similar to: python-theora: some comments

Hi, in the past two days I wrote Python bindings to libtheora: http://github.com/certik/python-theora Currently it can read any ogv file (that seems to work quite well) and also encode to ogv (this mostly works, but there are some subtle issues to be fixed, I suspect I still have some bug in the way I half the dimensions of the Cb and Cr planes, so when you read ogv and write ogv, the resulting

Thanks Cristoph, I have that code in my backend, but unless I do the following, the registers are never considered 'live' into the call. / Handle a function call let isCall = 1, Defs = [ R0, R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, R11, R12, R13, R14, R15, R16, R17, R18, R19, R20, R21, R22, R23, R24, R25, R26, R27, R28, R29, R30, R31, R32, R33, R34, R35, R36, R37, R38, R39, R40, R41,

Folks: I'm trying to port some code from python over to R, and I'm running into a wall finding R code that can solve a generalized eigenvalue problem following this function model: http://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eig.html Any ideas? I don't want to call python from within R for various reasons, I'd prefer a "native" R solution if one

HI, I've noticed what I think is an incorrect behavior of stats::psmirnov function and consequently of ks.test when run in an exact mode. For example: psmirnov(1, sizes=c(50, 50), z=1:100, two.sided = FALSE, lower.tail = F, exact=TRUE) produces 2.775558e-15 However, the exact value should be 1/combination(100, 50), which is 9.9e-30. While the absolute error is small, the relative error is

Hi all, While running the anscombe.test in R, I'm getting an error of *Error in if (pval > 1) pval <- 2 - pval : missing value where TRUE/FALSE needed* for a few time series columns whereas for most of the series the function is working fine. I have checked for those specific columns for missing values. However, there is no NA/NAN value in the dataset. I have also run kurtosis for

Hi, We are looking for some guidance in installing an upgraded Python on our cluster. Our cluster was installed with Rocks 6.0, is running CentOs 6.2, and has python-2.6.6, gcc-4.4.6. We would like to install an upgraded version of Python along with the following modules NumPy Scipy (which will require a compatible version of the Atlas libraries) Mayavi (which will require a compatible

moments::anscombe.test(x) does give errors when x has too few values or if all the values in x are the same > moments::anscombe.test(c(1,2,6)) Error in if (pval > 1) pval <- 2 - pval : missing value where TRUE/FALSE needed > moments::anscombe.test(c(2,2,2,2,2,2,2,2)) Error in if (pval > 1) pval <- 2 - pval : missing value where TRUE/FALSE needed You can use tryCatch() to

I am using the non-linear least squares routine in "R" -- nls. I have a dataset where the nls routine outputs tight confidence intervals on the 2 parameters I am solving for. As a check on my results, I used the Python SciPy leastsq module on the same data set and it yields the same answer as "R" for the coefficients. However, what was somewhat surprising was the the

This thread reveals that R has some holes in the solution of some of the linear algebra problems that may arise. It looks like Jim Ramsay used a quick and dirty approach to the generalized eigenproblem by using B^(-1) %*% A, which is usually not too successful due to issues with condition of B and making a symmetric/Hermitian problem unsymmetric. In short, the problem is stated as follows:

Another bad case is > moments::anscombe.test(rep(c(1,1.1),length=35)) Error in if (pval > 1) pval <- 2 - pval : missing value where TRUE/FALSE needed I haven't checked the formulas carefully, but I suspect the problem is from taking the cube root of a negative number in z <- (1 - 2/(9 * a) - ((1 - 2/a)/(1 + xx * sqrt(2/(a - 4))))^(1/3))/sqrt(2/(9 * a)) In R, the

Hi Bill, Thanks for prompt reply and letting me know a way around it. I have more than 1200 observations and not all the values are the same. However, my data points are quite similar, for example, 0.079275, 0.078867, 0.070716 in millions and etc. I have run the data without converting it to millions and I still get the same error message. As I have kurtosis value, it should be fine for the

Thanks for the response. I was not very clear in my original request. What I am asking is if in a non-linear estimation problem using nls(), as the condition number of the Hessian matrix becomes larger, will the t-values of one or more of the parameters being estimated in general become smaller in absolute value -- that is, are low t-values a sign of an ill-conditioned Hessian? Typical

include/Rmath.h declares a set of 'logspace' functions for use at the C level. I don't think there are core R functions that call them. /* Compute the log of a sum or difference from logs of terms, i.e., * * log (exp (logx) + exp (logy)) * or log (exp (logx) - exp (logy)) * * without causing overflows or throwing away too much accuracy: */ double Rf_logspace_add(double

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi All, I found an interesting thread discussing R and other packages here: http://anyall.org/blog/2009/02/comparison-of-data-analysis-packages-r-matlab-scipy-excel-sas-spss-stata/ Plenty of well-reasoned comments. I thought it may be informative for people on this list. Best, - -Jose - -- Jose Quesada, PhD. Max Planck Institute, Center for

Hi All, Thanks for all your comments which allows me to appreciate more of these in Python and R. I just came across the matrixStats package, ## EXAMPLE #1 lx <- c(1000.01, 1000.02) y0 <- log(sum(exp(lx))) print(y0) ## Inf y1 <- logSumExp(lx) print(y1) ## 1000.708 and > ly <- lx*100000 > ly [1] 100001000 100002000 > y1 <- logSumExp(ly) > print(y1) [1] 100002000

I agree with many the sentiments about the wisdom of computing very small p-values (although the example below may win some kind of a prize: I've seen people talking about p-values of the order of 10^(-2000), but never 10^(-(10^8)) !). That said, there are a several tricks for getting more reasonable sums of very small probabilities. The first is to scale the p-values by dividing the

Hello, I don't know if this question ought to go here, or rather on R-devel, so please bear with me. I'm interfacing to R via RPy (rpy.sf.net) and an embedded Python interpreter. This is really quite convenient. I use this approach to calculate the correlation coefficient of 1 independent dataset (vector) with 4 dependent vectors. It'd be nice if that could be done in 4 parallel

Hello, I don't know if this question ought to go here, or rather on R-devel, so please bear with me. I'm interfacing to R via RPy (rpy.sf.net) and an embedded Python interpreter. This is really quite convenient. I use this approach to calculate the correlation coefficient of 1 independent dataset (vector) with 4 dependent vectors. It'd be nice if that could be done in 4 parallel

Per the recent (seeming) consensus regarding incubating new projects under the LLVM organization, I would like to trial the process by requesting to incubate mlir-npcomp <https://github.com/google/mlir-npcomp>. The project is still quite young and has been primarily developed part time by myself and Sean Silva over the last ~2 months. We set it up following discussion of a Numpy/Scipy op set

Dear Colleagues/Developers/R enthusiasts, Would it be possible to develop a code of conduct (CoC) document for R lists, CRAN submissions that all developers/maintainers to follow? This may help all of us to better communicate and move forward together. There is a similar effort from Python community, here are the links: * https://mail.python.org/pipermail/scipy-dev/2017-August/022044.html *