search for: predited

Hello all, I’m pretty new to LLVM. I'm writing a pass for loop optimization. I clone and rearrange loops, setting the cloned loop as the original loop’s parent. This can be done multiple times, until there is no more work to do. The trouble is, after the first time I do this, the cloned loop's SCEVs become unknown types when they should be AddRecExpr. If I re-run the whole pass on the

hello, In my time series data, I was able to successfully fit its ARIMA model (Box-Jenkins) and its GARCH model and estimate their parameters. I was also able to forecast future values of the time series based on my fitted ARIMA model using the predict() function call. However, I'm not sure what is the correct function command to call in order to forecast future values of my time series

Hi all, for(pred_iterator PI=pred_begin(BB); PI!=pred_end(BB); PI++) { BasicBlock *Pres = *PI; pred.insert(Pres); } for(predit = pred.begin(); predit!=pred.end();predit++) (BB)->removePredecessor(*predit); Is this code enough to remove the predecessor for each basic block As I read in documentation that this removePredecessor() doesn't update the predecessor list. So,

Hello running R 1.7.1 on Windows 2000 I have a model notmar1 <- glm(yprisx~age+harddrug+sex, subset = marcom == 0, family = quasipoisson) and summary(notmar1) gives (as it should) 433 df for the null model but when I run predict(notmar1 <- glm(yprisx~age+harddrug+sex, subset = marcom == 0, family = quasipoisson)) I get preditions for 528 people (the full data set, not the subset)

VorbisHi Any help is appreciate. I have two questions. First, I looked into the vorbis-?-specific and was confused by the floor1 algorithm. I think at last the aim is to derive the piecewise curve with the list X and Y,then when encodering ,why not use the selected point orderly to get the curve ? In other words, the specific use the list of [0,128,64,32,96,16,48,80] , why can not use

Hi, I'm planning on a new mail infrastructure which constists of multiple 'frontends' running webmail & public access pop/imap, which would communicate over imap/pop to 'backend mail stores'. My original idea was to run dovecot on the backends, use a predition on the frontends to proxy imap/pop and also run imapproxy in front of webmail [squirrelmail]. I've since

Good morning to you all, Sorry for taking your time from your research and teaching schedules.   If you have a non-stationary univariate time Series data that has the transformation: Say; l.dat<-log (series) d.ldat<-diff (l.dat, differences=1) and you fit say arima model. predit.arima<-predict (fit.series, n.ahead=10, xregnew= (n+1) :( n+10)) How could I re-transform

Hi there, I have just started using the MASS package in R to run M-estimator robust regressions. The final output appears to only give coefficients, degrees of freedom and t-stats. Does anyone know why R doesn't compute R or R-squared and why doesn't give you any other indices of goodness of fit? Does anyone know how to compute these in R? Sophie -- View this message in context:

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

Hello all, I am working at the FORTH institute in Crete and it's been a long now that I am trying to reproduce the results of the paper : "Gene expression profiling predits clinical outcome of breast cancer", by Van't Veer et al. It has been published in NATURE, vol 415, 31 January 2002. http://www.nature.com/nature/journal/v415/n6871/full/415530a.html I am facing some

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

Dear List! I am trying to calculate the distance between original data points and their position in the PLS model. In order to do this, I tried to predict the scores using the predict.mvr function and calculate the corresponding positions in variable space. The prediction of scores works perfectly: ------ data(trees) # build model t<-plsr(Volume~.,data=trees) # predict scores for training

...o select some cases at random (at least I think so). Second, and more important, I want to classify the data in order to process the available information about a dependence of X from the two explaining variables "prediction horizon" and "prediction level", i.e. the level of the predited power output in relation to the maximum power output, the latter also called nominal power or rated power. That?s why the sample I want to analyse is reduced down to about 300 cases. As the mean of X is unsurprisingly always close to zero, I want to gather information about the dispersion of X as...