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::*;
8use scirs2_core::ndarray::Array1;
9
10/// Stub KMeans implementation for testing refactored structure
11#[pyclass(name = "KMeans")]
12pub struct PyKMeans {
13    n_clusters: usize,
14}
15
16#[pymethods]
17impl PyKMeans {
18    #[new]
19    fn new(n_clusters: usize) -> Self {
20        Self { n_clusters }
21    }
22
23    fn fit(&mut self, _x: PyReadonlyArray2<f64>) -> PyResult<()> {
24        // Stub implementation - n_clusters used for configuration
25        let _clusters = self.n_clusters;
26        Ok(())
27    }
28
29    fn predict(&self, _x: PyReadonlyArray2<f64>, py: Python<'_>) -> PyResult<Py<PyArray1<i32>>> {
30        let labels = Array1::<i32>::zeros(1);
31        Ok(PyArray1::from_array(py, &labels).unbind())
32    }
33}
34
35/// Stub DBSCAN implementation for testing refactored structure
36#[pyclass(name = "DBSCAN")]
37pub struct PyDBSCAN {
38    eps: f64,
39}
40
41#[pymethods]
42impl PyDBSCAN {
43    #[new]
44    fn new(eps: f64) -> Self {
45        Self { eps }
46    }
47
48    fn fit(&mut self, _x: PyReadonlyArray2<f64>) -> PyResult<()> {
49        // Stub implementation - eps used for configuration
50        let _epsilon = self.eps;
51        Ok(())
52    }
53
54    fn predict(&self, _x: PyReadonlyArray2<f64>, py: Python<'_>) -> PyResult<Py<PyArray1<i32>>> {
55        let labels = Array1::<i32>::zeros(1);
56        Ok(PyArray1::from_array(py, &labels).unbind())
57    }
58}
sklears_python/clustering.rs

sklears_python/
clustering.rs
