quantrs2_device/quantum_network/network_optimization/
dummymlmodel_traits.rs

1//! # DummyMLModel - Trait Implementations
2//!
3//! This module contains trait implementations for `DummyMLModel`.
4//!
5//! ## Implemented Traits
6//!
7//! - `MLModel`
8//!
9//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)
10
11use async_trait::async_trait;
12use chrono::Utc;
13use std::collections::HashMap;
14use std::time::Duration;
15
16use super::type_definitions::*;
17use crate::quantum_network::distributed_protocols::TrainingDataPoint;
18
19#[async_trait]
20impl MLModel for DummyMLModel {
21    async fn predict(&self, _features: &FeatureVector) -> Result<PredictionResult> {
22        Ok(PredictionResult {
23            predicted_values: HashMap::new(),
24            confidence_intervals: HashMap::new(),
25            uncertainty_estimate: 0.1,
26            prediction_timestamp: Utc::now(),
27        })
28    }
29    async fn train(&mut self, _training_data: &[TrainingDataPoint]) -> Result<TrainingResult> {
30        Ok(TrainingResult {
31            training_accuracy: 0.8,
32            validation_accuracy: 0.75,
33            loss_value: 0.2,
34            training_duration: Duration::from_secs(100),
35            model_size_bytes: 1024,
36        })
37    }
38    async fn update_weights(&mut self, _feedback: &FeedbackData) -> Result<()> {
39        Ok(())
40    }
41    fn get_model_metrics(&self) -> ModelMetrics {
42        ModelMetrics {
43            accuracy: 0.8,
44            precision: 0.8,
45            recall: 0.8,
46            f1_score: 0.8,
47            mae: 0.1,
48            rmse: 0.1,
49        }
50    }
51}