elementwise-operations

问题 I want to realize component-wise matrix multiplication in MATLAB, which can be done using numpy.einsum in Python as below: import numpy as np M = 2 N = 4 I = 2000 J = 300 A = np.random.randn(M, M, I) B = np.random.randn(M, M, N, J, I) C = np.random.randn(M, J, I) # using einsum D = np.einsum('mki, klnji, lji -> mnji', A, B, C) # naive for-loop E = np.zeros(M, N, J, I) for i in range(I): for j in range(J): for n in range(N): E[:,n,j,i] = B[:,:,i] @ A[:,:,n,j,i] @ C[:,j,i] print(np.sum(np.abs(D

问题 I want to do the element-wise outer product of two 2d arrays in numpy. A.shape = (100, 3) # A numpy ndarray B.shape = (100, 5) # A numpy ndarray C = element_wise_outer_product(A, B) # A function that does the trick C.shape = (100, 3, 5) # This should be the result C[i] = np.outer(A[i], B[i]) # This should be the result A naive implementation can the following. tmp = [] for i in range(len(A): outer_product = np.outer(A[i], B[i]) tmp.append(outer_product) C = np.array(tmp) A better solution

问题 This question already has answers here : Repeat copies of array elements: Run-length decoding in MATLAB (5 answers) Closed 5 years ago . My question is similar to this one, but I would like to replicate each element according to a count specified in a second array of the same size. An example of this, say I had an array v = [3 1 9 4] , I want to use rep = [2 3 1 5] to replicate the first element 2 times, the second three times, and so on to get [3 3 1 1 1 9 4 4 4 4 4] . So far I'm using a

问题 This question already has answers here : Repeat copies of array elements: Run-length decoding in MATLAB (5 answers) Closed 5 years ago . My question is similar to this one, but I would like to replicate each element according to a count specified in a second array of the same size. An example of this, say I had an array v = [3 1 9 4] , I want to use rep = [2 3 1 5] to replicate the first element 2 times, the second three times, and so on to get [3 3 1 1 1 9 4 4 4 4 4] . So far I'm using a

问题 I am receiving a ValueError when using integration, but I cannot understand why. Here is my simplified code: import numpy as np import scipy.integrate as integrate pbar = 1 p = np.arange(0,pbar,pbar/1000) h = lambda p: p**2/2+p*(1-p) Kl = lambda p: h(p) +0.02 K = Kl(p) R = 0.5*h(p) + 0.5*h(pbar) Vl = lambda p: np.minimum.reduce([p, K, R]) integrate.quad(Vl, 0, pbar)[0] Vl is the element-wise minimum of the three arrays. The last line gives the exception: ValueError: setting an array element

问题 I have a dense column matrix y of size (m,1) and a sparse matrix x of size (m,n) . I want to do element-wise multiplication using y and every column of x . The resultant sparse matrix is still of size (m,n) . Sparse matrix x , when loaded into memory, is about 10GB. Can spfun help me accomplish my goal in a memory efficient manner? I am having difficulties understanding the logic behind it. Thank you. 回答1: Have you tried bsxfun? out = bsxfun( @times, x, y ); spfun is more suitable for element

问题 Take this simple example: a = [1 2i]; x = zeros(1,length(a)); for n=1:length(a) x(n) = isreal(a(n)); end In an attempt to vectorize the code, I tried: y = arrayfun(@isreal,a); But the results are not the same: x = 1 0 y = 0 0 What am I doing wrong? 回答1: This certainly appears to be a bug, but here's a workaround: >> y = arrayfun(@(x) isreal(x(1)),a) ans = 1 0 Why does this work? I'm not totally sure, but it appears that when you perform an indexing operation on the variable before calling

问题 I have two numpy arrays of the same shape. The elements in the arrays are random integers from [0,N]. I need to check which (if any) of the elements in the same position in the arrays are equal. The output I need are the positions of the same elements. mock code: A=np.array([0,1]) B=np.array([1,0]) C=np.array([1,1]) np.any_elemenwise(A,B) np.any_elemenwise(A,C) np.any_elemenwise(A,A) desired output: [] [1] [0,1] I can write a loop going through all of the elements one by one, but I assume

问题 I have a CSV that looks like this (and when brought into a pandas Dataframe with read_csv() , it looks the same). I want to update the values in column ad_requests according to the following logic: For a given row, if ad_requests has a value, leave it alone. Else, give it a value of the previous row's value for ad_requests minus the previous row's value for impressions . So in the first example, we would like to end up with: I get partially there: df["ad_requests"] = [i if not pd.isnull(i)

问题 Say, I have two lists: list.a <- as.list(c("a", "b", "c")) list.b <- as.list(c("d", "e", "f")) I would like to combine these lists recursively, such that the result would be a list of combined elements as a vector like the following: [[1]] [1] a d [[2]] [1] a e [[3]] [1] a f [[4]] [1] b d and so on. I feel like I'm missing something relatively simple here. Any help? Cheers. 回答1: expand.grid(list.1, list.b) gives you the desired result in a data.frame structure. This tends to be the most