Normal

Normal distributed

## Usage

torch_normal(mean, std, size = NULL, generator = NULL, ...)

## Arguments

mean

(tensor or scalar double) Mean of the normal distribution. If this is a torch_tensor() then the output has the same dim as mean and it represents the per-element mean. If it's a scalar value, it's reused for all elements.

std

(tensor or scalar double) The standard deviation of the normal distribution. If this is a torch_tensor() then the output has the same size as std and it represents the per-element standard deviation. If it's a scalar value, it's reused for all elements.

size

(integers, optional) only used if both mean and std are scalars.

generator

a random number generator created with torch_generator(). If NULL a default generator is used.

...

Tensor option parameters like dtype, layout, and device. Can only be used when mean and std are both scalar numerics.

## Note

When the shapes do not match, the shape of mean is used as the shape for the returned output tensor

## normal(mean, std, *) -> Tensor

Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given.

The mean is a tensor with the mean of each output element's normal distribution

The std is a tensor with the standard deviation of each output element's normal distribution

The shapes of mean and std don't need to match, but the total number of elements in each tensor need to be the same.

## normal(mean=0.0, std) -> Tensor

Similar to the function above, but the means are shared among all drawn elements.

## normal(mean, std=1.0) -> Tensor

Similar to the function above, but the standard-deviations are shared among all drawn elements.

## normal(mean, std, size, *) -> Tensor

Similar to the function above, but the means and standard deviations are shared among all drawn elements. The resulting tensor has size given by size.

## Examples

if (torch_is_installed()) {

torch_normal(mean=0, std=torch_arange(1, 0, -0.1) + 1e-6)
torch_normal(mean=0.5, std=torch_arange(1., 6.))
torch_normal(mean=torch_arange(1., 6.))
torch_normal(2, 3, size=c(1, 4))

}
#> torch_tensor
#>  0.0455  2.4500  3.9270  5.6783
#> [ CPUFloatType{1,4} ]