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
//! Simplified transformation pipeline for WASM operations
//! WASM操作の簡単化変換パイプライン

use crate::wasm::common::{WasmError, WasmResult, WasmValidation};
use crate::wasm::tensor::WasmTensor;
use js_sys::Array;
use wasm_bindgen::prelude::*;

/// Simple pipeline for chaining transformations
#[wasm_bindgen]
pub struct WasmTransformPipeline {
    transforms: Vec<String>,
    cache_enabled: bool,
}

#[wasm_bindgen]
impl WasmTransformPipeline {
    /// Create new pipeline
    #[wasm_bindgen(constructor)]
    pub fn new(cache_enabled: bool) -> WasmTransformPipeline {
        WasmTransformPipeline {
            transforms: Vec::new(),
            cache_enabled,
        }
    }

    /// Add transform to pipeline
    pub fn add_transform(&mut self, transform_name: &str) -> WasmResult<()> {
        if transform_name.is_empty() {
            return Err(WasmError::invalid_param(
                "transform_name",
                transform_name,
                "cannot be empty",
            ));
        }

        self.transforms.push(transform_name.to_string());
        Ok(())
    }

    /// Get number of transforms in pipeline
    pub fn length(&self) -> usize {
        self.transforms.len()
    }

    /// Clear all transforms
    pub fn clear(&mut self) {
        self.transforms.clear();
    }

    /// Execute pipeline on tensor (simplified)
    pub fn execute(&self, input: &WasmTensor) -> WasmResult<WasmTensor> {
        input.validate_non_empty()?;

        // Return input unchanged for now (placeholder)
        Ok(WasmTensor::new(input.data(), input.shape()))
    }

    /// Get pipeline statistics
    pub fn get_stats(&self) -> String {
        format!(
            "{{\"transforms\":{},\"cache_enabled\":{}}}",
            self.transforms.len(),
            self.cache_enabled
        )
    }
}

/// Processing pipeline for analysis operations
#[wasm_bindgen]
pub struct WasmProcessingPipeline {
    operations: Vec<String>,
    parallel_execution: bool,
}

#[wasm_bindgen]
impl WasmProcessingPipeline {
    /// Create new processing pipeline
    #[wasm_bindgen(constructor)]
    pub fn new(parallel_execution: bool) -> WasmProcessingPipeline {
        WasmProcessingPipeline {
            operations: Vec::new(),
            parallel_execution,
        }
    }

    /// Add operation to pipeline
    pub fn add_operation(&mut self, operation_name: &str) -> WasmResult<()> {
        if operation_name.is_empty() {
            return Err(WasmError::invalid_param(
                "operation_name",
                operation_name,
                "cannot be empty",
            ));
        }

        self.operations.push(operation_name.to_string());
        Ok(())
    }

    /// Get operation count
    pub fn operation_count(&self) -> usize {
        self.operations.len()
    }

    /// Get pipeline configuration
    pub fn get_config(&self) -> String {
        format!(
            "{{\"operations\":{},\"parallel\":{}}}",
            self.operations.len(),
            self.parallel_execution
        )
    }
}