search for: impurities

Hi Suman, I think you can ascertain pureness automatically leveraging the compiler instead of manually tagging attribute to each method and call-site. It would seem like impurity should be a transitive attribute. So this would conflict with below. __attribute__((annotate("pure"))) int add(uint32_t a, uint32_t b) { // impure by calling printf... ... printf("%d + %d =

Hello folks, I'm not a compiler expert or subscribed to this mailing list, but I have a unique problem. I need to split a large piece of C/C++ code into two separate libraries: one library that only has pure code (i.e., code that doesn't require operating system interactions) and other library that can have both pure code and side-effecting code. I was was wondering if it's possible

Hi, this should be an easy one, but I can't figure it out. I have a vector of tests, with their units between brackets (if they have units). eg tests <- c("pH", "Assay (%)", "Impurity A(%)", "content (mg/ml)") Now I would like to hava a function where I use a test as input, and which returns the units like: f <- function (x) sub("\\)",

Hello dear R-help members, I would appreciate any help in understanding how the rpart function computes the "improve" (which is given in fit$split) when using the split='information' parameter. Thanks to Professor Atkinson help, I was able to find how this is done in the case that split='gini'. By following the explanation here:

Hello everyone, I'm a newbie to R and to CART so I hope my questions don't seem too stupid. 1.) My first question concerns the rpart() method. Which method does rpart use in order to get the best split - entropy impurity, Bayes error (min. error) or Gini index? Is there a way to make it use the entropy impurity? The second and third question concern the output of the printcp() function.

Hi all, I apologies in advance if I am missing something very simple here, but since I failed at resolving this myself, I'm sending this question to the list. I would appreciate any help in understanding how the rpart function is (exactly) computing the "improve" (which is given in fit$split), and how it differs when using the split='information' vs split='gini'

Dear everybody, I'm a new user of R 2.4.1 and I'm searching for information on improving the output of regression tree graphs. In the terminal nodes I am up to now able to indicate the number of values (n) and the mean of all values in this terminal node by the command > text(tree, use.n=T, xpd=T) Yet I would like to indicate automatically in the output graph of the tree some

HI, Dear Andy, I run the RandomFOrest in R, and get the following resutls in variable importance: What is the meaning of MeanDecreaseAccuracy and MeanDecreaseGini? I found they are raw values, they are not scaled to 1, right? Which column if most similar to the variable rel.influence in Boosting? Thanks so much! > fit$importance 0 1

Dear R, Short verse of the question: Is there R code which will calculate surrogate splits and/or delta impurity for decision trees at each node? Long Version: I have local, legacy code which I use to calculate my decision trees. I would like to switch to R, but as I understand it surrogate splits are not implemented. Surrogate splits and feature ranking are described in Breiman et al

On Wed, Sep 19, 2012 at 6:30 PM, Andrew Trick <atrick at apple.com> wrote: > > On Sep 19, 2012, at 2:18 PM, Preston Briggs <preston.briggs at gmail.com> > wrote: > > On Tue, Sep 18, 2012 at 10:59 PM, Andrew Trick <atrick at apple.com> wrote: > >> >> On Sep 18, 2012, at 8:21 PM, Preston Briggs <preston.briggs at gmail.com> >> wrote:

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Dear R community, I have recently discovered the package oblique.tree and I must admit that it was a nice surprise for me, since I have actually made my own version of a kind of a classifier which uses the idea of oblique splits (splits by means of hyperplanes). So I am now interested in comparing these two classifiers. But what I do not seem to understand is why the function

I have a question about predictor importances in randomForest. Once I've run randomForest and got my object, I get their importances: rfresult$importance I also get the "standard errors" of the permutation-based importance measure: rfresult$importanceSD I have 2 questions: 1. Because I am dealing with regressions, I am getting an importance object (rfresult$importance) with two

Hi, > Sorry, I probably led you astray. No-self-wrap is useful for determining > trip count, but does not mean that sign/zero extension can be hoisted. > > But if you run your analysis after -indvars, the sign-extension should be > removed if possible. The algorithm walks the derived induction variables > specifically looking for add nsw/nuw and replacing

I appreciate the well-thought-out comments. To your first point, I am not sure what "glattering" means precisely (a Google search revealed nothing useful), but I assume it means something to the effect of overfilling the main namespace with too many names. Per Norm Matloff's counterpoint in The Art of R Programming regarding this issue, this is mostly avoided by well-defined,

On Sep 19, 2012, at 2:18 PM, Preston Briggs <preston.briggs at gmail.com> wrote: > On Tue, Sep 18, 2012 at 10:59 PM, Andrew Trick <atrick at apple.com> wrote: > > On Sep 18, 2012, at 8:21 PM, Preston Briggs <preston.briggs at gmail.com> wrote: > >> Given the following SCEV, >> >> (sext i32 {2,+,1}<nw><%for.body> to i64) >>

I am trying to use shfs to mount a remote root filesystem for a diskless workstation. The system downloads its kernel and initrd from a server. I have tried repeatedly to get a working installation of ssh on the initrd with no success. I finally got ssh to connect to the server. Normally when I use ssh the session goes as follows [arrummzen at localhost LFS-BOOK-6.0-HTML]$ ssh 192.168.11.10 -l

>> Are all your array indices uniformly sign-extended? >> I don't know if this is a good idea, but why can't you consider the sext >> operand the array index rather than the gep operand? If you prove that the >> narrow indices are disjoint, then the extended indices must be disjoint. >> SCEV operations should work fine on the narrow indices

Dear R community, as stated in Breiman et.al. (1984) and De'Ath & Fabricius (2000) using sums of absolute deviations about the median as an impurity measure gives robust trees. I would like to use this method in rpart. Has somebody already tried this method in rpart? Is there maybe already a script available somewhere? I am aware of the possibility to define usersplits myself with

Dear r-help-list: I' got a question about the computation of the improve of a split. The following is an extract of an output of the summary of a tree: Node number 1: 600 observations, complexity param=0.007272727 predicted class=0 expected loss=0.1666667 class counts: 500 100 probabilities: 0.833 0.167 left son=2 (211 obs) right son=3 (389 obs) Primary splits: x4