Struct MemoryDataLoader 

pub struct MemoryDataLoader { /* private fields */ }

Simple in-memory data loader

Implementations

impl MemoryDataLoader

pub fn new( inputs: SciRS2Array, targets: SciRS2Array, batch_size: usize, shuffle: bool, ) -> Result<Self>

Create new memory data loader

Examples found in repository

examples/pytorch_integration_demo.rs (lines 173-178)

fn create_quantum_datasets() -> Result<(MemoryDataLoader, MemoryDataLoader)> {
    // Create synthetic quantum-friendly dataset
    let num_train = 800;
    let num_test = 200;
    let num_features = 4;

    // Training data with quantum entanglement patterns
    let train_data = Array2::from_shape_fn((num_train, num_features), |(i, j)| {
        let phase = (i as f64 * 0.1) + (j as f64 * 0.2);
        (phase.sin() + (phase * 2.0).cos()) * 0.5
    });

    let train_labels = Array1::from_shape_fn(num_train, |i| {
        // Create labels based on quantum-like correlations
        let sum = (0..num_features).map(|j| train_data[[i, j]]).sum::<f64>();
        if sum > 0.0 {
            1.0
        } else {
            0.0
        }
    });

    // Test data
    let test_data = Array2::from_shape_fn((num_test, num_features), |(i, j)| {
        let phase = (i as f64 * 0.15) + (j as f64 * 0.25);
        (phase.sin() + (phase * 2.0).cos()) * 0.5
    });

    let test_labels = Array1::from_shape_fn(num_test, |i| {
        let sum = (0..num_features).map(|j| test_data[[i, j]]).sum::<f64>();
        if sum > 0.0 {
            1.0
        } else {
            0.0
        }
    });

    let train_loader = MemoryDataLoader::new(
        SciRS2Array::from_array(train_data.into_dyn()),
        SciRS2Array::from_array(train_labels.into_dyn()),
        32,
        true,
    )?;
    let test_loader = MemoryDataLoader::new(
        SciRS2Array::from_array(test_data.into_dyn()),
        SciRS2Array::from_array(test_labels.into_dyn()),
        32,
        false,
    )?;

    Ok((train_loader, test_loader))
}

Trait Implementations

impl DataLoader for MemoryDataLoader

fn next_batch(&mut self) -> Result<Option<(SciRS2Array, SciRS2Array)>>

Get next batch

fn reset(&mut self)

Reset to beginning

fn batch_size(&self) -> usize

Get batch size