How to Create a Tensor Whose Elements are Sampled from a Poisson Distribution in PyTorch
In this article, we will discuss how to create a Tensor whose elements are Sampled from a Poisson Distribution in PyTorch in Python.
torch.poisson() method
The torch.poisson() method helps us to create a tensor whose elements are sampled from a Poisson distribution. This method accepts a tensor as input and this input tensor contains the rates of the Poisson distribution. This method returns a tensor of the size same as the input tensor and the elements of this tensor are sampled from a Poisson distribution with the rate parameter. before moving further let’s see the syntax of the given method.
Syntax: torch.poisson(rates)
Parameters:
- rates (Tensor): This parameter is used to sample elements from a Poisson distribution.
Returns:This method returns a tensor and the elements of this tensor are sampled from a Poisson distribution with the rate parameter.
Example 1:
In this example, we will discuss how to create a 1D Tensor whose elements are Sampled from a Poisson Distribution.
Python
import torch
rates_tens = torch.tensor([ 2.7345 , 3.4347 ,
4.1237 , 1.3379 , 3.2343 ])
print ( "tensor of rate parameters: " , rates_tens)
pois_tens = torch.poisson(rates_tens)
print ( "Poisson Tensor: " , pois_tens)
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Output:
Example 2: In this example, we will discuss how to create a 2D Tensor whose elements are Sampled from a Poisson Distribution.
Python
import torch
rates_tens = torch.tensor([[ 4.1237 , 1.8373 , 3.2343 ],
[ 2.3344 , 3.3324 , 1.3378 ],
[ 3.2349 , 2.4447 , 4.5269 ]])
print ( "tensor of rate parameters: \n" , rates_tens)
pois_tens = torch.poisson(rates_tens)
print ( "Poisson Tensor: \n" , pois_tens)
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Output:
Last Updated :
07 Oct, 2022
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