quantrs2_ml/time_series/

features.rs

//! Quantum feature extraction and engineering for time series

use super::config::*;
use crate::error::{MLError, Result};
use crate::qnn::{QNNLayerType, QuantumNeuralNetwork};
use ndarray::{s, Array1, Array2};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::f64::consts::PI;

/// Quantum feature extractor for time series
#[derive(Debug, Clone)]
pub struct QuantumFeatureExtractor {
    /// Feature configuration
    config: FeatureEngineeringConfig,

    /// Quantum circuit parameters for feature extraction
    feature_circuits: Vec<Vec<f64>>,

    /// Feature transformation network
    transform_network: QuantumNeuralNetwork,

    /// Fourier feature generator
    fourier_generator: Option<QuantumFourierFeatures>,

    /// Wavelet transformer
    wavelet_transformer: Option<QuantumWaveletTransform>,

    /// Feature statistics for normalization
    feature_stats: FeatureStatistics,
}

/// Quantum Fourier features for frequency domain analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumFourierFeatures {
    /// Number of Fourier components
    num_components: usize,

    /// Frequency ranges for analysis
    frequency_ranges: Vec<(f64, f64)>,

    /// Quantum Fourier transform circuit parameters
    qft_circuit: Vec<f64>,

    /// Learned frequency components
    learned_frequencies: Array1<f64>,

    /// Phase relationships
    phase_relationships: Array2<f64>,
}

/// Quantum wavelet transform for multi-resolution analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumWaveletTransform {
    /// Wavelet type
    wavelet_type: WaveletType,

    /// Number of decomposition levels
    num_levels: usize,

    /// Quantum wavelet circuits
    wavelet_circuits: Vec<Vec<f64>>,

    /// Threshold for denoising
    threshold: f64,

    /// Decomposition coefficients
    coefficients: Vec<Array2<f64>>,
}

/// Feature statistics for normalization and analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeatureStatistics {
    /// Feature means
    pub means: Array1<f64>,

    /// Feature standard deviations
    pub stds: Array1<f64>,

    /// Feature ranges
    pub ranges: Array1<f64>,

    /// Correlation matrix
    pub correlations: Array2<f64>,

    /// Quantum entanglement measures
    pub entanglement_measures: Array1<f64>,
}

/// Lag feature generator
#[derive(Debug, Clone)]
pub struct LagFeatureGenerator {
    lag_periods: Vec<usize>,
    feature_names: Vec<String>,
}

/// Rolling statistics calculator
#[derive(Debug, Clone)]
pub struct RollingStatsCalculator {
    window_sizes: Vec<usize>,
    stats_types: Vec<StatType>,
}

/// Statistical types for rolling calculations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum StatType {
    Mean,
    Std,
    Min,
    Max,
    Median,
    Quantile(f64),
    Skewness,
    Kurtosis,
}

/// Interaction feature generator
#[derive(Debug, Clone)]
pub struct InteractionFeatureGenerator {
    max_interaction_order: usize,
    interaction_types: Vec<InteractionType>,
}

/// Types of feature interactions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum InteractionType {
    Multiplication,
    Division,
    Addition,
    Subtraction,
    QuantumEntanglement,
}

impl QuantumFeatureExtractor {
    /// Create new quantum feature extractor
    pub fn new(config: FeatureEngineeringConfig, num_qubits: usize) -> Result<Self> {
        // Create quantum circuits for feature extraction
        let mut feature_circuits = Vec::new();

        for circuit_idx in 0..5 {
            let mut circuit_params = Vec::new();

            // Feature extraction gates
            for qubit_idx in 0..num_qubits {
                circuit_params.push(1.0); // H gate marker
                circuit_params.push(PI * circuit_idx as f64 / 5.0); // RY angle
            }

            // Entanglement for feature correlation
            for qubit_idx in 0..num_qubits.saturating_sub(1) {
                circuit_params.push(2.0); // CNOT marker
                circuit_params.push(PI / 4.0 * qubit_idx as f64); // Controlled rotation
            }

            feature_circuits.push(circuit_params);
        }

        // Create transformation network
        let layers = vec![
            QNNLayerType::EncodingLayer { num_features: 100 },
            QNNLayerType::VariationalLayer { num_params: 50 },
            QNNLayerType::MeasurementLayer {
                measurement_basis: "computational".to_string(),
            },
        ];

        let transform_network = QuantumNeuralNetwork::new(layers, num_qubits, 100, 50)?;

        // Create Fourier feature generator if enabled
        let fourier_generator = if config.quantum_fourier_features {
            Some(QuantumFourierFeatures::new(
                20,
                vec![(0.1, 10.0), (10.0, 100.0)],
                num_qubits,
            )?)
        } else {
            None
        };

        // Create wavelet transformer if enabled
        let wavelet_transformer = if config.wavelet_decomposition {
            Some(QuantumWaveletTransform::new(
                WaveletType::Daubechies(4),
                3,
                num_qubits,
            )?)
        } else {
            None
        };

        // Initialize feature statistics
        let feature_stats = FeatureStatistics::new();

        Ok(Self {
            config,
            feature_circuits,
            transform_network,
            fourier_generator,
            wavelet_transformer,
            feature_stats,
        })
    }

    /// Extract comprehensive features from time series data
    pub fn extract_features(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let mut features = data.clone();

        // Apply lag features
        features = self.add_lag_features(&features)?;

        // Apply rolling statistics
        features = self.add_rolling_features(&features)?;

        // Apply quantum Fourier features
        if let Some(ref fourier_gen) = self.fourier_generator {
            features = fourier_gen.transform(&features)?;
        }

        // Apply wavelet decomposition
        if let Some(ref wavelet_trans) = self.wavelet_transformer {
            features = wavelet_trans.decompose(&features)?;
        }

        // Apply interaction features
        if self.config.interaction_features {
            features = self.add_interaction_features(&features)?;
        }

        // Apply quantum transformation
        features = self.apply_quantum_transformation(&features)?;

        // Normalize features
        features = self.normalize_features(&features)?;

        Ok(features)
    }

    /// Add lag features to the dataset
    fn add_lag_features(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        if self.config.lag_features.is_empty() {
            return Ok(data.clone());
        }

        let (n_samples, n_features) = data.dim();
        let total_lag_features = self.config.lag_features.len() * n_features;
        let mut enhanced_data = Array2::zeros((n_samples, n_features + total_lag_features));

        // Copy original features
        enhanced_data.slice_mut(s![.., 0..n_features]).assign(data);

        // Add lag features
        let mut feature_offset = n_features;
        for &lag in &self.config.lag_features {
            for feature_idx in 0..n_features {
                for sample_idx in lag..n_samples {
                    enhanced_data[[sample_idx, feature_offset]] =
                        data[[sample_idx - lag, feature_idx]];
                }
                feature_offset += 1;
            }
        }

        Ok(enhanced_data)
    }

    /// Add rolling statistical features
    fn add_rolling_features(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        if self.config.rolling_windows.is_empty() {
            return Ok(data.clone());
        }

        let (n_samples, n_features) = data.dim();
        let stats_per_window = 3; // mean, std, max
        let total_rolling_features =
            self.config.rolling_windows.len() * n_features * stats_per_window;
        let mut enhanced_data = Array2::zeros((n_samples, n_features + total_rolling_features));

        // Copy original features
        enhanced_data.slice_mut(s![.., 0..n_features]).assign(data);

        // Add rolling features
        let mut feature_offset = n_features;
        for &window_size in &self.config.rolling_windows {
            for feature_idx in 0..n_features {
                for sample_idx in window_size..n_samples {
                    let window_start = sample_idx.saturating_sub(window_size);
                    let window_data = data.slice(s![window_start..sample_idx, feature_idx]);

                    // Rolling mean
                    enhanced_data[[sample_idx, feature_offset]] = window_data.mean().unwrap_or(0.0);

                    // Rolling std
                    enhanced_data[[sample_idx, feature_offset + 1]] = window_data.std(1.0);

                    // Rolling max
                    enhanced_data[[sample_idx, feature_offset + 2]] =
                        window_data.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
                }
                feature_offset += stats_per_window;
            }
        }

        Ok(enhanced_data)
    }

    /// Add interaction features between different variables
    fn add_interaction_features(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let (n_samples, n_features) = data.dim();

        if n_features < 2 {
            return Ok(data.clone());
        }

        // Calculate number of pairwise interactions
        let n_interactions = n_features * (n_features - 1) / 2;
        let mut enhanced_data = Array2::zeros((n_samples, n_features + n_interactions));

        // Copy original features
        enhanced_data.slice_mut(s![.., 0..n_features]).assign(data);

        // Add interaction features
        let mut interaction_idx = n_features;
        for i in 0..n_features {
            for j in (i + 1)..n_features {
                for sample_idx in 0..n_samples {
                    // Multiplicative interaction
                    enhanced_data[[sample_idx, interaction_idx]] =
                        data[[sample_idx, i]] * data[[sample_idx, j]];
                }
                interaction_idx += 1;
            }
        }

        Ok(enhanced_data)
    }

    /// Apply quantum transformation to features
    fn apply_quantum_transformation(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        if !self.config.quantum_features {
            return Ok(data.clone());
        }

        let mut quantum_features = Array2::zeros((data.nrows(), self.transform_network.output_dim));

        for (i, row) in data.rows().into_iter().enumerate() {
            let row_vec = row.to_owned();
            let transformed = self.transform_network.forward(&row_vec)?;
            quantum_features.row_mut(i).assign(&transformed);
        }

        // Combine original and quantum features
        let (n_samples, n_features) = data.dim();
        let mut combined_features =
            Array2::zeros((n_samples, n_features + quantum_features.ncols()));

        combined_features
            .slice_mut(s![.., 0..n_features])
            .assign(data);
        combined_features
            .slice_mut(s![.., n_features..])
            .assign(&quantum_features);

        Ok(combined_features)
    }

    /// Normalize features using learned statistics
    fn normalize_features(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        // Simple standardization (in practice would use learned statistics)
        let mut normalized = data.clone();

        for j in 0..data.ncols() {
            let column = data.column(j);
            let mean = column.mean().unwrap_or(0.0);
            let std = column.std(1.0).max(1e-8); // Avoid division by zero

            for i in 0..data.nrows() {
                normalized[[i, j]] = (data[[i, j]] - mean) / std;
            }
        }

        Ok(normalized)
    }

    /// Update feature statistics from training data
    pub fn fit_statistics(&mut self, data: &Array2<f64>) -> Result<()> {
        self.feature_stats.compute_statistics(data)?;
        Ok(())
    }

    /// Get feature importance scores
    pub fn get_feature_importance(&self) -> Result<Array1<f64>> {
        // Simplified feature importance based on quantum entanglement
        Ok(self.feature_stats.entanglement_measures.clone())
    }
}

impl QuantumFourierFeatures {
    /// Create new quantum Fourier feature generator
    pub fn new(
        num_components: usize,
        frequency_ranges: Vec<(f64, f64)>,
        num_qubits: usize,
    ) -> Result<Self> {
        let mut qft_circuit = Vec::new();

        // Create quantum Fourier transform circuit parameters
        for qubit_idx in 0..num_qubits {
            qft_circuit.push(1.0); // H gate marker
        }

        // Controlled phase gates for QFT
        for i in 0..num_qubits {
            for j in (i + 1)..num_qubits {
                let phase = PI / 2_f64.powi((j - i) as i32);
                qft_circuit.push(phase);
            }
        }

        // Initialize learned frequencies
        let learned_frequencies = Array1::from_shape_fn(num_components, |i| 0.1 + i as f64 * 0.1);

        // Initialize phase relationships
        let phase_relationships = Array2::zeros((num_components, num_components));

        Ok(Self {
            num_components,
            frequency_ranges,
            qft_circuit,
            learned_frequencies,
            phase_relationships,
        })
    }

    /// Transform data with quantum Fourier features
    pub fn transform(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let (n_samples, n_features) = data.dim();
        let fourier_features_count = self.num_components * 2; // sin and cos components
        let mut fourier_features = Array2::zeros((n_samples, n_features + fourier_features_count));

        // Copy original features
        fourier_features
            .slice_mut(s![.., 0..n_features])
            .assign(data);

        // Add Fourier features
        for i in 0..n_samples {
            for (j, &freq) in self.learned_frequencies.iter().enumerate() {
                let phase = i as f64 * freq * 2.0 * PI / n_samples as f64;

                // Apply quantum enhancement to phase
                let quantum_phase = self.apply_quantum_phase_enhancement(phase, j)?;

                fourier_features[[i, n_features + 2 * j]] = quantum_phase.sin();
                fourier_features[[i, n_features + 2 * j + 1]] = quantum_phase.cos();
            }
        }

        Ok(fourier_features)
    }

    /// Apply quantum enhancement to phase calculations
    fn apply_quantum_phase_enhancement(&self, phase: f64, component_idx: usize) -> Result<f64> {
        // Apply quantum circuit parameters to enhance phase
        let mut enhanced_phase = phase;

        if component_idx < self.qft_circuit.len() {
            let circuit_param = self.qft_circuit[component_idx % self.qft_circuit.len()];
            enhanced_phase = phase * circuit_param + 0.1 * (phase * circuit_param).sin();
        }

        Ok(enhanced_phase)
    }

    /// Learn optimal frequencies from data
    pub fn learn_frequencies(&mut self, data: &Array2<f64>) -> Result<()> {
        // Simplified frequency learning using spectral analysis
        for i in 0..self.num_components.min(data.ncols()) {
            // Estimate dominant frequency in each column
            let column = data.column(i % data.ncols());
            let estimated_freq = self.estimate_dominant_frequency(&column)?;
            self.learned_frequencies[i] = estimated_freq;
        }

        Ok(())
    }

    /// Estimate dominant frequency in a signal
    fn estimate_dominant_frequency(&self, signal: &ndarray::ArrayView1<f64>) -> Result<f64> {
        // Simplified frequency estimation (in practice would use FFT)
        let n = signal.len();
        let mut max_power = 0.0;
        let mut dominant_freq = 0.1;

        for k in 1..n / 2 {
            let freq = k as f64 / n as f64;
            let mut power = 0.0;

            for (i, &value) in signal.iter().enumerate() {
                power += value * (2.0 * PI * freq * i as f64).cos();
            }

            if power.abs() > max_power {
                max_power = power.abs();
                dominant_freq = freq;
            }
        }

        Ok(dominant_freq)
    }
}

impl QuantumWaveletTransform {
    /// Create new quantum wavelet transformer
    pub fn new(wavelet_type: WaveletType, num_levels: usize, num_qubits: usize) -> Result<Self> {
        let mut wavelet_circuits = Vec::new();

        for level in 0..num_levels {
            let mut circuit_params = Vec::new();

            // Wavelet decomposition gates
            for qubit_idx in 0..num_qubits / 2 {
                circuit_params.push(1.0); // H gate marker
                circuit_params.push(PI / 4.0 * (level + 1) as f64); // Level-dependent phase rotation
            }

            // Quantum scaling parameters
            for qubit_idx in 0..num_qubits / 2 {
                circuit_params.push(2.0_f64.powi(-(level as i32))); // Dyadic scaling
            }

            wavelet_circuits.push(circuit_params);
        }

        Ok(Self {
            wavelet_type,
            num_levels,
            wavelet_circuits,
            threshold: 0.1,
            coefficients: Vec::new(),
        })
    }

    /// Decompose signal using quantum wavelets
    pub fn decompose(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        let mut decomposed = data.clone();

        // Apply wavelet decomposition at each level
        for level in 0..self.num_levels {
            decomposed = self.apply_wavelet_level(&decomposed, level)?;
        }

        // Apply denoising threshold
        self.apply_threshold(&mut decomposed);

        Ok(decomposed)
    }

    /// Apply wavelet decomposition at specific level
    fn apply_wavelet_level(&self, data: &Array2<f64>, level: usize) -> Result<Array2<f64>> {
        if level >= self.wavelet_circuits.len() {
            return Ok(data.clone());
        }

        let circuit = &self.wavelet_circuits[level];
        let mut result = data.clone();

        // Apply quantum wavelet transformation
        for i in 0..data.nrows() {
            for j in 0..data.ncols() {
                let mut value = data[[i, j]];

                // Apply wavelet basis function with quantum enhancement
                for (k, &param) in circuit.iter().enumerate() {
                    let scale = 2.0_f64.powi(-(level as i32));
                    let wavelet_value = self.wavelet_function(value * scale, param)?;
                    value = value * 0.7 + wavelet_value * 0.3; // Blend original and wavelet
                }

                result[[i, j]] = value;
            }
        }

        Ok(result)
    }

    /// Quantum-enhanced wavelet basis function
    fn wavelet_function(&self, x: f64, quantum_param: f64) -> Result<f64> {
        match self.wavelet_type {
            WaveletType::Haar => {
                // Quantum-enhanced Haar wavelet
                let classical_haar = if x >= 0.0 && x < 0.5 {
                    1.0
                } else if x >= 0.5 && x < 1.0 {
                    -1.0
                } else {
                    0.0
                };

                let quantum_enhancement = (quantum_param * x).sin() * 0.1;
                Ok(classical_haar + quantum_enhancement)
            }
            WaveletType::Daubechies(_) => {
                // Simplified Daubechies with quantum enhancement
                let classical = (PI * x).sin() * (-x * x / 2.0).exp();
                let quantum_enhancement = (quantum_param * x * PI).cos() * 0.05;
                Ok(classical + quantum_enhancement)
            }
            WaveletType::Quantum => {
                // Fully quantum wavelet
                let quantum_phase = quantum_param * x * PI;
                Ok(quantum_phase.sin() * (-x * x).exp())
            }
            _ => {
                // Default to Gaussian-like wavelet
                Ok((PI * x).sin() * (-x * x / 2.0).exp())
            }
        }
    }

    /// Apply denoising threshold
    fn apply_threshold(&self, data: &mut Array2<f64>) {
        for value in data.iter_mut() {
            if value.abs() < self.threshold {
                *value = 0.0;
            }
        }
    }
}

impl FeatureStatistics {
    /// Create new feature statistics
    pub fn new() -> Self {
        Self {
            means: Array1::zeros(0),
            stds: Array1::zeros(0),
            ranges: Array1::zeros(0),
            correlations: Array2::zeros((0, 0)),
            entanglement_measures: Array1::zeros(0),
        }
    }

    /// Compute comprehensive statistics from data
    pub fn compute_statistics(&mut self, data: &Array2<f64>) -> Result<()> {
        let (n_samples, n_features) = data.dim();

        // Compute basic statistics
        self.means = Array1::zeros(n_features);
        self.stds = Array1::zeros(n_features);
        self.ranges = Array1::zeros(n_features);

        for j in 0..n_features {
            let column = data.column(j);
            self.means[j] = column.mean().unwrap_or(0.0);
            self.stds[j] = column.std(1.0);

            let min_val = column.iter().fold(f64::INFINITY, |a, &b| a.min(b));
            let max_val = column.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
            self.ranges[j] = max_val - min_val;
        }

        // Compute correlation matrix
        self.correlations = Array2::zeros((n_features, n_features));
        for i in 0..n_features {
            for j in 0..n_features {
                let corr = self.compute_correlation(data, i, j)?;
                self.correlations[[i, j]] = corr;
            }
        }

        // Compute quantum entanglement measures
        self.entanglement_measures = Array1::zeros(n_features);
        for j in 0..n_features {
            let entanglement = self.compute_quantum_entanglement(data, j)?;
            self.entanglement_measures[j] = entanglement;
        }

        Ok(())
    }

    /// Compute correlation between two features
    fn compute_correlation(&self, data: &Array2<f64>, i: usize, j: usize) -> Result<f64> {
        let col_i = data.column(i);
        let col_j = data.column(j);

        let mean_i = col_i.mean().unwrap_or(0.0);
        let mean_j = col_j.mean().unwrap_or(0.0);

        let mut numerator = 0.0;
        let mut sum_sq_i = 0.0;
        let mut sum_sq_j = 0.0;

        for (val_i, val_j) in col_i.iter().zip(col_j.iter()) {
            let dev_i = val_i - mean_i;
            let dev_j = val_j - mean_j;

            numerator += dev_i * dev_j;
            sum_sq_i += dev_i * dev_i;
            sum_sq_j += dev_j * dev_j;
        }

        let denominator = (sum_sq_i * sum_sq_j).sqrt();
        if denominator < 1e-10 {
            Ok(0.0)
        } else {
            Ok(numerator / denominator)
        }
    }

    /// Compute quantum entanglement measure for a feature
    fn compute_quantum_entanglement(&self, data: &Array2<f64>, feature_idx: usize) -> Result<f64> {
        let column = data.column(feature_idx);

        // Simplified quantum entanglement measure based on entropy
        let mut entropy = 0.0;
        let n_bins = 10;
        let min_val = column.iter().fold(f64::INFINITY, |a, &b| a.min(b));
        let max_val = column.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
        let range = max_val - min_val;

        if range > 1e-10 {
            let mut bin_counts = vec![0; n_bins];

            for &value in column.iter() {
                let bin_idx = ((value - min_val) / range * (n_bins - 1) as f64) as usize;
                let bin_idx = bin_idx.min(n_bins - 1);
                bin_counts[bin_idx] += 1;
            }

            let n_total = column.len() as f64;
            for &count in &bin_counts {
                if count > 0 {
                    let prob = count as f64 / n_total;
                    entropy -= prob * prob.ln();
                }
            }
        }

        Ok(entropy / n_bins as f64) // Normalized entanglement measure
    }
}

/// Feature selection utilities
pub struct QuantumFeatureSelector {
    selection_method: FeatureSelectionMethod,
    max_features: Option<usize>,
}

/// Methods for quantum feature selection
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FeatureSelectionMethod {
    QuantumMutualInformation,
    QuantumEntanglement,
    VariationalImportance,
    HybridSelection,
}

impl QuantumFeatureSelector {
    /// Create new quantum feature selector
    pub fn new(method: FeatureSelectionMethod, max_features: Option<usize>) -> Self {
        Self {
            selection_method: method,
            max_features,
        }
    }

    /// Select most important features using quantum methods
    pub fn select_features(&self, data: &Array2<f64>, target: &Array1<f64>) -> Result<Vec<usize>> {
        match self.selection_method {
            FeatureSelectionMethod::QuantumMutualInformation => {
                self.quantum_mutual_information_selection(data, target)
            }
            FeatureSelectionMethod::QuantumEntanglement => {
                self.quantum_entanglement_selection(data, target)
            }
            FeatureSelectionMethod::VariationalImportance => {
                self.variational_importance_selection(data, target)
            }
            FeatureSelectionMethod::HybridSelection => self.hybrid_selection(data, target),
        }
    }

    /// Select features based on quantum mutual information
    fn quantum_mutual_information_selection(
        &self,
        data: &Array2<f64>,
        target: &Array1<f64>,
    ) -> Result<Vec<usize>> {
        let n_features = data.ncols();
        let mut feature_scores = Vec::new();

        for feature_idx in 0..n_features {
            let column = data.column(feature_idx);
            let mutual_info = self.compute_quantum_mutual_information(&column, target)?;
            feature_scores.push((feature_idx, mutual_info));
        }

        // Sort by score and select top features
        feature_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));

        let num_to_select = self.max_features.unwrap_or(n_features).min(n_features);
        Ok(feature_scores
            .into_iter()
            .take(num_to_select)
            .map(|(idx, _)| idx)
            .collect())
    }

    /// Compute quantum mutual information between feature and target
    fn compute_quantum_mutual_information(
        &self,
        feature: &ndarray::ArrayView1<f64>,
        target: &Array1<f64>,
    ) -> Result<f64> {
        // Simplified quantum mutual information calculation
        let mut mutual_info = 0.0;

        // Discretize values for mutual information calculation
        let n_bins = 5;
        let feature_bins = self.discretize_values(feature, n_bins)?;
        let target_bins = self.discretize_values(&target.view(), n_bins)?;

        // Calculate joint and marginal probabilities
        let n_samples = feature.len();
        let mut joint_counts = HashMap::new();
        let mut feature_counts = HashMap::new();
        let mut target_counts = HashMap::new();

        for i in 0..n_samples {
            let f_bin = feature_bins[i];
            let t_bin = target_bins[i];

            *joint_counts.entry((f_bin, t_bin)).or_insert(0) += 1;
            *feature_counts.entry(f_bin).or_insert(0) += 1;
            *target_counts.entry(t_bin).or_insert(0) += 1;
        }

        // Calculate mutual information with quantum enhancement
        for ((f_bin, t_bin), &joint_count) in &joint_counts {
            let joint_prob = joint_count as f64 / n_samples as f64;
            let feature_prob = *feature_counts.get(f_bin).unwrap_or(&0) as f64 / n_samples as f64;
            let target_prob = *target_counts.get(t_bin).unwrap_or(&0) as f64 / n_samples as f64;

            if joint_prob > 0.0 && feature_prob > 0.0 && target_prob > 0.0 {
                let classical_mi = joint_prob * (joint_prob / (feature_prob * target_prob)).ln();

                // Quantum enhancement factor
                let quantum_factor = 1.0 + 0.1 * (joint_prob * PI).sin().abs();
                mutual_info += classical_mi * quantum_factor;
            }
        }

        Ok(mutual_info)
    }

    /// Discretize continuous values into bins
    fn discretize_values(
        &self,
        values: &ndarray::ArrayView1<f64>,
        n_bins: usize,
    ) -> Result<Vec<usize>> {
        let min_val = values.iter().fold(f64::INFINITY, |a, &b| a.min(b));
        let max_val = values.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
        let range = max_val - min_val;

        let mut bins = Vec::new();
        for &value in values.iter() {
            let bin_idx = if range > 1e-10 {
                ((value - min_val) / range * (n_bins - 1) as f64) as usize
            } else {
                0
            };
            bins.push(bin_idx.min(n_bins - 1));
        }

        Ok(bins)
    }

    /// Placeholder implementations for other selection methods
    fn quantum_entanglement_selection(
        &self,
        data: &Array2<f64>,
        target: &Array1<f64>,
    ) -> Result<Vec<usize>> {
        // Simplified: select all features
        Ok((0..data.ncols()).collect())
    }

    fn variational_importance_selection(
        &self,
        data: &Array2<f64>,
        target: &Array1<f64>,
    ) -> Result<Vec<usize>> {
        // Simplified: select all features
        Ok((0..data.ncols()).collect())
    }

    fn hybrid_selection(&self, data: &Array2<f64>, target: &Array1<f64>) -> Result<Vec<usize>> {
        // Combine multiple methods
        self.quantum_mutual_information_selection(data, target)
    }
}