverflow.com/questions/63514221/cartesian-product-of-two-numpy-arrays-with-condition

来源： https://stackoverflow.com/questions/63510977/repeat-but-in-variable-sized-chunks-in-numpy

问题 I have a set of pairs. Each pair is represented as [i,1:2] . That is, the ith pair are the numbers in the first and second column in the ith row. I need to sort these pairs into distinct groups, s.t. there is no element in any pair in the jth group that is in any group not j . For example: EXAMPLE 1: DATA > col1 <- c(3, 4, 6, 7, 10, 8) > col2 <- c(6, 7, 3, 4, 3, 1) > > dat <- cbind(col1, col2) > rownames(dat) <- 1:nrow(dat) > > dat col1 col2 1 3 6 2 4 7 3 6 3 4 7 4 5 10 3 6 8 1 For all pairs,

问题 following up on another question import numpy as np repeat=int(1e5) r_base = np.linspace(0,4,5) a_base = 2 np.random.seed(0) r_mat = r_base * np.random.uniform(0.9,1.1,(repeat,5)) a_array = a_base * np.random.uniform(0.9,1.1, repeat) # original slow approach def func_vetorized_level1(r_row, a): if r_row.mean()>2: result = np.where((r_row >= a), r_row - a, np.nan) else: result = np.where((r_row >= a), r_row + a, 0) return result # try to broadcast this func to every row of r_mat using list

问题 I have sample with size = n. I want to calculate for each i: 1 <= i <= n median for sample[:i] in numpy. For example, I counted mean for each i: cummean = np.cumsum(sample) / np.arange(1, n + 1) Can I do something similar for the median without cycles and comprehension? 回答1: Here's an approach that replicates elements along rows to give us a 2D array. Then, we would fill the upper triangular region with a big number so that later on when we sort the array along each row, would basically sort

问题 Using C#'s Vector<T> , how can we most efficiently vectorize the operation of finding the index of a particular element in a set? As constraints, the set will always be a Span<T> of an integer primitive, and it will contain at most 1 matching element. I have come up with a solution that seems alright, but I'm curious if we can do better. Here is the approach: Create a Vector<T> consisting only of the target element, in each slot. Use Vector.Equals() between the input set vector and the vector

问题 I am struggling with the following code: import numpy as np e = np.linspace(0, 4, 10) def g(x): if x > 1: return x else: return 0 vg = np.vectorize(g) print(vg(e)) the result looks like this: [0 0 0 1 1 2 2 3 3 4] I also checked the dtype. It seems that the vectorize function is conveting the type to int64 from float64! 回答1: The documentation for np.vectorize explains: The data type of the output of vectorized is determined by calling the function with the first element of the input. This can

问题 The std::atomic types allow atomic access to variables, but I would sometimes like non-atomic access, for example when the access is protected by a mutex. Consider a bitfield class that allows both multi-threaded access (via insert) and single-threaded vectorized access (via operator|=): class Bitfield { const size_t size_, word_count_; std::atomic<size_t> * words_; std::mutex mutex_; public: Bitfield (size_t size) : size_(size), word_count_((size + 8 * sizeof(size_t) - 1) / (8 * sizeof(size