COMMunication
RESearch.NET

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====== Neural Network ======
<code>> install.packages("nnet")
> library(nnet)
> m <- nnet(Species ~ ., data=iris, size=3)
# weights:  27
initial  value 191.494035 
iter  10 value 65.618496
iter  20 value 40.493306
iter  30 value 8.542349
iter  40 value 6.034377
iter  50 value 6.000246
iter  60 value 5.998411
iter  70 value 5.983894
iter  80 value 5.972932
iter  90 value 5.968740
iter 100 value 5.965371
final  value 5.965371 
stopped after 100 iterations
> round(predict(m, newdata=iris),2)
    setosa versicolor virginica
1        1       0.00      0.00
2        1       0.00      0.00
3        1       0.00      0.00
4        1       0.00      0.00
5        1       0.00      0.00
6        1       0.00      0.00
7        1       0.00      0.00
8        1       0.00      0.00
9        1       0.00      0.00
10       1       0.00      0.00
11       1       0.00      0.00
12       1       0.00      0.00
13       1       0.00      0.00
14       1       0.00      0.00
15       1       0.00      0.00
16       1       0.00      0.00
17       1       0.00      0.00
18       1       0.00      0.00
19       1       0.00      0.00
20       1       0.00      0.00
21       1       0.00      0.00
22       1       0.00      0.00
23       1       0.00      0.00
24       1       0.00      0.00
25       1       0.00      0.00
26       1       0.00      0.00
27       1       0.00      0.00
28       1       0.00      0.00
29       1       0.00      0.00
30       1       0.00      0.00
31       1       0.00      0.00
32       1       0.00      0.00
33       1       0.00      0.00
34       1       0.00      0.00
35       1       0.00      0.00
36       1       0.00      0.00
37       1       0.00      0.00
38       1       0.00      0.00
39       1       0.00      0.00
40       1       0.00      0.00
41       1       0.00      0.00
42       1       0.00      0.00
43       1       0.00      0.00
44       1       0.00      0.00
45       1       0.00      0.00
46       1       0.00      0.00
47       1       0.00      0.00
48       1       0.00      0.00
49       1       0.00      0.00
50       1       0.00      0.00
51       0       1.00      0.00
52       0       1.00      0.00
53       0       1.00      0.00
54       0       1.00      0.00
55       0       1.00      0.00
56       0       1.00      0.00
57       0       1.00      0.00
58       0       1.00      0.00
59       0       1.00      0.00
60       0       1.00      0.00
61       0       1.00      0.00
62       0       1.00      0.00
63       0       1.00      0.00
64       0       1.00      0.00
65       0       1.00      0.00
66       0       1.00      0.00
67       0       1.00      0.00
68       0       1.00      0.00
69       0       0.95      0.05
70       0       1.00      0.00
71       0       0.59      0.41
72       0       1.00      0.00
73       0       0.77      0.23
74       0       1.00      0.00
75       0       1.00      0.00
76       0       1.00      0.00
77       0       1.00      0.00
78       0       0.73      0.27
79       0       1.00      0.00
80       0       1.00      0.00
81       0       1.00      0.00
82       0       1.00      0.00
83       0       1.00      0.00
84       0       0.12      0.88
85       0       1.00      0.00
86       0       1.00      0.00
87       0       1.00      0.00
88       0       1.00      0.00
89       0       1.00      0.00
90       0       1.00      0.00
91       0       1.00      0.00
92       0       1.00      0.00
93       0       1.00      0.00
94       0       1.00      0.00
95       0       1.00      0.00
96       0       1.00      0.00
97       0       1.00      0.00
98       0       1.00      0.00
99       0       1.00      0.00
100      0       1.00      0.00
101      0       0.00      1.00
102      0       0.00      1.00
103      0       0.00      1.00
104      0       0.00      1.00
105      0       0.00      1.00
106      0       0.00      1.00
107      0       0.11      0.89
108      0       0.00      1.00
109      0       0.00      1.00
110      0       0.00      1.00
111      0       0.01      0.99
112      0       0.00      1.00
113      0       0.00      1.00
114      0       0.00      1.00
115      0       0.00      1.00
116      0       0.00      1.00
117      0       0.00      1.00
118      0       0.00      1.00
119      0       0.00      1.00
120      0       0.08      0.92
121      0       0.00      1.00
122      0       0.00      1.00
123      0       0.00      1.00
124      0       0.06      0.94
125      0       0.00      1.00
126      0       0.00      1.00
127      0       0.19      0.81
128      0       0.20      0.80
129      0       0.00      1.00
130      0       0.03      0.97
131      0       0.00      1.00
132      0       0.00      1.00
133      0       0.00      1.00
134      0       0.77      0.23
135      0       0.03      0.97
136      0       0.00      1.00
137      0       0.00      1.00
138      0       0.00      1.00
139      0       0.33      0.67
140      0       0.00      1.00
141      0       0.00      1.00
142      0       0.00      1.00
143      0       0.00      1.00
144      0       0.00      1.00
145      0       0.00      1.00
146      0       0.00      1.00
147      0       0.00      1.00
148      0       0.00      1.00
149      0       0.00      1.00
150      0       0.02      0.98
> predict(m, newdata=iris, type="class")
  [1] "setosa"     "setosa"     "setosa"     "setosa"    
  [5] "setosa"     "setosa"     "setosa"     "setosa"    
  [9] "setosa"     "setosa"     "setosa"     "setosa"    
 [13] "setosa"     "setosa"     "setosa"     "setosa"    
 [17] "setosa"     "setosa"     "setosa"     "setosa"    
 [21] "setosa"     "setosa"     "setosa"     "setosa"    
 [25] "setosa"     "setosa"     "setosa"     "setosa"    
 [29] "setosa"     "setosa"     "setosa"     "setosa"    
 [33] "setosa"     "setosa"     "setosa"     "setosa"    
 [37] "setosa"     "setosa"     "setosa"     "setosa"    
 [41] "setosa"     "setosa"     "setosa"     "setosa"    
 [45] "setosa"     "setosa"     "setosa"     "setosa"    
 [49] "setosa"     "setosa"     "versicolor" "versicolor"
 [53] "versicolor" "versicolor" "versicolor" "versicolor"
 [57] "versicolor" "versicolor" "versicolor" "versicolor"
 [61] "versicolor" "versicolor" "versicolor" "versicolor"
 [65] "versicolor" "versicolor" "versicolor" "versicolor"
 [69] "versicolor" "versicolor" "versicolor" "versicolor"
 [73] "versicolor" "versicolor" "versicolor" "versicolor"
 [77] "versicolor" "versicolor" "versicolor" "versicolor"
 [81] "versicolor" "versicolor" "versicolor" "virginica" 
 [85] "versicolor" "versicolor" "versicolor" "versicolor"
 [89] "versicolor" "versicolor" "versicolor" "versicolor"
 [93] "versicolor" "versicolor" "versicolor" "versicolor"
 [97] "versicolor" "versicolor" "versicolor" "versicolor"
[101] "virginica"  "virginica"  "virginica"  "virginica" 
[105] "virginica"  "virginica"  "virginica"  "virginica" 
[109] "virginica"  "virginica"  "virginica"  "virginica" 
[113] "virginica"  "virginica"  "virginica"  "virginica" 
[117] "virginica"  "virginica"  "virginica"  "virginica" 
[121] "virginica"  "virginica"  "virginica"  "virginica" 
[125] "virginica"  "virginica"  "virginica"  "virginica" 
[129] "virginica"  "virginica"  "virginica"  "virginica" 
[133] "virginica"  "versicolor" "virginica"  "virginica" 
[137] "virginica"  "virginica"  "virginica"  "virginica" 
[141] "virginica"  "virginica"  "virginica"  "virginica" 
[145] "virginica"  "virginica"  "virginica"  "virginica" 
[149] "virginica"  "virginica" 
> 
</code>
====== E.G. 2 ======
{{:r:wine.csv}}
for the description of the data: https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.names

<code>wine <- read.csv("wine.csv")
head(wine)
summary(wine)

wine.scale <- cbind(wine[1], scale(wine[-1]))
summary(wine.scale)
apply(wine.scale[-1], 2, sd)

# Partitioning the data into training and test data
data.size <- nrow(wine.scale)
set.seed(1111)
samp <- c(sample(1:data.size, data.size * 0.7))
data.tr <- wine.scale[samp, ]
data.test <- wine.scale[-samp, ]
summary(data.tr)
summary(data.test)

# Fitting the neural network for the training data
library(nnet)
model.nnet <- nnet(Type ~ ., data = data.tr, size = 2, decay = 5e-04, maxit = 200)

names(model.nnet)


# Creating the confusion matrix for the model
predicted <- predict(model.nnet, data.test, type = "class")
predicted


actual <- data.test$Type
model.confusion.matrix <- table(actual, predicted)
model.confusion.matrix

confusion.matrix.rate = prop.table(model.confusion.matrix) * 100
round(confusion.matrix.rate, digit = 2)

diag.index <- cbind(1:3, 1:3)

error.overall = sum(confusion.matrix.rate) - sum(confusion.matrix.rate[diag.index])
paste("오차율 =", round(error.overall, digit = 2), "%")
</code>


<code>> wine <- read.csv("wine.csv")
> head(wine)
  Type Alcohol Malic  Ash Alcalinity Magnesium Phenols Flavanoids
1   t1   14.23  1.71 2.43       15.6       127    2.80       3.06
2   t1   13.20  1.78 2.14       11.2       100    2.65       2.76
3   t1   13.16  2.36 2.67       18.6       101    2.80       3.24
4   t1   14.37  1.95 2.50       16.8       113    3.85       3.49
5   t1   13.24  2.59 2.87       21.0       118    2.80       2.69
6   t1   14.20  1.76 2.45       15.2       112    3.27       3.39
  Nonflavanoids Proanthocyanins Color  Hue Dilution Proline
1          0.28            2.29  5.64 1.04     3.92    1065
2          0.26            1.28  4.38 1.05     3.40    1050
3          0.30            2.81  5.68 1.03     3.17    1185
4          0.24            2.18  7.80 0.86     3.45    1480
5          0.39            1.82  4.32 1.04     2.93     735
6          0.34            1.97  6.75 1.05     2.85    1450
> summary(wine)
 Type       Alcohol          Malic            Ash       
 t1:59   Min.   :11.03   Min.   :0.740   Min.   :1.360  
 t2:71   1st Qu.:12.36   1st Qu.:1.603   1st Qu.:2.210  
 t3:48   Median :13.05   Median :1.865   Median :2.360  
         Mean   :13.00   Mean   :2.336   Mean   :2.367  
         3rd Qu.:13.68   3rd Qu.:3.083   3rd Qu.:2.558  
         Max.   :14.83   Max.   :5.800   Max.   :3.230  
   Alcalinity      Magnesium         Phenols        Flavanoids   
 Min.   :10.60   Min.   : 70.00   Min.   :0.980   Min.   :0.340  
 1st Qu.:17.20   1st Qu.: 88.00   1st Qu.:1.742   1st Qu.:1.205  
 Median :19.50   Median : 98.00   Median :2.355   Median :2.135  
 Mean   :19.49   Mean   : 99.74   Mean   :2.295   Mean   :2.029  
 3rd Qu.:21.50   3rd Qu.:107.00   3rd Qu.:2.800   3rd Qu.:2.875  
 Max.   :30.00   Max.   :162.00   Max.   :3.880   Max.   :5.080  
 Nonflavanoids    Proanthocyanins     Color       
 Min.   :0.1300   Min.   :0.410   Min.   : 1.280  
 1st Qu.:0.2700   1st Qu.:1.250   1st Qu.: 3.220  
 Median :0.3400   Median :1.555   Median : 4.690  
 Mean   :0.3619   Mean   :1.591   Mean   : 5.058  
 3rd Qu.:0.4375   3rd Qu.:1.950   3rd Qu.: 6.200  
 Max.   :0.6600   Max.   :3.580   Max.   :13.000  
      Hue            Dilution        Proline      
 Min.   :0.4800   Min.   :1.270   Min.   : 278.0  
 1st Qu.:0.7825   1st Qu.:1.938   1st Qu.: 500.5  
 Median :0.9650   Median :2.780   Median : 673.5  
 Mean   :0.9574   Mean   :2.612   Mean   : 746.9  
 3rd Qu.:1.1200   3rd Qu.:3.170   3rd Qu.: 985.0  
 Max.   :1.7100   Max.   :4.000   Max.   :1680.0  
> </code>

<code>> wine.scale <- cbind(wine[1], scale(wine[-1]))
> summary(wine.scale)
 Type       Alcohol             Malic              Ash          
 t1:59   Min.   :-2.42739   Min.   :-1.4290   Min.   :-3.66881  
 t2:71   1st Qu.:-0.78603   1st Qu.:-0.6569   1st Qu.:-0.57051  
 t3:48   Median : 0.06083   Median :-0.4219   Median :-0.02375  
         Mean   : 0.00000   Mean   : 0.0000   Mean   : 0.00000  
         3rd Qu.: 0.83378   3rd Qu.: 0.6679   3rd Qu.: 0.69615  
         Max.   : 2.25341   Max.   : 3.1004   Max.   : 3.14745  
   Alcalinity          Magnesium          Phenols        
 Min.   :-2.663505   Min.   :-2.0824   Min.   :-2.10132  
 1st Qu.:-0.687199   1st Qu.:-0.8221   1st Qu.:-0.88298  
 Median : 0.001514   Median :-0.1219   Median : 0.09569  
 Mean   : 0.000000   Mean   : 0.0000   Mean   : 0.00000  
 3rd Qu.: 0.600395   3rd Qu.: 0.5082   3rd Qu.: 0.80672  
 Max.   : 3.145637   Max.   : 4.3591   Max.   : 2.53237  
   Flavanoids      Nonflavanoids     Proanthocyanins   
 Min.   :-1.6912   Min.   :-1.8630   Min.   :-2.06321  
 1st Qu.:-0.8252   1st Qu.:-0.7381   1st Qu.:-0.59560  
 Median : 0.1059   Median :-0.1756   Median :-0.06272  
 Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.00000  
 3rd Qu.: 0.8467   3rd Qu.: 0.6078   3rd Qu.: 0.62741  
 Max.   : 3.0542   Max.   : 2.3956   Max.   : 3.47527  
     Color              Hue              Dilution      
 Min.   :-1.6297   Min.   :-2.08884   Min.   :-1.8897  
 1st Qu.:-0.7929   1st Qu.:-0.76540   1st Qu.:-0.9496  
 Median :-0.1588   Median : 0.03303   Median : 0.2371  
 Mean   : 0.0000   Mean   : 0.00000   Mean   : 0.0000  
 3rd Qu.: 0.4926   3rd Qu.: 0.71116   3rd Qu.: 0.7864  
 Max.   : 3.4258   Max.   : 3.29241   Max.   : 1.9554  
    Proline       
 Min.   :-1.4890  
 1st Qu.:-0.7824  
 Median :-0.2331  
 Mean   : 0.0000  
 3rd Qu.: 0.7561  
 Max.   : 2.9631  
> apply(wine.scale[-1], 2, sd)
        Alcohol           Malic             Ash      Alcalinity 
              1               1               1               1 
      Magnesium         Phenols      Flavanoids   Nonflavanoids 
              1               1               1               1 
Proanthocyanins           Color             Hue        Dilution 
              1               1               1               1 
        Proline 
              1 
></code> 

<code>> data.size <- nrow(wine.scale)
> set.seed(1111)
> samp <- c(sample(1:data.size, data.size * 0.7))
> data.tr <- wine.scale[samp, ]
> data.test <- wine.scale[-samp, ]
> summary(data.tr)
 Type       Alcohol              Malic              Ash          
 t1:41   Min.   :-1.897718   Min.   :-1.2947   Min.   :-3.66881  
 t2:50   1st Qu.:-0.816822   1st Qu.:-0.6815   1st Qu.:-0.57051  
 t3:33   Median : 0.023875   Median :-0.4712   Median :-0.02375  
         Mean   : 0.004802   Mean   :-0.0207   Mean   :-0.01053  
         3rd Qu.: 0.778346   3rd Qu.: 0.6992   3rd Qu.: 0.56857  
         Max.   : 2.253415   Max.   : 2.9662   Max.   : 3.14745  
   Alcalinity         Magnesium           Phenols        
 Min.   :-2.66350   Min.   :-2.08238   Min.   :-2.10132  
 1st Qu.:-0.74709   1st Qu.:-0.62955   1st Qu.:-0.79110  
 Median :-0.14821   Median :-0.08693   Median : 0.16759  
 Mean   :-0.04002   Mean   : 0.08867   Mean   : 0.04969  
 3rd Qu.: 0.60039   3rd Qu.: 0.71825   3rd Qu.: 0.80672  
 Max.   : 3.14564   Max.   : 4.35908   Max.   : 2.53237  
   Flavanoids       Nonflavanoids      Proanthocyanins   
 Min.   :-1.69120   Min.   :-1.86298   Min.   :-2.06321  
 1st Qu.:-0.79267   1st Qu.:-0.81841   1st Qu.:-0.59560  
 Median : 0.13589   Median :-0.17560   Median : 0.05958  
 Mean   : 0.02124   Mean   :-0.05118   Mean   : 0.08015  
 3rd Qu.: 0.88424   3rd Qu.: 0.54756   3rd Qu.: 0.66672  
 Max.   : 3.05422   Max.   : 2.15459   Max.   : 3.47527  
     Color               Hue              Dilution       
 Min.   :-1.62969   Min.   :-2.08884   Min.   :-1.88972  
 1st Qu.:-0.82737   1st Qu.:-0.69978   1st Qu.:-0.96365  
 Median :-0.17603   Median : 0.05491   Median : 0.28636  
 Mean   : 0.00959   Mean   :-0.02603   Mean   : 0.01557  
 3rd Qu.: 0.51953   3rd Qu.: 0.59085   3rd Qu.: 0.80398  
 Max.   : 3.42577   Max.   : 2.15491   Max.   : 1.95540  
    Proline        
 Min.   :-1.38102  
 1st Qu.:-0.77926  
 Median :-0.21560  
 Mean   : 0.01084  
 3rd Qu.: 0.72039  
 Max.   : 2.96311  
> summary(data.test)
 Type       Alcohol             Malic               Ash          
 t1:18   Min.   :-2.42739   Min.   :-1.42895   Min.   :-2.42949  
 t2:21   1st Qu.:-0.76139   1st Qu.:-0.62109   1st Qu.:-0.58874  
 t3:15   Median : 0.06083   Median :-0.21156   Median :-0.18778  
         Mean   :-0.01103   Mean   : 0.04753   Mean   : 0.02417  
         3rd Qu.: 0.90768   3rd Qu.: 0.61196   3rd Qu.: 0.92396  
         Max.   : 1.69911   Max.   : 3.10045   Max.   : 2.01748  
   Alcalinity        Magnesium          Phenols       
 Min.   :-2.4838   Min.   :-1.5223   Min.   :-1.6220  
 1st Qu.:-0.5974   1st Qu.:-0.8221   1st Qu.:-1.0188  
 Median : 0.1512   Median :-0.4370   Median :-0.1839  
 Mean   : 0.0919   Mean   :-0.2036   Mean   :-0.1141  
 3rd Qu.: 0.6004   3rd Qu.: 0.1581   3rd Qu.: 0.7468  
 Max.   : 2.6965   Max.   : 2.3986   Max.   : 1.6056  
   Flavanoids         Nonflavanoids     Proanthocyanins  
 Min.   :-1.5610513   Min.   :-1.7826   Min.   :-1.6614  
 1st Qu.:-0.9828915   1st Qu.:-0.7381   1st Qu.:-0.7834  
 Median : 0.0007312   Median :-0.2158   Median :-0.2724  
 Mean   :-0.0487698   Mean   : 0.1175   Mean   :-0.1841  
 3rd Qu.: 0.7315653   3rd Qu.: 1.0498   3rd Qu.: 0.3610  
 Max.   : 1.6125708   Max.   : 2.3956   Max.   : 2.3920  
     Color               Hue              Dilution       
 Min.   :-1.36225   Min.   :-1.82634   Min.   :-1.84747  
 1st Qu.:-0.74973   1st Qu.:-0.88572   1st Qu.:-0.80872  
 Median :-0.13721   Median : 0.01116   Median : 0.18073  
 Mean   :-0.02202   Mean   : 0.05977   Mean   :-0.03576  
 3rd Qu.: 0.42247   3rd Qu.: 0.82053   3rd Qu.: 0.75820  
 Max.   : 2.24386   Max.   : 3.29241   Max.   : 1.33567  
    Proline        
 Min.   :-1.48899  
 1st Qu.:-0.77608  
 Median :-0.38708  
 Mean   :-0.02489  
 3rd Qu.: 0.91092  
 Max.   : 2.54077  </code>

<code>> library(nnet)
> model.nnet <- nnet(Type ~ ., data = data.tr, size = 2, decay = 5e-04, maxit = 200)
# weights:  37
initial  value 164.152084 
iter  10 value 7.066118
iter  20 value 1.286566
iter  30 value 0.542334
iter  40 value 0.444873
iter  50 value 0.365821
iter  60 value 0.335803
iter  70 value 0.311584
iter  80 value 0.298015
iter  90 value 0.288199
iter 100 value 0.281759
iter 110 value 0.276935
iter 120 value 0.270048
iter 130 value 0.261452
iter 140 value 0.258495
iter 150 value 0.257073
iter 160 value 0.256035
iter 170 value 0.255792
iter 180 value 0.255743
iter 190 value 0.255725
iter 200 value 0.255714
final  value 0.255714 
stopped after 200 iterations
> 
> names(model.nnet)
 [1] "n"             "nunits"        "nconn"        
 [4] "conn"          "nsunits"       "decay"        
 [7] "entropy"       "softmax"       "censored"     
[10] "value"         "wts"           "convergence"  
[13] "fitted.values" "residuals"     "lev"          
[16] "call"          "terms"         "coefnames"    
[19] "xlevels"      
> </code>


<code>> # Creating the confusion matrix for the model
> predicted <- predict(model.nnet, data.test, type = "class")
> predicted
 [1] "t1" "t1" "t1" "t1" "t1" "t1" "t1" "t1" "t1" "t1" "t1" "t1"
[13] "t1" "t1" "t1" "t1" "t1" "t1" "t2" "t2" "t2" "t2" "t2" "t2"
[25] "t2" "t2" "t2" "t2" "t2" "t2" "t2" "t2" "t2" "t2" "t2" "t2"
[37] "t2" "t2" "t2" "t3" "t2" "t3" "t3" "t3" "t3" "t3" "t3" "t3"
[49] "t3" "t3" "t3" "t3" "t3" "t3"
> </code>

<code>> actual <- data.test$Type
> model.confusion.matrix <- table(actual, predicted)
> model.confusion.matrix
      predicted
actual t1 t2 t3
    t1 18  0  0
    t2  0 21  0
    t3  0  1 14
</code>

<code>> confusion.matrix.rate = prop.table(model.confusion.matrix) * 100
> round(confusion.matrix.rate, digit = 2)
      predicted
actual    t1    t2    t3
    t1 33.33  0.00  0.00
    t2  0.00 38.89  0.00
    t3  0.00  1.85 25.93
</code>

<code>> diag.index <- cbind(1:3, 1:3)
> 
> error.overall = sum(confusion.matrix.rate) - sum(confusion.matrix.rate[diag.index])
> paste("오차율 =", round(error.overall, digit = 2), "%")
[1] "오차율 = 1.85 %"</code>

====== E.G. 3 ======
{{:r:creditset.csv}}
<WRAP info 70%>The dataset contains information on different clients who received a loan at least 10 years ago. The variables income (yearly), age, loan (size in euros) and LTI (the loan to yearly income ratio) are available. Our goal is to devise a model which predicts, based on the input variables LTI and age, whether or not a default will occur within 10 years.
</WRAP>

<code>set.seed(1234567890)
library("neuralnet")

dataset <- read.csv("creditset.csv")
head(dataset)

# extract a set to train the NN
trainset <- dataset[1:800, ]

# select the test set
testset <- dataset[801:2000, ]

## build the neural network (NN)
creditnet <- neuralnet(default10yr ~ LTI + age, trainset, hidden = 4, lifesign = "minimal", 
    linear.output = FALSE, threshold = 0.1)

## plot the NN
plot(creditnet, rep = "best")
</code>

__test the resulting output__
<code>temp_test <- subset(testset, select = c("LTI", "age"))

creditnet.results <- compute(creditnet, temp_test)
head(temp_test)

results <- data.frame(actual = testset$default10yr, prediction = creditnet.results$net.result)
results[100:115, ]

results$prediction <- round(results$prediction)
results[100:115, ]

pred.table <- table(testset$default10yr, results$prediction)
pred.table

library(plyr)
count(testset, 'default10yr')
</code>

with outputs
<code>> set.seed(1234567890)
> library("neuralnet")
> 
> dataset <- read.csv("creditset.csv")
> head(dataset)
  clientid      income         age          loan             LTI
1        1 66155.92510 59.01701507 8106.53213129 0.1225367511623
2        2 34415.15397 48.11715310 6564.74501768 0.1907515806612
3        3 57317.17006 63.10804949 8020.95329639 0.1399397996720
4        4 42709.53420 45.75197235 6103.64226014 0.1429105321411
5        5 66952.68885 18.58433593 8770.09923520 0.1309894999955
6        6 24904.06414 57.47160710   15.49859844 0.0006223320961
  default10yr
1           0
2           0
3           0
4           0
5           1
6           0
> 
> # extract a set to train the NN
> trainset <- dataset[1:800, ]
> 
> # select the test set
> testset <- dataset[801:2000, ]
> 
> ## build the neural network (NN)
> creditnet <- neuralnet(default10yr ~ LTI + age, trainset, hidden = 4, lifesign = "minimal", 
+                        linear.output = FALSE, threshold = 0.1)
hidden: 4    thresh: 0.1    rep: 1/1    steps:    7266	error: 0.79202	time: 2.94 secs
> 
> ## plot the NN
> plot(creditnet, rep = "best")
> 
</code>

<code>> temp_test <- subset(testset, select = c("LTI", "age"))
> 
> creditnet.results <- compute(creditnet, temp_test)
> head(temp_test)
              LTI         age
801 0.02306808811 25.90644520
802 0.13729704954 40.77430558
803 0.10456984914 32.47350580
804 0.15985046411 53.22813215
805 0.11161429579 46.47915325
806 0.11489364221 47.12736998
> 
> results <- data.frame(actual = testset$default10yr, prediction = creditnet.results$net.result)
> results[100:115, ]
    actual                                 prediction
900      0 0.0000000000000000000000000015964854322398
901      0 0.0000000000000000000000000065162871249459
902      0 0.0000000000164043993271687692878796349660
903      1 0.9999999999219191249011373656685464084148
904      0 0.0000000000000000013810778585990655628959
905      0 0.0000000000000000539636283549268839978413
906      0 0.0000000000000000000234592312583964807452
907      1 0.9581419934268182725389806364546529948711
908      0 0.2499229633059938393557786184828728437424
909      0 0.0000000000000007044361454974903653282470
910      0 0.0006082559674722681341413332845036165963
911      1 0.9999999878713862200285689141310285776854
912      0 0.0000000000000000000000000015562211243506
913      1 0.9999999993455563895849991240538656711578
914      0 0.0000000000000000000000000000003082538282
915      0 0.0000000019359618836434052080615331181690
> 
> results$prediction <- round(results$prediction)
> results[100:115, ]
    actual prediction
900      0          0
901      0          0
902      0          0
903      1          1
904      0          0
905      0          0
906      0          0
907      1          1
908      0          0
909      0          0
910      0          0
911      1          1
912      0          0
913      1          1
914      0          0
915      0          0
</code>

<code>> pred.table <- table(testset$default10yr, results$prediction)
> pred.table
   
       0    1
  0 1035    1
  1    3  161
>

> library(plyr)
> count(testset, 'default10yr')
  default10yr freq
1           0 1036
2           1  164

</code>

====== E.G 5 ======