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//! Quick start example for sklears
//!
//! This example demonstrates basic usage of linear regression
//! and clustering algorithms.
use sklears::dataset::{make_blobs, make_regression};
use sklears::prelude::*;
fn main() -> Result<()> {
println!("sklears Quick Start Example\n");
// Example 1: Linear Regression
println!("=== Linear Regression Example ===");
linear_regression_example()?;
// Example 2: Clustering
println!("\n=== K-Means Clustering Example ===");
clustering_example()?;
Ok(())
}
fn linear_regression_example() -> Result<()> {
// Generate synthetic regression data
let dataset = make_regression(100, 5, 0.1)?;
let (x_train, _y_train) = (&dataset.data, &dataset.target);
// This would work once LinearRegression is implemented:
/*
let model = LinearRegression::new()
.fit_intercept(true)
.fit(x_train, y_train)?;
// Make predictions
let predictions = model.predict(x_train)?;
// Calculate R² score
let score = model.score(x_train, y_train)?;
println!("R² score: {:.4}", score);
*/
println!(
"Generated regression dataset with {} samples and {} features",
x_train.nrows(),
x_train.ncols()
);
Ok(())
}
fn clustering_example() -> Result<()> {
// Generate synthetic clustered data
let dataset = make_blobs(150, 2, 3, 0.5)?;
let x = &dataset.data;
// This would work once KMeans is implemented:
/*
let kmeans = KMeans::new(3)
.max_iter(300)
.random_state(42);
let fitted = kmeans.fit(x)?;
let labels = fitted.labels();
let centers = fitted.cluster_centers();
println!("Found {} clusters", centers.nrows());
println!("Inertia: {:.4}", fitted.inertia());
// Predict cluster for new points
let new_points = array![[0.0, 0.0], [5.0, 5.0]];
let predictions = fitted.predict(&new_points)?;
*/
println!(
"Generated blob dataset with {} samples and {} features",
x.nrows(),
x.ncols()
);
Ok(())
}