Displaying 20 results from an estimated 22 matches for "nn1".
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2009 Feb 18
1
Training nnet in two ways, trying to understand the performance difference - with (i hope!) commented, minimal, self-contained, reproducible code
...m dataset from package kernlab
>data(list = "spam", package = "kernlab")
>set.seed(42)
>my.sample <- sample(nrow(spam), 3221)
>spam.train <- spam[my.sample, ]
>spam.test <- spam[-my.sample, ]
## Example 1 - my own code
# train artificial neural network (nn1)
>( nn1 <- nnet(type~., data=spam.train, size=3, decay=0.1, maxit=1000) )
# predict spam.test dataset on nn1
> ( nn1.pr.test <- predict(nn1, spam.test, type='class') )
[1] "spam" "spam" "spam" "spam" "nonspam" &quo...
2006 Aug 16
0
Regular expressions: retrieving matches depending on intervening strings [Follow-up]
Dear all
This is a follow-up to an earlier posting today regarding a regular expression question. In the meantime, this is the best approximation I could come up with and should give you a better idea what I am talking about.
a<-c("<w AT0>a <w NN1>blockage <w CJC>and <w DT0>that<c PUN>.",
"<w AT0>a <w NN1>blockage <w CJC>and <ptr target=KB2LC003><w DT0>that<c PUN>.",
"<w AT0>a <w NN1>blockage <w CJC>and<c PUN>, <w DT0>that<...
2009 May 12
0
FW: neural network not using all observations
As a follow-up to my email below:
The input data frame to nnet() has dimensions:
> dim(coreaff.trn.nn)
[1] 5088 8
And the predictions from the neural network (35 records are dropped -
see email below for more details) has dimensions:
> pred <- predict(coreaff.nn1)
> dim(pred)
[1] 5053 1
So, the following line of R code does not work as the dimensions are
different.
> sum((coreaff.trn.nn$hh.iast.y - predict(coreaff.nn1))^2)
Error: dims [product 5053] do not match the length of object [5088]
In addition: Warning message:
In coreaff.trn.n...
2009 May 12
0
How do I extract the scoring equations for neural networks and support vector machines?
...bservations
As a follow-up to my email below:
The input data frame to nnet() has dimensions:
> dim(coreaff.trn.nn)
[1] 5088 8
And the predictions from the neural network (35 records are dropped -
see email below for more details) has dimensions:
> pred <- predict(coreaff.nn1)
> dim(pred)
[1] 5053 1
So, the following line of R code does not work as the dimensions are
different.
> sum((coreaff.trn.nn$hh.iast.y - predict(coreaff.nn1))^2)
Error: dims [product 5053] do not match the length of object [5088]
In addition: Warning message:
In coreaff.trn.nn$...
2006 Sep 11
2
Translating R code + library into Fortran?
...ead.table("bayest.par")
names(stadler)=c("exp.shift","exp.scale","exp.shape")
cell.size=20
sim.size=600
#first train initial neural nets
training.data=data.gen(1e4,cell.size,cell.size,.1,1,.1,1,1,4)
#train nn.shift with error checking
ok=F
while(ok==F){
nn1.shift=nnet(exp.shift~min+q1+med+mean+q3+max+samples,data=training.data,size=8,linout=T,rang=1e-08,maxit=500,trace=F)
cor.shift=predict(nn.shift,training.data[,c(1:7)],type="raw")
temp=hist(cor.shift,plot=F)
if(length(temp$counts[temp$counts>0])>10){
ok=T
}
}...
2012 Jul 24
3
Nearest Neighbors
I was wondering if there is a way in R to find k nearest neighbors of various
orders, say order 2, 3, or 4. In otherwords neighbors of neighbors of
neighbors. You get the idea. I know that I can use knearneigh(matrix.data,
k) but this only gives me the k nearest neighbors and not of a particular
order.
Thanks in advance.
--
View this message in context:
2009 May 12
0
neural network not using all observations
...0 0
5 484253 -0.6112205 -0.7306664 0.64013414 0.07979137
1 0 0
6 799054 0.6580506 1.1763114 0.24784295 0.07979137
0 1 1
> coreaff.nn1 <- nnet(hh.iast.y ~ WC_Total_Assets + all_assets_per_hh +
age + tenure + max_acc_ownr_liq_asts_n_med +
+ max_acc_ownr_nwrth_n_med +
max_acc_ownr_ann_incm_n_med, coreaff.trn.nn, size = 2, decay = 1e-3,
+ linout = T, skip = T, maxit = 1000, Hess = T)
# we...
2008 Jun 12
1
About Mcneil Hanley test for a portion of AUC!
...ion
cROC. I can only find the value of "r" for the whole AUC's .
> seROC<-function(AUC,na,nn){
> a<-AUC
> q1<-a/(2-a)
> q2<-(2*a^2)/(1+a)
> se<-sqrt((a*(1-a)+(na-1)*(q1-a^2)+(nn-1)*(q2-a^2))/(nn*na))
> se
> }
>
> cROC<-function(AUC1,na1,nn1,AUC2,na2,nn2,r){
> se1<-seROC(AUC1,na1,nn1)
> se2<-seROC(AUC2,na2,nn2)
>
> sed<-sqrt(se1^2+se2^2-2*r*se1*se2)
> zad<-(AUC1-AUC2)/sed
> p<-dnorm(zad)
> a<-list(zad,p)
> a
Could somebody kindly suggest me how to calculate the value of "r" or
some w...
2006 Mar 15
1
How to compare areas under ROC curves calculated with ROCR package
....
I used the following program I found on R-help archives :
From: Bernardo Rangel Tura
Date: Thu 16 Dec 2004 - 07:30:37 EST
seROC<-function(AUC,na,nn){
a<-AUC
q1<-a/(2-a)
q2<-(2*a^2)/(1+a)
se<-sqrt((a*(1-a)+(na-1)*(q1-a^2)+(nn-1)*(q2-a^2))/(nn*na))
se
}
cROC<-function(AUC1,na1,nn1,AUC2,na2,nn2,r){
se1<-seROC(AUC1,na1,nn1)
se2<-seROC(AUC2,na2,nn2)
sed<-sqrt(se1^2+se2^2-2*r*se1*se2)
zad<-(AUC1-AUC2)/sed
p<-dnorm(zad)
a<-list(zad,p)
a
}
The author of this script says: "The first function (seROC) calculate the standard error of ROC curve, the
second fun...
2006 Mar 20
1
How to compare areas under ROC curves calculated with ROC R package
....
I used the following program I found on R-help archives :
From: Bernardo Rangel Tura
Date: Thu 16 Dec 2004 - 07:30:37 EST
seROC<-function(AUC,na,nn){
a<-AUC
q1<-a/(2-a)
q2<-(2*a^2)/(1+a)
se<-sqrt((a*(1-a)+(na-1)*(q1-a^2)+(nn-1)*(q2-a^2))/(nn*na))
se
}
cROC<-function(AUC1,na1,nn1,AUC2,na2,nn2,r){
se1<-seROC(AUC1,na1,nn1)
se2<-seROC(AUC2,na2,nn2)
sed<-sqrt(se1^2+se2^2-2*r*se1*se2)
zad<-(AUC1-AUC2)/sed
p<-dnorm(zad)
a<-list(zad,p)
a
}
The author of this script says: "The first function (seROC) calculate the
standard error of ROC curve, the
second fun...
2012 Aug 01
3
Neuralnet Error
I require some help in debugging this code
library(neuralnet)
ir<-read.table(file="iris_data.txt",header=TRUE,row.names=NULL)
ir1 <- data.frame(ir[1:100,2:6])
ir2 <- data.frame(ifelse(ir1$Species=="setosa",1,ifelse(ir1$Species=="versicolor",0,"")))
colnames(ir2)<-("Output")
ir3 <- data.frame(rbind(ir1[1:4],ir2))
2006 Jun 23
1
Problems creating packages.
...root
Here is a sample .r file format:
###########################################################################
#
# Freq function returns the frequencies of numerical vectors
#
###########################################################################
freq = function(x1,x2,x3){
nn1=rep(0, length(x))
nn2=rep(0, length(x))
nn3=rep(0, length(x))
for ( i in 1:length(x)){
nn1[i]=sum(x[i]==x1)
nn2[i]=sum(x[i]==x2)
nn3[i]=sum(x[i]==x3)
}
mm = as.matrix(rbind(nn1,nn2,nn3))
mm
}
Here is a sample man page format:
\name{freq}
\ali...
2009 Jun 02
2
What do you think about my function?
...a mistake!)
########## My function #############################################
dzieci<-transform(dzieci, zywnosc=0)
zywnoscCalosc<- function( jedzenie, sklep, n1, n2, n3, n4, d1, d2, d3, d4 )
{
skl <- sklep
wynik <- vector()
wynik <- jedzenie
ndf <- data.frame(nn1=n1,nn2=n2,nn3=n3,nn4=n4)
ddf <- data.frame(dd1=d1,dd2=d2,dd3=d3,dd4=d4)
for (i in 1:length(n1)){
wekt_n = ndf[i,]
wekt_d = ddf[i,]
wekt_n_ok = wekt_n[!is.na(wekt_n)]
wekt_n_ok = as.numeric(wekt_n_ok)
wekt_d_ok = wekt_d[!is.na(wekt_d)]
wekt_d_ok = as...
2009 May 31
1
Error:non-numeric argument in my function
Hello!
I have a function:
zywnoscCalosc<- function( jedzenie, n1, n2, n3, n4, d1, d2, d3, d4 ) {
ndf <- data.frame(nn1=n1,nn2=n2,nn3=n3,nn4=n4)
ddf <- data.frame(dd1=d1,dd2=d2,dd3=d3,dd4=d4)
for (i in 1:length(n1)){
wekt_n = ndf[i,]
wekt_n_ok = wekt_n[!is.na(wekt_n)]
dl_n = length(wekt_n_ok)
wynik = (1*wekt_n_ok)/(1*dl_n)
}
}
and I get an error like this:
Error in 1 * wekt_n_ok : non-numeric argument to bin...
2009 Oct 28
1
need help explain the routine input parameters for seROC and cROC found in the R archive
...e.
> From: Bernardo Rangel Tura
> Date: Thu 16 Dec 2004 - 07:30:37 EST
>
> seROC<-function(AUC,na,nn){
> a<-AUC
> q1<-a/(2-a)
> q2<-(2*a^2)/(1+a)
> se<-sqrt((a*(1-a)+(na-1)*(q1-a^2)+(nn-1)*(q2-a^2))/(nn*na))
> se
> }
>
> cROC<-function(AUC1,na1,nn1,AUC2,na2,nn2,r){
> se1<-seROC(AUC1,na1,nn1)
> se2<-seROC(AUC2,na2,nn2)
>
> sed<-sqrt(se1^2+se2^2-2*r*se1*se2)
> zad<-(AUC1-AUC2)/sed
> p<-dnorm(zad)
> a<-list(zad,p)
> a
> }
>
--
Waverley @ Palo Alto
2004 Jun 21
2
visualizing a list of probabilities
Hi,
I'm using nnet to work on a 2 class classification problem. The result
of my code is data.frame of true class, predicted class and associated
probability.
One way of summarizing the data is by a confusion matrix. However are
there any graphical ways I could represent the data - specifically, I'd
like to show the probabilities associated with each member of my
prediction set?
(I
2004 Dec 15
3
(no subject)
Dear R-helper,
I would like to compare the AUC of two logistic regression models (same
population). Is it possible with R ?
Thank you
Roman Rouzier
[[alternative HTML version deleted]]
2001 Sep 27
1
Making a factor with common levels ...
...h vector.
I can get the list of common names quite easily, using:
nn<-sort(unique(c(levels(n1)[table(n1)>N],levels(n0)[table(n0)>N])))
Some of the factor levels may be empty for one of the factors but the same
level must be present in the other.
Is there a simple way to extract nn0 and nn1 so that the pairs remain
correctly aligned and each list has at least N cases of each name? Or do
I have to jump into my steamroller and do a couple of loops?
TIA
John
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r-help mailing list -- Read http://www.ci.tuwie...
2006 Feb 11
2
Xend crashes repeatedly starting DomU - please help...
...to get a
DomU booting. I constantly get the following error "Error: Received
invalid response from Xend, twice." on trying to start the domain
Where do I actually look to try and debug this further? I do have
SELinux compiled in on this kernel, but it''s in non-enforcing mode
nn1 xen # xm create -c ttylinux
Using config file "ttylinux".
Error: Received invalid response from Xend, twice.
nn1 xen # xm list
Name ID Mem(MiB) VCPUs State Time(s)
Domain-0 0 125 2 r----- 641.6
Domain-16...
2011 Jan 05
0
Nnet and AIC: selection of a parsimonious parameterisation
...IC ) {
cat('\n j',j,'AIC'=AIC.tmp,'AIC_1',AIC,'\n')
break
} else {
nn=nn.tmp; AIC=AIC.tmp; RSS=RSS.tmp
}
}
list(choice=sqrt(RSS/100),nparam=sum(nn$wts!=0),AIC=AIC,nn=nn)
}
#Modified function for optimisation
CVnn1 <- function(decay, formula, data, nreps=1, ri, size, linout, skip,
maxit, optimFlag=FALSE, alpha) {
truth <- log10(data$perf)
nn <- nnet(formula, data[ri !=1,], trace=FALSE, size=size, linout=linout,
skip=skip, maxit=maxit, Hess = TRUE)
RSS=(alpha-1)*sum((truth[ri != 1] - pre...