setwd("E:/code/dm_R/")
Data = read.csv("./chapter6/example/model.csv", header = TRUE)
#把数据分为训练集、测试集两部分
#数据命名
colnames(Data) <- c("time", "userid", "ele_ind", "loss_ind", "alarm_ind", "class")
#数据分割
set.seed(1234)
#定义序列ind，随机抽取1和2,1占80%，2占20%
ind <- sample(2, nrow(Data), replace = TRUE, prob = c(0.8, 0.2))
trainData <- Data[ind == 1,]#训练集,240个对象
testData<- Data[ind == 2,]#测试集，51个对象
#构建神经网络代码
#将class列转为factor类型
trainData <- transform(trainData, class = as.factor(class))
str(trainData)

'data.frame':    240 obs. of  6 variables:
 $ time     : Factor w/ 19 levels "2014年9月10日",..: 16 11 8 5 4 13 11 10 19 9 ...
 $ userid   : num  9.9e+09 9.9e+09 9.9e+09 9.9e+09 9.9e+09 ...
 $ ele_ind  : int  4 4 2 9 2 5 3 3 4 10 ...
 $ loss_ind : num  1 0 1 0 0 0 1 0 1 1 ...
 $ alarm_ind: int  1 4 1 0 0 2 3 0 0 2 ...
 $ class    : Factor w/ 2 levels "0","1": 2 2 2 1 1 2 2 1 1 2 ...

#使用神经网络构建模型

library(nnet)

#利用nnet建立神经网络

nnet.model <- nnet(class~ele_ind + loss_ind + alarm_ind, trainData, size = 10, decay = 0.05)

[203,] -0.1604506 1.114802 0.3928444
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[232,] 3.455055 -0.2956536 0.4491234
[233,] 3.455055 -0.2956536 0.4491234
[234,] 3.455055 -0.2956536 0.4491234
[235,] 3.455055 -0.2956536 0.4491234
[236,] 3.455055 -0.2956536 0.4491234
[237,] 3.455055 -0.2956536 0.4491234
[238,] 3.455055 -0.2956536 0.4491234
[239,] 3.455055 -0.2956536 0.4491234
[240,] 3.455055 -0.2956536 0.4491234
[241,] 3.455055 -0.2956536 0.4491234
[242,] 3.455055 -0.2956536 0.4491234
[243,] 3.455055 -0.2956536 0.4491234
[244,] 3.455055 -0.2956536 0.4491234
[245,] 3.455055 -0.2956536 0.4491234
[246,] 3.455055 -0.2956536 0.4491234
[247,] 3.455055 -0.2956536 0.4491234
[248,] 3.455055 -0.2956536 0.4491234
[249,] 3.455055 -0.2956536 0.4491234
[250,] 3.455055 -0.2956536 0.4491234
[251,] 3.455055 -0.2956536 0.4491234
[252,] 3.455055 -0.2956536 0.4491234
[253,] 3.455055 -0.2956536 0.4491234
[254,] 3.455055 -0.2956536 0.4491234
[255,] 3.455055 -0.2956536 0.4491234
[256,] 3.455055 -0.2956536 0.4491234
[257,] 3.455055 -0.2956536 0.4491234
[258,] 3.455055 -0.2956536 0.4491234
[259,] 3.455055 -0.2956536 0.4491234
[260,] 3.455055 -0.2956536 0.4491234
[261,] 3.455055 -0.2956536 0.4491234
[262,] 3.455055 -0.2956536 0.4491234
[263,] 3.455055 -0.2956536 0.4491234
[264,] 3.455055 -0.2956536 0.4491234
[265,] 3.455055 -0.2956536 0.4491234
[266,] 3.455055 -0.2956536 0.4491234
[267,] 3.455055 -0.2956536 0.4491234
[268,] 3.455055 -0.2956536 0.4491234
[269,] 3.455055 -0.2956536 0.4491234
[270,] 3.455055 -0.2956536 0.4491234
[271,] 3.455055 -0.2956536 0.4491234
[272,] 3.455055 -0.2956536 0.4491234
[273,] 3.455055 -0.2956536 0.4491234
[274,] 3.455055 -0.2956536 0.4491234
[275,] 3.455055 -0.2956536 0.4491234
[276,] 3.455055 -0.2956536 0.4491234
[277,] 3.455055 -0.2956536 0.4491234
[278,] 3.455055 -0.2956536 0.4491234
[279,] 3.455055 -0.2956536 0.4491234
[280,] 3.455055 -0.2956536 0.4491234
[281,] 3.455055 -0.2956536 0.4491234
[282,] 3.455055 -0.2956536 0.4491234
[283,] 3.455055 -0.2956536 0.4491234
[284,] 3.455055 -0.2956536 0.4491234
[285,] 3.455055 -0.2956536 0.4491234
[286,] 3.455055 -0.2956536 0.4491234
[287,] 3.455055 -0.2956536 0.4491234
[288,] 3.455055 -0.2956536 0.4491234
[289,] 3.455055 -0.2956536 0.4491234
[290,] 3.455055 -0.2956536 0.4491234
[291,] 3.455055 -0.2956536 0.4491234
[292,] 3.455055 -0.2956536 0.4491234
[293,] 3.455055 -0.2956536 0.4491234
[294,] 3.455055 -0.2956536 0.4491234
[295,] 3.455055 -0.2956536 0.4491234
[296,] 3.455055 -0.2956536 0.4491234
[297,] 3.455055 -0.2956536 0.4491234
[298,] 3.455055 -0.2956536 0.4491234
[299,] 3.455055 -0.2956536 0.4491234
[300,] 3.455055 -0.2956536 0.4491234
[301,] 3.455055 -0.2956536 0.4491234
[302,] 3.455055 -0.2956536 0.4491234
[303,] 3.455055 -0.2956536 0.4491234
[304,] 3.455055 -0.2956536 0.4491234
[305,] 3.455055 -0.2956536 0.4491234
[306,] 3.455055 -0.2956536 0.4491234
[307,] 3.455055 -0.2956536 0.4491234
[308,] 3.455055 -0.2956536 0.4491234
[309,] 3.455055 -0.2956536 0.4491234
[310,] 3.455055 -0.2956536 0.4491234
[311,] 3.455055 -0.2956536 0.4491234
[312,] 3.455055 -0.2956536 0.4491234
[313,] 3.455055 -0.2956536 0.4491234
[314,] 3.455055 -0.2956536 0.4491234
[315,] 3.455055 -0.2956536 0.4491234
[316,] 3.455055 -0.2956536 0.4491234
[317,] 3.455055 -0.2956536 0.4491234
[318,] 3.455055 -0.2956536 0.4491234
[319,] 3.455055 -0.2956536 0.4491234
[320,] 3.455055 -0.2956536 0.4491234
[321,] 3.455055 -0.2956536 0.4491234
[322,] 3.455055 -0.2956536 0.4491234
[323,] 3.455055 -0.2956536 0.4491234
[324,] 3.455055 -0.2956536 0.4491234
[325,] 3.455055 -0.2956536 0.4491234
[326,] 3.455055 -0.2956536 0.4491234
[327,] 3.455055 -0.2956536 0.4491234
[328,] 3.455055 -0.2956536 0.4491234
[329,] 3.455055 -0.2956536 0.4491234
[330,] 3.455055 -0.2956536 0.4491234
[331,] 3.455055 -0.2956536 0.4491234
[332,] 3.455055 -0.2956536 0.4491234
[333,] 3.455055 -0.2956536 0.4491234
 [ reached getOption("max.print") -- omitted 607 rows ]

> x1 = x[,1]
Error in x[, 1] : incorrect number of dimensions
> x1 = x[1]
> x1
[[1]]
             [,1]      [,2]       [,3]
  [1,] -0.1493534 -0.658893 -0.2717798
  [2,] -0.1493534 -0.658893 -0.2717798
  [3,] -0.1493534 -0.658893 -0.2717798
  [4,] -0.1493534 -0.658893 -0.2717798
  [5,] -0.1493534 -0.658893 -0.2717798
  [6,] -0.1493534 -0.658893 -0.2717798
  [7,] -0.1493534 -0.658893 -0.2717798
  [8,] -0.1493534 -0.658893 -0.2717798
  [9,] -0.1493534 -0.658893 -0.2717798
 [10,] -0.1493534 -0.658893 -0.2717798
 [11,] -0.1493534 -0.658893 -0.2717798
 [12,] -0.1493534 -0.658893 -0.2717798
 [13,] -0.1493534 -0.658893 -0.2717798
 [14,] -0.1493534 -0.658893 -0.2717798
 [15,] -0.1493534 -0.658893 -0.2717798
 [16,] -0.1493534 -0.658893 -0.2717798
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 [18,] -0.1493534 -0.658893 -0.2717798
 [19,] -0.1493534 -0.658893 -0.2717798
 [20,] -0.1493534 -0.658893 -0.2717798
 [21,] -0.1493534 -0.658893 -0.2717798
 [22,] -0.1493534 -0.658893 -0.2717798
 [23,] -0.1493534 -0.658893 -0.2717798
 [24,] -0.1493534 -0.658893 -0.2717798
 [25,] -0.1493534 -0.658893 -0.2717798
 [26,] -0.1493534 -0.658893 -0.2717798
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 [28,] -0.1493534 -0.658893 -0.2717798
 [29,] -0.1493534 -0.658893 -0.2717798
 [30,] -0.1493534 -0.658893 -0.2717798
 [31,] -0.1493534 -0.658893 -0.2717798
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 [33,] -0.1493534 -0.658893 -0.2717798
 [34,] -0.1493534 -0.658893 -0.2717798
 [35,] -0.1493534 -0.658893 -0.2717798
 [36,] -0.1493534 -0.658893 -0.2717798
 [37,] -0.1493534 -0.658893 -0.2717798
 [38,] -0.1493534 -0.658893 -0.2717798
 [39,] -0.1493534 -0.658893 -0.2717798
 [40,] -0.1493534 -0.658893 -0.2717798
 [41,] -0.1493534 -0.658893 -0.2717798
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 [44,] -0.1493534 -0.658893 -0.2717798
 [45,] -0.1493534 -0.658893 -0.2717798
 [46,] -0.1493534 -0.658893 -0.2717798
 [47,] -0.1493534 -0.658893 -0.2717798
 [48,] -0.1493534 -0.658893 -0.2717798
 [49,] -0.1493534 -0.658893 -0.2717798
 [50,] -0.1493534 -0.658893 -0.2717798
 [51,] -0.1493534 -0.658893 -0.2717798
 [52,] -0.1493534 -0.658893 -0.2717798
 [53,] -0.1493534 -0.658893 -0.2717798
 [54,] -0.1493534 -0.658893 -0.2717798
 [55,] -0.1493534 -0.658893 -0.2717798
 [56,] -0.1493534 -0.658893 -0.2717798
 [57,] -0.1493534 -0.658893 -0.2717798
 [58,] -0.1493534 -0.658893 -0.2717798
 [59,] -0.1493534 -0.658893 -0.2717798
 [60,] -0.1493534 -0.658893 -0.2717798
 [61,] -0.1493534 -0.658893 -0.2717798
 [62,] -0.1493534 -0.658893 -0.2717798
 [63,] -0.1493534 -0.658893 -0.2717798
 [64,] -0.1493534 -0.658893 -0.2717798
 [65,] -0.1493534 -0.658893 -0.2717798
 [66,] -0.1493534 -0.658893 -0.2717798
 [67,] -0.1493534 -0.658893 -0.2717798
 [68,] -0.1493534 -0.658893 -0.2717798
 [69,] -0.1493534 -0.658893 -0.2717798
 [70,] -0.1493534 -0.658893 -0.2717798
 [71,] -0.1493534 -0.658893 -0.2717798
 [72,] -0.1493534 -0.658893 -0.2717798
 [73,] -0.1493534 -0.658893 -0.2717798
 [74,] -0.1493534 -0.658893 -0.2717798
 [75,] -0.1493534 -0.658893 -0.2717798
 [76,] -0.1493534 -0.658893 -0.2717798
 [77,] -0.1493534 -0.658893 -0.2717798
 [78,] -0.1493534 -0.658893 -0.2717798
 [79,] -0.1493534 -0.658893 -0.2717798
 [80,] -0.1493534 -0.658893 -0.2717798
 [81,] -0.1493534 -0.658893 -0.2717798
 [82,] -0.1493534 -0.658893 -0.2717798
 [83,] -0.1493534 -0.658893 -0.2717798
 [84,] -0.1493534 -0.658893 -0.2717798
 [85,] -0.1493534 -0.658893 -0.2717798
 [86,] -0.1493534 -0.658893 -0.2717798
 [87,] -0.1493534 -0.658893 -0.2717798
 [88,] -0.1493534 -0.658893 -0.2717798
 [89,] -0.1493534 -0.658893 -0.2717798
 [90,] -0.1493534 -0.658893 -0.2717798
 [91,] -0.1493534 -0.658893 -0.2717798
 [92,] -0.1493534 -0.658893 -0.2717798
 [93,] -0.1493534 -0.658893 -0.2717798
 [94,] -0.1493534 -0.658893 -0.2717798
 [95,] -0.1493534 -0.658893 -0.2717798
 [96,] -0.1493534 -0.658893 -0.2717798
 [97,] -0.1493534 -0.658893 -0.2717798
 [98,] -0.1493534 -0.658893 -0.2717798
 [99,] -0.1493534 -0.658893 -0.2717798
[100,] -0.1493534 -0.658893 -0.2717798
[101,] -0.1493534 -0.658893 -0.2717798
[102,] -0.1493534 -0.658893 -0.2717798
[103,] -0.1493534 -0.658893 -0.2717798
[104,] -0.1493534 -0.658893 -0.2717798
[105,] -0.1493534 -0.658893 -0.2717798
[106,] -0.1493534 -0.658893 -0.2717798
[107,] -0.1493534 -0.658893 -0.2717798
[108,] -0.1493534 -0.658893 -0.2717798
[109,] -0.1493534 -0.658893 -0.2717798
[110,] -0.1493534 -0.658893 -0.2717798
[111,] -0.1493534 -0.658893 -0.2717798
[112,] -0.1493534 -0.658893 -0.2717798
[113,] -0.1493534 -0.658893 -0.2717798
[114,] -0.1493534 -0.658893 -0.2717798
[115,] -0.1493534 -0.658893 -0.2717798
[116,] -0.1493534 -0.658893 -0.2717798
[117,] -0.1493534 -0.658893 -0.2717798
[118,] -0.1493534 -0.658893 -0.2717798
[119,] -0.1493534 -0.658893 -0.2717798
[120,] -0.1493534 -0.658893 -0.2717798
[121,] -0.1493534 -0.658893 -0.2717798
[122,] -0.1493534 -0.658893 -0.2717798
[123,] -0.1493534 -0.658893 -0.2717798
[124,] -0.1493534 -0.658893 -0.2717798
[125,] -0.1493534 -0.658893 -0.2717798
[126,] -0.1493534 -0.658893 -0.2717798
[127,] -0.1493534 -0.658893 -0.2717798
[128,] -0.1493534 -0.658893 -0.2717798
[129,] -0.1493534 -0.658893 -0.2717798
[130,] -0.1493534 -0.658893 -0.2717798
[131,] -0.1493534 -0.658893 -0.2717798
[132,] -0.1493534 -0.658893 -0.2717798
[133,] -0.1493534 -0.658893 -0.2717798
[134,] -0.1493534 -0.658893 -0.2717798
[135,] -0.1493534 -0.658893 -0.2717798
[136,] -0.1493534 -0.658893 -0.2717798
[137,] -0.1493534 -0.658893 -0.2717798
[138,] -0.1493534 -0.658893 -0.2717798
[139,] -0.1493534 -0.658893 -0.2717798
[140,] -0.1493534 -0.658893 -0.2717798
[141,] -0.1493534 -0.658893 -0.2717798
[142,] -0.1493534 -0.658893 -0.2717798
[143,] -0.1493534 -0.658893 -0.2717798
[144,] -0.1493534 -0.658893 -0.2717798
[145,] -0.1493534 -0.658893 -0.2717798
[146,] -0.1493534 -0.658893 -0.2717798
[147,] -0.1493534 -0.658893 -0.2717798
[148,] -0.1493534 -0.658893 -0.2717798
[149,] -0.1493534 -0.658893 -0.2717798
[150,] -0.1493534 -0.658893 -0.2717798
[151,] -0.1493534 -0.658893 -0.2717798
[152,] -0.1493534 -0.658893 -0.2717798
[153,] -0.1493534 -0.658893 -0.2717798
[154,] -0.1493534 -0.658893 -0.2717798
[155,] -0.1493534 -0.658893 -0.2717798
[156,] -0.1493534 -0.658893 -0.2717798
[157,] -0.1493534 -0.658893 -0.2717798
[158,] -0.1493534 -0.658893 -0.2717798
[159,] -0.1493534 -0.658893 -0.2717798
[160,] -0.1493534 -0.658893 -0.2717798
[161,] -0.1493534 -0.658893 -0.2717798
[162,] -0.1493534 -0.658893 -0.2717798
[163,] -0.1493534 -0.658893 -0.2717798
[164,] -0.1493534 -0.658893 -0.2717798
[165,] -0.1493534 -0.658893 -0.2717798
[166,] -0.1493534 -0.658893 -0.2717798
[167,] -0.1493534 -0.658893 -0.2717798
[168,] -0.1493534 -0.658893 -0.2717798
[169,] -0.1493534 -0.658893 -0.2717798
[170,] -0.1493534 -0.658893 -0.2717798
[171,] -0.1493534 -0.658893 -0.2717798
[172,] -0.1493534 -0.658893 -0.2717798
[173,] -0.1493534 -0.658893 -0.2717798
[174,] -0.1493534 -0.658893 -0.2717798
[175,] -0.1493534 -0.658893 -0.2717798
[176,] -0.1493534 -0.658893 -0.2717798
[177,] -0.1493534 -0.658893 -0.2717798
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[179,] -0.1493534 -0.658893 -0.2717798
[180,] -0.1493534 -0.658893 -0.2717798
[181,] -0.1493534 -0.658893 -0.2717798
[182,] -0.1493534 -0.658893 -0.2717798
[183,] -0.1493534 -0.658893 -0.2717798
[184,] -0.1493534 -0.658893 -0.2717798
[185,] -0.1493534 -0.658893 -0.2717798
[186,] -0.1493534 -0.658893 -0.2717798
[187,] -0.1493534 -0.658893 -0.2717798
[188,] -0.1493534 -0.658893 -0.2717798
[189,] -0.1493534 -0.658893 -0.2717798
[190,] -0.1493534 -0.658893 -0.2717798
[191,] -0.1493534 -0.658893 -0.2717798
[192,] -0.1493534 -0.658893 -0.2717798
[193,] -0.1493534 -0.658893 -0.2717798
[194,] -0.1493534 -0.658893 -0.2717798
[195,] -0.1493534 -0.658893 -0.2717798
[196,] -0.1493534 -0.658893 -0.2717798
[197,] -0.1493534 -0.658893 -0.2717798
[198,] -0.1493534 -0.658893 -0.2717798
[199,] -0.1493534 -0.658893 -0.2717798
[200,] -0.1493534 -0.658893 -0.2717798
[201,] -0.1493534 -0.658893 -0.2717798
[202,] -0.1493534 -0.658893 -0.2717798
[203,] -0.1493534 -0.658893 -0.2717798
[204,] -0.1493534 -0.658893 -0.2717798
[205,] -0.1493534 -0.658893 -0.2717798
[206,] -0.1493534 -0.658893 -0.2717798
[207,] -0.1493534 -0.658893 -0.2717798
[208,] -0.1493534 -0.658893 -0.2717798
[209,] -0.1493534 -0.658893 -0.2717798
[210,] -0.1493534 -0.658893 -0.2717798
[211,] -0.1493534 -0.658893 -0.2717798
[212,] -0.1493534 -0.658893 -0.2717798
[213,] -0.1493534 -0.658893 -0.2717798
[214,] -0.1493534 -0.658893 -0.2717798
[215,] -0.1493534 -0.658893 -0.2717798
[216,] -0.1493534 -0.658893 -0.2717798
[217,] -0.1493534 -0.658893 -0.2717798
[218,] -0.1493534 -0.658893 -0.2717798
[219,] -0.1493534 -0.658893 -0.2717798
[220,] -0.1493534 -0.658893 -0.2717798
[221,] -0.1493534 -0.658893 -0.2717798
[222,] -0.1493534 -0.658893 -0.2717798
[223,] -0.1493534 -0.658893 -0.2717798
[224,] -0.1493534 -0.658893 -0.2717798
[225,] -0.1493534 -0.658893 -0.2717798
[226,] -0.1493534 -0.658893 -0.2717798
[227,] -0.1493534 -0.658893 -0.2717798
[228,] -0.1493534 -0.658893 -0.2717798
[229,] -0.1493534 -0.658893 -0.2717798
[230,] -0.1493534 -0.658893 -0.2717798
[231,] -0.1493534 -0.658893 -0.2717798
[232,] -0.1493534 -0.658893 -0.2717798
[233,] -0.1493534 -0.658893 -0.2717798
[234,] -0.1493534 -0.658893 -0.2717798
[235,] -0.1493534 -0.658893 -0.2717798
[236,] -0.1493534 -0.658893 -0.2717798
[237,] -0.1493534 -0.658893 -0.2717798
[238,] -0.1493534 -0.658893 -0.2717798
[239,] -0.1493534 -0.658893 -0.2717798
[240,] -0.1493534 -0.658893 -0.2717798
[241,] -0.1493534 -0.658893 -0.2717798
[242,] -0.1493534 -0.658893 -0.2717798
[243,] -0.1493534 -0.658893 -0.2717798
[244,] -0.1493534 -0.658893 -0.2717798
[245,] -0.1493534 -0.658893 -0.2717798
[246,] -0.1493534 -0.658893 -0.2717798
[247,] -0.1493534 -0.658893 -0.2717798
[248,] -0.1493534 -0.658893 -0.2717798
[249,] -0.1493534 -0.658893 -0.2717798
[250,] -0.1493534 -0.658893 -0.2717798
[251,] -0.1493534 -0.658893 -0.2717798
[252,] -0.1493534 -0.658893 -0.2717798
[253,] -0.1493534 -0.658893 -0.2717798
[254,] -0.1493534 -0.658893 -0.2717798
[255,] -0.1493534 -0.658893 -0.2717798
[256,] -0.1493534 -0.658893 -0.2717798
[257,] -0.1493534 -0.658893 -0.2717798
[258,] -0.1493534 -0.658893 -0.2717798
[259,] -0.1493534 -0.658893 -0.2717798
[260,] -0.1493534 -0.658893 -0.2717798
[261,] -0.1493534 -0.658893 -0.2717798
[262,] -0.1493534 -0.658893 -0.2717798
[263,] -0.1493534 -0.658893 -0.2717798
[264,] -0.1493534 -0.658893 -0.2717798
[265,] -0.1493534 -0.658893 -0.2717798
[266,] -0.1493534 -0.658893 -0.2717798
[267,] -0.1493534 -0.658893 -0.2717798
[268,] -0.1493534 -0.658893 -0.2717798
[269,] -0.1493534 -0.658893 -0.2717798
[270,] -0.1493534 -0.658893 -0.2717798
[271,] -0.1493534 -0.658893 -0.2717798
[272,] -0.1493534 -0.658893 -0.2717798
[273,] -0.1493534 -0.658893 -0.2717798
[274,] -0.1493534 -0.658893 -0.2717798
[275,] -0.1493534 -0.658893 -0.2717798
[276,] -0.1493534 -0.658893 -0.2717798
[277,] -0.1493534 -0.658893 -0.2717798
[278,] -0.1493534 -0.658893 -0.2717798
[279,] -0.1493534 -0.658893 -0.2717798
[280,] -0.1493534 -0.658893 -0.2717798
[281,] -0.1493534 -0.658893 -0.2717798
[282,] -0.1493534 -0.658893 -0.2717798
[283,] -0.1493534 -0.658893 -0.2717798
[284,] -0.1493534 -0.658893 -0.2717798
[285,] -0.1493534 -0.658893 -0.2717798
[286,] -0.1493534 -0.658893 -0.2717798
[287,] -0.1493534 -0.658893 -0.2717798
[288,] -0.1493534 -0.658893 -0.2717798
[289,] -0.1493534 -0.658893 -0.2717798
[290,] -0.1493534 -0.658893 -0.2717798
[291,] -0.1493534 -0.658893 -0.2717798
[292,] -0.1493534 -0.658893 -0.2717798
[293,] -0.1493534 -0.658893 -0.2717798
[294,] -0.1493534 -0.658893 -0.2717798
[295,] -0.1493534 -0.658893 -0.2717798
[296,] -0.1493534 -0.658893 -0.2717798
[297,] -0.1493534 -0.658893 -0.2717798
[298,] -0.1493534 -0.658893 -0.2717798
[299,] -0.1493534 -0.658893 -0.2717798
[300,] -0.1493534 -0.658893 -0.2717798
[301,] -0.1493534 -0.658893 -0.2717798
[302,] -0.1493534 -0.658893 -0.2717798
[303,] -0.1493534 -0.658893 -0.2717798
[304,] -0.1493534 -0.658893 -0.2717798
[305,] -0.1493534 -0.658893 -0.2717798
[306,] -0.1493534 -0.658893 -0.2717798
[307,] -0.1493534 -0.658893 -0.2717798
[308,] -0.1493534 -0.658893 -0.2717798
[309,] -0.1493534 -0.658893 -0.2717798
[310,] -0.1493534 -0.658893 -0.2717798
[311,] -0.1493534 -0.658893 -0.2717798
[312,] -0.1493534 -0.658893 -0.2717798
[313,] -0.1493534 -0.658893 -0.2717798
[314,] -0.1493534 -0.658893 -0.2717798
[315,] -0.1493534 -0.658893 -0.2717798
[316,] -0.1493534 -0.658893 -0.2717798
[317,] -0.1493534 -0.658893 -0.2717798
[318,] -0.1493534 -0.658893 -0.2717798
[319,] -0.1493534 -0.658893 -0.2717798
[320,] -0.1493534 -0.658893 -0.2717798
[321,] -0.1493534 -0.658893 -0.2717798
[322,] -0.1493534 -0.658893 -0.2717798
[323,] -0.1493534 -0.658893 -0.2717798
[324,] -0.1493534 -0.658893 -0.2717798
[325,] -0.1493534 -0.658893 -0.2717798
[326,] -0.1493534 -0.658893 -0.2717798
[327,] -0.1493534 -0.658893 -0.2717798
[328,] -0.1493534 -0.658893 -0.2717798
[329,] -0.1493534 -0.658893 -0.2717798
[330,] -0.1493534 -0.658893 -0.2717798
[331,] -0.1493534 -0.658893 -0.2717798
[332,] -0.1493534 -0.658893 -0.2717798
[333,] -0.1493534 -0.658893 -0.2717798
 [ reached getOption("max.print") -- omitted 607 rows ]

> juli1 = juli[1]
> juli2 = juli[2]
> juli3 = juli[3]
> dist = data.frame(juli1, juli2, juli3)
> View(dist)
> colnames(dist) <- c('juli1', 'juli2', 'juli3')
> y = apply(dist, 1, min)
> y
  [1] 1.2839514 1.2517772 1.1052795 1.5070744 1.4935138 0.9726273 0.8921393 0.9597807
  [9] 1.0796595 1.0488114 0.6159521 1.0597333 1.0975862 1.4545486 1.4207439 1.0816366
 [17] 2.4048476 0.2861651 0.9661754 1.3203545 1.2949491 1.3987975 0.6928462 0.9798814
 [25] 0.6325604 1.2926673 1.5713442 1.1522974 0.6286930 5.5058217 1.1768097 0.6037162
 [33] 1.3051681 0.9464992 0.4643231 1.4708567 0.7424111 1.3555286 3.1001436 1.0507244
 [41] 0.8439410 1.4240501 1.1827495 0.6438475 0.8841970 0.8055016 1.0505353 0.5552708
 [49] 1.2919281 1.0715975 1.3171011 1.8530298 1.6843018 1.0936926 0.8152268 0.4500286
 [57] 0.8841592 2.2107706 1.0473566 1.0244698 0.7398935 0.9720879 1.1936466 1.3228729
 [65] 0.9834741 1.0023424 1.0432600 0.9211457 1.3713181 1.0388900 1.4831576 1.0446892
 [73] 0.8611032 1.0666477 1.3811832 0.7429663 1.2167127 1.0988258 0.7188866 1.3123011
 [81] 1.3861779 1.0776628 0.5389992 1.0653812 1.1816154 0.7753244 1.0017056 1.0768776
 [89] 1.0351799 0.2978925 0.6418134 1.1217043 1.5621522 0.7424412 0.9598346 1.5982183
 [97] 1.2035453 0.4941361 0.4020193 1.3480132 0.8968158 0.9457947 1.7271597 1.1043144
[105] 1.3140138 1.2023263 1.2047381 0.7257287 1.2473410 1.0251931 0.4051376 1.2259243
[113] 1.5983494 1.0829995 0.5511577 2.2837502 1.1996493 1.2984391 0.3631780 0.7039784
[121] 1.3479714 1.5546965 1.1581293 1.1325073 1.2384725 1.4306388 0.3347727 0.9377885
[129] 1.5933367 1.8630912 1.4738865 1.0206379 0.9899630 1.0059593 0.8987748 0.7843134
[137] 1.0557005 1.3127282 1.4868505 1.3524569 1.3107396 1.1718289 0.8424027 0.7687681
[145] 0.3840095 0.6969193 1.4316009 0.2908066 0.8668981 1.6141233 1.2046086 0.8715482
[153] 0.9968797 0.9636232 1.1557989 1.5130763 1.3197081 1.1133957 1.4560506 0.8665945
[161] 1.1684736 1.0754526 1.4902143 0.9895615 0.5466386 0.8408925 0.8705366 0.6394344
[169] 0.3215736 0.4849082 0.6758050 1.1300405 1.1763062 1.1273946 1.0581313 1.4060679
[177] 0.9389915 0.5535159 1.1141892 1.0912659 1.2498473 1.0798504 0.9431308 1.4791674
[185] 1.1454491 1.1220533 0.1605346 0.8745886 1.1120990 0.6669449 0.8276200 1.0920278
[193] 1.1489228 0.5550147 1.0548300 1.1398306 1.4955479 1.3552737 0.7975900 1.3604131
[201] 0.4564041 0.4961539 0.9691897 0.8691432 0.6231899 1.4609199 0.8045874 1.2412889
[209] 0.9560649 0.4915211 0.7429527 1.4128889 0.6900214 1.0425451 1.2190204 0.8717731
[217] 1.1038467 0.6005712 0.9159237 0.6918378 1.4822200 0.8709222 1.2206467 0.4787106
[225] 1.3636101 5.3579148 0.8842671 1.0043205 1.2397042 1.1304026 1.6138967 0.7829579
[233] 1.4375962 1.4754184 1.5482245 0.9110524 2.1435041 0.9849496 0.7030380 1.4998396
[241] 0.9838546 1.2649096 0.7003884 1.5043285 1.2141310 1.3468569 0.4211875 0.5016135
[249] 1.3179935 1.4700683 1.0127141 2.2931364 1.5279684 1.0394141 0.7735913 1.8600937
[257] 1.2523426 0.1744778 1.4197043 1.0254327 0.2392494 1.1073483 1.1355131 1.4675341
[265] 0.9577999 1.1856144 0.7666811 0.7225896 1.7060141 1.3333393 1.0600361 0.7690634
[273] 1.3223654 0.2499185 0.4633498 1.2939895 1.1791938 1.1969636 0.4309354 1.1659037
[281] 1.1460749 1.1508381 1.0191954 0.5998564 0.8234549 1.4408193 1.2722395 0.8608374
[289] 0.8912637 1.0076047 1.0315105 1.5363909 0.9943001 1.4883903 1.0219607 1.3085799
[297] 1.2566572 1.1234713 0.7745607 0.7573539 0.1280823 0.8732647 1.1811365 1.1112783
[305] 1.5854657 1.0109497 1.3876504 0.8288070 0.9787849 0.5775379 1.1651816 1.0957784
[313] 0.5020928 0.5589391 0.9585456 1.2532868 0.6936860 2.2314103 1.3364297 0.9759277
[321] 0.4355542 1.2516414 0.5230440 0.8621161 1.5326653 1.3380629 1.1131484 0.7472479
[329] 0.8960827 0.9576644 1.4445607 0.7158241 0.6571699 1.5236752 1.1791586 0.7694449
[337] 0.7203636 1.2391367 8.1495960 0.9801997 1.4521694 1.0408281 0.9243646 1.2131164
[345] 0.6085620 0.8447841 0.9531615 0.9455160 0.6596088 1.0995886 1.1383126 0.8187668
[353] 1.2313983 0.7983616 1.2822536 0.4928538 2.1047009 0.5437321 1.2934844 0.6950751
[361] 0.9542976 1.0126004 1.0886037 0.6574972 0.9866520 0.8043054 0.9156735 0.8592554
[369] 1.1602786 1.7178113 1.3969374 1.1071634 1.0324079 0.6639653 1.1813941 1.2440756
[377] 0.9649058 1.2931110 1.6557284 1.1590156 1.1840715 1.3068176 1.2542298 1.0233000
[385] 0.7887185 1.0343381 0.7913306 1.3493341 0.8229570 0.7129816 1.5419717 1.0242896
[393] 0.7129623 1.1579323 0.6126775 0.9154135 0.5178629 1.0260977 1.2532844 1.9539405
[401] 1.0793116 1.0659384 1.0464752 0.4411357 1.1519487 1.5673818 1.2302121 1.0309159
[409] 1.4306000 1.3484580 1.1754497 1.0779455 1.1146398 1.0368314 0.7619855 0.6156820
[417] 0.5784987 0.9912607 0.4225005 0.6026715 0.8424346 0.9594051 1.3426729 0.6902286
[425] 1.0200561 0.7854999 0.4498094 1.1542191 0.9419381 0.5301805 1.3459199 1.1832069
[433] 1.3178452 1.0898924 0.6531858 1.4078479 1.1044566 1.0753511 0.5456547 1.2808286
[441] 0.7812876 0.3181437 1.0843608 1.0568619 1.3858093 1.3830416 1.0094683 0.7941849
[449] 0.8753278 1.1061292 0.9210019 0.9222120 1.1198201 0.4416677 0.7239107 1.4268632
[457] 0.9065780 0.6545739 0.7167731 0.9751824 1.3891474 0.5655475 1.2059896 0.9593023
[465] 1.0981839 1.2839040 1.0857146 1.4028372 1.4181303 1.0849792 1.0937756 0.9629356
[473] 1.4621202 1.2946438 1.2119843 1.1713047 1.8615012 1.2463369 1.7145545 0.6261349
[481] 1.7205662 1.3175104 1.1851732 6.7226493 1.1306398 0.7763717 1.2311144 0.8408301
[489] 1.2914871 0.7262230 1.7935285 1.0254019 1.3933172 1.1716277 1.2915157 1.2379393
[497] 1.4984881 0.8153921 0.9895112 1.2091044 1.0583671 1.3834109 1.0755894 0.5557379
[505] 0.8155748 1.0063111 0.6783922 1.8651345 0.7920476 1.1173837 0.9243145 1.6990830
[513] 1.0899565 1.0096415 1.0688348 0.9247384 0.3362163 1.1835113 1.1767888 0.3992950
[521] 1.5031308 0.9795107 1.2226379 0.9538496 7.3607207 2.1624678 0.5466497 1.4793876
[529] 1.5659406 0.9569004 0.6318081 1.2680456 1.0944916 1.2645807 1.2111500 1.5102204
[537] 1.1662645 1.4086313 1.2819908 0.2578604 1.0996600 0.8127780 0.5698445 0.8213166
[545] 1.7438906 1.1794479 1.1677803 1.2243349 0.6700831 1.5031828 1.5022064 1.1510338
[553] 1.1235531 1.2472845 1.0567613 0.8163719 1.1663425 0.9744137 1.8280838 0.6025739
[561] 0.8447845 0.9428083 1.1686558 1.5320960 1.2334774 0.7626252 1.4222879 0.8216348
[569] 1.3188548 0.5717187 1.6319220 1.6282943 1.4051307 1.1699042 0.4788885 0.9245620
[577] 0.6728018 1.1792337 1.5204019 0.9380297 0.8175747 1.6486445 1.2575291 1.2001917
[585] 1.0361429 1.3451754 0.9854510 0.3916743 1.0074249 1.4795447 0.7315399 0.9170269
[593] 0.6726788 1.1400752 1.8974033 0.6668925 1.3160288 0.5829186 1.1374810 0.7212238
[601] 0.4029057 0.7471073 1.4190277 1.1812355 1.5668834 1.3393016 0.5577225 1.2492498
[609] 0.8713848 1.3724018 1.5441540 1.0760632 0.7390731 1.5150137 1.0501453 0.9417341
[617] 1.4632591 0.6506913 0.5536888 0.7522189 1.4454102 1.1845700 0.9397655 1.0423330
[625] 1.0527009 2.3179884 1.2105093 1.2615439 0.6398723 0.6730281 0.7341352 1.3923201
[633] 1.8094685 0.8339744 1.2576877 1.1016637 0.6375617 1.2870095 1.5405691 0.8497679
[641] 2.4977510 1.3455889 0.7428110 1.2514015 0.6077994 1.4118518 1.4725076 1.3524016
[649] 0.9145420 0.9059670 1.2199542 1.1449830 0.7217522 3.3299508 1.3759212 1.1725989
[657] 1.2378863 1.2327485 1.2339038 0.1553428 0.5507687 1.4858672 0.7224308 1.0881287
[665] 1.3174189 0.1089261 1.5215424 0.8558387 1.5862416 4.5950978 1.5066765 0.5075528
[673] 0.7959311 1.7374380 1.2137935 1.6781089 1.4256482 0.7179093 1.4537657 1.0982290
[681] 0.2625644 0.9407392 0.9528060 1.4204629 0.6396742 1.1104935 0.9989661 0.7670441
[689] 0.5012360 0.7652255 1.1181520 1.5973491 0.5495272 0.9766241 1.5729960 0.8916578
[697] 1.3287877 1.5292093 0.6357204 0.3911841 0.6838091 1.0120706 1.3733250 1.2901076
[705] 1.2818088 1.3354822 1.3806010 1.0555691 0.6892187 0.5773910 1.3859900 0.7799393
[713] 0.7174006 0.4748022 0.7681028 1.0485436 0.9709246 0.8992905 0.9234304 1.1247343
[721] 2.0227839 1.0407366 1.5800071 1.1231196 1.4048875 1.1098788 0.9472814 1.3526454
[729] 0.9561335 1.3663523 1.3188120 0.8900342 0.5694400 0.7783630 0.9319654 1.1237027
[737] 1.3642690 1.3187594 1.2482680 1.0488772 1.2938513 1.3580272 0.8490998 1.0359414
[745] 0.9270210 0.4286338 0.8452944 0.5947655 1.4761152 1.1273610 0.5907828 0.4428753
[753] 1.2266418 0.5527903 0.6698934 1.4055399 1.1511738 0.9171768 1.5482786 0.9790308
[761] 1.3395725 0.7971436 1.5218741 0.9784529 1.2201897 0.4563178 0.8263704 1.6064781
[769] 0.8974280 0.9953865 1.6134941 1.5072936 1.3398629 1.1019856 0.9489962 1.4880601
[777] 1.3767848 0.4349203 0.9882961 0.9162950 1.6760009 1.0595125 0.9969116 1.2097352
[785] 1.2222840 1.0132668 1.6311103 1.3224185 0.9466660 1.0341740 1.0082048 0.7820773
[793] 0.8813724 2.1416474 1.1947087 1.0784591 1.2158145 1.4877730 0.8976137 0.8942452
[801] 0.7232144 1.6174230 0.8356251 1.2269545 1.5943899 1.7901508 0.9139183 1.2788182
[809] 0.6548575 1.0951896 1.2438860 1.2577716 1.0847958 0.7748141 0.9343389 1.2166194
[817] 1.2488675 1.4325804 1.1017457 1.2322249 1.2937197 0.9083477 0.8568408 1.2953291
[825] 1.3942516 1.4329445 0.9759928 0.9686806 1.1847879 1.1587197 1.0422539 1.3491388
[833] 1.0455122 0.9950813 0.8574708 0.7266451 0.4840801 1.1419342 0.9147633 1.2666787
[841] 0.5038670 1.1600358 0.6211254 0.9853673 1.5302166 1.5166395 0.7643040 0.7763888
[849] 1.5637569 0.7311832 1.0214453 0.7782809 1.0798627 0.9885680 1.3758187 1.0442081
[857] 1.2087186 0.9723205 1.5263603 1.7749692 1.4054425 0.2929259 1.1921241 1.3165523
[865] 0.8608230 1.2494763 1.1233349 1.1556149 0.9494448 0.9867714 1.3515372 1.8010708
[873] 1.4315911 1.9217633 1.6963755 1.3884189 0.8390164 1.0862745 1.1363216 1.0742549
[881] 1.0756769 0.9633683 1.2342443 0.8375643 1.5691265 1.2436118 1.5439945 0.5788142
[889] 1.2465572 0.7108082 0.7623006 0.6641609 1.1632840 1.0202232 1.0466293 1.3569105
[897] 1.2806858 1.5351610 0.2647251 0.9822875 1.4055886 1.2667593 1.6250036 0.2471722
[905] 0.9054815 1.4612098 1.1870253 0.7930448 1.1971517 0.9809554 0.7736964 1.4442581
[913] 1.2857693 1.4769917 0.7481013 1.6846935 0.8899082 1.0976217 0.8799863 0.4368026
[921] 0.5773471 0.9323268 0.9559382 1.1428471 0.4795261 0.9274134 0.9429120 1.4250533
[929] 1.1500366 1.1380509 1.3432753 1.6506144 9.1067729 1.6860776 1.7314868 0.5321686
[937] 0.5591202 1.1538210 1.1422847 0.7589459
> plot(1:940, y, xlim = c(0, 940), xlab = "样本点", ylab = "欧氏距离")
Warning message:
In grepl("\n", lines, fixed = TRUE) :
  input string 1 is invalid in this locale
> points(which(y > 2.5), y[which(y > 2.5)], pch = 19, col = "red")
> which(y > 2.5)
[1]  30  39 226 339 484 525 654 670 933
> setwd("E:/code/dm_R")
> Data = read.csv("./chapter6/model.csv", header = TRUE)
Error in file(file, "rt") : cannot open the connection
In addition: Warning message:
In file(file, "rt") :
  cannot open file './chapter6/model.csv': No such file or directory
> setwd("E:/code/dm_R/")
> Data = read.csv("./chapter6/model.csv", header = TRUE)
Error in file(file, "rt") : cannot open the connection
In addition: Warning message:
In file(file, "rt") :
  cannot open file './chapter6/model.csv': No such file or directory
> Data = read.csv("./chapter6/example/model.csv", header = TRUE)
> colnames(Data) <- c("time", "userid", "ele_ind", "loss_ind", "alarm_ind", "class")
> View(Data)
> set.seed(1234)
> ind <- sample(2, nrow(Data), replace = TRUE, prob = c(0.8, 0.2))
> ind
  [1] 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 2 1 2 2 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1
 [45] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1
 [89] 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 2 1 1 1 1 2 2 2 1 1 1 1 1 1 2 1
[133] 1 1 2 1 2 1 1 2 1 2 1 1 1 1 1 1 2 1 1 1 1 2 1 2 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1
[177] 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 2 1 2 2 2 2 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 2
[221] 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
[265] 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 2 1
> trainData <- Data[ind == 1,]#训练集
> testData<- Data[ind == 2,]#测试集
> View(trainData)
> write.csv(trainData, "./chapter6/train.csv", row.names = FALSE)
> write.csv(testData, "./chapter6/test.csv", row.names = FALSE)
> trainData <- transform(trainData, class = as.factor(class))
> View(trainData)
> str(trainData)
'data.frame':    240 obs. of  6 variables:
 $ time     : Factor w/ 19 levels "2014年9月10日",..: 16 11 8 5 4 13 11 10 19 9 ...
 $ userid   : num  9.9e+09 9.9e+09 9.9e+09 9.9e+09 9.9e+09 ...
 $ ele_ind  : int  4 4 2 9 2 5 3 3 4 10 ...
 $ loss_ind : num  1 0 1 0 0 0 1 0 1 1 ...
 $ alarm_ind: int  1 4 1 0 0 2 3 0 0 2 ...
 $ class    : Factor w/ 2 levels "0","1": 2 2 2 1 1 2 2 1 1 2 ...
> library(nnet)
> nnet.model <- nnet(class~ele_ind + loss_ind + alarm_ind, trainData, size = 10, decay = 0.05)
# weights:  51
initial  value 186.182167 
iter  10 value 77.276797
iter  20 value 55.683287
iter  30 value 51.056399
iter  40 value 48.988954
iter  50 value 48.110299
iter  60 value 47.880343
iter  70 value 47.829561
iter  80 value 47.809978
iter  90 value 47.790313
iter 100 value 47.784068
final  value 47.784068 
stopped after 100 iterations

> summary(nnet.model)
a 3-10-1 network with 51 weights
options were - entropy fitting  decay=0.05
 b->h1 i1->h1 i2->h1 i3->h1 
  0.82  -0.30  -0.68  -0.17 
 b->h2 i1->h2 i2->h2 i3->h2 
 -1.50  -1.14   1.13   3.02 
 b->h3 i1->h3 i2->h3 i3->h3 
  0.74  -0.33  -0.81  -0.11 
 b->h4 i1->h4 i2->h4 i3->h4 
  2.13  -0.12  -0.62  -0.62 
 b->h5 i1->h5 i2->h5 i3->h5 
  1.99  -0.11  -0.61  -0.61 
 b->h6 i1->h6 i2->h6 i3->h6 
 -1.23   0.03   0.71   0.49 
 b->h7 i1->h7 i2->h7 i3->h7 
 -1.28   0.03   0.73   0.50 
 b->h8 i1->h8 i2->h8 i3->h8 
 -2.28   0.15   0.50   0.67 
 b->h9 i1->h9 i2->h9 i3->h9 
  0.83  -0.33  -0.69  -0.14 
 b->h10 i1->h10 i2->h10 i3->h10 
  -2.80    0.19    0.44    0.79 
  b->o  h1->o  h2->o  h3->o  h4->o  h5->o  h6->o  h7->o  h8->o  h9->o h10->o 
 -0.11  -1.51  -3.79  -1.53  -2.59  -2.52   1.84   1.93   2.71  -1.52   3.15 

#建立混淆矩阵

> confusion = table(trainData$class, predict(nnet.model, trainData, type = "class"))
> confusion
   
      0   1
  0 202   3
  1  10  25

> accuracy = sum(diag(confusion)) * 100/sum(confusion)#计算准确率
> accuracy
[1] 94.58333

write.csv(output_nnet.trainData,"./output_nnet.trainData.csv", row.names = FALSE)

#构建cart决策树模型

library(tree)

#利用tree建立cart决策树

tree.model <- tree(class~ele_ind + loss_ind + alarm_ind, trainData)

summary(tree.model)

Classification tree:
tree(formula = class ~ ele_ind + loss_ind + alarm_ind, data = trainData)
Number of terminal nodes:  11 
Residual mean deviance:  0.3757 = 86.04 / 229 
Misclassification error rate: 0.08333 = 20 / 240 

#画决策树

plot(tree.model);text(tree.model)

> confusion = table(trainData$class, predict(tree.model, trainData, type = "class"))
> confusion
   
      0   1
  0 193  12
  1   8  27
> accuracy = sum(diag(confusion)) * 100/sum(confusion)
> accuracy
[1] 91.66667

#ROC曲线
library(ROCR)
#画出神经网络模型的ROC曲线
nnet.pred <- prediction(predict(nnet.model, testData), testData$class)
nnet.perf <- performance(nnet.pred, "tpr", "fpr")
plot(nnet.perf)
#画出决策树模型的ROC曲线
tree.pred <- prediction(predict(tree.model, testData)[,2], testData$class)
tree.perf <- performance(tree.pred, "tpr", "fpr")
plot(tree.perf)