use rustlearn::prelude::*;

use rustlearn::prelude::*;

use rustlearn::linear_models::sgdclassifier::Hyperparameters;
// more imports

use rustlearn::prelude::*;
use rustlearn::datasets::iris;
use rustlearn::cross_validation::CrossValidation;
use rustlearn::linear_models::sgdclassifier::Hyperparameters;
use rustlearn::metrics::accuracy_score;


let (X, y) = iris::load_data();

let num_splits = 10;
let num_epochs = 5;

let mut accuracy = 0.0;

for (train_idx, test_idx) in CrossValidation::new(X.rows(), num_splits) {

    let X_train = X.get_rows(&train_idx);
    let y_train = y.get_rows(&train_idx);
    let X_test = X.get_rows(&test_idx);
    let y_test = y.get_rows(&test_idx);

    let mut model = Hyperparameters::new(X.cols())
                                    .learning_rate(0.5)
                                    .l2_penalty(0.0)
                                    .l1_penalty(0.0)
                                    .one_vs_rest();

    for _ in 0..num_epochs {
        model.fit(&X_train, &y_train).unwrap();
    }

    let prediction = model.predict(&X_test).unwrap();
    accuracy += accuracy_score(&y_test, &prediction);
}

accuracy /= num_splits as f32;

use rustlearn::prelude::*;

use rustlearn::ensemble::random_forest::Hyperparameters;
use rustlearn::datasets::iris;
use rustlearn::trees::decision_tree;

let (data, target) = iris::load_data();

let mut tree_params = decision_tree::Hyperparameters::new(data.cols());
tree_params.min_samples_split(10)
    .max_features(4);

let mut model = Hyperparameters::new(tree_params, 10)
    .one_vs_rest();

model.fit(&data, &target).unwrap();

// Optionally serialize and deserialize the model

// let encoded = bincode::serialize(&model).unwrap();
// let decoded: OneVsRestWrapper<RandomForest> = bincode::deserialize(&encoded).unwrap();

let prediction = model.predict(&data).unwrap();