scirs2-ndimage 0.4.2

//! Knowledge Management for Multi-Modal Learning
//!
//! This module handles multi-modal knowledge bases, cross-modal associations,
//! and contextual knowledge for the AI-driven adaptive processing system.

use scirs2_core::ndarray::{Array1, Array2};
use std::collections::HashMap;

use super::config::{AlgorithmType, PatternType};
use super::learning::PredictionModel;

/// Multi-Modal Knowledge Base
#[derive(Debug, Clone)]
pub struct MultiModalKnowledgeBase {
    /// Visual pattern knowledge
    pub visual_patterns: HashMap<String, VisualKnowledge>,
    /// Temporal pattern knowledge
    pub temporal_patterns: HashMap<String, TemporalKnowledge>,
    /// Contextual knowledge
    pub contextual_knowledge: HashMap<String, ContextualKnowledge>,
    /// Cross-modal associations
    pub cross_modal_associations: Vec<CrossModalAssociation>,
}

/// Visual Knowledge
#[derive(Debug, Clone)]
pub struct VisualKnowledge {
    /// Feature descriptors
    pub features: Array1<f64>,
    /// Optimal processing methods
    pub optimal_methods: Vec<AlgorithmType>,
    /// Expected outcomes
    pub expected_outcomes: Array1<f64>,
    /// Confidence level
    pub confidence: f64,
}

/// Temporal Knowledge
#[derive(Debug, Clone)]
pub struct TemporalKnowledge {
    /// Temporal patterns
    pub patterns: Array2<f64>,
    /// Prediction models
    pub prediction_models: Vec<PredictionModel>,
    /// Temporal dependencies
    pub dependencies: Vec<TemporalDependency>,
}

/// Contextual Knowledge
#[derive(Debug, Clone)]
pub struct ContextualKnowledge {
    /// Context descriptors
    pub contextfeatures: Array1<f64>,
    /// Contextual preferences
    pub preferences: HashMap<String, f64>,
    /// Adaptation strategies
    pub adaptation_strategies: Vec<super::strategies::AdaptationStrategy>,
}

/// Cross-Modal Association
#[derive(Debug, Clone)]
pub struct CrossModalAssociation {
    /// Source modality
    pub source_modality: String,
    /// Target modality
    pub target_modality: String,
    /// Association strength
    pub strength: f64,
    /// Transfer function
    pub transfer_function: Array2<f64>,
}

/// Temporal Dependency
#[derive(Debug, Clone)]
pub struct TemporalDependency {
    /// Source time step
    pub source_step: usize,
    /// Target time step
    pub target_step: usize,
    /// Dependency strength
    pub strength: f64,
    /// Dependency type
    pub dependency_type: String,
}

/// Processing Context
#[derive(Debug, Clone)]
pub struct ProcessingContext {
    /// Current image type
    pub image_type: PatternType,
    /// User preferences
    pub user_preferences: HashMap<String, f64>,
    /// Available resources
    pub available_resources: ResourceAvailability,
    /// Time constraints
    pub time_constraints: Option<f64>,
    /// Quality requirements
    pub quality_requirements: Option<f64>,
}

/// Resource Availability
#[derive(Debug, Clone)]
pub struct ResourceAvailability {
    /// CPU cores available
    pub cpu_cores: usize,
    /// Memory available (MB)
    pub memory_mb: f64,
    /// GPU available
    pub gpu_available: bool,
    /// Quantum processor available
    pub quantum_available: bool,
}

impl MultiModalKnowledgeBase {
    /// Create a new empty knowledge base
    pub fn new() -> Self {
        Self {
            visual_patterns: HashMap::new(),
            temporal_patterns: HashMap::new(),
            contextual_knowledge: HashMap::new(),
            cross_modal_associations: Vec::new(),
        }
    }

    /// Add visual knowledge
    pub fn add_visual_knowledge(&mut self, key: String, knowledge: VisualKnowledge) {
        self.visual_patterns.insert(key, knowledge);
    }

    /// Get visual knowledge by key
    pub fn get_visual_knowledge(&self, key: &str) -> Option<&VisualKnowledge> {
        self.visual_patterns.get(key)
    }

    /// Add cross-modal association
    pub fn add_cross_modal_association(&mut self, association: CrossModalAssociation) {
        self.cross_modal_associations.push(association);
    }

    /// Find relevant knowledge for a given pattern
    pub fn find_relevant_knowledge(&self, pattern_features: &Array1<f64>) -> Vec<String> {
        let mut relevant_keys = Vec::new();

        for (key, knowledge) in &self.visual_patterns {
            // Calculate similarity (simplified dot product)
            let similarity = self.calculate_similarity(pattern_features, &knowledge.features);
            if similarity > 0.7 {
                // Threshold for relevance
                relevant_keys.push(key.clone());
            }
        }

        relevant_keys
    }

    /// Calculate similarity between feature vectors
    fn calculate_similarity(&self, features1: &Array1<f64>, features2: &Array1<f64>) -> f64 {
        if features1.len() != features2.len() {
            return 0.0;
        }

        let mut dot_product = 0.0;
        let mut norm1 = 0.0;
        let mut norm2 = 0.0;

        for i in 0..features1.len() {
            dot_product += features1[i] * features2[i];
            norm1 += features1[i] * features1[i];
            norm2 += features2[i] * features2[i];
        }

        if norm1 == 0.0 || norm2 == 0.0 {
            return 0.0;
        }

        dot_product / (norm1.sqrt() * norm2.sqrt())
    }
}

impl ProcessingContext {
    /// Create a default processing context
    pub fn default() -> Self {
        Self {
            image_type: PatternType::Natural,
            user_preferences: HashMap::new(),
            available_resources: ResourceAvailability {
                cpu_cores: 4,
                memory_mb: 8192.0,
                gpu_available: false,
                quantum_available: false,
            },
            time_constraints: None,
            quality_requirements: None,
        }
    }

    /// Update context with new information
    pub fn update_context(&mut self, image_type: PatternType, preferences: HashMap<String, f64>) {
        self.image_type = image_type;
        self.user_preferences.extend(preferences);
    }

    /// Check if processing is time-constrained
    pub fn is_time_constrained(&self) -> bool {
        self.time_constraints.is_some()
    }

    /// Get available processing power score
    pub fn get_processing_power_score(&self) -> f64 {
        let mut score = self.available_resources.cpu_cores as f64;

        if self.available_resources.gpu_available {
            score += 10.0; // GPU adds significant processing power
        }

        if self.available_resources.quantum_available {
            score += 50.0; // Quantum processing adds substantial power for specific tasks
        }

        score
    }
}