This notebook will demonstrate the process of training a binary classifier using gradient descent on a toy two-dimensional dataset.
First we will load the dataset.
In [1]:
class1<-read.table("Class1.txt",header=TRUE,sep=",")
class1.dim<-dim(class1)
class1.label<-rep(1,class1.dim[1])
class1<-cbind(class1,class1.label)
names(class1)<-c("weight","height","label")
class1
class2<-read.table("Class2.txt",header=TRUE,sep=",")
class2.dim<-dim(class2)
class2.label<-rep(-1,class2.dim[1])
class2<-cbind(class2,class2.label)
names(class2)<-c("weight","height","label")
class2
class1.2<-rbind(class1,class2)
class1.2
| weight | height | label |
|---|---|---|
| 0.132 | 0.757 | 1 |
| 0.722 | 0.888 | 1 |
| 0.095 | 0.804 | 1 |
| 0.633 | 0.530 | 1 |
| 0.472 | 0.701 | 1 |
| 0.294 | 0.183 | 1 |
| 0.179 | 0.874 | 1 |
| 0.023 | 0.664 | 1 |
| 0.116 | 0.978 | 1 |
| 0.054 | 0.786 | 1 |
| 0.394 | 0.970 | 1 |
| 0.165 | 0.372 | 1 |
| 0.654 | 0.906 | 1 |
| 0.486 | 0.871 | 1 |
| 0.237 | 0.691 | 1 |
| 0.584 | 0.990 | 1 |
| 0.133 | 0.396 | 1 |
| 0.068 | 0.234 | 1 |
| 0.114 | 0.464 | 1 |
| 0.108 | 0.241 | 1 |
| 0.392 | 0.697 | 1 |
| 0.049 | 0.361 | 1 |
| 0.169 | 0.877 | 1 |
| 0.709 | 0.863 | 1 |
| 0.046 | 0.646 | 1 |
| 0.069 | 0.525 | 1 |
| 0.432 | 0.841 | 1 |
| 0.366 | 0.888 | 1 |
| 0.386 | 0.570 | 1 |
| 0.576 | 0.912 | 1 |
| ... | ... | ... |
| 0.577 | 0.902 | 1 |
| 0.033 | 0.473 | 1 |
| 0.773 | 0.938 | 1 |
| 0.303 | 0.680 | 1 |
| 0.576 | 0.721 | 1 |
| 0.326 | 0.989 | 1 |
| 0.407 | 0.719 | 1 |
| 0.406 | 0.921 | 1 |
| 0.649 | 0.905 | 1 |
| 0.579 | 0.841 | 1 |
| 0.095 | 0.621 | 1 |
| 0.254 | 0.646 | 1 |
| 0.093 | 0.570 | 1 |
| 0.126 | 0.420 | 1 |
| 0.387 | 0.710 | 1 |
| 0.063 | 0.996 | 1 |
| 0.016 | 0.867 | 1 |
| 0.161 | 0.885 | 1 |
| 0.010 | 0.700 | 1 |
| 0.154 | 0.564 | 1 |
| 0.565 | 0.961 | 1 |
| 0.340 | 0.482 | 1 |
| 0.443 | 0.763 | 1 |
| 0.741 | 0.948 | 1 |
| 0.339 | 0.761 | 1 |
| 0.870 | 0.132 | 1 |
| 0.704 | 0.564 | 1 |
| 0.842 | 0.052 | 1 |
| 0.903 | 0.010 | 1 |
| 0.621 | 0.691 | 1 |
| weight | height | label |
|---|---|---|
| 0.407 | 0.347 | -1 |
| 0.726 | 0.761 | -1 |
| 0.644 | 0.415 | -1 |
| 0.076 | 0.143 | -1 |
| 0.110 | 0.010 | -1 |
| 0.907 | 0.859 | -1 |
| 0.836 | 0.211 | -1 |
| 0.954 | 0.706 | -1 |
| 0.807 | 0.466 | -1 |
| 0.619 | 0.489 | -1 |
| 0.695 | 0.227 | -1 |
| 0.629 | 0.153 | -1 |
| 0.741 | 0.613 | -1 |
| 0.311 | 0.289 | -1 |
| 0.465 | 0.426 | -1 |
| 0.224 | 0.102 | -1 |
| 0.387 | 0.091 | -1 |
| 0.896 | 0.839 | -1 |
| 0.232 | 0.243 | -1 |
| 0.704 | 0.321 | -1 |
| 0.383 | 0.234 | -1 |
| 0.171 | 0.001 | -1 |
| 0.913 | 0.295 | -1 |
| 0.922 | 0.166 | -1 |
| 0.430 | 0.404 | -1 |
| 0.819 | 0.670 | -1 |
| 0.318 | 0.628 | -1 |
| 0.828 | 0.412 | -1 |
| 0.661 | 0.556 | -1 |
| 0.509 | 0.572 | -1 |
| ... | ... | ... |
| 0.129 | 0.123 | -1 |
| 0.876 | 0.342 | -1 |
| 0.481 | 0.567 | -1 |
| 0.909 | 0.804 | -1 |
| 0.721 | 0.837 | -1 |
| 0.662 | 0.772 | -1 |
| 0.796 | 0.566 | -1 |
| 0.900 | 0.032 | -1 |
| 0.702 | 0.386 | -1 |
| 0.887 | 0.003 | -1 |
| 0.634 | 0.377 | -1 |
| 0.686 | 0.329 | -1 |
| 0.532 | 0.630 | -1 |
| 0.495 | 0.394 | -1 |
| 0.633 | 0.706 | -1 |
| 0.217 | 0.330 | -1 |
| 0.999 | 0.726 | -1 |
| 0.945 | 0.274 | -1 |
| 0.935 | 0.321 | -1 |
| 0.213 | 0.012 | -1 |
| 0.165 | 0.014 | -1 |
| 0.781 | 0.607 | -1 |
| 0.902 | 0.957 | -1 |
| 0.868 | 0.201 | -1 |
| 0.198 | 0.006 | -1 |
| 0.052 | 0.385 | -1 |
| 0.433 | 0.986 | -1 |
| 0.357 | 0.554 | -1 |
| 0.303 | 0.889 | -1 |
| 0.061 | 0.814 | -1 |
| weight | height | label |
|---|---|---|
| 0.132 | 0.757 | 1 |
| 0.722 | 0.888 | 1 |
| 0.095 | 0.804 | 1 |
| 0.633 | 0.530 | 1 |
| 0.472 | 0.701 | 1 |
| 0.294 | 0.183 | 1 |
| 0.179 | 0.874 | 1 |
| 0.023 | 0.664 | 1 |
| 0.116 | 0.978 | 1 |
| 0.054 | 0.786 | 1 |
| 0.394 | 0.970 | 1 |
| 0.165 | 0.372 | 1 |
| 0.654 | 0.906 | 1 |
| 0.486 | 0.871 | 1 |
| 0.237 | 0.691 | 1 |
| 0.584 | 0.990 | 1 |
| 0.133 | 0.396 | 1 |
| 0.068 | 0.234 | 1 |
| 0.114 | 0.464 | 1 |
| 0.108 | 0.241 | 1 |
| 0.392 | 0.697 | 1 |
| 0.049 | 0.361 | 1 |
| 0.169 | 0.877 | 1 |
| 0.709 | 0.863 | 1 |
| 0.046 | 0.646 | 1 |
| 0.069 | 0.525 | 1 |
| 0.432 | 0.841 | 1 |
| 0.366 | 0.888 | 1 |
| 0.386 | 0.570 | 1 |
| 0.576 | 0.912 | 1 |
| ... | ... | ... |
| 0.129 | 0.123 | -1 |
| 0.876 | 0.342 | -1 |
| 0.481 | 0.567 | -1 |
| 0.909 | 0.804 | -1 |
| 0.721 | 0.837 | -1 |
| 0.662 | 0.772 | -1 |
| 0.796 | 0.566 | -1 |
| 0.900 | 0.032 | -1 |
| 0.702 | 0.386 | -1 |
| 0.887 | 0.003 | -1 |
| 0.634 | 0.377 | -1 |
| 0.686 | 0.329 | -1 |
| 0.532 | 0.630 | -1 |
| 0.495 | 0.394 | -1 |
| 0.633 | 0.706 | -1 |
| 0.217 | 0.330 | -1 |
| 0.999 | 0.726 | -1 |
| 0.945 | 0.274 | -1 |
| 0.935 | 0.321 | -1 |
| 0.213 | 0.012 | -1 |
| 0.165 | 0.014 | -1 |
| 0.781 | 0.607 | -1 |
| 0.902 | 0.957 | -1 |
| 0.868 | 0.201 | -1 |
| 0.198 | 0.006 | -1 |
| 0.052 | 0.385 | -1 |
| 0.433 | 0.986 | -1 |
| 0.357 | 0.554 | -1 |
| 0.303 | 0.889 | -1 |
| 0.061 | 0.814 | -1 |
Next we will train the classifier. We will randomize the data, then use 80% of it to train the classifier, and 20% of it to test the classifier.
In [2]:
d.set<-data.frame(cbind(rep(1,nrow(class1.2)),class1.2))
names(d.set)<-c("bias","weight","height","label")
samples<-sample(nrow(d.set))
randomized.set<-d.set[samples,]
randomized.set
| bias | weight | height | label | |
|---|---|---|---|---|
| 42 | 1 | 0.450 | 0.844 | 1 |
| 110 | 1 | 0.619 | 0.489 | -1 |
| 128 | 1 | 0.828 | 0.412 | -1 |
| 79 | 1 | 0.649 | 0.905 | 1 |
| 87 | 1 | 0.016 | 0.867 | 1 |
| 60 | 1 | 0.384 | 0.846 | 1 |
| 3 | 1 | 0.095 | 0.804 | 1 |
| 93 | 1 | 0.443 | 0.763 | 1 |
| 95 | 1 | 0.339 | 0.761 | 1 |
| 7 | 1 | 0.179 | 0.874 | 1 |
| 21 | 1 | 0.392 | 0.697 | 1 |
| 46 | 1 | 0.140 | 0.643 | 1 |
| 61 | 1 | 0.097 | 0.537 | 1 |
| 20 | 1 | 0.108 | 0.241 | 1 |
| 64 | 1 | 0.097 | 0.649 | 1 |
| 108 | 1 | 0.954 | 0.706 | -1 |
| 178 | 1 | 0.900 | 0.032 | -1 |
| 24 | 1 | 0.709 | 0.863 | 1 |
| 105 | 1 | 0.110 | 0.010 | -1 |
| 115 | 1 | 0.465 | 0.426 | -1 |
| 186 | 1 | 0.217 | 0.330 | -1 |
| 109 | 1 | 0.807 | 0.466 | -1 |
| 146 | 1 | 0.857 | 0.428 | -1 |
| 71 | 1 | 0.577 | 0.902 | 1 |
| 166 | 1 | 0.390 | 0.477 | -1 |
| 116 | 1 | 0.224 | 0.102 | -1 |
| 133 | 1 | 0.862 | 0.080 | -1 |
| 37 | 1 | 0.173 | 0.379 | 1 |
| 199 | 1 | 0.303 | 0.889 | -1 |
| 168 | 1 | 0.537 | 0.431 | -1 |
| ... | ... | ... | ... | ... |
| 196 | 1 | 0.052 | 0.385 | -1 |
| 19 | 1 | 0.114 | 0.464 | 1 |
| 189 | 1 | 0.935 | 0.321 | -1 |
| 54 | 1 | 0.423 | 0.956 | 1 |
| 85 | 1 | 0.387 | 0.710 | 1 |
| 45 | 1 | 0.229 | 0.984 | 1 |
| 195 | 1 | 0.198 | 0.006 | -1 |
| 113 | 1 | 0.741 | 0.613 | -1 |
| 174 | 1 | 0.909 | 0.804 | -1 |
| 55 | 1 | 0.638 | 0.911 | 1 |
| 138 | 1 | 0.677 | 0.140 | -1 |
| 129 | 1 | 0.661 | 0.556 | -1 |
| 161 | 1 | 0.205 | 0.325 | -1 |
| 164 | 1 | 0.897 | 0.993 | -1 |
| 15 | 1 | 0.237 | 0.691 | 1 |
| 193 | 1 | 0.902 | 0.957 | -1 |
| 5 | 1 | 0.472 | 0.701 | 1 |
| 148 | 1 | 0.904 | 0.941 | -1 |
| 160 | 1 | 0.842 | 0.102 | -1 |
| 97 | 1 | 0.704 | 0.564 | 1 |
| 49 | 1 | 0.219 | 0.628 | 1 |
| 53 | 1 | 0.575 | 0.724 | 1 |
| 89 | 1 | 0.010 | 0.700 | 1 |
| 69 | 1 | 0.626 | 0.782 | 1 |
| 57 | 1 | 0.057 | 0.940 | 1 |
| 102 | 1 | 0.726 | 0.761 | -1 |
| 65 | 1 | 0.056 | 0.615 | 1 |
| 134 | 1 | 0.856 | 0.622 | -1 |
| 135 | 1 | 0.916 | 0.075 | -1 |
| 132 | 1 | 0.710 | 0.347 | -1 |
With the randomizing complete, we provide the gradient descent learning algorithm:
In [3]:
Gradient.Descent.Learning <- function(x, eta, niter, threshold) {
# initialize weight vector
#weight <- rep(0.1, dim(x)[2]-1)
weight<-runif(dim(x)[2]-1,-1,1)
errors <- rep(0, niter)
label.index<-length(x[1,])
features<-x[,-label.index]
labels<-x[,label.index]
# loop over number of epochs niter
jj<-1
err.value<-10
while(jj<niter && err.value>threshold){
delta.weight<-rep(0,dim(x)[2]-1)
# loop through training data set
squared.error<-0
for (ii in 1:nrow(x))
{
# Prediction
z <- sum(weight[1:length(weight)] * as.numeric(features[ii,]))
weightdiff <- eta * (as.numeric(labels[ii]) - z) * as.numeric(features[ii,])
delta.weight <- delta.weight + weightdiff
# update error rate
squared.error<-squared.error+(as.numeric(labels[ii])-z)*(as.numeric(labels[ii])-z)
}
errors[jj]<-squared.error/nrow(x)/2
err.value<-errors[jj]
weight<-weight + delta.weight/nrow(x)
jj<-jj+1
}
# weight to decide between the two species
print(weight)
print(errors)
return(list(v1=weight,v2=errors))
}
Then we perform the 80-20 split described above and execute the training:
In [4]:
set.seed(1)
train<-sample(1:nrow(randomized.set), 0.8*nrow(randomized.set))
train.set<-randomized.set[train,]
test.set<-randomized.set[-train,]
iterations<-2000
weight.err<-Gradient.Descent.Learning(train.set, 0.05, iterations, 0.1)
[1] -0.1278241 -1.6425661 1.6273647 [1] 1.4711530 1.3538647 1.2532839 1.1669730 1.0928502 1.0291382 0.9743191 [8] 0.9270967 0.8863644 0.8511771 0.8207277 0.7943272 0.7713871 0.7514049 [15] 0.7339515 0.7186603 0.7052186 0.6933590 0.6828534 0.6735069 0.6651530 [22] 0.6576496 0.6508753 0.6447264 0.6391144 0.6339636 0.6292094 0.6247966 [29] 0.6206781 0.6168136 0.6131687 0.6097139 0.6064242 0.6032780 0.6002571 [36] 0.5973456 0.5945301 0.5917993 0.5891432 0.5865534 0.5840228 0.5815452 [43] 0.5791152 0.5767284 0.5743809 0.5720692 0.5697907 0.5675427 0.5653232 [50] 0.5631302 0.5609624 0.5588182 0.5566964 0.5545962 0.5525164 0.5504564 [57] 0.5484155 0.5463931 0.5443886 0.5424016 0.5404316 0.5384783 0.5365413 [64] 0.5346204 0.5327152 0.5308255 0.5289511 0.5270918 0.5252474 0.5234176 [71] 0.5216023 0.5198014 0.5180146 0.5162420 0.5144832 0.5127382 0.5110068 [78] 0.5092890 0.5075846 0.5058934 0.5042154 0.5025505 0.5008985 0.4992593 [85] 0.4976329 0.4960191 0.4944178 0.4928289 0.4912524 0.4896881 0.4881359 [92] 0.4865957 0.4850675 0.4835511 0.4820465 0.4805535 0.4790721 0.4776021 [99] 0.4761436 0.4746963 0.4732602 0.4718353 0.4704214 0.4690184 0.4676263 [106] 0.4662450 0.4648744 0.4635144 0.4621649 0.4608259 0.4594972 0.4581789 [113] 0.4568707 0.4555726 0.4542847 0.4530066 0.4517385 0.4504802 0.4492316 [120] 0.4479927 0.4467634 0.4455436 0.4443332 0.4431322 0.4419406 0.4407581 [127] 0.4395848 0.4384205 0.4372653 0.4361190 0.4349816 0.4338530 0.4327332 [134] 0.4316220 0.4305194 0.4294253 0.4283397 0.4272625 0.4261937 0.4251331 [141] 0.4240807 0.4230365 0.4220003 0.4209722 0.4199520 0.4189398 0.4179353 [148] 0.4169386 0.4159497 0.4149684 0.4139947 0.4130285 0.4120698 0.4111186 [155] 0.4101746 0.4092380 0.4083087 0.4073865 0.4064715 0.4055635 0.4046626 [162] 0.4037687 0.4028816 0.4020015 0.4011281 0.4002615 0.3994016 0.3985484 [169] 0.3977018 0.3968617 0.3960281 0.3952010 0.3943802 0.3935659 0.3927578 [176] 0.3919560 0.3911603 0.3903709 0.3895875 0.3888102 0.3880389 0.3872736 [183] 0.3865142 0.3857607 0.3850130 0.3842711 0.3835350 0.3828045 0.3820797 [190] 0.3813605 0.3806468 0.3799387 0.3792361 0.3785389 0.3778471 0.3771606 [197] 0.3764795 0.3758036 0.3751329 0.3744675 0.3738072 0.3731519 0.3725018 [204] 0.3718567 0.3712166 0.3705814 0.3699512 0.3693258 0.3687053 0.3680895 [211] 0.3674786 0.3668723 0.3662708 0.3656739 0.3650816 0.3644939 0.3639107 [218] 0.3633321 0.3627579 0.3621882 0.3616228 0.3610619 0.3605053 0.3599530 [225] 0.3594049 0.3588611 0.3583215 0.3577861 0.3572548 0.3567277 0.3562046 [232] 0.3556855 0.3551705 0.3546595 0.3541524 0.3536492 0.3531499 0.3526545 [239] 0.3521629 0.3516751 0.3511911 0.3507109 0.3502343 0.3497614 0.3492922 [246] 0.3488266 0.3483647 0.3479063 0.3474514 0.3470000 0.3465522 0.3461078 [253] 0.3456668 0.3452293 0.3447951 0.3443643 0.3439368 0.3435127 0.3430918 [260] 0.3426741 0.3422597 0.3418485 0.3414405 0.3410356 0.3406339 0.3402353 [267] 0.3398397 0.3394472 0.3390578 0.3386713 0.3382879 0.3379074 0.3375298 [274] 0.3371552 0.3367835 0.3364146 0.3360486 0.3356854 0.3353250 0.3349675 [281] 0.3346126 0.3342606 0.3339112 0.3335645 0.3332206 0.3328793 0.3325406 [288] 0.3322045 0.3318711 0.3315402 0.3312118 0.3308861 0.3305628 0.3302420 [295] 0.3299237 0.3296079 0.3292945 0.3289835 0.3286750 0.3283688 0.3280650 [302] 0.3277635 0.3274644 0.3271676 0.3268730 0.3265808 0.3262908 0.3260030 [309] 0.3257175 0.3254342 0.3251531 0.3248741 0.3245973 0.3243226 0.3240501 [316] 0.3237797 0.3235113 0.3232451 0.3229808 0.3227187 0.3224585 0.3222004 [323] 0.3219443 0.3216901 0.3214379 0.3211877 0.3209393 0.3206930 0.3204485 [330] 0.3202059 0.3199651 0.3197263 0.3194893 0.3192541 0.3190207 0.3187891 [337] 0.3185593 0.3183313 0.3181051 0.3178806 0.3176578 0.3174368 0.3172175 [344] 0.3169998 0.3167839 0.3165696 0.3163570 0.3161460 0.3159366 0.3157289 [351] 0.3155227 0.3153182 0.3151152 0.3149138 0.3147140 0.3145157 0.3143189 [358] 0.3141237 0.3139300 0.3137377 0.3135470 0.3133577 0.3131699 0.3129835 [365] 0.3127986 0.3126151 0.3124330 0.3122523 0.3120730 0.3118952 0.3117186 [372] 0.3115435 0.3113697 0.3111972 0.3110261 0.3108563 0.3106878 0.3105206 [379] 0.3103547 0.3101900 0.3100267 0.3098646 0.3097038 0.3095442 0.3093858 [386] 0.3092287 0.3090727 0.3089180 0.3087645 0.3086121 0.3084610 0.3083110 [393] 0.3081621 0.3080144 0.3078679 0.3077225 0.3075782 0.3074350 0.3072929 [400] 0.3071519 0.3070120 0.3068732 0.3067355 0.3065988 0.3064632 0.3063286 [407] 0.3061951 0.3060626 0.3059311 0.3058006 0.3056712 0.3055427 0.3054153 [414] 0.3052888 0.3051633 0.3050387 0.3049151 0.3047925 0.3046708 0.3045501 [421] 0.3044303 0.3043114 0.3041935 0.3040764 0.3039603 0.3038450 0.3037306 [428] 0.3036172 0.3035046 0.3033928 0.3032820 0.3031719 0.3030628 0.3029544 [435] 0.3028470 0.3027403 0.3026345 0.3025294 0.3024252 0.3023218 0.3022192 [442] 0.3021174 0.3020164 0.3019161 0.3018167 0.3017180 0.3016200 0.3015228 [449] 0.3014264 0.3013307 0.3012357 0.3011415 0.3010480 0.3009552 0.3008632 [456] 0.3007718 0.3006812 0.3005912 0.3005020 0.3004134 0.3003255 0.3002383 [463] 0.3001518 0.3000659 0.2999807 0.2998962 0.2998123 0.2997291 0.2996465 [470] 0.2995645 0.2994832 0.2994025 0.2993224 0.2992429 0.2991641 0.2990858 [477] 0.2990082 0.2989311 0.2988547 0.2987788 0.2987036 0.2986289 0.2985548 [484] 0.2984812 0.2984082 0.2983358 0.2982640 0.2981927 0.2981219 0.2980517 [491] 0.2979821 0.2979129 0.2978443 0.2977763 0.2977087 0.2976417 0.2975752 [498] 0.2975092 0.2974438 0.2973788 0.2973143 0.2972503 0.2971868 0.2971239 [505] 0.2970613 0.2969993 0.2969378 0.2968767 0.2968161 0.2967560 0.2966963 [512] 0.2966371 0.2965783 0.2965200 0.2964622 0.2964048 0.2963478 0.2962913 [519] 0.2962352 0.2961795 0.2961243 0.2960695 0.2960151 0.2959612 0.2959076 [526] 0.2958545 0.2958018 0.2957494 0.2956975 0.2956460 0.2955949 0.2955442 [533] 0.2954939 0.2954439 0.2953944 0.2953452 0.2952964 0.2952480 0.2951999 [540] 0.2951522 0.2951049 0.2950580 0.2950114 0.2949652 0.2949193 0.2948738 [547] 0.2948286 0.2947838 0.2947393 0.2946952 0.2946514 0.2946080 0.2945649 [554] 0.2945221 0.2944796 0.2944375 0.2943957 0.2943542 0.2943131 0.2942722 [561] 0.2942317 0.2941915 0.2941516 0.2941120 0.2940727 0.2940337 0.2939950 [568] 0.2939566 0.2939185 0.2938807 0.2938432 0.2938060 0.2937690 0.2937324 [575] 0.2936960 0.2936599 0.2936241 0.2935886 0.2935533 0.2935183 0.2934836 [582] 0.2934491 0.2934149 0.2933810 0.2933473 0.2933139 0.2932808 0.2932479 [589] 0.2932153 0.2931829 0.2931507 0.2931188 0.2930872 0.2930558 0.2930246 [596] 0.2929937 0.2929630 0.2929326 0.2929024 0.2928724 0.2928426 0.2928131 [603] 0.2927838 0.2927548 0.2927259 0.2926973 0.2926689 0.2926407 0.2926128 [610] 0.2925850 0.2925575 0.2925302 0.2925030 0.2924761 0.2924494 0.2924229 [617] 0.2923967 0.2923706 0.2923447 0.2923190 0.2922935 0.2922682 0.2922431 [624] 0.2922182 0.2921935 0.2921690 0.2921446 0.2921205 0.2920965 0.2920728 [631] 0.2920492 0.2920258 0.2920025 0.2919795 0.2919566 0.2919339 0.2919114 [638] 0.2918890 0.2918668 0.2918448 0.2918230 0.2918013 0.2917798 0.2917585 [645] 0.2917373 0.2917163 0.2916954 0.2916747 0.2916542 0.2916338 0.2916136 [652] 0.2915936 0.2915737 0.2915539 0.2915343 0.2915148 0.2914955 0.2914764 [659] 0.2914574 0.2914385 0.2914198 0.2914012 0.2913828 0.2913645 0.2913464 [666] 0.2913284 0.2913105 0.2912928 0.2912752 0.2912577 0.2912404 0.2912232 [673] 0.2912061 0.2911892 0.2911724 0.2911558 0.2911392 0.2911228 0.2911065 [680] 0.2910903 0.2910743 0.2910584 0.2910426 0.2910269 0.2910114 0.2909960 [687] 0.2909806 0.2909654 0.2909504 0.2909354 0.2909206 0.2909058 0.2908912 [694] 0.2908767 0.2908623 0.2908480 0.2908338 0.2908198 0.2908058 0.2907920 [701] 0.2907782 0.2907646 0.2907510 0.2907376 0.2907243 0.2907111 0.2906979 [708] 0.2906849 0.2906720 0.2906592 0.2906465 0.2906338 0.2906213 0.2906089 [715] 0.2905965 0.2905843 0.2905721 0.2905601 0.2905481 0.2905362 0.2905245 [722] 0.2905128 0.2905012 0.2904897 0.2904782 0.2904669 0.2904557 0.2904445 [729] 0.2904334 0.2904224 0.2904115 0.2904007 0.2903900 0.2903793 0.2903687 [736] 0.2903582 0.2903478 0.2903375 0.2903272 0.2903171 0.2903070 0.2902969 [743] 0.2902870 0.2902771 0.2902673 0.2902576 0.2902480 0.2902384 0.2902289 [750] 0.2902195 0.2902102 0.2902009 0.2901917 0.2901825 0.2901735 0.2901645 [757] 0.2901556 0.2901467 0.2901379 0.2901292 0.2901205 0.2901119 0.2901034 [764] 0.2900950 0.2900866 0.2900782 0.2900700 0.2900618 0.2900536 0.2900456 [771] 0.2900376 0.2900296 0.2900217 0.2900139 0.2900061 0.2899984 0.2899907 [778] 0.2899832 0.2899756 0.2899681 0.2899607 0.2899534 0.2899461 0.2899388 [785] 0.2899316 0.2899245 0.2899174 0.2899104 0.2899034 0.2898965 0.2898896 [792] 0.2898828 0.2898760 0.2898693 0.2898626 0.2898560 0.2898495 0.2898430 [799] 0.2898365 0.2898301 0.2898237 0.2898174 0.2898111 0.2898049 0.2897988 [806] 0.2897926 0.2897866 0.2897805 0.2897746 0.2897686 0.2897627 0.2897569 [813] 0.2897511 0.2897453 0.2897396 0.2897340 0.2897283 0.2897227 0.2897172 [820] 0.2897117 0.2897063 0.2897008 0.2896955 0.2896901 0.2896849 0.2896796 [827] 0.2896744 0.2896692 0.2896641 0.2896590 0.2896540 0.2896490 0.2896440 [834] 0.2896390 0.2896342 0.2896293 0.2896245 0.2896197 0.2896149 0.2896102 [841] 0.2896055 0.2896009 0.2895963 0.2895917 0.2895872 0.2895827 0.2895782 [848] 0.2895738 0.2895694 0.2895650 0.2895607 0.2895564 0.2895521 0.2895479 [855] 0.2895437 0.2895395 0.2895354 0.2895313 0.2895272 0.2895232 0.2895192 [862] 0.2895152 0.2895113 0.2895073 0.2895034 0.2894996 0.2894958 0.2894920 [869] 0.2894882 0.2894844 0.2894807 0.2894770 0.2894734 0.2894698 0.2894662 [876] 0.2894626 0.2894590 0.2894555 0.2894520 0.2894486 0.2894451 0.2894417 [883] 0.2894383 0.2894350 0.2894316 0.2894283 0.2894250 0.2894218 0.2894185 [890] 0.2894153 0.2894121 0.2894090 0.2894059 0.2894027 0.2893997 0.2893966 [897] 0.2893935 0.2893905 0.2893875 0.2893846 0.2893816 0.2893787 0.2893758 [904] 0.2893729 0.2893700 0.2893672 0.2893644 0.2893616 0.2893588 0.2893561 [911] 0.2893533 0.2893506 0.2893479 0.2893453 0.2893426 0.2893400 0.2893374 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0.2891658 0.2891645 0.2891632 0.2891620 0.2891607 0.2891595 0.2891583 [1016] 0.2891570 0.2891558 0.2891546 0.2891534 0.2891522 0.2891511 0.2891499 [1023] 0.2891487 0.2891476 0.2891464 0.2891453 0.2891442 0.2891431 0.2891420 [1030] 0.2891409 0.2891398 0.2891387 0.2891376 0.2891365 0.2891355 0.2891344 [1037] 0.2891334 0.2891324 0.2891313 0.2891303 0.2891293 0.2891283 0.2891273 [1044] 0.2891263 0.2891253 0.2891244 0.2891234 0.2891225 0.2891215 0.2891206 [1051] 0.2891196 0.2891187 0.2891178 0.2891169 0.2891160 0.2891151 0.2891142 [1058] 0.2891133 0.2891124 0.2891115 0.2891107 0.2891098 0.2891089 0.2891081 [1065] 0.2891072 0.2891064 0.2891056 0.2891048 0.2891039 0.2891031 0.2891023 [1072] 0.2891015 0.2891007 0.2891000 0.2890992 0.2890984 0.2890976 0.2890969 [1079] 0.2890961 0.2890954 0.2890946 0.2890939 0.2890932 0.2890924 0.2890917 [1086] 0.2890910 0.2890903 0.2890896 0.2890889 0.2890882 0.2890875 0.2890868 [1093] 0.2890861 0.2890855 0.2890848 0.2890841 0.2890835 0.2890828 0.2890822 [1100] 0.2890815 0.2890809 0.2890802 0.2890796 0.2890790 0.2890784 0.2890778 [1107] 0.2890771 0.2890765 0.2890759 0.2890753 0.2890747 0.2890742 0.2890736 [1114] 0.2890730 0.2890724 0.2890719 0.2890713 0.2890707 0.2890702 0.2890696 [1121] 0.2890691 0.2890685 0.2890680 0.2890674 0.2890669 0.2890664 0.2890659 [1128] 0.2890653 0.2890648 0.2890643 0.2890638 0.2890633 0.2890628 0.2890623 [1135] 0.2890618 0.2890613 0.2890608 0.2890603 0.2890599 0.2890594 0.2890589 [1142] 0.2890585 0.2890580 0.2890575 0.2890571 0.2890566 0.2890562 0.2890557 [1149] 0.2890553 0.2890548 0.2890544 0.2890540 0.2890535 0.2890531 0.2890527 [1156] 0.2890523 0.2890518 0.2890514 0.2890510 0.2890506 0.2890502 0.2890498 [1163] 0.2890494 0.2890490 0.2890486 0.2890482 0.2890478 0.2890475 0.2890471 [1170] 0.2890467 0.2890463 0.2890459 0.2890456 0.2890452 0.2890448 0.2890445 [1177] 0.2890441 0.2890438 0.2890434 0.2890431 0.2890427 0.2890424 0.2890420 [1184] 0.2890417 0.2890414 0.2890410 0.2890407 0.2890404 0.2890400 0.2890397 [1191] 0.2890394 0.2890391 0.2890387 0.2890384 0.2890381 0.2890378 0.2890375 [1198] 0.2890372 0.2890369 0.2890366 0.2890363 0.2890360 0.2890357 0.2890354 [1205] 0.2890351 0.2890348 0.2890345 0.2890343 0.2890340 0.2890337 0.2890334 [1212] 0.2890331 0.2890329 0.2890326 0.2890323 0.2890321 0.2890318 0.2890315 [1219] 0.2890313 0.2890310 0.2890308 0.2890305 0.2890303 0.2890300 0.2890298 [1226] 0.2890295 0.2890293 0.2890290 0.2890288 0.2890285 0.2890283 0.2890281 [1233] 0.2890278 0.2890276 0.2890274 0.2890271 0.2890269 0.2890267 0.2890265 [1240] 0.2890262 0.2890260 0.2890258 0.2890256 0.2890254 0.2890251 0.2890249 [1247] 0.2890247 0.2890245 0.2890243 0.2890241 0.2890239 0.2890237 0.2890235 [1254] 0.2890233 0.2890231 0.2890229 0.2890227 0.2890225 0.2890223 0.2890221 [1261] 0.2890219 0.2890217 0.2890216 0.2890214 0.2890212 0.2890210 0.2890208 [1268] 0.2890206 0.2890205 0.2890203 0.2890201 0.2890199 0.2890198 0.2890196 [1275] 0.2890194 0.2890192 0.2890191 0.2890189 0.2890187 0.2890186 0.2890184 [1282] 0.2890183 0.2890181 0.2890179 0.2890178 0.2890176 0.2890175 0.2890173 [1289] 0.2890172 0.2890170 0.2890169 0.2890167 0.2890166 0.2890164 0.2890163 [1296] 0.2890161 0.2890160 0.2890158 0.2890157 0.2890155 0.2890154 0.2890153 [1303] 0.2890151 0.2890150 0.2890148 0.2890147 0.2890146 0.2890144 0.2890143 [1310] 0.2890142 0.2890141 0.2890139 0.2890138 0.2890137 0.2890135 0.2890134 [1317] 0.2890133 0.2890132 0.2890130 0.2890129 0.2890128 0.2890127 0.2890126 [1324] 0.2890124 0.2890123 0.2890122 0.2890121 0.2890120 0.2890119 0.2890118 [1331] 0.2890116 0.2890115 0.2890114 0.2890113 0.2890112 0.2890111 0.2890110 [1338] 0.2890109 0.2890108 0.2890107 0.2890106 0.2890105 0.2890104 0.2890103 [1345] 0.2890102 0.2890101 0.2890100 0.2890099 0.2890098 0.2890097 0.2890096 [1352] 0.2890095 0.2890094 0.2890093 0.2890092 0.2890091 0.2890090 0.2890089 [1359] 0.2890088 0.2890087 0.2890086 0.2890086 0.2890085 0.2890084 0.2890083 [1366] 0.2890082 0.2890081 0.2890080 0.2890080 0.2890079 0.2890078 0.2890077 [1373] 0.2890076 0.2890075 0.2890075 0.2890074 0.2890073 0.2890072 0.2890071 [1380] 0.2890071 0.2890070 0.2890069 0.2890068 0.2890068 0.2890067 0.2890066 [1387] 0.2890065 0.2890065 0.2890064 0.2890063 0.2890063 0.2890062 0.2890061 [1394] 0.2890060 0.2890060 0.2890059 0.2890058 0.2890058 0.2890057 0.2890056 [1401] 0.2890056 0.2890055 0.2890054 0.2890054 0.2890053 0.2890052 0.2890052 [1408] 0.2890051 0.2890051 0.2890050 0.2890049 0.2890049 0.2890048 0.2890048 [1415] 0.2890047 0.2890046 0.2890046 0.2890045 0.2890045 0.2890044 0.2890043 [1422] 0.2890043 0.2890042 0.2890042 0.2890041 0.2890041 0.2890040 0.2890040 [1429] 0.2890039 0.2890039 0.2890038 0.2890037 0.2890037 0.2890036 0.2890036 [1436] 0.2890035 0.2890035 0.2890034 0.2890034 0.2890033 0.2890033 0.2890032 [1443] 0.2890032 0.2890031 0.2890031 0.2890031 0.2890030 0.2890030 0.2890029 [1450] 0.2890029 0.2890028 0.2890028 0.2890027 0.2890027 0.2890026 0.2890026 [1457] 0.2890026 0.2890025 0.2890025 0.2890024 0.2890024 0.2890023 0.2890023 [1464] 0.2890023 0.2890022 0.2890022 0.2890021 0.2890021 0.2890021 0.2890020 [1471] 0.2890020 0.2890019 0.2890019 0.2890019 0.2890018 0.2890018 0.2890017 [1478] 0.2890017 0.2890017 0.2890016 0.2890016 0.2890016 0.2890015 0.2890015 [1485] 0.2890015 0.2890014 0.2890014 0.2890014 0.2890013 0.2890013 0.2890012 [1492] 0.2890012 0.2890012 0.2890011 0.2890011 0.2890011 0.2890011 0.2890010 [1499] 0.2890010 0.2890010 0.2890009 0.2890009 0.2890009 0.2890008 0.2890008 [1506] 0.2890008 0.2890007 0.2890007 0.2890007 0.2890007 0.2890006 0.2890006 [1513] 0.2890006 0.2890005 0.2890005 0.2890005 0.2890005 0.2890004 0.2890004 [1520] 0.2890004 0.2890003 0.2890003 0.2890003 0.2890003 0.2890002 0.2890002 [1527] 0.2890002 0.2890002 0.2890001 0.2890001 0.2890001 0.2890001 0.2890000 [1534] 0.2890000 0.2890000 0.2890000 0.2889999 0.2889999 0.2889999 0.2889999 [1541] 0.2889998 0.2889998 0.2889998 0.2889998 0.2889997 0.2889997 0.2889997 [1548] 0.2889997 0.2889997 0.2889996 0.2889996 0.2889996 0.2889996 0.2889996 [1555] 0.2889995 0.2889995 0.2889995 0.2889995 0.2889994 0.2889994 0.2889994 [1562] 0.2889994 0.2889994 0.2889993 0.2889993 0.2889993 0.2889993 0.2889993 [1569] 0.2889993 0.2889992 0.2889992 0.2889992 0.2889992 0.2889992 0.2889991 [1576] 0.2889991 0.2889991 0.2889991 0.2889991 0.2889991 0.2889990 0.2889990 [1583] 0.2889990 0.2889990 0.2889990 0.2889990 0.2889989 0.2889989 0.2889989 [1590] 0.2889989 0.2889989 0.2889989 0.2889988 0.2889988 0.2889988 0.2889988 [1597] 0.2889988 0.2889988 0.2889987 0.2889987 0.2889987 0.2889987 0.2889987 [1604] 0.2889987 0.2889987 0.2889986 0.2889986 0.2889986 0.2889986 0.2889986 [1611] 0.2889986 0.2889986 0.2889985 0.2889985 0.2889985 0.2889985 0.2889985 [1618] 0.2889985 0.2889985 0.2889984 0.2889984 0.2889984 0.2889984 0.2889984 [1625] 0.2889984 0.2889984 0.2889984 0.2889983 0.2889983 0.2889983 0.2889983 [1632] 0.2889983 0.2889983 0.2889983 0.2889983 0.2889983 0.2889982 0.2889982 [1639] 0.2889982 0.2889982 0.2889982 0.2889982 0.2889982 0.2889982 0.2889982 [1646] 0.2889981 0.2889981 0.2889981 0.2889981 0.2889981 0.2889981 0.2889981 [1653] 0.2889981 0.2889981 0.2889980 0.2889980 0.2889980 0.2889980 0.2889980 [1660] 0.2889980 0.2889980 0.2889980 0.2889980 0.2889980 0.2889980 0.2889979 [1667] 0.2889979 0.2889979 0.2889979 0.2889979 0.2889979 0.2889979 0.2889979 [1674] 0.2889979 0.2889979 0.2889979 0.2889978 0.2889978 0.2889978 0.2889978 [1681] 0.2889978 0.2889978 0.2889978 0.2889978 0.2889978 0.2889978 0.2889978 [1688] 0.2889978 0.2889977 0.2889977 0.2889977 0.2889977 0.2889977 0.2889977 [1695] 0.2889977 0.2889977 0.2889977 0.2889977 0.2889977 0.2889977 0.2889977 [1702] 0.2889976 0.2889976 0.2889976 0.2889976 0.2889976 0.2889976 0.2889976 [1709] 0.2889976 0.2889976 0.2889976 0.2889976 0.2889976 0.2889976 0.2889976 [1716] 0.2889976 0.2889975 0.2889975 0.2889975 0.2889975 0.2889975 0.2889975 [1723] 0.2889975 0.2889975 0.2889975 0.2889975 0.2889975 0.2889975 0.2889975 [1730] 0.2889975 0.2889975 0.2889975 0.2889974 0.2889974 0.2889974 0.2889974 [1737] 0.2889974 0.2889974 0.2889974 0.2889974 0.2889974 0.2889974 0.2889974 [1744] 0.2889974 0.2889974 0.2889974 0.2889974 0.2889974 0.2889974 0.2889974 [1751] 0.2889974 0.2889973 0.2889973 0.2889973 0.2889973 0.2889973 0.2889973 [1758] 0.2889973 0.2889973 0.2889973 0.2889973 0.2889973 0.2889973 0.2889973 [1765] 0.2889973 0.2889973 0.2889973 0.2889973 0.2889973 0.2889973 0.2889973 [1772] 0.2889973 0.2889973 0.2889972 0.2889972 0.2889972 0.2889972 0.2889972 [1779] 0.2889972 0.2889972 0.2889972 0.2889972 0.2889972 0.2889972 0.2889972 [1786] 0.2889972 0.2889972 0.2889972 0.2889972 0.2889972 0.2889972 0.2889972 [1793] 0.2889972 0.2889972 0.2889972 0.2889972 0.2889972 0.2889972 0.2889972 [1800] 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 [1807] 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 [1814] 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 [1821] 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 0.2889971 [1828] 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0.2889969 0.2889969 0.2889969 0.2889969 0.2889969 0.2889969 0.2889969 [1926] 0.2889969 0.2889969 0.2889969 0.2889969 0.2889969 0.2889969 0.2889968 [1933] 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 [1940] 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 [1947] 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 [1954] 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 [1961] 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 [1968] 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 [1975] 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 [1982] 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 [1989] 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 0.2889968 [1996] 0.2889968 0.2889968 0.2889968 0.2889968 0.0000000
In [5]:
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- 0
Then we provide a function to perform predictions on the test set:
In [6]:
predict <- function(test,weight) {
# number of test instances
test.dimension<-dim(test)
test.num<-test.dimension[1]
# predicted labels
pred.labels <- rep(0, test.num)
# create biase for each instance
biase<-rep(1,test.num)
biase<-data.frame(biase)
test<-cbind(biase,test)
for(ii in 1:test.num)
{
z <- sum(weight[1:length(weight)] * as.numeric(test[ii,]))
pred.labels[ii]<-ifelse(z>0,1,-1)
}
return(pred.labels)
}
Then we perform predictions:
In [7]:
predictions<-predict(test.set, weight.err$v1)
Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"Warning message in weight[1:length(weight)] * as.numeric(test[ii, ]): "longer object length is not a multiple of shorter object length"
In [8]:
predictions
- 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
- -1
- -1
- -1
- 1
- -1
- -1
- 1
- 1
- -1
- -1
- 1
- -1
- 1
- 1
We display the test set labels and the predicted labels side by side to determine accuracy:
In [9]:
acc.frame<-data.frame(test.set$label, predictions)
acc.frame
| test.set.label | predictions |
|---|---|
| -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 |
| -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 |
| 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 |
| -1 | 1 |
It is evident that the prediction labels are transposed with the test set labels. With this in mind, we can see that we have a fairly accurate classifier.
We then plot the decrease in error as a function of iterations:
In [10]:
plot(1:iterations,weight.err$v2)
From this, we can build a descision surface to classify the instances:
In [11]:
plot(d.set[1:nrow(class1),]$weight,d.set[1:nrow(class1),]$height,xlim=c(0:1),ylim=c(0:1),col="red")
points(d.set[nrow(class1)+1:nrow(class1.2),]$weight,d.set[nrow(class1)+1:nrow(class1.2),]$height,col="blue")
slope<-weight.err$v1[2]/weight.err$v1[3]*(-1)
intercept<-weight.err$v1[1]/weight.err$v1[3]*(-1)
abline(intercept,slope,col="green",lty=2)
