automl 0.3.0

Automated machine learning for classification and regression
Documentation 
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use smartcore::linalg::basic::matrix::DenseMatrix;

/// Return regression data for tests and examples.
///
/// # Returns
///
/// * `(x, y)` - Feature matrix and target vector.
pub fn regression_testing_data() -> (DenseMatrix<f64>, Vec<f64>) {
    let x = DenseMatrix::from_2d_array(&[
        &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
        &[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
        &[258.054, 368.2, 161.6, 109.773, 1949., 60.171],
        &[284.599, 335.1, 165.0, 110.929, 1950., 61.187],
        &[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
        &[346.999, 193.2, 359.4, 113.270, 1952., 63.639],
        &[365.385, 187.0, 354.7, 115.094, 1953., 64.989],
        &[363.112, 357.8, 335.0, 116.219, 1954., 63.761],
        &[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
        &[419.180, 282.2, 285.7, 118.734, 1956., 67.857],
        &[442.769, 293.6, 279.8, 120.445, 1957., 68.169],
        &[444.546, 468.1, 263.7, 121.950, 1958., 66.513],
        &[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
        &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
        &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
        &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
    ])
    .unwrap();

    let y = vec![
        83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2,
        115.7, 116.9,
    ];

    (x, y)
}