Hi all, I have a df and I want to use supervised Self Organizing Map to do classification. I should use Kohonen library and xyf function from it. As you know the xyf function looks like this and I have problem defining my Y: xyf(data,Y,grid=somgrid(),rlen=100,alpha=c(0.05,0.01)) I want to do classification based on a column which shows the speed that a protocols is run, and this column is the following: $speed :num 4 4 3 3 3 1 1 1 2 1 4 4 3 numbers from 1 to 4 show the speed from very fast to very slow protocols. so the property I want to be modeled is df$speed, but I don't know how should I bring it in xyf function. Does anyone know how to do that? I also added my train set ans test set: dt=sort(sample(nrow(df),nrow(df)*.7)) train=df[dt,] Xtraining=scale(trian) Xtest=scale(-trian) center=attr(Xtrianing,"scaled:center") scale=attr(Xtraining,"scaled:scale") xyf(Xtraining,........,grid=somgrid(10,10,"hexagonal")) Thanks for any Help, Elahe
Is there any answer? Hi all, I have a df and I want to use supervised Self Organizing Map to do classification. I should use Kohonen library and xyf function from it. As you know the xyf function looks like this and I have problem defining my Y: xyf(data,Y,grid=somgrid(),rlen=100,alpha=c(0.05,0.01)) I want to do classification based on a column which shows the speed that a protocols is run, and this column is the following: $speed :num 4 4 3 3 3 1 1 1 2 1 4 4 3 numbers from 1 to 4 show the speed from very fast to very slow protocols. so the property I want to be modeled is df$speed, but I don't know how should I bring it in xyf function. Does anyone know how to do that? I also added my train set ans test set: dt=sort(sample(nrow(df),nrow(df)*.7)) train=df[dt,] Xtraining=scale(trian) Xtest=scale(-trian) center=attr(Xtrianing,"scaled:center") scale=attr(Xtraining,"scaled:scale") xyf(Xtraining,........,grid=somgrid(10,10,"hexagonal")) Thanks for any Help, Elahe ______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Hi! Some sample data could help us to help you... But have you read '?xyf' in order to ensure that your 'Y' is what 'xyf' expects it to be? What kind of error messages do you get? Regards, Kimmo 16.06.2016, 15:13, ch.elahe via R-help wrote:> Is there any answer? > > > Hi all, I have a df and I want to use supervised Self Organizing Map > to do classification. I should use Kohonen library and xyf function > from it. As you know the xyf function looks like this and I have > problem defining my Y: > > xyf(data,Y,grid=somgrid(),rlen=100,alpha=c(0.05,0.01)) I want to do > classification based on a column which shows the speed that a > protocols is run, and this column is the following: > > $speed :num 4 4 3 3 3 1 1 1 2 1 4 4 3 numbers from 1 to 4 show the > speed from very fast to very slow protocols. so the property I want > to be modeled is df$speed, but I don't know how should I bring it in > xyf function. Does anyone know how to do that? I also added my train > set ans test set: > > dt=sort(sample(nrow(df),nrow(df)*.7)) train=df[dt,] > Xtraining=scale(trian) Xtest=scale(-trian) > center=attr(Xtrianing,"scaled:center") > scale=attr(Xtraining,"scaled:scale") > xyf(Xtraining,........,grid=somgrid(10,10,"hexagonal")) > > > Thanks for any Help, Elahe > > ______________________________________________ R-help at r-project.org > mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the > posting guide http://www.R-project.org/posting-guide.html and provide > commented, minimal, self-contained, reproducible code. > > ______________________________________________ R-help at r-project.org > mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the > posting guide http://www.R-project.org/posting-guide.html and provide > commented, minimal, self-contained, reproducible code. >