Updated 4 years ago
5 Statistical Functions for Random Sampling in PyTorch
An short introduction about PyTorch and about the chosen functions.
- torch.bernoulli()
- torch.normal()
- torch.poisson()
- torch.randn()
- torch.randperm()
# Import torch and other required modules
import torch
Function 1 - torch.bernoulli(input, *, generator=None, out=None)
It draws binary random numbers (0 or 1) from a Bernoulli distribution and the output
is of the same shape as input.
Parameters:
input (Tensor) – the input tensor of probability values for the Bernoulli distribution
Keyword Arguments:
generator (torch.Generator, optional) – a pseudorandom number generator for sampling
out (Tensor, optional) – the output tensor
# Example 1 - working
rand_m = torch.rand(4, 4) # generate a random matrix of shape 4x4
print(rand_m)
torch.bernoulli(rand_m) # draws a binary random number (0 or 1)
tensor([[0.0643, 0.0690, 0.5069, 0.0323],
[0.3004, 0.6761, 0.1933, 0.7579],
[0.2958, 0.8812, 0.2917, 0.3713],
[0.8113, 0.0845, 0.9176, 0.9883]])
tensor([[0., 0., 1., 0.],
[1., 1., 0., 1.],
[0., 1., 0., 0.],
[1., 1., 1., 1.]])
Draws a binary random number (0
or 1
) for given random matrix.