R Under development (unstable) (2024-04-29 r86495 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(MixAll) Loading required package: rtkore Loading required package: Rcpp Attaching package: 'rtkore' The following object is masked from 'package:Rcpp': LdFlags > ## get data and target from iris data set > data(iris) > x <- as.matrix(iris[,1:4]); z <- as.vector(iris[,5]); n <- nrow(x); p <- ncol(x) > ## add missing values at random > indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2) > cbind(indexes, x[indexes]) [,1] [,2] [,3] [1,] 77 4 1.4 [2,] 147 4 1.9 [3,] 64 2 2.9 [4,] 135 1 6.1 [5,] 119 2 2.6 > x[indexes] <- NA > ## learn continuous model > model <- learnDiagGaussian( data=x, labels= z, prop = c(1/3,1/3,1/3) + , models = clusterDiagGaussianNames(prop = "equal") + , algo = "simul", nbIter = 2, epsilon = 1e-08 + ) > missingValues(model) row col value 1 135 1 8.185348 2 64 2 2.832163 3 119 2 2.468325 4 77 4 1.626138 5 147 4 2.177216 > print(model) **************************************** * model name = gaussian_p_sjk * data = Sepal.Length Sepal.Width Petal.Length Petal.Width [1,] 5.100000 3.500000 1.400000 0.200000 [2,] 4.900000 3.000000 1.400000 0.200000 [3,] 4.700000 3.200000 1.300000 0.200000 [4,] 4.600000 3.100000 1.500000 0.200000 [5,] 5.000000 3.600000 1.400000 0.200000 [6,] 5.400000 3.900000 1.700000 0.400000 [7,] 4.600000 3.400000 1.400000 0.300000 [8,] 5.000000 3.400000 1.500000 0.200000 [9,] 4.400000 2.900000 1.400000 0.200000 [10,] 4.900000 3.100000 1.500000 0.100000 [11,] 5.400000 3.700000 1.500000 0.200000 [12,] 4.800000 3.400000 1.600000 0.200000 [13,] 4.800000 3.000000 1.400000 0.100000 [14,] 4.300000 3.000000 1.100000 0.100000 [15,] 5.800000 4.000000 1.200000 0.200000 [16,] 5.700000 4.400000 1.500000 0.400000 [17,] 5.400000 3.900000 1.300000 0.400000 [18,] 5.100000 3.500000 1.400000 0.300000 [19,] 5.700000 3.800000 1.700000 0.300000 [20,] 5.100000 3.800000 1.500000 0.300000 [21,] 5.400000 3.400000 1.700000 0.200000 [22,] 5.100000 3.700000 1.500000 0.400000 [23,] 4.600000 3.600000 1.000000 0.200000 [24,] 5.100000 3.300000 1.700000 0.500000 [25,] 4.800000 3.400000 1.900000 0.200000 [26,] 5.000000 3.000000 1.600000 0.200000 [27,] 5.000000 3.400000 1.600000 0.400000 [28,] 5.200000 3.500000 1.500000 0.200000 [29,] 5.200000 3.400000 1.400000 0.200000 [30,] 4.700000 3.200000 1.600000 0.200000 [31,] 4.800000 3.100000 1.600000 0.200000 [32,] 5.400000 3.400000 1.500000 0.400000 [33,] 5.200000 4.100000 1.500000 0.100000 [34,] 5.500000 4.200000 1.400000 0.200000 [35,] 4.900000 3.100000 1.500000 0.200000 [36,] 5.000000 3.200000 1.200000 0.200000 [37,] 5.500000 3.500000 1.300000 0.200000 [38,] 4.900000 3.600000 1.400000 0.100000 [39,] 4.400000 3.000000 1.300000 0.200000 [40,] 5.100000 3.400000 1.500000 0.200000 [41,] 5.000000 3.500000 1.300000 0.300000 [42,] 4.500000 2.300000 1.300000 0.300000 [43,] 4.400000 3.200000 1.300000 0.200000 [44,] 5.000000 3.500000 1.600000 0.600000 [45,] 5.100000 3.800000 1.900000 0.400000 [46,] 4.800000 3.000000 1.400000 0.300000 [47,] 5.100000 3.800000 1.600000 0.200000 [48,] 4.600000 3.200000 1.400000 0.200000 [49,] 5.300000 3.700000 1.500000 0.200000 [50,] 5.000000 3.300000 1.400000 0.200000 [51,] 7.000000 3.200000 4.700000 1.400000 [52,] 6.400000 3.200000 4.500000 1.500000 [53,] 6.900000 3.100000 4.900000 1.500000 [54,] 5.500000 2.300000 4.000000 1.300000 [55,] 6.500000 2.800000 4.600000 1.500000 [56,] 5.700000 2.800000 4.500000 1.300000 [57,] 6.300000 3.300000 4.700000 1.600000 [58,] 4.900000 2.400000 3.300000 1.000000 [59,] 6.600000 2.900000 4.600000 1.300000 [60,] 5.200000 2.700000 3.900000 1.400000 [61,] 5.000000 2.000000 3.500000 1.000000 [62,] 5.900000 3.000000 4.200000 1.500000 [63,] 6.000000 2.200000 4.000000 1.000000 [64,] 6.100000 2.832163 4.700000 1.400000 [65,] 5.600000 2.900000 3.600000 1.300000 [66,] 6.700000 3.100000 4.400000 1.400000 [67,] 5.600000 3.000000 4.500000 1.500000 [68,] 5.800000 2.700000 4.100000 1.000000 [69,] 6.200000 2.200000 4.500000 1.500000 [70,] 5.600000 2.500000 3.900000 1.100000 [71,] 5.900000 3.200000 4.800000 1.800000 [72,] 6.100000 2.800000 4.000000 1.300000 [73,] 6.300000 2.500000 4.900000 1.500000 [74,] 6.100000 2.800000 4.700000 1.200000 [75,] 6.400000 2.900000 4.300000 1.300000 [76,] 6.600000 3.000000 4.400000 1.400000 [77,] 6.800000 2.800000 4.800000 1.626138 [78,] 6.700000 3.000000 5.000000 1.700000 [79,] 6.000000 2.900000 4.500000 1.500000 [80,] 5.700000 2.600000 3.500000 1.000000 [81,] 5.500000 2.400000 3.800000 1.100000 [82,] 5.500000 2.400000 3.700000 1.000000 [83,] 5.800000 2.700000 3.900000 1.200000 [84,] 6.000000 2.700000 5.100000 1.600000 [85,] 5.400000 3.000000 4.500000 1.500000 [86,] 6.000000 3.400000 4.500000 1.600000 [87,] 6.700000 3.100000 4.700000 1.500000 [88,] 6.300000 2.300000 4.400000 1.300000 [89,] 5.600000 3.000000 4.100000 1.300000 [90,] 5.500000 2.500000 4.000000 1.300000 [91,] 5.500000 2.600000 4.400000 1.200000 [92,] 6.100000 3.000000 4.600000 1.400000 [93,] 5.800000 2.600000 4.000000 1.200000 [94,] 5.000000 2.300000 3.300000 1.000000 [95,] 5.600000 2.700000 4.200000 1.300000 [96,] 5.700000 3.000000 4.200000 1.200000 [97,] 5.700000 2.900000 4.200000 1.300000 [98,] 6.200000 2.900000 4.300000 1.300000 [99,] 5.100000 2.500000 3.000000 1.100000 [100,] 5.700000 2.800000 4.100000 1.300000 [101,] 6.300000 3.300000 6.000000 2.500000 [102,] 5.800000 2.700000 5.100000 1.900000 [103,] 7.100000 3.000000 5.900000 2.100000 [104,] 6.300000 2.900000 5.600000 1.800000 [105,] 6.500000 3.000000 5.800000 2.200000 [106,] 7.600000 3.000000 6.600000 2.100000 [107,] 4.900000 2.500000 4.500000 1.700000 [108,] 7.300000 2.900000 6.300000 1.800000 [109,] 6.700000 2.500000 5.800000 1.800000 [110,] 7.200000 3.600000 6.100000 2.500000 [111,] 6.500000 3.200000 5.100000 2.000000 [112,] 6.400000 2.700000 5.300000 1.900000 [113,] 6.800000 3.000000 5.500000 2.100000 [114,] 5.700000 2.500000 5.000000 2.000000 [115,] 5.800000 2.800000 5.100000 2.400000 [116,] 6.400000 3.200000 5.300000 2.300000 [117,] 6.500000 3.000000 5.500000 1.800000 [118,] 7.700000 3.800000 6.700000 2.200000 [119,] 7.700000 2.468325 6.900000 2.300000 [120,] 6.000000 2.200000 5.000000 1.500000 [121,] 6.900000 3.200000 5.700000 2.300000 [122,] 5.600000 2.800000 4.900000 2.000000 [123,] 7.700000 2.800000 6.700000 2.000000 [124,] 6.300000 2.700000 4.900000 1.800000 [125,] 6.700000 3.300000 5.700000 2.100000 [126,] 7.200000 3.200000 6.000000 1.800000 [127,] 6.200000 2.800000 4.800000 1.800000 [128,] 6.100000 3.000000 4.900000 1.800000 [129,] 6.400000 2.800000 5.600000 2.100000 [130,] 7.200000 3.000000 5.800000 1.600000 [131,] 7.400000 2.800000 6.100000 1.900000 [132,] 7.900000 3.800000 6.400000 2.000000 [133,] 6.400000 2.800000 5.600000 2.200000 [134,] 6.300000 2.800000 5.100000 1.500000 [135,] 8.185348 2.600000 5.600000 1.400000 [136,] 7.700000 3.000000 6.100000 2.300000 [137,] 6.300000 3.400000 5.600000 2.400000 [138,] 6.400000 3.100000 5.500000 1.800000 [139,] 6.000000 3.000000 4.800000 1.800000 [140,] 6.900000 3.100000 5.400000 2.100000 [141,] 6.700000 3.100000 5.600000 2.400000 [142,] 6.900000 3.100000 5.100000 2.300000 [143,] 5.800000 2.700000 5.100000 1.900000 [144,] 6.800000 3.200000 5.900000 2.300000 [145,] 6.700000 3.300000 5.700000 2.500000 [146,] 6.700000 3.000000 5.200000 2.300000 [147,] 6.300000 2.500000 5.000000 2.177216 [148,] 6.500000 3.000000 5.200000 2.000000 [149,] 6.200000 3.400000 5.400000 2.300000 [150,] 5.900000 3.000000 5.100000 1.800000 * missing = row col [1,] 135 1 [2,] 64 2 [3,] 119 2 [4,] 77 4 [5,] 147 4 * nbSample = 150 * nbCluster = 3 * lnLikelihood = -1038.039 * nbFreeParameter= 70 * criterion name = ICL * criterion value= 2434.038 * zi = [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [149] 2 2 **************************************** *** Cluster: 1 * Proportion = 0.3333333 * Means = 5.006 3.428 1.462 0.246 * S.D. = 0.3489470 0.3752546 0.1719186 0.1043264 **************************************** *** Cluster: 2 * Proportion = 0.3333333 * Means = 5.936000 2.768643 4.260000 1.330523 * S.D. = 0.5109834 0.3102216 0.4651881 0.1999892 **************************************** *** Cluster: 3 * Proportion = 0.3333333 * Means = 6.629707 2.971367 5.552000 2.031544 * S.D. = 0.6639157 0.3228524 0.5463479 0.2720902 **************************************** > model <- learnDiagGaussian( data=x, labels= z, + , models = clusterDiagGaussianNames(prop = "equal") + , algo = "impute", nbIter = 2, epsilon = 1e-08) > missingValues(model) row col value > print(model) **************************************** * model name = gaussian_p_sjk * data = Sepal.Length Sepal.Width Petal.Length Petal.Width [1,] 5.100000 3.500000 1.400000 0.200000 [2,] 4.900000 3.000000 1.400000 0.200000 [3,] 4.700000 3.200000 1.300000 0.200000 [4,] 4.600000 3.100000 1.500000 0.200000 [5,] 5.000000 3.600000 1.400000 0.200000 [6,] 5.400000 3.900000 1.700000 0.400000 [7,] 4.600000 3.400000 1.400000 0.300000 [8,] 5.000000 3.400000 1.500000 0.200000 [9,] 4.400000 2.900000 1.400000 0.200000 [10,] 4.900000 3.100000 1.500000 0.100000 [11,] 5.400000 3.700000 1.500000 0.200000 [12,] 4.800000 3.400000 1.600000 0.200000 [13,] 4.800000 3.000000 1.400000 0.100000 [14,] 4.300000 3.000000 1.100000 0.100000 [15,] 5.800000 4.000000 1.200000 0.200000 [16,] 5.700000 4.400000 1.500000 0.400000 [17,] 5.400000 3.900000 1.300000 0.400000 [18,] 5.100000 3.500000 1.400000 0.300000 [19,] 5.700000 3.800000 1.700000 0.300000 [20,] 5.100000 3.800000 1.500000 0.300000 [21,] 5.400000 3.400000 1.700000 0.200000 [22,] 5.100000 3.700000 1.500000 0.400000 [23,] 4.600000 3.600000 1.000000 0.200000 [24,] 5.100000 3.300000 1.700000 0.500000 [25,] 4.800000 3.400000 1.900000 0.200000 [26,] 5.000000 3.000000 1.600000 0.200000 [27,] 5.000000 3.400000 1.600000 0.400000 [28,] 5.200000 3.500000 1.500000 0.200000 [29,] 5.200000 3.400000 1.400000 0.200000 [30,] 4.700000 3.200000 1.600000 0.200000 [31,] 4.800000 3.100000 1.600000 0.200000 [32,] 5.400000 3.400000 1.500000 0.400000 [33,] 5.200000 4.100000 1.500000 0.100000 [34,] 5.500000 4.200000 1.400000 0.200000 [35,] 4.900000 3.100000 1.500000 0.200000 [36,] 5.000000 3.200000 1.200000 0.200000 [37,] 5.500000 3.500000 1.300000 0.200000 [38,] 4.900000 3.600000 1.400000 0.100000 [39,] 4.400000 3.000000 1.300000 0.200000 [40,] 5.100000 3.400000 1.500000 0.200000 [41,] 5.000000 3.500000 1.300000 0.300000 [42,] 4.500000 2.300000 1.300000 0.300000 [43,] 4.400000 3.200000 1.300000 0.200000 [44,] 5.000000 3.500000 1.600000 0.600000 [45,] 5.100000 3.800000 1.900000 0.400000 [46,] 4.800000 3.000000 1.400000 0.300000 [47,] 5.100000 3.800000 1.600000 0.200000 [48,] 4.600000 3.200000 1.400000 0.200000 [49,] 5.300000 3.700000 1.500000 0.200000 [50,] 5.000000 3.300000 1.400000 0.200000 [51,] 7.000000 3.200000 4.700000 1.400000 [52,] 6.400000 3.200000 4.500000 1.500000 [53,] 6.900000 3.100000 4.900000 1.500000 [54,] 5.500000 2.300000 4.000000 1.300000 [55,] 6.500000 2.800000 4.600000 1.500000 [56,] 5.700000 2.800000 4.500000 1.300000 [57,] 6.300000 3.300000 4.700000 1.600000 [58,] 4.900000 2.400000 3.300000 1.000000 [59,] 6.600000 2.900000 4.600000 1.300000 [60,] 5.200000 2.700000 3.900000 1.400000 [61,] 5.000000 2.000000 3.500000 1.000000 [62,] 5.900000 3.000000 4.200000 1.500000 [63,] 6.000000 2.200000 4.000000 1.000000 [64,] 6.100000 2.832163 4.700000 1.400000 [65,] 5.600000 2.900000 3.600000 1.300000 [66,] 6.700000 3.100000 4.400000 1.400000 [67,] 5.600000 3.000000 4.500000 1.500000 [68,] 5.800000 2.700000 4.100000 1.000000 [69,] 6.200000 2.200000 4.500000 1.500000 [70,] 5.600000 2.500000 3.900000 1.100000 [71,] 5.900000 3.200000 4.800000 1.800000 [72,] 6.100000 2.800000 4.000000 1.300000 [73,] 6.300000 2.500000 4.900000 1.500000 [74,] 6.100000 2.800000 4.700000 1.200000 [75,] 6.400000 2.900000 4.300000 1.300000 [76,] 6.600000 3.000000 4.400000 1.400000 [77,] 6.800000 2.800000 4.800000 1.626138 [78,] 6.700000 3.000000 5.000000 1.700000 [79,] 6.000000 2.900000 4.500000 1.500000 [80,] 5.700000 2.600000 3.500000 1.000000 [81,] 5.500000 2.400000 3.800000 1.100000 [82,] 5.500000 2.400000 3.700000 1.000000 [83,] 5.800000 2.700000 3.900000 1.200000 [84,] 6.000000 2.700000 5.100000 1.600000 [85,] 5.400000 3.000000 4.500000 1.500000 [86,] 6.000000 3.400000 4.500000 1.600000 [87,] 6.700000 3.100000 4.700000 1.500000 [88,] 6.300000 2.300000 4.400000 1.300000 [89,] 5.600000 3.000000 4.100000 1.300000 [90,] 5.500000 2.500000 4.000000 1.300000 [91,] 5.500000 2.600000 4.400000 1.200000 [92,] 6.100000 3.000000 4.600000 1.400000 [93,] 5.800000 2.600000 4.000000 1.200000 [94,] 5.000000 2.300000 3.300000 1.000000 [95,] 5.600000 2.700000 4.200000 1.300000 [96,] 5.700000 3.000000 4.200000 1.200000 [97,] 5.700000 2.900000 4.200000 1.300000 [98,] 6.200000 2.900000 4.300000 1.300000 [99,] 5.100000 2.500000 3.000000 1.100000 [100,] 5.700000 2.800000 4.100000 1.300000 [101,] 6.300000 3.300000 6.000000 2.500000 [102,] 5.800000 2.700000 5.100000 1.900000 [103,] 7.100000 3.000000 5.900000 2.100000 [104,] 6.300000 2.900000 5.600000 1.800000 [105,] 6.500000 3.000000 5.800000 2.200000 [106,] 7.600000 3.000000 6.600000 2.100000 [107,] 4.900000 2.500000 4.500000 1.700000 [108,] 7.300000 2.900000 6.300000 1.800000 [109,] 6.700000 2.500000 5.800000 1.800000 [110,] 7.200000 3.600000 6.100000 2.500000 [111,] 6.500000 3.200000 5.100000 2.000000 [112,] 6.400000 2.700000 5.300000 1.900000 [113,] 6.800000 3.000000 5.500000 2.100000 [114,] 5.700000 2.500000 5.000000 2.000000 [115,] 5.800000 2.800000 5.100000 2.400000 [116,] 6.400000 3.200000 5.300000 2.300000 [117,] 6.500000 3.000000 5.500000 1.800000 [118,] 7.700000 3.800000 6.700000 2.200000 [119,] 7.700000 2.468325 6.900000 2.300000 [120,] 6.000000 2.200000 5.000000 1.500000 [121,] 6.900000 3.200000 5.700000 2.300000 [122,] 5.600000 2.800000 4.900000 2.000000 [123,] 7.700000 2.800000 6.700000 2.000000 [124,] 6.300000 2.700000 4.900000 1.800000 [125,] 6.700000 3.300000 5.700000 2.100000 [126,] 7.200000 3.200000 6.000000 1.800000 [127,] 6.200000 2.800000 4.800000 1.800000 [128,] 6.100000 3.000000 4.900000 1.800000 [129,] 6.400000 2.800000 5.600000 2.100000 [130,] 7.200000 3.000000 5.800000 1.600000 [131,] 7.400000 2.800000 6.100000 1.900000 [132,] 7.900000 3.800000 6.400000 2.000000 [133,] 6.400000 2.800000 5.600000 2.200000 [134,] 6.300000 2.800000 5.100000 1.500000 [135,] 8.185348 2.600000 5.600000 1.400000 [136,] 7.700000 3.000000 6.100000 2.300000 [137,] 6.300000 3.400000 5.600000 2.400000 [138,] 6.400000 3.100000 5.500000 1.800000 [139,] 6.000000 3.000000 4.800000 1.800000 [140,] 6.900000 3.100000 5.400000 2.100000 [141,] 6.700000 3.100000 5.600000 2.400000 [142,] 6.900000 3.100000 5.100000 2.300000 [143,] 5.800000 2.700000 5.100000 1.900000 [144,] 6.800000 3.200000 5.900000 2.300000 [145,] 6.700000 3.300000 5.700000 2.500000 [146,] 6.700000 3.000000 5.200000 2.300000 [147,] 6.300000 2.500000 5.000000 2.177216 [148,] 6.500000 3.000000 5.200000 2.000000 [149,] 6.200000 3.400000 5.400000 2.300000 [150,] 5.900000 3.000000 5.100000 1.800000 * missing = row col * nbSample = 150 * nbCluster = 3 * lnLikelihood = -1044.786 * nbFreeParameter= 70 * criterion name = ICL * criterion value= 2448.149 * zi = [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [149] 2 2 **************************************** *** Cluster: 1 * Proportion = 0.3333333 * Means = 5.006 3.428 1.462 0.246 * S.D. = 0.3489470 0.3752546 0.1719186 0.1043264 **************************************** *** Cluster: 2 * Proportion = 0.3333333 * Means = 5.936000 2.768643 4.260000 1.330523 * S.D. = 0.5109834 0.3102216 0.4651881 0.1999892 **************************************** *** Cluster: 3 * Proportion = 0.3333333 * Means = 6.629707 2.971367 5.552000 2.031544 * S.D. = 0.6639157 0.3228524 0.5463479 0.2720902 **************************************** > ##set.seed(2) > ## model <- learnGamma( data=x, labels= z, > ## , models = clusterGammaNames(prop = "equal") > ## , algo = "simul", nbIter = 2, epsilon = 1e-08 > ## ) > ## missingValues(model) > ## print(model) > > ## get data and target from DebTrivedi data set > data(DebTrivedi) > x <- DebTrivedi[, c(1, 6,8, 15)] > z <- DebTrivedi$medicaid; > n <- nrow(x); p <- ncol(x); > model <- learnPoisson( data=x, labels=z + , models = clusterPoissonNames(prop = "equal") + , algo="simul", nbIter = 2, epsilon = 1e-08 + ) > print(model) **************************************** * model name = poisson_p_ljk * data = ofp hosp numchron school 1 5 1 2 6 2 1 0 2 10 3 13 3 4 10 4 16 1 2 3 5 3 0 2 6 6 17 0 5 7 7 9 0 0 8 8 3 0 0 8 9 1 0 0 8 10 0 0 0 8 11 0 0 1 8 12 44 1 5 15 13 2 0 1 8 14 1 0 1 8 15 19 1 1 12 16 19 0 0 8 17 0 0 1 8 18 3 0 2 8 19 2 0 3 10 20 12 0 4 12 21 2 2 1 12 22 3 0 2 8 23 1 0 1 9 24 1 0 2 9 25 5 0 3 8 26 1 0 1 4 27 12 0 5 6 28 6 1 2 8 29 2 0 0 12 30 4 0 3 8 31 2 0 0 16 32 2 0 0 9 33 1 0 1 8 34 0 0 0 9 35 1 0 0 12 36 4 1 3 8 37 26 0 2 12 38 3 1 0 12 39 1 0 0 8 40 1 0 0 12 41 1 0 1 12 42 3 0 1 12 43 0 0 0 13 44 17 0 3 12 45 5 0 3 10 46 5 1 2 13 47 9 0 0 16 48 2 0 1 11 49 1 0 2 8 50 9 2 1 12 51 11 0 1 13 52 3 0 0 14 53 1 0 3 8 54 1 0 0 0 55 0 0 0 0 56 3 0 0 12 57 2 0 2 8 58 4 1 1 8 59 3 0 1 12 60 5 0 1 8 61 5 1 4 13 62 1 0 2 11 63 4 0 2 8 64 0 1 0 18 65 8 0 2 12 66 8 2 1 8 67 1 0 0 12 68 5 1 1 10 69 0 0 0 9 70 4 0 1 7 71 3 1 1 9 72 11 0 1 8 73 13 0 1 10 74 0 0 1 8 75 7 0 2 8 76 5 0 3 10 77 3 0 2 13 78 2 0 3 13 79 22 0 1 5 80 6 0 2 12 81 1 0 1 9 82 8 0 2 12 83 0 0 1 9 84 1 0 1 8 85 5 0 2 12 86 2 0 1 10 87 6 0 5 12 88 10 1 0 10 89 1 0 1 12 90 3 0 1 10 91 7 0 0 12 92 10 0 2 8 93 13 1 2 12 94 1 0 0 12 95 16 0 2 10 96 29 1 4 6 97 8 1 2 12 98 4 1 2 12 99 1 0 3 12 100 8 0 2 12 101 9 0 1 12 102 1 0 1 6 103 0 0 1 7 104 0 0 5 5 105 7 0 1 13 106 0 0 1 9 107 8 1 4 8 108 0 0 0 12 109 22 1 3 8 110 4 0 1 9 111 3 0 3 8 112 1 0 2 12 113 3 0 1 12 114 7 0 1 12 115 0 0 1 9 116 11 2 2 12 117 3 2 1 12 118 0 0 1 6 119 0 0 1 12 120 0 0 1 12 121 7 0 1 9 122 4 1 2 6 123 9 0 4 12 124 15 0 3 3 125 2 0 3 10 126 5 1 0 11 127 0 0 0 9 128 5 0 1 16 129 13 2 1 16 130 1 0 1 12 131 0 0 2 10 132 10 0 5 10 133 10 0 1 9 134 9 0 1 14 135 4 0 0 12 136 7 0 1 11 137 0 0 2 12 138 29 0 2 16 139 39 1 1 16 140 3 0 4 12 141 17 0 1 16 142 0 0 1 0 143 1 0 1 0 144 14 0 2 12 145 0 0 1 12 146 1 0 0 15 147 6 0 3 12 148 4 0 2 8 149 7 1 2 12 150 3 0 0 8 151 2 0 0 12 152 0 0 1 12 153 7 0 3 14 154 7 0 1 12 155 6 1 2 18 156 12 0 1 8 157 8 0 1 7 158 5 0 1 9 159 0 1 3 9 160 5 0 2 9 161 12 2 2 5 162 3 0 0 7 163 4 0 3 10 164 1 0 0 10 165 2 0 1 3 166 2 0 0 10 167 17 3 5 9 168 11 0 1 4 169 3 1 4 9 170 4 0 1 10 171 5 0 0 8 172 4 0 1 6 173 18 0 2 8 174 7 0 2 14 175 31 0 3 13 176 23 0 1 12 177 9 0 2 12 178 8 0 3 16 179 10 0 2 16 180 10 0 2 16 181 2 0 0 16 182 6 1 3 12 183 0 0 5 8 184 7 0 1 14 185 2 0 1 12 186 1 0 1 12 187 4 0 3 12 188 19 0 2 14 189 10 0 1 12 190 5 0 0 16 191 5 1 5 8 192 7 0 2 7 193 3 0 1 10 194 0 0 0 8 195 5 1 0 12 196 2 0 1 11 197 1 0 0 11 198 1 0 2 7 199 1 0 1 5 200 0 0 1 12 201 0 0 0 8 202 0 0 0 7 203 0 2 2 8 204 3 0 0 12 205 8 0 1 12 206 0 0 1 9 207 3 0 1 12 208 3 0 0 8 209 5 0 0 12 210 5 0 3 16 211 7 0 3 14 212 3 0 3 14 213 0 0 0 12 214 1 0 1 12 215 3 0 1 10 216 18 3 1 14 217 3 0 1 12 218 1 0 2 14 219 0 2 3 15 220 1 0 1 12 221 0 0 0 12 222 0 0 0 14 223 3 0 1 15 224 1 0 1 10 225 0 2 0 8 226 2 0 1 7 227 1 3 2 12 228 0 0 1 12 229 17 0 2 4 230 0 0 0 4 231 0 0 0 8 232 1 0 2 8 233 3 0 2 12 234 0 0 1 3 235 2 0 2 14 236 8 2 1 13 237 3 1 0 14 238 5 0 6 12 239 2 0 3 12 240 7 0 2 14 241 1 0 2 12 242 18 0 0 15 243 13 0 3 12 244 3 0 0 12 245 2 0 1 7 246 9 0 1 12 247 8 0 0 12 248 6 0 1 12 249 2 0 0 8 250 4 0 0 6 251 0 0 2 0 252 11 0 0 14 253 14 2 0 14 254 4 0 2 8 255 12 1 1 6 256 2 0 2 12 257 2 0 0 15 258 1 0 2 12 259 40 0 2 13 260 15 1 2 8 261 2 0 2 0 262 6 0 0 12 263 9 0 1 16 264 2 0 0 12 265 2 0 0 12 266 3 1 0 7 267 8 0 1 11 268 5 0 0 12 269 0 0 1 4 270 27 0 3 11 271 0 0 1 12 272 5 0 3 8 273 4 1 3 0 274 7 0 1 6 275 14 0 5 10 276 17 2 3 5 277 4 0 1 14 278 3 0 2 12 279 6 0 2 16 280 1 0 2 12 281 5 0 2 14 282 4 1 2 16 283 4 0 1 10 284 7 0 5 10 285 1 0 1 10 286 2 0 2 12 287 15 0 2 12 288 6 0 2 6 289 9 0 1 18 290 9 0 1 12 291 1 0 1 7 292 4 0 2 12 293 3 0 0 8 294 2 0 2 14 295 3 0 3 8 296 6 0 3 11 297 26 3 6 8 298 8 0 2 12 299 12 1 1 10 300 9 0 2 8 301 4 0 1 10 302 2 0 2 12 303 1 0 1 12 304 20 0 4 10 305 1 0 0 8 306 7 0 4 6 307 5 0 3 12 308 0 0 0 8 309 5 0 0 11 310 1 0 0 8 311 2 0 1 10 312 4 0 2 10 313 9 0 2 8 314 0 0 1 12 315 17 2 1 8 316 0 0 0 8 317 2 0 0 11 318 9 0 2 12 319 3 0 2 13 320 2 0 1 8 321 5 0 3 11 322 58 0 0 12 323 0 0 1 0 324 5 0 3 12 325 1 0 0 12 326 0 0 0 10 327 8 0 0 7 328 7 0 2 12 329 4 1 3 10 330 1 0 0 9 331 7 0 1 16 332 2 0 1 14 333 11 0 5 6 334 16 0 1 12 335 2 1 0 4 336 10 0 3 14 337 16 0 3 12 338 3 0 0 14 339 3 3 4 7 340 4 0 1 3 341 2 0 1 11 342 14 0 5 12 343 24 0 4 10 344 9 0 2 14 345 1 0 0 16 346 6 1 2 13 347 4 0 1 18 348 36 0 0 17 349 6 0 0 16 350 1 0 3 16 351 0 0 1 12 352 10 2 2 10 353 3 0 2 11 354 0 0 1 8 355 19 0 3 12 356 16 1 3 12 357 3 0 1 12 358 6 0 1 13 359 5 0 0 12 360 2 0 1 16 361 4 2 3 12 362 5 0 1 12 363 1 0 1 14 364 5 0 5 12 365 4 0 4 7 366 6 0 1 8 367 6 0 0 12 368 8 1 1 18 369 12 0 3 10 370 2 0 3 14 371 21 4 2 18 372 4 1 0 16 373 2 0 1 16 374 58 1 0 18 375 21 0 1 18 376 11 0 2 11 377 2 0 2 9 378 4 0 3 12 379 6 0 0 14 380 0 0 0 8 381 9 1 2 12 382 7 5 0 16 383 0 0 1 14 384 0 0 0 2 385 8 2 3 0 386 43 0 1 11 387 7 0 0 13 388 2 0 0 12 389 23 0 5 5 390 10 0 1 12 391 6 0 0 12 392 0 0 0 13 393 3 0 4 12 394 17 0 1 12 395 9 0 2 15 396 1 0 3 13 397 16 0 2 8 398 0 0 0 7 399 1 0 0 5 400 0 0 0 9 401 1 0 3 10 402 9 3 2 8 403 4 1 2 18 404 1 0 1 13 405 2 0 0 16 406 17 1 1 12 407 0 3 3 6 408 1 5 3 16 409 8 0 0 16 410 4 0 2 14 411 4 0 2 18 412 2 0 1 12 413 8 1 1 12 414 0 0 1 11 415 1 0 1 10 416 2 0 1 18 417 13 0 3 3 418 8 0 4 12 419 4 0 0 8 420 3 1 1 10 421 3 0 1 15 422 4 0 1 12 423 2 0 2 12 424 0 0 0 9 425 3 4 0 9 426 5 1 2 7 427 6 1 2 9 428 5 2 1 8 429 4 0 0 3 430 2 0 3 6 431 2 0 0 12 432 9 1 3 15 433 10 0 4 12 434 1 0 1 13 435 7 0 2 16 436 5 0 5 8 437 3 0 1 12 438 7 0 1 12 439 12 0 3 10 440 6 0 1 9 441 1 0 2 8 442 0 0 1 12 443 0 0 0 10 444 5 0 1 11 445 1 0 0 12 446 1 0 0 8 447 8 0 1 8 448 11 0 6 12 449 7 0 3 8 450 1 0 0 12 451 16 1 3 7 452 12 0 0 7 453 0 0 0 8 454 18 0 3 6 455 6 0 0 11 456 1 0 0 0 457 7 0 2 8 458 24 3 2 8 459 20 2 1 6 460 1 0 0 6 461 4 0 0 6 462 11 0 1 12 463 13 1 2 7 464 20 0 1 5 465 8 1 1 16 466 17 1 5 12 467 12 0 2 12 468 0 0 1 12 469 0 0 1 9 470 1 0 1 7 471 7 0 1 12 472 10 0 2 10 473 1 0 1 16 474 5 0 1 11 475 24 2 3 8 476 3 0 1 12 477 0 0 1 16 478 0 0 0 14 479 4 0 1 11 480 2 0 1 8 481 7 0 2 8 482 5 0 1 7 483 2 0 1 8 484 11 0 2 9 485 0 0 0 12 486 2 0 0 8 487 0 3 3 7 488 3 0 1 5 489 1 0 2 0 490 13 1 2 7 491 7 0 3 6 492 4 0 2 4 493 3 0 0 12 494 8 0 2 16 495 4 0 1 16 496 2 0 1 12 497 0 0 0 4 498 0 0 0 18 499 5 1 1 8 500 0 0 2 8 501 7 0 1 9 502 0 0 2 9 503 9 0 4 12 504 0 0 0 13 505 0 0 0 12 506 1 0 0 8 507 0 0 0 12 508 4 1 0 12 509 8 0 0 8 510 17 0 0 12 511 17 2 4 12 512 3 0 2 12 513 0 0 0 8 514 3 0 2 8 515 3 0 1 8 516 2 1 1 0 517 4 0 4 8 518 4 0 1 10 519 0 0 0 10 520 0 0 1 12 521 1 1 0 12 522 0 0 0 4 523 0 0 0 8 524 0 0 1 10 525 3 1 2 10 526 2 0 4 6 527 2 0 1 12 528 5 1 1 18 529 3 0 0 14 530 5 0 1 12 531 3 0 2 11 532 27 1 2 12 533 30 0 1 6 534 1 0 0 8 535 7 1 0 8 536 12 0 3 14 537 21 4 1 13 538 41 0 2 12 539 2 0 1 11 540 13 0 5 12 541 2 0 3 12 542 3 0 2 12 543 4 0 0 12 544 7 0 1 12 545 9 3 2 12 546 0 2 1 12 547 13 0 1 15 548 0 0 1 16 549 6 0 2 16 550 5 0 1 18 551 7 0 0 18 552 0 0 0 12 553 0 0 0 12 554 15 0 1 12 555 3 0 2 4 556 1 0 1 8 557 9 0 3 4 558 0 0 0 8 559 12 0 3 2 560 16 0 2 11 561 4 1 2 12 562 6 1 2 9 563 13 0 4 0 564 51 0 1 5 565 0 0 0 6 566 0 0 0 0 567 3 0 1 9 568 4 0 1 12 569 0 0 2 12 570 4 0 1 6 571 14 1 5 2 572 13 0 6 5 573 2 0 1 8 574 1 0 0 8 575 9 1 4 11 576 3 0 1 8 577 1 0 1 11 578 1 0 1 10 579 0 0 1 4 580 0 0 0 7 581 7 0 1 12 582 8 0 0 18 583 27 0 2 14 584 3 0 0 18 585 0 0 0 12 586 4 0 0 12 587 5 2 6 0 588 13 3 3 8 589 0 0 0 12 590 7 0 5 7 591 0 0 0 18 592 4 0 1 13 593 15 1 1 13 594 19 0 4 4 595 1 0 1 7 596 18 0 4 7 597 5 2 1 0 598 1 0 1 5 599 12 0 4 6 600 1 0 3 12 601 7 2 1 13 602 4 0 0 7 603 8 0 1 8 604 34 0 6 3 605 0 0 1 4 606 4 0 1 9 607 7 0 3 8 608 0 0 0 11 609 0 2 1 1 610 3 0 0 5 611 0 0 4 8 612 2 0 4 5 613 1 0 1 10 614 14 3 5 9 615 9 0 2 5 616 3 0 1 6 617 11 0 3 12 618 7 0 3 10 619 0 0 0 12 620 2 0 1 7 621 55 0 5 10 622 14 2 1 6 623 61 0 1 16 624 2 0 3 8 625 6 0 1 8 626 5 0 1 18 627 8 1 3 3 628 0 0 3 3 629 2 0 2 10 630 0 0 1 9 631 4 0 1 12 632 3 0 2 10 633 19 1 6 8 634 9 0 4 10 635 0 0 2 9 636 13 0 1 7 637 1 0 1 12 638 8 0 1 12 639 2 0 1 12 640 0 0 1 4 641 3 2 2 10 642 10 0 1 7 643 9 0 0 7 644 1 1 4 9 645 4 0 0 9 646 9 0 2 8 647 0 0 2 12 648 0 0 1 12 649 3 0 0 12 650 7 0 2 12 651 8 2 3 14 652 9 2 6 6 653 1 0 2 8 654 2 0 2 8 655 1 0 1 11 656 6 1 2 12 657 22 0 1 0 658 0 0 0 14 659 0 0 1 3 660 3 0 1 12 661 6 1 2 7 662 4 1 3 7 663 13 1 1 0 664 2 0 1 5 665 5 0 2 8 666 0 0 0 8 667 7 4 2 7 668 3 0 2 8 669 1 0 1 18 670 2 2 3 10 671 0 0 2 8 672 5 0 3 12 673 4 0 5 7 674 1 0 3 3 675 5 0 2 0 676 3 0 1 12 677 3 2 2 2 678 0 1 0 8 679 0 0 1 4 680 0 1 2 12 681 2 0 1 5 682 0 0 6 8 683 31 1 4 11 684 3 0 0 12 685 0 0 1 10 686 12 0 1 10 687 1 0 3 7 688 8 2 3 7 689 4 1 3 11 690 0 2 4 9 691 0 0 0 12 692 0 0 3 12 693 0 0 2 3 694 0 0 0 12 695 0 1 1 9 696 1 0 2 18 697 5 0 2 8 698 0 0 0 8 699 6 1 3 8 700 11 1 0 7 701 16 2 0 12 702 6 0 3 11 703 0 0 2 10 704 0 1 0 8 705 2 1 5 9 706 3 1 1 8 707 8 3 5 7 708 1 0 3 11 709 47 0 3 16 710 14 0 4 8 711 0 0 2 7 712 6 0 2 9 713 0 0 3 12 714 4 0 2 16 715 14 0 3 12 716 0 1 1 6 717 2 0 0 9 718 6 2 5 10 719 1 0 2 12 720 2 0 1 9 721 0 0 2 12 722 4 0 4 12 723 5 0 2 7 724 3 0 2 9 725 0 0 4 15 726 6 0 2 5 727 1 0 2 14 728 7 1 2 12 729 0 0 3 4 730 15 1 2 12 731 15 0 0 15 732 0 0 1 5 733 1 0 1 8 734 10 0 1 5 735 3 1 0 4 736 1 0 1 8 737 10 0 1 8 738 5 0 1 6 739 4 0 3 6 740 11 2 6 9 741 0 0 1 8 742 13 1 2 8 743 3 0 3 7 744 4 0 2 4 745 1 0 0 7 746 0 0 1 18 747 0 0 1 14 748 10 0 3 9 749 15 0 2 4 750 11 0 3 18 751 2 0 1 15 752 1 0 2 12 753 1 0 2 13 754 17 0 1 13 755 3 0 1 13 756 1 0 1 12 757 3 0 1 12 758 3 1 3 14 759 0 0 0 8 760 3 1 2 12 761 5 1 1 10 762 1 1 1 13 763 3 0 0 8 764 4 1 2 8 765 9 0 2 12 766 2 0 1 8 767 4 0 1 12 768 6 1 1 13 769 2 0 0 8 770 4 1 1 16 771 8 0 0 12 772 1 0 1 17 773 1 0 1 8 774 3 0 1 7 775 3 0 2 12 776 3 0 2 12 777 7 0 1 11 778 8 0 3 11 779 0 1 0 12 780 7 0 5 6 781 2 0 0 12 782 3 2 0 12 783 14 0 3 12 784 5 0 3 13 785 1 0 0 15 786 4 0 4 13 787 0 0 1 14 788 2 0 1 12 789 1 0 0 12 790 4 0 2 14 791 11 0 2 12 792 8 0 2 12 793 16 0 0 9 794 9 1 1 12 795 0 0 0 12 796 2 0 3 8 797 0 0 0 12 798 30 0 2 15 799 10 0 3 8 800 4 0 1 12 801 10 1 2 9 802 2 0 2 5 803 11 0 2 11 804 1 0 3 15 805 0 0 1 8 806 8 0 4 8 807 1 1 3 14 808 3 3 0 8 809 0 0 0 9 810 0 0 0 9 811 4 0 2 16 812 1 0 2 14 813 2 0 3 10 814 0 0 1 11 815 8 0 1 12 816 13 0 1 16 817 10 1 3 12 818 5 1 1 14 819 0 0 1 12 820 9 1 2 10 821 0 0 1 11 822 6 1 0 16 823 2 0 1 11 824 14 2 4 11 825 2 0 1 8 826 1 0 0 10 827 11 8 6 12 828 5 0 2 8 829 5 0 2 10 830 0 0 0 8 831 2 0 1 12 832 0 0 1 12 833 15 1 2 11 834 4 0 2 8 835 6 0 4 11 836 24 4 4 12 837 4 0 1 4 838 10 0 3 8 839 8 1 2 8 840 3 0 0 6 841 17 2 4 10 842 16 0 1 10 843 3 0 0 12 844 8 1 3 11 845 12 0 0 10 846 8 0 3 15 847 13 0 4 9 848 3 0 0 5 849 1 0 0 7 850 2 1 1 11 851 1 0 1 13 852 18 2 2 17 853 1 0 1 12 854 4 0 2 8 855 16 2 4 10 856 1 0 1 11 857 0 0 1 16 858 0 0 0 12 859 7 2 1 12 860 6 0 2 16 861 12 0 1 8 862 5 0 1 12 863 4 0 2 18 864 6 0 3 13 865 6 0 2 7 866 4 0 5 8 867 0 0 0 11 868 2 0 2 11 869 9 0 2 8 870 10 0 2 10 871 23 0 6 9 872 6 0 2 13 873 5 0 3 12 874 6 0 0 14 875 5 0 1 12 876 2 0 3 12 877 0 0 2 11 878 7 0 1 14 879 2 0 0 12 880 6 1 1 12 881 11 0 4 13 882 4 0 2 12 883 4 0 3 12 884 4 0 2 13 885 3 0 1 12 886 4 0 1 14 887 16 1 2 18 888 2 0 2 8 889 8 1 1 12 890 2 0 2 12 891 12 1 0 12 892 2 0 1 13 893 3 0 1 12 894 6 0 2 14 895 4 0 1 12 896 7 0 1 12 897 12 1 3 13 898 1 0 0 12 899 3 0 2 10 900 0 0 1 12 901 0 0 0 14 902 4 1 1 13 903 3 0 2 13 904 4 0 0 0 905 0 0 0 0 906 5 0 1 12 907 21 0 3 11 908 3 0 0 12 909 7 1 2 5 910 10 0 1 12 911 9 0 1 14 912 0 0 1 9 913 8 2 3 12 914 4 0 0 14 915 9 1 2 13 916 7 0 1 11 917 10 0 2 13 918 4 0 0 16 919 4 0 0 12 920 5 0 1 12 921 0 0 1 12 922 6 0 2 15 923 3 0 2 12 924 4 1 3 15 925 3 0 1 12 926 7 0 2 12 927 1 0 2 12 928 5 0 2 14 929 11 0 2 12 930 0 0 0 12 931 7 0 3 4 932 32 0 1 8 933 14 0 0 11 934 5 0 2 8 935 6 1 1 13 936 0 0 6 16 937 10 0 0 12 938 5 0 4 4 939 11 0 5 9 940 1 1 2 10 941 6 0 2 16 942 5 0 2 14 943 1 0 1 12 944 6 0 1 8 945 4 0 2 12 946 10 0 1 12 947 1 0 0 8 948 4 0 5 14 949 3 0 1 9 950 6 0 2 11 951 11 1 0 12 952 5 3 1 8 953 6 1 1 12 954 8 0 1 7 955 1 0 2 14 956 4 0 3 12 957 1 0 1 10 958 3 0 3 11 959 9 0 2 9 960 5 0 3 8 961 5 0 1 10 962 6 0 1 12 963 0 0 0 0 964 9 1 5 12 965 4 0 2 12 966 21 0 2 12 967 11 0 2 12 968 10 0 0 14 969 14 1 5 14 970 4 0 3 14 971 13 0 1 12 972 2 0 0 9 973 6 0 1 15 974 8 0 2 9 975 0 0 0 8 976 0 0 0 18 977 6 0 1 16 978 5 0 1 14 979 1 0 0 12 980 13 1 2 14 981 3 0 0 12 982 4 0 3 9 983 4 0 2 10 984 11 0 1 6 985 4 0 3 4 986 9 0 5 4 987 0 1 3 3 988 3 0 3 8 989 3 0 1 8 990 5 0 1 12 991 3 0 0 12 992 1 0 0 12 993 20 1 2 3 994 0 0 1 12 995 1 0 0 10 996 2 0 0 6 997 5 0 0 6 998 13 3 2 3 999 1 0 0 6 1000 1 0 1 12 1001 0 0 0 12 1002 0 0 2 14 1003 2 0 1 8 1004 0 0 1 4 1005 0 0 0 15 1006 16 0 1 12 1007 0 0 0 11 1008 24 0 1 8 1009 21 1 4 12 1010 3 0 1 10 1011 6 0 1 10 1012 0 0 0 12 1013 3 0 1 16 1014 7 0 2 14 1015 6 0 2 12 1016 7 0 3 11 1017 34 0 2 12 1018 7 0 3 16 1019 5 0 1 8 1020 37 1 3 12 1021 1 0 1 9 1022 2 0 1 12 1023 4 0 1 12 1024 0 1 0 16 1025 6 0 3 8 1026 5 0 1 12 1027 0 0 2 13 1028 22 2 3 12 1029 9 1 3 12 1030 2 0 1 8 1031 6 0 3 12 1032 2 0 1 12 1033 35 0 2 10 1034 0 0 0 12 1035 7 1 0 12 1036 7 0 2 12 1037 9 0 3 12 1038 6 0 1 16 1039 10 0 1 16 1040 5 1 0 16 1041 2 0 1 12 1042 7 1 1 12 1043 0 0 1 12 1044 4 0 2 10 1045 0 0 0 3 1046 0 1 2 10 1047 8 0 2 7 1048 7 0 1 12 1049 3 0 4 8 1050 4 0 3 14 1051 11 0 0 9 1052 42 0 1 5 1053 1 0 2 6 1054 1 0 0 9 1055 11 0 1 12 1056 5 0 3 10 1057 22 0 0 10 1058 20 1 3 16 1059 12 0 1 8 1060 2 0 1 15 1061 1 0 2 11 1062 1 2 1 12 1063 0 0 2 14 1064 53 2 4 12 1065 13 0 0 12 1066 3 0 1 16 1067 0 0 1 12 1068 3 0 1 12 1069 2 0 1 15 1070 12 0 2 12 1071 0 0 0 16 1072 0 0 0 13 1073 1 0 0 17 1074 3 1 1 17 1075 20 1 4 12 1076 49 2 1 12 1077 2 0 0 13 1078 9 0 1 17 1079 5 0 0 8 1080 0 0 0 16 1081 8 0 3 16 1082 4 0 0 8 1083 6 0 1 12 1084 0 0 0 12 1085 5 1 0 15 1086 5 0 1 10 1087 26 0 1 12 1088 0 0 0 12 1089 5 0 0 12 1090 3 0 0 12 1091 5 0 1 12 1092 12 0 5 12 1093 8 0 2 12 1094 0 0 1 0 1095 3 0 4 10 1096 6 0 1 10 1097 0 0 1 0 1098 1 0 1 10 1099 0 0 2 8 1100 5 1 3 12 1101 5 0 0 10 1102 7 0 2 11 1103 5 0 1 8 1104 18 0 3 4 1105 7 0 1 12 1106 8 0 1 13 1107 2 0 0 11 1108 17 0 2 14 1109 5 1 1 17 1110 3 0 1 10 1111 5 0 0 12 1112 1 0 1 7 1113 2 1 3 4 1114 0 0 0 5 1115 1 0 0 12 1116 9 0 3 8 1117 2 0 1 11 1118 2 0 2 12 1119 6 0 1 12 1120 27 0 0 12 1121 7 0 1 12 1122 6 0 0 8 1123 5 1 1 10 1124 9 1 3 12 1125 6 0 2 8 1126 4 1 0 12 1127 3 0 0 10 1128 7 0 5 8 1129 2 0 0 13 1130 2 1 3 12 1131 1 0 1 10 1132 5 1 1 12 1133 0 0 1 12 1134 4 0 2 8 1135 1 0 1 10 1136 3 0 1 6 1137 0 0 2 8 1138 8 4 1 12 1139 3 0 1 8 1140 2 0 1 7 1141 5 0 1 12 1142 6 1 1 12 1143 2 0 1 9 1144 5 0 2 8 1145 3 0 0 12 1146 0 0 1 12 1147 4 1 0 8 1148 3 0 1 10 1149 0 0 1 8 1150 4 0 1 8 1151 1 0 1 6 1152 5 1 2 12 1153 5 0 0 12 1154 3 0 1 7 1155 4 0 1 5 1156 1 0 1 7 1157 2 0 2 7 1158 4 0 1 12 1159 8 0 0 8 1160 5 3 3 8 1161 0 0 1 8 1162 8 0 1 0 1163 8 2 3 12 1164 3 0 1 12 1165 5 1 4 8 1166 2 0 2 4 1167 5 0 1 8 1168 2 0 2 8 1169 3 0 2 5 1170 0 0 2 5 1171 3 0 1 12 1172 2 0 3 12 1173 1 0 1 7 1174 15 0 3 4 1175 13 1 2 10 1176 23 0 1 2 1177 0 0 1 3 1178 1 0 0 6 1179 0 0 1 12 1180 2 1 2 0 1181 1 0 1 6 1182 10 0 2 8 1183 9 0 1 7 1184 2 0 0 3 1185 1 0 1 10 1186 6 1 4 8 1187 4 0 1 12 1188 0 0 1 4 1189 9 2 2 4 1190 3 0 1 1 1191 5 0 1 6 1192 8 0 0 12 1193 12 0 0 12 1194 4 0 3 12 1195 11 0 1 18 1196 5 0 2 17 1197 0 0 0 9 1198 3 0 1 12 1199 2 0 1 13 1200 10 0 4 7 1201 0 0 0 7 1202 9 0 3 8 1203 1 0 2 8 1204 0 0 0 10 1205 5 1 1 18 1206 4 0 2 12 1207 39 0 1 14 1208 0 0 0 8 1209 17 0 1 6 1210 2 0 1 8 1211 2 0 0 15 1212 23 0 3 8 1213 3 0 3 2 1214 1 0 3 12 1215 0 0 2 12 1216 0 0 1 2 1217 7 1 2 17 1218 4 0 0 16 1219 5 3 4 12 1220 5 0 3 16 1221 3 0 1 9 1222 4 0 2 13 1223 0 0 0 12 1224 0 0 0 12 1225 1 0 0 12 1226 9 0 1 10 1227 0 0 0 4 1228 2 1 2 6 1229 1 0 0 9 1230 6 1 2 8 1231 6 0 3 12 1232 3 0 3 8 1233 2 1 4 8 1234 11 0 1 10 1235 2 0 1 9 1236 0 0 1 6 1237 17 0 0 15 1238 14 2 4 7 1239 0 0 1 10 1240 17 3 4 16 1241 1 0 1 16 1242 1 0 0 17 1243 16 0 3 9 1244 4 0 0 12 1245 0 0 1 15 1246 3 0 3 12 1247 2 0 0 13 1248 5 0 2 9 1249 3 0 1 12 1250 5 0 1 10 1251 2 0 0 14 1252 0 0 1 7 1253 42 0 4 8 1254 0 0 1 6 1255 2 0 0 13 1256 3 0 1 10 1257 5 0 2 2 1258 1 0 2 12 1259 1 1 5 12 1260 1 0 1 8 1261 26 1 5 12 1262 25 2 4 4 1263 10 3 0 3 1264 7 3 5 8 1265 1 0 1 5 1266 4 0 1 5 1267 1 1 2 12 1268 1 1 1 12 1269 9 0 2 6 1270 15 0 1 12 1271 19 0 1 7 1272 6 0 2 8 1273 10 0 1 7 1274 4 0 1 8 1275 4 0 1 7 1276 2 2 2 4 1277 4 0 5 2 1278 8 4 1 12 1279 19 1 4 12 1280 7 0 0 12 1281 10 0 2 5 1282 1 0 1 2 1283 4 0 4 3 1284 5 1 2 4 1285 2 0 0 3 1286 6 2 2 3 1287 9 0 0 9 1288 4 0 1 12 1289 4 0 1 12 1290 17 0 3 5 1291 0 0 0 0 1292 10 0 3 4 1293 16 1 5 14 1294 3 1 1 12 1295 12 5 5 9 1296 6 1 1 8 1297 2 0 2 3 1298 7 0 3 11 1299 3 0 2 6 1300 3 1 3 7 1301 5 0 2 11 1302 4 0 1 6 1303 10 2 1 8 1304 0 0 0 8 1305 8 0 1 8 1306 2 0 0 18 1307 4 0 4 8 1308 2 0 3 14 1309 4 0 0 12 1310 4 0 1 11 1311 10 0 0 16 1312 8 0 1 17 1313 4 0 2 14 1314 5 0 4 0 1315 0 0 0 10 1316 13 0 4 12 1317 6 0 1 15 1318 13 0 2 12 1319 3 0 2 18 1320 14 1 2 15 1321 1 0 2 10 1322 8 0 2 12 1323 1 0 2 15 1324 6 0 4 12 1325 3 0 5 8 1326 0 0 1 4 1327 11 0 1 9 1328 10 0 3 18 1329 5 0 1 14 1330 3 0 0 12 1331 0 0 0 14 1332 9 0 3 14 1333 15 1 3 14 1334 4 0 1 12 1335 14 1 2 12 1336 7 0 5 12 1337 3 0 1 14 1338 8 0 4 0 1339 4 0 1 14 1340 14 0 5 13 1341 17 0 4 12 1342 8 0 1 9 1343 22 0 2 10 1344 5 0 1 12 1345 3 0 1 6 1346 2 0 2 10 1347 24 3 1 14 1348 1 0 3 11 1349 1 0 3 12 1350 12 0 2 11 1351 7 0 2 14 1352 17 1 2 12 1353 8 0 1 14 1354 17 0 0 14 1355 2 0 3 12 1356 3 0 2 12 1357 1 0 0 14 1358 11 1 3 14 1359 2 0 4 10 1360 2 0 0 18 1361 1 0 1 12 1362 4 0 0 12 1363 4 0 2 12 1364 6 0 0 12 1365 1 0 1 18 1366 1 0 1 15 1367 15 0 0 12 1368 5 0 0 15 1369 1 0 6 7 1370 4 0 0 12 1371 5 0 1 12 1372 7 1 2 12 1373 5 0 2 12 1374 0 0 2 7 1375 13 0 2 12 1376 18 2 1 12 1377 15 1 0 14 1378 5 0 3 14 1379 16 0 2 8 1380 1 0 1 8 1381 2 0 5 10 1382 1 0 1 10 1383 12 0 4 8 1384 16 1 1 6 1385 1 0 1 16 1386 3 0 1 4 1387 0 0 0 10 1388 0 0 1 11 1389 13 1 2 9 1390 27 0 2 12 1391 16 2 3 12 1392 7 0 5 12 1393 2 0 0 6 1394 1 0 1 4 1395 13 1 2 8 1396 5 2 1 5 1397 4 0 2 7 1398 11 0 3 3 1399 3 1 1 3 1400 0 0 0 3 1401 0 0 1 9 1402 13 0 1 10 1403 4 1 1 2 1404 6 2 1 9 1405 8 0 3 1 1406 12 0 2 12 1407 24 1 4 4 1408 0 0 0 8 1409 0 0 3 4 1410 4 1 5 4 1411 0 0 2 5 1412 5 1 1 13 1413 1 0 0 13 1414 6 0 1 8 1415 1 0 0 8 1416 25 1 2 6 1417 8 0 1 6 1418 6 0 0 10 1419 21 0 2 15 1420 1 1 0 11 1421 2 0 1 12 1422 9 0 1 0 1423 8 1 3 8 1424 7 0 2 4 1425 8 0 1 7 1426 5 0 2 18 1427 3 0 0 12 1428 9 1 3 12 1429 17 0 1 9 1430 0 0 0 2 1431 1 0 3 12 1432 3 0 1 11 1433 19 0 2 13 1434 4 0 2 13 1435 0 0 1 12 1436 3 0 0 8 1437 9 3 4 0 1438 2 0 5 12 1439 8 0 2 7 1440 7 0 0 16 1441 2 0 2 13 1442 2 0 0 10 1443 8 0 0 5 1444 5 0 2 12 1445 0 0 1 9 1446 1 0 0 8 1447 11 0 1 12 1448 3 0 2 14 1449 1 0 2 8 1450 10 1 2 12 1451 4 0 1 14 1452 8 0 0 5 1453 2 0 0 12 1454 3 0 0 12 1455 3 0 1 10 1456 6 0 0 17 1457 4 0 1 17 1458 2 0 1 11 1459 1 0 3 4 1460 0 0 0 0 1461 2 0 1 8 1462 2 0 1 6 1463 14 0 2 14 1464 15 0 0 13 1465 5 0 1 12 1466 10 0 0 12 1467 9 0 1 14 1468 3 0 1 17 1469 5 0 0 13 1470 2 0 1 8 1471 1 0 1 4 1472 0 0 1 5 1473 0 2 1 8 1474 3 1 2 8 1475 4 1 1 10 1476 1 0 1 12 1477 2 0 1 9 1478 7 0 2 12 1479 0 0 3 12 1480 7 0 0 5 1481 19 0 1 3 1482 0 0 0 2 1483 2 0 1 8 1484 13 1 2 5 1485 7 2 0 12 1486 3 2 0 5 1487 1 0 0 14 1488 1 0 1 14 1489 6 0 1 8 1490 5 0 1 12 1491 5 0 2 12 1492 2 0 0 12 1493 5 1 1 10 1494 17 0 2 8 1495 0 0 0 11 1496 0 0 0 12 1497 4 0 3 8 1498 12 1 2 18 1499 3 2 2 9 1500 15 2 1 8 1501 0 0 4 8 1502 50 0 2 12 1503 0 0 0 16 1504 0 0 1 12 1505 7 0 1 14 1506 6 0 1 14 1507 1 0 1 12 1508 6 0 2 9 1509 0 0 2 10 1510 2 0 1 10 1511 1 0 0 6 1512 1 0 0 6 1513 1 0 0 7 1514 1 0 0 14 1515 68 1 2 16 1516 4 0 0 16 1517 4 1 2 12 1518 6 0 2 18 1519 0 0 0 12 1520 7 0 0 16 1521 0 0 0 12 1522 65 0 1 10 1523 2 0 1 12 1524 4 0 3 16 1525 9 0 1 12 1526 0 0 2 7 1527 3 0 1 4 1528 5 0 1 8 1529 26 1 4 8 1530 12 0 0 7 1531 12 0 2 9 1532 1 0 0 12 1533 1 0 0 12 1534 5 0 1 12 1535 1 0 2 9 1536 7 0 1 8 1537 8 1 3 7 1538 3 2 1 8 1539 0 0 1 12 1540 0 0 1 8 1541 0 0 1 10 1542 0 0 2 10 1543 3 0 0 8 1544 3 0 1 12 1545 3 0 1 8 1546 2 0 2 8 1547 13 0 1 12 1548 6 0 3 12 1549 10 0 1 8 1550 8 0 1 12 1551 5 0 1 12 1552 4 0 0 2 1553 4 0 4 12 1554 16 2 3 12 1555 6 0 1 12 1556 1 0 0 8 1557 8 1 3 12 1558 5 0 1 12 1559 6 0 1 13 1560 6 0 0 14 1561 13 1 2 3 1562 0 0 3 4 1563 1 1 2 12 1564 5 0 4 12 1565 7 1 6 7 1566 2 0 2 12 1567 10 0 3 10 1568 5 0 3 18 1569 9 0 0 14 1570 2 0 1 12 1571 1 0 1 16 1572 12 1 3 12 1573 89 5 2 12 1574 3 0 0 9 1575 0 0 1 12 1576 0 0 1 16 1577 19 0 4 6 1578 14 0 4 6 1579 12 0 2 6 1580 3 0 2 8 1581 3 0 1 6 1582 3 0 0 8 1583 17 0 6 4 1584 10 1 4 16 1585 6 1 2 16 1586 4 0 3 18 1587 6 0 0 18 1588 5 0 0 12 1589 13 0 2 12 1590 1 0 2 11 1591 3 0 1 11 1592 0 0 0 12 1593 3 0 0 10 1594 1 0 0 9 1595 2 0 1 12 1596 9 0 1 12 1597 5 1 1 8 1598 3 0 2 12 1599 1 0 0 12 1600 5 0 3 12 1601 0 1 1 12 1602 0 1 0 14 1603 11 0 0 10 1604 6 0 0 12 1605 9 0 2 10 1606 4 0 1 8 1607 24 2 4 18 1608 6 0 1 14 1609 3 0 1 16 1610 14 0 2 14 1611 7 1 1 14 1612 8 0 4 10 1613 2 0 1 8 1614 0 1 3 16 1615 4 0 1 8 1616 21 1 3 11 1617 5 0 1 15 1618 4 0 1 11 1619 8 0 2 12 1620 0 0 1 8 1621 3 0 1 10 1622 8 3 3 12 1623 2 0 1 7 1624 8 0 2 8 1625 8 0 1 10 1626 0 0 0 8 1627 13 0 1 10 1628 9 1 1 8 1629 4 0 0 8 1630 3 0 1 12 1631 3 0 0 7 1632 0 0 0 11 1633 6 0 1 7 1634 14 2 3 11 1635 4 0 0 7 1636 3 0 0 11 1637 2 0 2 7 1638 3 0 0 5 1639 4 0 3 12 1640 11 0 2 10 1641 2 0 0 13 1642 5 0 3 12 1643 10 0 4 12 1644 14 0 2 12 1645 10 1 4 11 1646 15 0 1 12 1647 5 0 2 7 1648 4 0 1 5 1649 3 1 4 12 1650 2 0 0 12 1651 0 0 0 10 1652 0 0 2 6 1653 0 0 0 0 1654 9 0 1 9 1655 3 0 0 16 1656 4 0 0 16 1657 3 0 0 13 1658 6 0 2 14 1659 3 0 1 13 1660 5 0 3 13 1661 11 0 2 11 1662 2 0 3 12 1663 0 5 2 5 1664 25 0 2 12 1665 2 0 0 6 1666 6 0 1 15 1667 18 0 1 16 1668 13 0 2 15 1669 4 0 1 14 1670 20 1 1 16 1671 3 0 3 16 1672 4 0 1 16 1673 31 2 0 14 1674 5 1 0 16 1675 11 0 1 18 1676 5 1 2 17 1677 5 0 1 18 1678 3 0 2 15 1679 0 0 0 8 1680 6 2 2 12 1681 4 0 1 7 1682 3 0 0 12 1683 2 0 0 16 1684 0 0 2 9 1685 0 0 1 8 1686 4 0 1 12 1687 3 0 1 12 1688 13 0 3 12 1689 0 0 0 8 1690 3 0 1 12 1691 2 0 1 12 1692 14 1 2 7 1693 1 0 1 0 1694 9 1 2 8 1695 6 0 0 8 1696 12 1 4 14 1697 7 0 2 12 1698 3 0 2 12 1699 10 1 2 8 1700 10 1 2 12 1701 1 0 2 13 1702 1 0 2 6 1703 3 1 2 5 1704 6 1 2 9 1705 2 0 2 10 1706 0 0 0 0 1707 4 0 1 2 1708 16 0 5 11 1709 5 0 1 16 1710 3 0 1 12 1711 1 0 1 11 1712 4 0 3 8 1713 3 0 1 11 1714 16 0 3 12 1715 7 0 2 11 1716 14 3 1 12 1717 7 0 2 12 1718 4 0 1 11 1719 5 0 2 9 1720 5 0 1 14 1721 0 0 0 11 1722 1 0 2 8 1723 0 0 1 12 1724 9 2 3 9 1725 7 0 0 9 1726 1 0 4 10 1727 4 0 4 10 1728 2 0 1 10 1729 3 3 3 13 1730 4 0 1 12 1731 4 0 1 15 1732 0 0 0 12 1733 9 0 2 16 1734 4 0 2 14 1735 2 0 2 16 1736 0 2 0 12 1737 0 0 3 12 1738 0 1 0 14 1739 4 0 1 9 1740 2 0 1 4 1741 1 0 2 12 1742 1 0 0 12 1743 0 0 0 12 1744 0 0 1 8 1745 10 0 3 9 1746 5 0 1 8 1747 5 0 0 11 1748 3 0 5 12 1749 0 0 2 8 1750 4 0 2 12 1751 9 0 2 12 1752 4 1 1 8 1753 6 1 2 16 1754 6 1 1 14 1755 3 0 0 10 1756 1 0 0 14 1757 4 1 2 12 1758 6 0 0 16 1759 0 1 2 6 1760 1 0 1 12 1761 10 0 0 6 1762 9 1 3 6 1763 1 0 1 12 1764 4 0 0 14 1765 11 0 1 13 1766 4 0 3 12 1767 4 0 1 13 1768 0 0 1 9 1769 28 2 5 11 1770 3 0 1 8 1771 2 0 2 7 1772 8 1 2 6 1773 8 0 1 8 1774 3 0 1 12 1775 0 0 1 9 1776 9 1 5 7 1777 3 0 1 16 1778 3 0 1 16 1779 0 0 2 12 1780 7 0 2 12 1781 5 0 1 6 1782 1 0 1 12 1783 2 0 1 14 1784 11 0 1 0 1785 12 3 5 10 1786 9 1 3 8 1787 11 1 1 8 1788 9 1 5 8 1789 0 0 2 8 1790 2 0 0 14 1791 30 1 3 12 1792 0 1 2 8 1793 2 1 1 12 1794 4 0 1 12 1795 0 0 1 12 1796 10 2 1 12 1797 0 1 2 5 1798 4 0 0 7 1799 1 0 0 12 1800 6 0 1 6 1801 15 0 1 8 1802 1 0 0 11 1803 5 0 2 8 1804 6 0 2 12 1805 9 0 1 14 1806 1 0 1 10 1807 4 1 2 12 1808 0 0 1 12 1809 14 1 3 12 1810 9 0 5 12 1811 3 0 0 12 1812 3 0 1 16 1813 0 0 2 0 1814 14 0 0 8 1815 7 1 1 8 1816 18 0 3 12 1817 4 1 3 12 1818 5 0 6 10 1819 9 0 2 8 1820 0 0 2 14 1821 14 1 1 12 1822 5 0 4 10 1823 9 0 3 12 1824 38 3 2 10 1825 10 2 2 8 1826 0 0 2 8 1827 16 1 1 12 1828 7 1 1 12 1829 0 0 2 12 1830 3 1 5 12 1831 20 0 5 8 1832 2 0 1 12 1833 29 2 2 12 1834 6 0 0 8 1835 5 0 0 8 1836 10 0 0 9 1837 3 0 4 8 1838 22 2 7 10 1839 1 1 1 14 1840 7 0 0 12 1841 3 0 1 8 1842 2 1 0 12 1843 15 0 6 13 1844 8 0 2 12 1845 8 0 3 8 1846 5 0 1 16 1847 14 0 2 12 1848 8 1 0 12 1849 15 1 0 12 1850 7 0 0 12 1851 5 1 1 9 1852 6 0 4 9 1853 12 0 1 12 1854 1 0 0 12 1855 9 0 2 18 1856 4 0 2 15 1857 6 0 0 12 1858 1 2 2 8 1859 1 0 2 8 1860 6 0 3 8 1861 1 0 5 5 1862 8 0 1 6 1863 15 1 3 6 1864 4 0 0 8 1865 3 0 0 11 1866 3 0 0 11 1867 2 0 0 8 1868 4 0 0 8 1869 4 0 3 10 1870 6 0 1 12 1871 1 0 0 9 1872 4 0 2 15 1873 4 0 3 6 1874 11 0 1 2 1875 2 0 2 8 1876 6 0 1 12 1877 0 0 0 12 1878 0 0 1 12 1879 7 2 1 12 1880 28 0 0 15 1881 5 0 0 16 1882 1 0 0 12 1883 10 1 3 11 1884 6 0 3 8 1885 6 0 1 12 1886 5 0 1 12 1887 1 0 0 10 1888 4 0 0 14 1889 2 0 3 13 1890 5 0 1 7 1891 11 3 2 12 1892 5 2 5 3 1893 0 0 0 8 1894 0 0 0 11 1895 14 1 3 8 1896 4 0 0 16 1897 5 0 2 11 1898 4 0 1 13 1899 2 0 0 5 1900 13 0 2 12 1901 14 0 1 11 1902 8 0 1 16 1903 14 1 3 11 1904 2 1 0 12 1905 13 0 3 14 1906 5 2 5 12 1907 4 0 3 7 1908 0 0 1 12 1909 5 0 2 6 1910 7 0 2 12 1911 7 0 0 12 1912 1 0 0 8 1913 1 0 0 6 1914 7 0 1 8 1915 7 1 2 12 1916 1 0 1 18 1917 17 2 5 16 1918 0 0 1 7 1919 1 0 4 14 1920 9 0 1 12 1921 2 0 3 16 1922 3 0 1 12 1923 11 0 1 12 1924 1 1 3 12 1925 0 0 0 12 1926 1 0 1 7 1927 0 0 0 8 1928 0 0 3 15 1929 3 0 1 11 1930 1 0 1 12 1931 0 0 2 8 1932 15 1 2 12 1933 8 0 1 8 1934 1 1 0 12 1935 1 0 1 12 1936 0 0 1 12 1937 1 0 0 5 1938 0 0 0 0 1939 10 1 2 3 1940 0 0 1 11 1941 0 0 1 10 1942 0 0 2 0 1943 5 0 1 8 1944 10 0 0 11 1945 3 0 0 16 1946 4 0 0 12 1947 11 0 1 8 1948 5 0 2 16 1949 12 0 3 10 1950 0 0 1 12 1951 6 0 1 12 1952 5 2 3 13 1953 6 0 1 15 1954 1 0 2 14 1955 2 0 1 13 1956 7 0 2 7 1957 11 0 3 14 1958 4 0 1 13 1959 0 0 0 8 1960 8 0 0 10 1961 0 0 1 12 1962 5 0 1 12 1963 2 0 1 12 1964 0 0 1 14 1965 4 0 5 13 1966 1 0 0 13 1967 1 0 1 11 1968 8 0 1 11 1969 5 0 0 16 1970 1 0 0 16 1971 9 0 1 14 1972 8 4 3 9 1973 8 0 2 11 1974 2 0 1 18 1975 2 0 1 10 1976 3 0 0 12 1977 10 2 4 8 1978 3 0 0 8 1979 10 0 1 12 1980 3 0 0 12 1981 7 1 1 8 1982 0 1 1 8 1983 9 0 1 12 1984 1 0 0 12 1985 9 0 1 12 1986 21 3 4 12 1987 3 0 1 8 1988 1 0 1 17 1989 1 0 0 16 1990 1 0 2 12 1991 11 1 0 9 1992 4 0 1 12 1993 48 0 2 18 1994 2 0 1 17 1995 4 0 5 5 1996 2 0 0 11 1997 6 0 3 6 1998 5 0 1 10 1999 1 0 1 11 2000 0 0 1 15 2001 0 0 2 12 2002 4 0 1 0 2003 1 0 2 7 2004 0 0 0 9 2005 3 0 1 12 2006 5 0 2 9 2007 1 0 1 18 2008 5 0 4 12 2009 4 0 2 9 2010 5 0 2 8 2011 6 0 1 10 2012 1 0 1 0 2013 0 0 0 12 2014 0 0 1 12 2015 0 0 1 0 2016 13 0 1 0 2017 1 0 0 0 2018 7 0 5 0 2019 4 0 2 1 2020 8 0 1 5 2021 2 0 2 5 2022 5 0 2 2 2023 5 0 0 11 2024 3 0 1 9 2025 2 0 3 12 2026 0 0 0 3 2027 4 1 1 12 2028 4 0 1 12 2029 1 0 1 0 2030 9 0 5 12 2031 2 0 0 12 2032 22 0 3 14 2033 5 0 0 12 2034 11 0 4 10 2035 2 1 2 0 2036 2 0 1 9 2037 11 0 3 12 2038 1 0 1 8 2039 1 0 1 12 2040 1 0 3 16 2041 9 0 3 12 2042 21 0 1 13 2043 8 0 1 13 2044 14 1 4 13 2045 2 0 0 17 2046 2 0 0 16 2047 0 0 4 14 2048 5 0 0 9 2049 7 0 5 14 2050 5 0 1 15 2051 2 0 1 9 2052 6 0 3 18 2053 7 0 1 16 2054 0 0 1 16 2055 6 0 4 7 2056 9 0 1 9 2057 5 0 4 14 2058 8 0 2 8 2059 3 0 0 12 2060 1 0 2 9 2061 4 0 0 5 2062 10 0 3 9 2063 42 1 3 3 2064 0 0 0 11 2065 2 0 1 9 2066 0 0 1 6 2067 1 0 1 12 2068 10 0 0 17 2069 2 0 2 7 2070 2 0 1 9 2071 5 0 2 13 2072 4 0 2 12 2073 3 0 3 11 2074 8 1 2 11 2075 5 0 0 12 2076 9 1 4 12 2077 6 0 3 12 2078 6 0 1 8 2079 9 0 3 7 2080 7 0 3 12 2081 2 0 2 4 2082 4 0 4 12 2083 6 5 3 12 2084 0 0 2 3 2085 1 0 1 14 2086 4 0 1 16 2087 0 0 1 16 2088 1 0 0 13 2089 8 0 3 7 2090 3 0 4 7 2091 3 0 1 18 2092 1 0 1 17 2093 8 0 2 6 2094 11 0 0 10 2095 9 2 2 10 2096 4 0 1 14 2097 2 0 1 12 2098 15 0 1 12 2099 3 0 2 12 2100 4 0 0 12 2101 3 0 1 8 2102 2 0 1 12 2103 2 0 2 12 2104 0 0 0 0 2105 0 1 0 6 2106 0 0 1 8 2107 5 0 4 12 2108 7 0 1 12 2109 9 1 2 12 2110 4 0 2 8 2111 1 0 1 12 2112 5 0 1 12 2113 4 0 1 10 2114 7 0 3 9 2115 16 1 2 7 2116 2 0 1 12 2117 2 1 1 8 2118 5 0 1 10 2119 0 0 0 8 2120 3 0 0 8 2121 11 0 2 8 2122 6 0 2 8 2123 1 0 0 12 2124 6 0 2 14 2125 1 0 0 8 2126 6 0 8 11 2127 9 0 3 8 2128 12 0 0 9 2129 1 0 0 12 2130 1 0 0 16 2131 12 0 2 12 2132 14 1 2 8 2133 1 0 2 12 2134 10 0 0 12 2135 0 0 2 12 2136 1 0 1 8 2137 5 0 1 12 2138 19 2 0 18 2139 11 0 0 15 2140 0 0 3 0 2141 9 0 3 8 2142 0 0 1 8 2143 3 0 5 7 2144 6 0 1 8 2145 10 0 1 14 2146 2 0 1 7 2147 0 0 1 12 2148 11 0 1 12 2149 7 0 1 8 2150 5 0 1 12 2151 6 0 2 8 2152 3 0 3 12 2153 6 1 1 12 2154 5 0 0 10 2155 2 0 0 8 2156 6 0 2 12 2157 0 0 1 12 2158 1 0 1 12 2159 1 0 0 7 2160 1 0 1 8 2161 3 0 0 8 2162 3 0 4 8 2163 12 1 1 12 2164 14 1 2 12 2165 6 2 1 12 2166 1 0 1 8 2167 9 0 3 12 2168 0 0 0 12 2169 20 7 2 12 2170 0 0 0 12 2171 3 0 3 10 2172 11 0 0 6 2173 3 0 1 12 2174 4 1 0 12 2175 12 0 2 14 2176 0 0 0 8 2177 0 1 2 14 2178 2 0 2 15 2179 4 0 0 11 2180 16 0 2 14 2181 1 0 0 12 2182 2 0 1 12 2183 5 1 2 12 2184 8 0 1 12 2185 1 0 0 15 2186 0 0 1 7 2187 7 0 3 12 2188 6 1 2 10 2189 3 0 3 16 2190 3 0 2 12 2191 0 0 1 12 2192 4 0 1 12 2193 12 1 1 12 2194 8 1 3 8 2195 4 0 1 8 2196 1 0 1 3 2197 16 0 2 11 2198 9 0 2 16 2199 12 1 1 15 2200 3 0 1 12 2201 3 1 1 12 2202 4 0 2 11 2203 4 1 5 12 2204 1 0 0 14 2205 1 0 4 8 2206 1 0 3 0 2207 0 0 3 12 2208 8 2 3 8 2209 0 0 4 12 2210 7 0 1 10 2211 5 0 2 7 2212 5 0 1 9 2213 8 0 3 8 2214 2 0 0 8 2215 2 0 1 12 2216 9 0 2 12 2217 0 0 0 9 2218 8 1 1 8 2219 7 0 2 12 2220 4 1 3 14 2221 1 0 2 12 2222 2 0 1 6 2223 4 0 2 11 2224 0 0 1 10 2225 2 0 1 9 2226 1 0 2 5 2227 10 0 1 8 2228 1 0 0 11 2229 3 0 2 12 2230 5 0 2 13 2231 9 0 4 11 2232 3 0 0 8 2233 8 1 5 12 2234 3 0 0 16 2235 5 0 1 13 2236 10 1 5 18 2237 13 0 2 12 2238 1 0 1 14 2239 3 0 1 11 2240 5 0 3 12 2241 0 1 1 9 2242 0 0 0 11 2243 3 0 1 10 2244 3 1 0 13 2245 1 0 1 13 2246 0 0 2 13 2247 22 0 0 0 2248 2 0 2 6 2249 2 0 2 12 2250 4 0 2 12 2251 0 0 0 0 2252 6 0 3 16 2253 11 0 2 12 2254 0 3 3 7 2255 15 0 1 18 2256 13 0 1 12 2257 2 0 1 11 2258 5 0 3 12 2259 7 0 2 9 2260 6 0 1 14 2261 0 0 0 9 2262 1 0 1 12 2263 4 0 0 8 2264 6 0 0 15 2265 3 1 0 16 2266 6 0 1 12 2267 8 0 3 18 2268 7 1 4 14 2269 2 0 0 15 2270 0 0 1 11 2271 2 0 2 8 2272 1 0 0 8 2273 2 0 3 9 2274 0 0 1 7 2275 2 1 0 12 2276 2 0 1 8 2277 0 0 0 10 2278 6 0 4 14 2279 11 0 1 12 2280 3 0 5 9 2281 0 0 0 9 2282 0 1 0 12 2283 5 1 3 8 2284 2 0 1 16 2285 1 0 0 18 2286 2 0 2 13 2287 1 0 1 12 2288 16 0 2 15 2289 8 0 2 14 2290 3 0 2 14 2291 5 0 3 14 2292 12 0 3 17 2293 5 0 0 17 2294 5 0 2 14 2295 10 0 5 16 2296 7 0 1 12 2297 7 0 3 10 2298 1 0 1 9 2299 5 1 1 12 2300 7 1 3 13 2301 3 0 3 16 2302 9 0 1 13 2303 5 0 2 12 2304 1 0 3 12 2305 1 1 4 16 2306 4 2 5 12 2307 0 0 0 14 2308 3 0 3 14 2309 9 0 4 14 2310 22 2 3 8 2311 2 0 2 14 2312 2 0 1 12 2313 3 0 2 8 2314 3 0 0 12 2315 2 0 0 12 2316 8 1 5 8 2317 4 0 2 18 2318 7 0 1 18 2319 14 0 2 12 2320 0 0 1 8 2321 0 0 1 16 2322 1 0 1 12 2323 6 0 1 11 2324 9 2 3 10 2325 0 0 2 10 2326 1 0 4 14 2327 7 1 2 12 2328 9 1 2 18 2329 33 3 3 18 2330 0 0 1 10 2331 0 0 1 15 2332 0 0 1 7 2333 2 0 2 7 2334 12 0 2 10 2335 0 0 1 14 2336 0 0 0 16 2337 1 0 0 18 2338 2 0 1 16 2339 6 0 2 16 2340 0 1 3 18 2341 0 0 0 16 2342 4 0 0 12 2343 3 0 2 10 2344 2 0 0 12 2345 0 0 1 8 2346 0 0 0 12 2347 10 1 3 12 2348 0 0 8 8 2349 6 0 2 7 2350 6 1 1 6 2351 0 0 0 12 2352 1 0 2 0 2353 2 0 0 8 2354 8 1 2 2 2355 0 0 1 16 2356 1 0 0 14 2357 10 0 1 7 2358 2 0 1 8 2359 10 0 1 12 2360 0 0 0 12 2361 0 0 0 12 2362 7 0 0 18 2363 3 0 0 12 2364 3 0 0 8 2365 4 1 2 6 2366 15 1 4 10 2367 17 0 3 7 2368 0 2 0 6 2369 5 0 3 12 2370 3 0 1 6 2371 2 1 1 12 2372 10 0 2 2 2373 0 0 1 10 2374 2 0 0 10 2375 14 0 4 10 2376 12 0 1 8 2377 8 0 1 12 2378 7 3 3 12 2379 0 0 0 8 2380 7 1 3 12 2381 1 1 5 10 2382 6 0 2 12 2383 9 0 1 10 2384 2 0 2 8 2385 14 0 3 18 2386 5 0 0 9 2387 4 0 0 9 2388 5 0 0 10 2389 0 0 1 8 2390 1 0 2 5 2391 18 0 1 8 2392 13 0 0 16 2393 12 0 2 8 2394 0 0 1 13 2395 30 0 1 12 2396 1 0 2 12 2397 15 0 1 8 2398 1 1 2 9 2399 0 0 0 12 2400 5 0 2 12 2401 3 0 0 12 2402 1 0 0 14 2403 8 0 2 12 2404 5 0 0 18 2405 3 0 1 9 2406 2 0 1 16 2407 9 1 1 16 2408 0 0 0 12 2409 2 1 3 8 2410 9 0 0 12 2411 7 0 1 11 2412 9 0 2 8 2413 14 1 3 12 2414 3 0 1 11 2415 3 0 2 10 2416 0 0 1 8 2417 21 0 5 8 2418 5 0 1 11 2419 3 0 2 9 2420 10 0 1 7 2421 5 0 1 9 2422 1 0 1 8 2423 0 1 1 5 2424 6 0 2 7 2425 5 0 1 8 2426 7 0 3 6 2427 6 0 1 9 2428 2 0 0 15 2429 3 0 0 8 2430 1 2 0 8 2431 3 0 0 8 2432 3 0 1 8 2433 5 1 3 12 2434 8 4 4 3 2435 0 0 1 16 2436 1 0 0 11 2437 12 0 1 12 2438 0 0 1 9 2439 1 0 1 10 2440 2 0 1 13 2441 0 0 0 12 2442 2 0 0 16 2443 0 0 2 8 2444 1 1 1 17 2445 26 1 5 13 2446 1 0 3 16 2447 18 1 2 6 2448 3 0 2 12 2449 5 0 1 6 2450 18 1 1 8 2451 4 0 0 7 2452 0 0 2 4 2453 0 0 1 6 2454 26 1 5 8 2455 9 1 3 8 2456 10 1 2 8 2457 1 2 2 11 2458 0 0 0 11 2459 0 1 2 8 2460 2 0 0 12 2461 0 0 0 12 2462 3 0 0 16 2463 0 0 2 13 2464 4 0 3 7 2465 1 2 0 10 2466 3 0 0 12 2467 5 0 1 11 2468 3 0 0 12 2469 0 2 1 12 2470 15 1 1 15 2471 2 0 1 16 2472 3 0 0 18 2473 0 0 1 16 2474 0 0 2 16 2475 14 0 1 18 2476 3 0 2 12 2477 3 0 1 12 2478 10 0 1 18 2479 4 0 2 17 2480 2 0 0 18 2481 2 0 0 16 2482 4 0 2 16 2483 4 0 2 16 2484 7 1 4 12 2485 4 0 1 13 2486 1 0 1 12 2487 1 0 0 12 2488 8 0 1 13 2489 18 0 1 13 2490 8 0 3 15 2491 17 0 0 13 2492 8 0 2 14 2493 7 1 4 13 2494 1 0 1 12 2495 2 0 2 12 2496 6 0 2 0 2497 10 0 2 12 2498 2 1 4 12 2499 11 1 1 6 2500 5 0 3 12 2501 8 0 2 6 2502 0 0 0 7 2503 0 0 1 6 2504 5 0 2 8 2505 0 0 3 5 2506 6 1 3 8 2507 1 0 3 11 2508 0 0 0 8 2509 0 0 1 8 2510 0 0 2 8 2511 4 0 1 12 2512 9 1 1 12 2513 3 0 2 18 2514 3 0 2 17 2515 19 0 2 12 2516 4 0 0 11 2517 5 0 2 16 2518 0 0 1 16 2519 3 0 0 5 2520 2 0 1 12 2521 66 0 2 12 2522 0 0 0 10 2523 2 1 1 9 2524 4 0 1 9 2525 9 0 2 3 2526 2 0 0 14 2527 13 0 2 12 2528 3 0 5 8 2529 5 0 2 7 2530 0 0 0 9 2531 2 1 3 11 2532 3 0 0 8 2533 7 0 2 12 2534 6 0 3 12 2535 4 0 3 12 2536 6 1 1 8 2537 3 0 1 12 2538 7 0 3 10 2539 4 0 2 12 2540 5 1 1 8 2541 1 0 0 12 2542 8 0 3 5 2543 3 0 1 6 2544 7 1 1 12 2545 1 0 1 12 2546 5 0 2 5 2547 37 3 5 12 2548 6 1 2 9 2549 6 0 2 6 2550 17 6 5 8 2551 4 0 3 4 2552 3 0 1 11 2553 0 0 0 12 2554 7 0 3 8 2555 6 0 0 10 2556 3 0 0 8 2557 0 0 2 8 2558 15 1 2 7 2559 14 1 0 9 2560 7 1 1 7 2561 4 0 4 12 2562 7 0 1 8 2563 0 0 0 4 2564 2 0 3 13 2565 12 1 2 8 2566 2 0 2 8 2567 3 0 1 11 2568 7 0 1 5 2569 13 1 4 12 2570 10 0 1 10 2571 7 0 1 13 2572 12 1 3 16 2573 0 0 2 6 2574 1 0 1 8 2575 5 0 3 7 2576 6 0 2 7 2577 6 0 1 7 2578 0 0 1 8 2579 2 0 3 3 2580 0 0 0 10 2581 28 0 4 11 2582 13 1 0 8 2583 10 1 2 2 2584 0 0 1 5 2585 0 0 0 8 2586 10 0 3 6 2587 4 2 2 10 2588 3 0 0 7 2589 5 0 2 7 2590 7 0 0 16 2591 0 0 2 12 2592 0 0 0 7 2593 6 0 4 6 2594 4 1 1 11 2595 0 0 2 8 2596 2 0 1 8 2597 19 0 4 7 2598 6 1 2 8 2599 0 0 0 11 2600 2 0 2 16 2601 4 0 2 14 2602 24 2 1 15 2603 3 0 6 12 2604 0 0 0 5 2605 0 0 0 8 2606 1 0 0 13 2607 0 0 0 12 2608 17 2 2 8 2609 4 0 3 9 2610 8 0 3 15 2611 2 0 1 12 2612 8 1 7 10 2613 10 0 1 8 2614 1 2 3 9 2615 2 1 0 13 2616 7 0 1 10 2617 0 0 0 12 2618 0 0 1 12 2619 0 0 0 18 2620 18 0 5 14 2621 2 1 2 10 2622 6 0 3 14 2623 16 0 3 18 2624 13 1 4 8 2625 1 1 0 6 2626 8 0 1 9 2627 7 0 2 11 2628 4 0 1 0 2629 0 0 1 2 2630 0 0 0 12 2631 6 0 2 8 2632 4 2 1 8 2633 18 0 5 11 2634 4 0 4 8 2635 9 0 2 7 2636 5 1 2 10 2637 3 0 0 10 2638 8 0 0 4 2639 12 0 2 10 2640 0 0 1 8 2641 11 0 1 12 2642 3 0 0 10 2643 8 0 2 16 2644 2 0 3 11 2645 0 0 0 11 2646 4 0 3 11 2647 12 1 1 12 2648 6 0 2 8 2649 0 0 1 11 2650 6 0 4 14 2651 4 0 2 12 2652 15 0 5 5 2653 14 1 4 10 2654 2 0 3 8 2655 2 0 0 10 2656 0 0 0 8 2657 21 0 1 3 2658 22 1 5 7 2659 9 0 2 5 2660 4 1 0 6 2661 3 1 1 8 2662 2 0 1 8 2663 0 0 1 8 2664 0 0 1 8 2665 1 0 2 9 2666 1 1 3 8 2667 1 0 2 0 2668 1 0 0 8 2669 10 2 2 9 2670 18 0 1 8 2671 5 0 1 12 2672 26 1 3 12 2673 1 0 1 14 2674 0 0 1 12 2675 0 0 0 12 2676 1 0 1 12 2677 4 0 2 8 2678 1 0 1 8 2679 0 0 0 11 2680 4 0 0 11 2681 5 0 4 6 2682 3 0 2 6 2683 10 0 3 12 2684 12 1 5 11 2685 3 1 2 9 2686 5 0 3 5 2687 3 0 3 16 2688 1 0 0 8 2689 3 0 0 12 2690 5 0 1 4 2691 0 0 1 6 2692 11 2 0 14 2693 0 0 1 6 2694 6 0 1 0 2695 1 0 1 9 2696 0 0 1 11 2697 0 0 3 0 2698 4 1 1 0 2699 16 1 4 8 2700 0 0 0 16 2701 7 3 2 16 2702 7 0 1 12 2703 5 0 2 16 2704 6 0 2 7 2705 3 0 0 7 2706 3 0 1 7 2707 27 1 2 15 2708 11 1 2 12 2709 8 0 1 18 2710 0 1 1 0 2711 0 0 0 10 2712 2 0 0 13 2713 4 0 1 12 2714 6 0 3 13 2715 1 0 0 16 2716 6 0 1 16 2717 3 0 2 12 2718 8 1 2 16 2719 9 0 2 16 2720 11 4 2 12 2721 6 0 1 12 2722 13 0 4 15 2723 5 0 1 14 2724 16 0 1 12 2725 1 0 0 10 2726 4 0 3 7 2727 9 0 3 18 2728 4 0 3 18 2729 13 0 2 13 2730 5 0 1 14 2731 15 0 2 16 2732 8 0 1 18 2733 14 0 1 8 2734 0 0 0 8 2735 12 0 1 11 2736 8 0 3 12 2737 10 0 3 11 2738 23 1 3 8 2739 2 0 2 8 2740 0 0 1 9 2741 0 0 0 0 2742 1 0 1 0 2743 2 1 3 12 2744 15 1 1 12 2745 19 0 0 18 2746 4 0 1 16 2747 4 0 1 16 2748 4 0 1 16 2749 0 0 1 18 2750 0 0 0 16 2751 1 0 1 15 2752 2 0 0 12 2753 8 0 2 8 2754 20 0 2 11 2755 0 0 6 4 2756 20 0 1 12 2757 9 1 2 10 2758 9 0 3 8 2759 10 0 2 13 2760 6 0 2 10 2761 1 0 1 10 2762 7 0 3 12 2763 0 0 0 10 2764 0 0 1 6 2765 0 2 2 12 2766 0 0 1 3 2767 11 0 5 12 2768 2 0 1 9 2769 1 0 1 11 2770 3 0 0 12 2771 2 0 1 12 2772 4 0 1 12 2773 2 0 1 12 2774 17 0 0 14 2775 0 0 2 8 2776 6 0 4 10 2777 3 0 2 12 2778 5 0 2 12 2779 1 0 1 8 2780 3 0 0 18 2781 6 0 1 12 2782 0 0 0 10 2783 1 1 0 12 2784 11 0 1 18 2785 9 2 3 5 2786 3 0 0 7 2787 6 1 1 12 2788 0 0 2 6 2789 7 2 2 9 2790 5 0 0 6 2791 14 1 1 7 2792 0 0 0 12 2793 15 0 2 12 2794 8 0 0 9 2795 8 0 4 18 2796 3 0 1 8 2797 4 0 0 12 2798 9 0 1 13 2799 19 0 1 12 2800 3 0 0 8 2801 3 0 0 14 2802 15 1 2 12 2803 6 0 0 16 2804 4 0 0 16 2805 1 0 1 10 2806 15 1 4 12 2807 5 0 1 12 2808 0 0 0 7 2809 9 0 1 12 2810 9 0 1 12 2811 9 0 2 12 2812 1 0 0 8 2813 6 0 1 12 2814 10 0 1 17 2815 10 1 1 13 2816 0 0 2 12 2817 9 0 0 11 2818 6 0 1 13 2819 0 0 0 12 2820 13 1 2 7 2821 8 1 2 8 2822 20 2 2 7 2823 16 1 1 7 2824 2 0 2 9 2825 14 0 0 12 2826 6 0 1 13 2827 9 0 3 12 2828 8 1 4 1 2829 2 0 0 8 2830 2 0 3 8 2831 0 0 0 14 2832 2 2 3 10 2833 11 1 2 11 2834 8 0 1 8 2835 12 0 3 12 2836 1 0 3 12 2837 6 0 0 18 2838 1 0 1 6 2839 4 1 1 11 2840 2 0 1 12 2841 1 0 1 12 2842 11 2 4 11 2843 4 0 2 5 2844 8 0 0 8 2845 2 0 2 10 2846 1 0 2 14 2847 2 2 2 14 2848 2 0 3 12 2849 13 1 2 11 2850 5 0 1 18 2851 7 0 1 12 2852 6 0 3 13 2853 18 2 3 5 2854 2 0 0 2 2855 9 0 2 5 2856 9 0 3 3 2857 3 2 3 8 2858 16 0 1 10 2859 9 0 1 12 2860 0 0 0 3 2861 6 2 2 12 2862 4 0 1 7 2863 2 0 1 18 2864 0 0 1 8 2865 19 0 2 8 2866 9 0 0 12 2867 0 0 0 12 2868 0 0 1 12 2869 2 0 0 12 2870 6 0 0 4 2871 18 0 1 10 2872 12 0 0 14 2873 0 1 2 12 2874 5 0 2 9 2875 2 0 0 10 2876 3 0 1 16 2877 3 0 2 17 2878 0 0 2 2 2879 2 0 2 16 2880 3 1 3 12 2881 6 0 1 8 2882 2 0 1 11 2883 4 1 4 3 2884 5 0 3 13 2885 0 0 0 5 2886 7 0 4 16 2887 8 0 2 12 2888 2 0 0 12 2889 9 0 1 12 2890 8 0 5 8 2891 5 0 3 7 2892 0 0 0 11 2893 4 0 0 7 2894 2 0 1 8 2895 0 0 1 10 2896 7 0 1 7 2897 3 0 2 2 2898 38 4 2 8 2899 5 0 2 1 2900 7 1 0 9 2901 8 0 1 12 2902 4 0 0 10 2903 0 4 8 10 2904 1 0 1 8 2905 4 0 2 13 2906 6 0 1 12 2907 10 0 1 12 2908 8 0 0 14 2909 10 1 0 13 2910 5 1 3 12 2911 2 0 1 9 2912 1 0 1 7 2913 4 0 0 12 2914 5 0 1 14 2915 11 0 2 18 2916 2 0 0 13 2917 1 0 2 16 2918 2 1 2 7 2919 9 0 5 7 2920 2 0 1 12 2921 4 0 0 12 2922 4 0 1 17 2923 10 0 2 8 2924 0 0 1 8 2925 2 0 1 12 2926 2 0 2 10 2927 12 0 2 12 2928 6 1 1 14 2929 6 0 1 12 2930 1 1 1 4 2931 9 1 1 10 2932 2 0 0 12 2933 17 1 1 15 2934 1 0 3 8 2935 4 0 1 9 2936 5 0 3 12 2937 11 0 3 12 2938 9 0 2 7 2939 4 0 2 12 2940 7 0 0 10 2941 10 0 3 6 2942 2 0 6 8 2943 8 0 6 12 2944 14 0 3 12 2945 6 0 0 8 2946 1 0 3 12 2947 19 0 1 12 2948 1 0 0 14 2949 1 0 0 14 2950 1 0 2 8 2951 0 0 1 8 2952 1 0 0 8 2953 4 0 3 10 2954 7 0 2 16 2955 0 0 0 9 2956 5 0 3 12 2957 4 0 2 8 2958 3 0 4 8 2959 3 0 3 7 2960 17 1 5 12 2961 2 0 3 12 2962 4 0 1 8 2963 5 0 2 12 2964 0 0 0 6 2965 5 1 3 6 2966 3 0 3 8 2967 3 0 1 8 2968 2 0 1 8 2969 21 1 1 8 2970 3 0 0 8 2971 6 2 2 15 2972 12 0 2 8 2973 0 0 0 11 2974 2 0 1 8 2975 0 0 0 8 2976 1 0 0 7 2977 11 2 1 8 2978 1 0 1 10 2979 15 0 3 18 2980 2 0 2 16 2981 1 0 0 16 2982 6 0 0 12 2983 12 2 4 3 2984 2 0 0 12 2985 1 0 0 8 2986 2 0 1 8 2987 17 1 0 8 2988 6 0 1 16 2989 4 0 1 9 2990 6 0 2 10 2991 0 0 2 12 2992 0 0 1 12 2993 10 0 2 12 2994 5 0 3 8 2995 7 0 1 10 2996 3 0 2 9 2997 4 0 3 10 2998 8 0 1 6 2999 1 0 3 8 3000 2 3 2 4 3001 0 0 1 4 3002 0 0 1 6 3003 2 1 3 7 3004 2 0 2 7 3005 5 0 0 12 3006 3 0 1 12 3007 6 0 1 8 3008 12 1 2 6 3009 5 0 4 6 3010 7 0 2 12 3011 9 0 2 11 3012 0 0 1 12 3013 8 0 2 10 3014 9 0 2 16 3015 0 0 0 8 3016 4 0 2 14 3017 10 0 3 12 3018 0 0 1 12 3019 3 5 2 12 3020 5 0 2 12 3021 0 0 0 12 3022 4 0 1 9 3023 2 0 0 12 3024 2 0 1 12 3025 6 1 0 16 3026 3 0 1 12 3027 2 0 0 14 3028 1 0 2 8 3029 4 0 0 8 3030 2 0 2 6 3031 4 2 2 4 3032 0 0 0 7 3033 0 0 2 5 3034 1 0 1 2 3035 7 1 2 12 3036 3 0 0 8 3037 0 0 1 12 3038 0 0 0 12 3039 10 1 1 12 3040 8 2 3 12 3041 1 0 3 4 3042 2 0 0 5 3043 0 0 0 4 3044 6 0 2 10 3045 7 0 4 16 3046 1 0 1 12 3047 5 0 1 13 3048 4 0 1 8 3049 4 0 3 13 3050 11 0 4 12 3051 13 0 1 12 3052 7 0 2 7 3053 0 0 3 4 3054 7 1 4 16 3055 2 0 0 11 3056 6 0 2 11 3057 4 0 2 6 3058 2 0 2 0 3059 8 0 2 12 3060 9 0 2 4 3061 0 0 3 12 3062 3 0 3 12 3063 8 1 1 12 3064 0 0 1 12 3065 3 0 2 12 3066 17 1 2 12 3067 5 0 1 12 3068 2 0 1 8 3069 2 0 1 7 3070 1 2 5 10 3071 2 0 0 3 3072 9 0 2 16 3073 0 0 0 5 3074 0 0 0 12 3075 3 0 1 16 3076 0 0 1 9 3077 15 1 3 2 3078 16 0 3 5 3079 2 0 0 12 3080 0 0 1 13 3081 4 0 5 14 3082 2 0 1 12 3083 6 1 4 12 3084 2 0 0 6 3085 3 0 1 12 3086 1 0 0 12 3087 8 0 1 10 3088 0 0 3 12 3089 1 0 0 12 3090 1 0 0 16 3091 3 0 1 18 3092 4 0 2 12 3093 0 0 2 13 3094 5 1 1 12 3095 3 0 0 12 3096 7 0 3 8 3097 3 1 4 11 3098 3 0 3 11 3099 22 0 1 18 3100 8 0 3 13 3101 1 0 0 11 3102 3 0 0 16 3103 0 0 1 12 3104 10 1 5 7 3105 4 0 2 13 3106 4 0 1 16 3107 2 0 2 4 3108 4 0 3 8 3109 9 0 1 10 3110 6 2 1 9 3111 14 0 2 0 3112 2 0 1 10 3113 0 0 1 12 3114 4 0 4 12 3115 0 1 1 6 3116 1 0 0 8 3117 13 2 3 13 3118 3 0 3 15 3119 6 0 0 7 3120 0 0 0 8 3121 1 0 0 9 3122 22 0 2 11 3123 1 0 0 11 3124 9 4 2 6 3125 5 1 2 6 3126 6 0 1 10 3127 14 1 4 16 3128 6 0 1 0 3129 3 0 1 12 3130 13 2 6 12 3131 0 0 4 12 3132 0 0 0 10 3133 0 0 0 10 3134 0 0 0 12 3135 17 0 3 13 3136 8 0 2 7 3137 0 0 1 12 3138 4 0 4 10 3139 5 0 1 14 3140 6 0 1 13 3141 7 0 1 14 3142 0 0 0 15 3143 7 0 2 12 3144 7 0 3 12 3145 7 0 5 13 3146 11 0 1 12 3147 7 2 4 12 3148 14 1 2 4 3149 5 0 2 8 3150 5 0 1 11 3151 5 1 3 12 3152 13 0 2 12 3153 7 0 1 9 3154 1 0 2 15 3155 9 1 0 14 3156 4 0 1 12 3157 7 0 1 12 3158 0 0 1 13 3159 3 0 1 16 3160 0 0 0 16 3161 7 2 0 9 3162 14 0 2 12 3163 16 0 0 14 3164 3 0 0 12 3165 5 0 0 13 3166 1 0 3 11 3167 3 0 2 12 3168 2 0 0 12 3169 18 0 0 12 3170 31 0 3 14 3171 1 1 2 12 3172 8 1 2 7 3173 3 0 1 9 3174 1 0 2 8 3175 1 0 0 8 3176 10 0 0 12 3177 4 0 1 9 3178 8 2 7 9 3179 0 0 1 10 3180 2 0 3 8 3181 9 1 3 3 3182 1 0 2 8 3183 5 0 0 12 3184 7 0 3 15 3185 11 0 3 14 3186 6 2 4 12 3187 4 1 4 10 3188 3 0 1 8 3189 3 0 0 11 3190 3 0 0 9 3191 4 1 1 8 3192 5 0 2 12 3193 24 2 4 11 3194 0 0 1 12 3195 1 0 0 8 3196 17 0 3 8 3197 1 0 1 12 3198 2 0 1 15 3199 14 0 1 12 3200 4 0 0 11 3201 0 0 2 8 3202 12 3 3 8 3203 3 0 1 8 3204 0 0 1 12 3205 2 0 1 12 3206 4 0 1 8 3207 5 1 1 13 3208 1 0 1 12 3209 13 0 1 8 3210 0 0 0 8 3211 3 0 1 8 3212 3 0 0 14 3213 8 0 1 8 3214 2 0 1 8 3215 17 1 1 12 3216 0 0 0 6 3217 9 0 2 8 3218 7 0 2 12 3219 8 0 2 8 3220 9 0 1 12 3221 12 0 1 12 3222 6 1 0 12 3223 0 1 0 8 3224 5 0 2 16 3225 3 0 0 8 3226 1 1 0 9 3227 1 0 0 12 3228 3 0 2 12 3229 2 1 1 8 3230 1 0 2 8 3231 2 0 2 17 3232 2 0 2 11 3233 7 1 2 8 3234 7 0 1 17 3235 0 0 0 8 3236 0 0 0 8 3237 1 0 0 8 3238 2 2 3 8 3239 0 0 0 8 3240 17 0 3 8 3241 19 0 5 12 3242 2 0 1 12 3243 9 2 1 13 3244 5 0 1 12 3245 2 0 2 14 3246 8 0 1 16 3247 4 0 0 12 3248 4 1 3 12 3249 3 0 0 12 3250 5 0 1 8 3251 8 0 0 16 3252 14 0 0 12 3253 3 0 1 12 3254 10 1 1 7 3255 7 0 1 8 3256 4 1 1 14 3257 5 0 0 12 3258 1 0 0 13 3259 6 0 1 9 3260 2 0 1 5 3261 6 0 0 12 3262 11 0 2 9 3263 3 0 1 7 3264 4 0 0 12 3265 5 0 1 10 3266 0 1 3 13 3267 1 0 0 12 3268 21 0 2 9 3269 0 0 0 12 3270 12 0 3 12 3271 1 0 0 16 3272 4 0 4 8 3273 6 0 1 8 3274 13 0 0 8 3275 7 0 3 12 3276 0 0 0 12 3277 4 0 3 10 3278 19 0 2 8 3279 6 0 0 12 3280 7 0 4 12 3281 5 0 0 12 3282 14 0 1 8 3283 0 0 0 7 3284 5 0 1 7 3285 7 0 1 12 3286 16 1 1 10 3287 0 0 1 7 3288 10 8 5 8 3289 0 0 1 12 3290 0 0 0 11 3291 4 0 1 12 3292 3 0 0 11 3293 0 0 1 8 3294 0 0 0 6 3295 0 0 1 14 3296 0 0 0 6 3297 1 0 1 6 3298 5 1 2 12 3299 7 0 0 12 3300 11 0 5 8 3301 12 0 1 11 3302 2 0 2 5 3303 10 2 6 8 3304 4 0 2 12 3305 0 0 0 6 3306 0 0 3 8 3307 6 1 0 11 3308 0 0 2 12 3309 4 0 1 12 3310 3 0 5 12 3311 1 0 1 10 3312 6 1 2 7 3313 13 0 4 12 3314 0 0 0 10 3315 8 0 5 8 3316 2 0 0 10 3317 5 0 3 14 3318 9 1 2 12 3319 5 0 0 10 3320 15 2 1 12 3321 5 0 1 16 3322 9 0 2 18 3323 6 0 5 8 3324 11 0 2 8 3325 0 0 3 8 3326 6 0 1 0 3327 6 0 3 8 3328 4 0 1 4 3329 12 0 1 18 3330 11 0 2 16 3331 2 0 0 10 3332 3 0 0 14 3333 8 0 5 10 3334 5 1 1 12 3335 15 1 1 6 3336 22 2 4 8 3337 4 0 0 12 3338 28 1 2 8 3339 0 0 1 11 3340 12 1 4 12 3341 6 0 1 9 3342 5 0 2 8 3343 2 0 1 8 3344 0 0 0 6 3345 0 0 0 12 3346 1 0 1 8 3347 3 0 1 0 3348 0 0 4 18 3349 12 0 2 13 3350 14 0 0 8 3351 14 1 6 14 3352 14 0 1 7 3353 4 0 1 14 3354 23 0 1 17 3355 3 2 2 12 3356 4 0 1 17 3357 13 1 2 18 3358 0 0 0 7 3359 2 0 1 12 3360 5 0 5 14 3361 3 0 2 13 3362 1 0 2 16 3363 0 0 1 18 3364 3 0 2 18 3365 10 1 2 12 3366 26 1 3 11 3367 12 0 2 17 3368 6 0 1 16 3369 6 0 0 12 3370 1 0 0 14 3371 2 0 0 13 3372 3 0 0 12 3373 0 0 2 7 3374 3 1 1 8 3375 4 0 1 9 3376 11 1 1 8 3377 6 0 2 11 3378 2 0 2 13 3379 5 0 3 15 3380 10 0 2 12 3381 5 0 3 6 3382 8 0 1 7 3383 4 0 1 12 3384 8 0 0 12 3385 11 0 0 13 3386 2 0 0 8 3387 21 0 2 9 3388 4 0 1 14 3389 0 0 0 11 3390 0 1 0 10 3391 2 0 2 11 3392 1 5 3 10 3393 0 0 1 8 3394 0 0 0 10 3395 10 2 4 12 3396 6 0 2 8 3397 6 0 1 12 3398 0 0 0 16 3399 11 1 0 12 3400 1 0 1 17 3401 11 5 2 16 3402 7 0 3 15 3403 2 0 1 14 3404 5 0 2 12 3405 8 0 2 17 3406 1 0 0 9 3407 2 0 4 2 3408 4 0 1 12 3409 9 1 1 16 3410 5 0 1 14 3411 15 0 0 18 3412 4 0 0 16 3413 1 0 2 16 3414 0 0 1 16 3415 2 0 0 12 3416 6 0 0 12 3417 1 0 1 18 3418 6 0 0 16 3419 1 0 1 3 3420 1 0 3 12 3421 2 0 0 17 3422 2 0 3 17 3423 18 1 3 12 3424 4 0 1 12 3425 10 0 4 8 3426 10 1 2 11 3427 1 1 2 0 3428 1 0 1 8 3429 9 0 3 14 3430 23 8 3 12 3431 6 0 0 16 3432 6 1 2 3 3433 4 0 0 12 3434 1 0 1 9 3435 7 0 2 8 3436 3 1 1 9 3437 11 1 2 8 3438 14 1 1 17 3439 4 0 4 14 3440 1 0 1 12 3441 3 0 3 16 3442 3 0 1 14 3443 5 0 1 15 3444 16 0 3 15 3445 11 1 1 12 3446 2 0 1 12 3447 2 0 1 12 3448 24 0 2 12 3449 4 0 0 12 3450 23 0 2 8 3451 8 0 2 0 3452 2 0 1 6 3453 12 0 2 11 3454 7 0 2 11 3455 0 2 2 0 3456 2 0 0 12 3457 3 0 4 16 3458 0 0 3 12 3459 0 0 0 14 3460 0 0 2 16 3461 0 0 0 12 3462 4 0 1 18 3463 5 2 3 0 3464 5 0 2 0 3465 0 0 0 12 3466 1 0 1 8 3467 11 0 1 0 3468 20 0 1 0 3469 6 1 2 12 3470 4 0 1 11 3471 15 1 1 13 3472 7 0 1 16 3473 0 0 0 2 3474 43 0 4 8 3475 22 2 7 11 3476 0 0 0 11 3477 3 0 1 8 3478 7 3 1 12 3479 14 0 3 12 3480 18 0 2 8 3481 2 0 0 11 3482 3 0 2 12 3483 0 0 0 12 3484 2 0 0 12 3485 2 0 0 15 3486 1 0 0 16 3487 11 0 3 9 3488 3 0 3 8 3489 18 0 2 9 3490 9 0 2 3 3491 0 0 1 5 3492 0 2 1 0 3493 13 1 2 12 3494 1 0 1 10 3495 11 2 3 5 3496 1 0 1 12 3497 8 0 2 17 3498 1 0 3 11 3499 2 0 0 8 3500 4 0 0 10 3501 0 0 0 8 3502 0 0 1 3 3503 3 1 0 12 3504 0 0 1 8 3505 2 0 0 16 3506 6 0 1 12 3507 4 0 1 12 3508 2 0 0 8 3509 10 1 0 6 3510 10 1 3 16 3511 1 1 4 6 3512 5 0 0 10 3513 10 0 1 8 3514 2 1 1 6 3515 1 0 2 16 3516 0 1 1 4 3517 8 2 4 12 3518 4 0 2 12 3519 5 0 2 10 3520 1 1 0 12 3521 5 0 2 12 3522 4 0 1 14 3523 0 0 1 8 3524 10 0 1 14 3525 17 1 1 8 3526 2 0 0 8 3527 2 0 1 8 3528 0 0 1 7 3529 1 0 0 9 3530 14 1 0 0 3531 10 0 3 8 3532 6 1 2 8 3533 3 0 5 0 3534 13 8 2 0 3535 3 0 1 16 3536 9 1 2 8 3537 15 0 3 12 3538 16 1 5 13 3539 12 1 1 10 3540 5 0 1 16 3541 5 0 0 12 3542 8 0 3 12 3543 2 0 1 12 3544 4 0 0 11 3545 7 0 1 12 3546 5 0 2 12 3547 5 0 1 12 3548 0 0 0 4 3549 5 0 0 12 3550 3 0 1 12 3551 13 0 1 14 3552 0 0 0 9 3553 3 0 1 3 3554 63 0 1 12 3555 2 1 4 9 3556 0 0 0 6 3557 5 0 1 14 3558 9 0 1 12 3559 2 0 1 12 3560 1 2 3 12 3561 0 0 1 11 3562 7 0 4 8 3563 27 0 2 14 3564 3 1 2 10 3565 4 0 3 12 3566 6 0 1 9 3567 3 0 3 12 3568 10 1 3 12 3569 2 0 0 16 3570 8 0 2 13 3571 4 0 1 12 3572 14 2 1 12 3573 5 0 1 16 3574 2 0 0 7 3575 8 0 2 13 3576 8 1 3 12 3577 2 0 1 16 3578 6 3 1 13 3579 1 0 1 12 3580 2 0 1 12 3581 2 0 1 14 3582 0 0 2 12 3583 16 0 3 9 3584 10 0 1 12 3585 0 0 0 12 3586 0 0 0 9 3587 5 1 2 12 3588 7 0 2 12 3589 6 2 2 10 3590 0 0 2 15 3591 3 0 1 12 3592 0 0 0 11 3593 7 2 5 7 3594 1 1 1 1 3595 10 0 6 5 3596 0 0 1 17 3597 1 0 0 15 3598 0 0 0 12 3599 0 0 1 13 3600 8 0 1 6 3601 1 0 0 8 3602 0 0 0 7 3603 3 2 2 6 3604 1 0 0 12 3605 1 0 0 12 3606 12 0 1 4 3607 2 0 0 9 3608 2 2 2 4 3609 5 0 2 0 3610 3 0 3 8 3611 11 1 0 8 3612 3 0 0 14 3613 11 0 1 16 3614 4 0 0 16 3615 5 0 0 13 3616 8 0 2 12 3617 10 2 1 7 3618 1 0 2 5 3619 0 2 0 7 3620 2 1 2 7 3621 0 0 4 7 3622 6 0 4 0 3623 3 0 1 0 3624 11 0 0 12 3625 3 0 2 12 3626 8 0 1 12 3627 2 0 2 5 3628 4 0 1 9 3629 1 0 0 8 3630 4 0 0 7 3631 7 1 1 12 3632 7 0 3 5 3633 1 0 0 15 3634 4 0 1 13 3635 3 0 1 9 3636 3 0 2 6 3637 5 1 3 3 3638 7 0 1 6 3639 5 1 1 2 3640 17 3 4 8 3641 11 0 2 4 3642 5 0 2 12 3643 9 0 4 10 3644 5 0 6 3 3645 1 0 0 12 3646 6 0 2 8 3647 0 0 0 7 3648 2 0 1 14 3649 4 0 0 10 3650 0 1 2 8 3651 9 0 0 8 3652 1 0 1 9 3653 13 1 2 12 3654 0 0 0 12 3655 9 0 1 12 3656 0 0 0 12 3657 0 0 0 12 3658 0 0 1 12 3659 12 0 3 8 3660 1 0 2 10 3661 18 1 2 12 3662 11 0 3 11 3663 7 1 1 12 3664 11 0 4 12 3665 10 1 5 8 3666 6 0 0 8 3667 1 0 4 8 3668 3 0 1 7 3669 1 2 5 12 3670 0 0 1 12 3671 3 1 2 8 3672 2 0 0 8 3673 7 0 4 8 3674 8 0 3 10 3675 9 1 2 8 3676 39 2 2 8 3677 7 0 2 12 3678 39 1 4 12 3679 1 0 1 12 3680 9 0 2 8 3681 1 0 0 18 3682 6 0 2 12 3683 11 0 1 2 3684 11 0 2 10 3685 1 0 2 12 3686 0 0 1 12 3687 6 0 0 12 3688 5 0 2 12 3689 4 1 2 11 3690 10 1 0 9 3691 5 0 2 9 3692 7 0 2 8 3693 6 0 1 12 3694 5 0 3 8 3695 8 0 1 8 3696 4 0 1 8 3697 10 0 2 9 3698 2 4 2 2 3699 0 0 1 12 3700 3 0 0 12 3701 0 0 0 16 3702 5 1 0 12 3703 3 0 1 14 3704 2 0 0 8 3705 4 0 2 7 3706 11 0 4 8 3707 3 0 1 12 3708 3 0 1 13 3709 1 0 0 14 3710 3 0 2 16 3711 0 0 0 16 3712 2 0 2 16 3713 2 0 2 4 3714 4 0 0 10 3715 7 2 5 12 3716 6 0 2 12 3717 5 0 0 12 3718 11 0 1 0 3719 14 0 5 12 3720 11 0 3 12 3721 0 0 1 9 3722 4 0 2 11 3723 0 6 4 8 3724 1 0 3 8 3725 4 0 1 7 3726 8 2 2 9 3727 9 0 4 14 3728 9 0 6 9 3729 1 0 1 8 3730 6 0 1 8 3731 7 0 2 11 3732 3 1 3 16 3733 0 0 2 8 3734 0 0 2 5 3735 0 5 7 9 3736 1 1 0 8 3737 4 0 0 9 3738 0 0 1 8 3739 10 0 1 12 3740 3 0 0 6 3741 6 1 4 11 3742 0 1 0 5 3743 0 0 1 12 3744 5 0 1 8 3745 4 1 0 12 3746 12 0 3 8 3747 7 6 2 9 3748 5 1 5 10 3749 2 1 0 6 3750 8 0 2 8 3751 5 0 0 8 3752 5 0 0 10 3753 1 5 2 12 3754 12 3 4 8 3755 4 0 1 8 3756 1 0 1 10 3757 11 0 6 8 3758 2 0 1 14 3759 16 0 0 12 3760 2 0 3 14 3761 0 0 0 8 3762 4 0 2 12 3763 8 0 0 12 3764 0 0 1 12 3765 1 0 1 10 3766 1 0 1 6 3767 6 0 4 3 3768 2 0 1 4 3769 8 0 3 12 3770 7 0 1 12 3771 0 0 1 12 3772 17 0 0 10 3773 18 4 3 12 3774 12 1 1 12 3775 6 1 1 9 3776 5 0 1 12 3777 0 1 3 14 3778 4 0 1 8 3779 0 0 0 12 3780 15 2 3 6 3781 7 4 1 14 3782 5 0 1 11 3783 0 0 0 11 3784 1 0 1 11 3785 1 0 1 11 3786 1 0 3 7 3787 0 0 1 5 3788 5 0 5 3 3789 2 2 1 12 3790 3 0 2 8 3791 6 0 1 12 3792 1 0 1 16 3793 1 0 4 6 3794 4 0 1 4 3795 2 0 2 8 3796 5 1 1 8 3797 4 0 0 18 3798 1 0 1 13 3799 4 0 3 12 3800 1 0 1 12 3801 20 0 3 10 3802 2 0 1 12 3803 0 0 0 12 3804 2 0 4 6 3805 1 0 2 16 3806 0 0 1 11 3807 1 1 2 3 3808 5 0 0 15 3809 6 0 4 7 3810 1 0 0 9 3811 1 0 1 12 3812 20 0 3 12 3813 11 0 1 12 3814 3 0 0 7 3815 5 0 1 8 3816 0 0 1 12 3817 4 0 1 9 3818 2 0 0 12 3819 8 0 1 15 3820 12 0 3 8 3821 0 0 4 8 3822 8 0 0 2 3823 8 0 3 12 3824 0 0 0 8 3825 0 0 0 14 3826 2 0 0 12 3827 13 0 5 10 3828 6 2 1 8 3829 0 0 0 10 3830 0 0 1 10 3831 3 0 1 12 3832 10 2 5 12 3833 12 0 1 12 3834 0 0 0 10 3835 6 0 2 12 3836 1 0 0 12 3837 2 0 0 12 3838 1 0 1 12 3839 3 2 2 14 3840 20 0 1 16 3841 11 0 4 13 3842 4 0 1 11 3843 5 0 1 12 3844 6 0 1 9 3845 0 0 0 9 3846 0 1 0 11 3847 1 0 2 12 3848 5 0 2 16 3849 7 0 2 11 3850 3 1 3 8 3851 53 0 3 8 3852 42 5 1 12 3853 1 0 1 10 3854 8 0 3 9 3855 2 0 1 12 3856 18 0 1 16 3857 7 0 0 9 3858 1 0 3 12 3859 6 1 1 12 3860 12 0 0 8 3861 10 0 1 11 3862 11 0 1 8 3863 0 0 0 8 3864 9 0 2 15 3865 2 1 1 14 3866 7 0 2 9 3867 4 0 0 12 3868 12 4 1 18 3869 2 0 1 16 3870 9 1 1 9 3871 1 0 0 12 3872 2 2 2 11 3873 3 0 1 12 3874 4 1 3 12 3875 7 0 1 15 3876 5 0 4 13 3877 8 0 2 14 3878 6 0 4 12 3879 14 0 1 9 3880 4 1 4 12 3881 7 0 2 12 3882 16 0 5 8 3883 6 0 1 13 3884 13 0 1 7 3885 15 0 4 8 3886 4 0 3 12 3887 2 0 1 3 3888 2 0 3 11 3889 7 1 2 10 3890 5 0 4 12 3891 8 1 3 11 3892 0 0 1 9 3893 6 0 1 0 3894 3 0 1 16 3895 7 0 2 16 3896 9 0 0 0 3897 0 0 0 0 3898 4 0 3 9 3899 8 0 4 15 3900 0 1 1 12 3901 1 0 1 10 3902 1 0 1 8 3903 0 0 1 8 3904 6 2 0 8 3905 16 1 3 10 3906 8 0 4 9 3907 18 1 1 8 3908 9 0 1 12 3909 0 0 0 11 3910 8 1 1 6 3911 7 1 2 12 3912 2 0 0 0 3913 9 0 2 13 3914 11 0 1 3 3915 5 0 1 8 3916 4 0 2 9 3917 1 0 1 12 3918 0 0 2 12 3919 3 0 1 8 3920 5 0 2 8 3921 5 0 1 12 3922 1 0 0 12 3923 13 0 4 8 3924 8 1 2 14 3925 14 0 1 14 3926 2 0 5 12 3927 9 0 0 11 3928 14 0 1 12 3929 39 0 0 12 3930 1 0 2 4 3931 13 2 3 13 3932 13 0 0 13 3933 2 1 0 9 3934 10 0 1 12 3935 6 0 0 14 3936 5 0 1 12 3937 0 0 0 12 3938 10 0 0 8 3939 4 0 1 12 3940 2 0 0 12 3941 10 2 1 12 3942 0 0 0 10 3943 7 0 3 12 3944 2 0 0 13 3945 1 0 0 12 3946 3 0 0 13 3947 2 0 1 12 3948 4 0 1 14 3949 7 0 1 6 3950 3 0 2 11 3951 1 0 2 14 3952 6 0 1 8 3953 4 0 3 8 3954 18 0 1 12 3955 5 0 0 12 3956 1 0 1 9 3957 19 1 1 8 3958 14 0 2 13 3959 4 0 1 7 3960 2 0 1 7 3961 6 0 3 6 3962 6 0 2 12 3963 0 0 1 13 3964 15 1 3 7 3965 1 0 2 7 3966 15 0 1 8 3967 2 0 1 12 3968 3 0 3 12 3969 2 0 3 11 3970 8 0 2 16 3971 37 1 3 13 3972 0 0 2 12 3973 2 0 1 12 3974 4 0 3 12 3975 4 0 3 16 3976 9 0 4 16 3977 7 1 1 8 3978 15 2 3 12 3979 3 0 0 12 3980 5 2 0 9 3981 2 0 2 6 3982 2 0 1 17 3983 1 0 1 12 3984 0 0 4 8 3985 5 0 3 7 3986 19 0 1 9 3987 3 0 0 8 3988 8 0 3 7 3989 11 0 3 4 3990 4 0 0 12 3991 1 0 0 8 3992 6 0 0 18 3993 4 0 1 15 3994 0 2 4 9 3995 10 0 3 12 3996 6 0 1 12 3997 1 0 0 13 3998 0 0 1 8 3999 14 2 1 8 4000 5 0 0 12 4001 19 0 1 12 4002 9 0 4 16 4003 3 0 1 14 4004 15 0 2 12 4005 4 0 2 16 4006 8 0 3 8 4007 2 0 0 8 4008 5 0 0 9 4009 4 1 2 9 4010 9 3 2 12 4011 4 1 5 8 4012 0 0 1 12 4013 6 0 3 8 4014 5 0 2 9 4015 14 1 1 11 4016 6 1 3 10 4017 0 0 0 4 4018 0 0 0 10 4019 2 0 1 12 4020 3 0 0 6 4021 3 0 1 4 4022 1 1 1 3 4023 1 0 0 12 4024 7 0 0 5 4025 2 0 0 10 4026 22 0 2 8 4027 6 0 1 12 4028 4 0 1 12 4029 8 0 2 16 4030 2 0 2 12 4031 0 0 0 12 4032 3 0 3 12 4033 7 2 2 12 4034 40 1 2 7 4035 11 0 0 0 4036 2 0 1 8 4037 6 0 1 13 4038 4 0 4 16 4039 3 0 5 5 4040 17 0 1 14 4041 12 0 1 8 4042 17 0 2 12 4043 3 0 0 12 4044 4 0 6 6 4045 0 0 1 17 4046 7 1 1 12 4047 9 0 1 12 4048 7 1 2 10 4049 6 0 1 10 4050 1 0 3 11 4051 4 1 4 12 4052 1 0 0 12 4053 6 1 1 12 4054 3 1 3 12 4055 3 0 1 16 4056 21 0 2 14 4057 0 0 0 5 4058 3 1 1 12 4059 7 1 2 12 4060 5 0 1 10 4061 5 0 2 13 4062 1 0 1 13 4063 4 0 2 12 4064 0 0 0 12 4065 9 1 3 7 4066 1 0 0 12 4067 6 0 1 17 4068 3 0 1 13 4069 8 0 2 12 4070 3 0 2 12 4071 0 0 2 9 4072 4 0 0 12 4073 5 0 0 7 4074 4 0 3 7 4075 9 0 2 8 4076 4 0 2 7 4077 3 1 1 9 4078 1 0 1 12 4079 0 0 0 8 4080 12 4 2 12 4081 3 1 2 8 4082 9 2 2 14 4083 4 0 2 10 4084 6 0 2 13 4085 4 0 4 2 4086 9 0 1 9 4087 17 0 0 12 4088 0 0 2 12 4089 4 1 2 10 4090 7 0 0 2 4091 14 0 2 3 4092 6 0 1 5 4093 5 1 5 12 4094 9 1 3 7 4095 15 0 1 10 4096 14 0 3 6 4097 10 0 1 12 4098 7 0 3 8 4099 1 0 1 3 4100 8 0 1 10 4101 9 0 1 12 4102 4 0 1 12 4103 8 2 5 16 4104 18 1 2 13 4105 12 0 0 9 4106 9 0 2 13 4107 18 1 1 18 4108 7 0 3 17 4109 5 0 2 16 4110 9 0 1 12 4111 5 0 2 9 4112 1 0 1 8 4113 23 3 2 6 4114 0 0 0 8 4115 0 0 1 14 4116 9 1 4 4 4117 8 0 4 4 4118 1 0 1 12 4119 1 2 3 14 4120 0 0 0 4 4121 4 0 5 6 4122 2 0 6 0 4123 5 0 1 6 4124 0 0 2 18 4125 4 0 1 18 4126 0 1 2 4 4127 0 0 1 4 4128 17 4 4 5 4129 0 0 0 12 4130 3 0 2 1 4131 14 0 4 1 4132 14 0 1 1 4133 11 0 1 0 4134 0 0 2 0 4135 5 0 2 3 4136 5 1 3 16 4137 5 0 2 7 4138 2 0 2 7 4139 3 0 2 5 4140 1 0 0 6 4141 0 0 1 16 4142 6 0 2 13 4143 3 6 3 11 4144 2 0 3 8 4145 6 0 0 16 4146 17 2 2 10 4147 7 0 1 7 4148 1 0 3 0 4149 17 1 4 16 4150 0 1 2 9 4151 1 0 1 11 4152 13 0 2 8 4153 5 0 2 8 4154 6 6 3 5 4155 0 0 0 8 4156 13 1 1 8 4157 19 0 1 8 4158 15 2 5 12 4159 1 0 0 13 4160 9 0 2 8 4161 12 0 1 12 4162 0 0 1 8 4163 8 0 0 14 4164 11 0 2 18 4165 11 0 2 8 4166 1 0 4 12 4167 0 0 0 8 4168 0 0 1 12 4169 11 1 3 8 4170 3 0 2 6 4171 0 1 0 16 4172 9 0 2 8 4173 13 3 2 8 4174 3 1 0 6 4175 2 0 4 7 4176 7 0 3 6 4177 3 0 2 12 4178 6 0 0 12 4179 10 0 2 0 4180 0 0 1 7 4181 4 0 1 7 4182 0 0 1 7 4183 10 0 0 12 4184 7 0 6 4 4185 0 0 0 7 4186 3 0 1 12 4187 6 0 2 8 4188 0 0 4 5 4189 6 0 2 11 4190 0 0 0 14 4191 1 0 2 7 4192 7 0 0 1 4193 3 0 3 12 4194 16 0 1 6 4195 11 0 2 11 4196 14 1 1 11 4197 6 0 1 8 4198 3 0 2 12 4199 4 0 1 5 4200 5 0 1 12 4201 2 0 1 12 4202 2 0 1 11 4203 1 0 1 0 4204 10 0 3 12 4205 10 0 3 7 4206 4 1 0 8 4207 2 0 0 8 4208 2 0 4 5 4209 0 0 0 5 4210 15 0 6 5 4211 1 0 0 7 4212 6 0 1 0 4213 16 1 2 7 4214 10 0 1 14 4215 21 0 0 16 4216 1 0 2 4 4217 5 0 4 3 4218 0 0 1 4 4219 3 0 1 5 4220 2 0 1 7 4221 4 0 1 7 4222 6 0 1 8 4223 16 1 5 12 4224 8 0 2 12 4225 3 0 1 4 4226 0 0 0 7 4227 6 1 0 9 4228 2 0 2 3 4229 4 0 1 4 4230 0 0 0 5 4231 0 0 1 12 4232 1 0 0 4 4233 2 0 2 14 4234 0 0 2 13 4235 0 0 0 14 4236 4 1 4 7 4237 3 0 2 10 4238 2 0 2 7 4239 0 0 0 8 4240 7 0 4 9 4241 7 2 2 6 4242 3 0 0 13 4243 3 0 2 11 4244 14 0 1 4 4245 0 0 0 10 4246 0 1 3 1 4247 0 0 1 2 4248 3 0 3 12 4249 9 0 0 14 4250 7 0 2 14 4251 5 0 0 10 4252 4 0 3 0 4253 1 0 1 0 4254 0 0 0 0 4255 4 1 1 2 4256 3 0 0 3 4257 11 0 1 11 4258 0 0 0 0 4259 0 0 0 2 4260 5 0 2 12 4261 8 0 1 2 4262 0 0 2 6 4263 0 0 0 8 4264 0 0 2 8 4265 1 0 0 9 4266 5 0 0 12 4267 0 0 0 0 4268 5 0 4 0 4269 56 3 4 16 4270 2 0 1 12 4271 6 0 1 11 4272 24 1 2 16 4273 0 0 1 6 4274 0 1 1 12 4275 7 1 1 13 4276 3 0 6 3 4277 3 0 0 12 4278 5 0 3 16 4279 4 0 4 12 4280 11 0 0 18 4281 6 0 1 12 4282 7 0 3 12 4283 3 0 1 10 4284 3 0 3 12 4285 2 1 0 12 4286 4 0 0 5 4287 0 0 1 8 4288 14 0 1 9 4289 3 0 1 12 4290 4 0 0 3 4291 2 0 2 10 4292 5 0 1 18 4293 3 0 1 16 4294 11 4 3 12 4295 6 0 1 8 4296 11 1 3 5 4297 2 0 3 5 4298 0 0 1 12 4299 7 0 1 4 4300 12 3 2 0 4301 6 1 1 5 4302 2 0 1 7 4303 3 0 2 10 4304 3 0 1 7 4305 5 0 2 7 4306 10 0 1 16 4307 6 0 5 4 4308 13 0 2 7 4309 1 0 0 4 4310 0 0 0 12 4311 5 0 2 8 4312 15 0 4 17 4313 9 1 1 11 4314 5 1 1 1 4315 14 1 5 12 4316 1 0 2 6 4317 5 0 3 12 4318 4 0 2 10 4319 3 0 3 4 4320 2 2 1 7 4321 3 0 0 3 4322 9 1 2 4 4323 13 0 2 4 4324 6 0 1 8 4325 12 0 3 8 4326 15 0 3 7 4327 11 0 2 7 4328 2 1 2 6 4329 14 3 1 8 4330 12 0 1 8 4331 16 2 4 6 4332 3 0 2 3 4333 3 0 1 6 4334 0 0 0 6 4335 3 1 3 6 4336 8 1 5 6 4337 9 0 4 8 4338 2 0 0 8 4339 2 0 1 8 4340 5 0 3 7 4341 6 0 2 3 4342 3 0 1 9 4343 4 0 1 6 4344 10 0 2 11 4345 11 0 3 11 4346 7 0 5 12 4347 0 0 4 12 4348 1 0 0 12 4349 3 0 1 6 4350 1 0 0 8 4351 0 0 2 16 4352 11 2 3 14 4353 10 0 1 14 4354 21 0 2 16 4355 4 1 0 16 4356 3 0 0 12 4357 1 0 1 12 4358 1 0 1 12 4359 0 0 0 8 4360 4 0 0 12 4361 5 1 0 12 4362 4 0 2 12 4363 2 0 3 0 4364 6 1 2 15 4365 7 0 2 14 4366 2 0 0 12 4367 4 0 0 12 4368 15 1 7 17 4369 13 0 1 14 4370 2 0 1 14 4371 14 2 2 13 4372 3 0 1 3 4373 1 0 0 10 4374 0 0 0 9 4375 7 1 2 11 4376 1 0 2 10 4377 11 0 1 15 4378 10 0 0 14 4379 1 0 3 12 4380 1 0 1 16 4381 0 0 0 12 4382 1 0 0 11 4383 2 1 2 12 4384 3 0 0 13 4385 10 1 1 12 4386 7 0 1 18 4387 0 0 1 15 4388 1 0 1 15 4389 16 0 2 8 4390 6 0 1 11 4391 3 1 2 12 4392 8 0 0 12 4393 11 0 1 12 4394 3 0 1 10 4395 2 0 0 12 4396 1 0 3 16 4397 2 0 0 12 4398 2 0 1 12 4399 11 1 2 4 4400 2 0 1 8 4401 12 0 3 7 4402 11 0 0 8 4403 12 0 2 11 4404 10 1 5 12 4405 16 0 0 12 4406 0 0 0 0 * missing = row col * nbSample = 4406 * nbCluster = 2 * lnLikelihood = -161277.5 * nbFreeParameter= 17 * criterion name = ICL * criterion value= 324634.3 * zi = [1] 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [112] 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [149] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 [186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [223] 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [260] 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 [297] 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [334] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [371] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 [408] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [445] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 [482] 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 [519] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [556] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [593] 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 [630] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 [667] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 [704] 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 [741] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [778] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [815] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 [852] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [926] 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [963] 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 [1000] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1037] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 [1074] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 [1111] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1148] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 [1185] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 [1222] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 [1259] 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 0 1 0 0 0 [1296] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1333] 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1370] 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 [1407] 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 [1444] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1518] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1555] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1592] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1629] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 [1666] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1703] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1740] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1777] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1814] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1851] 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1888] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 [1925] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 [1962] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1999] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 [2036] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 [2073] 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2110] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2147] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 [2184] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 0 [2221] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2258] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2295] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2332] 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2369] 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 [2406] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2443] 0 0 0 0 0 0 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 [2480] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2517] 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 [2554] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 [2591] 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 [2628] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 [2665] 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 [2702] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2739] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 [2776] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2813] 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 [2850] 0 0 0 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2887] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2924] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 [2961] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2998] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 [3035] 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 [3072] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 [3109] 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 [3146] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 [3183] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [3220] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 [3257] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 [3294] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 [3331] 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 [3368] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [3405] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [3442] 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 [3479] 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 [3516] 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [3553] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 [3590] 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 [3627] 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 [3664] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [3701] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [3738] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 [3775] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [3812] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [3849] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 [3886] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [3923] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [3960] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 [3997] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [4034] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [4071] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 [4108] 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 1 1 0 0 1 1 0 0 0 0 0 [4145] 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 [4182] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [4219] 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 [4256] 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [4293] 0 0 1 1 0 0 1 0 1 0 1 1 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 [4330] 0 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [4367] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [4404] 0 0 0 **************************************** *** Cluster: 1 * Proportion = 0.9087608 * lambda = 5.680070 0.283966 1.500500 10.615135 **************************************** *** Cluster: 2 * Proportion = 0.09123922 * lambda = 6.7139303 0.4154229 1.9552239 7.0547264 **************************************** > > ## get data and target from bird data set > data(birds) > > ## add 10 missing values > x <- birds[,2:5]; x = as.matrix(x); z <- birds[,1]; n <- nrow(x); p <- ncol(x); > indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2); > cbind(indexes, x[indexes]) [,1] [,2] [,3] [1,] "52" "2" "none" [2,] "65" "3" "white" [3,] "62" "2" "none" [4,] "41" "3" "white" [5,] "44" "3" "white" > x[indexes] <- NA; > model <- learnCategorical( data=x, labels=z + , models = clusterCategoricalNames(prop = "equal") + , algo="simul", nbIter = 2, epsilon = 1e-08 + ) > missingValues(model) row col value 1 52 2 2 2 62 2 1 3 41 3 3 4 44 3 1 5 65 3 3 > print(model) **************************************** * model name = categorical_p_pjk * data = eyebrow collar sub-caudal border 1 2 1 3 1 2 1 1 1 3 3 3 2 3 3 4 3 1 3 3 5 3 1 3 3 6 3 1 3 3 7 3 1 3 3 8 2 1 3 3 9 2 1 3 3 10 2 1 3 3 11 2 1 1 3 12 3 1 3 3 13 2 1 1 3 14 2 1 2 3 15 2 1 3 3 16 2 1 1 3 17 2 1 1 3 18 2 1 1 3 19 2 1 3 3 20 1 1 3 3 21 2 1 1 3 22 3 1 3 3 23 2 1 2 3 24 2 1 1 3 25 2 1 1 3 26 2 2 1 3 27 2 1 2 3 28 2 1 2 3 29 1 1 1 3 30 1 2 3 3 31 2 1 3 3 32 1 2 3 3 33 2 1 3 1 34 1 2 3 3 35 3 2 3 3 36 3 2 3 3 37 3 2 3 3 38 3 2 3 3 39 3 2 3 3 40 3 2 3 3 41 3 2 3 3 42 3 2 3 3 43 3 2 3 3 44 3 2 1 3 45 3 2 3 3 46 3 2 3 3 47 3 2 3 3 48 3 2 3 3 49 3 2 3 3 50 4 2 2 3 51 3 2 3 3 52 3 2 3 3 53 3 2 3 2 54 3 2 3 3 55 3 2 3 3 56 3 2 3 3 57 4 2 3 1 58 4 2 3 3 59 3 2 3 3 60 3 2 3 3 61 3 2 3 3 62 3 1 3 3 63 3 2 3 3 64 3 2 3 3 65 3 2 3 3 66 3 2 3 3 67 3 2 3 3 68 4 2 3 3 69 3 2 3 3 * missing = row col 52 52 2 62 62 2 41 41 3 44 44 3 65 65 3 **************************************** * nbModalities = 4 **************************************** * levels = [1] "none , poor pronounced, pronounced , very pronounced" [2] "dotted, none " [3] "black , black & white, white " [4] "few , many, none" **************************************** * nbSample = 69 * nbCluster = 2 * lnLikelihood = -469.2742 * nbFreeParameter= 23 * criterion name = ICL * criterion value= 1124.621 * zi = [1] 1 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0 0 1 [39] 1 0 0 1 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 1 1 1 1 0 0 0 1 1 **************************************** *** Cluster: 1 * Proportion = 0.5217391 * probabilities = [,1] [,2] [,3] [,4] [1,] 0.05555556 0.38888889 0.19444444 0.05555556 [2,] 0.33333333 0.61111111 0.05555556 0.00000000 [3,] 0.52777778 0.00000000 0.75000000 0.94444444 [4,] 0.08333333 0.00000000 0.00000000 0.00000000 * probabilities = [,1] [,2] [,3] [,4] [1,] 0.05555556 0.38888889 0.19444444 0.05555556 [2,] 0.33333333 0.61111111 0.05555556 0.00000000 [3,] 0.52777778 0.00000000 0.75000000 0.94444444 [4,] 0.08333333 0.00000000 0.00000000 0.00000000 **************************************** *** Cluster: 2 * Proportion = 0.4782609 * probabilities = [,1] [,2] [,3] [,4] [1,] 0.12121212 0.48484848 0.15151515 0.03030303 [2,] 0.27272727 0.51515152 0.09090909 0.03030303 [3,] 0.57575758 0.00000000 0.75757576 0.93939394 [4,] 0.03030303 0.00000000 0.00000000 0.00000000 * probabilities = [,1] [,2] [,3] [,4] [1,] 0.12121212 0.48484848 0.15151515 0.03030303 [2,] 0.27272727 0.51515152 0.09090909 0.03030303 [3,] 0.57575758 0.00000000 0.75757576 0.93939394 [4,] 0.03030303 0.00000000 0.00000000 0.00000000 **************************************** > > ## A quantitative example with the heart disease data set > data(HeartDisease.cat) > data(HeartDisease.cont) > data(HeartDisease.target) > ## with default values > lcomponent = list(HeartDisease.cat, HeartDisease.cont); > models = c("categorical_pk_pjk","gaussian_pk_sjk") > z<-HeartDisease.target[[1]]; > model <- learnMixedData(lcomponent, models, z, algo="simul", nbIter=2) > missingValues(model) [[1]] row col value [1,] 167 7 1 [2,] 193 7 1 [3,] 288 7 1 [4,] 303 7 1 [5,] 88 8 3 [6,] 267 8 1 [[2]] row col value > print(model) **************************************** * nbSample = 303 * nbCluster = 5 * lnLikelihood = -7527.475 * nbFreeParameter= 129 * criterion name = ICL * criterion value= 16168.93 * zi = [1] 0 2 1 0 0 0 3 0 2 1 0 0 2 0 0 0 1 0 0 0 0 0 1 3 4 0 0 0 0 3 0 2 1 0 0 0 3 [38] 1 3 0 4 0 0 0 1 4 0 4 0 0 0 0 2 0 1 1 1 1 0 0 2 0 1 0 2 2 1 0 2 1 0 3 1 1 [75] 1 0 1 0 0 3 0 0 0 3 0 0 0 0 0 0 0 3 0 0 0 1 2 3 0 0 0 0 0 0 3 0 2 1 2 3 1 [112] 1 0 2 2 0 0 0 3 2 3 4 0 3 1 0 3 3 0 0 0 0 0 0 0 0 4 3 1 0 0 1 0 1 0 1 4 0 [149] 0 0 0 0 0 4 3 1 1 1 2 0 0 4 0 0 0 0 0 0 1 0 3 0 1 0 4 1 0 1 0 0 3 2 0 0 1 [186] 0 0 2 1 2 0 3 1 2 0 3 0 0 0 1 0 0 0 0 0 3 3 3 0 1 0 4 0 3 1 0 0 0 0 0 0 0 [223] 0 3 1 0 0 0 3 2 0 2 1 0 0 3 2 1 0 0 0 0 0 2 0 2 2 1 3 0 0 1 0 0 0 0 0 0 0 [260] 1 0 3 0 0 4 2 2 2 1 0 1 0 2 0 1 0 0 0 1 0 2 0 3 0 2 4 2 0 0 0 1 0 2 2 1 0 [297] 3 1 1 2 3 1 0 * model name = categorical_pk_pjk sex cp fbs restecg exang slope ca thal [1,] 2 1 2 3 1 3 1 2 [2,] 2 4 1 3 2 2 4 1 [3,] 2 4 1 3 2 2 3 3 [4,] 2 3 1 1 1 3 1 1 [5,] 1 2 1 3 1 1 1 1 [6,] 2 2 1 1 1 1 1 1 [7,] 1 4 1 3 1 3 3 1 [8,] 1 4 1 1 2 1 1 1 [9,] 2 4 1 3 1 2 2 3 [10,] 2 4 2 3 2 3 1 3 [11,] 2 4 1 1 1 2 1 2 [12,] 1 2 1 3 1 2 1 1 [13,] 2 3 2 3 2 2 2 2 [14,] 2 2 1 1 1 1 1 3 [15,] 2 3 2 1 1 1 1 3 [16,] 2 3 1 1 1 1 1 1 [17,] 2 2 1 1 1 3 1 3 [18,] 2 4 1 1 1 1 1 1 [19,] 1 3 1 1 1 1 1 1 [20,] 2 2 1 1 1 1 1 1 [21,] 2 1 1 3 2 2 1 1 [22,] 1 1 2 3 1 1 1 1 [23,] 2 2 1 3 1 2 1 1 [24,] 2 3 1 3 1 1 3 3 [25,] 2 4 1 3 2 2 3 3 [26,] 1 3 1 1 1 2 1 1 [27,] 1 3 1 1 1 1 1 1 [28,] 1 1 1 1 1 3 1 1 [29,] 2 4 1 1 1 1 1 1 [30,] 2 4 1 3 2 2 1 3 [31,] 1 1 1 1 1 1 3 1 [32,] 2 4 2 1 2 1 3 3 [33,] 2 3 1 1 1 1 1 1 [34,] 2 4 1 1 1 2 1 3 [35,] 2 3 1 1 2 1 1 1 [36,] 2 4 1 1 1 1 1 1 [37,] 2 4 1 3 2 2 1 3 [38,] 2 4 1 3 2 2 2 2 [39,] 2 4 1 1 2 2 2 3 [40,] 2 3 2 1 2 2 1 1 [41,] 1 4 1 3 1 2 4 3 [42,] 2 1 1 1 2 1 1 3 [43,] 1 2 1 1 1 1 3 1 [44,] 2 3 2 1 1 1 1 1 [45,] 1 4 1 3 1 1 1 1 [46,] 2 3 1 3 1 2 2 3 [47,] 2 3 1 1 1 1 1 1 [48,] 2 4 1 3 1 2 1 3 [49,] 1 3 2 3 1 1 2 1 [50,] 2 3 2 3 1 3 1 1 [51,] 1 2 1 1 1 1 2 1 [52,] 2 4 1 1 1 1 1 3 [53,] 2 4 1 3 1 1 2 1 [54,] 2 2 1 3 1 1 1 1 [55,] 2 4 1 1 2 1 2 3 [56,] 2 4 1 3 2 2 2 3 [57,] 2 3 1 1 1 2 2 3 [58,] 2 4 1 3 1 1 1 3 [59,] 2 3 1 3 1 3 2 1 [60,] 2 1 1 3 2 1 2 1 [61,] 1 4 1 1 2 2 1 3 [62,] 1 3 1 3 2 3 1 1 [63,] 2 4 1 3 2 2 4 3 [64,] 1 3 2 1 1 1 1 1 [65,] 2 4 1 1 1 2 2 3 [66,] 2 4 1 3 2 2 3 3 [67,] 2 3 1 3 1 2 1 1 [68,] 2 3 1 3 1 1 1 3 [69,] 2 4 1 3 2 3 1 3 [70,] 2 3 1 1 1 2 1 1 [71,] 1 3 1 1 1 1 1 1 [72,] 2 4 2 1 1 2 3 3 [73,] 2 4 1 1 2 2 3 3 [74,] 2 4 1 3 1 1 3 2 [75,] 2 4 1 3 1 1 2 1 [76,] 1 3 1 3 1 1 1 1 [77,] 2 4 1 3 2 2 2 3 [78,] 1 3 1 3 1 1 2 1 [79,] 2 2 1 3 1 2 1 1 [80,] 2 4 1 3 2 1 1 3 [81,] 2 4 1 3 2 2 1 1 [82,] 1 4 1 3 1 2 1 1 [83,] 2 3 1 3 1 1 1 1 [84,] 2 3 2 3 2 2 1 3 [85,] 2 2 1 1 1 1 1 1 [86,] 2 3 1 3 1 1 1 1 [87,] 2 3 1 3 1 1 1 1 [88,] 1 3 1 3 1 1 1 3 [89,] 1 4 1 3 1 1 1 1 [90,] 1 3 1 3 1 1 1 1 [91,] 2 4 1 3 1 2 1 1 [92,] 1 4 1 3 1 3 4 3 [93,] 2 3 1 1 1 2 4 3 [94,] 1 3 1 1 1 2 1 1 [95,] 1 3 1 3 1 1 1 1 [96,] 2 4 1 1 2 1 2 3 [97,] 2 4 1 3 2 2 2 3 [98,] 1 4 1 3 1 2 3 3 [99,] 2 2 1 1 1 1 2 1 [100,] 2 4 1 3 1 1 1 1 [101,] 2 4 1 3 1 1 1 1 [102,] 2 1 1 3 1 1 1 1 [103,] 1 4 1 3 1 1 2 1 [104,] 1 3 2 3 1 1 2 1 [105,] 2 3 1 1 1 2 4 3 [106,] 2 2 1 1 1 1 1 3 [107,] 2 4 1 1 2 1 2 3 [108,] 2 3 1 3 1 2 2 3 [109,] 2 4 1 1 2 2 2 3 [110,] 2 4 1 1 1 2 1 3 [111,] 1 4 1 3 2 2 1 3 [112,] 2 4 2 3 2 2 2 1 [113,] 2 1 1 3 1 2 1 2 [114,] 1 4 2 3 2 2 1 3 [115,] 1 3 1 1 1 2 2 3 [116,] 2 2 1 1 1 2 1 2 [117,] 2 3 2 3 1 1 1 1 [118,] 1 4 1 1 1 1 1 1 [119,] 2 4 2 3 2 1 4 3 [120,] 2 4 1 3 1 2 2 3 [121,] 2 4 2 3 2 1 3 3 [122,] 1 4 1 3 1 2 4 3 [123,] 2 3 1 1 2 2 1 1 [124,] 2 4 1 1 2 3 1 3 [125,] 2 1 2 3 1 2 2 1 [126,] 1 2 1 3 1 2 1 1 [127,] 1 4 2 3 2 3 3 3 [128,] 2 4 1 1 2 2 2 3 [129,] 2 2 1 1 1 1 1 1 [130,] 1 4 1 1 1 1 1 1 [131,] 2 3 1 3 1 2 1 3 [132,] 2 3 1 1 2 1 2 3 [133,] 2 2 1 3 1 1 1 1 [134,] 2 4 1 3 2 1 1 1 [135,] 1 3 1 1 1 2 1 1 [136,] 1 2 1 3 1 2 1 1 [137,] 2 4 1 1 2 3 1 3 [138,] 2 2 1 3 1 2 2 3 [139,] 2 4 1 1 2 2 1 3 [140,] 2 3 2 3 1 2 1 1 [141,] 2 2 1 1 2 1 1 1 [142,] 2 1 1 3 1 2 1 3 [143,] 2 2 2 1 1 1 1 1 [144,] 2 3 1 1 2 2 1 3 [145,] 2 3 1 3 2 2 1 3 [146,] 2 3 1 1 1 1 1 1 [147,] 2 4 2 3 1 2 4 3 [148,] 2 3 1 1 1 1 1 1 [149,] 2 2 1 3 1 1 1 1 [150,] 1 3 1 1 1 1 2 1 [151,] 2 1 2 1 1 2 1 3 [152,] 1 4 1 3 1 2 1 1 [153,] 1 3 1 3 1 2 1 3 [154,] 2 4 1 3 2 2 2 3 [155,] 2 4 1 3 2 3 2 1 [156,] 2 4 1 3 1 2 4 1 [157,] 2 4 1 1 2 1 1 3 [158,] 2 4 1 3 1 1 3 3 [159,] 2 4 1 3 1 2 3 3 [160,] 2 3 1 1 1 1 2 3 [161,] 2 2 2 1 1 1 1 3 [162,] 2 4 1 3 2 1 4 1 [163,] 1 3 1 1 1 2 1 1 [164,] 1 4 1 3 1 2 1 1 [165,] 2 3 2 1 1 1 3 1 [166,] 2 4 1 1 2 1 1 3 [167,] 2 3 1 1 1 1 1 1 [168,] 1 2 2 3 2 1 2 1 [169,] 2 4 1 3 2 1 1 3 [170,] 1 2 1 1 1 2 1 1 [171,] 2 3 1 1 2 2 2 3 [172,] 2 4 1 3 2 1 1 3 [173,] 1 4 1 1 2 2 1 1 [174,] 1 4 1 3 1 2 1 1 [175,] 2 4 1 3 1 2 3 2 [176,] 2 4 1 1 2 2 2 3 [177,] 2 4 2 1 1 1 4 3 [178,] 2 4 1 3 2 2 2 2 [179,] 2 3 1 1 1 1 2 1 [180,] 2 3 2 3 1 1 4 1 [181,] 2 4 1 3 1 2 1 3 [182,] 1 4 1 3 2 2 3 3 [183,] 2 1 1 3 1 1 3 1 [184,] 2 1 1 3 1 3 1 3 [185,] 1 4 1 3 1 1 1 1 [186,] 1 2 1 1 1 1 3 1 [187,] 2 3 2 1 1 3 1 3 [188,] 2 2 1 1 2 2 4 2 [189,] 2 2 1 3 1 1 2 3 [190,] 2 3 1 3 1 2 4 3 [191,] 2 3 1 1 1 1 1 1 [192,] 2 4 1 1 2 2 4 3 [193,] 2 4 2 3 2 2 1 3 [194,] 1 4 2 1 1 2 4 1 [195,] 1 3 1 3 1 2 1 1 [196,] 2 4 1 3 2 2 3 1 [197,] 2 1 2 3 1 2 2 1 [198,] 1 4 1 3 2 2 1 1 [199,] 1 2 1 1 1 1 1 1 [200,] 2 1 1 3 1 1 1 1 [201,] 1 4 1 3 1 1 1 1 [202,] 1 4 1 1 2 1 1 1 [203,] 2 3 2 1 1 1 2 3 [204,] 1 3 1 1 1 1 1 3 [205,] 2 4 1 1 1 1 1 3 [206,] 2 4 1 3 2 2 4 3 [207,] 2 4 1 3 2 2 3 3 [208,] 2 4 1 3 2 2 1 3 [209,] 2 2 1 1 1 1 1 1 [210,] 1 4 1 1 2 2 1 1 [211,] 1 3 1 1 1 1 1 1 [212,] 2 1 1 1 2 2 1 3 [213,] 2 3 1 3 1 2 1 1 [214,] 1 4 2 1 2 2 3 3 [215,] 2 4 1 1 1 1 2 1 [216,] 2 1 1 3 1 2 1 3 [217,] 1 2 1 1 1 1 1 1 [218,] 1 4 1 3 2 2 1 1 [219,] 1 4 1 1 1 2 3 1 [220,] 2 4 1 3 1 1 1 1 [221,] 1 3 1 3 2 1 1 1 [222,] 1 3 1 3 1 1 1 1 [223,] 1 3 1 1 1 1 1 1 [224,] 2 4 1 1 2 2 3 3 [225,] 1 4 1 1 2 2 3 1 [226,] 1 2 1 1 1 1 1 1 [227,] 2 4 1 1 1 1 1 1 [228,] 1 3 1 1 1 1 2 1 [229,] 2 4 1 3 2 2 2 1 [230,] 2 4 1 3 2 1 2 1 [231,] 1 3 1 3 1 2 1 1 [232,] 1 4 1 2 2 2 1 1 [233,] 2 3 1 3 1 1 4 1 [234,] 1 2 1 3 2 1 2 1 [235,] 1 3 1 1 1 1 2 1 [236,] 2 4 1 3 2 2 3 1 [237,] 2 4 2 3 2 3 1 3 [238,] 2 4 1 3 1 1 1 3 [239,] 1 2 1 1 1 2 1 1 [240,] 2 2 1 1 1 1 1 1 [241,] 2 2 1 1 1 1 1 1 [242,] 1 2 1 1 1 1 1 1 [243,] 1 4 1 1 1 1 1 1 [244,] 2 1 1 1 1 2 3 1 [245,] 1 3 2 1 1 1 1 1 [246,] 2 4 1 1 1 2 1 1 [247,] 2 4 1 1 1 1 2 3 [248,] 2 4 1 3 2 2 2 1 [249,] 2 4 1 1 1 1 3 3 [250,] 2 2 2 3 1 1 1 1 [251,] 2 4 1 1 2 2 1 2 [252,] 2 4 1 1 1 2 2 3 [253,] 2 4 1 1 2 2 2 3 [254,] 1 3 1 3 1 1 1 1 [255,] 2 4 1 1 1 2 1 1 [256,] 1 3 1 1 1 2 1 1 [257,] 1 4 1 1 1 1 3 1 [258,] 1 3 1 2 1 2 1 1 [259,] 2 2 1 3 1 1 1 1 [260,] 2 2 1 1 1 1 1 3 [261,] 1 3 1 1 1 2 2 1 [262,] 1 2 2 3 1 1 3 1 [263,] 1 1 1 1 1 1 1 1 [264,] 2 3 1 1 1 1 1 1 [265,] 2 4 1 3 2 2 2 1 [266,] 2 4 1 1 2 2 1 2 [267,] 2 4 2 1 2 2 1 1 [268,] 2 3 2 1 1 2 2 2 [269,] 2 4 1 1 1 1 1 3 [270,] 2 3 1 1 1 1 1 1 [271,] 2 4 1 3 2 1 2 3 [272,] 2 4 1 3 1 1 1 2 [273,] 2 4 1 1 2 2 3 3 [274,] 1 4 1 1 1 2 1 1 [275,] 2 1 1 1 1 1 3 1 [276,] 2 1 1 3 1 2 1 3 [277,] 1 3 1 3 1 2 2 1 [278,] 1 3 1 1 1 2 1 1 [279,] 2 2 1 3 1 1 2 1 [280,] 1 4 1 1 1 2 1 1 [281,] 2 4 1 1 2 2 2 3 [282,] 2 3 1 1 1 1 1 1 [283,] 1 4 1 2 2 2 2 3 [284,] 2 2 1 1 1 1 1 1 [285,] 2 4 1 1 1 1 2 3 [286,] 2 4 1 2 1 3 4 2 [287,] 1 4 2 3 2 2 3 2 [288,] 2 2 1 1 1 2 1 3 [289,] 2 2 1 3 1 1 1 3 [290,] 2 2 1 1 1 3 1 1 [291,] 2 3 1 3 1 2 1 3 [292,] 1 2 1 1 1 1 1 1 [293,] 2 4 1 1 2 3 1 2 [294,] 2 4 1 3 2 1 3 3 [295,] 1 4 1 1 2 2 1 1 [296,] 2 2 1 1 1 1 1 1 [297,] 2 4 2 3 1 2 3 2 [298,] 1 4 1 1 2 2 1 3 [299,] 2 1 1 1 1 2 1 3 [300,] 2 4 2 1 1 2 3 3 [301,] 2 4 1 1 2 2 2 3 [302,] 1 2 1 3 1 2 2 1 [303,] 2 3 1 1 1 1 1 1 * model name = gaussian_pk_sjk age trestbps chol thalach oldpeak [1,] 63.0 145.0 233.0 150.0 2.3 [2,] 67.0 160.0 286.0 108.0 1.5 [3,] 67.0 120.0 229.0 129.0 2.6 [4,] 37.0 130.0 250.0 187.0 3.5 [5,] 41.0 130.0 204.0 172.0 1.4 [6,] 56.0 120.0 236.0 178.0 0.8 [7,] 62.0 140.0 268.0 160.0 3.6 [8,] 57.0 120.0 354.0 163.0 0.6 [9,] 63.0 130.0 254.0 147.0 1.4 [10,] 53.0 140.0 203.0 155.0 3.1 [11,] 57.0 140.0 192.0 148.0 0.4 [12,] 56.0 140.0 294.0 153.0 1.3 [13,] 56.0 130.0 256.0 142.0 0.6 [14,] 44.0 120.0 263.0 173.0 0.0 [15,] 52.0 172.0 199.0 162.0 0.5 [16,] 57.0 150.0 168.0 174.0 1.6 [17,] 48.0 110.0 229.0 168.0 1.0 [18,] 54.0 140.0 239.0 160.0 1.2 [19,] 48.0 130.0 275.0 139.0 0.2 [20,] 49.0 130.0 266.0 171.0 0.6 [21,] 64.0 110.0 211.0 144.0 1.8 [22,] 58.0 150.0 283.0 162.0 1.0 [23,] 58.0 120.0 284.0 160.0 1.8 [24,] 58.0 132.0 224.0 173.0 3.2 [25,] 60.0 130.0 206.0 132.0 2.4 [26,] 50.0 120.0 219.0 158.0 1.6 [27,] 58.0 120.0 340.0 172.0 0.0 [28,] 66.0 150.0 226.0 114.0 2.6 [29,] 43.0 150.0 247.0 171.0 1.5 [30,] 40.0 110.0 167.0 114.0 2.0 [31,] 69.0 140.0 239.0 151.0 1.8 [32,] 60.0 117.0 230.0 160.0 1.4 [33,] 64.0 140.0 335.0 158.0 0.0 [34,] 59.0 135.0 234.0 161.0 0.5 [35,] 44.0 130.0 233.0 179.0 0.4 [36,] 42.0 140.0 226.0 178.0 0.0 [37,] 43.0 120.0 177.0 120.0 2.5 [38,] 57.0 150.0 276.0 112.0 0.6 [39,] 55.0 132.0 353.0 132.0 1.2 [40,] 61.0 150.0 243.0 137.0 1.0 [41,] 65.0 150.0 225.0 114.0 1.0 [42,] 40.0 140.0 199.0 178.0 1.4 [43,] 71.0 160.0 302.0 162.0 0.4 [44,] 59.0 150.0 212.0 157.0 1.6 [45,] 61.0 130.0 330.0 169.0 0.0 [46,] 58.0 112.0 230.0 165.0 2.5 [47,] 51.0 110.0 175.0 123.0 0.6 [48,] 50.0 150.0 243.0 128.0 2.6 [49,] 65.0 140.0 417.0 157.0 0.8 [50,] 53.0 130.0 197.0 152.0 1.2 [51,] 41.0 105.0 198.0 168.0 0.0 [52,] 65.0 120.0 177.0 140.0 0.4 [53,] 44.0 112.0 290.0 153.0 0.0 [54,] 44.0 130.0 219.0 188.0 0.0 [55,] 60.0 130.0 253.0 144.0 1.4 [56,] 54.0 124.0 266.0 109.0 2.2 [57,] 50.0 140.0 233.0 163.0 0.6 [58,] 41.0 110.0 172.0 158.0 0.0 [59,] 54.0 125.0 273.0 152.0 0.5 [60,] 51.0 125.0 213.0 125.0 1.4 [61,] 51.0 130.0 305.0 142.0 1.2 [62,] 46.0 142.0 177.0 160.0 1.4 [63,] 58.0 128.0 216.0 131.0 2.2 [64,] 54.0 135.0 304.0 170.0 0.0 [65,] 54.0 120.0 188.0 113.0 1.4 [66,] 60.0 145.0 282.0 142.0 2.8 [67,] 60.0 140.0 185.0 155.0 3.0 [68,] 54.0 150.0 232.0 165.0 1.6 [69,] 59.0 170.0 326.0 140.0 3.4 [70,] 46.0 150.0 231.0 147.0 3.6 [71,] 65.0 155.0 269.0 148.0 0.8 [72,] 67.0 125.0 254.0 163.0 0.2 [73,] 62.0 120.0 267.0 99.0 1.8 [74,] 65.0 110.0 248.0 158.0 0.6 [75,] 44.0 110.0 197.0 177.0 0.0 [76,] 65.0 160.0 360.0 151.0 0.8 [77,] 60.0 125.0 258.0 141.0 2.8 [78,] 51.0 140.0 308.0 142.0 1.5 [79,] 48.0 130.0 245.0 180.0 0.2 [80,] 58.0 150.0 270.0 111.0 0.8 [81,] 45.0 104.0 208.0 148.0 3.0 [82,] 53.0 130.0 264.0 143.0 0.4 [83,] 39.0 140.0 321.0 182.0 0.0 [84,] 68.0 180.0 274.0 150.0 1.6 [85,] 52.0 120.0 325.0 172.0 0.2 [86,] 44.0 140.0 235.0 180.0 0.0 [87,] 47.0 138.0 257.0 156.0 0.0 [88,] 53.0 128.0 216.0 115.0 0.0 [89,] 53.0 138.0 234.0 160.0 0.0 [90,] 51.0 130.0 256.0 149.0 0.5 [91,] 66.0 120.0 302.0 151.0 0.4 [92,] 62.0 160.0 164.0 145.0 6.2 [93,] 62.0 130.0 231.0 146.0 1.8 [94,] 44.0 108.0 141.0 175.0 0.6 [95,] 63.0 135.0 252.0 172.0 0.0 [96,] 52.0 128.0 255.0 161.0 0.0 [97,] 59.0 110.0 239.0 142.0 1.2 [98,] 60.0 150.0 258.0 157.0 2.6 [99,] 52.0 134.0 201.0 158.0 0.8 [100,] 48.0 122.0 222.0 186.0 0.0 [101,] 45.0 115.0 260.0 185.0 0.0 [102,] 34.0 118.0 182.0 174.0 0.0 [103,] 57.0 128.0 303.0 159.0 0.0 [104,] 71.0 110.0 265.0 130.0 0.0 [105,] 49.0 120.0 188.0 139.0 2.0 [106,] 54.0 108.0 309.0 156.0 0.0 [107,] 59.0 140.0 177.0 162.0 0.0 [108,] 57.0 128.0 229.0 150.0 0.4 [109,] 61.0 120.0 260.0 140.0 3.6 [110,] 39.0 118.0 219.0 140.0 1.2 [111,] 61.0 145.0 307.0 146.0 1.0 [112,] 56.0 125.0 249.0 144.0 1.2 [113,] 52.0 118.0 186.0 190.0 0.0 [114,] 43.0 132.0 341.0 136.0 3.0 [115,] 62.0 130.0 263.0 97.0 1.2 [116,] 41.0 135.0 203.0 132.0 0.0 [117,] 58.0 140.0 211.0 165.0 0.0 [118,] 35.0 138.0 183.0 182.0 1.4 [119,] 63.0 130.0 330.0 132.0 1.8 [120,] 65.0 135.0 254.0 127.0 2.8 [121,] 48.0 130.0 256.0 150.0 0.0 [122,] 63.0 150.0 407.0 154.0 4.0 [123,] 51.0 100.0 222.0 143.0 1.2 [124,] 55.0 140.0 217.0 111.0 5.6 [125,] 65.0 138.0 282.0 174.0 1.4 [126,] 45.0 130.0 234.0 175.0 0.6 [127,] 56.0 200.0 288.0 133.0 4.0 [128,] 54.0 110.0 239.0 126.0 2.8 [129,] 44.0 120.0 220.0 170.0 0.0 [130,] 62.0 124.0 209.0 163.0 0.0 [131,] 54.0 120.0 258.0 147.0 0.4 [132,] 51.0 94.0 227.0 154.0 0.0 [133,] 29.0 130.0 204.0 202.0 0.0 [134,] 51.0 140.0 261.0 186.0 0.0 [135,] 43.0 122.0 213.0 165.0 0.2 [136,] 55.0 135.0 250.0 161.0 1.4 [137,] 70.0 145.0 174.0 125.0 2.6 [138,] 62.0 120.0 281.0 103.0 1.4 [139,] 35.0 120.0 198.0 130.0 1.6 [140,] 51.0 125.0 245.0 166.0 2.4 [141,] 59.0 140.0 221.0 164.0 0.0 [142,] 59.0 170.0 288.0 159.0 0.2 [143,] 52.0 128.0 205.0 184.0 0.0 [144,] 64.0 125.0 309.0 131.0 1.8 [145,] 58.0 105.0 240.0 154.0 0.6 [146,] 47.0 108.0 243.0 152.0 0.0 [147,] 57.0 165.0 289.0 124.0 1.0 [148,] 41.0 112.0 250.0 179.0 0.0 [149,] 45.0 128.0 308.0 170.0 0.0 [150,] 60.0 102.0 318.0 160.0 0.0 [151,] 52.0 152.0 298.0 178.0 1.2 [152,] 42.0 102.0 265.0 122.0 0.6 [153,] 67.0 115.0 564.0 160.0 1.6 [154,] 55.0 160.0 289.0 145.0 0.8 [155,] 64.0 120.0 246.0 96.0 2.2 [156,] 70.0 130.0 322.0 109.0 2.4 [157,] 51.0 140.0 299.0 173.0 1.6 [158,] 58.0 125.0 300.0 171.0 0.0 [159,] 60.0 140.0 293.0 170.0 1.2 [160,] 68.0 118.0 277.0 151.0 1.0 [161,] 46.0 101.0 197.0 156.0 0.0 [162,] 77.0 125.0 304.0 162.0 0.0 [163,] 54.0 110.0 214.0 158.0 1.6 [164,] 58.0 100.0 248.0 122.0 1.0 [165,] 48.0 124.0 255.0 175.0 0.0 [166,] 57.0 132.0 207.0 168.0 0.0 [167,] 52.0 138.0 223.0 169.0 0.0 [168,] 54.0 132.0 288.0 159.0 0.0 [169,] 35.0 126.0 282.0 156.0 0.0 [170,] 45.0 112.0 160.0 138.0 0.0 [171,] 70.0 160.0 269.0 112.0 2.9 [172,] 53.0 142.0 226.0 111.0 0.0 [173,] 59.0 174.0 249.0 143.0 0.0 [174,] 62.0 140.0 394.0 157.0 1.2 [175,] 64.0 145.0 212.0 132.0 2.0 [176,] 57.0 152.0 274.0 88.0 1.2 [177,] 52.0 108.0 233.0 147.0 0.1 [178,] 56.0 132.0 184.0 105.0 2.1 [179,] 43.0 130.0 315.0 162.0 1.9 [180,] 53.0 130.0 246.0 173.0 0.0 [181,] 48.0 124.0 274.0 166.0 0.5 [182,] 56.0 134.0 409.0 150.0 1.9 [183,] 42.0 148.0 244.0 178.0 0.8 [184,] 59.0 178.0 270.0 145.0 4.2 [185,] 60.0 158.0 305.0 161.0 0.0 [186,] 63.0 140.0 195.0 179.0 0.0 [187,] 42.0 120.0 240.0 194.0 0.8 [188,] 66.0 160.0 246.0 120.0 0.0 [189,] 54.0 192.0 283.0 195.0 0.0 [190,] 69.0 140.0 254.0 146.0 2.0 [191,] 50.0 129.0 196.0 163.0 0.0 [192,] 51.0 140.0 298.0 122.0 4.2 [193,] 43.0 132.0 247.0 143.0 0.1 [194,] 62.0 138.0 294.0 106.0 1.9 [195,] 68.0 120.0 211.0 115.0 1.5 [196,] 67.0 100.0 299.0 125.0 0.9 [197,] 69.0 160.0 234.0 131.0 0.1 [198,] 45.0 138.0 236.0 152.0 0.2 [199,] 50.0 120.0 244.0 162.0 1.1 [200,] 59.0 160.0 273.0 125.0 0.0 [201,] 50.0 110.0 254.0 159.0 0.0 [202,] 64.0 180.0 325.0 154.0 0.0 [203,] 57.0 150.0 126.0 173.0 0.2 [204,] 64.0 140.0 313.0 133.0 0.2 [205,] 43.0 110.0 211.0 161.0 0.0 [206,] 45.0 142.0 309.0 147.0 0.0 [207,] 58.0 128.0 259.0 130.0 3.0 [208,] 50.0 144.0 200.0 126.0 0.9 [209,] 55.0 130.0 262.0 155.0 0.0 [210,] 62.0 150.0 244.0 154.0 1.4 [211,] 37.0 120.0 215.0 170.0 0.0 [212,] 38.0 120.0 231.0 182.0 3.8 [213,] 41.0 130.0 214.0 168.0 2.0 [214,] 66.0 178.0 228.0 165.0 1.0 [215,] 52.0 112.0 230.0 160.0 0.0 [216,] 56.0 120.0 193.0 162.0 1.9 [217,] 46.0 105.0 204.0 172.0 0.0 [218,] 46.0 138.0 243.0 152.0 0.0 [219,] 64.0 130.0 303.0 122.0 2.0 [220,] 59.0 138.0 271.0 182.0 0.0 [221,] 41.0 112.0 268.0 172.0 0.0 [222,] 54.0 108.0 267.0 167.0 0.0 [223,] 39.0 94.0 199.0 179.0 0.0 [224,] 53.0 123.0 282.0 95.0 2.0 [225,] 63.0 108.0 269.0 169.0 1.8 [226,] 34.0 118.0 210.0 192.0 0.7 [227,] 47.0 112.0 204.0 143.0 0.1 [228,] 67.0 152.0 277.0 172.0 0.0 [229,] 54.0 110.0 206.0 108.0 0.0 [230,] 66.0 112.0 212.0 132.0 0.1 [231,] 52.0 136.0 196.0 169.0 0.1 [232,] 55.0 180.0 327.0 117.0 3.4 [233,] 49.0 118.0 149.0 126.0 0.8 [234,] 74.0 120.0 269.0 121.0 0.2 [235,] 54.0 160.0 201.0 163.0 0.0 [236,] 54.0 122.0 286.0 116.0 3.2 [237,] 56.0 130.0 283.0 103.0 1.6 [238,] 46.0 120.0 249.0 144.0 0.8 [239,] 49.0 134.0 271.0 162.0 0.0 [240,] 42.0 120.0 295.0 162.0 0.0 [241,] 41.0 110.0 235.0 153.0 0.0 [242,] 41.0 126.0 306.0 163.0 0.0 [243,] 49.0 130.0 269.0 163.0 0.0 [244,] 61.0 134.0 234.0 145.0 2.6 [245,] 60.0 120.0 178.0 96.0 0.0 [246,] 67.0 120.0 237.0 71.0 1.0 [247,] 58.0 100.0 234.0 156.0 0.1 [248,] 47.0 110.0 275.0 118.0 1.0 [249,] 52.0 125.0 212.0 168.0 1.0 [250,] 62.0 128.0 208.0 140.0 0.0 [251,] 57.0 110.0 201.0 126.0 1.5 [252,] 58.0 146.0 218.0 105.0 2.0 [253,] 64.0 128.0 263.0 105.0 0.2 [254,] 51.0 120.0 295.0 157.0 0.6 [255,] 43.0 115.0 303.0 181.0 1.2 [256,] 42.0 120.0 209.0 173.0 0.0 [257,] 67.0 106.0 223.0 142.0 0.3 [258,] 76.0 140.0 197.0 116.0 1.1 [259,] 70.0 156.0 245.0 143.0 0.0 [260,] 57.0 124.0 261.0 141.0 0.3 [261,] 44.0 118.0 242.0 149.0 0.3 [262,] 58.0 136.0 319.0 152.0 0.0 [263,] 60.0 150.0 240.0 171.0 0.9 [264,] 44.0 120.0 226.0 169.0 0.0 [265,] 61.0 138.0 166.0 125.0 3.6 [266,] 42.0 136.0 315.0 125.0 1.8 [267,] 52.0 128.0 204.0 156.0 1.0 [268,] 59.0 126.0 218.0 134.0 2.2 [269,] 40.0 152.0 223.0 181.0 0.0 [270,] 42.0 130.0 180.0 150.0 0.0 [271,] 61.0 140.0 207.0 138.0 1.9 [272,] 66.0 160.0 228.0 138.0 2.3 [273,] 46.0 140.0 311.0 120.0 1.8 [274,] 71.0 112.0 149.0 125.0 1.6 [275,] 59.0 134.0 204.0 162.0 0.8 [276,] 64.0 170.0 227.0 155.0 0.6 [277,] 66.0 146.0 278.0 152.0 0.0 [278,] 39.0 138.0 220.0 152.0 0.0 [279,] 57.0 154.0 232.0 164.0 0.0 [280,] 58.0 130.0 197.0 131.0 0.6 [281,] 57.0 110.0 335.0 143.0 3.0 [282,] 47.0 130.0 253.0 179.0 0.0 [283,] 55.0 128.0 205.0 130.0 2.0 [284,] 35.0 122.0 192.0 174.0 0.0 [285,] 61.0 148.0 203.0 161.0 0.0 [286,] 58.0 114.0 318.0 140.0 4.4 [287,] 58.0 170.0 225.0 146.0 2.8 [288,] 58.0 125.0 220.0 144.0 0.4 [289,] 56.0 130.0 221.0 163.0 0.0 [290,] 56.0 120.0 240.0 169.0 0.0 [291,] 67.0 152.0 212.0 150.0 0.8 [292,] 55.0 132.0 342.0 166.0 1.2 [293,] 44.0 120.0 169.0 144.0 2.8 [294,] 63.0 140.0 187.0 144.0 4.0 [295,] 63.0 124.0 197.0 136.0 0.0 [296,] 41.0 120.0 157.0 182.0 0.0 [297,] 59.0 164.0 176.0 90.0 1.0 [298,] 57.0 140.0 241.0 123.0 0.2 [299,] 45.0 110.0 264.0 132.0 1.2 [300,] 68.0 144.0 193.0 141.0 3.4 [301,] 57.0 130.0 131.0 115.0 1.2 [302,] 57.0 130.0 236.0 174.0 0.0 [303,] 38.0 138.0 175.0 173.0 0.0 **************************************** * model name = categorical_pk_pjk *** Cluster: 1 * Proportion = 0.5412541 * probabilities = [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.439024390 0.097560976 0.859756098 0.579268293 0.859756098 0.646341463 [2,] 0.560975610 0.250000000 0.140243902 0.006097561 0.140243902 0.298780488 [3,] 0.000000000 0.414634146 0.000000000 0.414634146 0.000000000 0.054878049 [4,] 0.000000000 0.237804878 0.000000000 0.000000000 0.000000000 0.000000000 [,7] [,8] [1,] 0.810975610 0.786585366 [2,] 0.128048780 0.036585366 [3,] 0.042682927 0.176829268 [4,] 0.018292683 0.000000000 *** Cluster: 2 * Proportion = 0.1815182 * probabilities = [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.16363636 0.09090909 0.92727273 0.41818182 0.54545455 0.40000000 [2,] 0.83636364 0.10909091 0.07272727 0.00000000 0.45454545 0.56363636 [3,] 0.00000000 0.16363636 0.00000000 0.58181818 0.00000000 0.03636364 [4,] 0.00000000 0.63636364 0.00000000 0.00000000 0.00000000 0.00000000 [,7] [,8] [1,] 0.49090909 0.40000000 [2,] 0.34545455 0.05454545 [3,] 0.10909091 0.54545455 [4,] 0.05454545 0.00000000 *** Cluster: 3 * Proportion = 0.1188119 * probabilities = [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.19444444 0.02777778 0.75000000 0.52777778 0.38888889 0.19444444 [2,] 0.80555556 0.02777778 0.25000000 0.02777778 0.61111111 0.72222222 [3,] 0.00000000 0.11111111 0.00000000 0.44444444 0.00000000 0.08333333 [4,] 0.00000000 0.83333333 0.00000000 0.00000000 0.00000000 0.00000000 [,7] [,8] [1,] 0.25000000 0.22222222 [2,] 0.38888889 0.16666667 [3,] 0.25000000 0.61111111 [4,] 0.11111111 0.00000000 *** Cluster: 4 * Proportion = 0.1155116 * probabilities = [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.20000000 0.00000000 0.77142857 0.34285714 0.34285714 0.17142857 [2,] 0.80000000 0.05714286 0.22857143 0.02857143 0.65714286 0.68571429 [3,] 0.00000000 0.11428571 0.00000000 0.62857143 0.00000000 0.14285714 [4,] 0.00000000 0.82857143 0.00000000 0.00000000 0.00000000 0.00000000 [,7] [,8] [1,] 0.22857143 0.17142857 [2,] 0.22857143 0.02857143 [3,] 0.40000000 0.80000000 [4,] 0.14285714 0.00000000 *** Cluster: 5 * Proportion = 0.04290429 * probabilities = [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.15384615 0.07692308 0.92307692 0.15384615 0.53846154 0.07692308 [2,] 0.84615385 0.00000000 0.07692308 0.07692308 0.46153846 0.76923077 [3,] 0.00000000 0.07692308 0.00000000 0.76923077 0.00000000 0.15384615 [4,] 0.00000000 0.84615385 0.00000000 0.00000000 0.00000000 0.00000000 [,7] [,8] [1,] 0.23076923 0.15384615 [2,] 0.23076923 0.15384615 [3,] 0.15384615 0.69230769 [4,] 0.38461538 0.00000000 * model name = gaussian_pk_sjk *** Cluster: 1 * Proportion = 0.5412541 * Means = 52.5853659 129.2500000 242.6402439 158.3780488 0.3536585 * S.D. = 9.482913 16.155259 53.293354 19.140457 0.669400 *** Cluster: 2 * Proportion = 0.1815182 * Means = 55.3818182 133.2545455 249.1090909 145.9272727 0.7090909 * S.D. = 7.9280650 17.8633380 40.3789651 22.6337275 0.8877495 *** Cluster: 3 * Proportion = 0.1188119 * Means = 58.027778 134.194444 259.277778 135.583333 1.416667 * S.D. = 7.119949 17.645930 52.993506 20.571522 1.063929 *** Cluster: 4 * Proportion = 0.1155116 * Means = 56.000000 135.457143 246.457143 132.057143 1.657143 * S.D. = 7.668116 21.349263 50.996551 22.372104 1.491746 *** Cluster: 5 * Proportion = 0.04290429 * Means = 59.69231 138.76923 253.38462 140.61538 2.00000 * S.D. = 9.050156 16.511785 63.687384 19.101101 1.240347 **************************************** > > > proc.time() user system elapsed 1.17 0.37 1.53