use sklears::linear_model::LinearRegression;

// Model starts in Untrained state
let model = LinearRegression::new()
    .fit_intercept(true)
    .regularization(0.1);

// ❌ This won't compile - can't predict with untrained model
// let predictions = model.predict(&x);

// ✅ After fitting, model transitions to Trained state
let trained_model = model.fit(&x_train, &y_train)?;
let predictions = trained_model.predict(&x_test)?;

use sklears::prelude::*;

fn evaluate_model<M>(model: M, x: &Array2<f64>, y: &Array1<f64>) -> Result<f64>
where
    M: Predict<Array2<f64>, Array1<f64>> + Score<Array2<f64>, Array1<f64>>,
{
    model.score(x, y)  // Monomorphized at compile time
}

{
    let large_model = train_neural_network(&training_data)?;
    // Use model...
} // Model automatically freed here, no GC needed

use sklears::prelude::*;

fn train_pipeline() -> Result<Pipeline, SklearsError> {
    let scaler = StandardScaler::new()
        .fit(&x_train)
        .context("Failed to fit scaler")?;
    
    let model = LinearRegression::new()
        .fit(&scaled_x, &y_train)
        .context("Failed to train model")?;
    
    Ok(Pipeline::new()
        .add_step("scaler", scaler)
        .add_step("model", model))
}

use sklears::ensemble::RandomForestClassifier;

// Automatically uses all CPU cores safely
let model = RandomForestClassifier::new()
    .n_estimators(1000)
    .n_jobs(-1)  // Parallel tree construction
    .fit(&x_train, &y_train)?;

// Automatically vectorized operations
let scaled = StandardScaler::new()
    .fit(&data)?
    .transform(&data)?;  // Uses SIMD when available

use sklears::prelude::*;
use sklears::linear_model::LinearRegression;
use sklears::model_selection::train_test_split;

fn main() -> Result<()> {
    // Load or generate data
    let dataset = sklears::dataset::make_regression(100, 10, 0.1)?;
    
    // Split into train/test sets
    let (x_train, x_test, y_train, y_test) = 
        train_test_split(&dataset.data, &dataset.target, 0.2, Some(42))?;
    
    // Create and train model
    let model = LinearRegression::new()
        .fit_intercept(true)
        .fit(&x_train, &y_train)?;
    
    // Make predictions
    let predictions = model.predict(&x_test)?;
    
    // Evaluate
    let r2_score = model.score(&x_test, &y_test)?;
    println!("R² score: {:.4}", r2_score);
    
    Ok(())
}

use sklears::prelude::*;
use sklears::pipeline::Pipeline;
use sklears::preprocessing::{StandardScaler, PolynomialFeatures};
use sklears::linear_model::Ridge;
use sklears::model_selection::{GridSearchCV, KFold};

fn main() -> Result<()> {
    // Create a pipeline
    let pipeline = Pipeline::new()
        .add_step("poly", PolynomialFeatures::new().degree(2))
        .add_step("scaler", StandardScaler::new())
        .add_step("ridge", Ridge::new());
    
    // Define parameter grid
    let param_grid = vec![
        ("ridge__alpha", vec![0.1, 1.0, 10.0]),
        ("poly__degree", vec![1, 2, 3]),
    ];
    
    // Grid search with cross-validation
    let grid_search = GridSearchCV::new(pipeline)
        .param_grid(param_grid)
        .cv(KFold::new(5))
        .scoring("r2")
        .n_jobs(-1);  // Use all CPU cores
    
    // Fit and find best parameters
    let best_model = grid_search.fit(&x_train, &y_train)?;
    println!("Best parameters: {:?}", best_model.best_params());
    println!("Best score: {:.4}", best_model.best_score());
    
    Ok(())
}

// Linear Algebra (via scirs2::linalg)
- Matrix decompositions (SVD, QR, Cholesky)
- Eigenvalue problems
- Linear solvers
- BLAS/LAPACK bindings

// Optimization (via scirs2::optimize)
- Gradient descent variants
- L-BFGS and Newton methods
- Constrained optimization
- Global optimization

// Statistics (via scirs2::stats)
- Probability distributions
- Statistical tests
- Correlation analysis
- Random sampling

// Neural Networks (via scirs2::neural)
- Activation functions
- Automatic differentiation
- Layer abstractions
- Optimizers (SGD, Adam)

// Signal Processing (via scirs2::signal)
- FFT and spectral analysis
- Digital filters
- Wavelet transforms

// Models have compile-time state tracking
let untrained = LinearRegression::new();
// untrained.predict(&x);  // ❌ Compile error!

let trained = untrained.fit(&x, &y)?;
let predictions = trained.predict(&x_test)?;  // ✅ Works!

// Rust (sklears)
use sklears::prelude::*;
use sklears::ensemble::RandomForestClassifier;
use sklears::preprocessing::StandardScaler;
use sklears::pipeline::Pipeline;

let pipeline = Pipeline::new()
    .add_step("scaler", StandardScaler::new())
    .add_step("rf", RandomForestClassifier::new().n_estimators(100));

let fitted = pipeline.fit(&x_train, &y_train)?;
let predictions = fitted.predict(&x_test)?;

// Errors are handled explicitly and checked at compile time
match model.fit(&x, &y) {
    Ok(trained_model) => {
        let predictions = trained_model.predict(&x_test)?;
        // Handle success
    }
    Err(e) => {
        eprintln!("Training failed: {}", e);
        // Handle error with full context
    }
}

// Deterministic memory management, zero overhead
{
    let large_dataset = load_massive_dataset()?;
    let model = train_model(&large_dataset)?;
    // Memory freed immediately when variables go out of scope
}

// Shape mismatches caught at compile time
let x = Array2::random((100, 10), Uniform::new(0., 1.));
let y = Array1::random(50, Uniform::new(0., 1.));  // Wrong size!
// model.fit(&x, &y)?;  // ❌ Won't compile!

// True parallelism with compile-time safety guarantees
use rayon::prelude::*;

let results: Vec<_> = folds
    .par_iter()  // Parallel iterator
    .map(|fold| train_fold(fold))  // No data races possible
    .collect();

use sklears::prelude::*;
use std::marker::PhantomData;

#[derive(Debug, Clone)]
pub struct MyEstimatorConfig {
    pub learning_rate: f64,
    pub max_iter: usize,
}

pub struct MyEstimator<State = Untrained> {
    config: MyEstimatorConfig,
    state: PhantomData<State>,
    // Fitted parameters (only available after training)
    weights_: Option<Array1<f64>>,
}

impl MyEstimator<Untrained> {
    pub fn new() -> Self {
        Self {
            config: MyEstimatorConfig {
                learning_rate: 0.01,
                max_iter: 1000,
            },
            state: PhantomData,
            weights_: None,
        }
    }
    
    // Builder pattern methods
    pub fn learning_rate(mut self, lr: f64) -> Self {
        self.config.learning_rate = lr;
        self
    }
}

impl Estimator for MyEstimator<Untrained> {
    type Config = MyEstimatorConfig;
    type Error = SklearsError;
}

impl Fit<Array2<f64>, Array1<f64>> for MyEstimator<Untrained> {
    type Fitted = MyEstimator<Trained>;
    
    fn fit(self, x: &Array2<f64>, y: &Array1<f64>) -> Result<Self::Fitted> {
        // Validation with comprehensive error context
        validate::check_consistent_length(x, y)
            .context("Input validation failed")?;
        
        // Training algorithm with RAII cleanup
        let weights = self.train_algorithm(x, y)?;
        
        Ok(MyEstimator {
            config: self.config,
            state: PhantomData,
            weights_: Some(weights),
        })
    }
}

// Only trained models can predict (compile-time safety)
impl Predict<Array2<f64>, Array1<f64>> for MyEstimator<Trained> {
    fn predict(&self, x: &Array2<f64>) -> Result<Array1<f64>> {
        let weights = self.weights_.as_ref().expect("Model is trained");
        Ok(x.dot(weights))
    }
}

use sklears::prelude::*;

// Process data without unnecessary copies
fn efficient_pipeline(data: &ArrayView2<f64>) -> Result<Array1<f64>> {
    let scaled_view = StandardScaler::new()
        .fit(data)?
        .transform_view(data)?;  // Zero-copy transformation
    
    let model = LinearRegression::new()
        .fit(&scaled_view, &targets)?;
    
    model.predict(&scaled_view)
}

use sklears::prelude::*;
use tokio::fs;

async fn train_async_pipeline() -> Result<Pipeline> {
    // Async data loading
    let data = fs::read("large_dataset.parquet").await?;
    let dataset = parse_parquet(&data)?;
    
    // Non-blocking training with progress updates
    let model = LinearRegression::new()
        .fit_async(&dataset.features, &dataset.targets)
        .with_progress_callback(|progress| {
            println!("Training progress: {:.1}%", progress * 100.0);
        })
        .await?;
    
    Ok(Pipeline::new().add_step("model", model))
}

use sklears::prelude::*;
use sklears::memory::{ArenaAllocator, PoolAllocator};

// Use custom allocator for performance-critical code
fn high_performance_training() -> Result<RandomForest> {
    let arena = ArenaAllocator::new(1024 * 1024 * 1024); // 1GB arena
    
    let model = RandomForestClassifier::new()
        .with_allocator(arena)
        .n_estimators(1000)
        .fit(&x_train, &y_train)?;
    
    Ok(model)
}

use sklears::prelude::*;
use rayon::{ThreadPoolBuilder, ThreadPool};

// Configure custom thread pool for ML workloads
fn configure_parallel_training() -> Result<()> {
    let pool = ThreadPoolBuilder::new()
        .num_threads(16)
        .stack_size(8 * 1024 * 1024)  // 8MB stack for deep recursion
        .thread_name(|i| format!("ml-worker-{}", i))
        .build()?;
    
    pool.install(|| {
        let model = RandomForestRegressor::new()
            .n_estimators(1000)
            .max_depth(20)
            .n_jobs(-1)  // Use all threads in this pool
            .fit(&x_train, &y_train)
    })?
}

use sklears::prelude::*;
use std::simd::{f64x4, SimdFloat};

// Leverage SIMD for custom operations
fn simd_feature_engineering(data: &mut Array2<f64>) {
    // Automatically vectorized operations
    data.par_mapv_inplace(|x| x.sqrt() + x.ln());
    
    // Manual SIMD for maximum performance
    let chunks = data.as_slice_mut().unwrap().chunks_exact_mut(4);
    for chunk in chunks {
        let simd_vec = f64x4::from_slice(chunk);
        let result = simd_vec.sqrt() + simd_vec.ln();
        result.copy_to_slice(chunk);
    }
}

#![no_std]
#![no_main]

use sklears_core::prelude::*;
use heapless::Vec; // Stack-allocated vectors

// Deploy ML models on microcontrollers
fn embedded_inference(features: &[f32; 10]) -> f32 {
    // Pre-trained model weights stored in flash
    const WEIGHTS: [f32; 10] = [0.1, 0.2, /* ... */];
    const BIAS: f32 = 0.5;
    
    // Simple linear model inference
    let mut result = BIAS;
    for (i, &feature) in features.iter().enumerate() {
        result += feature * WEIGHTS[i];
    }
    
    result
}

use sklears::prelude::*;
use sklears::backends::CudaBackend;

let model = MLPRegressor::new()
    .hidden_layers(&[512, 256, 128])
    .backend(CudaBackend::new()?)
    .batch_size(1024)
    .mixed_precision(true)  // FP16 training
    .fit(&x, &y)?;