//::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: /** @author John Miller * @version 1.6 * @date Sun Oct 28 16:16:38 EDT 2018 * @see LICENSE (MIT style license file). * * @title Example Dataset: Motor Trend Car Road Tests */ package scalation.analytics.classifier import scalation.linalgebra.MatrixD //::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: /** The `ExampleMtcars` object provides the Motor Trend Car Road Tests dataset (mtcars) * as a combined 'xy' matrix. * @see https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html * @see https://gist.github.com/seankross/a412dfbd88b3db70b74b */ object ExampleMtcars { // mtcars dataset: y = b0 * 1 + b1 * x1 // y = V/S (e.g., V-6 vs. I-4) // x1 = Mpg // 32 data points: One x1 y val xy = new MatrixD ((32, 3), 1.0, 21.0, 0, // 1 - Mazda RX4 1.0, 21.0, 0, // 2 - Mazda RX4 Wa 1.0, 22.8, 1, // 3 - Datsun 710 1.0, 21.4, 1, // 4 - Hornet 4 Drive 1.0, 18.7, 0, // 5 - Hornet Sportabout 1.0, 18.1, 1, // 6 - Valiant 1.0, 14.3, 0, // 7 - Duster 360 1.0, 24.4, 1, // 8 - Merc 240D 1.0, 22.8, 1, // 9 - Merc 230 1.0, 19.2, 1, // 10 - Merc 280 1.0, 17.8, 1, // 11 - Merc 280C 1.0, 16.4, 0, // 12 - Merc 450S 1.0, 17.3, 0, // 13 - Merc 450SL 1.0, 15.2, 0, // 14 - Merc 450SLC 1.0, 10.4, 0, // 15 - Cadillac Fleetwood 1.0, 10.4, 0, // 16 - Lincoln Continental 1.0, 14.7, 0, // 17 - Chrysler Imperial 1.0, 32.4, 1, // 18 - Fiat 128 1.0, 30.4, 1, // 19 - Honda Civic 1.0, 33.9, 1, // 20 - Toyota Corolla 1.0, 21.5, 1, // 21 - Toyota Corona 1.0, 15.5, 0, // 22 - Dodge Challenger 1.0, 15.2, 0, // 23 - AMC Javelin 1.0, 13.3, 0, // 24 - Camaro Z28 1.0, 19.2, 0, // 25 - Pontiac Firebird 1.0, 27.3, 1, // 26 - Fiat X1-9 1.0, 26.0, 0, // 27 - Porsche 914-2 1.0, 30.4, 1, // 28 - Lotus Europa 1.0, 15.8, 0, // 29 - Ford Pantera L 1.0, 19.7, 0, // 30 - Ferrari Dino 1.0, 15.0, 0, // 31 - Maserati Bora 1.0, 21.4, 1) // 32 - Volvo 142E } // ExampleMtcars object