tacet-core 0.4.2

//! Block bootstrap resampling for time series data.
//!
//! This module implements block bootstrap resampling which preserves
//! the autocorrelation structure of timing measurements. This is critical
//! for accurate covariance estimation when measurements have temporal
//! dependencies.

extern crate alloc;

use alloc::vec::Vec;

use rand::Rng;

use crate::math;
use crate::types::TimingSample;

/// Counter-based RNG seed generation using SplitMix64.
///
/// This is a stateless PRF that generates deterministic, well-distributed
/// seeds from a base seed and counter. Using this instead of simple addition
/// provides better statistical properties and avoids sequential correlation.
///
/// # Arguments
///
/// * `base_seed` - Base random seed
/// * `counter` - Iteration counter (0, 1, 2, ...)
///
/// # Returns
///
/// A 64-bit seed suitable for initializing an RNG.
///
/// # Performance
///
/// ~2-3ns per call (negligible compared to ~100ns for RNG initialization).
#[inline]
pub fn counter_rng_seed(base_seed: u64, counter: u64) -> u64 {
    // SplitMix64: high-quality 64-bit hash function
    // See: https://xoshiro.di.unimi.it/splitmix64.c
    let mut z = base_seed.wrapping_add(counter.wrapping_mul(0x9e3779b97f4a7c15));
    z = (z ^ (z >> 30)).wrapping_mul(0xbf58476d1ce4e5b9);
    z = (z ^ (z >> 27)).wrapping_mul(0x94d049bb133111eb);
    z ^ (z >> 31)
}

/// Compute the optimal block size for block bootstrap using n^(1/3) scaling.
///
/// The block length follows Politis & Romano (1994) and Kunsch (1989):
/// l(n) = O(n^(1/3)) balances bias-variance tradeoff for stationary data.
///
/// The constant 1.3 is an engineering default that works well empirically.
/// At n=30k, this gives ~40 blocks (vs ~173 with sqrt(n)), preserving
/// autocorrelation structure better.
///
/// # Arguments
///
/// * `n` - Number of samples in the original data
///
/// # Returns
///
/// The recommended block size, minimum 1.
pub fn compute_block_size(n: usize) -> usize {
    let block_size = math::ceil(1.3 * math::cbrt(n as f64)) as usize;
    block_size.max(1)
}

/// Perform block bootstrap resampling into an existing buffer.
///
/// This is an optimized version of `block_bootstrap_resample` that writes
/// into a preallocated buffer instead of allocating a new Vec. This eliminates
/// allocation overhead in hot loops.
///
/// # Arguments
///
/// * `data` - Slice of timing measurements for one class
/// * `block_size` - Size of contiguous blocks to resample
/// * `rng` - Random number generator
/// * `out` - Output buffer (must have same length as `data`)
///
/// # Panics
///
/// Panics if `out.len() != data.len()`.
///
/// # Performance
///
/// ~2-3× faster than allocating version when called repeatedly with
/// the same buffer, due to eliminating allocator overhead and using
/// fast `copy_from_slice` memcpy.
pub fn block_bootstrap_resample_into<R: Rng>(
    data: &[f64],
    block_size: usize,
    rng: &mut R,
    out: &mut [f64],
) {
    assert_eq!(
        out.len(),
        data.len(),
        "Output buffer must have same length as input data"
    );

    if data.is_empty() {
        return;
    }

    let n = data.len();
    let block_size = block_size.max(1).min(n);

    // Number of possible starting positions for blocks
    let num_block_starts = n.saturating_sub(block_size) + 1;
    if num_block_starts == 0 {
        out.copy_from_slice(data);
        return;
    }

    let mut pos = 0;

    // Sample blocks until we fill the output buffer
    while pos < n {
        // Random starting position for this block
        let start = rng.random_range(0..num_block_starts);
        let block_end = (start + block_size).min(n);
        let block_len = block_end - start;

        // How much can we copy without overflowing output?
        let copy_len = block_len.min(n - pos);

        // Fast memcpy of the block
        out[pos..pos + copy_len].copy_from_slice(&data[start..start + copy_len]);

        pos += copy_len;
    }
}

/// Perform block bootstrap resampling on timing measurements.
///
/// Resamples measurements from a single class (fixed or random) while
/// preserving autocorrelation structure. Blocks of consecutive measurements
/// are sampled with replacement.
///
/// # Arguments
///
/// * `data` - Slice of timing measurements for one class
/// * `block_size` - Size of contiguous blocks to resample
/// * `rng` - Random number generator
///
/// # Returns
///
/// A new vector of resampled measurements with the same length as input.
///
/// # Algorithm
///
/// 1. Divide data into overlapping blocks of size `block_size`
/// 2. Sample blocks with replacement until we have n samples
/// 3. Truncate to exactly n samples
pub fn block_bootstrap_resample<R: Rng>(data: &[f64], block_size: usize, rng: &mut R) -> Vec<f64> {
    if data.is_empty() {
        return Vec::new();
    }

    let n = data.len();
    let block_size = block_size.max(1).min(n);

    // Number of possible starting positions for blocks
    let num_block_starts = n.saturating_sub(block_size) + 1;
    if num_block_starts == 0 {
        return data.to_vec();
    }

    let mut result = Vec::with_capacity(n);

    // Sample blocks until we have enough data
    while result.len() < n {
        // Random starting position for this block
        let start = rng.random_range(0..num_block_starts);
        let end = (start + block_size).min(n);

        // Add the block to our resampled data
        for &value in data.iter().take(end).skip(start) {
            if result.len() >= n {
                break;
            }
            result.push(value);
        }
    }

    result
}

/// Perform stratified block bootstrap on interleaved measurements.
///
/// When measurements alternate between fixed and random classes,
/// this function resamples within each class separately to preserve
/// both the autocorrelation structure and class separation.
///
/// # Arguments
///
/// * `fixed_data` - Measurements for the fixed input class
/// * `random_data` - Measurements for the random input class
/// * `block_size` - Size of contiguous blocks to resample
/// * `rng` - Random number generator
///
/// # Returns
///
/// A tuple of (resampled_fixed, resampled_random) vectors.
#[cfg(test)]
pub fn stratified_block_bootstrap<R: Rng>(
    fixed_data: &[f64],
    random_data: &[f64],
    block_size: usize,
    rng: &mut R,
) -> (Vec<f64>, Vec<f64>) {
    let resampled_fixed = block_bootstrap_resample(fixed_data, block_size, rng);
    let resampled_random = block_bootstrap_resample(random_data, block_size, rng);

    (resampled_fixed, resampled_random)
}

/// Perform joint block bootstrap resampling on interleaved measurements.
///
/// This preserves temporal pairing between fixed and random samples, which
/// captures the cross-covariance Cov(q_F, q_R) > 0 from common-mode noise.
/// Joint resampling gives the correct (smaller) Var(Δ), improving statistical
/// power compared to independent resampling.
///
/// # Arguments
///
/// * `interleaved` - Samples in measurement order, each tagged with its class
/// * `block_size` - Size of contiguous blocks to resample
/// * `rng` - Random number generator
///
/// # Returns
///
/// A new vector of resampled `TimingSample`s with the same length as input.
///
/// # Algorithm
///
/// 1. Sample blocks of consecutive measurements (preserving F/R pairing)
/// 2. The class labels travel with their timing values
/// 3. After resampling, caller splits by class and computes quantile difference
#[allow(dead_code)]
pub fn block_bootstrap_resample_joint<R: Rng>(
    interleaved: &[TimingSample],
    block_size: usize,
    rng: &mut R,
) -> Vec<TimingSample> {
    if interleaved.is_empty() {
        return Vec::new();
    }

    let n = interleaved.len();
    let block_size = block_size.max(1).min(n);

    // Number of possible starting positions for blocks
    let num_block_starts = n.saturating_sub(block_size) + 1;
    if num_block_starts == 0 {
        return interleaved.to_vec();
    }

    let mut result = Vec::with_capacity(n);

    // Sample blocks until we have enough data
    while result.len() < n {
        // Random starting position for this block
        let start = rng.random_range(0..num_block_starts);
        let end = (start + block_size).min(n);

        // Add the entire block (preserving class labels)
        for sample in interleaved.iter().take(end).skip(start) {
            if result.len() >= n {
                break;
            }
            result.push(*sample);
        }
    }

    result
}

/// Perform joint block bootstrap resampling into an existing buffer.
///
/// This is an optimized version that writes into a preallocated buffer.
/// Used in parallel bootstrap for better performance.
///
/// # Arguments
///
/// * `interleaved` - Samples in measurement order, each tagged with its class
/// * `block_size` - Size of contiguous blocks to resample
/// * `rng` - Random number generator
/// * `out` - Output buffer (must have same length as `interleaved`)
///
/// # Panics
///
/// Panics if `out.len() != interleaved.len()`.
pub fn block_bootstrap_resample_joint_into<R: Rng>(
    interleaved: &[TimingSample],
    block_size: usize,
    rng: &mut R,
    out: &mut [TimingSample],
) {
    assert_eq!(
        out.len(),
        interleaved.len(),
        "Output buffer must have same length as input data"
    );

    if interleaved.is_empty() {
        return;
    }

    let n = interleaved.len();
    let block_size = block_size.max(1).min(n);

    // Number of possible starting positions for blocks
    let num_block_starts = n.saturating_sub(block_size) + 1;
    if num_block_starts == 0 {
        out.copy_from_slice(interleaved);
        return;
    }

    let mut pos = 0;

    // Sample blocks until we fill the output buffer
    while pos < n {
        // Random starting position for this block
        let start = rng.random_range(0..num_block_starts);
        let block_end = (start + block_size).min(n);
        let block_len = block_end - start;

        // How much can we copy without overflowing output?
        let copy_len = block_len.min(n - pos);

        // Copy the block (preserving class labels)
        out[pos..pos + copy_len].copy_from_slice(&interleaved[start..start + copy_len]);

        pos += copy_len;
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use rand::SeedableRng;
    use rand_xoshiro::Xoshiro256PlusPlus;

    #[test]
    fn test_block_size_computation() {
        // Formula: ceil(1.3 * n^(1/3))
        // n=100:   1.3 * 100^(1/3) = 1.3 * 4.6416 = 6.03 -> ceil = 7
        // n=10000: 1.3 * 10000^(1/3) = 1.3 * 21.544 = 28.007 -> ceil = 29
        // n=30000: 1.3 * 30000^(1/3) = 1.3 * 31.072 = 40.39 -> ceil = 41
        assert_eq!(compute_block_size(100), 7);
        assert_eq!(compute_block_size(10000), 29);
        assert_eq!(compute_block_size(30000), 41);
        assert_eq!(compute_block_size(1), 2); // 1.3 * 1^(1/3) = 1.3 -> ceil = 2
        assert_eq!(compute_block_size(0), 1); // Minimum is 1
    }

    #[test]
    fn test_bootstrap_preserves_length() {
        let data: Vec<f64> = (0..100).map(|x| x as f64).collect();
        let mut rng = Xoshiro256PlusPlus::seed_from_u64(42);

        let resampled = block_bootstrap_resample(&data, 10, &mut rng);
        assert_eq!(resampled.len(), data.len());
    }

    #[test]
    fn test_bootstrap_samples_from_data() {
        let data: Vec<f64> = (0..100).map(|x| x as f64).collect();
        let mut rng = Xoshiro256PlusPlus::seed_from_u64(42);

        let resampled = block_bootstrap_resample(&data, 10, &mut rng);

        // All resampled values should be from original data
        for val in &resampled {
            assert!(data.contains(val));
        }
    }

    #[test]
    fn test_empty_data() {
        let data: Vec<f64> = vec![];
        let mut rng = Xoshiro256PlusPlus::seed_from_u64(42);

        let resampled = block_bootstrap_resample(&data, 10, &mut rng);
        assert!(resampled.is_empty());
    }

    #[test]
    fn test_stratified_bootstrap() {
        let fixed: Vec<f64> = (0..50).map(|x| x as f64).collect();
        let random: Vec<f64> = (50..100).map(|x| x as f64).collect();
        let mut rng = Xoshiro256PlusPlus::seed_from_u64(42);

        let (resampled_fixed, resampled_random) =
            stratified_block_bootstrap(&fixed, &random, 7, &mut rng);

        assert_eq!(resampled_fixed.len(), fixed.len());
        assert_eq!(resampled_random.len(), random.len());

        // Fixed resamples should come from fixed data
        for val in &resampled_fixed {
            assert!(fixed.contains(val));
        }

        // Random resamples should come from random data
        for val in &resampled_random {
            assert!(random.contains(val));
        }
    }
}