search for: impure

...n 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 = %d\n", a, b, c) __attribute__((annotate("call_ impure"))); Finding purity of functions should be possible by parsing each function across files and build up a call dag tree. You can traverse bottom-up to mark each function as impure...

...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 to achieve this by adding something like __attribute__((annotate("pure"))) and __attribute__((annotate("call_impure"))) and how much effort will it be to add such a functionality. Basically, I want "pure" attribute to be sticky, in the sense that every function that's pure, can only call pure code unless explicitly marked inside the function to make impure call. For example, the following code...

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

...d 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 which shouldn't have > any impure type casting operations. > > This can all be avoided by limiting your optimization to code that uses > pointer-size loop counters! > > -Andy > The array indices are out of my control; depends on the code people write. If they'd use long ints for everything, life would be good...

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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

...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 which shouldn't have > any impure type casting operations. > > This can all be avoided by limiting your optimization to code that uses > pointer-size loop counters! > > -Andy > > > The array indices are out of my control; depends on the code people write. If > they'd use long ints for ever...

...Organizing the functions we want to be pure so that they are not defined in the same environment in which they are called actually helps to ensure function purity in the input direction, since those functions will not have lexical-scope access to called variables. (That is, you may have written an impure function without realizing it; organizing functions so they are not defined in the same environment as when they are called helps to ensure purity.) Perhaps I am mistaken, but in either case, your points actually favor a main() idiom, unless you take using main() to mean using main() with extra bi...

...now 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 which shouldn't have any impure type casting operations. This can all be avoided by limiting your optimization to code that uses pointer-size loop counters! -Andy -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20120919/c7b80b55/attachment...

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

...nsider 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 which shouldn't have >> any impure type casting operations. >> >> This can all be avoided by limiting your optimization to code that uses >> pointer-size loop counters! >> >> The array indices are out of my control; depends on the code people >> write. If they'd use long ints for every...

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