similar to: convolution bug (PR#408)

To add to the wish-list for "convolve": For modeling processes that decay exponentially in time, e.g., fluorescence, it is desirable to have a function that convolves an arbitrary vector with an exponential using an iterative method. In the TIMP package (which won't be on CRAN till R 2.5.0 is official, but is for now at www.nat.vu.nl/~kate/TIMP) we implemented this special-purpose

Dear R-ophiles, I've found something very odd when I apply convolve to ever larger vectors. Here is an example below with vectors ranging from 2^11 to 2^17. There is a funny bump up at 2^12. Then it gets very slow at 2^16. > for( i in 11:20 )print( system.time(convolve(1:2^i,1:2^i,type="o"))) user system elapsed 0.002 0.000 0.002 user system elapsed 0.373

Hello everyone, I'm a new comer to this mailing list. I found that the echo_diagnostic.m included in speex-1.2rc1 seems to have grammar mistakes which cause parse error in octave 3.6.2. I wonder if anyone have ever executed this code under octave/matlab. I also made some minor modifications to the file to correct those errors and publish it here. If it can helps the project to be more

Hi, Man page for 'convolve' says: conj: logical; if 'TRUE', take the complex _conjugate_ before back-transforming (default, and used for usual convolution). The complex conjugate of 'x', of 'y', of both? In fact it seems that it takes the complex conjugate of 'y' only which is OK but might be worth mentioning because (1) conj=TRUE is the

I have been experimenting with convolve(). What I know about convolution I learned from engineering (they call this stuff linear systems theory), not statistics, so maybe this is all just a matter of different conventions. BUT I notice very weird things with convolve(). 1. First example, from the classic Bracewell The Fourier transform and its applications, chap 3 (p.32 in 2nd edition): {2 2 3 3

Hi I have made a R function 'convolve2' for convolution of two real valued vectors based on Rs 'convolve' with option type="open" - see below. (exp.length and irf.length are variables set in another part of the program) I wish to implement the function convolve2 in C and use it in a function used from R with .Call - e.g. I need to call fft in C. All I can find in the

Hi, I'm new to R, and I'm a bit confused with the "convolve()" function. If I do: x<-c(1, 2, 3) convolve(x, rev(x), TRUE, "open") = 9 12 10 4 1 But I expected: 3 8 14 8 3 (like in Octave/MATLAB - conv(x, reverse(x)) ) 3 2 1 x 1 2 3 = 3 2 1 0 6 4 2 0 0 9 6 3 = 3 8 14 8 3 The thing is, that "convolve(x, x, TRUE, "open")" works. For me

Dear all, I have an image that I need to filter, and so I'm looking for a method to convolve it with a matrix. If I understood the docs for convolve correctly, it only works in 1D (and I have tried to convolve, it didn't look good). So, I wondered if anybody have implemented 2D convolution in R, or have any good advices to share (beyond having a look at mvfft), before I go hacking?

I want to generate the following outcome using convolution of two sequences. x <- c(1,2,3,4,5) y <- c(6,7,8,9) The resulting convolution vector is 6 19 40 70 100 94 76 45 When using convolve(), it is hard to produce the result above. Would you help me out to get that? Best regards Moohwan Kim

Dear R-Helpers, I want to try the 2D (two-dimensional) convolution in R. For example, let us we have the following kernel and data. kernel <- (1,2,3,2,1) data <- array(1:100, dim=c(10,10)) I know the function 'convolve' only for one-dimensional convolution, but it is just for a 1D sequence. Is there any function for 2D convolution? For theory, please refer to the following link:

We obtained some disturbing results from convolve() (inaccuracies and negative probabilities). We'll try to make the context clear in as few lines as possible... Our function panjer() (code below) basically computes recursively the probability mass function of a compound Poisson distribution. When the Poisson parameter lambda is very large, the starting value of the recursive scheme ---

Dear R-users, I am trying to translate a matlab code for calculating the Local Whittle estimator in time series with long memory originally written by Shimotsu and available free in his webpage ( http://www.econ.queensu.ca/pub/faculty/shimotsu/ ) The Matlab code is ======================================================================================= function[r] = whittle(d,x,m) % WHITTLE.M

Dear R People: Here is something that I am sure is very simple. I'm just trying to re-create the C convolution example in the Extensions manual. Here is the subroutine: void convolve(double *a, int *na, double *b, int *nb, double *ab) { R_len_t i, j, nab = *na + *nb - 1; for(i = 0; i < nab; i++) ab[i] = 0.0; for(i = 0; i < *na; i++) for(j = 0; j < *nb; j++) ab[i + j] += a[i] *

Hello, May be somebody can help me... I am trying to find a solution of a convolution equation using fft (and unfortunately I do not have a good background for this). So I am just trying to figure out how it can be implemented in R. I have two multidimensional independent variables X and Z and I know their densities fx and fz, which are multidimensional arrays. So I have to find the density of

Hi, the function stats::convolve does not mention efficient usage of the underlying FFT algorithm, such as (a) if type="circular", then length(x)=length(y) should have many factors (e.g. length(x) = length(y) = 2^n) (b) if type="open" or "filter", then length(x)+length(y)-1 should have many factors (e.g. length(x)+length(y)-1 = 2^n) In particular the latter may

Ok. I did some quality tests. First off; never do quality tests with ints. I had serious problems interpreting my results until it dawned on me that the signal differences were just 0 or 1. So, after a lot of scratching my head, these are done comparing the result from the _float versions (which is how both resamplers work internally anyway). What I did was this: Load speex_wb.wav as one

Jeff Brown wrote: > > Hi, > > I'm trying to learn to use .C, which lets one invoke compiled C code from > within R. To do that, one has to first get the C code into R as a shared > object, which (I think) means first compiling it (with COMPILE or SHLIB) > and then loading it (with dyn.load()). > I would suggest taking it a step further and building an R package

I read through the "Writing R Extensions" and the "Debugging in R" website (http://www.stats.uwo.ca/faculty/murdoch/software/debuggingR/), looking for some hints about how to solve the issue of debugging problems encountered during the "R CMD check" process, but nothing seems to be mentioned about addressing issues encountered. Specifically, I am working with the R

Hello opus-dev, Can we please have review on RFCv2? We have quite a few optimizations (Eg: ifft/mdct_backwards, fixed point fft/ifft mdct_forward/backward etc) that are in my pipeline that depend on this patch series being accepted. Regards, Vish On 4 February 2015 at 12:21, Viswanath Puttagunta <viswanath.puttagunta at linaro.org> wrote: > Changes from RFC PATCH v1: > - passing

I am in the process of dropping the TI DSP lib fft routines into the AEC. Currently, FFTWrap.c uses the USE_SMALLFT and USE_KISS_FFT to select between two packages of FFT routines. ] #ifdef USE_SMALLFT ... #elif defined(USE_KISS_FFT) ... #else #error No other FFT implemented #endif My guess is that every DSP and many other processors will have their own specific FFT and iFFT. What method