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
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//! Real-time processing traits
//! リアルタイム処理トレイト

use crate::wasm::common::error::{WasmError, WasmResult};
use crate::wasm::common::traits::WasmOperation;
use wasm_bindgen::prelude::*;

/// Trait for real-time data processing
pub trait WasmRealtime: WasmOperation {
    /// Process single value in real-time
    fn process_realtime(&mut self, value: f32) -> WasmResult<JsValue>;

    /// Reset internal state
    fn reset_state(&mut self);

    /// Get processing statistics
    fn get_processing_stats(&self) -> String {
        format!("{{\"type\":\"{}\"}}", self.name())
    }
}

/// Trait for time series analysis
pub trait WasmTimeSeries: WasmOperation {
    /// Add data point with timestamp
    fn add_point(&mut self, timestamp: f64, value: f32) -> WasmResult<JsValue>;

    /// Get trend analysis
    fn trend_analysis(&self) -> WasmResult<String>;

    /// Get seasonal analysis
    fn seasonal_analysis(&self) -> WasmResult<String>;

    /// Get time series statistics
    fn get_time_stats(&self) -> String {
        format!("{{\"analyzer\":\"{}\"}}", self.name())
    }
}