flow-utils 0.1.1

//! DBSCAN clustering implementation

use crate::clustering::{ClusteringError, ClusteringResult};
use ndarray::Array2;

/// Configuration for DBSCAN clustering
#[derive(Debug, Clone)]
pub struct DbscanConfig {
    /// Maximum distance between samples for one to be considered in the neighborhood of the other
    pub eps: f64,
    /// Minimum number of samples in a neighborhood for a point to be a core point
    pub min_samples: usize,
}

impl Default for DbscanConfig {
    fn default() -> Self {
        Self {
            eps: 0.5,
            min_samples: 5,
        }
    }
}

/// DBSCAN clustering result
#[derive(Debug, Clone)]
pub struct DbscanResult {
    /// Cluster assignments for each point (-1 indicates noise/outlier)
    pub assignments: Vec<i32>,
    /// Number of clusters found
    pub n_clusters: usize,
    /// Number of noise points
    pub n_noise: usize,
}

/// DBSCAN clustering
pub struct Dbscan;

impl Dbscan {
    /// Perform DBSCAN clustering
    ///
    /// # Arguments
    /// * `data` - Input data matrix (n_samples × n_features)
    /// * `config` - Configuration for DBSCAN
    ///
    /// # Returns
    /// DbscanResult with cluster assignments
    pub fn fit(data: &Array2<f64>, _config: &DbscanConfig) -> ClusteringResult<DbscanResult> {
        if data.nrows() == 0 {
            return Err(ClusteringError::EmptyData);
        }

        // Use linfa-clustering for DBSCAN
        // NOTE: DBSCAN in linfa-clustering 0.8 has trait bound issues with ParamGuard
        // This is a known limitation - DBSCAN clustering is temporarily disabled
        // TODO: Fix DBSCAN once linfa-clustering API is updated or use alternative implementation
        Err(ClusteringError::ClusteringFailed(
            "DBSCAN clustering is temporarily disabled due to linfa-clustering API limitations. \
             Please use K-means or GMM clustering instead.".to_string()
        ))
        
        // Original implementation (commented out until API issue is resolved):
        /*
        let dataset = DatasetBase::new(data.clone(), ());
        let model = LinfaDbscan::params(config.min_samples)
            .tolerance(config.eps)
            .check()
            .map_err(|e| ClusteringError::ValidationFailed(format!("DBSCAN params validation failed: {:?}", e)))?
            .fit(&dataset)
            .map_err(|e| ClusteringError::ClusteringFailed(format!("{}", e)))?;
        
        let assignments: Vec<i32> = model
            .labels()
            .iter()
            .map(|&label| label as i32)
            .collect();
        
        let n_clusters = assignments
            .iter()
            .filter(|&&a| a >= 0)
            .map(|&a| a as usize)
            .max()
            .map(|m| m + 1)
            .unwrap_or(0);
        let n_noise = assignments.iter().filter(|&&a| a == -1).count();
        
        Ok(DbscanResult {
            assignments,
            n_clusters,
            n_noise,
        })
        */
    }
}