quantrs2_ml/dimensionality_reduction/linear/
qpca.rs

1//! Quantum Principal Component Analysis
2
3use crate::error::{MLError, Result};
4use scirs2_core::ndarray::{s, Array1, Array2};
5use std::collections::HashMap;
6
7use super::super::config::{DRTrainedState, QPCAConfig};
8
9/// Quantum Principal Component Analysis implementation
10#[derive(Debug)]
11pub struct QPCA {
12    config: QPCAConfig,
13    trained_state: Option<DRTrainedState>,
14}
15
16impl QPCA {
17    /// Create new QPCA instance
18    pub fn new(config: QPCAConfig) -> Self {
19        Self {
20            config,
21            trained_state: None,
22        }
23    }
24
25    /// Fit the QPCA model
26    pub fn fit(&mut self, data: &Array2<f64>) -> Result<()> {
27        let n_samples = data.nrows();
28        let n_features = data.ncols();
29        let n_components = self.config.n_components.min(n_features);
30
31        // Compute mean
32        let mean = data
33            .mean_axis(scirs2_core::ndarray::Axis(0))
34            .unwrap_or_else(|| scirs2_core::ndarray::Array1::zeros(data.ncols()));
35
36        // Center the data
37        let centered = data - &mean;
38
39        // Compute covariance matrix
40        let cov = centered.t().dot(&centered) / (n_samples - 1) as f64;
41
42        // Placeholder eigendecomposition - create components matrix with correct dimensions
43        // Components stored as (n_components, n_features) to work with transform method
44        let components = Array2::eye(n_features)
45            .slice(s![..n_components, ..])
46            .to_owned();
47        let eigenvalues =
48            Array1::from_vec((0..n_components).map(|i| 1.0 / (i + 1) as f64).collect());
49        let explained_variance_ratio = &eigenvalues / eigenvalues.sum();
50
51        // Create trained state
52        self.trained_state = Some(DRTrainedState {
53            components,
54            explained_variance_ratio,
55            mean,
56            scale: None,
57            quantum_parameters: HashMap::new(),
58            model_parameters: HashMap::new(),
59            training_statistics: HashMap::new(),
60        });
61
62        Ok(())
63    }
64
65    /// Transform data using fitted QPCA
66    pub fn transform(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
67        if let Some(state) = &self.trained_state {
68            let centered = data - &state.mean;
69            Ok(centered.dot(&state.components.t()))
70        } else {
71            Err(MLError::ModelNotTrained(
72                "QPCA model must be fitted before transform".to_string(),
73            ))
74        }
75    }
76
77    /// Get trained state
78    pub fn get_trained_state(&self) -> Option<DRTrainedState> {
79        self.trained_state.clone()
80    }
81}
quantrs2_ml/dimensionality_reduction/linear/qpca.rs

quantrs2_ml/dimensionality_reduction/linear/
qpca.rs
