search for: coef1

....90,2188.80,2371.70,2563.60) Y<-c(2208.30,2271.40,2365.60,2423.30,2416.20,2484.80,2608.50,2744.10,2729.30,2695.00,2826.70,2958.60,3115.20,3192.40,3187.10,3248.80,3166.00,3277.70,3492.00,3573.50) money<-data.frame(r,M,Y) attach(money) ols1<-lm(log(M)~log(r)+log(Y)) output1<-summary(ols1) coef1<-ols1$coefficients a1<-coef1[[1]] b11<-coef1[[2]] b21<-coef1[[3]] money.m1<-nls(log(M)~a+b*r^g+c*Y^g,data=money,start=list(a=a1,b=b11,g=1,c=b21)) summary(money.m1) money.m2<-boxcox(money.m1) Prof. Ikerne del Valle Erkiaga Department of Applied Economics V Faculty of Econom...

Jonathan Lennox wrote: > +opus_int32 silk_noise_shape_quantizer_short_prediction_neon(const opus_int32 *buf32, const opus_int32 *coef32) > +{ > + int32x4_t coef0 = vld1q_s32(coef32); > + int32x4_t coef1 = vld1q_s32(coef32 + 4); > + int32x4_t coef2 = vld1q_s32(coef32 + 8); > + int32x4_t coef3 = vld1q_s32(coef32 + 12); > + > + int32x4_t a0 = vld1q_s32(buf32 - 15); > + int32x4_t a1 = vld1q_s32(buf32 - 11); > + int32x4_t a2 = vld1q_s32(buf32 - 7); > + int32x4_t a...

Hello, I want to model my data with the following model: Y1=X1*coef1+X2*coef2 Y2=X1*coef2+X2*coef3 Note: coef2 appears in both lines Xi, Yi is input versus output data respectively How can I do this in R? I got this far: lm(Y1~X1+X2,mydata) now how do I add the second line of the model including the cross dependency? Your help is greatly appreciated...

...zes and variances, the function seems to be extremely sensitive both of these. I am wondering if I've missed something in my function? I'd be very grateful for any tips. Thanks! Martin TwoSlope <-function(lm1, lm2) { ## lm1 and lm2 are two linear models on independent data sets coef1 <-summary(lm1)$coef coef2 <-summary(lm2)$coef sigma <-(sum(lm1$residuals^2)+sum(lm2$residuals^2))/(lm1$df.residual + lm2$df.residual-4) SSall <-sum(lm1$model[,2]^2) + sum(lm2$model[,2]^2) SSprod <-sum(lm1$model[,2]^2) * sum(lm2$model[,2]^2) F.val <-(as.numeric(coefficients(lm1)...

> On Nov 19, 2015, at 5:47 PM, John Ridges <jridges at masque.com> wrote: > > Any speedup from the intrinsics may just be swamped by the rest of the encode/decode process. But I think you really want SIG2WORD16 to be (vqmovns_s32(PSHR32((x), SIG_SHIFT))) Yes, you?re right. I forgot to run the vectors under qemu with my previous version (oh, the embarrassment!) Fixed forthcoming

...PM, John Ridges <jridges at masque.com<mailto:jridges at masque.com>> wrote: Hi Jonathan. I really, really hate to bring this up this late in the game, but I just noticed that your NEON code doesn't use any of the "high" intrinsics for ARM64, e.g. instead of: int32x4_t coef1 = vmovl_s16(vget_high_s16(coef16)); you could use: int32x4_t coef1 = vmovl_high_s16(coef16); and instead of: int64x2_t b1 = vmlal_s32(b0, vget_high_s32(a0), vget_high_s32(coef0)); you could use: int64x2_t b1 = vmlal_high_s32(b0, a0, coef0); and instead of: int64x1_t c = vadd_s64(vget_low_s6...

I'm looking for a good form in which to store matrix results of a simulation. I am doing a simulation study. Each simulation generates some data and then analyzes it. I want to record the results of many simulations and analyze them. Say r has the results of one simulation, and I care about r$coefficients, a vector of coefficients, and r$var, the estimated covariance matrix. I'll do

...Timothy B. Terriberry <tterribe at xiph.org> wrote: > > Jonathan Lennox wrote: >> +opus_int32 silk_noise_shape_quantizer_short_prediction_neon(const opus_int32 *buf32, const opus_int32 *coef32) >> +{ >> + int32x4_t coef0 = vld1q_s32(coef32); >> + int32x4_t coef1 = vld1q_s32(coef32 + 4); >> + int32x4_t coef2 = vld1q_s32(coef32 + 8); >> + int32x4_t coef3 = vld1q_s32(coef32 + 12); >> + >> + int32x4_t a0 = vld1q_s32(buf32 - 15); >> + int32x4_t a1 = vld1q_s32(buf32 - 11); >> + int32x4_t a2 = vld1q_s32(buf32 - 7...

Hi, I have a question about running multiple in regressions in R and then storing the coefficients. I have a large dataset with several variables, one of which is a state variable, coded 1-50 for each state. I'd like to run a regression of 28 select variables on the remaining 27 variables of the dataset (there are 55 variables total), and specific for each state, ie run a regression of

...(order == 8) + { + int32x4_t a00 = vdupq_n_s32(data0[0]); + int32x4_t a01 = vld1q_s32(data1); // data1[0] ... [3] + + int32x4_t a0 = vextq_s32 (a00, a01, 3); // data0[0] data1[0] ...[2] + int32x4_t a1 = vld1q_s32(data1 + 3); // data1[3] ... [6] + + int16x8_t coef16 = vld1q_s16(coef); + int32x4_t coef0 = vmovl_s16(vget_low_s16(coef16)); + int32x4_t coef1 = vmovl_s16(vget_high_s16(coef16)); + + int64x2_t b0 = vmull_s32(vget_low_s32(a0), vget_low_s32(coef0)); + int64x2_t b1 = vmlal_s32(b0, vget_high_s32(a0), vget_high_s32(coef0)); +...

...(order == 8) + { + int32x4_t a00 = vdupq_n_s32(data0[0]); + int32x4_t a01 = vld1q_s32(data1); // data1[0] ... [3] + + int32x4_t a0 = vextq_s32 (a00, a01, 3); // data0[0] data1[0] ...[2] + int32x4_t a1 = vld1q_s32(data1 + 3); // data1[3] ... [6] + + int16x8_t coef16 = vld1q_s16(coef); + int32x4_t coef0 = vmovl_s16(vget_low_s16(coef16)); + int32x4_t coef1 = vmovl_s16(vget_high_s16(coef16)); + + int64x2_t b0 = vmull_s32(vget_low_s32(a0), vget_low_s32(coef0)); + int64x2_t b1 = vmlal_s32(b0, vget_high_s32(a0), vget_high_s32(coef0)); +...

Hi Jonathan. I really, really hate to bring this up this late in the game, but I just noticed that your NEON code doesn't use any of the "high" intrinsics for ARM64, e.g. instead of: int32x4_t coef1 = vmovl_s16(vget_high_s16(coef16)); you could use: int32x4_t coef1 = vmovl_high_s16(coef16); and instead of: int64x2_t b1 = vmlal_s32(b0, vget_high_s32(a0), vget_high_s32(coef0)); you could use: int64x2_t b1 = vmlal_high_s32(b0, a0, coef0); and instead of: int64x1_t c = vadd_s64(vget_low_s6...

As promised, here's a re-send of all my Aarch64 patches, following comments by John Ridges. Note that they actually affect more than just Aarch64 -- other than the ones specifically guarded by AARCH64_NEON defines, the Neon intrinsics all also apply on armv7; and the OPUS_FAST_INT64 patches apply on any 64-bit machine. The patches should largely be independent and independently useful, other

Hello! I've a problem with the combn function and a set of vector. I would like to make a simple combination where, instead of scalars, i would like to combine vector, in order to form matrixes. In other words, i have nineteen 6-items vectors (for example coef1-coef19), that i would like to combine in n!/k!(n-k)! 6x6 matrixes. I tried with a code like this mma <- combn(c(coeff1,coeff2,...,coeff19),function(x) matrix(x,6,6)) but i think that R doesn't recognize that these are not scalars. In fact the error is: Errore in matrix(r, nrow = len.r...

...function (x) K(object$SV[x,], newdata)) ## compute raw prediction for classifier (i,j) predone <- function (i,j) { ## ranges for class i and j: ri <- start[i] : (start[i] + object$nSV[i] - 1) rj <- start[j] : (start[j] + object$nSV[j] - 1) ## coefs for (i,j): coef1 <- object$coefs[ri, j-1] coef2 <- object$coefs[rj, i] ## return raw values: crossprod(coef1, kernel[ri]) + crossprod(coef2, kernel[rj]) } ## compute votes for all classifiers votes <- rep(0,object$nclasses) c <- 0 # rho counter for (i in 1 : (object$nclasses - 1)...

...SUCH DAMAGE. +***********************************************************************/ +#ifndef SILK_NSQ_H +#define SILK_NSQ_H + +#define optional_coef_reversal(out, in, order) + +static OPUS_INLINE opus_int32 silk_noise_shape_quantizer_short_prediction_c(const opus_int32 *buf32, const opus_int16 *coef16, opus_int order) +{ + opus_int32 out; + silk_assert( order == 10 || order == 16 ); + + /* Avoids introducing a bias because silk_SMLAWB() always rounds to -inf */ + out = silk_RSHIFT( order, 1 ); + out = silk_SMLAWB( out, buf32[ 0 ], coef16[ 0 ] ); + out = silk_SMLAWB( out, buf3...

...SUCH DAMAGE. +***********************************************************************/ +#ifndef SILK_NSQ_H +#define SILK_NSQ_H + +#define optional_coef_reversal(out, in, order) + +static OPUS_INLINE opus_int32 silk_noise_shape_quantizer_short_prediction_c(const opus_int32 *buf32, const opus_int16 *coef16, opus_int order) +{ + opus_int32 out; + silk_assert( order == 10 || order == 16 ); + + /* Avoids introducing a bias because silk_SMLAWB() always rounds to -inf */ + out = silk_RSHIFT( order, 1 ); + out = silk_SMLAWB( out, buf32[ 0 ], coef16[ 0 ] ); + out = silk_SMLAWB( out, buf3...

Following Tim's comments, here are my reworked patches for the Neon intrinsic function patches of of my Aarch64 patchset, i.e. replacing patches 5-8 of the v2 series. Patches 1-4 and 9-18 of the old series still apply unmodified. The one new (as opposed to changed) patch is the first one in this series, to add named constants for the ARM architecture variants. There are also some minor code

This sequence of patches provides arm64 support for Opus. Tested on iOS, Android, and Ubuntu 14.04. The patch sequence was written on top of Viswanath Puttagunta's Ne10 patches, but all but the second ("Reorganize pitch_arm.h") should, I think, apply independently of it. It does depends on my previous intrinsics configury reorganization, however. Comments welcome. With this and

Here are my aarch64 patches rebased to the current tip of Opus master. They're largely the same as my previous patch set, with the addition of the final one (the Neon fixed-point implementation of xcorr_kernel). This replaces Viswanath's Neon fixed-point celt_pitch_xcorr, since xcorr_kernel is used in celt_fir and celt_iir as well. These have been tested for correctness under qemu