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.
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Answer
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:
torch.rand(a, b)
produces ana 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]])
(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]])
(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
- 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]])
(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]])
(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).