way to create a 3d matrix of 2 vectors and 1 matrix

Question

Hello i have a question regarding a problem I am facing in python. I was studying about tensors and I saw that each row/column of a tensor must have the same size. Is it possible to create a tensor of perhaps a 3d object or matrix where lets say we have 3 axis : x,y,z In the x axis I want to create a vector to work as an index. So let x be from 0 to N Then on the y axis I want to have N random integer vectors of size m (where mm Is it possible? My first approach was to create a big vector of Nm and a big matrix of (N m,N m) dimensions where i would store all my random vectors and matrices and then if I wanted to change for example the my second vector then i would have to play with the indexes. However is there another way to approach this problem with tensors or numpy that I m unaware of? Thank you in advance for your advices

Answer 1

First vector, N = 3, [1,2, 3] Second N vectors with length m, m = 2 [[4,5], [6,7], [7,8]]

So, N matrices of size (m,m) [[[1,1], [2,2]], [[1,1], [2,2]], [[1,1], [2,2]] ]

Lets create numpy arrays from them.

import numpy as np
N = 3
m = 2
a = np.array([1,2,3])
b = np.random.randn(N, m)
c = np.random.randn(N, m, m)

You see the problem here? The last matrix c has already 3 dimensions according to your definitions.

Your argument can be simplified.

Let's say our final matrix is - a = np.zeros((3,2,2)) # 3 dimensions, x,y,z 1) For first dimension -

a[0,:,:] = 0 # first axis, first index = 0

a[1,:,:] = 1 # first axis, 2nd index = 1

a[2,:,:] = 2 # first axis, 3rd index = 2

2) Now, we need to fill up the rest of the positions, but dimensions don't match up.

So, it's better to create separate tensors for them.