Struct SparseDistanceMatrix 

pub struct SparseDistanceMatrix { /* private fields */ }

Compressed Sparse Row (CSR) matrix for efficient sparse distance storage

Implementations

impl SparseDistanceMatrix

pub fn new(n_rows: usize, n_cols: usize, symmetric: bool) -> Self

Create a new empty sparse distance matrix

Arguments

• n_rows - Number of rows
• n_cols - Number of columns
• symmetric - Whether the matrix should be symmetric

pub fn from_data( data: &Array2<Float>, config: SparseMatrixConfig, ) -> Result<Self>

Create sparse distance matrix from dense data

Arguments

• data - Input data matrix (n_samples × n_features)
• config - Configuration for sparse matrix creation

pub fn get(&self, row: usize, col: usize) -> Float

Get the value at position (row, col)

pub fn set(&mut self, row: usize, col: usize, value: Float) -> Result<()>

Set the value at position (row, col)

Note: This is an expensive operation for CSR matrices as it may require rebuilding the entire structure. Use during construction only.

pub fn row_entries(&self, row: usize) -> Vec<(usize, Float)>

Get all non-zero entries in a row

pub fn k_nearest_neighbors(&self, row: usize, k: usize) -> Vec<(usize, Float)>

Get k-nearest neighbors for a specific row

Arguments

• row - Row index to find neighbors for
• k - Number of nearest neighbors to find

Returns

Vector of (column_index, distance) pairs sorted by distance

pub fn neighbors_within_radius( &self, row: usize, radius: Float, ) -> Vec<(usize, Float)>

Get all neighbors within a specific radius

pub fn to_dense(&self) -> Array2<Float>

Convert to dense matrix (for small matrices or debugging)

pub fn stats(&self) -> SparseMatrixStats

Get matrix statistics

pub fn shape(&self) -> (usize, usize)

Matrix dimensions

pub fn nnz(&self) -> usize

Number of non-zero entries

pub fn is_symmetric(&self) -> bool

Check if matrix is symmetric

pub fn connected_components(&self) -> Vec<usize>

Advanced: Get connected components using union-find algorithm Returns vector where each element is the component ID for that vertex

pub fn graph_diameter(&self) -> Option<usize>

Advanced: Compute graph diameter (longest shortest path) Uses BFS from multiple starting points for efficiency

pub fn clustering_coefficient(&self, vertex: usize) -> Float

Advanced: Compute clustering coefficient for a vertex Measures how connected a vertex’s neighbors are to each other

pub fn average_clustering_coefficient(&self) -> Float

Advanced: Compute average clustering coefficient for the entire graph

pub fn approximate_betweenness_centrality( &self, sample_size: usize, ) -> Vec<Float>

Advanced: Approximate betweenness centrality using sampling Measures how often a vertex lies on shortest paths between other vertices

Trait Implementations

impl Clone for SparseDistanceMatrix

fn clone(&self) -> SparseDistanceMatrix

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SparseDistanceMatrix

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.