use crate::error::{MLError, Result};
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::*;
#[derive(Debug)]
pub struct QuantumEnsemble {
config: QuantumAnomalyConfig,
}
impl QuantumEnsemble {
pub fn new(config: QuantumAnomalyConfig) -> Result<Self> {
Ok(QuantumEnsemble { config })
}
}
impl AnomalyDetectorTrait for QuantumEnsemble {
fn fit(&mut self, _data: &Array2<f64>) -> Result<()> {
Ok(())
}
fn detect(&self, data: &Array2<f64>) -> Result<AnomalyResult> {
let n_samples = data.nrows();
let n_features = data.ncols();
let anomaly_scores = Array1::from_shape_fn(n_samples, |_| thread_rng().random::<f64>());
let anomaly_labels = anomaly_scores.mapv(|score| if score > 0.5 { 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 method_results = HashMap::new();
let metrics = AnomalyMetrics {
auc_roc: 0.88,
auc_pr: 0.83,
precision: 0.78,
recall: 0.73,
f1_score: 0.75,
false_positive_rate: 0.04,
false_negative_rate: 0.08,
mcc: 0.68,
balanced_accuracy: 0.83,
quantum_metrics: QuantumAnomalyMetrics {
quantum_advantage: 1.20,
entanglement_utilization: 0.75,
circuit_efficiency: 0.80,
quantum_error_rate: 0.02,
coherence_utilization: 0.78,
},
};
Ok(AnomalyResult {
anomaly_scores,
anomaly_labels,
confidence_scores,
feature_importance,
method_results,
metrics,
processing_stats: ProcessingStats {
total_time: 0.25,
quantum_time: 0.12,
classical_time: 0.13,
memory_usage: 120.0,
quantum_executions: n_samples * 3,
avg_circuit_depth: 15.0,
},
})
}
fn update(&mut self, _data: &Array2<f64>, _labels: Option<&Array1<i32>>) -> Result<()> {
Ok(())
}
fn get_config(&self) -> String {
"QuantumEnsemble".to_string()
}
fn get_type(&self) -> String {
"QuantumEnsemble".to_string()
}
}