I have a code where I zero a column or row if it contains zero , I have my own function but when I use it it returns None , I made a copy of my matrix and performed changes on it and then I converted the values to the original one and yet I still get no return .
Tag: matrix
Numpy: Optimal way to count indexs occurrence in an array
I have an array indexs. It’s very long (>10k), and each int value is rather small (<100). e.g. Now I want to count occurrence of each index value (e.g. 0 for 3 times, 1 for 2 times…), and get counts as np.array([3, 2, 1, 1, 1]). I have tested 4 methods as follows: UPDATE: _test4 is @Ch3steR’s sol: Run for
A robust way to keep the n-largest elements in rows or colums in the matrix
I would like to make a sparse matrix from the dense one, such that in each row or column only n-largest elements are preserved. I do the following: This approach works as intended but seems to be not the most efficient, and the robust one. What would you recommend to do it an better way? The example of usage: Answer
Combination of rows in numpy.ndarray
I have the following numpy.ndarray I want to find all the possible combinations of sum of each row (sum of individual elements of a row except the last column) of S[0,:,:] with each row of S[1,:,:], i.e., my desired result is (order does not matter): which is a 9-by-2 array resulting from 9 possible combinations of S[0,:,:] and S[1,:,:]. Although
Why is ‘scipy.sparse.linalg.spilu’ less efficient than ‘scipy.linalg.lu’ for sparse matrix?
I posted this question on https://scicomp.stackexchange.com, but received no attention. As long as I get answer in one of them, I will inform in the other. I have a matrix B which is sparse and try to utilize a function scipy.sparse.linalg.spilu specialized for sparse matrix to factorize B. Could you please explain why this function is significantly less efficient than
Create a matrix with np.random.normal
I need to create an nxn matrix in which the numbers in the cells are distributed following a Gaussian distribution. This code may not go well because it fills a cell with a sequence. how can I do? Answer Edited for border of zeros np.random.normal takes a size keyword argument. You can use it like this:
How to update the 1st value of the sub list with another list
I want to add the 1st element of each sublist with the values of another list Output (comming): Output (want): Answer Here let me tell you what is happening in your code… In this line you made a nested list in which all the sublists are pointing to the same sublist in memory. In this line you now made your
Delete values over the diagonal in a matrix with python
I have the next problem with a matrix in python and numpy given this matrix i want to obtain this: I was trying with np.diag() but doesnt works Thanks! Answer Use np.tril(a) to extract the lower triangular matrix. Refer this : https://docs.scipy.org/doc/numpy/reference/generated/numpy.tril.html
Python-Scipy sparse Matrices – what is A[i, j] doing?
According to my previous question here (Python – Multiply sparse matrix row with non-sparse vector by index) direct indexing of sparse matrices is not possible (at least not if you don’t want to work with the three arrays by which the sparse.csr matrix is defined, data, indices, indptr). But I just found out, that given a csr-sparse matrix A, this
supposedly incorrect output np.reshape function
I have an array called “foto_dct” with shape (16,16,8,8) which means 16×16 matrices of 8×8. When I print foto_dct[0,15], being the last matrix of the first row I get: when i do foto_dct_big = np.reshape(foto_dct,(128,128)) and print foto_dct_big I get this: As you can see is the top righter corner( which is supposed to be the matrix above with all