* installing *source* package ‘SVMFeature’ ... ** using staged installation ** R ** data *** moving datasets to lazyload DB ** inst ** byte-compile and prepare package for lazy loading Datos cargados y normalizados: x0 x1 x2 x3 x4 x5 x6 x7 1 0.9036145 0 0.12592593 0.4352941 0.2706767 0.3683206 0.5018051 0.23333333 2 0.6746988 1 0.08888889 0.3411765 0.1954887 0.3320611 0.6101083 0.14074074 3 0.6506024 0 0.09925926 0.5235294 0.6240602 0.3110687 0.5559567 0.27407407 4 0.6626506 0 0.10370370 0.5176471 0.3383459 0.3854962 0.5342960 0.25185185 5 0.1566265 0 0.09481481 0.2647059 0.3383459 0.3187023 0.3212996 0.23333333 6 0.4819277 1 0.08148148 0.2705882 0.1654135 0.2461832 0.5234657 0.08148148 x8 x9 x10 x11 x12 x13 x14 x15 1 0.5902439 0.4574780 0.5297450 0.6763006 0.4418605 0.15966387 0 0.3333333 2 0.1902439 0.5777126 0.5977337 0.4421965 0.5514950 0.07563025 0 0.3076923 3 0.4975610 0.7859238 0.5892351 0.6936416 0.6677741 0.26050420 0 0.2564103 4 0.6975610 0.5865103 0.5240793 0.4768786 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0 0.5 0.5 2 0 0.5 0 0 0 0.0000000 0.46875 0.0000000 0 0 0.25 0 0 0.5 0.5 3 0 0.5 0 0 0 0.0000000 1.00000 0.0000000 0 0 0.30 0 1 0.5 0.5 4 0 0.5 0 0 0 0.0000000 0.46875 0.3666667 0 0 0.40 0 0 0.5 0.5 5 0 0.5 0 0 0 0.2727273 0.34375 0.3333333 0 0 0.55 0 0 0.5 0.5 6 0 0.5 0 0 0 0.0000000 0.34375 0.1333333 0 0 0.30 0 0 0.5 0.5 x84 x85 x86 x87 x88 x89 x90 x91 x92 x93 x94 x95 x96 1 0 0 0.5714286 0.00000000 0.0000000 0.00 0 0.0000000 0 0 0 0 0 2 0 0 0.0000000 0.11111111 0.4482759 0.00 0 0.1379310 0 0 0 0 0 3 0 0 0.0000000 0.11111111 0.3103448 0.95 0 0.8620690 0 0 0 0 0 4 0 0 0.4000000 0.00000000 0.0000000 0.00 0 0.0000000 0 0 0 0 0 5 0 0 0.0000000 0.16666667 0.5172414 0.00 0 0.2068966 0 0 0 0 0 6 0 0 0.0000000 0.09259259 0.4827586 0.00 0 0.1034483 0 0 0 0 0 x97 x98 x99 x100 x101 x102 x103 x104 x105 x106 1 0 0.0000000 0.1851852 0.4814815 0.0000000 0 0.2916667 0 0 0 2 0 0.0000000 0.1481481 0.4814815 0.0000000 0 0.2083333 0 0 0 3 0 0.0000000 0.1851852 0.2592593 0.8333333 0 1.0000000 0 0 0 4 0 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1.0000000 0.07017544 0.8521940 0.0000000 1 0.4464286 0.5275229 2 0.3191489 1.0000000 0.09122807 0.8175520 0.0000000 1 0.5535714 0.4862385 3 0.2269504 1.0000000 0.07719298 0.9053118 0.4966443 1 0.5000000 0.2844037 4 0.4397163 1.0000000 0.04210526 0.8406467 0.0000000 1 0.3214286 0.5275229 5 0.3546099 0.9831081 0.41754386 0.0000000 0.0000000 1 0.5535714 0.5504587 6 0.4113475 1.0000000 0.08070175 0.5958430 0.0000000 1 0.3035714 0.6009174 x236 x237 x238 x239 x240 x241 x242 1 0.5549818 0.5043439 0.1739130 1.0000000 0.2957746 0.7933884 0.0000000 2 0.5434386 0.4403292 0.1793478 1.0000000 0.2042254 0.8409091 0.0000000 3 0.6567436 0.4229538 0.1793478 1.0000000 0.2042254 0.9152893 0.5714286 4 0.5546780 0.5171468 0.2173913 1.0000000 0.3169014 0.8367769 0.0000000 5 0.2548603 0.0000000 0.2880435 0.9728814 0.6971831 0.0000000 0.0000000 6 0.4526124 0.4288980 0.2391304 1.0000000 0.1302817 0.7727273 0.0000000 x243 x244 x245 x246 x247 x248 x249 x250 1 1.0000000 0.6607143 0.5784314 0.5848455 0.7003521 0.2248062 1 0.4175824 2 1.0000000 0.7142857 0.5098039 0.5804316 0.6123239 0.2325581 1 0.2609890 3 0.9107143 0.6250000 0.3954248 0.6444335 0.5968310 0.3023256 1 0.2747253 4 1.0000000 0.5714286 0.5196078 0.6130456 0.6943662 0.2790698 1 0.4120879 5 1.0000000 0.8392857 0.6699346 0.3136341 0.2584507 0.4031008 1 0.8516484 6 1.0000000 0.4642857 0.5196078 0.5458558 0.5894366 0.2558140 1 0.2115385 x251 x252 x253 x254 x255 x256 x257 x258 x259 1 0.7711172 0 1 0.5000000 0.5588235 0.6244522 0.6752982 0.5365854 1 2 0.8637602 0 1 0.3947368 0.4537815 0.5985977 0.5702648 0.5365854 1 3 0.8446866 0 1 0.3947368 0.4369748 0.5337423 0.5298225 0.7439024 1 4 0.8501362 0 1 0.2894737 0.4831933 0.6730938 0.6531859 0.5975610 1 5 0.2997275 0 1 0.4736842 0.5924370 0.6748466 0.7192319 0.6585366 1 6 0.8256131 0 1 0.3684211 0.4243697 0.5530237 0.5144021 0.5853659 1 x260 x261 x262 x263 x264 x265 x266 x267 1 0.4576271 0.8701299 0 0.5 0.4864865 0.6691729 0.5925126 0.6605744 2 0.3728814 0.9220779 0 0.5 0.3513514 0.5714286 0.5644348 0.5587467 3 0.3762712 0.8896104 0 0.5 0.3513514 0.6315789 0.4917207 0.5838120 4 0.5152542 0.8798701 0 0.5 0.4054054 0.6015038 0.6738661 0.6872063 5 0.6610169 0.6298701 0 0.5 0.4594595 0.6240602 0.5536357 0.5885117 6 0.2508475 0.9188312 0 0.5 0.3513514 0.4736842 0.4758819 0.4308094 x268 x269 x270 x271 x272 x273 x274 x275 x276 1 0.6385542 1.0000000 0.3813559 0.9685315 0 0.5 0.53125 0.7416667 0.5075188 2 0.6144578 1.0000000 0.3601695 1.0000000 0 0.5 0.31250 0.6750000 0.4857143 3 0.7831325 1.0000000 0.4025424 0.9160839 0 0.5 0.34375 0.7833333 0.4248120 4 0.6867470 1.0000000 0.5169492 0.9230769 0 0.5 0.37500 0.7166667 0.5924812 5 0.6746988 0.8536585 0.5169492 0.9020979 0 0.5 0.53125 0.6833333 0.4338346 6 0.6265060 1.0000000 0.2754237 1.0000000 0 0.5 0.37500 0.5833333 0.4398496 x277 y1 1 0.5695793 -1 2 0.5009709 -1 3 0.5669903 -1 4 0.6485437 1 5 0.4511327 -1 6 0.3825243 1 Generando poblacion inicial de tamaño 50 Número de soluciones por frente: # A tibble: 10 × 2 FRONT count 1 1 6 2 2 7 3 3 7 4 4 8 5 5 4 6 6 6 7 7 5 8 8 3 9 9 2 10 10 2 ITERACION 0: 0.000105 Creando nueva población por cruce desde 50 hasta 100.... Reduciendo de tamaño 2N a tamaño N.... FRONT 1: 6 soluciones FRONT 2: 12 soluciones FRONT 3: 19 soluciones FRONT 4: 28 soluciones FRONT 5: 35 soluciones FRONT 6: 44 soluciones Número de soluciones por frente: # A tibble: 7 × 2 FRONT count 1 1 6 2 2 6 3 3 7 4 4 9 5 5 7 6 6 9 7 7 6 ITERACION 1: 5.104739 Creando nueva población por cruce desde 50 hasta 100.... Reduciendo de tamaño 2N a tamaño N.... FRONT 1: 6 soluciones FRONT 2: 12 soluciones FRONT 3: 21 soluciones FRONT 4: 30 soluciones FRONT 5: 37 soluciones FRONT 6: 42 soluciones FRONT 7: 50 soluciones Número de soluciones por frente: # A tibble: 7 × 2 FRONT count 1 1 6 2 2 6 3 3 9 4 4 9 5 5 7 6 6 5 7 7 8 ITERACION 2: 10.068604 Creando nueva población por cruce desde 50 hasta 100.... Reduciendo de tamaño 2N a tamaño N.... FRONT 1: 6 soluciones FRONT 2: 11 soluciones FRONT 3: 20 soluciones FRONT 4: 30 soluciones FRONT 5: 40 soluciones FRONT 6: 50 soluciones Número de soluciones por frente: # A tibble: 6 × 2 FRONT count 1 1 6 2 2 5 3 3 9 4 4 10 5 5 10 6 6 10 ITERACION 3: 15.013678 Creando nueva población por cruce desde 50 hasta 100.... Reduciendo de tamaño 2N a tamaño N.... FRONT 1: 5 soluciones FRONT 2: 12 soluciones FRONT 3: 20 soluciones FRONT 4: 31 soluciones FRONT 5: 40 soluciones Número de soluciones por frente: # A tibble: 6 × 2 FRONT count 1 1 5 2 2 7 3 3 8 4 4 11 5 5 9 6 6 10 ITERACION 4: 19.902457 Creando nueva población por cruce desde 50 hasta 100.... Reduciendo de tamaño 2N a tamaño N.... FRONT 1: 6 soluciones FRONT 2: 14 soluciones FRONT 3: 22 soluciones FRONT 4: 32 soluciones FRONT 5: 45 soluciones Número de soluciones por frente: # A tibble: 6 × 2 FRONT count 1 1 6 2 2 8 3 3 8 4 4 10 5 5 13 6 6 5 ITERACION 5: 24.855590 Creando nueva población por cruce desde 50 hasta 100.... Reduciendo de tamaño 2N a tamaño N.... FRONT 1: 8 soluciones FRONT 2: 15 soluciones FRONT 3: 23 soluciones FRONT 4: 34 soluciones FRONT 5: 45 soluciones Número de soluciones por frente: # A tibble: 6 × 2 FRONT count 1 1 8 2 2 7 3 3 8 4 4 11 5 5 11 6 6 5 ITERACION 6: 29.770616 Creando nueva población por cruce desde 50 hasta 100.... Reduciendo de tamaño 2N a tamaño N.... FRONT 1: 2 soluciones FRONT 2: 9 soluciones FRONT 3: 17 soluciones FRONT 4: 25 soluciones FRONT 5: 33 soluciones FRONT 6: 45 soluciones Número de soluciones por frente: # A tibble: 7 × 2 FRONT count 1 1 2 2 2 7 3 3 8 4 4 8 5 5 8 6 6 12 7 7 5 ITERACION 7: 34.779522 Creando nueva población por cruce desde 50 hasta 100.... Reduciendo de tamaño 2N a tamaño N.... FRONT 1: 4 soluciones FRONT 2: 9 soluciones FRONT 3: 15 soluciones FRONT 4: 23 soluciones FRONT 5: 33 soluciones FRONT 6: 39 soluciones Número de soluciones por frente: # A tibble: 7 × 2 FRONT count 1 1 4 2 2 5 3 3 6 4 4 8 5 5 10 6 6 6 7 7 11 ITERACION 8: 39.891341 Creando nueva población por cruce desde 50 hasta 100.... Reduciendo de tamaño 2N a tamaño N.... FRONT 1: 4 soluciones FRONT 2: 9 soluciones FRONT 3: 15 soluciones FRONT 4: 21 soluciones FRONT 5: 30 soluciones FRONT 6: 42 soluciones Número de soluciones por frente: # A tibble: 7 × 2 FRONT count 1 1 4 2 2 5 3 3 6 4 4 6 5 5 9 6 6 12 7 7 8 ITERACION 9: 44.971378 Creando nueva población por cruce desde 50 hasta 100.... Reduciendo de tamaño 2N a tamaño N.... FRONT 1: 6 soluciones FRONT 2: 12 soluciones FRONT 3: 18 soluciones FRONT 4: 24 soluciones FRONT 5: 36 soluciones FRONT 6: 46 soluciones Número de soluciones por frente: # A tibble: 7 × 2 FRONT count 1 1 6 2 2 6 3 3 6 4 4 6 5 5 12 6 6 10 7 7 4 VECTORS FEATURES DIST EPS 1 350, 189 x51, x261, x0, x174, x155 0.9024884 263.6587 2 178, 140 x262, x270, x110, x178, x175 0.5719376 0.898516 3 82, 248 x98, x223, x88, x108, x255 0.665769 187.4533 4 278, 132 x107, x13, x64, x68, x262 0.2331164 0.3813256 5 359, 254 x229, x174, x88, x244, x205 0.4908358 0.4502955 6 350, 132 x51, x261, x0, x174, x155 0.6883909 196.1886 7 82, 132 x219, x159, x199, x103, x21 0.6579759 1.201289 8 310, 248 x267, x252, x221, x108, x262 0.6644959 6.083927 9 82, 140 x98, x223, x88, x108, x255 0.6395265 182.2838 10 350, 115 x225, x60, x161, x41, x165 0.6101179 1.773734 11 350, 115 x1, x261, x129, x48, x155 0.3961562 1.358437 12 178, 227 x262, x270, x7, x61, x175 0.468417 1.533915 13 278, 234 x267, x13, x64, x68, x262 0.3245112 1.082633 14 350, 6 x225, x84, x34, x48, x165 0.2231695 0.4879786 15 86, 21 x1, x261, x129, x202, x100 0.6378376 2.7515 16 310, 134 x267, x252, x237, x68, x262 0.6039528 5.350106 17 286, 378 x228, x1, x249, x163, x208 0.5054949 2.512338 18 350, 254 x235, x261, x129, x143, x100 0.4727077 2.319762 19 237, 140 x74, x103, x159, x54, x180 0.5738887 5.296207 20 350, 134 x225, x60, x161, x41, x165 0.5626566 5.149435 21 146, 248 x46, x137, x80, x142, x135 0.0003195906 1.106297 22 342, 234 x267, x13, x64, x68, x262 0.3118114 1.277374 23 350, 254 x1, x261, x129, x143, x100 0.4112763 1.586993 24 186, 234 x18, x1, x249, x60, x208 0.524266 16.1291 25 82, 248 x20, x190, x88, x108, x255 0.5551436 181.6982 26 359, 189 x229, x174, x88, x211, x205 0.4387724 2.790489 27 237, 6 x74, x183, x159, x54, x180 0.4471587 3.393258 28 350, 390 x164, x60, x161, x41, x155 0.476198 7.373679 29 108, 6 x229, x163, x150, x47, x240 0.008751995 1.503585 30 342, 254 x267, x13, x64, x68, x262 0.2673215 1.914297 31 16, 234 x18, x13, x83, x241, x262 0.07601363 1.584543 32 329, 316 x205, x261, x231, x211, x141 0.2987225 1.981171 33 359, 254 x229, x27, x88, x211, x17 0.5434726 27.8223 34 278, 118 x267, x47, x64, x68, x1 0.5161236 12.23865 35 186, 390 x18, x1, x249, x60, x208 0.4931772 19.32128 36 350, 189 x1, x261, x0, x48, x155 0.5113155 127.9917 37 137, 140 x120, x197, x124, x1, x92 0.392186 4.653796 38 197, 102 x229, x192, x273, x208, x0 0.4246956 5.196433 39 350, 140 x1, x261, x129, x143, x100 0.3664546 3.678099 40 350, 115 x164, x60, x161, x41, x155 0.461518 9.859955 41 57, 11 x149, x270, x7, x61, x175 0.2839685 3.14734 42 211, 130 x253, x154, x232, x54, x180 0.06051199 1.799255 43 145, 140 x99, x242, x130, x126, x193 0.06782545 2.679166 44 342, 106 x267, x13, x64, x68, x262 0.2418894 2.726781 45 82, 132 x98, x223, x88, x103, x21 0.4940747 75.78514 46 271, 309 x253, x172, x232, x54, x193 0 2.125089 47 350, 106 x51, x261, x0, x174, x155 0.4993117 175.7842 48 67, 247 x3, x197, x162, x68, x42 0.1558145 2.736241 49 272, 384 x229, x163, x150, x47, x240 0.0005021004 2.275455 50 286, 254 x228, x1, x249, x163, x208 0.423437 7.348837 DOMINA_A 1 NULL 2 11, 12, 13, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 3 NULL 4 14, 21, 29, 31, 42, 43, 46, 48, 49 5 11, 12, 13, 14, 18, 21, 22, 23, 26, 27, 28, 29, 30, 31, 32, 37, 38, 39, 40, 41, 42, 43, 44, 46, 48, 49, 50 6 NULL 7 9, 10, 11, 12, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 8 9, 24, 25, 28, 33, 34, 35, 36, 40, 45, 47, 50 9 NULL 10 16, 17, 18, 19, 20, 24, 25, 26, 27, 28, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 11 29, 30, 31, 32, 37, 39, 41, 42, 43, 44, 46, 48, 49 12 23, 26, 27, 30, 31, 32, 37, 38, 39, 40, 41, 42, 43, 44, 46, 48, 49, 50 13 21, 22, 29, 30, 31, 32, 41, 42, 43, 44, 46, 48, 49 14 21, 29, 31, 42, 43, 46, 48, 49 15 16, 19, 20, 24, 25, 26, 27, 28, 33, 34, 35, 36, 37, 38, 39, 40, 41, 45, 47, 50 16 24, 25, 28, 33, 34, 35, 36, 40, 45, 47, 50 17 26, 27, 28, 35, 37, 38, 39, 40, 41, 43, 44, 45, 47, 48, 50 18 26, 27, 37, 38, 39, 40, 41, 43, 44, 48, 50 19 24, 25, 28, 33, 34, 35, 36, 40, 45, 47, 50 20 24, 25, 28, 33, 34, 35, 36, 38, 40, 45, 47, 50 21 46 22 29, 30, 31, 32, 41, 42, 43, 44, 46, 48, 49 23 30, 32, 37, 39, 41, 42, 43, 44, 46, 48, 49 24 35, 36, 45, 47 25 NULL 26 37, 38, 39, 41, 50 27 37, 38, 39, 50 28 40 29 46, 49 30 43, 44, 46, 48, 49 31 42, 43, 46, 49 32 41, 43, 44, 46, 48, 49 33 36, 45, 47 34 35, 36, 45, 47 35 NULL 36 47 37 NULL 38 50 39 NULL 40 NULL 41 NULL 42 46, 49 43 NULL 44 48 45 NULL 46 NULL 47 NULL 48 NULL 49 NULL 50 NULL DOMINADO_POR SOL_DOM_POR 1 NULL 0 2 NULL 0 3 NULL 0 4 NULL 0 5 NULL 0 6 NULL 0 7 NULL 0 8 NULL 0 9 7, 8 0 10 7 0 11 2, 5, 7 0 12 2, 5, 7 0 13 2, 5 0 14 4, 5 0 15 7 0 16 7, 10, 15 0 17 2, 7, 10 0 18 2, 5, 7, 10 0 19 7, 10, 15 0 20 2, 7, 10, 15 0 21 2, 4, 5, 13, 14 0 22 2, 5, 7, 13 0 23 2, 5, 7, 12 0 24 2, 7, 8, 10, 15, 16, 19, 20 0 25 2, 7, 8, 10, 15, 16, 19, 20 0 26 2, 5, 7, 10, 12, 15, 17, 18 0 27 2, 5, 7, 10, 12, 15, 17, 18 0 28 2, 5, 7, 8, 10, 15, 16, 17, 19, 20 0 29 2, 4, 5, 7, 11, 13, 14, 22 0 30 2, 5, 7, 10, 11, 12, 13, 22, 23 0 31 2, 4, 5, 7, 11, 12, 13, 14, 22 0 32 2, 5, 7, 10, 11, 12, 13, 22, 23 0 33 2, 7, 8, 10, 15, 16, 19, 20 0 34 2, 7, 8, 10, 15, 16, 19, 20 0 35 2, 7, 8, 10, 15, 16, 17, 19, 20, 24, 34 0 36 2, 7, 8, 10, 15, 16, 19, 20, 24, 33, 34 0 37 2, 5, 7, 10, 11, 12, 15, 17, 18, 23, 26, 27 0 38 2, 5, 7, 10, 12, 15, 17, 18, 20, 26, 27 0 39 2, 5, 7, 10, 11, 12, 15, 17, 18, 23, 26, 27 0 40 2, 5, 7, 8, 10, 12, 15, 16, 17, 18, 19, 20, 28 0 41 2, 5, 7, 10, 11, 12, 13, 15, 17, 18, 22, 23, 26, 32 0 42 2, 4, 5, 7, 10, 11, 12, 13, 14, 22, 23, 31 0 43 2, 4, 5, 7, 10, 11, 12, 13, 14, 17, 18, 22, 23, 30, 31, 32 0 44 2, 5, 7, 10, 11, 12, 13, 17, 18, 22, 23, 30, 32 0 45 2, 7, 8, 10, 15, 16, 17, 19, 20, 24, 33, 34 0 46 2, 4, 5, 7, 10, 11, 12, 13, 14, 21, 22, 23, 29, 30, 31, 32, 42 0 47 2, 7, 8, 10, 15, 16, 17, 19, 20, 24, 33, 34, 36 0 48 2, 4, 5, 7, 10, 11, 12, 13, 14, 17, 18, 22, 23, 30, 32, 44 0 49 2, 4, 5, 7, 10, 11, 12, 13, 14, 22, 23, 29, 30, 31, 32, 42 0 50 2, 5, 7, 8, 10, 12, 15, 16, 17, 18, 19, 20, 26, 27, 38 0 FRONT CROW_DIST 1 1 0 2 1 0 3 1 0 4 1 0 5 1 0 6 1 0 7 1 0 8 1 0 9 2 0 10 2 0 11 2 0 12 2 0 13 2 0 14 2 0 15 2 0 16 3 0 17 3 0 18 3 0 19 3 0 20 3 0 21 3 0 22 3 0 23 3 0 24 4 0 25 4 0 26 4 0 27 4 0 28 4 0 29 4 0 30 4 0 31 4 0 32 4 0 33 4 0 34 4 0 35 5 0 36 5 0 37 5 0 38 5 0 39 5 0 40 5 0 41 5 0 42 5 0 43 5 0 44 5 0 45 5 0 46 6 Inf 47 6 Inf 48 6 1.467396 49 6 1.014322 50 6 0.5707333 Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0. ℹ Please use the `linewidth` argument instead. ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (SVMFeature)