ruthril 0.1.2

A powerful AI/ML framework is under development
Documentation 
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/// ONNX Runtime adapter for Ruthril Framework
/// Provides statistical analysis of model performance
use ruthril::core::statistics::Statistics;

pub struct OnnxRuntime {
    model_path: String,
    session: Option<String>, // Placeholder for actual ONNX session
}

impl OnnxRuntime {
    /// Create new ONNX runtime instance
    pub fn new(model_path: &str) -> Self {
        Self {
            model_path: model_path.to_string(),
            session: None,
        }
    }
    
    /// Load ONNX model
    pub fn load_model(&mut self) -> Result<(), Box<dyn std::error::Error>> {
        println!("Loading ONNX model from: {}", self.model_path);
        // TODO: Implement actual ONNX loading
        self.session = Some("mock_session".to_string());
        Ok(())
    }
    
    /// Run inference with statistical validation
    pub fn run_inference(&self, input_data: &[f64]) -> Result<Vec<f64>, Box<dyn std::error::Error>> {
        if self.session.is_none() {
            return Err("Model not loaded".into());
        }
        
        // Validate input data using statistics
        if let Some(mean) = Statistics::mean(input_data) {
            println!("Input data mean: {}", mean);
        }
        
        // Mock inference - replace with actual ONNX inference
        let output: Vec<f64> = input_data.iter().map(|x| x * 2.0).collect();
        
        // Validate output using statistics
        if let Some(std) = Statistics::std_deviation(&output) {
            println!("Output standard deviation: {}", std);
        }
        
        Ok(output)
    }
    
    /// Benchmark model performance using Ruthril's statistics
    pub fn benchmark(&self, test_data: &[Vec<f64>]) -> Result<BenchmarkResults, Box<dyn std::error::Error>> {
        let mut inference_times = Vec::new();
        let mut all_outputs = Vec::new();
        
        for input in test_data {
            let start = std::time::Instant::now();
            let output = self.run_inference(input)?;
            let duration = start.elapsed().as_millis() as f64;
            
            inference_times.push(duration);
            all_outputs.extend(output);
        }
        
        Ok(BenchmarkResults {
            mean_inference_time: Statistics::mean(&inference_times).unwrap_or(0.0),
            inference_time_std: Statistics::std_deviation(&inference_times).unwrap_or(0.0),
            output_mean: Statistics::mean(&all_outputs).unwrap_or(0.0),
            output_std: Statistics::std_deviation(&all_outputs).unwrap_or(0.0),
        })
    }
}

/// Benchmark results using Ruthril's statistical analysis
#[derive(Debug)]
pub struct BenchmarkResults {
    pub mean_inference_time: f64,
    pub inference_time_std: f64,
    pub output_mean: f64,
    pub output_std: f64,
}