search for: ffpe

...he code is given below fdata<- function(mat){? nb = 27 # number of basis functions for the data? fbf = create.fourier.basis(rangeval=c(0,1), nbasis=nb) # basis for data? args=seq(0,1,length=24)? fdata1=Data2fd(args,y=t(mat),fbf) # functions generated from discretized y? return(fdata1)}prediction.ffpe = function(fdata1){? n = ncol(fdata1$coef)? D = nrow(fdata1$coef)? #center the data? #mu = mean.fd(fdata1)? data = center.fd(fdata1)? #ffpe = fFPE(fdata1, Pmax=10)? #p.hat = ffpe[2] #order of the model? d.hat=23? p.hat=6? #fPCA? fpca = pca.fd(data,nharm=D, centerfns=TRUE)? scores = fpca$scores[,0:d...

...below ? fdata<- function(mat){ ? nb = 27 # number of basis functions for the data ? fbf = create.fourier.basis(rangeval=c(0,1), nbasis=nb) #basis for data ? args=seq(0,1,length=24) ? fdata1=Data2fd(args,y=t(mat),fbf) # functions generatedfrom discretized y ? return(fdata1) } prediction.ffpe = function(fdata1){ ? n = ncol(fdata1$coef) ? D = nrow(fdata1$coef) ? #center the data ? #mu = mean.fd(fdata1) ? data = center.fd(fdata1) ? #ffpe = fFPE(fdata1, Pmax=10) ? #p.hat = ffpe[2] #order of the model ? d.hat=23 ? p.hat=6 ? #fPCA ? fpca = pca.fd(data,nharm=D, centerfns=TRUE) ? s...

...eters on the VAR model(4) Using VAR for the analysis and forecasting .(1)?nb = 23 # number of basis functions for the data? fbf = create.fourier.basis(rangeval=c(0,1), nbasis=nb) # basis for data? args=seq(0,1,length=24)? fdata1=Data2fd(args,y=t(mat),fbf) # functions generated from discretized y(2)?ffpe = fFPE(fdata1, Pmax=10)? d.hat = ffpe[1] #order of the model? p.hat = ffpe[2] #lag of the model (3) n = ncol(fdata1$coef)? D = nrow(fdata1$coef)? #center the data? mu = mean.fd(fdata1)? data = center.fd(fdata1)? #fPCA? fpca = pca.fd(data,nharm=D)? scores = fpca$scores[,1:d.hat](4)?# to avoid warnin...

...eters on the VAR model(4) Using VAR for the analysis and forecasting .(1)?nb = 23 # number of basis functions for the data? fbf = create.fourier.basis(rangeval=c(0,1), nbasis=nb) # basis for data? args=seq(0,1,length=24)? fdata1=Data2fd(args,y=t(mat),fbf) # functions generated from discretized y(2)?ffpe = fFPE(fdata1, Pmax=10)? d.hat = ffpe[1] #order of the model? p.hat = ffpe[2] #lag of the model (3) n = ncol(fdata1$coef)? D = nrow(fdata1$coef)? #center the data? mu = mean.fd(fdata1)? data = center.fd(fdata1)? #fPCA? fpca = pca.fd(data,nharm=D)? scores = fpca$scores[,1:d.hat](4)?# to avoid warnin...

...great lengths not to constant fold an integer division by zero. We would also like LLVM to do the same. Are there others in the community who feel this way? I can envision an option which preserves faults during constant folding. E.g. an integer version of -enable-unsafe-fp-math or gfortran's -ffpe-trap. More accurately, this hypothetical option would suppress the folding of unsafe integer expressions altogether. Would an option such as this benefit the community at large? To be complete, I've also explored the idea of generating a __builtin_trap() call for such expressions before the IR...

...ths not to constant fold an integer division by zero. We would also like LLVM to do the same. Are there others in the community who feel this way? > > I can envision an option which preserves faults during constant folding. E.g. an integer version of -enable-unsafe-fp-math or gfortran's -ffpe-trap. More accurately, this hypothetical option would suppress the folding of unsafe integer expressions altogether. Would an option such as this benefit the community at large? Clang's -fsanitize=integer-divide-by-zero option seems to provide what you want. See the Clang User Manual: http://...

On 4/5/2013 1:23 PM, Cameron McInally wrote: > Hey guys, > > I'm learning that LLVM does not preserve faults during constant > folding. I realize that this is an architecture dependent problem, but > I'm not sure if it's safe to constant fold away a fault on x86-64. > > A little testcase: > > #include <stdio.h> > > int foo(int j, int d) { >

...=======================================================================================| 100% 1 MB |=======================================================================================================| 100% Starting to add information to samples => Add clinical information to samples Add FFPE information. More information at: => https://cancergenome.nih.gov/cancersselected/biospeccriteria => http://gdac.broadinstitute.org/runs/sampleReports/latest/FPPP_FFPE_Cases.html => Adding subtype information to samples luad subtype information from:doi:10.1038/nature13385 Request to BioM...

Hey guys, I'm learning that LLVM does not preserve faults during constant folding. I realize that this is an architecture dependent problem, but I'm not sure if it's safe to constant fold away a fault on x86-64. A little testcase: #include <stdio.h> int foo(int j, int d) { return j / d ; } int bar (int k, int d) { return foo(k + 1, d); } int main( void ) { int r =