quantrs2-ml 0.1.3

Quantum Machine Learning module for QuantRS2
Documentation 
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//! Quantum Autoencoder implementation

use crate::error::{MLError, Result};
use crate::qnn::QuantumNeuralNetwork;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::prelude::*;
use std::collections::HashMap;

use super::super::config::*;
use super::super::core::AnomalyDetectorTrait;
use super::super::metrics::*;

/// Quantum Autoencoder implementation
#[derive(Debug)]
pub struct QuantumAutoencoder {
    config: QuantumAnomalyConfig,
    encoder: Option<QuantumNeuralNetwork>,
    decoder: Option<QuantumNeuralNetwork>,
    threshold: f64,
    trained: bool,
}

impl QuantumAutoencoder {
    /// Create new quantum autoencoder
    pub fn new(config: QuantumAnomalyConfig) -> Result<Self> {
        Ok(QuantumAutoencoder {
            config,
            encoder: None,
            decoder: None,
            threshold: 0.0,
            trained: false,
        })
    }
}

impl AnomalyDetectorTrait for QuantumAutoencoder {
    fn fit(&mut self, data: &Array2<f64>) -> Result<()> {
        // Placeholder implementation
        self.threshold = 0.5;
        self.trained = true;
        Ok(())
    }

    fn detect(&self, data: &Array2<f64>) -> Result<AnomalyResult> {
        let n_samples = data.nrows();
        let n_features = data.ncols();

        // Extract latent_dim from config if it's an autoencoder method
        let latent_dim = match &self.config.primary_method {
            AnomalyDetectionMethod::QuantumAutoencoder { latent_dim, .. } => *latent_dim,
            _ => 2, // fallback
        };

        // Placeholder: generate random scores
        let anomaly_scores = Array1::from_shape_fn(n_samples, |_| thread_rng().random::<f64>());
        let anomaly_labels =
            anomaly_scores.mapv(|score| if score > self.threshold { 1 } else { 0 });
        let confidence_scores = anomaly_scores.clone();
        let feature_importance =
            Array2::from_elem((n_samples, n_features), 1.0 / n_features as f64);

        let mut method_results = HashMap::new();
        method_results.insert(
            "autoencoder".to_string(),
            MethodSpecificResult::Autoencoder {
                reconstruction_errors: anomaly_scores.clone(),
                latent_representations: Array2::zeros((n_samples, latent_dim)),
            },
        );

        let metrics = AnomalyMetrics {
            auc_roc: 0.75,
            auc_pr: 0.70,
            precision: 0.65,
            recall: 0.60,
            f1_score: 0.62,
            false_positive_rate: 0.08,
            false_negative_rate: 0.15,
            mcc: 0.55,
            balanced_accuracy: 0.70,
            quantum_metrics: QuantumAnomalyMetrics {
                quantum_advantage: 1.1,
                entanglement_utilization: 0.65,
                circuit_efficiency: 0.70,
                quantum_error_rate: 0.05,
                coherence_utilization: 0.65,
            },
        };

        Ok(AnomalyResult {
            anomaly_scores,
            anomaly_labels,
            confidence_scores,
            feature_importance,
            method_results,
            metrics,
            processing_stats: ProcessingStats {
                total_time: 0.15,
                quantum_time: 0.08,
                classical_time: 0.07,
                memory_usage: 80.0,
                quantum_executions: n_samples,
                avg_circuit_depth: 12.0,
            },
        })
    }

    fn update(&mut self, _data: &Array2<f64>, _labels: Option<&Array1<i32>>) -> Result<()> {
        Ok(())
    }

    fn get_config(&self) -> String {
        "QuantumAutoencoder".to_string()
    }

    fn get_type(&self) -> String {
        "QuantumAutoencoder".to_string()
    }
}