normal_gaussian_like

normal_gaussian_like(
    x: &Tensor<T>,
    mean: T,
    std: T
) -> Result<Tensor<T>, TensorError>

Same as normal_gaussian but the shape will be based on x. Creates a Tensor with values drawn from a normal distribution with specified mean and standard deviation.

Parameters:

x: Input Tensor to derive the shape from

mean: Mean (μ) of the distribution, determining the center of the bell curve.

std: Standard deviation (σ) of the distribution, determining the spread. Must be positive.

Returns:

Tensor with type T containing random values from the normal distribution.

Examples:

use hpt::{error::TensorError, ops::Random, Tensor};

fn main() -> Result<(), TensorError> {
    // Create an initial tensor
    let x = Tensor::<f32>::randn(&[10, 10])?;
    
    // Create a new tensor with same shape as x but with normal distribution
    let n = x.normal_gaussian_like(0.0, 1.0)?;
    println!("{}", n);
    Ok(())
}

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