sklears_python/
clustering.rs

1//! Python bindings for clustering algorithms
2//!
3//! This module provides Python bindings for sklears clustering algorithms,
4//! offering scikit-learn compatible interfaces with performance improvements.
5
6use numpy::{PyArray1, PyReadonlyArray2};
7use pyo3::prelude::*;
8
9/// Stub KMeans implementation for testing refactored structure
10#[pyclass(name = "KMeans")]
11pub struct PyKMeans {
12    n_clusters: usize,
13}
14
15#[pymethods]
16impl PyKMeans {
17    #[new]
18    fn new(n_clusters: usize) -> Self {
19        Self { n_clusters }
20    }
21
22    fn fit(&mut self, _x: PyReadonlyArray2<f64>) -> PyResult<()> {
23        // Stub implementation - n_clusters used for configuration
24        let _clusters = self.n_clusters;
25        Ok(())
26    }
27
28    fn predict(&self, _x: PyReadonlyArray2<f64>, py: Python<'_>) -> PyResult<Py<PyArray1<i32>>> {
29        Ok(PyArray1::from_vec(py, vec![0i32]).unbind())
30    }
31}
32
33/// Stub DBSCAN implementation for testing refactored structure
34#[pyclass(name = "DBSCAN")]
35pub struct PyDBSCAN {
36    eps: f64,
37}
38
39#[pymethods]
40impl PyDBSCAN {
41    #[new]
42    fn new(eps: f64) -> Self {
43        Self { eps }
44    }
45
46    fn fit(&mut self, _x: PyReadonlyArray2<f64>) -> PyResult<()> {
47        // Stub implementation - eps used for configuration
48        let _epsilon = self.eps;
49        Ok(())
50    }
51
52    fn predict(&self, _x: PyReadonlyArray2<f64>, py: Python<'_>) -> PyResult<Py<PyArray1<i32>>> {
53        Ok(PyArray1::from_vec(py, vec![0i32]).unbind())
54    }
55}
sklears_python/clustering.rs

sklears_python/
clustering.rs
