## Create all binary valued vectors of specific length

How do I efficiently create a matrix of shape `(2**N, N)`

with all binary vectors of length N as its rows? I call a vector binary iff each of its components equals either 0 or 1. I want the matrix to have `dtype=torch.float`

.

For modest values of `N`

, this may work alright:

import numpy as np def bin_array(N, dtype): return (np.arange(1<<N, dtype=dtype)[:, None] >> np.arange(N, dtype=dtype)[::-1]) & 0b1

For maximum efficiency though, I'd suggest to generate the individual bits packed in `uint8`

values and design the arithmetic to operate on that. This will depend on what exactly you want to do with the array.

**Creating all possible binary sequences for specific length under ,** My constraints are for example: At least x values of the sequence have to be 1; No more than 5 repeating 0s are� Multiplication of vectors and matrices must follow strict rules. Actually, so must addition. In the example above, the vectors are both column vectors with three entries. You cannot add a row vector to a column vector. Multiplication, though, can be a bit trickier.

Seb's answer but modified for pytorch:

def gen_all_binary_vectors(length: int) -> torch.Tensor: return ((torch.arange(2**length).unsqueeze(1) >> torch.arange(length-1, -1, -1)) & 1).float()

**How to create all range of given vector (possible permutations ,** How to create all possible values of vector. For example, I have binary vector of length 2, then are possible four vectors. [0 0; 1 1; 1 0; 0 1];. Our framework then utilizes an asymmetric locality sensitive hashing (ALSH) in order to convert the generated fixed-length but real valued GMM-SVM feature vector to a binary bit string. This binarization step transforms the matching process to calculating Hamming distance between binary vectors and expedites fingerprint matching.

you can do this

import random import torch res = [ [ random.randint(0, 1) for i in range(2**N) ] for j in range(N) ] pt_tensor_from_list = torch.FloatTensor(res)

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