similar to: [LLVMdev] newbie with pass registering Problem

Hi!! This is my first time with llvm. I'm still learning and really need help. I wrote only one Function Pass, which uses another Function Pass (blockNrs), and registered this: RegisterPass<FunctionAnalysis> X("gasched", "Genom Scheduling Pass"); When I compile my sourcecode, everything was okay. But when I tried to test it, i got this error Message: nicole at

Hi John!! >1) Have you ensured that your passes have different arguments to the = >RegisterPass constructor (i.e., they have different names)? Yes, I only write one Pass (Function Pass) and gave it that one name. >2) Are you sure that variable X is only being defined once? There are = >some ways in which you could have unintentionally defined it twice. For = >example, if you

Hi!! It's me again. I guess that there is a problem in the Makefile of my source code. But I don't know where. Could you please verify it? The Makefile is written like this: # Makefile for Genetic Algorithm Pass CXXFLAGS = -Wall -I/iss/fpga3/nicole/galib247/ LDFLAGS= -L/iss/fpga3/nicole/galib247/ga/ -lga -Wl,-E # Path to top level of LLVM heirarchy LEVEL=../../../ # Name of the

Hello, my name is Marc. I wrote a llvm function pass by using the "hello world" tutorial. But this selfwritten pass (GA_VHDL_Pass) dependes on another function pass (GenomePass). GenomePass also dependes on two other function passes. The Pass GenomePass generates an object, which is used in my GA-VHDL_Pass. When I compile my Make file I always get this error message:

Hi, I would like to give a quick update on what my current design and implementation go like. Basically, the `FunctionToLoopPassAdaptor` is now modified to a generic version `FunctionToLoopUnitPassAdaptor`, which allows re-using the existing codes to implement `FunctionToLoopNestPassAdaptor`, with the help of additional metadata associated with `Loop` and `LoopNest`. Both

Hi I would like to eliminate a large number of lines of the dataframe df1 The lines to delete are given here by the values of Mat (ex : 2,4,7,10). but I have a large number (300) values of Mat dput(df1) structure(list(Mat = c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 10, 10, 10, 11,

To detect heteroscedasticity for a multiple linear OLS regression (no time dependencies): What if the residuals vs. fitted values plot shows well behaved residuals (cloud) - but the some of the x versus residuals plots are a megaphone? Also, it seems that textbooks and internet tutorials in R do not agree what is the best plot for detecting heteroscedasticity. What do you use? I found so

Hi Devang, > Did you use static ID ? For example ... > > class VISIBILITY_HIDDEN GVN : public FunctionPass { > bool runOnFunction(Function &F); > public: > static char ID; // Pass identification, replacement for typeid > GVN() : FunctionPass((intptr_t)&ID) { } > ... > Yes, I did. And actually everything was doing fine until I added galib (

I have what I hope is a simple question - is it possible to do time series analysis on a small data set specifically only four data points? I have collected human threat data (mean number of threats per kilometre walked/ survey) every 3 months in eight different sites (four with an experimental element and four control sites). I am trying to determine the best way to determine if there is a

Dear R-users, we are happy to announce the release of our R package parcor. The package contains tools to estimate the matrix of partial correlations based on different regularized regression methods: Lasso, adaptive Lasso, PLS, and Ridge Regression. In addition, parcor provides cross-validation based model selection for Lasso, adaptive Lasso and Ridge Regression. More details can be found

Dear R-users, we are happy to announce the release of our R package parcor. The package contains tools to estimate the matrix of partial correlations based on different regularized regression methods: Lasso, adaptive Lasso, PLS, and Ridge Regression. In addition, parcor provides cross-validation based model selection for Lasso, adaptive Lasso and Ridge Regression. More details can be found

Dear R users, we proudly announce the latest release of our R package plsdof: Degrees of Freedom and Statistical Inference for Partial Least Squares. Features include: * Degrees of Freedom estimates for Partial Least Squares (PLS) Regression * Model selection for PLS based on various information criteria and on cross-validation * approximate confidence intervals and significance tests for PLS

Dear R users, we proudly announce the latest release of our R package plsdof: Degrees of Freedom and Statistical Inference for Partial Least Squares. Features include: * Degrees of Freedom estimates for Partial Least Squares (PLS) Regression * Model selection for PLS based on various information criteria and on cross-validation * approximate confidence intervals and significance tests for PLS

Hello, I have a very simple data set i imported from excel including 96 averages in a column along with 96 standard errors associated with those averages (calculated in excel). I plotted the 95 averages using r and I am wondering if it is possible to plot the second column of standard errors while applying error bars to each value so they represent the error corresponding to each average? thanks,

Hello, I'm doing a comparative analysis of mammal brain and body size data. I'm following Charlie Nunn and Natalie Cooper's instructions for "Running PGLS in R using caper". I run into the following error when I create my comparative dataset, combining my phylogenetic tree (mammaltree) and taxon measures (mammaldata): "Error in phy$node.label[which(newNb > 0) -

Hi, I wonder if you can help me: I cannot seem to be able to import my data anymore. When I try to import the attached file with the string FunctRes<-read.table("C:/Documents and Settings/FunctRes.txt", header=FALSE) I obtain: > FunctRes V1 1 ??C 2 \n 3 C 4 0 5 0 6 B 7 \n 8 C 9 0 10 1 11 B 12 \n 13 C 14 0 15 0 16 B 17 \n 18 C 19 0

Hello everybody, I'm trying to install rJava to use JRI in a Java program as JRI is a part of rJava. There should be the option to install this package via install.packages("rJava"). Unfortunately I'm connected to the internet via a proxy. I have no idea how to tell R to connect via the proxy. The R-help tells me something about environment variables like "http_proxy".

What is the exact formula used in R lm() for the Adjusted R-squared? How can I interpret it? There seem to exist several formula's to calculate Adjusted R-squared. Wherry’s formula [1-(1-R2)·(n-1)/(n-v)] McNemar’s formula [1-(1-R2)·(n-1)/(n-v-1)] Lord’s formula [1-(1-R2)(n+v-1)/(n-v-1)] Stein 1-(n-1/n-k-1)(n-2)/n-k-2) (n+1/n) Theil's formula (found here:

Hi, I’m Nicole Haenni and I'm doing research for my thesis at the University of Berne (scg.unibe.ch) with Mircea Lungu and Niko Schwarz. We are researching on monitoring the activity in software ecosystems. This is a study about information needs that arise in such software ecosystems. I need your help to fill out the survey below. It takes about 10 minutes to complete it. A software

Dear R users, an update of the package ppls - Penalized Partial Least Squares - is now available on CRAN. It implements the methods described in N. Kr?mer, A.-L. Boulesteix, G. Tutz "Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data" Chem. Intell. Lab. Sys. 2008 http://dx.doi.org/10.1016/j.chemolab.2008.06.009 Features of the package