# How to get a uniform distribution in a range [r1,r2] in PyTorch?

## Question:

I want to get a 2-D `torch.Tensor` with size `[a,b]` filled with values from a uniform distribution (in range `[r1,r2]`) in PyTorch.

If `U` is a random variable uniformly distributed on [0, 1], then `(r1 - r2) * U + r2` is uniformly distributed on [r1, r2].

Thus, you just need:

``````(r1 - r2) * torch.rand(a, b) + r2
``````

Alternatively, you can simply use:

``````torch.FloatTensor(a, b).uniform_(r1, r2)
``````

To fully explain this formulation, let’s look at some concrete numbers:

``````r1 = 2 # Create uniform random numbers in half-open interval [2.0, 5.0)
r2 = 5

a = 1  # Create tensor shape 1 x 7
b = 7
``````

We can break down the expression `(r1 - r2) * torch.rand(a, b) + r2` as follows:

1. `torch.rand(a, b)` produces an `a x b` (1×7) tensor with numbers uniformly distributed in the range [0.0, 1.0).
``````x = torch.rand(a, b)
print(x)
# tensor([[0.5671, 0.9814, 0.8324, 0.0241, 0.2072, 0.6192, 0.4704]])
``````
1. `(r1 - r2) * torch.rand(a, b)` produces numbers distributed in the uniform range [0.0, -3.0)
``````print((r1 - r2) * x)
tensor([[-1.7014, -2.9441, -2.4972, -0.0722, -0.6216, -1.8577, -1.4112]])
``````
1. `(r1 - r2) * torch.rand(a, b) + r2` produces numbers in the uniform range [5.0, 2.0)
``````print((r1 - r2) * x + r2)
tensor([[3.2986, 2.0559, 2.5028, 4.9278, 4.3784, 3.1423, 3.5888]])
``````

Now, let’s break down the answer suggested by @Jonasson: `(r2 - r1) * torch.rand(a, b) + r1`

1. Again, `torch.rand(a, b)` produces (1×7) numbers uniformly distributed in the range [0.0, 1.0).
``````x = torch.rand(a, b)
print(x)
# tensor([[0.5671, 0.9814, 0.8324, 0.0241, 0.2072, 0.6192, 0.4704]])
``````
1. `(r2 - r1) * torch.rand(a, b)` produces numbers uniformly distributed in the range [0.0, 3.0).
``````print((r2 - r1) * x)
# tensor([[1.7014, 2.9441, 2.4972, 0.0722, 0.6216, 1.8577, 1.4112]])
``````
1. `(r2 - r1) * torch.rand(a, b) + r1` produces numbers uniformly distributed in the range [2.0, 5.0)
``````print((r2 - r1) * x + r1)
tensor([[3.7014, 4.9441, 4.4972, 2.0722, 2.6216, 3.8577, 3.4112]])
``````

In summary, `(r1 - r2) * torch.rand(a, b) + r2` produces numbers in the range [r2, r1), while `(r2 - r1) * torch.rand(a, b) + r1` produces numbers in the range [r1, r2).

``````torch.FloatTensor(a, b).uniform_(r1, r2)
``````

To get a uniform random distribution, you can use

``````torch.distributions.uniform.Uniform()
``````

example,

``````import torch
from torch.distributions import uniform

distribution = uniform.Uniform(torch.Tensor([0.0]),torch.Tensor([5.0]))
distribution.sample(torch.Size([2,3])
``````

This will give the output, tensor of size [2, 3].

Please Can you try something like:

``````import torch as pt
pt.empty(2,3).uniform_(5,10).type(pt.FloatTensor)
``````

This answer uses NumPy to first produce a random matrix and then converts the matrix to a PyTorch tensor. I find the NumPy API to be easier to understand.

``````import numpy as np

torch.from_numpy(np.random.uniform(low=r1, high=r2, size=(a, b)))
``````

See this for all distributions: https://pytorch.org/docs/stable/distributions.html#torch.distributions.uniform.Uniform

This is the way I found works:

``````# generating uniform variables

import numpy as np

num_samples = 3
Din = 1
lb, ub = -1, 1

xn = np.random.uniform(low=lb, high=ub, size=(num_samples,Din))
print(xn)

import torch

sampler = torch.distributions.Uniform(low=lb, high=ub)
r = sampler.sample((num_samples,Din))

print(r)

r2 = torch.torch.distributions.Uniform(low=lb, high=ub).sample((num_samples,Din))

print(r2)

# process input
f = nn.Sequential(OrderedDict([
('f1', nn.Linear(Din,Dout)),
('out', nn.SELU())
]))
Y = f(r2)
print(Y)
``````

but I have to admit I don’t know what the point of generating sampler is and why not just call it directly as I do in the one liner (last line of code).

Reference:

Utilize the `torch.distributions` package to generate samples from different distributions.

For example to sample a 2d PyTorch tensor of size `[a,b]` from a uniform distribution of `range(low, high)` try the following sample code

``````import torch
a,b = 2,3   #dimension of the pytorch tensor to be generated
low,high = 0,1 #range of uniform distribution

x = torch.distributions.uniform.Uniform(low,high).sample([a,b])
``````

PyTorch has a number of distributions built in. You can build a tensor of the desired `shape` with elements drawn from a uniform distribution like so:

``````from torch.distributions.uniform import Uniform

shape = 3,4
r1, r2 = 0,1

x = Uniform(r1, r2).sample(shape)
``````

Pytorch (now?) has a random integer function that allows:

``````torch.randint(low=r1, high=r2, size=(1,), **kwargs)
``````

and returns uniformly sampled random integers of shape size in range [r1, r2).

https://pytorch.org/docs/stable/generated/torch.randint.html

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