//! Predictive modeling for pipeline performance optimization
//!
//! This module provides machine learning models for predicting optimal pipeline
//! parameters based on system metrics and historical performance data.

use crate::error::{IoError, Result};
use super::auto_tuning::{OptimalParameters, OptimizedPipelineConfig, PrefetchStrategy, BatchProcessingMode};
use std::time::Duration;
use serde::{Deserialize, Serialize};

/// Machine learning model for parameter optimization
#[derive(Debug)]
pub struct ParameterOptimizationModel {
    weights: Vec<f64>,
    feature_count: usize,
    training_data: Vec<TrainingExample>,
}

impl Default for ParameterOptimizationModel {
    fn default() -> Self {
        Self::new()
    }
}

impl ParameterOptimizationModel {
    pub fn new() -> Self {
        let feature_count = 8; // Number of features we extract
        Self {
            weights: vec![0.0; feature_count * 6], // 6 parameters to optimize
            feature_count,
            training_data: Vec::new(),
        }
    }

    pub fn predict(&self, features: &[f64]) -> Result<OptimalParameters> {
        if features.len() != self.feature_count {
            return Err(IoError::Other("Feature dimension mismatch".to_string()));
        }

        // Simple linear model prediction
        let mut predictions = [0.0; 6];
        for (i, prediction) in predictions.iter_mut().enumerate().take(6) {
            let start_idx = i * self.feature_count;
            *prediction = features
                .iter()
                .zip(&self.weights[start_idx..start_idx + self.feature_count])
                .map(|(f, w)| f * w)
                .sum();
        }

        // Convert predictions to parameters with bounds
        Ok(OptimalParameters {
            thread_count: (predictions[0].exp().clamp(1.0, 64.0)) as usize,
            chunk_size: (predictions[1].exp().clamp(1024.0, 1024.0 * 1024.0)) as usize,
            simd_enabled: predictions[2] > 0.0,
            gpu_enabled: predictions[3] > 0.0,
            prefetch_strategy: if predictions[4] > 0.5 {
                PrefetchStrategy::Adaptive {
                    learning_window: 100,
                }
            } else {
                PrefetchStrategy::Sequential { distance: 4 }
            },
            compression_level: (predictions[5].clamp(1.0, 9.0)) as u8,
            io_buffer_size: 64 * 1024, // Default 64KB
            batch_processing: BatchProcessingMode::Dynamic {
                min_batch_size: 100,
                max_batch_size: 10000,
                latency_target: Duration::from_millis(100),
            },
        })
    }

    pub fn update(
        &mut self,
        config: &OptimizedPipelineConfig,
        performance_score: f64,
    ) -> Result<()> {
        // Store training example
        let example = TrainingExample {
            config: config.clone(),
            performance_score,
        };
        self.training_data.push(example);

        // Simple online learning update (could be replaced with more sophisticated algorithms)
        if self.training_data.len() >= 10 {
            self.update_weights()?;
        }

        Ok(())
    }

    fn update_weights(&mut self) -> Result<()> {
        // Simplified gradient descent update
        // In practice, this would use more sophisticated ML algorithms
        for example in &self.training_data {
            let features = self.config_to_features(&example.config);
            let learning_rate = 0.001;

            // Update weights based on performance feedback
            for i in 0..self.weights.len() {
                let feature_idx = i % self.feature_count;
                if feature_idx < features.len() {
                    self.weights[i] +=
                        learning_rate * example.performance_score * features[feature_idx];
                }
            }
        }

        // Clear old training data to prevent memory growth
        if self.training_data.len() > 1000 {
            self.training_data.drain(0..500);
        }

        Ok(())
    }

    fn config_to_features(&self, config: &OptimizedPipelineConfig) -> Vec<f64> {
        vec![
            (config.thread_count as f64).ln(),
            (config.chunk_size as f64).ln(),
            if config.simd_optimization { 1.0 } else { 0.0 },
            if config.gpu_acceleration { 1.0 } else { 0.0 },
            config.compression_level as f64 / 9.0,
            (config.io_buffer_size as f64).ln(),
        ]
    }

    /// Get model statistics for monitoring
    pub fn get_statistics(&self) -> ModelStatistics {
        ModelStatistics {
            training_examples: self.training_data.len(),
            feature_count: self.feature_count,
            weight_norm: self.weights.iter().map(|w| w * w).sum::<f64>().sqrt(),
            average_performance: if !self.training_data.is_empty() {
                self.training_data.iter().map(|e| e.performance_score).sum::<f64>() 
                / self.training_data.len() as f64
            } else {
                0.0
            },
        }
    }

    /// Save model state for persistence
    pub fn save_model(&self) -> ModelCheckpoint {
        ModelCheckpoint {
            weights: self.weights.clone(),
            feature_count: self.feature_count,
            training_examples_count: self.training_data.len(),
        }
    }

    /// Load model state from checkpoint
    pub fn load_model(&mut self, checkpoint: ModelCheckpoint) -> Result<()> {
        if checkpoint.feature_count != self.feature_count {
            return Err(IoError::Other("Feature count mismatch in checkpoint".to_string()));
        }

        self.weights = checkpoint.weights;
        Ok(())
    }
}

/// Training example for the optimization model
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingExample {
    pub config: OptimizedPipelineConfig,
    pub performance_score: f64,
}

/// Model performance statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelStatistics {
    pub training_examples: usize,
    pub feature_count: usize,
    pub weight_norm: f64,
    pub average_performance: f64,
}

/// Model checkpoint for saving/loading
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelCheckpoint {
    pub weights: Vec<f64>,
    pub feature_count: usize,
    pub training_examples_count: usize,
}

/// Performance prediction engine with multiple models
#[derive(Debug)]
pub struct PerformancePredictionEngine {
    linear_model: ParameterOptimizationModel,
    ensemble_weights: Vec<f64>,
    prediction_history: Vec<PredictionResult>,
}

impl PerformancePredictionEngine {
    pub fn new() -> Self {
        Self {
            linear_model: ParameterOptimizationModel::new(),
            ensemble_weights: vec![1.0], // Single model for now
            prediction_history: Vec::new(),
        }
    }

    pub fn predict_performance(
        &self,
        config: &OptimizedPipelineConfig,
        system_features: &[f64],
    ) -> Result<PerformancePrediction> {
        // Extract features from configuration
        let config_features = self.linear_model.config_to_features(config);
        
        // Combine system and configuration features
        let mut combined_features = system_features.to_vec();
        combined_features.extend(config_features);

        // Make prediction (simplified - would use the actual model)
        let predicted_throughput = self.predict_throughput(&combined_features)?;
        let predicted_memory = self.predict_memory_usage(&combined_features)?;
        let predicted_latency = self.predict_latency(&combined_features)?;

        Ok(PerformancePrediction {
            throughput: predicted_throughput,
            memory_usage: predicted_memory,
            latency: predicted_latency,
            confidence: self.calculate_confidence(&combined_features),
        })
    }

    fn predict_throughput(&self, features: &[f64]) -> Result<f64> {
        // Simplified throughput prediction
        let base_throughput = 1000.0;
        let feature_impact: f64 = features.iter().take(4).sum();
        Ok(base_throughput * (1.0 + feature_impact * 0.1))
    }

    fn predict_memory_usage(&self, features: &[f64]) -> Result<usize> {
        // Simplified memory usage prediction
        let base_memory = 1024 * 1024; // 1MB
        let feature_impact: f64 = features.iter().skip(2).take(3).sum();
        Ok((base_memory as f64 * (1.0 + feature_impact * 0.2)) as usize)
    }

    fn predict_latency(&self, features: &[f64]) -> Result<Duration> {
        // Simplified latency prediction
        let base_latency_ms = 10.0;
        let feature_impact: f64 = features.iter().take(6).map(|f| f.abs()).sum();
        let predicted_ms = base_latency_ms * (1.0 + feature_impact * 0.05);
        Ok(Duration::from_millis(predicted_ms as u64))
    }

    fn calculate_confidence(&self, _features: &[f64]) -> f64 {
        // Simplified confidence calculation based on prediction history
        if self.prediction_history.len() < 5 {
            0.5 // Low confidence with limited history
        } else {
            0.8 // Higher confidence with more history
        }
    }

    pub fn update_prediction_accuracy(&mut self, actual: &PerformancePrediction, predicted: &PerformancePrediction) {
        let result = PredictionResult {
            predicted: predicted.clone(),
            actual: actual.clone(),
            error: self.calculate_prediction_error(actual, predicted),
        };
        
        self.prediction_history.push(result);
        
        // Keep only recent history
        if self.prediction_history.len() > 1000 {
            self.prediction_history.drain(0..500);
        }
    }

    fn calculate_prediction_error(&self, actual: &PerformancePrediction, predicted: &PerformancePrediction) -> f64 {
        let throughput_error = (actual.throughput - predicted.throughput).abs() / actual.throughput;
        let memory_error = (actual.memory_usage as f64 - predicted.memory_usage as f64).abs() / actual.memory_usage as f64;
        let latency_error = (actual.latency.as_millis() as f64 - predicted.latency.as_millis() as f64).abs() / actual.latency.as_millis() as f64;
        
        (throughput_error + memory_error + latency_error) / 3.0
    }
}

impl Default for PerformancePredictionEngine {
    fn default() -> Self {
        Self::new()
    }
}

/// Performance prediction result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformancePrediction {
    pub throughput: f64,
    pub memory_usage: usize,
    pub latency: Duration,
    pub confidence: f64,
}

/// Prediction accuracy tracking
#[derive(Debug, Clone)]
pub struct PredictionResult {
    pub predicted: PerformancePrediction,
    pub actual: PerformancePrediction,
    pub error: f64,
}

#[cfg(test)]
mod tests {
    use super::*;
    use super::super::auto_tuning::{CacheStrategy, OptimizedPipelineConfig};

    #[test]
    fn test_parameter_optimization_model() {
        let model = ParameterOptimizationModel::new();
        assert_eq!(model.feature_count, 8);
        assert_eq!(model.weights.len(), 48); // 8 features * 6 parameters
    }

    #[test]
    fn test_model_prediction() {
        let model = ParameterOptimizationModel::new();
        let features = vec![0.5, 0.3, 0.8, 0.9, 0.7, 3.0, 1000.0, 6.0];
        
        let result = model.predict(&features);
        assert!(result.is_ok());
        
        let params = result.expect("Operation failed");
        assert!(params.thread_count >= 1 && params.thread_count <= 64);
        assert!(params.chunk_size >= 1024);
    }

    #[test]
    fn test_model_update() {
        let mut model = ParameterOptimizationModel::new();
        let config = OptimizedPipelineConfig {
            thread_count: 4,
            chunk_size: 8192,
            simd_optimization: true,
            gpu_acceleration: false,
            compression_level: 6,
            io_buffer_size: 64 * 1024,
            memory_strategy: crate::pipeline::optimization::memory::pool_management::MemoryStrategy::Standard,
            auto_scaling: true,
            cache_strategy: CacheStrategy::LRU { capacity: 1000 },
            prefetch_strategy: PrefetchStrategy::Sequential { distance: 4 },
            batch_processing: BatchProcessingMode::Fixed { batch_size: 100 },
        };

        let result = model.update(&config, 0.85);
        assert!(result.is_ok());
    }

    #[test]
    fn test_performance_prediction_engine() {
        let engine = PerformancePredictionEngine::new();
        let config = OptimizedPipelineConfig {
            thread_count: 4,
            chunk_size: 8192,
            simd_optimization: true,
            gpu_acceleration: false,
            compression_level: 6,
            io_buffer_size: 64 * 1024,
            memory_strategy: crate::pipeline::optimization::memory::pool_management::MemoryStrategy::Standard,
            auto_scaling: true,
            cache_strategy: CacheStrategy::LRU { capacity: 1000 },
            prefetch_strategy: PrefetchStrategy::Sequential { distance: 4 },
            batch_processing: BatchProcessingMode::Fixed { batch_size: 100 },
        };
        
        let system_features = vec![0.5, 0.3, 0.2, 0.9];
        let result = engine.predict_performance(&config, &system_features);
        
        assert!(result.is_ok());
        let prediction = result.expect("Operation failed");
        assert!(prediction.throughput > 0.0);
        assert!(prediction.memory_usage > 0);
        assert!(prediction.confidence >= 0.0 && prediction.confidence <= 1.0);
    }
}