//! Python bindings for clustering algorithms
//!
//! This module provides Python bindings for sklears clustering algorithms,
//! offering scikit-learn compatible interfaces with performance improvements.

use numpy::{PyArray1, PyReadonlyArray2};
use pyo3::prelude::*;
use scirs2_core::ndarray::Array1;

/// Stub KMeans implementation for testing refactored structure
#[pyclass(name = "KMeans")]
pub struct PyKMeans {
    n_clusters: usize,
}

#[pymethods]
impl PyKMeans {
    #[new]
    fn new(n_clusters: usize) -> Self {
        Self { n_clusters }
    }

    fn fit(&mut self, _x: PyReadonlyArray2<f64>) -> PyResult<()> {
        // Stub implementation - n_clusters used for configuration
        let _clusters = self.n_clusters;
        Ok(())
    }

    fn predict(&self, _x: PyReadonlyArray2<f64>, py: Python<'_>) -> PyResult<Py<PyArray1<i32>>> {
        let labels = Array1::<i32>::zeros(1);
        Ok(PyArray1::from_array(py, &labels).unbind())
    }
}

/// Stub DBSCAN implementation for testing refactored structure
#[pyclass(name = "DBSCAN")]
pub struct PyDBSCAN {
    eps: f64,
}

#[pymethods]
impl PyDBSCAN {
    #[new]
    fn new(eps: f64) -> Self {
        Self { eps }
    }

    fn fit(&mut self, _x: PyReadonlyArray2<f64>) -> PyResult<()> {
        // Stub implementation - eps used for configuration
        let _epsilon = self.eps;
        Ok(())
    }

    fn predict(&self, _x: PyReadonlyArray2<f64>, py: Python<'_>) -> PyResult<Py<PyArray1<i32>>> {
        let labels = Array1::<i32>::zeros(1);
        Ok(PyArray1::from_array(py, &labels).unbind())
    }
}
