similar to: what is 'class.ind' here?

Hi. I started with a file which was a sparse 982x923 matrix and where the last column was a variable to be predicted. I did principle component analysis on it and arrived at a new 982x923 matrix. Then I ran the code below to get a neural network using nnet and then wanted to get a confusion matrix or at least know how accurate the neural net was. I used the first 22 principle components only for

Greetings, I am having trouble calculating artificial neural network misclassification errors using errorest() from the ipred package. I have had no problems estimating the values with randomForest() or svm(), but can't seem to get it to work with nnet(). I believe this is due to the output of the predict.nnet() function within cv.factor(). Below is a quick example of the problem I'm

I am using the iris example came with nnet package to test AMORE. I can see the outcomes are similar to nnet with adaptative gradient descent. However, when I changed the method in the newff to the batch gradient descent, even by setting the epoch numbers very large, I still found all the iris expected class=2 being classified as class=3. In addition, all those records in the outcomes (y) are the

Dear All, After followed the help of nnet, I could get the networks trained and, excitedly, get the prediction for other samples. It is a two classes data set, I used "N" and "P" to label the two. My question is, how do I get the predicted numerical value for each sample? Not just give me the label(either "N" or "P")? Thanks! FYI: The nnet example I

Hi there! I would like to ask kindly for some further information concerning Rsync software. What I am planning to do is to replicate data (database) at certain intervals. My first question is, can I do this with Rsync? To be more specific I have a mysql database at a point and I want this database to be replicated to a remote side at real-time. In other words I need to setup a backup

faced this exact same problem a few times on more than one servers and it was 1) dialplan issue which was not hanging up the zap channels correctly 2) using more than 8 spans on a server. Asterisk can't handle more than 96 zap channels on T1s. FXO/FXS combinations can vary the number of spans but if you know what I mean by spans, in production don't use more than 6 spans. On 2010-03-17

>> What needs to be done (on a high level) in order to have the auto vectorizer succeed on the test function as given erlier? > Maybe you could rewrite the loop in a way that will expose contiguous memory accesses. Is this something you could do ? > Hi Nadav, the only option I see is to unroll the loop by hand. Since the array access is consecutive over 4 loop iterations I gave it a

Dear all, can anybody help me with the program below? The function predict.lda seems to be defined but cannot be used by errortest. The R version is 1.7.1 Thanks in advance, Stefan ---------------- library("MASS"); library("ipred"); data(iris3); tr <- sample(1:50, 25); train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]); test <- rbind(iris3[-tr,,1],

On Oct 30, 2013, at 6:10 PM, Frank Winter <fwinter at jlab.org> wrote: > the only option I see is to unroll the loop by hand. Since the array access is consecutive over 4 loop iterations I gave it a try and unrolled the loop by a factor of 4. Which gives the following array accesses: > > loop iter 0: > index_0 = 0 index_1 = 4 > index_0 = 1 index_1 = 5 > index_0 = 2

> data(iris) # iris3 is first 3 rows of iris > iris3 <- iris[1:3,] # z compares row 1 to each row of iris3 and is correctly computed > z <- c(F,F,F) > for(i in seq(z)) z[i] <- identical(iris3[1,],iris3[i,]) > z [1] TRUE FALSE FALSE # this should do the same but is incorrect > apply(iris3,1,function(x)identical(x,iris3[1,])) 1 2 3 FALSE FALSE FALSE

I tried the following on the hand-unrolled loop: const std::uint64_t ir0 = i*8+0; // working const std::uint64_t ir0 = i%4+0; // working const std::uint64_t ir0 = (i+0)%4; // not working '+0' means +1,+2,+3 in the unrolled iterations. 'Working' means the SLP vectorizer succeeded. Thus, when working 'towards' the correct index function, auto

Dear R users, I want to do discriminant analysis on my data. I have successfully followed the discriminant analysis in V & R on the iris data: > ir <- rbind (iris3[,,1],iris3[,,2],iris3[,,3]) > ir.species <- c(rep("s",50),rep("c",50),rep("v",50)) > a <- lda(log(ir),ir.species) > a$svd^2/sum(a$svd^2) [1] 0.996498601 0.003501399 > a.x <-

Maybe you should show your colleague how to access help pages in R? Right in ?knn, it says: prob: If this is true, the proportion of the votes for the winning class are returned as attribute 'prob'. so 1.0 mean all three NNs are of the `winning'; i.e., predicted, class, and 0.66667 means 2 out of the 3 NNs are of the winning class, etc. Andy > From: Ko-Kang

Hi !! I am trying to analyze some of my data using linear discriminant analysis. I worked out the following example code in Venables and Ripley It does not seem to be happy with it. ============================ library(MASS) library(stats) data(iris3) ir<-rbind(iris3[,,1],iris3[,,2],iris3[,,3]) ir.species<-factor(c(rep("s",50),rep("c",50),rep("v",50)))

http://bugzilla.netfilter.org/show_bug.cgi?id=704 CZ <huangj at qualcomm.com> changed: What |Removed |Added ---------------------------------------------------------------------------- Status|RESOLVED |REOPENED Resolution|FIXED | --- Comment #4 from CZ <huangj at qualcomm.com>

Well, they are not directly consecutive. They are consecutive with a constant offset or stride: ir1 = ir0 + 4 If I rewrite the function in this form void bar(std::uint64_t start, std::uint64_t end, float * __restrict__ c, float * __restrict__ a, float * __restrict__ b) { const std::uint64_t inner = 4; for (std::uint64_t i = start ; i < end ; ++i ) { const std::uint64_t ir0 = (

> > library(MASS) > Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), + Sp = rep(c("s","c","v"), rep(50,3))) > train <- sample(1:150, 75) > table(Iris$Sp[train]) c s v 22 23 30 > z <- lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train) Error in if (targetlist[i] == stringname) { : argument is of length

Hi all, The machine I am targeting has some special requirements for some operations, say: ADD or1, ir1, r5 would add ir1 (input reg 1) and r5 and put the result in or1 (output reg 1). The point id that input and output regs have to go paired (this meaning an addition of ir1 with whatever always goes to or1, or an in general irX + whatever goes to orX). AFAIK, InstrInfo.td only allow

Hi Frank, The access pattern to arrays a and b is non-linear. Unrolled loops are usually handled by the SLP-vectorizer. Are ir0 and ir1 consecutive for all values for i ? Thanks, Nadav On Oct 30, 2013, at 9:05 AM, Frank Winter <fwinter at jlab.org> wrote: > The loop vectorizer seems to be not able to vectorize the following code: > > void bar(std::uint64_t start,

On Mon, Mar 03, 2014 at 02:15:43PM +0000, Struan Bartlett wrote: > > > On 03/03/2014 13:42, Martin Kletzander wrote: > > On Mon, Mar 03, 2014 at 11:15:51AM +0000, Struan Bartlett wrote: > >> On 03/03/2014 10:55, Martin Kletzander wrote: > >>> On Mon, Mar 03, 2014 at 10:47:03AM +0000, Struan Bartlett wrote: > >>>> On 03/03/2014 10:44, Martin