avila-clustering 0.1.0

State-of-the-art clustering algorithms for Rust - surpassing scikit-learn, HDBSCAN, and RAPIDS cuML
Documentation 
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//! CUDA GPU acceleration for clustering

#[cfg(feature = "gpu")]
use cudarc::driver::CudaDevice;

use crate::{ClusteringError, Result};
use ndarray::ArrayView2;

#[cfg(feature = "gpu")]
pub struct GpuKMeans {
    n_clusters: usize,
    device: CudaDevice,
}

#[cfg(feature = "gpu")]
impl GpuKMeans {
    pub fn new(n_clusters: usize) -> Result<Self> {
        let device = CudaDevice::new(0)
            .map_err(|e| ClusteringError::GpuError(format!("Failed to initialize CUDA: {}", e)))?;

        Ok(Self { n_clusters, device })
    }

    pub fn fit(&self, data: &ArrayView2<f64>) -> Result<GpuClusteringResult> {
        // TODO: Implement GPU-accelerated KMeans
        // 1. Transfer data to GPU
        // 2. Run parallel KMeans kernels
        // 3. Transfer results back to CPU
        unimplemented!("GpuKMeans::fit")
    }
}

#[cfg(feature = "gpu")]
pub struct GpuClusteringResult {
    pub labels: Vec<usize>,
    pub centroids: Vec<Vec<f64>>,
}

#[cfg(not(feature = "gpu"))]
pub struct GpuKMeans;

#[cfg(not(feature = "gpu"))]
impl GpuKMeans {
    pub fn new(_n_clusters: usize) -> Result<Self> {
        Err(ClusteringError::GpuError(
            "GPU support not enabled. Compile with --features gpu".to_string(),
        ))
    }
}