search for: truth

Trying to resolve some general tablegen questions. Consider the test case for Tablegen called eq.td class Base<int V> { int Value = V; } class Derived<string Truth> : Base<!if(!eq(Truth, "true"), 1, 0)>; def TRUE : Derived<"true">; def FALSE : Derived<"false">; If I process this through tablegen I get: ------------- Classes ----------------- class Base<int Base:V = ?> { int Value = Base:V; s...

>From my understanding, NAME is a special builtin entry and dedicated for things related multiclass, So, is the following rewrite what you want? class Base<int V> { int Value = V; } class Derived<string Truth> : Base<!if(!eq(Truth, "true"), 1, 0)>; multiclass Derived_m<string T> { def #NAME# : Derived<T>; } defm TRUE : Derived_m<"true">; defm FALSE : Derived_m<"false">; tablegen result: ------------- Classes ----------------- class...

I have access to a cluster on which I have been supplied with R 3.1.0 which appears to have been built using the intel compiler tools. The following minimal Fortran file: subroutine truth(lind) logical lind lind = .TRUE. end Compiles thusly: arcadia> R CMD SHLIB truth.f ifort -fpic -O3 -xHOST -axCORE-AVX-I -fp-model precise -c truth.f -o truth.o ifort: command line warning #10212: -fp-model precise evaluates in source precision with Fortran. icc -std=gnu99...

...1:dim(rae.matrix)[1]) { if (rae.vector[k] == 1) rae.matrix[k,1] <- rae.matrix[k,1] + 1 else rae.matrix[k,2] <- rae.matrix[k,2] + 1 } rae.matrix } CVnn1 <- function(formula, data, nreps=1, ri, verbose, ...) { totalerror <- 0 truth <- data[,deparse(formula[[2]])] res <- matrix(0, nrow(data), length(levels(truth))) if(verbose > 20) cat(" inner fold") for (i in sort(unique(ri))) { if(verbose > 20) cat(" ", i, sep="") for(rep in 1:nreps)...

...try this: # find the index of the maximum value in each row of _data_, # disregarding the last column classified <- apply(data[,-(nclass+1)],1,which.max) ## or, if the maximum may be repeated: classified <- apply(data[,-(nclass+1)], 1, FUN = function(x) which(x == max(x))) # the variable _truth_ is just the last column of _data_ ? truth <- data[,nclass + 1] ?table z <- table(classified, truth) HTH Joe Joe Crombie Biosecurity and Information Sciences Bureau of Rural Science Canberra Australia e: joe.crombie at brs.gov.au -----Original Message----- From: r-help-bounces...

Has anyone seen this: http://www.microsoft.com/windows/Embedded/sak/evaluation/compare/advantage.asp And has anyone from the Samba team posted a response? It seems like some of the information presented there is contrary to what I know about Samba. ERX

...2012 11:28 PM, WANG.Jiong wrote: > From my understanding, NAME is a special builtin entry and dedicated for > things related multiclass, > > So, is the following rewrite what you want? > > class Base<int V> { > int Value = V; > } > > class Derived<string Truth> : > Base<!if(!eq(Truth, "true"), 1, 0)>; > > multiclass Derived_m<string T> { > def #NAME# : Derived<T>; > } > > defm TRUE : Derived_m<"true">; > defm FALSE : Derived_m<"false">; > > tablegen resu...

...nreps = 5, nifold = 10, alpha= 9/10, linout = TRUE, skip = TRUE, maxit = 1000,...){ #nreps=number of attempts to fit a nnet model with randomly chosen initial parameters # The one with the smallest RSS on the training data is then chosen nnWtsPrunning <-function(nn,data,alpha,i){ truth <- log10(data$perf) RSS=(1-alpha)*sum((truth[ri != i] - predict(nn, data[ri != i,]))^2) + alpha* sum((truth[ri == i] - predict(nn, data[ri == i,]))^2) AIC=2*sum(nn$wts!=0) + length(data$perf)*log(RSS) nn.tmp=nn for (j in (1:length(nn$wts))) { nn.tmp$wts[order(diag(...

According to the documentation at http://docs.puppetlabs.com/puppet/2.7/reference/lang_conditional.html#if-statements, you can test for truth with syntax like this: if $is_virtual == ''true'' { AFAICT that''s not the case. The quotes around ''true'' make the comparison always fail. Here''s my test case class users { define u($managehome=true) {...

...ot; can be used? Thanks. ### Start nclass <- dim(data)[[2]] - 1 z <- matrix(0, ncol = nclass, nrow = nclass) n <- dim(data)[[1]] x <- c(1:nclass) # loop starts for(loop in 1:n) { r <- data[loop, 1:nclass] classified <- x[r == max(r)] truth <- data[loop, nclass + 1] z[classified, truth] <- z[classified, truth] + 1 } # loop ends --------------------------------- [[alternative HTML version deleted]]

...)}) CVnn2 <- function(formula, data, size = c(0,4,4,10,10), lambda = c(0, rep(c(0.001, 0.01),2)), nreps = 1, nifold = 5, verbose = 99, ...) { CVnn1 <- function(formula, data, nreps=1, ri, verbose, ...) { totalerror <- 0 truth <- data[,deparse(formula[[2]])] res <- matrix(0, nrow(data), length(levels(truth))) if(verbose > 20) cat(" inner fold") for (i in sort(unique(ri))) { if(verbose > 20) cat(" ", i, sep="") data.training...

...(rae.vector[k] == 1) rae.matrix[k,1] <- rae.matrix[k,1] + 1 else rae.matrix[k,2] <- rae.matrix[k,2] + 1 } rae.matrix } CVnn1 <- function(formula, data, nreps=1, ri, verbose, ...) { totalerror <- 0 truth <- data[,deparse(formula[[2]])] res <- matrix(0, nrow(data), length(levels(truth))) if(verbose > 20) cat(" inner fold") for (i in sort(unique(ri))) { if(verbose > 20) cat(" ", i, sep="") data.training...

This was intended to go to the mailing list :) -------------- next part -------------- An embedded message was scrubbed... From: Maik Merten <maikmerten@gmx.net> Subject: Re: [Theora] troll or truth ? Date: Wed, 23 Mar 2005 01:40:00 +0100 Size: 1111 Url: http://lists.xiph.org/pipermail/theora/attachments/20050323/5fe9d818/Theoratrollortruth-0001.mht

Does Puppet have any genuine concept of truth and nil that survives the interface with Ruby? has_variable? only returns false if a variable has never been assigned a value at all in the enclosing scope; there is no way to assign undef to a regular variable (as opposed to a resource parameter) within a scope and so make it undefined for the te...

...'s property says that, for large sample size it is normally distributed [i.e. asymptotically normal]. On the other hand it is "Consistent" as well. My doubt is, how this two asymptotic properties exist simultaneously? If it is consistent then asymptotically it should collapse to "truth" i.e. for large sample size, variance of MLE should be zero. However asymptotic normality says, MLE have some distribution and hence variance.   Can anyone please clarify me? Your help will be highly appreciated. --- On Wed, 8/10/08, Ron Michael <ron_michael70@yahoo.com> wrote: From:...

...t; Not exactly - I think that future versions of qemu might lie > about some devices but not others. Can we keep this simple? QEMU currently lies about some devices. Let's implement a heuristic for the guest OS to know about that, and react accordingly. Then let's fix QEMU to tell the truth. All the time, unconditionally. Even on POWER/ARM where there's no obvious *way* for it to tell the truth (because you don't have the flexibility that DMAR tables do), and we need to devise a way to put it in the device-tree or fwcfg or something else. And only once QEMU consistently tells...

...t; Not exactly - I think that future versions of qemu might lie > about some devices but not others. Can we keep this simple? QEMU currently lies about some devices. Let's implement a heuristic for the guest OS to know about that, and react accordingly. Then let's fix QEMU to tell the truth. All the time, unconditionally. Even on POWER/ARM where there's no obvious *way* for it to tell the truth (because you don't have the flexibility that DMAR tables do), and we need to devise a way to put it in the device-tree or fwcfg or something else. And only once QEMU consistently tells...

...on is used (NULL, NA, or no default value at all), the documentation should probably explain how the argument will be handled in case it's missing (unless this is obvious). Anyway, what solution should be retained for mget() is not important. My point is that the \usage section should tell the truth, and, if not, 'R CMD check' should issue a warning. Many times we've seen argument default values for Bioconductor functions not reported in the \usage section. Each time it was *not* intended (typically the maintainer would add the argument default value in the function definition an...

...with pch=3, lwd=3. It has a high line width to attract attention to it. The following script plot(rnorm(10),rnorm(10),col="black") points(rnorm(10),rnorm(10),col="red") points(0,0,pch=3,lwd=3) if(TRUE){ legend("bottomleft",c("a","b","Truth"),pch=c(1,1,3),col=c ("black","red","black")) } else { legend("bottomleft",c("a","b","Truth"),pch=c(1,1,3),col=c ("black","red","black"),lwd=c(0,0,3)) } doesn't quite work as desired:...

Dear list, I think I'm being dense, but I can't get combn or expand.grid to give me this result. I need to generate a matrix of all 16 possible sequences of 4 boolean elements, 0000 0001 0010 0011 0100 ..... 1111 (in the end I'll have to assign NA to the 0s and some value to the 1s but let's keep it simple) OK, I could go the following route, but I feel like reinventing the