vectorization

问题 I have a calculation using a 1D and a 2D numpy array. It has two levels of if -conditions. I was able to use np.where to avoid one if -statement and further use the slow list comprehension to iterate through each row. Ideally, I would like to vectorize the whole calculation process. Is it possible? Here is my code: import numpy as np r_base = np.linspace(0, 4, 5) np.random.seed(0) r_mat = np.array([r_base * np.random.uniform(0.9, 1.1, 5), r_base * np.random.uniform(0.9, 1.1, 5), r_base * np

问题 I'm coming to python from Matlab. In Matlab, given two vectors that are not necessarily the same length, they can be added if one is a row vector and one is a column vector. v1 = [1 3 5 7] v2 = [2 4 6]' v1 + v2 ans = 3 5 7 9 5 7 9 11 7 9 11 13 I am trying to produce the same behavior in python given two numpy arrays. Looping first came to mind: import numpy as np v1 = np.array([1,3,5,7]) v2 = np.array([2,4,6]) v3 = np.empty((3,4,)) v3[:] = np.nan for i in range(0,3): v3[i,:] = v1 + v2[i] Is

问题 I'm coming to python from Matlab. In Matlab, given two vectors that are not necessarily the same length, they can be added if one is a row vector and one is a column vector. v1 = [1 3 5 7] v2 = [2 4 6]' v1 + v2 ans = 3 5 7 9 5 7 9 11 7 9 11 13 I am trying to produce the same behavior in python given two numpy arrays. Looping first came to mind: import numpy as np v1 = np.array([1,3,5,7]) v2 = np.array([2,4,6]) v3 = np.empty((3,4,)) v3[:] = np.nan for i in range(0,3): v3[i,:] = v1 + v2[i] Is

问题 I'm coming to python from Matlab. In Matlab, given two vectors that are not necessarily the same length, they can be added if one is a row vector and one is a column vector. v1 = [1 3 5 7] v2 = [2 4 6]' v1 + v2 ans = 3 5 7 9 5 7 9 11 7 9 11 13 I am trying to produce the same behavior in python given two numpy arrays. Looping first came to mind: import numpy as np v1 = np.array([1,3,5,7]) v2 = np.array([2,4,6]) v3 = np.empty((3,4,)) v3[:] = np.nan for i in range(0,3): v3[i,:] = v1 + v2[i] Is

问题 I'm coming to python from Matlab. In Matlab, given two vectors that are not necessarily the same length, they can be added if one is a row vector and one is a column vector. v1 = [1 3 5 7] v2 = [2 4 6]' v1 + v2 ans = 3 5 7 9 5 7 9 11 7 9 11 13 I am trying to produce the same behavior in python given two numpy arrays. Looping first came to mind: import numpy as np v1 = np.array([1,3,5,7]) v2 = np.array([2,4,6]) v3 = np.empty((3,4,)) v3[:] = np.nan for i in range(0,3): v3[i,:] = v1 + v2[i] Is

问题 Here is my problem : let's say my two array are : import numpy as np first = np.array(["hello", "hello", "hellllo"]) second = np.array(["hlo", "halo", "alle"]) Now I want to get the matrix of distance between each element of the two arrays so for example my distance function is : def diff_len(string1, string2): return abs(len(string1) - len(string2)) So I I would like to get the matrix : hello hello hellllo hlo result1 result2 result3 halo result4 result5 result6 alle result7 result8 result9

问题 I have a matrix which is 9x10000 size. So rows are R1, R2, upto R9. I want to generate all possible combination of the rows such as [R1;R2] [R1;R3].. [R1;R9] [R1;R2;R3]...[R1;R2;R4]... [R1;R2:R3;R4;..R8] I am currently doing this using for loops. Is there any better way of doing this. 回答1: Basically, counting up the binary from 1 to 2^9-i indicates which rows need to be selected: M=... your matrix S=dec2bin(1:2^size(M,1)-1)=='1'; allSubsets=cell(size(S,1),1); for ix=1:size(S,1) allSubsets{ix}

问题 Given n , I want to zero out the last n bytes of a __m128i vector. For instance consider the following __m128i vector: 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 After zeroing out the last n = 4 bytes, the vector should look like: 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 00000000 00000000 00000000 00000000 Is there a SSE

问题 I would like to vectorize ( apply ) a which operation on matrix X as illustrated by the following for loop having as result the vector ind : X = matrix( 1:20, 4, 5 ) V = sample( 1:20, 4 ) ind = numeric() for( i in 1:nrow(X) ) ind[i] = max( c(0, which(X[i,] < V[i]) )) The operation returns in ind for each row in X the index of the element with the highest value smaller than the value indicated by the X -row-corresponding element of V . The operation max maps the vector of all eligible indices

问题 I'd like to get an NxM matrix where numbers in each row are random samples generated from different normal distributions(same mean but different standard deviations). The following code works: import numpy as np mean = 0.0 # same mean stds = [1.0, 2.0, 3.0] # different stds matrix = np.random.random((3,10)) for i,std in enumerate(stds): matrix[i] = np.random.normal(mean, std, matrix.shape[1]) However, this code is not quite efficient as there is a for loop involved. Is there a faster way to