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 required librariesimport torch# create torch tensor of rate parametersrates_tens = torch.tensor([2.7345, 3.4347, 4.1237, 1.3379, 3.2343])print("tensor of rate parameters: ", rates_tens)# apply poisson() methodpois_tens = torch.poisson(rates_tens)# display resultprint("Poisson Tensor: ", pois_tens) |
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 required librariesimport torch# create torch tensor of rate parametersrates_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)# apply poisson() methodpois_tens = torch.poisson(rates_tens)# display resultprint("Poisson Tensor: \n", pois_tens) |
Output:
