In [6]:

    

library(dplyr)


    

In [7]:

    

iris


    

    




Warning message:
In `[<-.factor`(`*tmp*`, ri, value = "<e2><8b><ae>"): invalid factor level, NA generatedWarning message:
In `[<-.factor`(`*tmp*`, ri, value = "<e2><8b><ae>"): invalid factor level, NA generated





    






Sepal.Length
Sepal.Width
Petal.Length
Petal.Width
Species

	
1
5.1
3.5
1.4
0.2
setosa
	
2
4.9
3
1.4
0.2
setosa
	
3
4.7
3.2
1.3
0.2
setosa
	
4
4.6
3.1
1.5
0.2
setosa
	
5
5
3.6
1.4
0.2
setosa
	
6
5.4
3.9
1.7
0.4
setosa
	
7
4.6
3.4
1.4
0.3
setosa
	
8
5
3.4
1.5
0.2
setosa
	
9
4.4
2.9
1.4
0.2
setosa
	
10
4.9
3.1
1.5
0.1
setosa
	
11
5.4
3.7
1.5
0.2
setosa
	
12
4.8
3.4
1.6
0.2
setosa
	
13
4.8
3
1.4
0.1
setosa
	
14
4.3
3
1.1
0.1
setosa
	
15
5.8
4
1.2
0.2
setosa
	
16
5.7
4.4
1.5
0.4
setosa
	
17
5.4
3.9
1.3
0.4
setosa
	
18
5.1
3.5
1.4
0.3
setosa
	
19
5.7
3.8
1.7
0.3
setosa
	
20
5.1
3.8
1.5
0.3
setosa
	
21
5.4
3.4
1.7
0.2
setosa
	
22
5.1
3.7
1.5
0.4
setosa
	
23
4.6
3.6
1
0.2
setosa
	
24
5.1
3.3
1.7
0.5
setosa
	
25
4.8
3.4
1.9
0.2
setosa
	
26
5
3
1.6
0.2
setosa
	
27
5
3.4
1.6
0.4
setosa
	
28
5.2
3.5
1.5
0.2
setosa
	
29
5.2
3.4
1.4
0.2
setosa
	
30
4.7
3.2
1.6
0.2
setosa
	
31
<8b>
<8b>
<8b>
<8b>
NA
	
121
6.9
3.2
5.7
2.3
virginica
	
122
5.6
2.8
4.9
2
virginica
	
123
7.7
2.8
6.7
2
virginica
	
124
6.3
2.7
4.9
1.8
virginica
	
125
6.7
3.3
5.7
2.1
virginica
	
126
7.2
3.2
6
1.8
virginica
	
127
6.2
2.8
4.8
1.8
virginica
	
128
6.1
3
4.9
1.8
virginica
	
129
6.4
2.8
5.6
2.1
virginica
	
130
7.2
3
5.8
1.6
virginica
	
131
7.4
2.8
6.1
1.9
virginica
	
132
7.9
3.8
6.4
2
virginica
	
133
6.4
2.8
5.6
2.2
virginica
	
134
6.3
2.8
5.1
1.5
virginica
	
135
6.1
2.6
5.6
1.4
virginica
	
136
7.7
3
6.1
2.3
virginica
	
137
6.3
3.4
5.6
2.4
virginica
	
138
6.4
3.1
5.5
1.8
virginica
	
139
6
3
4.8
1.8
virginica
	
140
6.9
3.1
5.4
2.1
virginica
	
141
6.7
3.1
5.6
2.4
virginica
	
142
6.9
3.1
5.1
2.3
virginica
	
143
5.8
2.7
5.1
1.9
virginica
	
144
6.8
3.2
5.9
2.3
virginica
	
145
6.7
3.3
5.7
2.5
virginica
	
146
6.7
3
5.2
2.3
virginica
	
147
6.3
2.5
5
1.9
virginica
	
148
6.5
3
5.2
2
virginica
	
149
6.2
3.4
5.4
2.3
virginica
	
150
5.9
3
5.1
1.8
virginica

In [8]:

    

library(ggplot2)


    

In [9]:

    

ggplot(data=iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + geom_point(size=3)


    

In [12]:

    

library(nnet)


    

In [15]:

    

ir <- rbind(iris3[,,1],iris3[,,2],iris3[,,3])
targets <- class.ind( c(rep("s", 50), rep("c", 50), rep("v", 50)) )
samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
ir1 <- nnet(ir[samp,], targets[samp,], size = 2, rang = 0.1,
decay = 5e-4, maxit = 200)
test.cl <- function(true, pred) {
true <- max.col(true)
cres <- max.col(pred)
table(true, cres)
}
test.cl(targets[-samp,], predict(ir1, ir[-samp,]))


    

    




# weights:  19
initial  value 56.609187 
iter  10 value 36.566224
iter  20 value 19.574888
iter  30 value 17.906536
iter  40 value 8.253534
iter  50 value 2.906166
iter  60 value 1.609140
iter  70 value 1.447394
iter  80 value 1.352347
iter  90 value 1.226255
iter 100 value 1.164588
iter 110 value 1.150214
iter 120 value 1.142791
iter 130 value 1.133954
iter 140 value 1.133562
iter 150 value 1.133390
iter 160 value 1.133378
iter 170 value 1.133371
iter 180 value 1.133368
final  value 1.133368 
converged






    






    cres
true  1  2  3
   1 25  0  0
   2  0 25  0
   3  2  0 23

In [16]:

    

ird <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
species = factor(c(rep("s",50), rep("c", 50), rep("v", 50))))
ir.nn2 <- nnet(species ~ ., data = ird, subset = samp, size = 2, rang = 0.1,
decay = 5e-4, maxit = 200)
table(ird$species[-samp], predict(ir.nn2, ird[-samp,], type = "class"))


    

    




# weights:  19
initial  value 82.620508 
iter  10 value 35.760821
iter  20 value 9.731709
iter  30 value 5.199682
iter  40 value 4.964729
iter  50 value 4.474113
iter  60 value 4.409059
iter  70 value 4.286774
iter  80 value 4.241621
iter  90 value 4.022986
iter 100 value 2.356449
iter 110 value 1.830280
iter 120 value 1.421485
iter 130 value 1.208582
iter 140 value 1.139482
iter 150 value 1.057155
iter 160 value 1.041733
iter 170 value 1.028230
iter 180 value 1.021985
iter 190 value 1.019619
iter 200 value 1.019013
final  value 1.019013 
stopped after 200 iterations






    






   
     c  s  v
  c 25  0  0
  s  0 25  0
  v  2  0 23

In [17]:

    

# use half the iris data
ir <- rbind(iris3[,,1], iris3[,,2], iris3[,,3])
targets <- matrix(c(rep(c(1,0,0),50), rep(c(0,1,0),50), rep(c(0,0,1),50)),
150, 3, byrow=TRUE)
samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
ir1 <- nnet(ir[samp,], targets[samp,], size=2, rang=0.1, decay=5e-4, maxit=200)
eigen(nnetHess(ir1, ir[samp,], targets[samp,]), TRUE)$values


    

    




# weights:  19
initial  value 57.167362 
iter  10 value 50.007215
iter  20 value 29.624684
iter  30 value 21.615991
iter  40 value 18.500612
iter  50 value 11.298185
iter  60 value 3.728685
iter  70 value 2.848584
iter  80 value 2.614220
iter  90 value 2.334847
iter 100 value 2.268862
iter 110 value 2.249119
iter 120 value 2.241811
iter 130 value 2.239429
iter 140 value 2.238891
final  value 2.238827 
converged






    






	
909.832489262226
	
2.26011360626804
	
0.678166623724063
	
0.382993141933627
	
0.305181634249396
	
0.0735808095321853
	
0.0672736001276883
	
0.0515566433893583
	
0.0102448121099778
	
0.00880784100321814
	
0.00572286602392889
	
0.00442076703246651
	
0.00364186726756893
	
0.00345148171921274
	
0.00209564878999137
	
0.00135577713439145
	
0.00107061359380865
	
0.00103003696812595
	
0.000935570798758541

In [ ]: