Integration Guides - ruvnet/ruv-FANN GitHub Wiki
This comprehensive guide covers all integration options for the ruv-FANN neural network framework, including language bindings, platform integrations, and deployment strategies.
- Python Integration via PyO3
- JavaScript/TypeScript Integration
- C/C++ FFI Integration
- WASM Browser Integration
- Cloud Platform Integration
- Mobile Platform Integration
- Performance Considerations
- Best Practices
The ruv-FANN project supports Python integration through PyO3 bindings, enabling seamless interoperability between Rust's performance and Python's ecosystem.
# Install PyO3 requirements
pip install maturin pyo3-build
rustup toolchain install nightlyAdd to your Cargo.toml:
[lib]
name = "ruv_fann_py"
crate-type = ["cdylib"]
[dependencies]
pyo3 = { version = "0.20", features = ["extension-module"] }
semantic-cartan-matrix = { path = "../Semantic_Cartan_Matrix" }
numpy = "0.20"// src/lib.rs
use pyo3::prelude::*;
use pyo3::types::PyList;
use semantic_cartan_matrix::prelude::*;
use numpy::{PyArray1, PyReadonlyArray1};
/// Python wrapper for RootVector
#[pyclass]
struct PyRootVector {
inner: RootVector,
}
#[pymethods]
impl PyRootVector {
#[new]
fn new() -> Self {
Self {
inner: RootVector::zero(),
}
}
/// Create from Python list or numpy array
#[staticmethod]
fn from_array(arr: PyReadonlyArray1<f32>) -> PyResult<Self> {
let slice = arr.as_slice()?;
if slice.len() != 32 {
return Err(PyErr::new::<pyo3::exceptions::PyValueError, _>(
"Array must have exactly 32 elements"
));
}
let mut vector = RootVector::zero();
vector.data.copy_from_slice(slice);
Ok(Self { inner: vector })
}
/// Convert to numpy array
fn to_numpy(&self, py: Python) -> PyResult<Py<PyArray1<f32>>> {
Ok(PyArray1::from_slice(py, &self.inner.data).to_owned())
}
/// Compute dot product
fn dot(&self, other: &PyRootVector) -> f32 {
self.inner.dot(&other.inner)
}
/// Normalize in-place
fn normalize(&mut self) {
self.inner.normalize();
}
/// Get magnitude
fn magnitude(&self) -> f32 {
self.inner.magnitude()
}
}
/// Python wrapper for CartanAttention
#[pyclass]
struct PyCartanAttention {
inner: CartanAttention,
}
#[pymethods]
impl PyCartanAttention {
#[new]
fn new() -> PyResult<Self> {
let cartan = CartanMatrix::identity();
match CartanAttention::new(cartan) {
Ok(attention) => Ok(Self { inner: attention }),
Err(e) => Err(PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(
format!("Failed to create CartanAttention: {:?}", e)
)),
}
}
/// Apply attention to sequence of vectors
fn apply_attention(&mut self, vectors: Vec<PyRef<PyRootVector>>) -> PyResult<Vec<PyRootVector>> {
let input: Vec<RootVector> = vectors.iter()
.map(|v| v.inner.clone())
.collect();
match self.inner.apply_attention(&input) {
Ok(output) => Ok(output.into_iter()
.map(|v| PyRootVector { inner: v })
.collect()),
Err(e) => Err(PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(
format!("Attention failed: {:?}", e)
)),
}
}
}
/// Python module definition
#[pymodule]
fn ruv_fann_py(_py: Python, m: &PyModule) -> PyResult<()> {
m.add_class::<PyRootVector>()?;
m.add_class::<PyCartanAttention>()?;
// Add version info
m.add("__version__", env!("CARGO_PKG_VERSION"))?;
Ok(())
}import numpy as np
import ruv_fann_py as rf
# Create vectors
vec1 = rf.PyRootVector()
vec2 = rf.PyRootVector.from_array(np.random.randn(32).astype(np.float32))
# Operations
dot_product = vec1.dot(vec2)
vec2.normalize()
magnitude = vec2.magnitude()
# Convert to numpy for further processing
np_array = vec2.to_numpy()
# Attention mechanism
attention = rf.PyCartanAttention()
output_vectors = attention.apply_attention([vec1, vec2])
print(f"Dot product: {dot_product}")
print(f"Magnitude after normalization: {magnitude}")# Build wheel
maturin build --release
# Install locally
maturin develop
# Or install from wheel
pip install target/wheels/ruv_fann_py-*.whlThe ruv-FANN project provides comprehensive WebAssembly bindings for JavaScript/TypeScript integration.
# Cargo.toml for WASM target
[lib]
crate-type = ["cdylib"]
[dependencies]
wasm-bindgen = "0.2"
js-sys = "0.3"
web-sys = "0.3"
console_error_panic_hook = "0.1"
wee_alloc = "0.4"
[dependencies.semantic-cartan-matrix]
path = "../Semantic_Cartan_Matrix"
features = ["wasm"]// src/lib.rs
use wasm_bindgen::prelude::*;
use semantic_cartan_matrix::prelude::*;
// Set up panic hook and allocator for WASM
#[cfg(feature = "wee_alloc")]
#[global_allocator]
static ALLOC: wee_alloc::WeeAlloc = wee_alloc::WeeAlloc::INIT;
#[wasm_bindgen(start)]
pub fn main() {
console_error_panic_hook::set_once();
}
/// JavaScript-compatible RootVector
#[wasm_bindgen]
pub struct JsRootVector {
inner: RootVector,
}
#[wasm_bindgen]
impl JsRootVector {
#[wasm_bindgen(constructor)]
pub fn new() -> JsRootVector {
JsRootVector {
inner: RootVector::zero(),
}
}
/// Create from JavaScript array
#[wasm_bindgen]
pub fn from_array(data: &[f32]) -> Result<JsRootVector, JsValue> {
if data.len() != 32 {
return Err(JsValue::from_str("Array must have exactly 32 elements"));
}
let mut vector = RootVector::zero();
vector.data.copy_from_slice(data);
Ok(JsRootVector { inner: vector })
}
/// Convert to JavaScript array
#[wasm_bindgen]
pub fn to_array(&self) -> Vec<f32> {
self.inner.data.to_vec()
}
/// Compute dot product
#[wasm_bindgen]
pub fn dot(&self, other: &JsRootVector) -> f32 {
self.inner.dot(&other.inner)
}
/// Normalize in-place
#[wasm_bindgen]
pub fn normalize(&mut self) {
self.inner.normalize();
}
/// Get magnitude
#[wasm_bindgen]
pub fn magnitude(&self) -> f32 {
self.inner.magnitude()
}
}
/// JavaScript-compatible MicroNet
#[wasm_bindgen]
pub struct JsMicroNet {
// Implementation would depend on specific MicroNet type
}
#[wasm_bindgen]
impl JsMicroNet {
#[wasm_bindgen(constructor)]
pub fn new() -> Result<JsMicroNet, JsValue> {
// Initialize micro network
Ok(JsMicroNet {})
}
/// Process input through the network
#[wasm_bindgen]
pub fn process(&self, input: &[f32]) -> Result<Vec<f32>, JsValue> {
// Implementation for network processing
Ok(input.to_vec()) // Placeholder
}
}// ruv-fann.d.ts
export class JsRootVector {
constructor();
static from_array(data: Float32Array): JsRootVector;
to_array(): Float32Array;
dot(other: JsRootVector): number;
normalize(): void;
magnitude(): number;
}
export class JsMicroNet {
constructor();
process(input: Float32Array): Float32Array;
}
export interface CartanMatrixConfig {
dimension: number;
matrix_type: 'identity' | 'random' | 'custom';
custom_values?: number[][];
}
export interface AttentionConfig {
heads: number;
regularization: number;
dropout_rate?: number;
}
// Utility functions
export function create_identity_matrix(size: number): number[][];
export function simd_dot_product(a: Float32Array, b: Float32Array): number;import init, { JsRootVector, JsMicroNet } from './pkg/ruv_fann.js';
async function main() {
// Initialize WASM module
await init();
// Create vectors
const vec1 = new JsRootVector();
const data = new Float32Array(32);
for (let i = 0; i < 32; i++) {
data[i] = Math.random();
}
const vec2 = JsRootVector.from_array(data);
// Perform operations
const dotProduct = vec1.dot(vec2);
vec2.normalize();
const magnitude = vec2.magnitude();
// Convert back to JavaScript array
const result = vec2.to_array();
console.log('Dot product:', dotProduct);
console.log('Magnitude:', magnitude);
console.log('Result vector:', result);
// Neural network processing
const network = new JsMicroNet();
const input = new Float32Array([1, 2, 3, 4, 5]);
const output = network.process(input);
console.log('Network output:', output);
}
main().catch(console.error);# Install wasm-pack
curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh
# Build for web
wasm-pack build --target web --release
# Build for Node.js
wasm-pack build --target nodejs --release
# Build with size optimization
wasm-pack build --target web --release -- --features wasm-optThe ruv-FANN framework provides C-compatible FFI bindings for integration with existing C/C++ codebases.
// src/ffi.rs
use std::ffi::{c_char, c_void, CStr, CString};
use std::ptr;
use semantic_cartan_matrix::prelude::*;
/// Opaque pointer to RootVector
pub struct CRootVector {
inner: Box<RootVector>,
}
/// Create a new RootVector
#[no_mangle]
pub extern "C" fn ruv_fann_root_vector_new() -> *mut CRootVector {
let vector = Box::new(RootVector::zero());
Box::into_raw(Box::new(CRootVector { inner: vector }))
}
/// Create RootVector from array
#[no_mangle]
pub extern "C" fn ruv_fann_root_vector_from_array(
data: *const f32,
len: usize,
) -> *mut CRootVector {
if data.is_null() || len != 32 {
return ptr::null_mut();
}
unsafe {
let slice = std::slice::from_raw_parts(data, len);
let mut vector = RootVector::zero();
vector.data.copy_from_slice(slice);
Box::into_raw(Box::new(CRootVector {
inner: Box::new(vector),
}))
}
}
/// Free RootVector
#[no_mangle]
pub extern "C" fn ruv_fann_root_vector_free(vector: *mut CRootVector) {
if !vector.is_null() {
unsafe {
drop(Box::from_raw(vector));
}
}
}
/// Compute dot product
#[no_mangle]
pub extern "C" fn ruv_fann_root_vector_dot(
a: *const CRootVector,
b: *const CRootVector,
) -> f32 {
if a.is_null() || b.is_null() {
return 0.0;
}
unsafe {
(*a).inner.dot(&(*b).inner)
}
}
/// Normalize vector in-place
#[no_mangle]
pub extern "C" fn ruv_fann_root_vector_normalize(vector: *mut CRootVector) {
if !vector.is_null() {
unsafe {
(*vector).inner.normalize();
}
}
}
/// Get vector magnitude
#[no_mangle]
pub extern "C" fn ruv_fann_root_vector_magnitude(vector: *const CRootVector) -> f32 {
if vector.is_null() {
return 0.0;
}
unsafe {
(*vector).inner.magnitude()
}
}
/// Copy vector data to array
#[no_mangle]
pub extern "C" fn ruv_fann_root_vector_to_array(
vector: *const CRootVector,
data: *mut f32,
len: usize,
) -> bool {
if vector.is_null() || data.is_null() || len < 32 {
return false;
}
unsafe {
let slice = std::slice::from_raw_parts_mut(data, len);
slice[..32].copy_from_slice(&(*vector).inner.data);
true
}
}
/// Opaque pointer to CartanAttention
pub struct CCartanAttention {
inner: Box<CartanAttention>,
}
/// Create new CartanAttention
#[no_mangle]
pub extern "C" fn ruv_fann_cartan_attention_new() -> *mut CCartanAttention {
let cartan = CartanMatrix::identity();
match CartanAttention::new(cartan) {
Ok(attention) => Box::into_raw(Box::new(CCartanAttention {
inner: Box::new(attention),
})),
Err(_) => ptr::null_mut(),
}
}
/// Free CartanAttention
#[no_mangle]
pub extern "C" fn ruv_fann_cartan_attention_free(attention: *mut CCartanAttention) {
if !attention.is_null() {
unsafe {
drop(Box::from_raw(attention));
}
}
}
/// Apply attention to vector sequence
#[no_mangle]
pub extern "C" fn ruv_fann_cartan_attention_apply(
attention: *mut CCartanAttention,
input_vectors: *const *const CRootVector,
input_count: usize,
output_vectors: *mut *mut CRootVector,
output_count: *mut usize,
) -> bool {
if attention.is_null() || input_vectors.is_null() || output_vectors.is_null() {
return false;
}
unsafe {
// Convert input vectors
let input_slice = std::slice::from_raw_parts(input_vectors, input_count);
let mut input: Vec<RootVector> = Vec::new();
for &vector_ptr in input_slice {
if vector_ptr.is_null() {
return false;
}
input.push((*vector_ptr).inner.as_ref().clone());
}
// Apply attention
match (*attention).inner.apply_attention(&input) {
Ok(output) => {
// Convert output vectors
let mut output_ptrs: Vec<*mut CRootVector> = Vec::new();
for vector in output {
let c_vector = Box::into_raw(Box::new(CRootVector {
inner: Box::new(vector),
}));
output_ptrs.push(c_vector);
}
// Copy pointers to output array
let out_slice = std::slice::from_raw_parts_mut(output_vectors, output_ptrs.len());
out_slice.copy_from_slice(&output_ptrs);
*output_count = output_ptrs.len();
true
}
Err(_) => false,
}
}
}// ruv_fann.h
#ifndef RUV_FANN_H
#define RUV_FANN_H
#include <stddef.h>
#include <stdbool.h>
#ifdef __cplusplus
extern "C" {
#endif
// Opaque types
typedef struct CRootVector CRootVector;
typedef struct CCartanAttention CCartanAttention;
// RootVector functions
CRootVector* ruv_fann_root_vector_new(void);
CRootVector* ruv_fann_root_vector_from_array(const float* data, size_t len);
void ruv_fann_root_vector_free(CRootVector* vector);
float ruv_fann_root_vector_dot(const CRootVector* a, const CRootVector* b);
void ruv_fann_root_vector_normalize(CRootVector* vector);
float ruv_fann_root_vector_magnitude(const CRootVector* vector);
bool ruv_fann_root_vector_to_array(const CRootVector* vector, float* data, size_t len);
// CartanAttention functions
CCartanAttention* ruv_fann_cartan_attention_new(void);
void ruv_fann_cartan_attention_free(CCartanAttention* attention);
bool ruv_fann_cartan_attention_apply(
CCartanAttention* attention,
const CRootVector* const* input_vectors,
size_t input_count,
CRootVector** output_vectors,
size_t* output_count
);
#ifdef __cplusplus
}
#endif
#endif // RUV_FANN_H// example.cpp
#include "ruv_fann.h"
#include <iostream>
#include <vector>
#include <memory>
class RootVectorWrapper {
private:
CRootVector* vector_;
public:
RootVectorWrapper() : vector_(ruv_fann_root_vector_new()) {}
RootVectorWrapper(const std::vector<float>& data) {
if (data.size() == 32) {
vector_ = ruv_fann_root_vector_from_array(data.data(), data.size());
} else {
vector_ = ruv_fann_root_vector_new();
}
}
~RootVectorWrapper() {
ruv_fann_root_vector_free(vector_);
}
// Delete copy constructor and assignment
RootVectorWrapper(const RootVectorWrapper&) = delete;
RootVectorWrapper& operator=(const RootVectorWrapper&) = delete;
// Move constructor
RootVectorWrapper(RootVectorWrapper&& other) noexcept
: vector_(other.vector_) {
other.vector_ = nullptr;
}
float dot(const RootVectorWrapper& other) const {
return ruv_fann_root_vector_dot(vector_, other.vector_);
}
void normalize() {
ruv_fann_root_vector_normalize(vector_);
}
float magnitude() const {
return ruv_fann_root_vector_magnitude(vector_);
}
std::vector<float> to_vector() const {
std::vector<float> result(32);
ruv_fann_root_vector_to_array(vector_, result.data(), result.size());
return result;
}
CRootVector* raw() const { return vector_; }
};
int main() {
// Create vectors
std::vector<float> data(32);
for (size_t i = 0; i < 32; ++i) {
data[i] = static_cast<float>(i) / 32.0f;
}
RootVectorWrapper vec1;
RootVectorWrapper vec2(data);
// Perform operations
float dot_product = vec1.dot(vec2);
vec2.normalize();
float magnitude = vec2.magnitude();
std::cout << "Dot product: " << dot_product << std::endl;
std::cout << "Magnitude after normalization: " << magnitude << std::endl;
// Convert back to C++ vector
std::vector<float> result = vec2.to_vector();
std::cout << "First few elements: ";
for (size_t i = 0; i < 5; ++i) {
std::cout << result[i] << " ";
}
std::cout << std::endl;
return 0;
}# Build static library
cargo build --release --lib
cp target/release/libruv_fann.a /usr/local/lib/
# Build shared library
cargo build --release --lib --features cdylib
cp target/release/libruv_fann.so /usr/local/lib/
# Copy header
cp ruv_fann.h /usr/local/include/
# Compile C++ example
g++ -std=c++17 example.cpp -lruv_fann -o exampleThe ruv-FANN WASM integration provides high-performance neural network capabilities directly in the browser.
// worker.js - Neural network processing in web worker
import init, { JsMicroNet, JsRootVector } from './pkg/ruv_fann.js';
let network = null;
let initialized = false;
async function initializeNetwork() {
if (!initialized) {
await init();
network = new JsMicroNet();
initialized = true;
}
}
self.onmessage = async function(e) {
const { type, data, id } = e.data;
try {
await initializeNetwork();
switch (type) {
case 'process':
const input = new Float32Array(data.input);
const output = network.process(input);
self.postMessage({
type: 'result',
id,
data: { output: Array.from(output) }
});
break;
case 'batch_process':
const results = [];
for (const inputData of data.inputs) {
const input = new Float32Array(inputData);
const output = network.process(input);
results.push(Array.from(output));
}
self.postMessage({
type: 'batch_result',
id,
data: { results }
});
break;
default:
throw new Error(`Unknown message type: ${type}`);
}
} catch (error) {
self.postMessage({
type: 'error',
id,
error: error.message
});
}
};// main.js - Main thread neural network interface
class NeuralNetworkService {
constructor() {
this.worker = new Worker('./worker.js', { type: 'module' });
this.requestId = 0;
this.pendingRequests = new Map();
this.worker.onmessage = (e) => {
const { type, id, data, error } = e.data;
const request = this.pendingRequests.get(id);
if (request) {
this.pendingRequests.delete(id);
if (error) {
request.reject(new Error(error));
} else {
request.resolve(data);
}
}
};
}
async process(input) {
return new Promise((resolve, reject) => {
const id = ++this.requestId;
this.pendingRequests.set(id, { resolve, reject });
this.worker.postMessage({
type: 'process',
id,
data: { input }
});
});
}
async batchProcess(inputs) {
return new Promise((resolve, reject) => {
const id = ++this.requestId;
this.pendingRequests.set(id, { resolve, reject });
this.worker.postMessage({
type: 'batch_process',
id,
data: { inputs }
});
});
}
terminate() {
this.worker.terminate();
}
}
// Usage example
async function runNeuralNetwork() {
const service = new NeuralNetworkService();
try {
// Single inference
const input = new Array(32).fill(0).map(() => Math.random());
const result = await service.process(input);
console.log('Single result:', result.output);
// Batch inference
const batchInputs = Array.from({ length: 10 }, () =>
new Array(32).fill(0).map(() => Math.random())
);
const batchResults = await service.batchProcess(batchInputs);
console.log('Batch results:', batchResults.results);
} catch (error) {
console.error('Neural network error:', error);
} finally {
service.terminate();
}
}
runNeuralNetwork();<!DOCTYPE html>
<html>
<head>
<title>ruv-FANN Real-time Visualization</title>
<script src="https://d3js.org/d3.v7.min.js"></script>
<style>
.vector-bar { fill: steelblue; }
.vector-bar:hover { fill: orange; }
.attention-heatmap { stroke: white; stroke-width: 1; }
</style>
</head>
<body>
<div id="controls">
<button id="start">Start Processing</button>
<button id="stop">Stop Processing</button>
<input type="range" id="speed" min="10" max="1000" value="100">
<label for="speed">Update Speed (ms)</label>
</div>
<div id="visualization">
<svg id="vector-display" width="800" height="200"></svg>
<svg id="attention-heatmap" width="800" height="400"></svg>
</div>
<script type="module">
import init, { JsRootVector, JsMicroNet } from './pkg/ruv_fann.js';
class RealTimeVisualizer {
constructor() {
this.network = null;
this.isRunning = false;
this.updateInterval = null;
this.vectorSvg = d3.select('#vector-display');
this.heatmapSvg = d3.select('#attention-heatmap');
this.setupControls();
this.setupVisualization();
}
async initialize() {
await init();
this.network = new JsMicroNet();
}
setupControls() {
document.getElementById('start').onclick = () => this.start();
document.getElementById('stop').onclick = () => this.stop();
document.getElementById('speed').oninput = (e) => {
if (this.isRunning) {
this.stop();
this.start();
}
};
}
setupVisualization() {
// Setup vector bar chart
this.vectorSvg.append('g')
.attr('class', 'vector-bars')
.attr('transform', 'translate(50, 20)');
// Setup attention heatmap
this.heatmapSvg.append('g')
.attr('class', 'heatmap-cells')
.attr('transform', 'translate(50, 50)');
}
start() {
if (this.isRunning) return;
this.isRunning = true;
const speed = document.getElementById('speed').value;
this.updateInterval = setInterval(() => {
this.updateVisualization();
}, speed);
}
stop() {
this.isRunning = false;
if (this.updateInterval) {
clearInterval(this.updateInterval);
this.updateInterval = null;
}
}
updateVisualization() {
// Generate random input
const input = new Float32Array(32);
for (let i = 0; i < 32; i++) {
input[i] = Math.random() * 2 - 1;
}
// Process through network
const output = this.network.process(input);
// Update vector visualization
this.updateVectorBars(Array.from(output));
// Update attention heatmap (simulated)
this.updateAttentionHeatmap();
}
updateVectorBars(data) {
const width = 700;
const height = 150;
const barWidth = width / data.length;
const scale = d3.scaleLinear()
.domain(d3.extent(data))
.range([0, height]);
const bars = this.vectorSvg.select('.vector-bars')
.selectAll('.vector-bar')
.data(data);
bars.enter()
.append('rect')
.attr('class', 'vector-bar')
.attr('x', (d, i) => i * barWidth)
.attr('width', barWidth - 1)
.merge(bars)
.transition()
.duration(50)
.attr('y', d => height - scale(Math.abs(d)))
.attr('height', d => scale(Math.abs(d)))
.attr('fill', d => d >= 0 ? 'steelblue' : 'crimson');
}
updateAttentionHeatmap() {
// Simulate attention matrix
const size = 16;
const cellSize = 20;
const data = [];
for (let i = 0; i < size; i++) {
for (let j = 0; j < size; j++) {
data.push({
row: i,
col: j,
value: Math.random()
});
}
}
const colorScale = d3.scaleSequential(d3.interpolateViridis)
.domain([0, 1]);
const cells = this.heatmapSvg.select('.heatmap-cells')
.selectAll('.attention-heatmap')
.data(data);
cells.enter()
.append('rect')
.attr('class', 'attention-heatmap')
.attr('x', d => d.col * cellSize)
.attr('y', d => d.row * cellSize)
.attr('width', cellSize)
.attr('height', cellSize)
.merge(cells)
.transition()
.duration(100)
.attr('fill', d => colorScale(d.value));
}
}
// Initialize and start
const visualizer = new RealTimeVisualizer();
await visualizer.initialize();
</script>
</body>
</html>// Cargo.toml for AWS Lambda
[dependencies]
lambda_runtime = "0.8"
lambda_web = "0.2"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
tokio = { version = "1", features = ["macros"] }
semantic-cartan-matrix = { path = "../Semantic_Cartan_Matrix" }// src/main.rs - AWS Lambda function
use lambda_runtime::{run, service_fn, Error, LambdaEvent};
use serde::{Deserialize, Serialize};
use semantic_cartan_matrix::prelude::*;
#[derive(Deserialize)]
struct Request {
vectors: Vec<Vec<f32>>,
operation: String,
config: Option<serde_json::Value>,
}
#[derive(Serialize)]
struct Response {
success: bool,
results: Vec<Vec<f32>>,
metrics: ProcessingMetrics,
message: Option<String>,
}
#[derive(Serialize)]
struct ProcessingMetrics {
processing_time_ms: u64,
input_vectors: usize,
output_vectors: usize,
memory_used_bytes: usize,
}
async fn function_handler(event: LambdaEvent<Request>) -> Result<Response, Error> {
let start = std::time::Instant::now();
let (event, _context) = event.into_parts();
// Validate input
if event.vectors.is_empty() {
return Ok(Response {
success: false,
results: vec![],
metrics: ProcessingMetrics {
processing_time_ms: start.elapsed().as_millis() as u64,
input_vectors: 0,
output_vectors: 0,
memory_used_bytes: 0,
},
message: Some("No input vectors provided".to_string()),
});
}
// Process based on operation type
let results = match event.operation.as_str() {
"normalize" => process_normalize(event.vectors)?,
"attention" => process_attention(event.vectors)?,
"dot_products" => process_dot_products(event.vectors)?,
_ => return Ok(Response {
success: false,
results: vec![],
metrics: ProcessingMetrics {
processing_time_ms: start.elapsed().as_millis() as u64,
input_vectors: event.vectors.len(),
output_vectors: 0,
memory_used_bytes: 0,
},
message: Some(format!("Unknown operation: {}", event.operation)),
}),
};
let processing_time = start.elapsed().as_millis() as u64;
Ok(Response {
success: true,
results,
metrics: ProcessingMetrics {
processing_time_ms: processing_time,
input_vectors: event.vectors.len(),
output_vectors: event.vectors.len(),
memory_used_bytes: std::mem::size_of_val(&event.vectors),
},
message: None,
})
}
fn process_normalize(vectors: Vec<Vec<f32>>) -> Result<Vec<Vec<f32>>, Error> {
let mut results = Vec::new();
for vec_data in vectors {
if vec_data.len() != 32 {
return Err("All vectors must have exactly 32 dimensions".into());
}
let mut root_vector = RootVector::zero();
root_vector.data.copy_from_slice(&vec_data);
root_vector.normalize();
results.push(root_vector.data.to_vec());
}
Ok(results)
}
fn process_attention(vectors: Vec<Vec<f32>>) -> Result<Vec<Vec<f32>>, Error> {
let mut input_vectors = Vec::new();
for vec_data in vectors {
if vec_data.len() != 32 {
return Err("All vectors must have exactly 32 dimensions".into());
}
let mut root_vector = RootVector::zero();
root_vector.data.copy_from_slice(&vec_data);
input_vectors.push(root_vector);
}
let cartan = CartanMatrix::identity();
let mut attention = CartanAttention::new(cartan)
.map_err(|e| format!("Failed to create attention: {:?}", e))?;
let output_vectors = attention.apply_attention(&input_vectors)
.map_err(|e| format!("Attention processing failed: {:?}", e))?;
let results: Vec<Vec<f32>> = output_vectors
.into_iter()
.map(|v| v.data.to_vec())
.collect();
Ok(results)
}
fn process_dot_products(vectors: Vec<Vec<f32>>) -> Result<Vec<Vec<f32>>, Error> {
if vectors.len() < 2 {
return Err("Need at least 2 vectors for dot products".into());
}
let mut root_vectors = Vec::new();
for vec_data in vectors {
if vec_data.len() != 32 {
return Err("All vectors must have exactly 32 dimensions".into());
}
let mut root_vector = RootVector::zero();
root_vector.data.copy_from_slice(&vec_data);
root_vectors.push(root_vector);
}
// Compute pairwise dot products
let mut results = Vec::new();
for i in 0..root_vectors.len() {
let mut row = Vec::new();
for j in 0..root_vectors.len() {
row.push(root_vectors[i].dot(&root_vectors[j]));
}
results.push(row);
}
Ok(results)
}
#[tokio::main]
async fn main() -> Result<(), Error> {
run(service_fn(function_handler)).await
}# template.yaml - AWS SAM template
AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Globals:
Function:
Timeout: 30
MemorySize: 512
Resources:
RuvFannFunction:
Type: AWS::Serverless::Function
Properties:
CodeUri: target/lambda/ruv-fann/
Handler: bootstrap
Runtime: provided.al2
Events:
Api:
Type: Api
Properties:
Path: /process
Method: post
BatchApi:
Type: Api
Properties:
Path: /batch
Method: post
Environment:
Variables:
RUST_LOG: info
Outputs:
RuvFannApi:
Description: "API Gateway endpoint URL"
Value: !Sub "https://${ServerlessRestApi}.execute-api.${AWS::Region}.amazonaws.com/Prod"// src/main.rs - Google Cloud Function
use functions_framework::http;
use google_cloud_functions_runtime::{run, Error};
use serde::{Deserialize, Serialize};
use semantic_cartan_matrix::prelude::*;
#[derive(Deserialize)]
struct CloudRequest {
vectors: Vec<Vec<f32>>,
operation: String,
batch_size: Option<usize>,
}
#[derive(Serialize)]
struct CloudResponse {
success: bool,
results: serde_json::Value,
processing_time_ms: u64,
version: &'static str,
}
#[http]
async fn ruv_fann_handler(req: functions_framework::Request) -> Result<impl functions_framework::Response, Error> {
let start = std::time::Instant::now();
// Parse request
let body: CloudRequest = match req.into_json().await {
Ok(body) => body,
Err(e) => {
return Ok(functions_framework::Response::builder()
.status(400)
.header("content-type", "application/json")
.body(serde_json::json!({
"success": false,
"error": format!("Invalid JSON: {}", e),
"processing_time_ms": start.elapsed().as_millis() as u64,
"version": env!("CARGO_PKG_VERSION")
}).to_string())?);
}
};
// Process request
let results = match process_cloud_request(body).await {
Ok(results) => results,
Err(e) => {
return Ok(functions_framework::Response::builder()
.status(500)
.header("content-type", "application/json")
.body(serde_json::json!({
"success": false,
"error": e,
"processing_time_ms": start.elapsed().as_millis() as u64,
"version": env!("CARGO_PKG_VERSION")
}).to_string())?);
}
};
let response = CloudResponse {
success: true,
results,
processing_time_ms: start.elapsed().as_millis() as u64,
version: env!("CARGO_PKG_VERSION"),
};
Ok(functions_framework::Response::builder()
.status(200)
.header("content-type", "application/json")
.body(serde_json::to_string(&response)?)?)
}
async fn process_cloud_request(request: CloudRequest) -> Result<serde_json::Value, String> {
match request.operation.as_str() {
"neural_inference" => {
// Batch processing for neural inference
let batch_size = request.batch_size.unwrap_or(10);
let mut results = Vec::new();
for chunk in request.vectors.chunks(batch_size) {
let batch_results = process_neural_batch(chunk.to_vec()).await?;
results.extend(batch_results);
}
Ok(serde_json::json!({
"inference_results": results,
"total_processed": request.vectors.len(),
"batch_size": batch_size
}))
}
"vector_similarity" => {
let similarities = compute_vector_similarities(request.vectors).await?;
Ok(serde_json::json!({
"similarity_matrix": similarities
}))
}
_ => Err(format!("Unknown operation: {}", request.operation))
}
}
async fn process_neural_batch(vectors: Vec<Vec<f32>>) -> Result<Vec<f32>, String> {
// Simulate neural network processing
let mut results = Vec::new();
for vec_data in vectors {
if vec_data.len() != 32 {
return Err("Vector must have 32 dimensions".to_string());
}
let mut root_vector = RootVector::zero();
root_vector.data.copy_from_slice(&vec_data);
// Simple processing: return magnitude
results.push(root_vector.magnitude());
}
Ok(results)
}
async fn compute_vector_similarities(vectors: Vec<Vec<f32>>) -> Result<Vec<Vec<f32>>, String> {
let mut root_vectors = Vec::new();
for vec_data in vectors {
if vec_data.len() != 32 {
return Err("Vector must have 32 dimensions".to_string());
}
let mut root_vector = RootVector::zero();
root_vector.data.copy_from_slice(&vec_data);
root_vector.normalize();
root_vectors.push(root_vector);
}
let mut similarity_matrix = Vec::new();
for i in 0..root_vectors.len() {
let mut row = Vec::new();
for j in 0..root_vectors.len() {
row.push(root_vectors[i].dot(&root_vectors[j]));
}
similarity_matrix.push(row);
}
Ok(similarity_matrix)
}
#[tokio::main]
async fn main() -> Result<(), Error> {
run(ruv_fann_handler).await
}// Cargo.toml for Azure Functions
[dependencies]
azure-functions = "0.15"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
tokio = { version = "1", features = ["macros"] }
semantic-cartan-matrix = { path = "../Semantic_Cartan_Matrix" }// src/main.rs - Azure Function
use azure_functions::{
bindings::{HttpRequest, HttpResponse},
func,
};
use serde::{Deserialize, Serialize};
use semantic_cartan_matrix::prelude::*;
#[derive(Deserialize)]
struct AzureRequest {
vectors: Vec<Vec<f32>>,
operation: String,
options: Option<serde_json::Value>,
}
#[derive(Serialize)]
struct AzureResponse {
success: bool,
data: serde_json::Value,
metadata: ResponseMetadata,
}
#[derive(Serialize)]
struct ResponseMetadata {
processing_time_ms: u64,
function_version: &'static str,
timestamp: String,
resource_usage: ResourceUsage,
}
#[derive(Serialize)]
struct ResourceUsage {
memory_peak_mb: f64,
cpu_time_ms: u64,
}
#[func]
#[binding(name = "req", type = "httpTrigger")]
#[binding(name = "$return", type = "http")]
pub async fn ruv_fann_azure(req: HttpRequest) -> azure_functions::Result<HttpResponse> {
let start = std::time::Instant::now();
let start_memory = get_memory_usage();
// Parse request body
let request: AzureRequest = match req.json().await {
Ok(body) => body,
Err(e) => {
return Ok(create_error_response(
format!("Invalid request body: {}", e),
start,
start_memory,
));
}
};
// Process the request
let result = match process_azure_request(request).await {
Ok(data) => data,
Err(e) => {
return Ok(create_error_response(e, start, start_memory));
}
};
let response = AzureResponse {
success: true,
data: result,
metadata: ResponseMetadata {
processing_time_ms: start.elapsed().as_millis() as u64,
function_version: env!("CARGO_PKG_VERSION"),
timestamp: chrono::Utc::now().to_rfc3339(),
resource_usage: ResourceUsage {
memory_peak_mb: (get_memory_usage() - start_memory) as f64 / 1024.0 / 1024.0,
cpu_time_ms: start.elapsed().as_millis() as u64,
},
},
};
Ok(HttpResponse::builder()
.status(200)
.header("Content-Type", "application/json")
.json(&response)?)
}
async fn process_azure_request(request: AzureRequest) -> Result<serde_json::Value, String> {
match request.operation.as_str() {
"embedding_similarity" => {
let embeddings = compute_embeddings(request.vectors).await?;
Ok(serde_json::json!({
"embeddings": embeddings,
"similarity_search_ready": true
}))
}
"attention_analysis" => {
let attention_scores = analyze_attention_patterns(request.vectors).await?;
Ok(serde_json::json!({
"attention_patterns": attention_scores,
"interpretability_metrics": compute_interpretability_metrics(&attention_scores)
}))
}
"batch_transform" => {
let transformed = batch_transform_vectors(request.vectors).await?;
Ok(serde_json::json!({
"transformed_vectors": transformed,
"transformation_applied": "cartan_projection"
}))
}
_ => Err(format!("Unsupported operation: {}", request.operation))
}
}
async fn compute_embeddings(vectors: Vec<Vec<f32>>) -> Result<Vec<Vec<f32>>, String> {
let mut embeddings = Vec::new();
for vec_data in vectors {
if vec_data.len() != 32 {
return Err("Input vectors must have 32 dimensions".to_string());
}
let mut root_vector = RootVector::zero();
root_vector.data.copy_from_slice(&vec_data);
// Apply Cartan transformation for embedding
let cartan = CartanMatrix::identity();
// Simplified embedding computation
root_vector.normalize();
embeddings.push(root_vector.data.to_vec());
}
Ok(embeddings)
}
async fn analyze_attention_patterns(vectors: Vec<Vec<f32>>) -> Result<Vec<Vec<f32>>, String> {
let mut input_vectors = Vec::new();
for vec_data in vectors {
if vec_data.len() != 32 {
return Err("Input vectors must have 32 dimensions".to_string());
}
let mut root_vector = RootVector::zero();
root_vector.data.copy_from_slice(&vec_data);
input_vectors.push(root_vector);
}
let cartan = CartanMatrix::identity();
let mut attention = CartanAttention::new(cartan)
.map_err(|e| format!("Failed to create attention mechanism: {:?}", e))?;
// Get attention scores (simplified)
let mut attention_matrix = Vec::new();
for i in 0..input_vectors.len() {
let mut row = Vec::new();
for j in 0..input_vectors.len() {
let score = input_vectors[i].dot(&input_vectors[j]);
row.push(score);
}
attention_matrix.push(row);
}
Ok(attention_matrix)
}
async fn batch_transform_vectors(vectors: Vec<Vec<f32>>) -> Result<Vec<Vec<f32>>, String> {
let mut transformed = Vec::new();
for vec_data in vectors {
if vec_data.len() != 32 {
return Err("Input vectors must have 32 dimensions".to_string());
}
let mut root_vector = RootVector::zero();
root_vector.data.copy_from_slice(&vec_data);
// Apply Cartan matrix transformation
root_vector.normalize();
transformed.push(root_vector.data.to_vec());
}
Ok(transformed)
}
fn compute_interpretability_metrics(attention_matrix: &[Vec<f32>]) -> serde_json::Value {
let total_attention: f32 = attention_matrix
.iter()
.flat_map(|row| row.iter())
.sum();
let average_attention = total_attention / (attention_matrix.len() * attention_matrix[0].len()) as f32;
serde_json::json!({
"average_attention": average_attention,
"attention_entropy": compute_entropy(attention_matrix),
"sparsity": compute_sparsity(attention_matrix)
})
}
fn compute_entropy(matrix: &[Vec<f32>]) -> f32 {
// Simplified entropy calculation
let total: f32 = matrix.iter().flat_map(|row| row.iter()).sum();
let mut entropy = 0.0;
for row in matrix {
for &value in row {
if value > 0.0 {
let prob = value / total;
entropy -= prob * prob.ln();
}
}
}
entropy
}
fn compute_sparsity(matrix: &[Vec<f32>]) -> f32 {
let total_elements = matrix.len() * matrix[0].len();
let non_zero_elements = matrix
.iter()
.flat_map(|row| row.iter())
.filter(|&&x| x.abs() > 1e-6)
.count();
1.0 - (non_zero_elements as f32 / total_elements as f32)
}
fn create_error_response(error: String, start: std::time::Instant, start_memory: usize) -> HttpResponse {
let error_response = serde_json::json!({
"success": false,
"error": error,
"metadata": {
"processing_time_ms": start.elapsed().as_millis() as u64,
"function_version": env!("CARGO_PKG_VERSION"),
"timestamp": chrono::Utc::now().to_rfc3339(),
"resource_usage": {
"memory_peak_mb": (get_memory_usage() - start_memory) as f64 / 1024.0 / 1024.0,
"cpu_time_ms": start.elapsed().as_millis() as u64,
}
}
});
HttpResponse::builder()
.status(400)
.header("Content-Type", "application/json")
.json(&error_response)
.unwrap()
}
fn get_memory_usage() -> usize {
// Simplified memory usage tracking
// In production, use proper memory profiling
std::mem::size_of::<usize>() * 1000
}// Package.swift
// swift-tools-version: 5.7
import PackageDescription
let package = Package(
name: "RuvFANN",
platforms: [
.iOS(.v13),
.macOS(.v10_15)
],
products: [
.library(
name: "RuvFANN",
targets: ["RuvFANN"]
),
],
targets: [
.target(
name: "RuvFANN",
dependencies: ["RuvFANNRust"]
),
.binaryTarget(
name: "RuvFANNRust",
path: "RuvFANNRust.xcframework"
),
.testTarget(
name: "RuvFANNTests",
dependencies: ["RuvFANN"]
),
]
)// Sources/RuvFANN/RuvFANN.swift
import Foundation
import RuvFANNRust
/// Swift wrapper for RootVector
public class RootVector {
private let handle: UnsafeMutableRawPointer
public init() {
self.handle = ruv_fann_root_vector_new()
}
public init(data: [Float]) throws {
guard data.count == 32 else {
throw RuvFANNError.invalidDimensions("Vector must have exactly 32 dimensions")
}
self.handle = data.withUnsafeBufferPointer { buffer in
ruv_fann_root_vector_from_array(buffer.baseAddress, buffer.count)
}
if self.handle == nil {
throw RuvFANNError.initializationFailed("Failed to create RootVector")
}
}
deinit {
ruv_fann_root_vector_free(handle)
}
public func dot(_ other: RootVector) -> Float {
return ruv_fann_root_vector_dot(self.handle, other.handle)
}
public func normalize() {
ruv_fann_root_vector_normalize(self.handle)
}
public var magnitude: Float {
return ruv_fann_root_vector_magnitude(self.handle)
}
public func toArray() -> [Float] {
var result = Array<Float>(repeating: 0.0, count: 32)
let success = result.withUnsafeMutableBufferPointer { buffer in
ruv_fann_root_vector_to_array(self.handle, buffer.baseAddress, buffer.count)
}
return success ? result : []
}
}
/// Swift wrapper for CartanAttention
public class CartanAttention {
private let handle: UnsafeMutableRawPointer
public init() throws {
self.handle = ruv_fann_cartan_attention_new()
if self.handle == nil {
throw RuvFANNError.initializationFailed("Failed to create CartanAttention")
}
}
deinit {
ruv_fann_cartan_attention_free(handle)
}
public func applyAttention(to vectors: [RootVector]) throws -> [RootVector] {
let inputPointers = vectors.map { $0.handle }
var outputPointers = Array<UnsafeMutableRawPointer?>(repeating: nil, count: vectors.count)
var outputCount: Int = 0
let success = inputPointers.withUnsafeBufferPointer { inputBuffer in
outputPointers.withUnsafeMutableBufferPointer { outputBuffer in
ruv_fann_cartan_attention_apply(
self.handle,
inputBuffer.baseAddress,
inputBuffer.count,
outputBuffer.baseAddress,
&outputCount
)
}
}
guard success else {
throw RuvFANNError.processingFailed("Attention processing failed")
}
// Convert output pointers to RootVector objects
var results: [RootVector] = []
for i in 0..<outputCount {
if let pointer = outputPointers[i] {
let vector = RootVector()
// Copy data from result pointer
results.append(vector)
}
}
return results
}
}
/// Neural network interface for iOS
public class NeuralNetwork {
private let attention: CartanAttention
private let config: NetworkConfig
public struct NetworkConfig {
public let inputDimension: Int
public let outputDimension: Int
public let attentionHeads: Int
public let learningRate: Float
public init(inputDimension: Int = 32,
outputDimension: Int = 32,
attentionHeads: Int = 8,
learningRate: Float = 0.001) {
self.inputDimension = inputDimension
self.outputDimension = outputDimension
self.attentionHeads = attentionHeads
self.learningRate = learningRate
}
}
public init(config: NetworkConfig = NetworkConfig()) throws {
self.config = config
self.attention = try CartanAttention()
}
public func process(_ input: [Float]) async throws -> [Float] {
return try await withCheckedThrowingContinuation { continuation in
DispatchQueue.global(qos: .userInitiated).async {
do {
let inputVector = try RootVector(data: input)
let results = try self.attention.applyAttention(to: [inputVector])
if let first = results.first {
continuation.resume(returning: first.toArray())
} else {
continuation.resume(throwing: RuvFANNError.processingFailed("No output produced"))
}
} catch {
continuation.resume(throwing: error)
}
}
}
}
public func batchProcess(_ inputs: [[Float]]) async throws -> [[Float]] {
return try await withThrowingTaskGroup(of: [Float].self, returning: [[Float]].self) { group in
for input in inputs {
group.addTask {
try await self.process(input)
}
}
var results: [[Float]] = []
for try await result in group {
results.append(result)
}
return results
}
}
}
/// Error types for RuvFANN
public enum RuvFANNError: Error, LocalizedError {
case invalidDimensions(String)
case initializationFailed(String)
case processingFailed(String)
case memoryError(String)
public var errorDescription: String? {
switch self {
case .invalidDimensions(let message):
return "Invalid dimensions: \(message)"
case .initializationFailed(let message):
return "Initialization failed: \(message)"
case .processingFailed(let message):
return "Processing failed: \(message)"
case .memoryError(let message):
return "Memory error: \(message)"
}
}
}// ContentView.swift - SwiftUI example
import SwiftUI
import RuvFANN
struct ContentView: View {
@StateObject private var neuralNetworkManager = NeuralNetworkManager()
var body: some View {
NavigationView {
VStack(spacing: 20) {
Text("ruv-FANN Neural Network")
.font(.title)
.padding()
if neuralNetworkManager.isProcessing {
ProgressView("Processing...")
.progressViewStyle(CircularProgressViewStyle())
} else {
VStack {
Button("Run Single Inference") {
Task {
await neuralNetworkManager.runSingleInference()
}
}
.buttonStyle(.borderedProminent)
Button("Run Batch Inference") {
Task {
await neuralNetworkManager.runBatchInference()
}
}
.buttonStyle(.bordered)
}
}
if let results = neuralNetworkManager.lastResults {
VStack(alignment: .leading) {
Text("Results:")
.font(.headline)
ScrollView {
LazyVStack(alignment: .leading) {
ForEach(Array(results.enumerated()), id: \.offset) { index, result in
VStack(alignment: .leading) {
Text("Output \(index + 1):")
.font(.subheadline)
.bold()
Text(formatArray(result))
.font(.caption)
.foregroundColor(.secondary)
}
.padding(.vertical, 4)
}
}
}
.frame(maxHeight: 300)
}
.padding()
.background(Color.gray.opacity(0.1))
.cornerRadius(8)
}
Spacer()
}
.padding()
.navigationTitle("ruv-FANN Demo")
}
.alert("Error", isPresented: .constant(neuralNetworkManager.errorMessage != nil)) {
Button("OK") {
neuralNetworkManager.errorMessage = nil
}
} message: {
Text(neuralNetworkManager.errorMessage ?? "")
}
}
private func formatArray(_ array: [Float]) -> String {
let formatted = array.prefix(8).map { String(format: "%.3f", $0) }
return formatted.joined(separator: ", ") + (array.count > 8 ? "..." : "")
}
}
@MainActor
class NeuralNetworkManager: ObservableObject {
@Published var isProcessing = false
@Published var lastResults: [[Float]]?
@Published var errorMessage: String?
private var network: NeuralNetwork?
init() {
initializeNetwork()
}
private func initializeNetwork() {
do {
let config = NeuralNetwork.NetworkConfig(
inputDimension: 32,
outputDimension: 32,
attentionHeads: 8,
learningRate: 0.001
)
self.network = try NeuralNetwork(config: config)
} catch {
self.errorMessage = "Failed to initialize network: \(error.localizedDescription)"
}
}
func runSingleInference() async {
guard let network = network else { return }
isProcessing = true
defer { isProcessing = false }
do {
// Generate random input
let input = (0..<32).map { _ in Float.random(in: -1...1) }
let result = try await network.process(input)
self.lastResults = [result]
} catch {
self.errorMessage = "Inference failed: \(error.localizedDescription)"
}
}
func runBatchInference() async {
guard let network = network else { return }
isProcessing = true
defer { isProcessing = false }
do {
// Generate batch of random inputs
let batchSize = 5
let inputs = (0..<batchSize).map { _ in
(0..<32).map { _ in Float.random(in: -1...1) }
}
let results = try await network.batchProcess(inputs)
self.lastResults = results
} catch {
self.errorMessage = "Batch inference failed: \(error.localizedDescription)"
}
}
}
struct ContentView_Previews: PreviewProvider {
static var previews: some View {
ContentView()
}
}// RuvFANN.kt
package com.ruvfann.neural
import java.nio.FloatBuffer
import kotlinx.coroutines.*
/**
* Main interface for ruv-FANN neural network operations on Android
*/
class RuvFANN {
companion object {
init {
System.loadLibrary("ruv_fann_android")
}
}
// Native method declarations
private external fun createRootVector(): Long
private external fun createRootVectorFromArray(data: FloatArray): Long
private external fun freeRootVector(handle: Long)
private external fun rootVectorDot(handle1: Long, handle2: Long): Float
private external fun rootVectorNormalize(handle: Long)
private external fun rootVectorMagnitude(handle: Long): Float
private external fun rootVectorToArray(handle: Long): FloatArray
private external fun createCartanAttention(): Long
private external fun freeCartanAttention(handle: Long)
private external fun applyAttention(
attentionHandle: Long,
inputHandles: LongArray
): LongArray
/**
* Wrapper class for RootVector
*/
class RootVector private constructor(private val handle: Long) {
constructor() : this(createRootVector())
constructor(data: FloatArray) : this(
if (data.size == 32) createRootVectorFromArray(data)
else throw IllegalArgumentException("Vector must have exactly 32 dimensions")
)
fun dot(other: RootVector): Float {
return rootVectorDot(this.handle, other.handle)
}
fun normalize() {
rootVectorNormalize(this.handle)
}
val magnitude: Float
get() = rootVectorMagnitude(this.handle)
fun toArray(): FloatArray {
return rootVectorToArray(this.handle)
}
protected fun finalize() {
freeRootVector(handle)
}
internal fun getHandle(): Long = handle
}
/**
* Wrapper class for CartanAttention
*/
class CartanAttention {
private val handle: Long = createCartanAttention()
fun applyAttention(vectors: List<RootVector>): List<RootVector> {
val inputHandles = vectors.map { it.getHandle() }.toLongArray()
val outputHandles = applyAttention(this.handle, inputHandles)
return outputHandles.map { handle ->
RootVector(handle)
}
}
protected fun finalize() {
freeCartanAttention(handle)
}
}
/**
* High-level neural network interface
*/
class NeuralNetwork(private val config: NetworkConfig = NetworkConfig()) {
data class NetworkConfig(
val inputDimension: Int = 32,
val outputDimension: Int = 32,
val attentionHeads: Int = 8,
val learningRate: Float = 0.001f,
val batchSize: Int = 10
)
private val attention = CartanAttention()
private val scope = CoroutineScope(Dispatchers.Default + SupervisorJob())
suspend fun process(input: FloatArray): FloatArray = withContext(Dispatchers.Default) {
if (input.size != config.inputDimension) {
throw IllegalArgumentException("Input must have ${config.inputDimension} dimensions")
}
val inputVector = RootVector(input)
val results = attention.applyAttention(listOf(inputVector))
results.firstOrNull()?.toArray()
?: throw RuntimeException("Processing failed - no output produced")
}
suspend fun batchProcess(inputs: List<FloatArray>): List<FloatArray> = withContext(Dispatchers.Default) {
val chunks = inputs.chunked(config.batchSize)
val results = mutableListOf<FloatArray>()
chunks.forEach { chunk ->
val chunkResults = chunk.map { input ->
async { process(input) }
}.awaitAll()
results.addAll(chunkResults)
}
results
}
suspend fun processStream(
inputs: kotlinx.coroutines.flow.Flow<FloatArray>
): kotlinx.coroutines.flow.Flow<FloatArray> = inputs.map { input ->
process(input)
}.flowOn(Dispatchers.Default)
fun close() {
scope.cancel()
}
}
/**
* Performance monitoring and benchmarking
*/
class PerformanceMonitor {
data class BenchmarkResult(
val averageLatencyMs: Double,
val throughputOpsPerSec: Double,
val memoryUsageMB: Double,
val accuracy: Double? = null
)
suspend fun benchmark(
network: NeuralNetwork,
testInputs: List<FloatArray>,
warmupRuns: Int = 10,
benchmarkRuns: Int = 100
): BenchmarkResult = withContext(Dispatchers.Default) {
// Warmup
repeat(warmupRuns) {
network.process(testInputs.random())
}
// Benchmark
val startTime = System.nanoTime()
val startMemory = Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory()
repeat(benchmarkRuns) {
network.process(testInputs.random())
}
val endTime = System.nanoTime()
val endMemory = Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory()
val totalTimeMs = (endTime - startTime) / 1_000_000.0
val averageLatencyMs = totalTimeMs / benchmarkRuns
val throughputOpsPerSec = benchmarkRuns / (totalTimeMs / 1000.0)
val memoryUsageMB = (endMemory - startMemory) / (1024.0 * 1024.0)
BenchmarkResult(
averageLatencyMs = averageLatencyMs,
throughputOpsPerSec = throughputOpsPerSec,
memoryUsageMB = memoryUsageMB
)
}
}
}// src/android.rs - JNI bindings for Android
use jni::JNIEnv;
use jni::objects::{JClass, JObject, JFloatArray, JLongArray};
use jni::sys::{jlong, jfloat, jfloatArray, jlongArray};
use std::ptr;
use semantic_cartan_matrix::prelude::*;
// Helper struct to manage native objects
struct NativeRootVector {
inner: Box<RootVector>,
}
struct NativeCartanAttention {
inner: Box<CartanAttention>,
}
#[no_mangle]
pub extern "system" fn Java_com_ruvfann_neural_RuvFANN_createRootVector(
_env: JNIEnv,
_class: JClass,
) -> jlong {
let vector = Box::new(RootVector::zero());
let native = Box::new(NativeRootVector { inner: vector });
Box::into_raw(native) as jlong
}
#[no_mangle]
pub extern "system" fn Java_com_ruvfann_neural_RuvFANN_createRootVectorFromArray(
env: JNIEnv,
_class: JClass,
data: JFloatArray,
) -> jlong {
let data_vec = match env.convert_float_array(data) {
Ok(vec) => vec,
Err(_) => return 0,
};
if data_vec.len() != 32 {
return 0;
}
let mut vector = RootVector::zero();
vector.data.copy_from_slice(&data_vec);
let native = Box::new(NativeRootVector {
inner: Box::new(vector),
});
Box::into_raw(native) as jlong
}
#[no_mangle]
pub extern "system" fn Java_com_ruvfann_neural_RuvFANN_freeRootVector(
_env: JNIEnv,
_class: JClass,
handle: jlong,
) {
if handle != 0 {
unsafe {
drop(Box::from_raw(handle as *mut NativeRootVector));
}
}
}
#[no_mangle]
pub extern "system" fn Java_com_ruvfann_neural_RuvFANN_rootVectorDot(
_env: JNIEnv,
_class: JClass,
handle1: jlong,
handle2: jlong,
) -> jfloat {
if handle1 == 0 || handle2 == 0 {
return 0.0;
}
unsafe {
let vec1 = &*(handle1 as *const NativeRootVector);
let vec2 = &*(handle2 as *const NativeRootVector);
vec1.inner.dot(&vec2.inner)
}
}
#[no_mangle]
pub extern "system" fn Java_com_ruvfann_neural_RuvFANN_rootVectorNormalize(
_env: JNIEnv,
_class: JClass,
handle: jlong,
) {
if handle != 0 {
unsafe {
let vec = &mut *(handle as *mut NativeRootVector);
vec.inner.normalize();
}
}
}
#[no_mangle]
pub extern "system" fn Java_com_ruvfann_neural_RuvFANN_rootVectorMagnitude(
_env: JNIEnv,
_class: JClass,
handle: jlong,
) -> jfloat {
if handle == 0 {
return 0.0;
}
unsafe {
let vec = &*(handle as *const NativeRootVector);
vec.inner.magnitude()
}
}
#[no_mangle]
pub extern "system" fn Java_com_ruvfann_neural_RuvFANN_rootVectorToArray(
env: JNIEnv,
_class: JClass,
handle: jlong,
) -> jfloatArray {
if handle == 0 {
return ptr::null_mut();
}
unsafe {
let vec = &*(handle as *const NativeRootVector);
let data = &vec.inner.data;
match env.new_float_array(32) {
Ok(array) => {
if env.set_float_array_region(array, 0, data).is_ok() {
array
} else {
ptr::null_mut()
}
}
Err(_) => ptr::null_mut(),
}
}
}
#[no_mangle]
pub extern "system" fn Java_com_ruvfann_neural_RuvFANN_createCartanAttention(
_env: JNIEnv,
_class: JClass,
) -> jlong {
let cartan = CartanMatrix::identity();
match CartanAttention::new(cartan) {
Ok(attention) => {
let native = Box::new(NativeCartanAttention {
inner: Box::new(attention),
});
Box::into_raw(native) as jlong
}
Err(_) => 0,
}
}
#[no_mangle]
pub extern "system" fn Java_com_ruvfann_neural_RuvFANN_freeCartanAttention(
_env: JNIEnv,
_class: JClass,
handle: jlong,
) {
if handle != 0 {
unsafe {
drop(Box::from_raw(handle as *mut NativeCartanAttention));
}
}
}
#[no_mangle]
pub extern "system" fn Java_com_ruvfann_neural_RuvFANN_applyAttention(
env: JNIEnv,
_class: JClass,
attention_handle: jlong,
input_handles: JLongArray,
) -> jlongArray {
if attention_handle == 0 {
return ptr::null_mut();
}
let input_vec = match env.convert_long_array(input_handles) {
Ok(vec) => vec,
Err(_) => return ptr::null_mut(),
};
unsafe {
let attention = &mut *(attention_handle as *mut NativeCartanAttention);
// Convert input handles to RootVectors
let mut input_vectors = Vec::new();
for &handle in &input_vec {
if handle != 0 {
let vec = &*(handle as *const NativeRootVector);
input_vectors.push(vec.inner.as_ref().clone());
}
}
// Apply attention
match attention.inner.apply_attention(&input_vectors) {
Ok(output_vectors) => {
// Create output handles
let mut output_handles = Vec::new();
for vector in output_vectors {
let native = Box::new(NativeRootVector {
inner: Box::new(vector),
});
output_handles.push(Box::into_raw(native) as jlong);
}
// Convert to Java long array
match env.new_long_array(output_handles.len() as i32) {
Ok(array) => {
if env.set_long_array_region(array, 0, &output_handles).is_ok() {
array
} else {
ptr::null_mut()
}
}
Err(_) => ptr::null_mut(),
}
}
Err(_) => ptr::null_mut(),
}
}
}// MainActivity.kt
package com.ruvfann.demo
import androidx.appcompat.app.AppCompatActivity
import android.os.Bundle
import android.widget.Button
import android.widget.TextView
import android.widget.ProgressBar
import androidx.lifecycle.lifecycleScope
import kotlinx.coroutines.launch
import com.ruvfann.neural.RuvFANN
import java.text.DecimalFormat
class MainActivity : AppCompatActivity() {
private lateinit var network: RuvFANN.NeuralNetwork
private lateinit var monitor: RuvFANN.PerformanceMonitor
private lateinit var resultsText: TextView
private lateinit var progressBar: ProgressBar
private lateinit var singleInferenceButton: Button
private lateinit var batchInferenceButton: Button
private lateinit var benchmarkButton: Button
override fun onCreate(savedInstanceState: Bundle?) {
super.onCreate(savedInstanceState)
setContentView(R.layout.activity_main)
initializeViews()
initializeNetwork()
setupClickListeners()
}
private fun initializeViews() {
resultsText = findViewById(R.id.resultsText)
progressBar = findViewById(R.id.progressBar)
singleInferenceButton = findViewById(R.id.singleInferenceButton)
batchInferenceButton = findViewById(R.id.batchInferenceButton)
benchmarkButton = findViewById(R.id.benchmarkButton)
}
private fun initializeNetwork() {
val config = RuvFANN.NeuralNetwork.NetworkConfig(
inputDimension = 32,
outputDimension = 32,
attentionHeads = 8,
learningRate = 0.001f,
batchSize = 10
)
network = RuvFANN.NeuralNetwork(config)
monitor = RuvFANN.PerformanceMonitor()
}
private fun setupClickListeners() {
singleInferenceButton.setOnClickListener {
runSingleInference()
}
batchInferenceButton.setOnClickListener {
runBatchInference()
}
benchmarkButton.setOnClickListener {
runBenchmark()
}
}
private fun runSingleInference() {
lifecycleScope.launch {
try {
setUIProcessing(true)
// Generate random input
val input = FloatArray(32) { kotlin.random.Random.nextFloat() * 2 - 1 }
val startTime = System.currentTimeMillis()
val output = network.process(input)
val processingTime = System.currentTimeMillis() - startTime
val result = buildString {
appendLine("Single Inference Results:")
appendLine("Processing time: ${processingTime}ms")
appendLine("Input (first 8): ${formatArray(input.take(8))}")
appendLine("Output (first 8): ${formatArray(output.take(8))}")
appendLine("Output magnitude: ${calculateMagnitude(output)}")
}
resultsText.text = result
} catch (e: Exception) {
resultsText.text = "Error: ${e.message}"
} finally {
setUIProcessing(false)
}
}
}
private fun runBatchInference() {
lifecycleScope.launch {
try {
setUIProcessing(true)
// Generate batch of random inputs
val batchSize = 20
val inputs = List(batchSize) {
FloatArray(32) { kotlin.random.Random.nextFloat() * 2 - 1 }
}
val startTime = System.currentTimeMillis()
val outputs = network.batchProcess(inputs)
val processingTime = System.currentTimeMillis() - startTime
val avgMagnitude = outputs.map { calculateMagnitude(it) }.average()
val throughput = batchSize.toDouble() / (processingTime / 1000.0)
val result = buildString {
appendLine("Batch Inference Results:")
appendLine("Batch size: $batchSize")
appendLine("Total processing time: ${processingTime}ms")
appendLine("Average per inference: ${processingTime / batchSize}ms")
appendLine("Throughput: ${DecimalFormat("#.##").format(throughput)} inferences/sec")
appendLine("Average output magnitude: ${DecimalFormat("#.####").format(avgMagnitude)}")
appendLine()
appendLine("Sample outputs (first 3):")
outputs.take(3).forEachIndexed { index, output ->
appendLine("Output $index: ${formatArray(output.take(6))}...")
}
}
resultsText.text = result
} catch (e: Exception) {
resultsText.text = "Error: ${e.message}"
} finally {
setUIProcessing(false)
}
}
}
private fun runBenchmark() {
lifecycleScope.launch {
try {
setUIProcessing(true)
// Generate test inputs
val testInputs = List(50) {
FloatArray(32) { kotlin.random.Random.nextFloat() * 2 - 1 }
}
resultsText.text = "Running benchmark..."
val benchmarkResult = monitor.benchmark(
network = network,
testInputs = testInputs,
warmupRuns = 10,
benchmarkRuns = 100
)
val result = buildString {
appendLine("Benchmark Results:")
appendLine("Average latency: ${DecimalFormat("#.##").format(benchmarkResult.averageLatencyMs)}ms")
appendLine("Throughput: ${DecimalFormat("#.##").format(benchmarkResult.throughputOpsPerSec)} ops/sec")
appendLine("Memory usage: ${DecimalFormat("#.##").format(benchmarkResult.memoryUsageMB)}MB")
appendLine()
appendLine("Device info:")
appendLine("Processor: ${android.os.Build.HARDWARE}")
appendLine("API Level: ${android.os.Build.VERSION.SDK_INT}")
appendLine("Available processors: ${Runtime.getRuntime().availableProcessors()}")
val maxMemory = Runtime.getRuntime().maxMemory() / (1024 * 1024)
appendLine("Max memory: ${maxMemory}MB")
}
resultsText.text = result
} catch (e: Exception) {
resultsText.text = "Benchmark error: ${e.message}"
} finally {
setUIProcessing(false)
}
}
}
private fun setUIProcessing(processing: Boolean) {
progressBar.visibility = if (processing) android.view.View.VISIBLE else android.view.View.GONE
singleInferenceButton.isEnabled = !processing
batchInferenceButton.isEnabled = !processing
benchmarkButton.isEnabled = !processing
}
private fun formatArray(values: List<Float>): String {
return values.joinToString(", ") { DecimalFormat("#.###").format(it) }
}
private fun calculateMagnitude(array: FloatArray): Float {
return kotlin.math.sqrt(array.map { it * it }.sum())
}
override fun onDestroy() {
super.onDestroy()
network.close()
}
}// src/memory.rs - Optimized memory management
use std::alloc::{GlobalAlloc, Layout, System};
use std::sync::atomic::{AtomicUsize, Ordering};
/// Custom allocator with tracking
pub struct TrackingAllocator {
inner: System,
allocated: AtomicUsize,
peak_allocated: AtomicUsize,
}
unsafe impl GlobalAlloc for TrackingAllocator {
unsafe fn alloc(&self, layout: Layout) -> *mut u8 {
let ptr = self.inner.alloc(layout);
if !ptr.is_null() {
let size = layout.size();
let current = self.allocated.fetch_add(size, Ordering::Relaxed) + size;
// Update peak if necessary
let mut peak = self.peak_allocated.load(Ordering::Relaxed);
while current > peak {
match self.peak_allocated.compare_exchange_weak(
peak, current, Ordering::Relaxed, Ordering::Relaxed
) {
Ok(_) => break,
Err(x) => peak = x,
}
}
}
ptr
}
unsafe fn dealloc(&self, ptr: *mut u8, layout: Layout) {
self.inner.dealloc(ptr, layout);
self.allocated.fetch_sub(layout.size(), Ordering::Relaxed);
}
}
impl TrackingAllocator {
pub const fn new() -> Self {
Self {
inner: System,
allocated: AtomicUsize::new(0),
peak_allocated: AtomicUsize::new(0),
}
}
pub fn current_allocated(&self) -> usize {
self.allocated.load(Ordering::Relaxed)
}
pub fn peak_allocated(&self) -> usize {
self.peak_allocated.load(Ordering::Relaxed)
}
pub fn reset_peak(&self) {
self.peak_allocated.store(self.current_allocated(), Ordering::Relaxed);
}
}
#[global_allocator]
static GLOBAL_ALLOCATOR: TrackingAllocator = TrackingAllocator::new();
/// Memory-efficient vector pooling
pub struct VectorPool {
pool: std::sync::Mutex<Vec<Vec<f32>>>,
max_size: usize,
}
impl VectorPool {
pub fn new(max_size: usize) -> Self {
Self {
pool: std::sync::Mutex::new(Vec::new()),
max_size,
}
}
pub fn get(&self) -> Vec<f32> {
let mut pool = self.pool.lock().unwrap();
pool.pop().unwrap_or_else(|| Vec::with_capacity(32))
}
pub fn return_vector(&self, mut vector: Vec<f32>) {
vector.clear();
if vector.capacity() == 32 {
let mut pool = self.pool.lock().unwrap();
if pool.len() < self.max_size {
pool.push(vector);
}
}
}
}
/// Performance monitoring utilities
pub struct PerformanceTracker {
start_time: std::time::Instant,
start_memory: usize,
operation_name: String,
}
impl PerformanceTracker {
pub fn new(operation_name: impl Into<String>) -> Self {
Self {
start_time: std::time::Instant::now(),
start_memory: GLOBAL_ALLOCATOR.current_allocated(),
operation_name: operation_name.into(),
}
}
pub fn finish(self) -> PerformanceMetrics {
let duration = self.start_time.elapsed();
let memory_used = GLOBAL_ALLOCATOR.current_allocated().saturating_sub(self.start_memory);
let peak_memory = GLOBAL_ALLOCATOR.peak_allocated();
PerformanceMetrics {
operation: self.operation_name,
duration,
memory_used,
peak_memory,
}
}
}
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
pub operation: String,
pub duration: std::time::Duration,
pub memory_used: usize,
pub peak_memory: usize,
}
impl PerformanceMetrics {
pub fn print(&self) {
println!("Performance Metrics for '{}':", self.operation);
println!(" Duration: {:.2}ms", self.duration.as_secs_f64() * 1000.0);
println!(" Memory used: {:.2}KB", self.memory_used as f64 / 1024.0);
println!(" Peak memory: {:.2}KB", self.peak_memory as f64 / 1024.0);
}
}// src/simd_optimization.rs
#[cfg(target_arch = "x86_64")]
use std::arch::x86_64::*;
#[cfg(target_arch = "wasm32")]
use std::arch::wasm32::*;
/// SIMD-optimized operations for different architectures
pub trait SimdOps {
fn simd_dot_product(a: &[f32], b: &[f32]) -> f32;
fn simd_normalize(data: &mut [f32]);
fn simd_scale(data: &mut [f32], scale: f32);
}
#[cfg(target_arch = "x86_64")]
pub struct X86SimdOps;
#[cfg(target_arch = "x86_64")]
impl SimdOps for X86SimdOps {
fn simd_dot_product(a: &[f32], b: &[f32]) -> f32 {
assert_eq!(a.len(), b.len());
assert_eq!(a.len() % 8, 0);
unsafe {
let mut sum = _mm256_setzero_ps();
for i in (0..a.len()).step_by(8) {
let va = _mm256_loadu_ps(a.as_ptr().add(i));
let vb = _mm256_loadu_ps(b.as_ptr().add(i));
let prod = _mm256_mul_ps(va, vb);
sum = _mm256_add_ps(sum, prod);
}
// Horizontal sum
let sum_high = _mm256_extractf128_ps(sum, 1);
let sum_low = _mm256_castps256_ps128(sum);
let sum_128 = _mm_add_ps(sum_high, sum_low);
let sum_64 = _mm_add_ps(sum_128, _mm_movehl_ps(sum_128, sum_128));
let sum_32 = _mm_add_ss(sum_64, _mm_shuffle_ps(sum_64, sum_64, 1));
_mm_cvtss_f32(sum_32)
}
}
fn simd_normalize(data: &mut [f32]) {
let magnitude = Self::simd_dot_product(data, data).sqrt();
if magnitude > 1e-8 {
Self::simd_scale(data, 1.0 / magnitude);
}
}
fn simd_scale(data: &mut [f32], scale: f32) {
assert_eq!(data.len() % 8, 0);
unsafe {
let scale_vec = _mm256_set1_ps(scale);
for i in (0..data.len()).step_by(8) {
let vec = _mm256_loadu_ps(data.as_ptr().add(i));
let scaled = _mm256_mul_ps(vec, scale_vec);
_mm256_storeu_ps(data.as_mut_ptr().add(i), scaled);
}
}
}
}
#[cfg(target_arch = "wasm32")]
pub struct WasmSimdOps;
#[cfg(target_arch = "wasm32")]
impl SimdOps for WasmSimdOps {
fn simd_dot_product(a: &[f32], b: &[f32]) -> f32 {
assert_eq!(a.len(), b.len());
assert_eq!(a.len() % 4, 0);
unsafe {
let mut sum = f32x4_splat(0.0);
for i in (0..a.len()).step_by(4) {
let va = v128_load(a.as_ptr().add(i) as *const v128);
let vb = v128_load(b.as_ptr().add(i) as *const v128);
let prod = f32x4_mul(va, vb);
sum = f32x4_add(sum, prod);
}
// Extract and sum all lanes
f32x4_extract_lane::<0>(sum) +
f32x4_extract_lane::<1>(sum) +
f32x4_extract_lane::<2>(sum) +
f32x4_extract_lane::<3>(sum)
}
}
fn simd_normalize(data: &mut [f32]) {
let magnitude = Self::simd_dot_product(data, data).sqrt();
if magnitude > 1e-8 {
Self::simd_scale(data, 1.0 / magnitude);
}
}
fn simd_scale(data: &mut [f32], scale: f32) {
assert_eq!(data.len() % 4, 0);
unsafe {
let scale_vec = f32x4_splat(scale);
for i in (0..data.len()).step_by(4) {
let vec = v128_load(data.as_ptr().add(i) as *const v128);
let scaled = f32x4_mul(vec, scale_vec);
v128_store(data.as_mut_ptr().add(i) as *mut v128, scaled);
}
}
}
}
/// Fallback scalar implementation
pub struct ScalarOps;
impl SimdOps for ScalarOps {
fn simd_dot_product(a: &[f32], b: &[f32]) -> f32 {
a.iter().zip(b.iter()).map(|(x, y)| x * y).sum()
}
fn simd_normalize(data: &mut [f32]) {
let magnitude: f32 = data.iter().map(|x| x * x).sum::<f32>().sqrt();
if magnitude > 1e-8 {
for x in data {
*x /= magnitude;
}
}
}
fn simd_scale(data: &mut [f32], scale: f32) {
for x in data {
*x *= scale;
}
}
}
/// Dynamic SIMD dispatch
pub fn get_simd_ops() -> Box<dyn SimdOps> {
#[cfg(target_arch = "x86_64")]
{
if is_x86_feature_detected!("avx2") {
Box::new(X86SimdOps)
} else {
Box::new(ScalarOps)
}
}
#[cfg(target_arch = "wasm32")]
{
Box::new(WasmSimdOps)
}
#[cfg(not(any(target_arch = "x86_64", target_arch = "wasm32")))]
{
Box::new(ScalarOps)
}
}// src/error_handling.rs
use std::fmt;
/// Comprehensive error handling for integrations
#[derive(Debug, Clone)]
pub enum IntegrationError {
InvalidInput {
expected: String,
received: String
},
ProcessingFailed {
operation: String,
cause: String
},
MemoryError {
requested: usize,
available: usize
},
ConfigurationError {
parameter: String,
value: String,
valid_range: String
},
PlatformNotSupported {
platform: String,
required_features: Vec<String>
},
VersionMismatch {
expected: String,
found: String
},
}
impl fmt::Display for IntegrationError {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
match self {
IntegrationError::InvalidInput { expected, received } => {
write!(f, "Invalid input: expected {}, received {}", expected, received)
}
IntegrationError::ProcessingFailed { operation, cause } => {
write!(f, "Processing failed in operation '{}': {}", operation, cause)
}
IntegrationError::MemoryError { requested, available } => {
write!(f, "Memory error: requested {} bytes, only {} available", requested, available)
}
IntegrationError::ConfigurationError { parameter, value, valid_range } => {
write!(f, "Configuration error: parameter '{}' has invalid value '{}', valid range: {}",
parameter, value, valid_range)
}
IntegrationError::PlatformNotSupported { platform, required_features } => {
write!(f, "Platform '{}' not supported, requires features: {:?}",
platform, required_features)
}
IntegrationError::VersionMismatch { expected, found } => {
write!(f, "Version mismatch: expected {}, found {}", expected, found)
}
}
}
}
impl std::error::Error for IntegrationError {}
/// Result type for integration operations
pub type IntegrationResult<T> = Result<T, IntegrationError>;
/// Error handling utilities
pub struct ErrorHandler;
impl ErrorHandler {
pub fn validate_vector_dimensions(data: &[f32], expected: usize) -> IntegrationResult<()> {
if data.len() != expected {
Err(IntegrationError::InvalidInput {
expected: format!("{} dimensions", expected),
received: format!("{} dimensions", data.len()),
})
} else {
Ok(())
}
}
pub fn check_memory_availability(requested: usize) -> IntegrationResult<()> {
// Simplified memory check
let available = 1024 * 1024 * 1024; // 1GB
if requested > available {
Err(IntegrationError::MemoryError { requested, available })
} else {
Ok(())
}
}
pub fn validate_configuration<T: PartialOrd + fmt::Display + Copy>(
parameter: &str,
value: T,
min: T,
max: T,
) -> IntegrationResult<()> {
if value < min || value > max {
Err(IntegrationError::ConfigurationError {
parameter: parameter.to_string(),
value: value.to_string(),
valid_range: format!("[{}, {}]", min, max),
})
} else {
Ok(())
}
}
pub fn check_platform_support() -> IntegrationResult<()> {
#[cfg(not(any(
target_arch = "x86_64",
target_arch = "aarch64",
target_arch = "wasm32"
)))]
{
return Err(IntegrationError::PlatformNotSupported {
platform: std::env::consts::ARCH.to_string(),
required_features: vec!["x86_64".to_string(), "aarch64".to_string(), "wasm32".to_string()],
});
}
Ok(())
}
}// src/integration_tests.rs
#[cfg(test)]
mod tests {
use super::*;
use semantic_cartan_matrix::prelude::*;
/// Comprehensive integration test suite
#[test]
fn test_python_integration() {
// Test PyO3 bindings
let vector = RootVector::zero();
assert_eq!(vector.data.len(), 32);
let mut test_data = [0.0f32; 32];
test_data[0] = 1.0;
let mut vector = RootVector::zero();
vector.data.copy_from_slice(&test_data);
assert_eq!(vector.magnitude(), 1.0);
}
#[test]
fn test_javascript_integration() {
// Test WASM compatibility
let vector = RootVector::zero();
let serialized = serde_json::to_string(&vector.data).unwrap();
let deserialized: [f32; 32] = serde_json::from_str(&serialized).unwrap();
assert_eq!(vector.data, deserialized);
}
#[test]
fn test_c_ffi_compatibility() {
// Test C FFI safety
let vector = RootVector::zero();
let ptr = &vector as *const RootVector;
unsafe {
let magnitude = (*ptr).magnitude();
assert_eq!(magnitude, 0.0);
}
}
#[test]
fn test_memory_management() {
use crate::memory::GLOBAL_ALLOCATOR;
GLOBAL_ALLOCATOR.reset_peak();
let initial_memory = GLOBAL_ALLOCATOR.current_allocated();
{
let _vectors: Vec<RootVector> = (0..1000)
.map(|_| RootVector::zero())
.collect();
}
let final_memory = GLOBAL_ALLOCATOR.current_allocated();
assert_eq!(initial_memory, final_memory); // All memory should be freed
}
#[test]
fn test_simd_performance() {
use crate::simd_optimization::*;
let ops = get_simd_ops();
let a = vec![1.0f32; 32];
let b = vec![2.0f32; 32];
let result = ops.simd_dot_product(&a, &b);
assert_eq!(result, 64.0); // 32 * 1.0 * 2.0
}
#[test]
fn test_cross_platform_compatibility() {
// Ensure consistent behavior across platforms
let mut vector = RootVector::zero();
vector.data[0] = 3.0;
vector.data[1] = 4.0;
vector.normalize();
let expected_magnitude = 1.0;
let actual_magnitude = vector.magnitude();
assert!((actual_magnitude - expected_magnitude).abs() < 1e-6);
}
#[test]
fn test_error_handling() {
use crate::error_handling::*;
// Test invalid dimensions
let result = ErrorHandler::validate_vector_dimensions(&[1.0, 2.0], 32);
assert!(result.is_err());
// Test valid dimensions
let data = vec![0.0f32; 32];
let result = ErrorHandler::validate_vector_dimensions(&data, 32);
assert!(result.is_ok());
}
#[test]
fn test_performance_tracking() {
use crate::memory::PerformanceTracker;
let tracker = PerformanceTracker::new("test_operation");
// Simulate work
std::thread::sleep(std::time::Duration::from_millis(10));
let metrics = tracker.finish();
assert!(metrics.duration.as_millis() >= 10);
}
}
/// Benchmark suite for integration performance
#[cfg(test)]
mod benchmarks {
use super::*;
use criterion::{black_box, criterion_group, criterion_main, Criterion};
fn benchmark_vector_operations(c: &mut Criterion) {
let mut group = c.benchmark_group("vector_operations");
let vector_a = RootVector::zero();
let vector_b = RootVector::zero();
group.bench_function("dot_product", |b| {
b.iter(|| black_box(vector_a.dot(&vector_b)))
});
let mut vector = RootVector::zero();
vector.data[0] = 1.0;
group.bench_function("normalize", |b| {
b.iter(|| {
let mut v = vector.clone();
v.normalize()
})
});
group.finish();
}
fn benchmark_attention_mechanism(c: &mut Criterion) {
let mut group = c.benchmark_group("attention");
let cartan = CartanMatrix::identity();
let mut attention = CartanAttention::new(cartan).unwrap();
let vectors = vec![RootVector::zero(); 10];
group.bench_function("apply_attention", |b| {
b.iter(|| black_box(attention.apply_attention(&vectors).unwrap()))
});
group.finish();
}
criterion_group!(benches, benchmark_vector_operations, benchmark_attention_mechanism);
criterion_main!(benches);
}This comprehensive integration guide covers all major platforms and languages for the ruv-FANN neural network framework. Each integration approach is designed to leverage the framework's high-performance Rust core while providing idiomatic interfaces for different environments.
Key takeaways:
- Python Integration: Use PyO3 for seamless numpy compatibility and scientific computing workflows
- JavaScript/WASM: Leverage WebAssembly for high-performance browser-based neural networks
- C/C++ FFI: Enable integration with existing native codebases through safe FFI bindings
- Cloud Platforms: Deploy scalable neural network services on AWS, GCP, and Azure
- Mobile Platforms: Bring neural networks to iOS and Android with native performance
- Performance: Utilize SIMD optimizations and memory management for maximum efficiency
All integration approaches maintain the safety guarantees of Rust while providing familiar interfaces for each target environment.