Last updated on 2024-03-28 23:55:16 CET.
Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
---|---|---|---|---|---|---|
r-devel-linux-x86_64-debian-clang | 0.2.2 | 14.81 | 128.11 | 142.92 | NOTE | |
r-devel-linux-x86_64-debian-gcc | 0.2.2 | 11.70 | 96.54 | 108.24 | NOTE | |
r-devel-linux-x86_64-fedora-clang | 0.2.2 | 179.38 | NOTE | |||
r-devel-linux-x86_64-fedora-gcc | 0.2.2 | 169.27 | NOTE | |||
r-devel-windows-x86_64 | 0.2.2 | 14.00 | 104.00 | 118.00 | ERROR | |
r-patched-linux-x86_64 | 0.2.2 | 11.85 | 121.01 | 132.86 | NOTE | |
r-release-linux-x86_64 | 0.2.2 | 9.74 | 124.22 | 133.96 | NOTE | |
r-release-macos-arm64 | 0.2.2 | 53.00 | NOTE | |||
r-release-macos-x86_64 | 0.2.2 | 90.00 | NOTE | |||
r-release-windows-x86_64 | 0.2.2 | 18.00 | 127.00 | 145.00 | NOTE | |
r-oldrel-macos-arm64 | 0.2.2 | 52.00 | NOTE | |||
r-oldrel-windows-x86_64 | 0.2.2 | 19.00 | 129.00 | 148.00 | NOTE |
Version: 0.2.2
Check: LazyData
Result: NOTE
'LazyData' is specified without a 'data' directory
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-windows-x86_64
Version: 0.2.2
Check: examples
Result: ERROR
Running examples in 'RandPro-Ex.R' failed
The error most likely occurred in:
> ### Name: classify
> ### Title: Classification Function
> ### Aliases: classify
> ### Keywords: classification confusion_matrix feature_extraction k-nn svm
>
> ### ** Examples
>
> # Load Library
> library(RandPro)
>
> #Load Iris Data
> data("iris")
>
> #Split the data into training set and test set of 75:25 ratio.
> set.seed(101)
> sample <- sample.int(n = nrow(iris), size = floor(.75*nrow(iris)), replace = FALSE)
> trainn <- iris[sample, ]
> testt <- iris[-sample,]
>
> #Extract the train label and test label
> trainl <- trainn$Species
> testl <- testt$Species
> typeof(trainl)
[1] "integer"
>
> #Remove the label from training set and test set
> trainn <- trainn[,1:4]
> testt <- testt[,1:4]
>
> #classify the Iris data with default K-NN Classifier.
> res <- classify(trainn,testt,trainl,testl)
Function uses default value 0.5 for epsilon
Function uses Gaussian Projection function
Function uses default K-NN classifier
Flavor: r-devel-windows-x86_64