//! Robust pruning algorithm for DiskANN.
//!
//! **Key Innovation:** Unlike HNSW (which keeps k-nearest neighbors), DiskANN uses
//! **diversity-aware pruning** to ensure good graph connectivity.
//!
//! # Algorithm Intuition
//!
//! Given a candidate set of neighbors, we want to select R neighbors that:
//! 1. Are close to the query point (proximity)
//! 2. Cover different "directions" from the query (diversity)
//!
//! **Why diversity matters:**
//! ```text
//! Query (Q) with candidates:
//!
//!   A---B---C        If we only keep nearest neighbors (A, B),
//!    \  |  /         we miss D which provides a "bridge" to other
//!     \ | /          regions of the graph.
//!      \|/
//!       Q            Robust prune keeps (A, D) instead → better connectivity!
//!        \
//!         D
//! ```
//!
//! # Pseudocode
//!
//! ```text
//! RobustPrune(candidates, R, alpha):
//!   1. Sort candidates by distance to query
//!   2. selected = []
//!   3. For each candidate c (in sorted order):
//!        if selected is empty OR c is "far enough" from all selected:
//!          selected.add(c)
//!          if |selected| == R:
//!            break
//!   4. Return selected
//!
//! "Far enough" = distance(c, query) < alpha * distance(c, any selected neighbor)
//! ```

use crate::types::error::Result;

/// Robust pruning to select diverse, high-quality neighbors.
///
/// # Arguments
///
/// * `candidates` - List of (node_id, distance) pairs (unsorted)
/// * `vectors` - All vectors in the dataset (for computing inter-candidate distances)
/// * `max_neighbors` - Maximum number of neighbors to keep (R parameter)
/// * `alpha` - Diversity parameter (1.0 = pure nearest, 1.2 = more diversity)
///
/// # Returns
///
/// Selected neighbor IDs (at most `max_neighbors`, sorted by distance)
///
/// # Errors
///
/// Returns error if vectors are missing for any candidate
///
/// # Example
///
/// ```rust,ignore
/// let candidates = vec![(0, 0.5), (1, 0.6), (2, 0.7), (3, 0.8)];
/// let selected = robust_prune(&candidates, &vectors, 2, 1.2)?;
/// assert!(selected.len() <= 2);
/// ```
pub fn robust_prune(
    candidates: &[(u32, f32)],
    vectors: &[Vec<f32>],
    max_neighbors: usize,
    alpha: f32,
) -> Result<Vec<u32>> {
    todo!("Implement robust pruning with diversity")
}

/// Check if a candidate is "diverse enough" from selected neighbors.
///
/// A candidate is diverse if:
/// `distance(candidate, query) < alpha * min(distance(candidate, selected[i])) for all i`
///
/// Intuition: Candidate is closer to the query than it is to any existing neighbor
/// (scaled by alpha). This ensures we don't select redundant neighbors.
///
/// # Arguments
///
/// * `candidate_id` - Candidate node ID
/// * `candidate_dist` - Distance from candidate to query
/// * `selected` - Already selected neighbor IDs
/// * `vectors` - All vectors (for inter-candidate distances)
/// * `alpha` - Diversity threshold multiplier
///
/// # Returns
///
/// `true` if candidate should be added (diverse enough)
fn is_diverse(
    candidate_id: u32,
    candidate_dist: f32,
    selected: &[u32],
    vectors: &[Vec<f32>],
    alpha: f32,
) -> bool {
    todo!("Check diversity criterion")
}

/// Compute Euclidean distance between two vectors.
///
/// # Arguments
///
/// * `a` - First vector
/// * `b` - Second vector
///
/// # Returns
///
/// L2 distance
///
/// # Panics
///
/// Panics if vectors have different dimensions
fn euclidean_distance(a: &[f32], b: &[f32]) -> f32 {
    debug_assert_eq!(a.len(), b.len());
    a.iter()
        .zip(b.iter())
        .map(|(x, y)| (x - y) * (x - y))
        .sum::<f32>()
        .sqrt()
}

/// Compute squared Euclidean distance (faster, no sqrt).
///
/// Use when only relative distances matter (e.g., sorting).
///
/// # Arguments
///
/// * `a` - First vector
/// * `b` - Second vector
///
/// # Returns
///
/// Squared L2 distance
fn squared_distance(a: &[f32], b: &[f32]) -> f32 {
    debug_assert_eq!(a.len(), b.len());
    a.iter()
        .zip(b.iter())
        .map(|(x, y)| (x - y) * (x - y))
        .sum()
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_euclidean_distance() {
        todo!("Test distance computation")
    }

    #[test]
    fn test_squared_distance() {
        todo!("Test squared distance (no sqrt)")
    }

    #[test]
    fn test_robust_prune_empty() {
        todo!("Test pruning with empty candidates")
    }

    #[test]
    fn test_robust_prune_fewer_than_max() {
        todo!("Test pruning when candidates < max_neighbors")
    }

    #[test]
    fn test_robust_prune_diversity() {
        todo!("Test that diverse candidates are preferred over close clusters")
    }

    #[test]
    fn test_is_diverse() {
        todo!("Test diversity criterion")
    }
}
