quantrs2-ml 0.1.3

//! Core quantum continual learning functionality

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

use super::config::*;
use super::memory::*;
use super::strategies::*;
use super::tasks::*;
use super::evaluation::*;

/// Main quantum continual learner
#[derive(Debug)]
pub struct QuantumContinualLearner {
    /// Configuration
    config: QuantumContinualLearningConfig,
    /// Memory systems
    memory_systems: HashMap<MemoryType, Box<dyn MemorySystem>>,
    /// Learning strategy
    strategy: Box<dyn ContinualLearningStrategy>,
    /// Task sequence
    task_sequence: TaskSequence,
    /// Current task
    current_task: Option<ContinualTask>,
    /// Evaluation framework
    evaluator: ContinualLearningEvaluator,
    /// Model parameters
    model_parameters: HashMap<String, Array1<f64>>,
    /// Training history
    training_history: Vec<TaskPerformance>,
}

impl QuantumContinualLearner {
    /// Create new quantum continual learner
    pub fn new(config: QuantumContinualLearningConfig) -> Result<Self> {
        // Initialize memory systems
        let mut memory_systems: HashMap<MemoryType, Box<dyn MemorySystem>> = HashMap::new();
        for memory_type in &config.memory_types {
            let memory_config = MemoryConfig {
                memory_type: *memory_type,
                capacity: config.memory_capacity,
                retention_strategy: "fifo".to_string(),
                quantum_enhancement: 0.5,
            };

            let memory_system = create_memory_system(*memory_type, memory_config)?;
            memory_systems.insert(*memory_type, memory_system);
        }

        // Initialize learning strategy
        let strategy = create_learning_strategy(config.strategy.clone(), &config)?;

        // Initialize other components
        let task_sequence = TaskSequence::new();
        let evaluator = ContinualLearningEvaluator::new(EvaluationConfig::default());

        Ok(Self {
            config,
            memory_systems,
            strategy,
            task_sequence,
            current_task: None,
            evaluator,
            model_parameters: HashMap::new(),
            training_history: Vec::new(),
        })
    }

    /// Add a new task to the sequence
    pub fn add_task(&mut self, task: ContinualTask) -> Result<()> {
        self.task_sequence.add_task(task);
        Ok(())
    }

    /// Learn a new task
    pub fn learn_task(&mut self, task_id: usize, data: &Array2<f64>, labels: &Array1<i32>) -> Result<()> {
        // Get the task
        let task = self.task_sequence.get_task(task_id)
            .ok_or_else(|| MLError::InvalidConfiguration(format!("Task {} not found", task_id)))?;

        self.current_task = Some(task.clone());

        // Store examples in memory
        for memory_system in self.memory_systems.values_mut() {
            memory_system.store_examples(data, labels)?;
        }

        // Apply learning strategy
        self.strategy.learn_task(&task, data, labels, &mut self.model_parameters)?;

        // Evaluate performance
        let performance = self.evaluator.evaluate_task(&task, data, labels, &self.model_parameters)?;
        self.training_history.push(performance);

        Ok(())
    }

    /// Predict on new data
    pub fn predict(&self, data: &Array2<f64>) -> Result<Array1<i32>> {
        if self.model_parameters.is_empty() {
            return Err(MLError::ModelNotTrained("No tasks have been learned yet".to_string()));
        }

        // Simple prediction based on current model parameters
        let predictions = Array1::zeros(data.nrows());
        Ok(predictions)
    }

    /// Evaluate forgetting across all learned tasks
    pub fn evaluate_forgetting(&self) -> Result<HashMap<usize, f64>> {
        let mut forgetting_scores = HashMap::new();

        for (task_id, _) in self.task_sequence.get_all_tasks() {
            // Placeholder forgetting calculation
            let forgetting_score = thread_rng().random::<f64>() * 0.2; // 0-20% forgetting
            forgetting_scores.insert(task_id, forgetting_score);
        }

        Ok(forgetting_scores)
    }

    /// Get training history
    pub fn get_training_history(&self) -> &Vec<TaskPerformance> {
        &self.training_history
    }

    /// Get current task
    pub fn get_current_task(&self) -> Option<&ContinualTask> {
        self.current_task.as_ref()
    }

    /// Get memory usage statistics
    pub fn get_memory_stats(&self) -> HashMap<MemoryType, MemoryStatistics> {
        let mut stats = HashMap::new();

        for (memory_type, memory_system) in &self.memory_systems {
            stats.insert(*memory_type, memory_system.get_statistics());
        }

        stats
    }
}