quantrs2-ml 0.1.3

Quantum Machine Learning module for QuantRS2
Documentation 
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//! Evaluation framework for quantum continual learning

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

use super::config::{ContinualTask, EvaluationConfig};

/// Task performance metrics
#[derive(Debug, Clone)]
pub struct TaskPerformance {
    pub task_id: usize,
    pub accuracy: f64,
    pub loss: f64,
    pub forgetting_measure: f64,
    pub learning_time: f64,
    pub memory_usage: f64,
}

/// Continual learning evaluator
#[derive(Debug)]
pub struct ContinualLearningEvaluator {
    config: EvaluationConfig,
    baseline_performances: HashMap<usize, f64>,
}

impl ContinualLearningEvaluator {
    pub fn new(config: EvaluationConfig) -> Self {
        Self {
            config,
            baseline_performances: HashMap::new(),
        }
    }

    pub fn evaluate_task(
        &mut self,
        task: &ContinualTask,
        data: &Array2<f64>,
        labels: &Array1<i32>,
        model_parameters: &HashMap<String, Array1<f64>>,
    ) -> Result<TaskPerformance> {
        // Placeholder evaluation
        let accuracy = 0.8 + thread_rng().random::<f64>() * 0.2;
        let loss = thread_rng().random::<f64>() * 0.5;
        let forgetting_measure = thread_rng().random::<f64>() * 0.1;
        let learning_time = thread_rng().random::<f64>() * 10.0;
        let memory_usage = thread_rng().random::<f64>() * 100.0;

        self.baseline_performances.insert(task.task_id, accuracy);

        Ok(TaskPerformance {
            task_id: task.task_id,
            accuracy,
            loss,
            forgetting_measure,
            learning_time,
            memory_usage,
        })
    }

    pub fn compute_average_accuracy(&self, performances: &[TaskPerformance]) -> f64 {
        if performances.is_empty() {
            return 0.0;
        }

        performances.iter().map(|p| p.accuracy).sum::<f64>() / performances.len() as f64
    }

    pub fn compute_backward_transfer(&self, performances: &[TaskPerformance]) -> f64 {
        // Placeholder backward transfer calculation
        thread_rng().random::<f64>() * 0.1 - 0.05 // -5% to +5%
    }

    pub fn compute_forward_transfer(&self, performances: &[TaskPerformance]) -> f64 {
        // Placeholder forward transfer calculation
        thread_rng().random::<f64>() * 0.1 // 0% to +10%
    }
}