search for: predit

...opInfo *LI, const Twine &NameSuffix, ValueToValueMapTy &VMap) { // original preheader of the loop const auto PreHeader = L->getLoopPreheader(); // keep track of the original predecessors std::set<BasicBlock *> AllPredecessors; for (auto PredIt = pred_begin(PreHeader), E = pred_end(PreHeader); PredIt != E; PredIt++) AllPredecessors.insert(*PredIt); BasicBlock *ExitBlock = L->getExitBlock(); auto DT = DominatorTree(*F); SmallVector<BasicBlock *, 8> Blocks; const auto ClonedLoop = clo...

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, how could I update the predecessor list ? I...

...000 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) How do I get predict to work on just the subset of people for whom the model is estimated? Thanks Peter

...the previous Y directly? Second, from the previos letter,I know that floor is the least squared error approximation of the maximum amount of noise allowable for each mdct coefficient/frequency (determined by the psychoacustic model). Now I am just confused whatever the floor really is .Is it a predition to the MDCT coefficient or just an estimation to the maximum allowable noise? And how the psychoaustic result is applied to the compution of the floor? Looking forward to your letter! ????????Lisa ????????fangchao_dlut@sina.com ??????????2005-09-26

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 read about the dovecot proxying features and it seems to make sense to replace predition in that above scenario. I'm unclear about the benefits of the auth_cache and how it compar...

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 "prediction$pred" to the level data since it has been differenced once? I know exp (prediction$pred) will bring the inverse of the log transform but what about the differenced transform? This is m...

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:

...ial pca step, multinom, pls, plsda, gpls, nearest shrunken centroids, the lasso, the elastic net, supervised pca, knn, lvq and NaiveBayes. Recent changes include: - Estimation of class probabilities from PLS discriminant analysis using Bayes rule (in addition to softmax) - Added predict.train and predit.list - More lattice plots to visualize resampling results (xyplot, stripplot, densitplot, histogram) - User-specified performance metrics for resampling - User-specified algorithms for determining the optimal tuning parameters (instead of highest/lowest) - A CHANGES files now exists to track the sp...

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 difficulties in building the classifier and I was wondering if someone else has worked on...

...ial pca step, multinom, pls, plsda, gpls, nearest shrunken centroids, the lasso, the elastic net, supervised pca, knn, lvq and NaiveBayes. Recent changes include: - Estimation of class probabilities from PLS discriminant analysis using Bayes rule (in addition to softmax) - Added predict.train and predit.list - More lattice plots to visualize resampling results (xyplot, stripplot, densitplot, histogram) - User-specified performance metrics for resampling - User-specified algorithms for determining the optimal tuning parameters (instead of highest/lowest) - A CHANGES files now exists to track the sp...

...I ran into problems: My guess was to use the loadings matrix. In order to check the results, I took the scores from the model, mapped them to variable space and mapped them back to scores using the predict function. The result of this procedure should again be the scores. I know the last step - prediting scores - works (see above). Therefore, if I ended up with the original scores after my transformations, I knew my first step - mapping of scores to variable space - was correct too, I would indeed obtain the original scores. Obviously, I am mistaken (otherwise, I wouldn't ask you for help)...

...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 a...