rustyml 0.14.0

A high-performance machine learning & deep learning library in pure Rust, offering ML algorithms and neural network support
Documentation 
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//! Dimension and shape validators shared by the recurrent layers

use crate::error::Error;
use crate::neural_network::Tensor;

/// Validates that a dimension value is greater than 0
///
/// # Parameters
///
/// - `value` - The dimension value to validate
/// - `name` - The name of the dimension, used to build the error message
///
/// # Returns
///
/// - `Result<(), Error>` - `Ok(())` when `value` is greater than 0, otherwise the failing `Error`
///
/// # Errors
///
/// - [`Error::InvalidParameter`] - If `value` is 0
pub(super) fn validate_dimension_greater_than_zero(value: usize, name: &str) -> Result<(), Error> {
    if value == 0 {
        return Err(Error::invalid_parameter(name, "must be greater than 0"));
    }
    Ok(())
}

/// Validates input dimensions for recurrent layers
///
/// # Parameters
///
/// - `input_dim` - The input dimension to validate
/// - `units` - The units dimension to validate
///
/// # Returns
///
/// - `Result<(), Error>` - `Ok(())` when both dimensions are greater than 0, otherwise the
///   failing `Error`
///
/// # Errors
///
/// - [`Error::InvalidParameter`] - If `input_dim` or `units` is 0
pub(super) fn validate_recurrent_dimensions(input_dim: usize, units: usize) -> Result<(), Error> {
    validate_dimension_greater_than_zero(input_dim, "input_dim")?;
    validate_dimension_greater_than_zero(units, "units")?;
    Ok(())
}

/// Validates that the input tensor is 3D for recurrent layers
///
/// # Parameters
///
/// - `input` - The input tensor to validate
///
/// # Returns
///
/// - `Result<(), Error>` - `Ok(())` when `input` has 3 dimensions, otherwise the failing `Error`
///
/// # Errors
///
/// - [`Error::InvalidInput`] - If `input` is not 3D
pub(super) fn validate_input_3d(input: &Tensor) -> Result<(), Error> {
    if input.ndim() != 3 {
        return Err(Error::invalid_input("input tensor is not 3D"));
    }
    Ok(())
}