similar to: How should I improve the following R code?

From: Jyri Sarha <jsarha at ti.com> Also adds inline asm implementations of WORD2INT(x) macro for fixed and floating point. --- libspeex/resample_neon.h | 101 ++++++++++++++++++++++++++++++++++++++++++++++ 1 files changed, 101 insertions(+), 0 deletions(-) diff --git a/libspeex/resample_neon.h b/libspeex/resample_neon.h index ba93e41..e7e981e 100644 --- a/libspeex/resample_neon.h +++

- Use MAC16_16 macros instead of (sum += a*b) and unroll a loop by 2. It increase performance when using optimized macros (ex: ARMv5E). A possible side effect of loop unroll is that i don't check for odd length here. - Add NEON version of FIR filter and autocorr -- Aur?lien Zanelli Parrot SA 174, quai de Jemmapes 75010 Paris France -------------- next part -------------- diff --git

Hi, I have data in the following form: index count -7 32 1 9382 2 2192 7 190 11 201 I'd like to get quantiles from the data. I thought about something like this: index <- c(-7, 1, 2, 7, 11) count <- c(32, 9382, 2192, 190, 201) quantile(rep(index, count)) It answers correctly, but I feel it's wasteful especially when count is

Please ignore my previous mail and patch, there is a new version :). Patch changes are: - Use MAC16_16 macros instead of (sum += a*b) and unroll a loop by 2. It increase performance when using optimized macros (ex: ARMv5E). A possible side effect of loop unroll is that i don't check for odd length here. - Add NEON version of FIR filter and autocorr - Add a section in autoconf in order to

From: Jyri Sarha <jsarha at ti.com> I optimized Speex resampler for NEON capable ARM CPUs. The first patch should speed up resampling on any platform that can spare the increased memory usage. It would be nice to have these merged to the master branch. Please let me know if there is anything I can do to help the the merge. The patches have been rebased on top of master branch in

Hello all, I am having trouble running a count function in R using RODBC to query a table I created in Oracle. It may very well be that my SQL coding is incorrect; I just started learning it. But if someone could point me in the right direction or tell me if I am going about this the correct way that would be greatly appreciated! The script I have right now is: >require(RODBC)

Hello Aurelien, + "vdup.s16 d8, %1;\n" //Duplicate num in d8 lane + "vdup.s16 q5, %4;\n" //Duplicate mem in q5 lane + + /* We try to process 16 samples at a time */ + "movs %5, %3, lsr #4;\n" + "beq .celt_fir1_process16_done_%=;\n" + + ".celt_fir1_process16_%=:\n" + /* Load 16 x values in q0, q1 lanes */ +

struct pw_rail_t is allocated as an array in function nvios_iccsense_parse, and stored to a struct member of local variable. However, the array is not freed when the local variable becomes invalid, and the reference is not passed on, leading to a memory leak. Fix this by freeing struct pw_rail_t when exiting nvkm_iccsense_oneinit. Signed-off-by: Keita Suzuki <keitasuzuki.park at

Dear R-users, I don't know if there is a problem in wtd.quantile (from library "Hmisc"): -------------------------------- x <- c(1,2,3,4,5) w <- c(0.5,0.4,0.3,0.2,0.1) wtd.quantile(x,weights=w) ------------------------------- The output is: 0% 25% 50% 75% 100% 3.00 3.25 3.50 3.75 4.00 The version of R I am using is: 2.1.0 Best,Jing

I currently have a data frame whose rows correspond to each student and whose columns are different variables for the student, as shown below: Lastname Firstname CATALOG_NBR Email StudentID EMPLID Start 1 alastname afirstname 1213 *@uark.edu 10295236 # 12/2/2008 2 anotherlastname anotherfirstname 1213 **@uark.edu ## 10295236 9/3/2008 Xattempts Q1

This is my first post to R-help and I look forward receiving some advice for a novice like me... I?ve got a simple repeated (4 periods so far) 10-question survey data that is very easy to work on Excel. However, I?d like to move the compilation to R but I?m having some trouble operating on count list data in a neat way. The data C > str(C) 'data.frame': 551 obs. of 13

Dear All, I am economist and working on poverty / income inequality. I need descriptive statitics like the ratio of education expentitures between different income quintiles where each household has a different weight. After a bit of google search I found 'Hmisc' and 'quantreg' libraries for weighted quantiles. The problem is that these packages give me only weighted quintiles;

Hi Duncan I tried Ecdf and/or wtd.quantile from Hmisc and it is working (probably). Ecdf(x, q=.5) Ecdf(x, weights=xw,col=2, add=T, q=.5) wtd.quantile(x) 0% 25% 50% 75% 100% 10 10 10 100 300 wtd.quantile(x, weights=xw, type="i/n") 0% 25% 50% 75% 100% 10.0000 138.8667 192.5778 246.2889 300.0000 But could you please be more specific in this? >

Hello! First of all, 100x thanks to Monty and colleagues: you have done an excellent job! I just didn't believe my ears when I first tested Oggenc 1.0 at q0 to q1 - it sounds AMAZINGLY GOOD !!! But as HDD drives are getting larger and cheaper, most of us move toward higher quality settings ......... I use q8, because: - I was able to ABX some test samples up to q4.99 - at q8 Ogg is still

On 24/11/2017 6:27 AM, PIKAL Petr wrote: > Dear all > > Strictly speaking it is not R question but as you are the most capable persons I know I give it a try. > > I am strugling with recalculation of number weighted to volume weighted distribution. > > Suppose I have objects (cubes) with size > > x<- c(rep(10,20), rep(100, 10), rep(300,5)) > I can get >

Hi Petr, I think that Duncan suggests something like this: x<- c(rep(10,20), rep(300,5), rep(100, 10)) tx <- table(x) prop.x <- tx / sum(tx) vx <- as.integer(names(tx)) prop.wx <- tx * vx / sum(tx * vx) plot(ecdf(x)) plot(vx, cumsum(prop.x), ylim = 0:1) plot(vx, cumsum(prop.wx), ylim = 0:1) Best regards, Thierry ir. Thierry Onkelinx Statisticus / Statistician Vlaamse

Dear all, I found a strange result using R's weightedMedian function. Consider the following: > x <- c (0.2, 0.3, 0.5) > w <- c (1,1,2) > weightedMedian(x,w) > 0.3666 In cases like above, when the weights are integers, one could argue that the weighted median should be the same as the standard median with the elements repeated according to their weights. This is

The power sensors can be configured to sample the readout values over time. Nvidia does this too, so nouveau should probably do that too. Karol Herbst (4): iccsense: remove read function iccsense: convert to linked list iccsense: split sensor into own struct iccsense: configure sensors like nvidia does drm/nouveau/include/nvkm/subdev/iccsense.h | 6 +- drm/nouveau/nouveau_hwmon.c

Signed-off-by: Karol Herbst <karolherbst at gmail.com> --- drm/nouveau/include/nvkm/subdev/iccsense.h | 3 +++ drm/nouveau/nvkm/subdev/iccsense/base.c | 13 ++++++++++++- 2 files changed, 15 insertions(+), 1 deletion(-) diff --git a/drm/nouveau/include/nvkm/subdev/iccsense.h b/drm/nouveau/include/nvkm/subdev/iccsense.h index 3c2ddd9..b7a9b04 100644 ---

Hi everyone, now I am trying to finish writing the code (I had asked for assistance on subtracting arrays) This is what I what I am running in R: > source("/home/ie/Documents/TTU/GA_Research/GLUE/R-Project/R_GLUE_Example/NSEr.R") NSEr <- function (obs, sim) { {jjh <- (as.vector(obs) - sim)^2 Xjjhs <- apply(Xjjh, 2, sum) Yii <- (obs - mean(obs))^2 Yiis <- apply(Yii, 2,