quantrs2-ml 0.1.3

//! Core quantum dimensionality reduction functionality

use crate::error::{MLError, Result};
use scirs2_core::ndarray::{Array1, Array2};

use super::config::*;
use super::metrics::*;

/// Main quantum dimensionality reducer
#[derive(Debug)]
pub struct QuantumDimensionalityReducer {
    /// Algorithm to use
    pub algorithm: DimensionalityReductionAlgorithm,
    /// QPCA configuration
    pub qpca_config: Option<QPCAConfig>,
    /// QICA configuration
    pub qica_config: Option<QICAConfig>,
    /// Qt-SNE configuration
    pub qtsne_config: Option<QtSNEConfig>,
    /// QUMAP configuration
    pub qumap_config: Option<QUMAPConfig>,
    /// QLDA configuration
    pub qlda_config: Option<QLDAConfig>,
    /// QFA configuration
    pub qfa_config: Option<QFactorAnalysisConfig>,
    /// QCCA configuration
    pub qcca_config: Option<QCCAConfig>,
    /// QNMF configuration
    pub qnmf_config: Option<QNMFConfig>,
    /// Autoencoder configuration
    pub autoencoder_config: Option<QAutoencoderConfig>,
    /// Manifold learning configuration
    pub manifold_config: Option<QManifoldConfig>,
    /// Kernel PCA configuration
    pub kernel_pca_config: Option<QKernelPCAConfig>,
    /// Feature selection configuration
    pub feature_selection_config: Option<QFeatureSelectionConfig>,
    /// Specialized configuration
    pub specialized_config: Option<QSpecializedConfig>,
    /// Trained state
    pub trained_state: Option<DRTrainedState>,
}

impl QuantumDimensionalityReducer {
    /// Create a new quantum dimensionality reducer
    pub fn new(algorithm: DimensionalityReductionAlgorithm) -> Self {
        Self {
            algorithm,
            qpca_config: None,
            qica_config: None,
            qtsne_config: None,
            qumap_config: None,
            qlda_config: None,
            qfa_config: None,
            qcca_config: None,
            qnmf_config: None,
            autoencoder_config: None,
            manifold_config: None,
            kernel_pca_config: None,
            feature_selection_config: None,
            specialized_config: None,
            trained_state: None,
        }
    }

    /// Set QPCA configuration
    pub fn with_qpca_config(mut self, config: QPCAConfig) -> Self {
        self.qpca_config = Some(config);
        self
    }

    /// Set QICA configuration
    pub fn with_qica_config(mut self, config: QICAConfig) -> Self {
        self.qica_config = Some(config);
        self
    }

    /// Set Qt-SNE configuration
    pub fn with_qtsne_config(mut self, config: QtSNEConfig) -> Self {
        self.qtsne_config = Some(config);
        self
    }

    /// Set QUMAP configuration
    pub fn with_qumap_config(mut self, config: QUMAPConfig) -> Self {
        self.qumap_config = Some(config);
        self
    }

    /// Set QLDA configuration
    pub fn with_qlda_config(mut self, config: QLDAConfig) -> Self {
        self.qlda_config = Some(config);
        self
    }

    /// Set autoencoder configuration
    pub fn with_autoencoder_config(mut self, config: QAutoencoderConfig) -> Self {
        self.autoencoder_config = Some(config);
        self
    }

    /// Fit the dimensionality reduction model
    pub fn fit(&mut self, data: &Array2<f64>) -> Result<()> {
        match self.algorithm {
            DimensionalityReductionAlgorithm::QPCA => self.fit_qpca(data),
            DimensionalityReductionAlgorithm::QICA => self.fit_qica(data),
            DimensionalityReductionAlgorithm::QtSNE => self.fit_qtsne(data),
            DimensionalityReductionAlgorithm::QUMAP => self.fit_qumap(data),
            DimensionalityReductionAlgorithm::QLDA => self.fit_qlda(data),
            DimensionalityReductionAlgorithm::QVAE => self.fit_qvae(data),
            DimensionalityReductionAlgorithm::QDenoisingAE => self.fit_qdenoising_ae(data),
            DimensionalityReductionAlgorithm::QSparseAE => self.fit_qsparse_ae(data),
            DimensionalityReductionAlgorithm::QManifoldLearning => self.fit_qmanifold(data),
            DimensionalityReductionAlgorithm::QKernelPCA => self.fit_qkernel_pca(data),
            _ => {
                // Placeholder for other algorithms
                self.fit_placeholder(data)
            }
        }
    }

    /// Transform data using the fitted model
    pub fn transform(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        if self.trained_state.is_none() {
            return Err(MLError::ModelNotTrained(
                "Model must be fitted before transform".to_string(),
            ));
        }

        match self.algorithm {
            DimensionalityReductionAlgorithm::QPCA => self.transform_qpca(data),
            DimensionalityReductionAlgorithm::QICA => self.transform_qica(data),
            DimensionalityReductionAlgorithm::QtSNE => self.transform_qtsne(data),
            DimensionalityReductionAlgorithm::QUMAP => self.transform_qumap(data),
            DimensionalityReductionAlgorithm::QLDA => self.transform_qlda(data),
            DimensionalityReductionAlgorithm::QVAE => self.transform_qvae(data),
            DimensionalityReductionAlgorithm::QDenoisingAE => self.transform_qdenoising_ae(data),
            DimensionalityReductionAlgorithm::QSparseAE => self.transform_qsparse_ae(data),
            DimensionalityReductionAlgorithm::QManifoldLearning => self.transform_qmanifold(data),
            DimensionalityReductionAlgorithm::QKernelPCA => self.transform_qkernel_pca(data),
            _ => {
                // Placeholder for other algorithms
                self.transform_placeholder(data)
            }
        }
    }

    /// Fit and transform in one step
    pub fn fit_transform(&mut self, data: &Array2<f64>) -> Result<Array2<f64>> {
        self.fit(data)?;
        self.transform(data)
    }

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

    /// Get explained variance ratio (if applicable)
    pub fn explained_variance_ratio(&self) -> Option<&Array1<f64>> {
        self.trained_state
            .as_ref()
            .map(|state| &state.explained_variance_ratio)
    }

    /// Get the components (transformation matrix)
    pub fn components(&self) -> Option<&Array2<f64>> {
        self.trained_state.as_ref().map(|state| &state.components)
    }

    /// Inverse transform (reconstruction)
    pub fn inverse_transform(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        if let Some(state) = &self.trained_state {
            // Basic linear reconstruction
            let centered = data.dot(&state.components);
            let reconstructed = &centered + &state.mean;
            Ok(reconstructed)
        } else {
            Err(MLError::ModelNotTrained(
                "Model must be fitted before inverse transform".to_string(),
            ))
        }
    }

    // Private fitting methods (placeholder implementations)

    fn fit_qpca(&mut self, data: &Array2<f64>) -> Result<()> {
        use super::linear::QPCA;
        let binding = QPCAConfig::default();
        let config = self.qpca_config.as_ref().unwrap_or(&binding);
        let mut qpca = QPCA::new(config.clone());
        qpca.fit(data)?;
        self.trained_state = qpca.get_trained_state();
        Ok(())
    }

    fn fit_qica(&mut self, data: &Array2<f64>) -> Result<()> {
        use super::linear::QICA;
        let binding = QICAConfig::default();
        let config = self.qica_config.as_ref().unwrap_or(&binding);
        let mut qica = QICA::new(config.clone());
        qica.fit(data)?;
        self.trained_state = qica.get_trained_state();
        Ok(())
    }

    fn fit_qtsne(&mut self, data: &Array2<f64>) -> Result<()> {
        use super::manifold::QtSNE;
        let binding = QtSNEConfig::default();
        let config = self.qtsne_config.as_ref().unwrap_or(&binding);
        let mut qtsne = QtSNE::new(config.clone());
        qtsne.fit(data)?;
        self.trained_state = qtsne.get_trained_state();
        Ok(())
    }

    fn fit_qumap(&mut self, data: &Array2<f64>) -> Result<()> {
        use super::manifold::QUMAP;
        let binding = QUMAPConfig::default();
        let config = self.qumap_config.as_ref().unwrap_or(&binding);
        let mut qumap = QUMAP::new(config.clone());
        qumap.fit(data)?;
        self.trained_state = qumap.get_trained_state();
        Ok(())
    }

    fn fit_qlda(&mut self, data: &Array2<f64>) -> Result<()> {
        use super::linear::QLDA;
        let default_config = QLDAConfig::default();
        let config = self.qlda_config.as_ref().unwrap_or(&default_config);
        let mut qlda = QLDA::new(config.clone());
        qlda.fit(data)?;
        self.trained_state = qlda.get_trained_state();
        Ok(())
    }

    fn fit_qvae(&mut self, data: &Array2<f64>) -> Result<()> {
        use super::autoencoders::QVAE;
        let default_config = QAutoencoderConfig::default();
        let config = self.autoencoder_config.as_ref().unwrap_or(&default_config);
        let mut qvae = QVAE::new(config.clone());
        qvae.fit(data)?;
        self.trained_state = qvae.get_trained_state();
        Ok(())
    }

    fn fit_qdenoising_ae(&mut self, data: &Array2<f64>) -> Result<()> {
        use super::autoencoders::QDenoisingAE;
        let default_config = QAutoencoderConfig::default();
        let config = self.autoencoder_config.as_ref().unwrap_or(&default_config);
        let mut qdenoising = QDenoisingAE::new(config.clone());
        qdenoising.fit(data)?;
        self.trained_state = qdenoising.get_trained_state();
        Ok(())
    }

    fn fit_qsparse_ae(&mut self, data: &Array2<f64>) -> Result<()> {
        use super::autoencoders::QSparseAE;
        let default_config = QAutoencoderConfig::default();
        let config = self.autoencoder_config.as_ref().unwrap_or(&default_config);
        let mut qsparse = QSparseAE::new(config.clone());
        qsparse.fit(data)?;
        self.trained_state = qsparse.get_trained_state();
        Ok(())
    }

    fn fit_qmanifold(&mut self, data: &Array2<f64>) -> Result<()> {
        use super::manifold::QManifoldLearning;
        let default_config = QManifoldConfig::default();
        let config = self.manifold_config.as_ref().unwrap_or(&default_config);
        let mut qmanifold = QManifoldLearning::new(config.clone());
        qmanifold.fit(data)?;
        self.trained_state = qmanifold.get_trained_state();
        Ok(())
    }

    fn fit_qkernel_pca(&mut self, data: &Array2<f64>) -> Result<()> {
        use super::linear::QKernelPCA;
        let default_config = QKernelPCAConfig::default();
        let config = self.kernel_pca_config.as_ref().unwrap_or(&default_config);
        let mut qkernel_pca = QKernelPCA::new(config.clone());
        qkernel_pca.fit(data)?;
        self.trained_state = qkernel_pca.get_trained_state();
        Ok(())
    }

    fn fit_placeholder(&mut self, data: &Array2<f64>) -> Result<()> {
        // Placeholder implementation - creates a simple identity transformation
        let _n_samples = data.nrows();
        let n_features = data.ncols();
        let n_components = (n_features / 2).max(1);

        let components = Array2::eye(n_components);
        let explained_variance_ratio =
            Array1::from_vec((0..n_components).map(|i| 1.0 / (i + 1) as f64).collect());
        let mean = data
            .mean_axis(scirs2_core::ndarray::Axis(0))
            .ok_or_else(|| {
                MLError::ComputationError(
                    "Failed to compute mean axis for placeholder fit".to_string(),
                )
            })?;

        self.trained_state = Some(DRTrainedState {
            components,
            explained_variance_ratio,
            mean,
            scale: None,
            quantum_parameters: std::collections::HashMap::new(),
            model_parameters: std::collections::HashMap::new(),
            training_statistics: std::collections::HashMap::new(),
        });

        Ok(())
    }

    // Private transformation methods (placeholder implementations)

    fn transform_qpca(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let state = self
            .trained_state
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("QPCA model not trained".to_string()))?;
        let centered = data - &state.mean;
        Ok(centered.dot(&state.components.t()))
    }

    fn transform_qica(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let state = self
            .trained_state
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("QICA model not trained".to_string()))?;
        let centered = data - &state.mean;
        Ok(centered.dot(&state.components.t()))
    }

    fn transform_qtsne(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let state = self
            .trained_state
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("QtSNE model not trained".to_string()))?;
        // t-SNE doesn't have a direct transform, so use embedding
        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
    }

    fn transform_qumap(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let state = self
            .trained_state
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("QUMAP model not trained".to_string()))?;
        // UMAP transform placeholder
        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
    }

    fn transform_qlda(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let state = self
            .trained_state
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("QLDA model not trained".to_string()))?;
        let centered = data - &state.mean;
        Ok(centered.dot(&state.components.t()))
    }

    fn transform_qvae(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let state = self
            .trained_state
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("QVAE model not trained".to_string()))?;
        // VAE encoding placeholder
        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
    }

    fn transform_qdenoising_ae(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let state = self.trained_state.as_ref().ok_or_else(|| {
            MLError::ModelNotTrained("QDenoisingAE model not trained".to_string())
        })?;
        // Denoising AE encoding placeholder
        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
    }

    fn transform_qsparse_ae(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let state = self
            .trained_state
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("QSparseAE model not trained".to_string()))?;
        // Sparse AE encoding placeholder
        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
    }

    fn transform_qmanifold(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let state = self
            .trained_state
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("QManifold model not trained".to_string()))?;
        // Manifold learning transform placeholder
        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
    }

    fn transform_qkernel_pca(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let state = self
            .trained_state
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("QKernelPCA model not trained".to_string()))?;
        // Kernel PCA transform placeholder
        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
    }

    fn transform_placeholder(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let state = self
            .trained_state
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("Placeholder model not trained".to_string()))?;
        let centered = data - &state.mean;
        Ok(centered.dot(&state.components.t()))
    }
}