//::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: /** @author Hao Peng, John Miller * @version 1.6 * @date Tue Sep 29 14:14:15 EDT 2015 * @see LICENSE (MIT style license file). * * @title Example Dataset: Automobile Miles Per Gallon (MPG) */ package scalation.analytics import scalation.linalgebra.{MatrixD, VectorD, vectorD2StatVec} import scalation.plot.Plot import scalation.util.banner //::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: /** The `ExampleAutoMPG` object stored the UCI AutoMPG dataset in a matrix. * @see archive.ics.uci.edu/ml/datasets/Auto+MPG */ object ExampleAutoMPG { /** the names of the predictor variables and the response variable at the end */ val fnamer = Array ("cylinders", "displacement", "horsepower", "weight", "acceleration", "model_year", "origin", "mpg") val fname = Array ("cylinders", "displacement", "horsepower", "weight", "acceleration", "model_year", "mpg") /** the raw combined data matrix 'xyr' */ val xyr = new MatrixD ((392, 8), 8, 307, 130, 3504, 12, 70, 1, 18, 8, 350, 165, 3693, 11.5, 70, 1, 15, 8, 318, 150, 3436, 11, 70, 1, 18, 8, 304, 150, 3433, 12, 70, 1, 16, 8, 302, 140, 3449, 10.5, 70, 1, 17, 8, 429, 198, 4341, 10, 70, 1, 15, 8, 454, 220, 4354, 9, 70, 1, 14, 8, 440, 215, 4312, 8.5, 70, 1, 14, 8, 455, 225, 4425, 10, 70, 1, 14, 8, 390, 190, 3850, 8.5, 70, 1, 15, 8, 383, 170, 3563, 10, 70, 1, 15, 8, 340, 160, 3609, 8, 70, 1, 14, 8, 400, 150, 3761, 9.5, 70, 1, 15, 8, 455, 225, 3086, 10, 70, 1, 14, 4, 113, 95, 2372, 15, 70, 3, 24, 6, 198, 95, 2833, 15.5, 70, 1, 22, 6, 199, 97, 2774, 15.5, 70, 1, 18, 6, 200, 85, 2587, 16, 70, 1, 21, 4, 97, 88, 2130, 14.5, 70, 3, 27, 4, 97, 46, 1835, 20.5, 70, 2, 26, 4, 110, 87, 2672, 17.5, 70, 2, 25, 4, 107, 90, 2430, 14.5, 70, 2, 24, 4, 104, 95, 2375, 17.5, 70, 2, 25, 4, 121, 113, 2234, 12.5, 70, 2, 26, 6, 199, 90, 2648, 15, 70, 1, 21, 8, 360, 215, 4615, 14, 70, 1, 10, 8, 307, 200, 4376, 15, 70, 1, 10, 8, 318, 210, 4382, 13.5, 70, 1, 11, 8, 304, 193, 4732, 18.5, 70, 1, 9, 4, 97, 88, 2130, 14.5, 71, 3, 27, 4, 140, 90, 2264, 15.5, 71, 1, 28, 4, 113, 95, 2228, 14, 71, 3, 25, 6, 232, 100, 2634, 13, 71, 1, 19, 6, 225, 105, 3439, 15.5, 71, 1, 16, 6, 250, 100, 3329, 15.5, 71, 1, 17, 6, 250, 88, 3302, 15.5, 71, 1, 19, 6, 232, 100, 3288, 15.5, 71, 1, 18, 8, 350, 165, 4209, 12, 71, 1, 14, 8, 400, 175, 4464, 11.5, 71, 1, 14, 8, 351, 153, 4154, 13.5, 71, 1, 14, 8, 318, 150, 4096, 13, 71, 1, 14, 8, 383, 180, 4955, 11.5, 71, 1, 12, 8, 400, 170, 4746, 12, 71, 1, 13, 8, 400, 175, 5140, 12, 71, 1, 13, 6, 258, 110, 2962, 13.5, 71, 1, 18, 4, 140, 72, 2408, 19, 71, 1, 22, 6, 250, 100, 3282, 15, 71, 1, 19, 6, 250, 88, 3139, 14.5, 71, 1, 18, 4, 122, 86, 2220, 14, 71, 1, 23, 4, 116, 90, 2123, 14, 71, 2, 28, 4, 79, 70, 2074, 19.5, 71, 2, 30, 4, 88, 76, 2065, 14.5, 71, 2, 30, 4, 71, 65, 1773, 19, 71, 3, 31, 4, 72, 69, 1613, 18, 71, 3, 35, 4, 97, 60, 1834, 19, 71, 2, 27, 4, 91, 70, 1955, 20.5, 71, 1, 26, 4, 113, 95, 2278, 15.5, 72, 3, 24, 4, 97.5, 80, 2126, 17, 72, 1, 25, 4, 97, 54, 2254, 23.5, 72, 2, 23, 4, 140, 90, 2408, 19.5, 72, 1, 20, 4, 122, 86, 2226, 16.5, 72, 1, 21, 8, 350, 165, 4274, 12, 72, 1, 13, 8, 400, 175, 4385, 12, 72, 1, 14, 8, 318, 150, 4135, 13.5, 72, 1, 15, 8, 351, 153, 4129, 13, 72, 1, 14, 8, 304, 150, 3672, 11.5, 72, 1, 17, 8, 429, 208, 4633, 11, 72, 1, 11, 8, 350, 155, 4502, 13.5, 72, 1, 13, 8, 350, 160, 4456, 13.5, 72, 1, 12, 8, 400, 190, 4422, 12.5, 72, 1, 13, 3, 70, 97, 2330, 13.5, 72, 3, 19, 8, 304, 150, 3892, 12.5, 72, 1, 15, 8, 307, 130, 4098, 14, 72, 1, 13, 8, 302, 140, 4294, 16, 72, 1, 13, 8, 318, 150, 4077, 14, 72, 1, 14, 4, 121, 112, 2933, 14.5, 72, 2, 18, 4, 121, 76, 2511, 18, 72, 2, 22, 4, 120, 87, 2979, 19.5, 72, 2, 21, 4, 96, 69, 2189, 18, 72, 2, 26, 4, 122, 86, 2395, 16, 72, 1, 22, 4, 97, 92, 2288, 17, 72, 3, 28, 4, 120, 97, 2506, 14.5, 72, 3, 23, 4, 98, 80, 2164, 15, 72, 1, 28, 4, 97, 88, 2100, 16.5, 72, 3, 27, 8, 350, 175, 4100, 13, 73, 1, 13, 8, 304, 150, 3672, 11.5, 73, 1, 14, 8, 350, 145, 3988, 13, 73, 1, 13, 8, 302, 137, 4042, 14.5, 73, 1, 14, 8, 318, 150, 3777, 12.5, 73, 1, 15, 8, 429, 198, 4952, 11.5, 73, 1, 12, 8, 400, 150, 4464, 12, 73, 1, 13, 8, 351, 158, 4363, 13, 73, 1, 13, 8, 318, 150, 4237, 14.5, 73, 1, 14, 8, 440, 215, 4735, 11, 73, 1, 13, 8, 455, 225, 4951, 11, 73, 1, 12, 8, 360, 175, 3821, 11, 73, 1, 13, 6, 225, 105, 3121, 16.5, 73, 1, 18, 6, 250, 100, 3278, 18, 73, 1, 16, 6, 232, 100, 2945, 16, 73, 1, 18, 6, 250, 88, 3021, 16.5, 73, 1, 18, 6, 198, 95, 2904, 16, 73, 1, 23, 4, 97, 46, 1950, 21, 73, 2, 26, 8, 400, 150, 4997, 14, 73, 1, 11, 8, 400, 167, 4906, 12.5, 73, 1, 12, 8, 360, 170, 4654, 13, 73, 1, 13, 8, 350, 180, 4499, 12.5, 73, 1, 12, 6, 232, 100, 2789, 15, 73, 1, 18, 4, 97, 88, 2279, 19, 73, 3, 20, 4, 140, 72, 2401, 19.5, 73, 1, 21, 4, 108, 94, 2379, 16.5, 73, 3, 22, 3, 70, 90, 2124, 13.5, 73, 3, 18, 4, 122, 85, 2310, 18.5, 73, 1, 19, 6, 155, 107, 2472, 14, 73, 1, 21, 4, 98, 90, 2265, 15.5, 73, 2, 26, 8, 350, 145, 4082, 13, 73, 1, 15, 8, 400, 230, 4278, 9.5, 73, 1, 16, 4, 68, 49, 1867, 19.5, 73, 2, 29, 4, 116, 75, 2158, 15.5, 73, 2, 24, 4, 114, 91, 2582, 14, 73, 2, 20, 4, 121, 112, 2868, 15.5, 73, 2, 19, 8, 318, 150, 3399, 11, 73, 1, 15, 4, 121, 110, 2660, 14, 73, 2, 24, 6, 156, 122, 2807, 13.5, 73, 3, 20, 8, 350, 180, 3664, 11, 73, 1, 11, 6, 198, 95, 3102, 16.5, 74, 1, 20, 6, 232, 100, 2901, 16, 74, 1, 19, 6, 250, 100, 3336, 17, 74, 1, 15, 4, 79, 67, 1950, 19, 74, 3, 31, 4, 122, 80, 2451, 16.5, 74, 1, 26, 4, 71, 65, 1836, 21, 74, 3, 32, 4, 140, 75, 2542, 17, 74, 1, 25, 6, 250, 100, 3781, 17, 74, 1, 16, 6, 258, 110, 3632, 18, 74, 1, 16, 6, 225, 105, 3613, 16.5, 74, 1, 18, 8, 302, 140, 4141, 14, 74, 1, 16, 8, 350, 150, 4699, 14.5, 74, 1, 13, 8, 318, 150, 4457, 13.5, 74, 1, 14, 8, 302, 140, 4638, 16, 74, 1, 14, 8, 304, 150, 4257, 15.5, 74, 1, 14, 4, 98, 83, 2219, 16.5, 74, 2, 29, 4, 79, 67, 1963, 15.5, 74, 2, 26, 4, 97, 78, 2300, 14.5, 74, 2, 26, 4, 76, 52, 1649, 16.5, 74, 3, 31, 4, 83, 61, 2003, 19, 74, 3, 32, 4, 90, 75, 2125, 14.5, 74, 1, 28, 4, 90, 75, 2108, 15.5, 74, 2, 24, 4, 116, 75, 2246, 14, 74, 2, 26, 4, 120, 97, 2489, 15, 74, 3, 24, 4, 108, 93, 2391, 15.5, 74, 3, 26, 4, 79, 67, 2000, 16, 74, 2, 31, 6, 225, 95, 3264, 16, 75, 1, 19, 6, 250, 105, 3459, 16, 75, 1, 18, 6, 250, 72, 3432, 21, 75, 1, 15, 6, 250, 72, 3158, 19.5, 75, 1, 15, 8, 400, 170, 4668, 11.5, 75, 1, 16, 8, 350, 145, 4440, 14, 75, 1, 15, 8, 318, 150, 4498, 14.5, 75, 1, 16, 8, 351, 148, 4657, 13.5, 75, 1, 14, 6, 231, 110, 3907, 21, 75, 1, 17, 6, 250, 105, 3897, 18.5, 75, 1, 16, 6, 258, 110, 3730, 19, 75, 1, 15, 6, 225, 95, 3785, 19, 75, 1, 18, 6, 231, 110, 3039, 15, 75, 1, 21, 8, 262, 110, 3221, 13.5, 75, 1, 20, 8, 302, 129, 3169, 12, 75, 1, 13, 4, 97, 75, 2171, 16, 75, 3, 29, 4, 140, 83, 2639, 17, 75, 1, 23, 6, 232, 100, 2914, 16, 75, 1, 20, 4, 140, 78, 2592, 18.5, 75, 1, 23, 4, 134, 96, 2702, 13.5, 75, 3, 24, 4, 90, 71, 2223, 16.5, 75, 2, 25, 4, 119, 97, 2545, 17, 75, 3, 24, 6, 171, 97, 2984, 14.5, 75, 1, 18, 4, 90, 70, 1937, 14, 75, 2, 29, 6, 232, 90, 3211, 17, 75, 1, 19, 4, 115, 95, 2694, 15, 75, 2, 23, 4, 120, 88, 2957, 17, 75, 2, 23, 4, 121, 98, 2945, 14.5, 75, 2, 22, 4, 121, 115, 2671, 13.5, 75, 2, 25, 4, 91, 53, 1795, 17.5, 75, 3, 33, 4, 107, 86, 2464, 15.5, 76, 2, 28, 4, 116, 81, 2220, 16.9, 76, 2, 25, 4, 140, 92, 2572, 14.9, 76, 1, 25, 4, 98, 79, 2255, 17.7, 76, 1, 26, 4, 101, 83, 2202, 15.3, 76, 2, 27, 8, 305, 140, 4215, 13, 76, 1, 17.5, 8, 318, 150, 4190, 13, 76, 1, 16, 8, 304, 120, 3962, 13.9, 76, 1, 15.5, 8, 351, 152, 4215, 12.8, 76, 1, 14.5, 6, 225, 100, 3233, 15.4, 76, 1, 22, 6, 250, 105, 3353, 14.5, 76, 1, 22, 6, 200, 81, 3012, 17.6, 76, 1, 24, 6, 232, 90, 3085, 17.6, 76, 1, 22.5, 4, 85, 52, 2035, 22.2, 76, 1, 29, 4, 98, 60, 2164, 22.1, 76, 1, 24.5, 4, 90, 70, 1937, 14.2, 76, 2, 29, 4, 91, 53, 1795, 17.4, 76, 3, 33, 6, 225, 100, 3651, 17.7, 76, 1, 20, 6, 250, 78, 3574, 21, 76, 1, 18, 6, 250, 110, 3645, 16.2, 76, 1, 18.5, 6, 258, 95, 3193, 17.8, 76, 1, 17.5, 4, 97, 71, 1825, 12.2, 76, 2, 29.5, 4, 85, 70, 1990, 17, 76, 3, 32, 4, 97, 75, 2155, 16.4, 76, 3, 28, 4, 140, 72, 2565, 13.6, 76, 1, 26.5, 4, 130, 102, 3150, 15.7, 76, 2, 20, 8, 318, 150, 3940, 13.2, 76, 1, 13, 4, 120, 88, 3270, 21.9, 76, 2, 19, 6, 156, 108, 2930, 15.5, 76, 3, 19, 6, 168, 120, 3820, 16.7, 76, 2, 16.5, 8, 350, 180, 4380, 12.1, 76, 1, 16.5, 8, 350, 145, 4055, 12, 76, 1, 13, 8, 302, 130, 3870, 15, 76, 1, 13, 8, 318, 150, 3755, 14, 76, 1, 13, 4, 98, 68, 2045, 18.5, 77, 3, 31.5, 4, 111, 80, 2155, 14.8, 77, 1, 30, 4, 79, 58, 1825, 18.6, 77, 2, 36, 4, 122, 96, 2300, 15.5, 77, 1, 25.5, 4, 85, 70, 1945, 16.8, 77, 3, 33.5, 8, 305, 145, 3880, 12.5, 77, 1, 17.5, 8, 260, 110, 4060, 19, 77, 1, 17, 8, 318, 145, 4140, 13.7, 77, 1, 15.5, 8, 302, 130, 4295, 14.9, 77, 1, 15, 6, 250, 110, 3520, 16.4, 77, 1, 17.5, 6, 231, 105, 3425, 16.9, 77, 1, 20.5, 6, 225, 100, 3630, 17.7, 77, 1, 19, 6, 250, 98, 3525, 19, 77, 1, 18.5, 8, 400, 180, 4220, 11.1, 77, 1, 16, 8, 350, 170, 4165, 11.4, 77, 1, 15.5, 8, 400, 190, 4325, 12.2, 77, 1, 15.5, 8, 351, 149, 4335, 14.5, 77, 1, 16, 4, 97, 78, 1940, 14.5, 77, 2, 29, 4, 151, 88, 2740, 16, 77, 1, 24.5, 4, 97, 75, 2265, 18.2, 77, 3, 26, 4, 140, 89, 2755, 15.8, 77, 1, 25.5, 4, 98, 63, 2051, 17, 77, 1, 30.5, 4, 98, 83, 2075, 15.9, 77, 1, 33.5, 4, 97, 67, 1985, 16.4, 77, 3, 30, 4, 97, 78, 2190, 14.1, 77, 2, 30.5, 6, 146, 97, 2815, 14.5, 77, 3, 22, 4, 121, 110, 2600, 12.8, 77, 2, 21.5, 3, 80, 110, 2720, 13.5, 77, 3, 21.5, 4, 90, 48, 1985, 21.5, 78, 2, 43.1, 4, 98, 66, 1800, 14.4, 78, 1, 36.1, 4, 78, 52, 1985, 19.4, 78, 3, 32.8, 4, 85, 70, 2070, 18.6, 78, 3, 39.4, 4, 91, 60, 1800, 16.4, 78, 3, 36.1, 8, 260, 110, 3365, 15.5, 78, 1, 19.9, 8, 318, 140, 3735, 13.2, 78, 1, 19.4, 8, 302, 139, 3570, 12.8, 78, 1, 20.2, 6, 231, 105, 3535, 19.2, 78, 1, 19.2, 6, 200, 95, 3155, 18.2, 78, 1, 20.5, 6, 200, 85, 2965, 15.8, 78, 1, 20.2, 4, 140, 88, 2720, 15.4, 78, 1, 25.1, 6, 225, 100, 3430, 17.2, 78, 1, 20.5, 6, 232, 90, 3210, 17.2, 78, 1, 19.4, 6, 231, 105, 3380, 15.8, 78, 1, 20.6, 6, 200, 85, 3070, 16.7, 78, 1, 20.8, 6, 225, 110, 3620, 18.7, 78, 1, 18.6, 6, 258, 120, 3410, 15.1, 78, 1, 18.1, 8, 305, 145, 3425, 13.2, 78, 1, 19.2, 6, 231, 165, 3445, 13.4, 78, 1, 17.7, 8, 302, 139, 3205, 11.2, 78, 1, 18.1, 8, 318, 140, 4080, 13.7, 78, 1, 17.5, 4, 98, 68, 2155, 16.5, 78, 1, 30, 4, 134, 95, 2560, 14.2, 78, 3, 27.5, 4, 119, 97, 2300, 14.7, 78, 3, 27.2, 4, 105, 75, 2230, 14.5, 78, 1, 30.9, 4, 134, 95, 2515, 14.8, 78, 3, 21.1, 4, 156, 105, 2745, 16.7, 78, 1, 23.2, 4, 151, 85, 2855, 17.6, 78, 1, 23.8, 4, 119, 97, 2405, 14.9, 78, 3, 23.9, 5, 131, 103, 2830, 15.9, 78, 2, 20.3, 6, 163, 125, 3140, 13.6, 78, 2, 17, 4, 121, 115, 2795, 15.7, 78, 2, 21.6, 6, 163, 133, 3410, 15.8, 78, 2, 16.2, 4, 89, 71, 1990, 14.9, 78, 2, 31.5, 4, 98, 68, 2135, 16.6, 78, 3, 29.5, 6, 231, 115, 3245, 15.4, 79, 1, 21.5, 6, 200, 85, 2990, 18.2, 79, 1, 19.8, 4, 140, 88, 2890, 17.3, 79, 1, 22.3, 6, 232, 90, 3265, 18.2, 79, 1, 20.2, 6, 225, 110, 3360, 16.6, 79, 1, 20.6, 8, 305, 130, 3840, 15.4, 79, 1, 17, 8, 302, 129, 3725, 13.4, 79, 1, 17.6, 8, 351, 138, 3955, 13.2, 79, 1, 16.5, 8, 318, 135, 3830, 15.2, 79, 1, 18.2, 8, 350, 155, 4360, 14.9, 79, 1, 16.9, 8, 351, 142, 4054, 14.3, 79, 1, 15.5, 8, 267, 125, 3605, 15, 79, 1, 19.2, 8, 360, 150, 3940, 13, 79, 1, 18.5, 4, 89, 71, 1925, 14, 79, 2, 31.9, 4, 86, 65, 1975, 15.2, 79, 3, 34.1, 4, 98, 80, 1915, 14.4, 79, 1, 35.7, 4, 121, 80, 2670, 15, 79, 1, 27.4, 5, 183, 77, 3530, 20.1, 79, 2, 25.4, 8, 350, 125, 3900, 17.4, 79, 1, 23, 4, 141, 71, 3190, 24.8, 79, 2, 27.2, 8, 260, 90, 3420, 22.2, 79, 1, 23.9, 4, 105, 70, 2200, 13.2, 79, 1, 34.2, 4, 105, 70, 2150, 14.9, 79, 1, 34.5, 4, 85, 65, 2020, 19.2, 79, 3, 31.8, 4, 91, 69, 2130, 14.7, 79, 2, 37.3, 4, 151, 90, 2670, 16, 79, 1, 28.4, 6, 173, 115, 2595, 11.3, 79, 1, 28.8, 6, 173, 115, 2700, 12.9, 79, 1, 26.8, 4, 151, 90, 2556, 13.2, 79, 1, 33.5, 4, 98, 76, 2144, 14.7, 80, 2, 41.5, 4, 89, 60, 1968, 18.8, 80, 3, 38.1, 4, 98, 70, 2120, 15.5, 80, 1, 32.1, 4, 86, 65, 2019, 16.4, 80, 3, 37.2, 4, 151, 90, 2678, 16.5, 80, 1, 28, 4, 140, 88, 2870, 18.1, 80, 1, 26.4, 4, 151, 90, 3003, 20.1, 80, 1, 24.3, 6, 225, 90, 3381, 18.7, 80, 1, 19.1, 4, 97, 78, 2188, 15.8, 80, 2, 34.3, 4, 134, 90, 2711, 15.5, 80, 3, 29.8, 4, 120, 75, 2542, 17.5, 80, 3, 31.3, 4, 119, 92, 2434, 15, 80, 3, 37, 4, 108, 75, 2265, 15.2, 80, 3, 32.2, 4, 86, 65, 2110, 17.9, 80, 3, 46.6, 4, 156, 105, 2800, 14.4, 80, 1, 27.9, 4, 85, 65, 2110, 19.2, 80, 3, 40.8, 4, 90, 48, 2085, 21.7, 80, 2, 44.3, 4, 90, 48, 2335, 23.7, 80, 2, 43.4, 5, 121, 67, 2950, 19.9, 80, 2, 36.4, 4, 146, 67, 3250, 21.8, 80, 2, 30, 4, 91, 67, 1850, 13.8, 80, 3, 44.6, 4, 97, 67, 2145, 18, 80, 3, 33.8, 4, 89, 62, 1845, 15.3, 80, 2, 29.8, 6, 168, 132, 2910, 11.4, 80, 3, 32.7, 3, 70, 100, 2420, 12.5, 80, 3, 23.7, 4, 122, 88, 2500, 15.1, 80, 2, 35, 4, 107, 72, 2290, 17, 80, 3, 32.4, 4, 135, 84, 2490, 15.7, 81, 1, 27.2, 4, 151, 84, 2635, 16.4, 81, 1, 26.6, 4, 156, 92, 2620, 14.4, 81, 1, 25.8, 6, 173, 110, 2725, 12.6, 81, 1, 23.5, 4, 135, 84, 2385, 12.9, 81, 1, 30, 4, 79, 58, 1755, 16.9, 81, 3, 39.1, 4, 86, 64, 1875, 16.4, 81, 1, 39, 4, 81, 60, 1760, 16.1, 81, 3, 35.1, 4, 97, 67, 2065, 17.8, 81, 3, 32.3, 4, 85, 65, 1975, 19.4, 81, 3, 37, 4, 89, 62, 2050, 17.3, 81, 3, 37.7, 4, 91, 68, 1985, 16, 81, 3, 34.1, 4, 105, 63, 2215, 14.9, 81, 1, 34.7, 4, 98, 65, 2045, 16.2, 81, 1, 34.4, 4, 98, 65, 2380, 20.7, 81, 1, 29.9, 4, 105, 74, 2190, 14.2, 81, 2, 33, 4, 107, 75, 2210, 14.4, 81, 3, 33.7, 4, 108, 75, 2350, 16.8, 81, 3, 32.4, 4, 119, 100, 2615, 14.8, 81, 3, 32.9, 4, 120, 74, 2635, 18.3, 81, 3, 31.6, 4, 141, 80, 3230, 20.4, 81, 2, 28.1, 6, 145, 76, 3160, 19.6, 81, 2, 30.7, 6, 168, 116, 2900, 12.6, 81, 3, 25.4, 6, 146, 120, 2930, 13.8, 81, 3, 24.2, 6, 231, 110, 3415, 15.8, 81, 1, 22.4, 8, 350, 105, 3725, 19, 81, 1, 26.6, 6, 200, 88, 3060, 17.1, 81, 1, 20.2, 6, 225, 85, 3465, 16.6, 81, 1, 17.6, 4, 112, 88, 2605, 19.6, 82, 1, 28, 4, 112, 88, 2640, 18.6, 82, 1, 27, 4, 112, 88, 2395, 18, 82, 1, 34, 4, 112, 85, 2575, 16.2, 82, 1, 31, 4, 135, 84, 2525, 16, 82, 1, 29, 4, 151, 90, 2735, 18, 82, 1, 27, 4, 140, 92, 2865, 16.4, 82, 1, 24, 4, 105, 74, 1980, 15.3, 82, 2, 36, 4, 91, 68, 2025, 18.2, 82, 3, 37, 4, 91, 68, 1970, 17.6, 82, 3, 31, 4, 105, 63, 2125, 14.7, 82, 1, 38, 4, 98, 70, 2125, 17.3, 82, 1, 36, 4, 120, 88, 2160, 14.5, 82, 3, 36, 4, 107, 75, 2205, 14.5, 82, 3, 36, 4, 108, 70, 2245, 16.9, 82, 3, 34, 4, 91, 67, 1965, 15, 82, 3, 38, 4, 91, 67, 1965, 15.7, 82, 3, 32, 4, 91, 67, 1995, 16.2, 82, 3, 38, 6, 181, 110, 2945, 16.4, 82, 1, 25, 6, 262, 85, 3015, 17, 82, 1, 38, 4, 156, 92, 2585, 14.5, 82, 1, 26, 6, 232, 112, 2835, 14.7, 82, 1, 22, 4, 144, 96, 2665, 13.9, 82, 3, 32, 4, 135, 84, 2370, 13, 82, 1, 36, 4, 151, 90, 2950, 17.3, 82, 1, 27, 4, 140, 86, 2790, 15.6, 82, 1, 27, 4, 97, 52, 2130, 24.6, 82, 2, 44, 4, 135, 84, 2295, 11.6, 82, 1, 32, 4, 120, 79, 2625, 18.6, 82, 1, 28, 4, 119, 82, 2720, 19.4, 82, 1, 31) val xy = xyr.sliceEx (xyr.dim1, 6) // remove the origin column // val xy = xyr // use all columns - may cause multi-collinearity /** vector of all ones */ val _1 = VectorD.one (xy.dim1) /** index for the data points (instances) */ val t = VectorD.range (0, xy.dim1) /** the separation of the combine data matrix 'xy' into * a data/input matrix 'x' and a response/output vector 'y' */ val (x, y) = pullResponse (xy) /** the combined data matrix 'xy' with a column of all ones prepended * for intercept models */ val oxy = _1 +^: xy /** the data matrix 'x' with a column of all ones prepended for intercept models */ val ox = _1 +^: x } // ExampleAutoMPG object import ExampleAutoMPG._ //::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: /** The `ExampleAutoMPG_Correlation` object performs correlation analysis on * the UCI AutoMPG dataset. * @see archive.ics.uci.edu/ml/datasets/Auto+MPG * > runMain scalation.analytics.ExampleAutoMPG_Correlation */ object ExampleAutoMPG_Correlation extends App { banner ("Correlation Analysis: y vs. x.col(j") for (j <- x.range2) { val x_j = x.col (j) val correlation = y.asInstanceOf [VectorD] corr x_j.asInstanceOf [VectorD] // FIX val corr2 = correlation * correlation println (s"correlation of y vs. x.col($j) = $correlation \t $corr2 \t ${fname(j)}") new Plot (x_j, y, null, s"y vs, x.col($j)") } // for } // ExampleAutoMPG_Correlation object //::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: /** The `ExampleAutoMPG_NullModel` object performs `NullModel` on the UCI AutoMPG * dataset. * @see archive.ics.uci.edu/ml/datasets/Auto+MPG * > runMain scalation.analytics.ExampleAutoMPG_NullModel */ object ExampleAutoMPG_NullModel extends App { banner ("NullModel model: y = b₀") val rg = NullModel (xy) println (rg.analyze ().report) // perform basic analysis val yp = rg.predict () // predict y for all x rows val e = y - yp // error/residual vector println (s"error e = $e") new Plot (t, y, yp, "NullModel") new Plot (t, e, null, "NullModel error") } // ExampleAutoMPG_NullModel object //::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: /** The `ExampleAutoMPG_SimplerRegression` object performs `SimplerRegression` * on the UCI AutoMPG dataset. * @see archive.ics.uci.edu/ml/datasets/Auto+MPG * > runMain scalation.analytics.ExampleAutoMPG_SimplerRegression */ object ExampleAutoMPG_SimplerRegression extends App { banner ("SimplerRegression model: y = b₁*x₁") val rg = SimplerRegression (xy) // create a SimplerRegression model println (rg.analyze ().report) // perform basic analysis val x = xy.sliceCol (0, 1) // predictors values val yp = rg.predict (x) // predict y for all x rows val e = y - yp // error/residual vector println (s"error e = $e") new Plot (t, y, yp, "SimplerRegression") new Plot (t, e, null, "SimplerRegression error") } // ExampleAutoMPG_SimplerRegression object //::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: /** The `ExampleAutoMPG_SimpleRegression` object performs `SimpleRegression` * on the UCI AutoMPG dataset. * @see archive.ics.uci.edu/ml/datasets/Auto+MPG * > runMain scalation.analytics.ExampleAutoMPG_SimpleRegression */ object ExampleAutoMPG_SimpleRegression extends App { banner ("SimpleRegression model: y = b₀ + b₁*x₁") val rg = SimpleRegression (oxy) // create a SimpleRegression model println (rg.analyze ().report) // perform basic analysis val x = oxy.sliceCol (0, 2) // predictors values val yp = rg.predict (x) // predict y for all x rows val e = y - yp // error/residual vector println (s"error e = $e") new Plot (t, y, yp, "SimpleRegression") new Plot (t, e, null, "SimpleRegression error") } // ExampleAutoMPG_SimpleRegression object //::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: /** The `ExampleAutoMPG_Regression` object performs `Regression` on the UCI AutoMPG * dataset. * @see archive.ics.uci.edu/ml/datasets/Auto+MPG * > runMain scalation.analytics.ExampleAutoMPG_Regression */ object ExampleAutoMPG_Regression extends App { banner ("Regression model: y = b₀ + b₁*x₁ + b₂*x₂ + b₃*x₃ + b₄*x₄ + b₅*x₅ + b₆*x₆") val rg = Regression (oxy, fname) // create a Regression Model println (rg.analyze ().report) // perform basic analysis println ("Summary") println (rg.summary) // produce summary statistics val x = oxy.sliceCol (0, oxy.dim2-1) // predictors values val yp = rg.predict (x) // predict y for x rows val e = y - yp // error/residual vector println (s"error e = $e") new Plot (t, y, yp, "Regression") new Plot (t, e, null, "Regression error") } // ExampleAutoMPG_Regression object