Struct NabUtils

pub struct NabUtils;

Implementations

impl NabUtils

pub fn normalize(array: &mut NDArray)

Normalizes the array values to range [0, 1] using min-max normalization

Arguments

• array - The NDArray to normalize in-place

Example

use nabla_ml::nab_array::NDArray;
use nabla_ml::nab_utils::NabUtils;
 
let mut arr = NDArray::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0]);
NabUtils::normalize(&mut arr);
assert_eq!(arr.data(), &[0.0, 0.25, 0.5, 0.75, 1.0]);

pub fn normalize_with_range(array: &mut NDArray, min_val: f64, max_val: f64)

Normalizes the array values using specified min and max values

Arguments

• array - The NDArray to normalize in-place
• min_val - The minimum value in the original range
• max_val - The maximum value in the original range

Example

use nabla_ml::nab_array::NDArray;
use nabla_ml::nab_utils::NabUtils;
 
let mut arr = NDArray::from_vec(vec![0.0, 51.0, 102.0, 153.0, 204.0, 255.0]);
NabUtils::normalize_with_range(&mut arr, 0.0, 255.0);
assert_eq!(arr.data(), &[0.0, 0.2, 0.4, 0.6, 0.8, 1.0]);

pub fn load_and_split_dataset( path: &str, train_percent: f64, ) -> Result<((NDArray, NDArray), (NDArray, NDArray))>

Loads a dataset from .nab files and splits it into training and testing sets

Arguments

• path - Base path for the .nab files (e.g., “mnist”)
• train_percent - Percentage of data to use for training (e.g., 80 for 80%)

Returns

A tuple containing ((train_images, train_labels), (test_images, test_labels))

pub fn csv_to_nab( csv_path: &str, output_path: &str, shape: Vec<usize>, skip_first_column: bool, ) -> Result<()>

Converts CSV data to .nab format

Arguments

• csv_path - Path to the CSV file
• output_path - Path where to save the .nab file
• shape - Shape of the resulting array (e.g., [60000, 28, 28] for MNIST images)

pub fn display(&self, array: &NDArray) -> String

Displays the array in a formatted way similar to numpy’s print format

Returns

A string representation of the array