quantrs2-ml 0.1.3

//! Quantum Principal Component Analysis

use crate::error::{MLError, Result};
use scirs2_core::ndarray::{s, Array1, Array2};
use std::collections::HashMap;

use super::super::config::{DRTrainedState, QPCAConfig};

/// Quantum Principal Component Analysis implementation
#[derive(Debug)]
pub struct QPCA {
    config: QPCAConfig,
    trained_state: Option<DRTrainedState>,
}

impl QPCA {
    /// Create new QPCA instance
    pub fn new(config: QPCAConfig) -> Self {
        Self {
            config,
            trained_state: None,
        }
    }

    /// Fit the QPCA model
    pub fn fit(&mut self, data: &Array2<f64>) -> Result<()> {
        let n_samples = data.nrows();
        let n_features = data.ncols();
        let n_components = self.config.n_components.min(n_features);

        // Compute mean
        let mean = data
            .mean_axis(scirs2_core::ndarray::Axis(0))
            .unwrap_or_else(|| scirs2_core::ndarray::Array1::zeros(data.ncols()));

        // Center the data
        let centered = data - &mean;

        // Compute covariance matrix
        let cov = centered.t().dot(&centered) / (n_samples - 1) as f64;

        // Placeholder eigendecomposition - create components matrix with correct dimensions
        // Components stored as (n_components, n_features) to work with transform method
        let components = Array2::eye(n_features)
            .slice(s![..n_components, ..])
            .to_owned();
        let eigenvalues =
            Array1::from_vec((0..n_components).map(|i| 1.0 / (i + 1) as f64).collect());
        let explained_variance_ratio = &eigenvalues / eigenvalues.sum();

        // Create trained state
        self.trained_state = Some(DRTrainedState {
            components,
            explained_variance_ratio,
            mean,
            scale: None,
            quantum_parameters: HashMap::new(),
            model_parameters: HashMap::new(),
            training_statistics: HashMap::new(),
        });

        Ok(())
    }

    /// Transform data using fitted QPCA
    pub fn transform(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        if let Some(state) = &self.trained_state {
            let centered = data - &state.mean;
            Ok(centered.dot(&state.components.t()))
        } else {
            Err(MLError::ModelNotTrained(
                "QPCA model must be fitted before transform".to_string(),
            ))
        }
    }

    /// Get trained state
    pub fn get_trained_state(&self) -> Option<DRTrainedState> {
        self.trained_state.clone()
    }
}