Tag: multidimensional-array

TypeError: unhashable type: ‘numpy.ndarray’, Counter rows

I’m trying to view the frequency of the elements in a 2d array as in the code: Expected result: ({(22, 66): 2, (33, 77): 1, (55, 99): 1}) But I get: Answer Create a list of tuples: (tuples are hashable) Now Counter works: numpys own unique also works

(Conv1D) Tensorflow and Jax Resulting Different Outputs for The Same Input

I am trying to use conv1d functions to make a transposed convlotion repectively at jax and tensorflow. I read the documentation of both of jax and tensorflow for the con1d_transposed operation but they are resulting with different outputs for the same input. I can not find out what the problem is. And I don’t know which one produces the correct

Ordering a two-dimensional array relative to the main diagonal

Given a two-dimensional array T of size NxN, filled with various natural numbers (They do not have to be sorted in any way as in the example below.). My task is to write a program that transforms the array in such a way that all elements lying above the main diagonal are larger than each element lying on the diagonal

Smallest Submatrix in python

It contains an R*C matrix with unique digits. I have to print the submatrix having minimum and maximum integer This is my code: I need a solution without a NumPy array. I found the max and min values and also their index. After that, I don’t know what to do. Answer You can iterate over matrix again and get only

How to length the last dimension of a numpy array and fill it up with another array?

I have a numpy array of shape (5, 4, 3) and another numpy array of shape (4,) and what I want to do is expand the last dimension of the first array (5, 4, 3) -> (5, 4, 4) and then broadcast the other array with shape (4,) such that it fills up the new array cells respectively. Example: becomes

How to multiply specific rows/columns of matrices with each other in python?

I have to input matrices of shape I want to multiply each row (each n of size 3) with its correspondence of the other matrix, such that i get a (3,3) matrix for each row. When im trying to just use e.g. m1[0]@m2.T[0] the operation doesnt work, as m[0] delivers a (3,) list instead of a (3,1) matrix, on which