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codigo
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R version 3.4.2 (2017-09-28) -- "Short Summer"
Copyright (C) 2017 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R é um software livre e vem sem GARANTIA ALGUMA.
Você pode redistribuí-lo sob certas circunstâncias.
Digite 'license()' ou 'licence()' para detalhes de distribuição.
R é um projeto colaborativo com muitos contribuidores.
Digite 'contributors()' para obter mais informações e
'citation()' para saber como citar o R ou pacotes do R em publicações.
Digite 'demo()' para demonstrações, 'help()' para o sistema on-line de ajuda,
ou 'help.start()' para abrir o sistema de ajuda em HTML no seu navegador.
Digite 'q()' para sair do R.
[Área de trabalho anterior carregada]
> install.package("neuralnet")
Error in install.package("neuralnet") :
não foi possível encontrar a função "install.package"
> install.packages("neuralnet")
--- Please select a CRAN mirror for use in this session ---
tentando a URL 'https://cran.fiocruz.br/bin/windows/contrib/3.4/neuralnet_1.33.zip'
Content type 'application/zip' length 59672 bytes (58 KB)
downloaded 58 KB
package ‘neuralnet’ successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\marconunes\AppData\Local\Temp\Rtmpeq8mgQ\downloaded_packages
> library(neuralnet)
Warning message:
package ‘neuralnet’ was built under R version 3.4.4
> dados = read.csv(file.choose(), sep = ";" , head=T)
> dados
organizacao_mesa pontualidade trabalho_equipe comunicacao desempenho
1 3 3 2 3 regular
2 5 2 4 5 bom
3 1 3 3 3 ruim
4 3 4 3 5 otimo
5 2 2 4 4 regular
6 5 4 4 4 otimo
7 3 4 3 4 bom
8 2 3 2 2 ruim
9 3 5 4 4 bom
10 2 2 4 3 regular
11 5 5 2 3 regular
12 4 3 5 3 bom
13 4 3 3 4 otimo
14 3 5 2 4 regular
15 3 3 4 4 bom
16 3 3 5 4 bom
17 1 2 3 4 regular
18 2 3 5 3 bom
19 5 4 4 3 bom
20 3 4 3 5 otimo
> q()
> dados= cbind(dados, dados$desempenho="otimo")
Erro: '=' inesperado in "dados= cbind(dados, dados$desempenho="
> dados = cbind(dados, dados$desempenho=="otimo")
> dados = cbind(dados, dados$desempenho=="bom")
> dados = cbind(dados, dados$desempenho=="regular")
> dados = cbind(dados, dados$desempenho=="ruim")
> dados
organizacao_mesa pontualidade trabalho_equipe comunicacao desempenho
1 3 3 2 3 regular
2 5 2 4 5 bom
3 1 3 3 3 ruim
4 3 4 3 5 otimo
5 2 2 4 4 regular
6 5 4 4 4 otimo
7 3 4 3 4 bom
8 2 3 2 2 ruim
9 3 5 4 4 bom
10 2 2 4 3 regular
11 5 5 2 3 regular
12 4 3 5 3 bom
13 4 3 3 4 otimo
14 3 5 2 4 regular
15 3 3 4 4 bom
16 3 3 5 4 bom
17 1 2 3 4 regular
18 2 3 5 3 bom
19 5 4 4 3 bom
20 3 4 3 5 otimo
dados$desempenho == "otimo" dados$desempenho == "bom"
1 FALSE FALSE
2 FALSE TRUE
3 FALSE FALSE
4 TRUE FALSE
5 FALSE FALSE
6 TRUE FALSE
7 FALSE TRUE
8 FALSE FALSE
9 FALSE TRUE
10 FALSE FALSE
11 FALSE FALSE
12 FALSE TRUE
13 TRUE FALSE
14 FALSE FALSE
15 FALSE TRUE
16 FALSE TRUE
17 FALSE FALSE
18 FALSE TRUE
19 FALSE TRUE
20 TRUE FALSE
dados$desempenho == "regular" dados$desempenho == "ruim"
1 TRUE FALSE
2 FALSE FALSE
3 FALSE TRUE
4 FALSE FALSE
5 TRUE FALSE
6 FALSE FALSE
7 FALSE FALSE
8 FALSE TRUE
9 FALSE FALSE
10 TRUE FALSE
11 TRUE FALSE
12 FALSE FALSE
13 FALSE FALSE
14 TRUE FALSE
15 FALSE FALSE
16 FALSE FALSE
17 TRUE FALSE
18 FALSE FALSE
19 FALSE FALSE
20 FALSE FALSE
> names(dados)[6]="otimo"
> names(dados)[7]="bom"
> names(dados)[8]="regular"
> names(dados)[9]="ruim"
> dados
organizacao_mesa pontualidade trabalho_equipe comunicacao desempenho otimo
1 3 3 2 3 regular FALSE
2 5 2 4 5 bom FALSE
3 1 3 3 3 ruim FALSE
4 3 4 3 5 otimo TRUE
5 2 2 4 4 regular FALSE
6 5 4 4 4 otimo TRUE
7 3 4 3 4 bom FALSE
8 2 3 2 2 ruim FALSE
9 3 5 4 4 bom FALSE
10 2 2 4 3 regular FALSE
11 5 5 2 3 regular FALSE
12 4 3 5 3 bom FALSE
13 4 3 3 4 otimo TRUE
14 3 5 2 4 regular FALSE
15 3 3 4 4 bom FALSE
16 3 3 5 4 bom FALSE
17 1 2 3 4 regular FALSE
18 2 3 5 3 bom FALSE
19 5 4 4 3 bom FALSE
20 3 4 3 5 otimo TRUE
bom regular ruim
1 FALSE TRUE FALSE
2 TRUE FALSE FALSE
3 FALSE FALSE TRUE
4 FALSE FALSE FALSE
5 FALSE TRUE FALSE
6 FALSE FALSE FALSE
7 TRUE FALSE FALSE
8 FALSE FALSE TRUE
9 TRUE FALSE FALSE
10 FALSE TRUE FALSE
11 FALSE TRUE FALSE
12 TRUE FALSE FALSE
13 FALSE FALSE FALSE
14 FALSE TRUE FALSE
15 TRUE FALSE FALSE
16 TRUE FALSE FALSE
17 FALSE TRUE FALSE
18 TRUE FALSE FALSE
19 TRUE FALSE FALSE
20 FALSE FALSE FALSE
> fix(dados)
> amostra = sample(2,20, replace=T, prob = c(0.7,0.3))
> amostra
[1] 1 1 2 1 2 1 1 1 2 1 2 1 1 1 1 1 2 1 1 1
> model = neuralnet( otimo + bom + regular + ruim ~ organizacao_mesa + pontualidade + trabalho_equipe + comunicacao, dados)
> model
$call
neuralnet(formula = otimo + bom + regular + ruim ~ organizacao_mesa +
pontualidade + trabalho_equipe + comunicacao, data = dados)
$response
otimo bom regular ruim
1 FALSE FALSE TRUE FALSE
2 FALSE TRUE FALSE FALSE
3 FALSE FALSE FALSE TRUE
4 TRUE FALSE FALSE FALSE
5 FALSE FALSE TRUE FALSE
6 TRUE FALSE FALSE FALSE
7 FALSE TRUE FALSE FALSE
8 FALSE FALSE FALSE TRUE
9 FALSE TRUE FALSE FALSE
10 FALSE FALSE TRUE FALSE
11 FALSE FALSE TRUE FALSE
12 FALSE TRUE FALSE FALSE
13 TRUE FALSE FALSE FALSE
14 FALSE FALSE TRUE FALSE
15 FALSE TRUE FALSE FALSE
16 FALSE TRUE FALSE FALSE
17 FALSE FALSE TRUE FALSE
18 FALSE TRUE FALSE FALSE
19 FALSE TRUE FALSE FALSE
20 TRUE FALSE FALSE FALSE
$covariate
[,1] [,2] [,3] [,4]
[1,] 3 3 2 3
[2,] 5 2 4 5
[3,] 1 3 3 3
[4,] 3 4 3 5
[5,] 2 2 4 4
[6,] 5 4 4 4
[7,] 3 4 3 4
[8,] 2 3 2 2
[9,] 3 5 4 4
[10,] 2 2 4 3
[11,] 5 5 2 3
[12,] 4 3 5 3
[13,] 4 3 3 4
[14,] 3 5 2 4
[15,] 3 3 4 4
[16,] 3 3 5 4
[17,] 1 2 3 4
[18,] 2 3 5 3
[19,] 5 4 4 3
[20,] 3 4 3 5
$model.list
$model.list$response
[1] "otimo" "bom" "regular" "ruim"
$model.list$variables
[1] "organizacao_mesa" "pontualidade" "trabalho_equipe" "comunicacao"
$err.fct
function (x, y)
{
1/2 * (y - x)^2
}
<environment: 0x12934eac>
attr(,"type")
[1] "sse"
$act.fct
function (x)
{
1/(1 + exp(-x))
}
<environment: 0x12934eac>
attr(,"type")
[1] "logistic"
$linear.output
[1] TRUE
$data
organizacao_mesa pontualidade trabalho_equipe comunicacao desempenho otimo
1 3 3 2 3 regular FALSE
2 5 2 4 5 bom FALSE
3 1 3 3 3 ruim FALSE
4 3 4 3 5 otimo TRUE
5 2 2 4 4 regular FALSE
6 5 4 4 4 otimo TRUE
7 3 4 3 4 bom FALSE
8 2 3 2 2 ruim FALSE
9 3 5 4 4 bom FALSE
10 2 2 4 3 regular FALSE
11 5 5 2 3 regular FALSE
12 4 3 5 3 bom FALSE
13 4 3 3 4 otimo TRUE
14 3 5 2 4 regular FALSE
15 3 3 4 4 bom FALSE
16 3 3 5 4 bom FALSE
17 1 2 3 4 regular FALSE
18 2 3 5 3 bom FALSE
19 5 4 4 3 bom FALSE
20 3 4 3 5 otimo TRUE
bom regular ruim
1 FALSE TRUE FALSE
2 TRUE FALSE FALSE
3 FALSE FALSE TRUE
4 FALSE FALSE FALSE
5 FALSE TRUE FALSE
6 FALSE FALSE FALSE
7 TRUE FALSE FALSE
8 FALSE FALSE TRUE
9 TRUE FALSE FALSE
10 FALSE TRUE FALSE
11 FALSE TRUE FALSE
12 TRUE FALSE FALSE
13 FALSE FALSE FALSE
14 FALSE TRUE FALSE
15 TRUE FALSE FALSE
16 TRUE FALSE FALSE
17 FALSE TRUE FALSE
18 TRUE FALSE FALSE
19 TRUE FALSE FALSE
20 FALSE FALSE FALSE
$net.result
$net.result[[1]]
[,1] [,2] [,3] [,4]
1 0.09336638355 -0.08205495452 0.70811037965 0.278673003377
2 0.28453061220 0.78213337216 -0.02464879903 -0.041670864863
3 0.09417146601 -0.07841545108 0.70502438615 0.277323884701
4 0.23850827516 0.57408207137 0.15176124445 0.035451165636
5 0.15061379470 0.17674108070 0.48867305984 0.182740530736
6 0.28871983147 0.80107140472 -0.04070666137 -0.048690958076
7 0.18644050947 0.33870144802 0.35134426001 0.122703836225
8 0.09313882050 -0.08308368950 0.70898266057 0.279054342643
9 0.28828091217 0.79908720017 -0.03902422242 -0.047955438118
10 0.11589149279 0.01977338755 0.62176849096 0.240926503004
11 0.14587898342 0.15533661257 0.50682225257 0.190674900950
12 0.28856960328 0.80039227430 -0.04013081577 -0.048439212873
13 0.16112278435 0.22424864132 0.44839063525 0.165130068674
14 0.11377198463 0.01019181349 0.62989286168 0.244478273379
15 0.26472973419 0.69262035106 0.05125073097 -0.008489500855
16 0.28857286771 0.80040703166 -0.04014332877 -0.048444683243
17 0.09373641342 -0.08038217557 0.70669200347 0.278052925018
18 0.28276157784 0.77413617072 -0.01786784333 -0.038706401719
19 0.28759265939 0.79597584393 -0.03638605344 -0.046802097044
20 0.23850827516 0.57408207137 0.15176124445 0.035451165636
$weights
$weights[[1]]
$weights[[1]][[1]]
[,1]
[1,] -26.025466162
[2,] 1.144653997
[3,] 1.681270251
[4,] 3.723893273
[5,] 1.149501895
$weights[[1]][[2]]
[,1] [,2] [,3] [,4]
[1,] 0.0931132647 -0.08319921856 0.7090806195 0.2790971678
[2,] 0.1961336693 0.88665347424 -0.7518077385 -0.3286714191
$startweights
$startweights[[1]]
$startweights[[1]][[1]]
[,1]
[1,] 0.8015596194
[2,] -0.7132167355
[3,] -1.3845357344
[4,] -0.6819236960
[5,] -0.3271323721
$startweights[[1]][[2]]
[,1] [,2] [,3] [,4]
[1,] -0.1481174523 -1.6740596354 0.9747229680 -0.6699985166
[2,] -0.3093617764 0.6826869091 0.3052320653 -0.9349652753
$generalized.weights
$generalized.weights[[1]]
[,1] [,2] [,3] [,4]
1 0.003418342065 0.0050208681728 0.011120863649 0.0034328196069
2 0.025881339232 0.0380145666966 0.084199544453 0.0259909532403
3 0.014123024106 0.0207439281640 0.045946316198 0.0141828386796
4 0.237051071299 0.3481811230699 0.771196267568 0.2380550424549
5 0.363656225847 0.5341390462087 1.183079757933 0.3651964018064
6 0.002930105806 0.0043037457067 0.009532488713 0.0029425155445
7 0.369165560553 0.5422311688683 1.201003230454 0.3707290699369
8 0.000346286513 0.0005086263747 0.001126571017 0.0003477531239
9 0.005362809221 0.0078769057111 0.017446782384 0.0053855220723
10 0.224917042554 0.3303586355662 0.731720733339 0.2258696230332
11 0.354334854596 0.5204477960791 1.152754618806 0.3558355521941
12 0.003763480009 0.0055278075272 0.012243698036 0.0037794192975
13 0.376242127947 0.5526252462677 1.224025367968 0.3778356083810
14 0.209826241133 0.3081932340013 0.682625955295 0.2107149082569
15 0.126154334987 0.1852957583994 0.410416842860 0.1266886304564
16 0.003745379114 0.0055012208939 0.012184810546 0.0037612417408
17 0.008369915101 0.0122937492909 0.027229774791 0.0084053638049
18 0.035393291183 0.0519857421677 0.115144698176 0.0355431907097
19 0.009164253487 0.0134604752209 0.029813989221 0.0092030664153
20 0.237051071299 0.3481811230699 0.771196267568 0.2380550424549
[,5] [,6] [,7] [,8]
1 -0.014732807496 -0.02163957932 -0.047930119402 -0.014795204657
2 0.139777426043 0.20530538383 0.454736731050 0.140369418744
3 -0.064403141018 -0.09459547196 -0.209522200008 -0.064675904586
4 0.795998819000 1.16916477636 2.589616300132 0.799370075032
5 1.445402485818 2.12301027809 4.702315817789 1.451524130389
6 0.017070016479 0.02507247690 0.055533741838 0.017142312310
7 1.130154756999 1.65997373637 3.676723018349 1.134941247821
8 -0.001469369766 -0.00215821347 -0.004780288369 -0.001475592918
9 0.030982526893 0.04550720210 0.100795195603 0.031113745719
10 5.374940859986 7.89472468867 17.486250143880 5.397705091996
11 1.521143821155 2.23425931424 4.948724470542 1.527586249410
12 0.021862214214 0.03211126723 0.071124158646 0.021954806212
13 1.321515254773 1.94104444683 4.299274525223 1.327112205633
14 9.480688273569 13.92525531511 30.843443864091 9.520841382803
15 0.521414751973 0.76585510849 1.696314251490 0.523623075134
16 0.021758419137 0.03195881280 0.070786482992 0.021850571537
17 -0.037012508122 -0.05436405149 -0.120412483102 -0.037169265442
18 0.185584889250 0.27258748425 0.603761767967 0.186370888126
19 0.052266517560 0.07676917334 0.170038224422 0.052487879462
20 0.795998819000 1.16916477636 2.589616300132 0.799370075032
[,9] [,10] [,11] [,12]
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2 0.7996328722991 1.1745024816570 2.601438935335 0.8030195194646
3 -0.0222053393435 -0.0326152501584 -0.072240444761 -0.0222993845637
4 -1.2819946425773 -1.8829964866863 -4.170702448116 -1.2874242136628
5 -0.7136718180509 -1.0482427004025 -2.321782556526 -0.7166943984417
6 0.0544454307802 0.0799695657613 0.177126864576 0.0546760209300
7 -0.9417991317736 -1.3833165891619 -3.063947238208 -0.9457878889527
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9 0.1040187322407 0.1527829375052 0.338403271588 0.1044592778422
10 -0.3756191400572 -0.5517102003156 -1.221998606676 -0.3772099819798
11 -0.6770511838502 -0.9944542341720 -2.202644953576 -0.6799186666065
12 0.0709514540199 0.1042136481757 0.230825771926 0.0712519513468
13 -0.7881143828014 -1.1575840995236 -2.563965929793 -0.7914522462548
14 -0.3478572823053 -0.5109335239749 -1.131681187051 -0.3493305457489
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16 0.0705878204102 0.1036795423482 0.229642765745 0.0708867776570
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18 1.5128504550463 2.2220780002691 4.921743731947 1.5192577588013
19 0.1908540592586 0.2803268524830 0.620903914686 0.1916623744007
20 -1.2819946425773 -1.8829964866863 -4.170702448116 -1.2874242136628
[,13] [,14] [,15] [,16]
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6 0.0197476109189 0.0290053333654 0.0642447374336 0.019831247038
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8 -0.0002436270987 -0.0003578400063 -0.0007925900026 -0.000244658921
9 0.0366898517120 0.0538901330600 0.1193627877014 0.036845242500
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17 -0.0059355487069 -0.0087181466993 -0.0193100709634 -0.005960687255
18 0.2991844866908 0.4394428171534 0.9733343881773 0.300451608552
19 0.0642219023608 0.0943292682336 0.2089325777994 0.064493898337
20 -2.1099181549141 -3.0990523212418 -6.8641790860631 -2.118854191170
$result.matrix
1
error 4.539920480408
reached.threshold 0.009297192903
steps 5003.000000000000
Intercept.to.1layhid1 -26.025466162213
organizacao_mesa.to.1layhid1 1.144653996589
pontualidade.to.1layhid1 1.681270250642
trabalho_equipe.to.1layhid1 3.723893273236
comunicacao.to.1layhid1 1.149501895353
Intercept.to.otimo 0.093113264699
1layhid.1.to.otimo 0.196133669313
Intercept.to.bom -0.083199218559
1layhid.1.to.bom 0.886653474241
Intercept.to.regular 0.709080619525
1layhid.1.to.regular -0.751807738539
Intercept.to.ruim 0.279097167830
1layhid.1.to.ruim -0.328671419130
attr(,"class")
[1] "nn"
> plot(model)
> dadosTreino = dados[amostra == 1,]
> dadosTeste = dados[amostra == 2,]
> model = neuralnet( otimo + bom + regular + ruim ~ organizacao_mesa + pontualidade + trabalho_equipe + comunicacao, dadosTreino , hidden=(5,4))
Erro: ',' inesperado in "model = neuralnet( otimo + bom + regular + ruim ~ organizacao_mesa + pontualidade + trabalho_equipe + comunicacao, dadosTreino , hidden=(5,"
> model = neuralnet( otimo + bom + regular + ruim ~ organizacao_mesa + pontualidade + trabalho_equipe + comunicacao, dadosTreino , hidden=c(5,4))
> plot(model)
> teste = compute(model, dadosTeste[,1:4])
> resultado = as.data.frame(teste$net.result)
> names(resultado)[1] ='otimo'
> names(resultado)[2] ='bom'
> names(resultado)[3] ='regular'
> names(resultado)[4] ='ruim'
> resultado$desempenho = colnames(resultado[,1:4])[max.col(resultado[,1:4],ties.method='first')]
> head(resultado)
otimo bom regular ruim desempenho
3 0.67846961033 -0.5134821125 0.17166227814 0.714900930017 ruim
5 0.07166053213 -0.4108445254 1.89814222462 -0.540770632524 regular
9 0.15444324027 0.8526448204 -0.01323213139 0.002731724625 bom
11 1.45879688012 -2.6503656801 2.31908279652 -0.155045862696 regular
17 1.37155413261 -2.2495615696 1.97785662034 -0.063469020779 regular
> tbl = table(resultado$desempenho, teste$desempenho)
Error in table(resultado$desempenho, teste$desempenho) :
todos os argumentos devem ter o mesmo comprimento
> tbl = table(resultado$desempenho, dadosTeste$desempenho)
> tbl
bom otimo regular ruim
bom 1 0 0 0
regular 0 0 3 0
ruim 0 0 0 1
> sum(diag(confusao) *100 / sum(tbl))
Error in diag(confusao) : objeto 'confusao' não encontrado
> sum(diag(tab) *100 / sum(tbl))
Error in diag(tab) : objeto 'tab' não encontrado
> sum(diag(tbl) *100 / sum(tbl))
[1] 20
> resultado
otimo bom regular ruim desempenho
3 0.67846961033 -0.5134821125 0.17166227814 0.714900930017 ruim
5 0.07166053213 -0.4108445254 1.89814222462 -0.540770632524 regular
9 0.15444324027 0.8526448204 -0.01323213139 0.002731724625 bom
11 1.45879688012 -2.6503656801 2.31908279652 -0.155045862696 regular
17 1.37155413261 -2.2495615696 1.97785662034 -0.063469020779 regular
> novosFuncionarios = read.csv(file.choose(), sep = ";" , head=T)
> novosFuncionarios
organizacao_mesa pontualidade trabalho_equipe comunicacao
1 4 3 3 3
2 3 3 3 2
3 4 2 5 2
4 2 4 3 3
5 2 5 2 2
> aplicandoModelo = compute(model, novosFuncionarios [,1:4])
> resultado = as.data.frame(aplicandoModelo$net.result)
> resultado
V1 V2 V3 V4
1 -0.06721177569 1.035969445 0.03500178848 -0.009409280608
2 -0.34139047854 1.236329670 -0.06709690561 0.144487440072
3 -0.12380119953 1.127185069 -0.02191308042 0.005593971135
4 -0.41935754974 1.377072504 -0.05264700805 0.062880163823
5 -0.69959604805 0.541457084 0.06855085670 1.044924588024
> names(resultado)[1] ='otimo'
> names(resultado)[2] ='bom'
> names(resultado)[3] ='regular'
> names(resultado)[4] ='ruim'
> resultado$desempenho = colnames(resultado[,1:4])[max.col(resultado[,1:4],ties.method='first')]
> resultado
otimo bom regular ruim desempenho
1 -0.06721177569 1.035969445 0.03500178848 -0.009409280608 bom
2 -0.34139047854 1.236329670 -0.06709690561 0.144487440072 bom
3 -0.12380119953 1.127185069 -0.02191308042 0.005593971135 bom
4 -0.41935754974 1.377072504 -0.05264700805 0.062880163823 bom
5 -0.69959604805 0.541457084 0.06855085670 1.044924588024 ruim
>