Type Alias TrainTestSplitResult 

pub type TrainTestSplitResult<F> = (Array<F, IxDyn>, Array<F, IxDyn>, Array<F, IxDyn>, Array<F, IxDyn>);

Split data into training and testing sets

• x - Input data array
• y - Target data array
• test_size - Fraction of data to use for testing (between 0 and 1)
• shuffle - Whether to shuffle the data before splitting
• rng - Random number generator (only used if shuffle is true)
• A tuple of (x_train, x_test, y_train, y_test) use scirs2_neural::utils::train_test_split; use scirs2_core::ndarray::{Array, arr2}; let x = arr2(&[[1.0f64, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]]).into_dyn(); let y = arr2(&[[0.0f64], [1.0], [0.0], [1.0]]).into_dyn(); let (x_train, x_test, y_train, y_test) = train_test_split::(&x, &y, 0.25, true, &mut rng).unwrap(); // Note: Since the implementation is incomplete (TODO in the code), // we’re just checking that the shapes are what we expect assert_eq!(x_train.shape()[0], 3); assert_eq!(x_train.shape()[1], 2); assert_eq!(x_test.shape()[0], 1); assert_eq!(x_test.shape()[1], 2); Result type for train/test split