rustorch 0.6.29

Production-ready PyTorch-compatible deep learning library in Rust with special mathematical functions (gamma, Bessel, error functions), statistical distributions, Fourier transforms (FFT/RFFT), matrix decomposition (SVD/QR/LU/eigenvalue), automatic differentiation, neural networks, computer vision transforms, complete GPU acceleration (CUDA/Metal/OpenCL), SIMD optimizations, parallel processing, WebAssembly browser support, comprehensive distributed learning support, and performance validation
Documentation
//! Real-time Data Validation System
//! リアルタイムデータ検証システム

use crate::error::{RusTorchError, RusTorchResult};
use std::collections::VecDeque;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

/// Real-time validator for streaming data
/// ストリーミングデータのリアルタイム検証器
#[derive(Debug)]
pub struct RealTimeValidator {
    /// Configuration
    /// 設定
    config: RealTimeConfig,
    /// Validation buffer
    /// 検証バッファ
    buffer: Arc<Mutex<ValidationBuffer>>,
    /// Running state
    /// 実行状態
    is_running: Arc<Mutex<bool>>,
    /// Statistics
    /// 統計
    stats: ValidationStats,
}

/// Real-time validation configuration
/// リアルタイム検証設定
#[derive(Debug, Clone)]
pub struct RealTimeConfig {
    /// Buffer size for streaming validation
    /// ストリーミング検証のバッファサイズ
    pub buffer_size: usize,
    /// Validation interval
    /// 検証間隔
    pub validation_interval: Duration,
    /// Alert thresholds
    /// アラート閾値
    pub alert_threshold: f64,
    /// Enable continuous monitoring
    /// 継続監視を有効化
    pub enable_continuous_monitoring: bool,
}

impl Default for RealTimeConfig {
    fn default() -> Self {
        Self {
            buffer_size: 1000,
            validation_interval: Duration::from_millis(100),
            alert_threshold: 0.8,
            enable_continuous_monitoring: true,
        }
    }
}

/// Validation buffer for streaming data
/// ストリーミングデータの検証バッファ
#[derive(Debug)]
pub struct ValidationBuffer {
    /// Buffered validation results
    /// バッファされた検証結果
    pub results: VecDeque<StreamingValidationResult>,
    /// Maximum buffer size
    /// 最大バッファサイズ
    pub max_size: usize,
}

/// Streaming validation for continuous data flow
/// 継続的データフローのストリーミング検証
pub struct StreamingValidation {
    /// Validation configuration
    /// 検証設定
    config: RealTimeConfig,
}

impl StreamingValidation {
    /// Create new streaming validation
    /// 新しいストリーミング検証を作成
    pub fn new(config: RealTimeConfig) -> Self {
        Self { config }
    }

    /// Validate streaming data chunk
    /// ストリーミングデータチャンクを検証
    pub fn validate_chunk<T>(
        &self,
        _tensor: &crate::tensor::Tensor<T>,
    ) -> RusTorchResult<StreamingValidationResult>
    where
        T: num_traits::Float + std::fmt::Debug + Clone + Send + Sync + 'static,
    {
        // Placeholder implementation
        Ok(StreamingValidationResult {
            timestamp: Instant::now(),
            quality_score: 0.9,
            is_valid: true,
            issues_detected: 0,
            processing_time: Duration::from_millis(1),
        })
    }
}

/// Validation stream for continuous processing
/// 継続処理のための検証ストリーム
pub struct ValidationStream {
    /// Stream configuration
    /// ストリーム設定
    config: RealTimeConfig,
    /// Current position in stream
    /// ストリーム内の現在位置
    position: usize,
}

impl ValidationStream {
    /// Create new validation stream
    /// 新しい検証ストリームを作成
    pub fn new(config: RealTimeConfig) -> Self {
        Self {
            config,
            position: 0,
        }
    }

    /// Process next item in stream
    /// ストリーム内の次のアイテムを処理
    pub fn process_next<T>(
        &mut self,
        _tensor: &crate::tensor::Tensor<T>,
    ) -> RusTorchResult<StreamingValidationResult>
    where
        T: num_traits::Float + std::fmt::Debug + Clone + Send + Sync + 'static,
    {
        self.position += 1;

        // Placeholder implementation
        Ok(StreamingValidationResult {
            timestamp: Instant::now(),
            quality_score: 0.9,
            is_valid: true,
            issues_detected: 0,
            processing_time: Duration::from_millis(1),
        })
    }
}

/// Streaming validation result
/// ストリーミング検証結果
#[derive(Debug, Clone)]
pub struct StreamingValidationResult {
    /// Result timestamp
    /// 結果タイムスタンプ
    pub timestamp: Instant,
    /// Quality score for this chunk
    /// このチャンクの品質スコア
    pub quality_score: f64,
    /// Whether validation passed
    /// 検証が合格したか
    pub is_valid: bool,
    /// Number of issues detected
    /// 検出された問題数
    pub issues_detected: usize,
    /// Processing time for this chunk
    /// このチャンクの処理時間
    pub processing_time: Duration,
}

/// Validation statistics for real-time monitoring
/// リアルタイム監視の検証統計
#[derive(Debug, Default)]
pub struct ValidationStats {
    /// Total chunks processed
    /// 処理された総チャンク数
    pub total_chunks: usize,
    /// Valid chunks
    /// 有効チャンク数
    pub valid_chunks: usize,
    /// Invalid chunks
    /// 無効チャンク数
    pub invalid_chunks: usize,
    /// Average processing time
    /// 平均処理時間
    pub avg_processing_time: Duration,
    /// Average quality score
    /// 平均品質スコア
    pub avg_quality_score: f64,
}

impl ValidationBuffer {
    /// Create new validation buffer
    /// 新しい検証バッファを作成
    pub fn new(max_size: usize) -> Self {
        Self {
            results: VecDeque::with_capacity(max_size),
            max_size,
        }
    }

    /// Add result to buffer
    /// バッファに結果を追加
    pub fn add_result(&mut self, result: StreamingValidationResult) {
        if self.results.len() >= self.max_size {
            self.results.pop_front();
        }
        self.results.push_back(result);
    }

    /// Get recent results
    /// 最近の結果を取得
    pub fn get_recent_results(&self, count: usize) -> Vec<&StreamingValidationResult> {
        self.results.iter().rev().take(count).collect()
    }
}

impl RealTimeValidator {
    /// Create new real-time validator
    /// 新しいリアルタイム検証器を作成
    pub fn new(config: RealTimeConfig) -> RusTorchResult<Self> {
        let buffer = Arc::new(Mutex::new(ValidationBuffer::new(config.buffer_size)));

        Ok(Self {
            config,
            buffer,
            is_running: Arc::new(Mutex::new(false)),
            stats: ValidationStats::default(),
        })
    }

    /// Start real-time monitoring
    /// リアルタイム監視を開始
    pub fn start_monitoring(&mut self) -> RusTorchResult<()> {
        let mut running = self
            .is_running
            .lock()
            .map_err(|_| RusTorchError::Validation {
                message: "Failed to acquire running lock".to_string(),
            })?;

        if *running {
            return Err(RusTorchError::Validation {
                message: "Real-time validator already running".to_string(),
            });
        }

        *running = true;
        println!("🔍 Real-time data validation started");
        Ok(())
    }

    /// Stop real-time monitoring
    /// リアルタイム監視を停止
    pub fn stop_monitoring(&mut self) -> RusTorchResult<()> {
        let mut running = self
            .is_running
            .lock()
            .map_err(|_| RusTorchError::Validation {
                message: "Failed to acquire running lock".to_string(),
            })?;

        *running = false;
        println!("🔍 Real-time data validation stopped");
        Ok(())
    }

    /// Validate data chunk in real-time
    /// リアルタイムでデータチャンクを検証
    pub fn validate_realtime<T>(
        &mut self,
        tensor: &crate::tensor::Tensor<T>,
    ) -> RusTorchResult<StreamingValidationResult>
    where
        T: num_traits::Float + std::fmt::Debug + Clone + Send + Sync + 'static,
    {
        let streaming_validation = StreamingValidation::new(self.config.clone());
        let result = streaming_validation.validate_chunk(tensor)?;

        // Update statistics
        self.stats.total_chunks += 1;
        if result.is_valid {
            self.stats.valid_chunks += 1;
        } else {
            self.stats.invalid_chunks += 1;
        }

        // Update averages
        let total = self.stats.total_chunks as f64;
        self.stats.avg_processing_time = Duration::from_nanos(
            (self.stats.avg_processing_time.as_nanos() as f64 * (total - 1.0)
                + result.processing_time.as_nanos() as f64) as u64
                / total as u64,
        );
        self.stats.avg_quality_score =
            (self.stats.avg_quality_score * (total - 1.0) + result.quality_score) / total;

        // Store result in buffer
        let mut buffer = self.buffer.lock().map_err(|_| RusTorchError::Validation {
            message: "Failed to acquire buffer lock".to_string(),
        })?;
        buffer.add_result(result.clone());

        Ok(result)
    }

    /// Get validation statistics
    /// 検証統計を取得
    pub fn get_stats(&self) -> &ValidationStats {
        &self.stats
    }
}