solvr 0.2.0-beta.2

Advanced computing library for real-world problem solving - optimization, differential equations, interpolation, statistics, and more
Documentation 
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//! DBSCAN clustering trait.
use crate::DType;

use numr::error::Result;
use numr::ops::DistanceMetric;
use numr::runtime::Runtime;
use numr::tensor::Tensor;

/// Options for DBSCAN.
#[derive(Debug, Clone)]
pub struct DbscanOptions {
    /// Neighborhood radius.
    pub eps: f64,
    /// Minimum points to form a core point.
    pub min_samples: usize,
    /// Distance metric.
    pub metric: DistanceMetric,
}

impl Default for DbscanOptions {
    fn default() -> Self {
        Self {
            eps: 0.5,
            min_samples: 5,
            metric: DistanceMetric::Euclidean,
        }
    }
}

/// Result of DBSCAN clustering.
#[derive(Debug, Clone)]
pub struct DbscanResult<R: Runtime<DType = DType>> {
    /// Cluster labels `[n]` I64, -1 for noise.
    pub labels: Tensor<R>,
    /// Indices of core samples `[n_core]` I64.
    pub core_sample_indices: Tensor<R>,
    /// Number of clusters found.
    pub n_clusters: usize,
}

/// DBSCAN clustering algorithms.
pub trait DbscanAlgorithms<R: Runtime<DType = DType>> {
    /// Run DBSCAN on data `[n, d]`.
    fn dbscan(&self, data: &Tensor<R>, options: &DbscanOptions) -> Result<DbscanResult<R>>;
}