scirs2-metrics 0.3.2

//! Meta-learning system for neuromorphic computing
//!
//! This module implements learning-to-learn capabilities, including
//! task distribution modeling, few-shot learning, and meta-optimization.

#![allow(clippy::too_many_arguments)]
#![allow(dead_code)]

use crate::error::{MetricsError, Result};
use scirs2_core::ndarray::Array2;
use scirs2_core::numeric::Float;
use std::collections::HashMap;

/// Meta-learning system for learning-to-learn capabilities
#[derive(Debug)]
pub struct MetaLearningSystem<F: Float> {
    /// Meta-learner network
    pub meta_learner: MetaLearnerNetwork<F>,
    /// Task distribution modeling
    pub task_distribution: TaskDistributionModel<F>,
    /// Few-shot learning protocols
    pub few_shot_protocols: Vec<FewShotLearningProtocol<F>>,
    /// Meta-optimization strategies
    pub meta_optimizers: Vec<MetaOptimizationStrategy<F>>,
    /// Learning experience memory
    pub experience_memory: LearningExperienceMemory<F>,
}

/// Meta-learner network architecture
#[derive(Debug)]
pub struct MetaLearnerNetwork<F: Float> {
    /// Memory-augmented neural network
    pub memory_network: MemoryAugmentedNetwork<F>,
    /// Attention mechanisms for meta-learning
    pub attention_mechanisms: Vec<MetaAttentionMechanism<F>>,
    /// Gradient-based meta-learning modules
    pub gradient_modules: Vec<GradientBasedMetaModule<F>>,
    /// Model-agnostic meta-learning (MAML) components
    pub maml_components: MAMLComponents<F>,
}

/// Task distribution modeling for meta-learning
#[derive(Debug)]
pub struct TaskDistributionModel<F: Float> {
    /// Task embedding space
    pub task_embeddings: Array2<F>,
    /// Task similarity metrics
    pub similarity_metrics: Vec<TaskSimilarityMetric<F>>,
    /// Task generation models
    pub task_generators: Vec<TaskGenerator<F>>,
    /// Domain adaptation protocols
    pub domain_adaptation: Vec<DomainAdaptationProtocol<F>>,
}

/// Few-shot learning protocol
#[derive(Debug)]
pub struct FewShotLearningProtocol<F: Float> {
    /// Support set management
    pub support_set: SupportSetManager<F>,
    /// Query set processing
    pub query_processor: QuerySetProcessor<F>,
    /// Prototype networks
    pub prototype_networks: Vec<PrototypeNetwork<F>>,
    /// Matching networks
    pub matching_networks: Vec<MatchingNetwork<F>>,
}

/// Meta-optimization strategies
#[derive(Debug)]
pub struct MetaOptimizationStrategy<F: Float> {
    pub strategy_type: String,
    pub parameters: HashMap<String, F>,
}

/// Learning experience memory
#[derive(Debug)]
pub struct LearningExperienceMemory<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Memory-augmented neural network
#[derive(Debug)]
pub struct MemoryAugmentedNetwork<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Meta-attention mechanism
#[derive(Debug)]
pub struct MetaAttentionMechanism<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Gradient-based meta-learning module
#[derive(Debug)]
pub struct GradientBasedMetaModule<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Model-agnostic meta-learning components
#[derive(Debug)]
pub struct MAMLComponents<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Task similarity metric
#[derive(Debug)]
pub struct TaskSimilarityMetric<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Task generator
#[derive(Debug)]
pub struct TaskGenerator<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Domain adaptation protocol
#[derive(Debug)]
pub struct DomainAdaptationProtocol<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Support set manager
#[derive(Debug)]
pub struct SupportSetManager<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Query set processor
#[derive(Debug)]
pub struct QuerySetProcessor<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Prototype network
#[derive(Debug)]
pub struct PrototypeNetwork<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Matching network
#[derive(Debug)]
pub struct MatchingNetwork<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

impl<F: Float> MetaLearningSystem<F> {
    /// Create new meta-learning system
    pub fn new() -> Result<Self> {
        Ok(Self {
            meta_learner: MetaLearnerNetwork::new()?,
            task_distribution: TaskDistributionModel::new()?,
            few_shot_protocols: Vec::new(),
            meta_optimizers: Vec::new(),
            experience_memory: LearningExperienceMemory::new()?,
        })
    }

    /// Learn from a new task
    pub fn learn_task(&mut self, task_data: &[F]) -> Result<()> {
        // Simplified meta-learning implementation
        // In practice, this would involve:
        // 1. Encoding the task into the task distribution model
        // 2. Adapting the meta-learner based on task characteristics
        // 3. Storing learning experience for future tasks

        // Store experience
        // Update meta-learner parameters
        // Adapt few-shot protocols based on task type

        Ok(())
    }

    /// Adapt to a new task using few-shot learning
    pub fn few_shot_adapt(&mut self, support_set: &[F], query_set: &[F]) -> Result<Vec<F>> {
        // Simplified few-shot adaptation
        // In practice, this would:
        // 1. Use support set to adapt the meta-learner quickly
        // 2. Apply learned adaptation to query set
        // 3. Return predictions on query set

        // For now, return a simple transformation
        Ok(query_set.to_vec())
    }
}

impl<F: Float> MetaLearnerNetwork<F> {
    /// Create new meta-learner network
    pub fn new() -> Result<Self> {
        Ok(Self {
            memory_network: MemoryAugmentedNetwork::new()?,
            attention_mechanisms: Vec::new(),
            gradient_modules: Vec::new(),
            maml_components: MAMLComponents::new()?,
        })
    }
}

impl<F: Float> TaskDistributionModel<F> {
    /// Create new task distribution model
    pub fn new() -> Result<Self> {
        Ok(Self {
            task_embeddings: Array2::zeros((0, 0)),
            similarity_metrics: Vec::new(),
            task_generators: Vec::new(),
            domain_adaptation: Vec::new(),
        })
    }

    /// Add a new task to the distribution model
    pub fn add_task(&mut self, task_embedding: Vec<F>) -> Result<()> {
        // In practice, this would update the task embedding space
        // and recompute similarity metrics
        Ok(())
    }

    /// Find similar tasks to a given task
    pub fn find_similar_tasks(&self, task_embedding: &[F], k: usize) -> Result<Vec<usize>> {
        // Simplified similarity search
        // In practice, this would use the similarity metrics
        // to find the k most similar tasks
        Ok((0..k.min(10)).collect()) // Return dummy indices
    }
}

// Placeholder implementations for supporting types
macro_rules! impl_placeholder_new {
    ($($struct_name:ident),*) => {
        $(
            impl<F: Float> $struct_name<F> {
                pub fn new() -> Result<Self> {
                    Ok(Self {
                        _phantom: std::marker::PhantomData,
                    })
                }
            }
        )*
    };
}

impl_placeholder_new!(
    LearningExperienceMemory,
    MemoryAugmentedNetwork,
    MetaAttentionMechanism,
    GradientBasedMetaModule,
    MAMLComponents,
    TaskSimilarityMetric,
    TaskGenerator,
    DomainAdaptationProtocol,
    SupportSetManager,
    QuerySetProcessor,
    PrototypeNetwork,
    MatchingNetwork
);