datarust 0.6.1

//! Diabetes regression dataset.
//!
//! 442 samples, 10 features (age, sex, BMI, blood pressure, 6 blood serum
//! measurements), and a quantitative measure of disease progression after one
//! year (the regression target). Features have been mean-centered and scaled
//! to unit sum of squares (as in sklearn).
//!
//! ```rust
//! use datarust::datasets::diabetes;
//!
//! let data = diabetes::load();
//! assert_eq!(data.n_samples(), 442);
//! assert_eq!(data.n_features(), 10);
//! ```

use super::Dataset;

const FEATURE_NAMES: [&str; 10] = [
    "age", "sex", "bmi", "bp", "s1", "s2", "s3", "s4", "s5", "s6",
];

const TARGET_NAMES: [&str; 0] = [];

const DIABETES_DATA: [[f64; 10]; 442] = [
    [59.0, 2.0, 32.1, 101.0, 157.0, 93.2, 38.0, 4.0, 4.8598, 87.0],
    [48.0, 1.0, 21.6, 87.0, 183.0, 103.2, 70.0, 3.0, 3.8918, 69.0],
    [72.0, 2.0, 30.5, 93.0, 156.0, 93.6, 41.0, 4.0, 4.6728, 85.0],
    [24.0, 1.0, 25.3, 84.0, 198.0, 131.4, 40.0, 5.0, 4.8903, 89.0],
    [
        50.0, 1.0, 23.0, 101.0, 192.0, 125.4, 52.0, 4.0, 4.2905, 80.0,
    ],
    [23.0, 1.0, 22.6, 89.0, 139.0, 64.8, 61.0, 2.0, 4.1897, 68.0],
    [36.0, 2.0, 22.0, 90.0, 160.0, 99.6, 50.0, 3.0, 3.9512, 82.0],
    [
        66.0, 2.0, 26.2, 114.0, 255.0, 185.0, 56.0, 4.55, 4.2485, 92.0,
    ],
    [60.0, 2.0, 32.1, 83.0, 179.0, 119.4, 42.0, 4.0, 4.4773, 94.0],
    [29.0, 1.0, 30.0, 85.0, 180.0, 93.4, 43.0, 4.0, 5.3845, 88.0],
    [22.0, 1.0, 18.6, 97.0, 114.0, 57.6, 46.0, 2.0, 3.9512, 83.0],
    [56.0, 2.0, 28.0, 85.0, 184.0, 144.8, 32.0, 6.0, 3.5835, 77.0],
    [53.0, 1.0, 23.7, 92.0, 186.0, 109.2, 62.0, 3.0, 4.3041, 81.0],
    [50.0, 2.0, 26.2, 97.0, 186.0, 105.4, 49.0, 4.0, 5.0626, 88.0],
    [61.0, 1.0, 24.0, 91.0, 202.0, 115.4, 72.0, 3.0, 4.2905, 73.0],
    [34.0, 2.0, 24.7, 118.0, 254.0, 184.2, 39.0, 7.0, 5.037, 81.0],
    [
        47.0, 1.0, 30.3, 109.0, 207.0, 100.2, 70.0, 3.0, 5.2149, 98.0,
    ],
    [
        68.0, 2.0, 27.5, 111.0, 214.0, 147.0, 39.0, 5.0, 4.9416, 91.0,
    ],
    [38.0, 1.0, 25.4, 84.0, 162.0, 103.0, 42.0, 4.0, 4.4427, 87.0],
    [41.0, 1.0, 24.7, 83.0, 187.0, 108.2, 60.0, 3.0, 4.5433, 78.0],
    [35.0, 1.0, 21.1, 82.0, 156.0, 87.8, 50.0, 3.0, 4.5109, 95.0],
    [25.0, 2.0, 24.3, 95.0, 162.0, 98.6, 54.0, 3.0, 3.8501, 87.0],
    [25.0, 1.0, 26.0, 92.0, 187.0, 120.4, 56.0, 3.0, 3.9703, 88.0],
    [
        61.0, 2.0, 32.0, 103.67, 210.0, 85.2, 35.0, 6.0, 6.107, 124.0,
    ],
    [31.0, 1.0, 29.7, 88.0, 167.0, 103.4, 48.0, 4.0, 4.3567, 78.0],
    [30.0, 2.0, 25.2, 83.0, 178.0, 118.4, 34.0, 5.0, 4.852, 83.0],
    [19.0, 1.0, 19.2, 87.0, 124.0, 54.0, 57.0, 2.0, 4.1744, 90.0],
    [42.0, 1.0, 31.9, 83.0, 158.0, 87.6, 53.0, 3.0, 4.4659, 101.0],
    [63.0, 1.0, 24.4, 73.0, 160.0, 91.4, 48.0, 3.0, 4.6347, 78.0],
    [
        67.0, 2.0, 25.8, 113.0, 158.0, 54.2, 64.0, 2.0, 5.2933, 104.0,
    ],
    [32.0, 1.0, 30.5, 89.0, 182.0, 110.6, 56.0, 3.0, 4.3438, 89.0],
    [42.0, 1.0, 20.3, 71.0, 161.0, 81.2, 66.0, 2.0, 4.2341, 81.0],
    [
        58.0, 2.0, 38.0, 103.0, 150.0, 107.2, 22.0, 7.0, 4.6444, 98.0,
    ],
    [57.0, 1.0, 21.7, 94.0, 157.0, 58.0, 82.0, 2.0, 4.4427, 92.0],
    [53.0, 1.0, 20.5, 78.0, 147.0, 84.2, 52.0, 3.0, 3.989, 75.0],
    [
        62.0, 2.0, 23.5, 80.33, 225.0, 112.8, 86.0, 2.62, 4.8752, 96.0,
    ],
    [52.0, 1.0, 28.5, 110.0, 195.0, 97.2, 60.0, 3.0, 5.2417, 85.0],
    [46.0, 1.0, 27.4, 78.0, 171.0, 88.0, 58.0, 3.0, 4.8283, 90.0],
    [48.0, 2.0, 33.0, 123.0, 253.0, 163.6, 44.0, 6.0, 5.425, 97.0],
    [48.0, 2.0, 27.7, 73.0, 191.0, 119.4, 46.0, 4.0, 4.852, 92.0],
    [
        50.0, 2.0, 25.6, 101.0, 229.0, 162.2, 43.0, 5.0, 4.7791, 114.0,
    ],
    [21.0, 1.0, 20.1, 63.0, 135.0, 69.0, 54.0, 3.0, 4.0943, 89.0],
    [
        32.0, 2.0, 25.4, 90.33, 153.0, 100.4, 34.0, 4.5, 4.5326, 83.0,
    ],
    [
        54.0, 1.0, 24.2, 74.0, 204.0, 109.0, 82.0, 2.0, 4.1744, 109.0,
    ],
    [61.0, 2.0, 32.7, 97.0, 177.0, 118.4, 29.0, 6.0, 4.9972, 87.0],
    [
        56.0, 2.0, 23.1, 104.0, 181.0, 116.4, 47.0, 4.0, 4.4773, 79.0,
    ],
    [33.0, 1.0, 25.3, 85.0, 155.0, 85.0, 51.0, 3.0, 4.5539, 70.0],
    [27.0, 1.0, 19.6, 78.0, 128.0, 68.0, 43.0, 3.0, 4.4427, 71.0],
    [67.0, 2.0, 22.5, 98.0, 191.0, 119.2, 61.0, 3.0, 3.989, 86.0],
    [37.0, 2.0, 27.7, 93.0, 180.0, 119.4, 30.0, 6.0, 5.0304, 88.0],
    [58.0, 1.0, 25.7, 99.0, 157.0, 91.6, 49.0, 3.0, 4.4067, 93.0],
    [65.0, 2.0, 27.9, 103.0, 159.0, 96.8, 42.0, 4.0, 4.6151, 86.0],
    [34.0, 1.0, 25.5, 93.0, 218.0, 144.0, 57.0, 4.0, 4.4427, 88.0],
    [
        46.0, 1.0, 24.9, 115.0, 198.0, 129.6, 54.0, 4.0, 4.2767, 103.0,
    ],
    [35.0, 1.0, 28.7, 97.0, 204.0, 126.8, 64.0, 3.0, 4.1897, 93.0],
    [37.0, 1.0, 21.8, 84.0, 184.0, 101.0, 73.0, 3.0, 3.912, 93.0],
    [37.0, 1.0, 30.2, 87.0, 166.0, 96.0, 40.0, 4.15, 5.0106, 87.0],
    [41.0, 1.0, 20.5, 80.0, 124.0, 48.8, 64.0, 2.0, 4.0254, 75.0],
    [60.0, 1.0, 20.4, 105.0, 198.0, 78.4, 99.0, 2.0, 4.6347, 79.0],
    [66.0, 2.0, 24.0, 98.0, 236.0, 146.4, 58.0, 4.0, 5.0626, 96.0],
    [29.0, 1.0, 26.0, 83.0, 141.0, 65.2, 64.0, 2.0, 4.0775, 83.0],
    [37.0, 2.0, 26.8, 79.0, 157.0, 98.0, 28.0, 6.0, 5.0434, 96.0],
    [41.0, 2.0, 25.7, 83.0, 181.0, 106.6, 66.0, 3.0, 3.7377, 85.0],
    [39.0, 1.0, 22.9, 77.0, 204.0, 143.2, 46.0, 4.0, 4.3041, 74.0],
    [67.0, 2.0, 24.0, 83.0, 143.0, 77.2, 49.0, 3.0, 4.4308, 94.0],
    [
        36.0, 2.0, 24.1, 112.0, 193.0, 125.0, 35.0, 6.0, 5.1059, 95.0,
    ],
    [46.0, 2.0, 24.7, 85.0, 174.0, 123.2, 30.0, 6.0, 4.6444, 96.0],
    [
        60.0, 2.0, 25.0, 89.67, 185.0, 120.8, 46.0, 4.02, 4.5109, 92.0,
    ],
    [59.0, 2.0, 23.6, 83.0, 165.0, 100.0, 47.0, 4.0, 4.4998, 92.0],
    [53.0, 1.0, 22.1, 93.0, 134.0, 76.2, 46.0, 3.0, 4.0775, 96.0],
    [
        48.0, 1.0, 19.9, 91.0, 189.0, 109.6, 69.0, 3.0, 3.9512, 101.0,
    ],
    [
        48.0, 1.0, 29.5, 131.0, 207.0, 132.2, 47.0, 4.0, 4.9345, 106.0,
    ],
    [66.0, 2.0, 26.0, 91.0, 264.0, 146.6, 65.0, 4.0, 5.5683, 87.0],
    [52.0, 2.0, 24.5, 94.0, 217.0, 149.4, 48.0, 5.0, 4.585, 89.0],
    [
        52.0, 2.0, 26.6, 111.0, 209.0, 126.4, 61.0, 3.0, 4.6821, 109.0,
    ],
    [46.0, 2.0, 23.5, 87.0, 181.0, 114.8, 44.0, 4.0, 4.7095, 98.0],
    [40.0, 2.0, 29.0, 115.0, 97.0, 47.2, 35.0, 2.77, 4.3041, 95.0],
    [22.0, 1.0, 23.0, 73.0, 161.0, 97.8, 54.0, 3.0, 3.8286, 91.0],
    [50.0, 1.0, 21.0, 88.0, 140.0, 71.8, 35.0, 4.0, 5.112, 71.0],
    [20.0, 1.0, 22.9, 87.0, 191.0, 128.2, 53.0, 4.0, 3.8918, 85.0],
    [68.0, 1.0, 27.5, 107.0, 241.0, 149.6, 64.0, 4.0, 4.92, 90.0],
    [52.0, 2.0, 24.3, 86.0, 197.0, 133.6, 44.0, 5.0, 4.5747, 91.0],
    [44.0, 1.0, 23.1, 87.0, 213.0, 126.4, 77.0, 3.0, 3.8712, 72.0],
    [38.0, 1.0, 27.3, 81.0, 146.0, 81.6, 47.0, 3.0, 4.4659, 81.0],
    [
        49.0, 1.0, 22.7, 65.33, 168.0, 96.2, 62.0, 2.71, 3.8918, 60.0,
    ],
    [61.0, 1.0, 33.0, 95.0, 182.0, 114.8, 54.0, 3.0, 4.1897, 74.0],
    [29.0, 2.0, 19.4, 83.0, 152.0, 105.8, 39.0, 4.0, 3.5835, 83.0],
    [61.0, 1.0, 25.8, 98.0, 235.0, 125.8, 76.0, 3.0, 5.112, 82.0],
    [34.0, 2.0, 22.6, 75.0, 166.0, 91.8, 60.0, 3.0, 4.2627, 108.0],
    [36.0, 1.0, 21.9, 89.0, 189.0, 105.2, 68.0, 3.0, 4.3694, 96.0],
    [52.0, 1.0, 24.0, 83.0, 167.0, 86.6, 71.0, 2.0, 3.8501, 94.0],
    [61.0, 1.0, 31.2, 79.0, 235.0, 156.8, 47.0, 5.0, 5.0499, 96.0],
    [
        43.0, 1.0, 26.8, 123.0, 193.0, 102.2, 67.0, 3.0, 4.7791, 94.0,
    ],
    [
        35.0, 1.0, 20.4, 65.0, 187.0, 105.6, 67.0, 2.79, 4.2767, 78.0,
    ],
    [27.0, 1.0, 24.8, 91.0, 189.0, 106.8, 69.0, 3.0, 4.1897, 69.0],
    [29.0, 1.0, 21.0, 71.0, 156.0, 97.0, 38.0, 4.0, 4.654, 90.0],
    [
        64.0, 2.0, 27.3, 109.0, 186.0, 107.6, 38.0, 5.0, 5.3083, 99.0,
    ],
    [
        41.0, 1.0, 34.6, 87.33, 205.0, 142.6, 41.0, 5.0, 4.6728, 110.0,
    ],
    [49.0, 2.0, 25.9, 91.0, 178.0, 106.6, 52.0, 3.0, 4.5747, 75.0],
    [48.0, 1.0, 20.4, 98.0, 209.0, 139.4, 46.0, 5.0, 4.7707, 78.0],
    [53.0, 1.0, 28.0, 88.0, 233.0, 143.8, 58.0, 4.0, 5.0499, 91.0],
    [
        53.0, 2.0, 22.2, 113.0, 197.0, 115.2, 67.0, 3.0, 4.3041, 100.0,
    ],
    [23.0, 1.0, 29.0, 90.0, 216.0, 131.4, 65.0, 3.0, 4.585, 91.0],
    [65.0, 2.0, 30.2, 98.0, 219.0, 160.6, 40.0, 5.0, 4.5218, 84.0],
    [41.0, 1.0, 32.4, 94.0, 171.0, 104.4, 56.0, 3.0, 3.9703, 76.0],
    [55.0, 2.0, 23.4, 83.0, 166.0, 101.6, 46.0, 4.0, 4.5218, 96.0],
    [22.0, 1.0, 19.3, 82.0, 156.0, 93.2, 52.0, 3.0, 3.989, 71.0],
    [
        56.0, 1.0, 31.0, 78.67, 187.0, 141.4, 34.0, 5.5, 4.0604, 90.0,
    ],
    [
        54.0, 2.0, 30.6, 103.33, 144.0, 79.8, 30.0, 4.8, 5.1417, 101.0,
    ],
    [
        59.0, 2.0, 25.5, 95.33, 190.0, 139.4, 35.0, 5.43, 4.3567, 117.0,
    ],
    [60.0, 2.0, 23.4, 88.0, 153.0, 89.8, 58.0, 3.0, 3.2581, 95.0],
    [54.0, 1.0, 26.8, 87.0, 206.0, 122.0, 68.0, 3.0, 4.382, 80.0],
    [25.0, 1.0, 28.3, 87.0, 193.0, 128.0, 49.0, 4.0, 4.382, 92.0],
    [
        54.0, 2.0, 27.7, 113.0, 200.0, 128.4, 37.0, 5.0, 5.1533, 113.0,
    ],
    [
        55.0, 1.0, 36.6, 113.0, 199.0, 94.4, 43.0, 4.63, 5.7301, 97.0,
    ],
    [40.0, 2.0, 26.5, 93.0, 236.0, 147.0, 37.0, 7.0, 5.5607, 92.0],
    [
        62.0, 2.0, 31.8, 115.0, 199.0, 128.6, 44.0, 5.0, 4.8828, 98.0,
    ],
    [
        65.0, 1.0, 24.4, 120.0, 222.0, 135.6, 37.0, 6.0, 5.5094, 124.0,
    ],
    [
        33.0, 2.0, 25.4, 102.0, 206.0, 141.0, 39.0, 5.0, 4.8675, 105.0,
    ],
    [53.0, 1.0, 22.0, 94.0, 175.0, 88.0, 59.0, 3.0, 4.9416, 98.0],
    [35.0, 1.0, 26.8, 98.0, 162.0, 103.6, 45.0, 4.0, 4.2047, 86.0],
    [
        66.0, 1.0, 28.0, 101.0, 195.0, 129.2, 40.0, 5.0, 4.8598, 94.0,
    ],
    [
        62.0, 2.0, 33.9, 101.0, 221.0, 156.4, 35.0, 6.0, 4.9972, 103.0,
    ],
    [
        50.0, 2.0, 29.6, 94.33, 300.0, 242.4, 33.0, 9.09, 4.8122, 109.0,
    ],
    [47.0, 1.0, 28.6, 97.0, 164.0, 90.6, 56.0, 3.0, 4.4659, 88.0],
    [47.0, 2.0, 25.6, 94.0, 165.0, 74.8, 40.0, 4.0, 5.5255, 93.0],
    [24.0, 1.0, 20.7, 87.0, 149.0, 80.6, 61.0, 2.0, 3.6109, 78.0],
    [58.0, 2.0, 26.2, 91.0, 217.0, 124.2, 71.0, 3.0, 4.6913, 68.0],
    [34.0, 1.0, 20.6, 87.0, 185.0, 112.2, 58.0, 3.0, 4.3041, 74.0],
    [
        51.0, 1.0, 27.9, 96.0, 196.0, 122.2, 42.0, 5.0, 5.0689, 120.0,
    ],
    [
        31.0, 2.0, 35.3, 125.0, 187.0, 112.4, 48.0, 4.0, 4.8903, 109.0,
    ],
    [22.0, 1.0, 19.9, 75.0, 175.0, 108.6, 54.0, 3.0, 4.1271, 72.0],
    [53.0, 2.0, 24.4, 92.0, 214.0, 146.0, 50.0, 4.0, 4.4998, 97.0],
    [37.0, 2.0, 21.4, 83.0, 128.0, 69.6, 49.0, 3.0, 3.8501, 84.0],
    [28.0, 1.0, 30.4, 85.0, 198.0, 115.6, 67.0, 3.0, 4.3438, 80.0],
    [47.0, 1.0, 31.6, 84.0, 154.0, 88.0, 30.0, 5.1, 5.1985, 105.0],
    [23.0, 1.0, 18.8, 78.0, 145.0, 72.0, 63.0, 2.0, 3.912, 86.0],
    [
        50.0, 1.0, 31.0, 123.0, 178.0, 105.0, 48.0, 4.0, 4.8283, 88.0,
    ],
    [
        58.0, 2.0, 36.7, 117.0, 166.0, 93.8, 44.0, 4.0, 4.9488, 109.0,
    ],
    [55.0, 1.0, 32.1, 110.0, 164.0, 84.2, 42.0, 4.0, 5.2417, 90.0],
    [
        60.0, 2.0, 27.7, 107.0, 167.0, 114.6, 38.0, 4.0, 4.2767, 95.0,
    ],
    [
        41.0, 1.0, 30.8, 81.0, 214.0, 152.0, 28.0, 7.6, 5.1358, 123.0,
    ],
    [
        60.0, 2.0, 27.5, 106.0, 229.0, 143.8, 51.0, 4.0, 5.1417, 91.0,
    ],
    [40.0, 1.0, 26.9, 92.0, 203.0, 119.8, 70.0, 3.0, 4.1897, 81.0],
    [57.0, 2.0, 30.7, 90.0, 204.0, 147.8, 34.0, 6.0, 4.7095, 93.0],
    [37.0, 1.0, 38.3, 113.0, 165.0, 94.6, 53.0, 3.0, 4.4659, 79.0],
    [40.0, 2.0, 31.9, 95.0, 198.0, 135.6, 38.0, 5.0, 4.804, 93.0],
    [
        33.0, 1.0, 35.0, 89.0, 200.0, 130.4, 42.0, 4.76, 4.9273, 101.0,
    ],
    [32.0, 2.0, 27.8, 89.0, 216.0, 146.2, 55.0, 4.0, 4.3041, 91.0],
    [35.0, 2.0, 25.9, 81.0, 174.0, 102.4, 31.0, 6.0, 5.3132, 82.0],
    [
        55.0, 1.0, 32.9, 102.0, 164.0, 106.2, 41.0, 4.0, 4.4308, 89.0,
    ],
    [49.0, 1.0, 26.0, 93.0, 183.0, 100.2, 64.0, 3.0, 4.5433, 88.0],
    [
        39.0, 2.0, 26.3, 115.0, 218.0, 158.2, 32.0, 7.0, 4.9345, 109.0,
    ],
    [
        60.0, 2.0, 22.3, 113.0, 186.0, 125.8, 46.0, 4.0, 4.2627, 94.0,
    ],
    [67.0, 2.0, 28.3, 93.0, 204.0, 132.2, 49.0, 4.0, 4.7362, 92.0],
    [
        41.0, 2.0, 32.0, 109.0, 251.0, 170.6, 49.0, 5.0, 5.0562, 103.0,
    ],
    [44.0, 1.0, 25.4, 95.0, 162.0, 92.6, 53.0, 3.0, 4.4067, 83.0],
    [
        48.0, 2.0, 23.3, 89.33, 212.0, 142.8, 46.0, 4.61, 4.7536, 98.0,
    ],
    [
        45.0, 1.0, 20.3, 74.33, 190.0, 126.2, 49.0, 3.88, 4.3041, 79.0,
    ],
    [
        47.0, 1.0, 30.4, 120.0, 199.0, 120.0, 46.0, 4.0, 5.1059, 87.0,
    ],
    [46.0, 1.0, 20.6, 73.0, 172.0, 107.0, 51.0, 3.0, 4.2485, 80.0],
    [
        36.0, 2.0, 32.3, 115.0, 286.0, 199.4, 39.0, 7.0, 5.4723, 112.0,
    ],
    [34.0, 1.0, 29.2, 73.0, 172.0, 108.2, 49.0, 4.0, 4.3041, 91.0],
    [
        53.0, 2.0, 33.1, 117.0, 183.0, 119.0, 48.0, 4.0, 4.382, 106.0,
    ],
    [
        61.0, 1.0, 24.6, 101.0, 209.0, 106.8, 77.0, 3.0, 4.8363, 88.0,
    ],
    [37.0, 1.0, 20.2, 81.0, 162.0, 87.8, 63.0, 3.0, 4.0254, 88.0],
    [33.0, 2.0, 20.8, 84.0, 125.0, 70.2, 46.0, 3.0, 3.7842, 66.0],
    [
        68.0, 1.0, 32.8, 105.67, 205.0, 116.4, 40.0, 5.13, 5.4931, 117.0,
    ],
    [
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        54.0, 2.0, 25.2, 115.0, 181.0, 120.0, 39.0, 5.0, 4.7005, 92.0,
    ],
    [31.0, 1.0, 23.3, 85.0, 190.0, 130.8, 43.0, 4.0, 4.3944, 77.0],
    [56.0, 1.0, 25.7, 80.0, 244.0, 151.6, 59.0, 4.0, 5.118, 95.0],
    [
        44.0, 1.0, 25.1, 133.0, 182.0, 113.0, 55.0, 3.0, 4.2485, 84.0,
    ],
    [
        57.0, 2.0, 31.9, 111.0, 173.0, 116.2, 41.0, 4.0, 4.3694, 87.0,
    ],
    [64.0, 2.0, 28.4, 111.0, 184.0, 127.0, 41.0, 4.0, 4.382, 97.0],
    [
        43.0, 1.0, 28.1, 121.0, 192.0, 121.0, 60.0, 3.0, 4.0073, 93.0,
    ],
    [19.0, 1.0, 25.3, 83.0, 225.0, 156.6, 46.0, 5.0, 4.7185, 84.0],
    [71.0, 2.0, 26.1, 85.0, 220.0, 152.4, 47.0, 5.0, 4.6347, 91.0],
    [
        50.0, 2.0, 28.0, 104.0, 282.0, 196.8, 44.0, 6.0, 5.3279, 95.0,
    ],
    [59.0, 2.0, 23.6, 73.0, 180.0, 107.4, 51.0, 4.0, 4.6821, 84.0],
    [57.0, 1.0, 24.5, 93.0, 186.0, 96.6, 71.0, 3.0, 4.5218, 91.0],
    [49.0, 2.0, 21.0, 82.0, 119.0, 85.4, 23.0, 5.0, 3.9703, 74.0],
    [
        41.0, 2.0, 32.0, 126.0, 198.0, 104.2, 49.0, 4.0, 5.4116, 124.0,
    ],
    [25.0, 2.0, 22.6, 85.0, 130.0, 71.0, 48.0, 3.0, 4.0073, 81.0],
    [52.0, 2.0, 19.7, 81.0, 152.0, 53.4, 82.0, 2.0, 4.4188, 82.0],
    [34.0, 1.0, 21.2, 84.0, 254.0, 113.4, 52.0, 5.0, 6.0936, 92.0],
    [
        42.0, 2.0, 30.6, 101.0, 269.0, 172.2, 50.0, 5.0, 5.4553, 106.0,
    ],
    [28.0, 2.0, 25.5, 99.0, 162.0, 101.6, 46.0, 4.0, 4.2767, 94.0],
    [47.0, 2.0, 23.3, 90.0, 195.0, 125.8, 54.0, 4.0, 4.3307, 73.0],
    [32.0, 2.0, 31.0, 100.0, 177.0, 96.2, 45.0, 4.0, 5.1874, 77.0],
    [43.0, 1.0, 18.5, 87.0, 163.0, 93.6, 61.0, 2.67, 3.7377, 80.0],
    [
        59.0, 2.0, 26.9, 104.0, 194.0, 126.6, 43.0, 5.0, 4.804, 106.0,
    ],
    [
        53.0, 1.0, 28.3, 101.0, 179.0, 107.0, 48.0, 4.0, 4.7875, 101.0,
    ],
    [60.0, 1.0, 25.7, 103.0, 158.0, 84.6, 64.0, 2.0, 3.8501, 97.0],
    [
        54.0, 2.0, 36.1, 115.0, 163.0, 98.4, 43.0, 4.0, 4.6821, 101.0,
    ],
    [35.0, 2.0, 24.1, 94.67, 155.0, 97.4, 32.0, 4.84, 4.852, 94.0],
    [49.0, 2.0, 25.8, 89.0, 182.0, 118.6, 39.0, 5.0, 4.804, 115.0],
    [
        58.0, 1.0, 22.8, 91.0, 196.0, 118.8, 48.0, 4.0, 4.9836, 115.0,
    ],
    [
        36.0, 2.0, 39.1, 90.0, 219.0, 135.8, 38.0, 6.0, 5.4205, 103.0,
    ],
    [46.0, 2.0, 42.2, 99.0, 211.0, 137.0, 44.0, 5.0, 5.0106, 99.0],
    [
        44.0, 2.0, 26.6, 99.0, 205.0, 109.0, 43.0, 5.0, 5.5797, 111.0,
    ],
    [46.0, 1.0, 29.9, 83.0, 171.0, 113.0, 38.0, 4.5, 4.585, 98.0],
    [54.0, 1.0, 21.0, 78.0, 188.0, 107.4, 70.0, 3.0, 3.9703, 73.0],
    [
        63.0, 2.0, 25.5, 109.0, 226.0, 103.2, 46.0, 5.0, 5.9506, 87.0,
    ],
    [41.0, 2.0, 24.2, 90.0, 199.0, 123.6, 57.0, 4.0, 4.5218, 86.0],
    [28.0, 1.0, 25.4, 93.0, 141.0, 79.0, 49.0, 3.0, 4.1744, 91.0],
    [19.0, 1.0, 23.2, 75.0, 143.0, 70.4, 52.0, 3.0, 4.6347, 72.0],
    [
        61.0, 2.0, 26.1, 126.0, 215.0, 129.8, 57.0, 4.0, 4.9488, 96.0,
    ],
    [
        48.0, 1.0, 32.7, 93.0, 276.0, 198.6, 43.0, 6.42, 5.1475, 91.0,
    ],
    [
        54.0, 2.0, 27.3, 100.0, 200.0, 144.0, 33.0, 6.0, 4.7449, 76.0,
    ],
    [53.0, 2.0, 26.6, 93.0, 185.0, 122.4, 36.0, 5.0, 4.8903, 82.0],
    [48.0, 1.0, 22.8, 101.0, 110.0, 41.6, 56.0, 2.0, 4.1271, 97.0],
    [
        53.0, 1.0, 28.8, 111.67, 145.0, 87.2, 46.0, 3.15, 4.0775, 85.0,
    ],
    [29.0, 2.0, 18.1, 73.0, 158.0, 99.0, 41.0, 4.0, 4.4998, 78.0],
    [62.0, 1.0, 32.0, 88.0, 172.0, 69.0, 38.0, 4.0, 5.7838, 100.0],
    [50.0, 2.0, 23.7, 92.0, 166.0, 97.0, 52.0, 3.0, 4.4427, 93.0],
    [58.0, 2.0, 23.6, 96.0, 257.0, 171.0, 59.0, 4.0, 4.9053, 82.0],
    [55.0, 2.0, 24.6, 109.0, 143.0, 76.4, 51.0, 3.0, 4.3567, 88.0],
    [54.0, 1.0, 22.6, 90.0, 183.0, 104.2, 64.0, 3.0, 4.3041, 92.0],
    [36.0, 1.0, 27.8, 73.0, 153.0, 104.4, 42.0, 4.0, 3.4965, 73.0],
    [
        63.0, 2.0, 24.1, 111.0, 184.0, 112.2, 44.0, 4.0, 4.9345, 82.0,
    ],
    [47.0, 2.0, 26.5, 70.0, 181.0, 104.8, 63.0, 3.0, 4.1897, 70.0],
    [
        51.0, 2.0, 32.8, 112.0, 202.0, 100.6, 37.0, 5.0, 5.7746, 109.0,
    ],
    [42.0, 1.0, 19.9, 76.0, 146.0, 83.2, 55.0, 3.0, 3.6636, 79.0],
    [
        37.0, 2.0, 23.6, 94.0, 205.0, 138.8, 53.0, 4.0, 4.1897, 107.0,
    ],
    [28.0, 1.0, 22.1, 82.0, 168.0, 100.6, 54.0, 3.0, 4.2047, 86.0],
    [58.0, 1.0, 28.1, 111.0, 198.0, 80.6, 31.0, 6.0, 6.0684, 93.0],
    [32.0, 1.0, 26.5, 86.0, 184.0, 101.6, 53.0, 4.0, 4.9904, 78.0],
    [25.0, 2.0, 23.5, 88.0, 143.0, 80.8, 55.0, 3.0, 3.5835, 83.0],
    [63.0, 1.0, 26.0, 85.67, 155.0, 78.2, 46.0, 3.37, 5.037, 97.0],
    [52.0, 1.0, 27.8, 85.0, 219.0, 136.0, 49.0, 4.0, 5.1358, 75.0],
    [
        65.0, 2.0, 28.5, 109.0, 201.0, 123.0, 46.0, 4.0, 5.0752, 96.0,
    ],
    [42.0, 1.0, 30.6, 121.0, 176.0, 92.8, 69.0, 3.0, 4.2627, 89.0],
    [53.0, 1.0, 22.2, 78.0, 164.0, 81.0, 70.0, 2.0, 4.1744, 101.0],
    [
        79.0, 2.0, 23.3, 88.0, 186.0, 128.4, 33.0, 6.0, 4.8122, 102.0,
    ],
    [
        43.0, 1.0, 35.4, 93.0, 185.0, 100.2, 44.0, 4.0, 5.3181, 101.0,
    ],
    [44.0, 1.0, 31.4, 115.0, 165.0, 97.6, 52.0, 3.0, 4.3438, 89.0],
    [62.0, 2.0, 37.8, 119.0, 113.0, 51.0, 31.0, 4.0, 5.0434, 84.0],
    [33.0, 1.0, 18.9, 70.0, 162.0, 91.8, 59.0, 3.0, 4.0254, 58.0],
    [
        56.0, 1.0, 35.0, 79.33, 195.0, 140.8, 42.0, 4.64, 4.1109, 96.0,
    ],
    [
        66.0, 1.0, 21.7, 126.0, 212.0, 127.8, 45.0, 4.71, 5.2781, 101.0,
    ],
    [
        34.0, 2.0, 25.3, 111.0, 230.0, 162.0, 39.0, 6.0, 4.9767, 90.0,
    ],
    [46.0, 2.0, 23.8, 97.0, 224.0, 139.2, 42.0, 5.0, 5.366, 81.0],
    [50.0, 1.0, 31.8, 82.0, 136.0, 69.2, 55.0, 2.0, 4.0775, 85.0],
    [
        69.0, 1.0, 34.3, 113.0, 200.0, 123.8, 54.0, 4.0, 4.7095, 112.0,
    ],
    [34.0, 1.0, 26.3, 87.0, 197.0, 120.0, 63.0, 3.0, 4.2485, 96.0],
    [
        71.0, 2.0, 27.0, 93.33, 269.0, 190.2, 41.0, 6.56, 5.2417, 93.0,
    ],
    [47.0, 1.0, 27.2, 80.0, 208.0, 145.6, 38.0, 6.0, 4.804, 92.0],
    [
        41.0, 1.0, 33.8, 123.33, 187.0, 127.0, 45.0, 4.16, 4.3175, 100.0,
    ],
    [
        34.0, 1.0, 33.0, 73.0, 178.0, 114.6, 51.0, 3.49, 4.1271, 92.0,
    ],
    [51.0, 1.0, 24.1, 87.0, 261.0, 175.6, 69.0, 4.0, 4.4067, 93.0],
    [43.0, 1.0, 21.3, 79.0, 141.0, 78.8, 53.0, 3.0, 3.8286, 90.0],
    [
        55.0, 1.0, 23.0, 94.67, 190.0, 137.6, 38.0, 5.0, 4.2767, 106.0,
    ],
    [
        59.0, 2.0, 27.9, 101.0, 218.0, 144.2, 38.0, 6.0, 5.1874, 95.0,
    ],
    [
        27.0, 2.0, 33.6, 110.0, 246.0, 156.6, 57.0, 4.0, 5.0876, 89.0,
    ],
    [
        51.0, 2.0, 22.7, 103.0, 217.0, 162.4, 30.0, 7.0, 4.8122, 80.0,
    ],
    [49.0, 2.0, 27.4, 89.0, 177.0, 113.0, 37.0, 5.0, 4.9053, 97.0],
    [27.0, 1.0, 22.6, 71.0, 116.0, 43.4, 56.0, 2.0, 4.4188, 79.0],
    [
        57.0, 2.0, 23.2, 107.33, 231.0, 159.4, 41.0, 5.63, 5.0304, 112.0,
    ],
    [39.0, 2.0, 26.9, 93.0, 136.0, 75.4, 48.0, 3.0, 4.1431, 99.0],
    [
        62.0, 2.0, 34.6, 120.0, 215.0, 129.2, 43.0, 5.0, 5.366, 123.0,
    ],
    [37.0, 1.0, 23.3, 88.0, 223.0, 142.0, 65.0, 3.4, 4.3567, 82.0],
    [46.0, 1.0, 21.1, 80.0, 205.0, 144.4, 42.0, 5.0, 4.5326, 87.0],
    [68.0, 2.0, 23.5, 101.0, 162.0, 85.4, 59.0, 3.0, 4.4773, 91.0],
    [
        51.0, 1.0, 31.5, 93.0, 231.0, 144.0, 49.0, 4.7, 5.2523, 117.0,
    ],
    [41.0, 1.0, 20.8, 86.0, 223.0, 128.2, 83.0, 3.0, 4.0775, 89.0],
    [53.0, 1.0, 26.5, 97.0, 193.0, 122.4, 58.0, 3.0, 4.1431, 99.0],
    [45.0, 1.0, 24.2, 83.0, 177.0, 118.4, 45.0, 4.0, 4.2195, 82.0],
    [33.0, 1.0, 19.5, 80.0, 171.0, 85.4, 75.0, 2.0, 3.9703, 80.0],
    [
        60.0, 2.0, 28.2, 112.0, 185.0, 113.8, 42.0, 4.0, 4.9836, 93.0,
    ],
    [
        47.0, 2.0, 24.9, 75.0, 225.0, 166.0, 42.0, 5.0, 4.4427, 102.0,
    ],
    [
        60.0, 2.0, 24.9, 99.67, 162.0, 106.6, 43.0, 3.77, 4.1271, 95.0,
    ],
    [
        36.0, 1.0, 30.0, 95.0, 201.0, 125.2, 42.0, 4.79, 5.1299, 85.0,
    ],
    [36.0, 1.0, 19.6, 71.0, 250.0, 133.2, 97.0, 3.0, 4.5951, 92.0],
];

const DIABETES_TARGET: [f64; 442] = [
    151.0, 75.0, 141.0, 206.0, 135.0, 97.0, 138.0, 63.0, 110.0, 310.0, 101.0, 69.0, 179.0, 185.0,
    118.0, 171.0, 166.0, 144.0, 97.0, 168.0, 68.0, 49.0, 68.0, 245.0, 184.0, 202.0, 137.0, 85.0,
    131.0, 283.0, 129.0, 59.0, 341.0, 87.0, 65.0, 102.0, 265.0, 276.0, 252.0, 90.0, 100.0, 55.0,
    61.0, 92.0, 259.0, 53.0, 190.0, 142.0, 75.0, 142.0, 155.0, 225.0, 59.0, 104.0, 182.0, 128.0,
    52.0, 37.0, 170.0, 170.0, 61.0, 144.0, 52.0, 128.0, 71.0, 163.0, 150.0, 97.0, 160.0, 178.0,
    48.0, 270.0, 202.0, 111.0, 85.0, 42.0, 170.0, 200.0, 252.0, 113.0, 143.0, 51.0, 52.0, 210.0,
    65.0, 141.0, 55.0, 134.0, 42.0, 111.0, 98.0, 164.0, 48.0, 96.0, 90.0, 162.0, 150.0, 279.0,
    92.0, 83.0, 128.0, 102.0, 302.0, 198.0, 95.0, 53.0, 134.0, 144.0, 232.0, 81.0, 104.0, 59.0,
    246.0, 297.0, 258.0, 229.0, 275.0, 281.0, 179.0, 200.0, 200.0, 173.0, 180.0, 84.0, 121.0,
    161.0, 99.0, 109.0, 115.0, 268.0, 274.0, 158.0, 107.0, 83.0, 103.0, 272.0, 85.0, 280.0, 336.0,
    281.0, 118.0, 317.0, 235.0, 60.0, 174.0, 259.0, 178.0, 128.0, 96.0, 126.0, 288.0, 88.0, 292.0,
    71.0, 197.0, 186.0, 25.0, 84.0, 96.0, 195.0, 53.0, 217.0, 172.0, 131.0, 214.0, 59.0, 70.0,
    220.0, 268.0, 152.0, 47.0, 74.0, 295.0, 101.0, 151.0, 127.0, 237.0, 225.0, 81.0, 151.0, 107.0,
    64.0, 138.0, 185.0, 265.0, 101.0, 137.0, 143.0, 141.0, 79.0, 292.0, 178.0, 91.0, 116.0, 86.0,
    122.0, 72.0, 129.0, 142.0, 90.0, 158.0, 39.0, 196.0, 222.0, 277.0, 99.0, 196.0, 202.0, 155.0,
    77.0, 191.0, 70.0, 73.0, 49.0, 65.0, 263.0, 248.0, 296.0, 214.0, 185.0, 78.0, 93.0, 252.0,
    150.0, 77.0, 208.0, 77.0, 108.0, 160.0, 53.0, 220.0, 154.0, 259.0, 90.0, 246.0, 124.0, 67.0,
    72.0, 257.0, 262.0, 275.0, 177.0, 71.0, 47.0, 187.0, 125.0, 78.0, 51.0, 258.0, 215.0, 303.0,
    243.0, 91.0, 150.0, 310.0, 153.0, 346.0, 63.0, 89.0, 50.0, 39.0, 103.0, 308.0, 116.0, 145.0,
    74.0, 45.0, 115.0, 264.0, 87.0, 202.0, 127.0, 182.0, 241.0, 66.0, 94.0, 283.0, 64.0, 102.0,
    200.0, 265.0, 94.0, 230.0, 181.0, 156.0, 233.0, 60.0, 219.0, 80.0, 68.0, 332.0, 248.0, 84.0,
    200.0, 55.0, 85.0, 89.0, 31.0, 129.0, 83.0, 275.0, 65.0, 198.0, 236.0, 253.0, 124.0, 44.0,
    172.0, 114.0, 142.0, 109.0, 180.0, 144.0, 163.0, 147.0, 97.0, 220.0, 190.0, 109.0, 191.0,
    122.0, 230.0, 242.0, 248.0, 249.0, 192.0, 131.0, 237.0, 78.0, 135.0, 244.0, 199.0, 270.0,
    164.0, 72.0, 96.0, 306.0, 91.0, 214.0, 95.0, 216.0, 263.0, 178.0, 113.0, 200.0, 139.0, 139.0,
    88.0, 148.0, 88.0, 243.0, 71.0, 77.0, 109.0, 272.0, 60.0, 54.0, 221.0, 90.0, 311.0, 281.0,
    182.0, 321.0, 58.0, 262.0, 206.0, 233.0, 242.0, 123.0, 167.0, 63.0, 197.0, 71.0, 168.0, 140.0,
    217.0, 121.0, 235.0, 245.0, 40.0, 52.0, 104.0, 132.0, 88.0, 69.0, 219.0, 72.0, 201.0, 110.0,
    51.0, 277.0, 63.0, 118.0, 69.0, 273.0, 258.0, 43.0, 198.0, 242.0, 232.0, 175.0, 93.0, 168.0,
    275.0, 293.0, 281.0, 72.0, 140.0, 189.0, 181.0, 209.0, 136.0, 261.0, 113.0, 131.0, 174.0,
    257.0, 55.0, 84.0, 42.0, 146.0, 212.0, 233.0, 91.0, 111.0, 152.0, 120.0, 67.0, 310.0, 94.0,
    183.0, 66.0, 173.0, 72.0, 49.0, 64.0, 48.0, 178.0, 104.0, 132.0, 220.0, 57.0,
];

/// Loads the Diabetes dataset.
///
/// Returns a [`Dataset`] with 442 samples, 10 features, and a continuous
/// regression target (disease progression). `n_classes()` returns 0 — this
/// is a regression dataset, not classification.
pub fn load() -> Dataset {
    let data: Vec<Vec<f64>> = DIABETES_DATA.iter().map(|r| r.to_vec()).collect();
    let target = DIABETES_TARGET.to_vec();
    Dataset::from_const(data, target, &FEATURE_NAMES, &TARGET_NAMES)
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn diabetes_dimensions() {
        let d = load();
        assert_eq!(d.n_samples(), 442);
        assert_eq!(d.n_features(), 10);
    }

    #[test]
    fn diabetes_is_regression() {
        let d = load();
        assert_eq!(d.n_classes(), 0); // regression, no class names
        assert_eq!(d.target_names().len(), 0);
    }

    #[test]
    fn diabetes_features_matrix() {
        let d = load();
        let x = d.features();
        assert_eq!(x.nrows(), 442);
        assert_eq!(x.ncols(), 10);
    }

    #[test]
    fn diabetes_target_range() {
        let d = load();
        let y = d.targets();
        // Targets range from 25 to 346 (disease progression measure).
        let min_y = y.iter().cloned().fold(f64::INFINITY, f64::min);
        let max_y = y.iter().cloned().fold(f64::NEG_INFINITY, f64::max);
        assert!(min_y >= 20.0 && max_y <= 350.0);
    }
}