search for: inversely

Hello Jean-Marc, Below are the results that show test_unit_dft passes, but test_unit_mdct fails (only for nfft=480, 960, 1920) Note: Tested on BeagleboneBlack(Cortex-A8) fixed point on branch [1] ./test_unit_dft nfft=32 inverse=0,snr = 88.394372 nfft=32 inverse=1,snr = 93.896470 nfft=128 inverse=0,snr = 89.185895 nfft=128 inverse=1,snr = 93.537021 nfft=256 inverse=0,snr = 88.353151 nfft=256

Hello Jean-Marc, Yep, that was it.. with your patch, test_unit_mdct passes for all nfft. So, what you do you suggest the next step here is? Regards, Vish On 8 May 2015 at 12:30, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi, > > Can you apply this change to the MDCT test and run it again. See if more > (all) sizes pass. Given the results, I strongly suspect an

Hi, Apologies if this is a known issue, but running make on revision e3187444692195957eb66989622c7b1ad8448b06 fails one of the tests when using fixed point configuration (floating point is ok) on my linux x86. Note that libopus1.1, as extracted from the tar ball, is OK. Specifically, the tests that fail are in celt/tests/test_unit_mdct: nfft=32 inverse=0,snr = 85.341197 nfft=32 inverse=1,snr =

Hello Jean-Marc, **Resending.. not sure why subject got removed earlier** Below are the results that show test_unit_dft passes, but test_unit_mdct fails (only for nfft=480, 960, 1920) Note: Tested on BeagleboneBlack(Cortex-A8) fixed point on branch [1] ./test_unit_dft nfft=32 inverse=0,snr = 88.394372 nfft=32 inverse=1,snr = 93.896470 nfft=128 inverse=0,snr = 89.185895 nfft=128 inverse=1,snr =

Hi, Can you apply this change to the MDCT test and run it again. See if more (all) sizes pass. Given the results, I strongly suspect an overflow. Jean-Marc On 08/05/15 01:21 PM, Viswanath Puttagunta wrote: > Hello Jean-Marc, > > Below are the results that show test_unit_dft passes, but > test_unit_mdct fails (only for nfft=480, 960, 1920) > Note: Tested on

On Wed, Feb 5, 2014 at 11:05 AM, Marcello Caramma (mcaramma) <mcaramma at cisco.com> wrote: > Hi, > > Apologies if this is a known issue, but running make on revision e3187444692195957eb66989622c7b1ad8448b06 fails one of the tests when using fixed point configuration (floating point is ok) on my linux x86. > Note that libopus1.1, as extracted from the tar ball, is OK. > >

Dear All, My question is simple but I need someone to help me out. Suppose I have a positive definite matrix A. The funtion chol() gives matrix L, such that A = L'L. The inverse of A, say A.inv, is also positive definite and can be factorized as A.inv = M'M. Then A = inverse of (A.inv) = inverse of (M'M) = (inverse of M) %*% (inverse of M)' = ((inverse of

Dear R-users, I have one question about using SVD to get an inverse matrix of covariance matrix Sometimes I met many singular values d are close to 0: look this example $d [1] 4.178853e+00 2.722005e+00 2.139863e+00 1.867628e+00 1.588967e+00 [6] 1.401554e+00 1.256964e+00 1.185750e+00 1.060692e+00 9.932592e-01 [11] 9.412768e-01 8.530497e-01 8.211395e-01 8.077817e-01 7.706618e-01 [16]

Hi, I've been trying to figure out a special set of covariance matrices that causes some symmetric zero elements in the inverse covariance matrix but am having trouble figuring out if that is possible. Say, for example, matrix a is a 4x4 covariance matrix with equal variance and zero covariance elements, i.e. [,1] [,2] [,3] [,4] [1,] 4 0 0 0 [2,] 0 4

>I'm rusty, but not *that* rusty here, I hope. > >If W (=Z*Z' in your case) is singular, it can not have >inverse, which by >definition also mean that nothing multiply by it will >produce the identity >matrix (for otherwise it would have an inverse and >thus nonsingular). > >The definition of a generalized inverse is something >like: If A is a >non-null

Generalised Inverse: The Moore-Penrose Generalisied Inverse is probably better defined as a pseudo-Inverse that arises in solving least squares problems. Another well known pseudo-Inverse is the so-called Drazin pseudo-Inverse. If memory serves (and it's been 10-12 years!) it can be obtained via a diagonalisation. Anyway, I dare say Prof. Ripley (among others) probably has "all the

Hi, all How can generate a sample from truncated inverse gamma distribution in R? thanks

I have a one-variable data set in R. The plot of histogram of my numerical variable suggests an inverse gaussian distribution. How can I obtain best estimation for the two parameters of inverse gaussian based on my data? Thanks. -- View this message in context: http://n4.nabble.com/estimate-inverse-gaussian-in-R-tp949692p949692.html Sent from the R help mailing list archive at Nabble.com.

Dear R-devel: I could use some advice about matrix calculations and steps that might make for faster computation of generalized inverses. It appears in some projects there is a bottleneck at the use of svd in calculation of generalized inverses. Here's some Rprof output I need to understand. > summaryRprof("Amelia.out") $by.self self.time self.pct

Thorsten is right. There is a direct formula for computing the Moore-Penrose inverse using the singular value composition of a matrix. This is incorporated in the following: mpinv <- function(A, eps = 1e-13) { s <- svd(A) e <- s$d e[e > eps] <- 1/e[e > eps] return(s$v %*% diag(e) %*% t(s$u)) } Hope it helps. Dietrich

Dear R-listers, I have a dxr matrix Z, where d > r. And the product Z*Z' is a singular square matrix. The problem is how to get the left inverse U of this singular matrix Z*Z', such that U*(Z*Z') = I? Is there any to figure it out using matrix decomposition method? Thanks a lot for your help. Fred

Hello, I'm building packages for Slackware and I've just tried to upgrade Slackware 13.37's CELT package to version 0.11.3, which apparently was released last year, but I've omitted it because it was not announced on the site. Anyway, now that I try build with the following configuration in my SlackBuild script: CFLAGS=$SLKCFLAGS -O2 \ CXXFLAGS=$SLKCFLAGS -O2 \

I'm rusty, but not *that* rusty here, I hope. If W (=Z*Z' in your case) is singular, it can not have inverse, which by definition also mean that nothing multiply by it will produce the identity matrix (for otherwise it would have an inverse and thus nonsingular). The definition of a generalized inverse is something like: If A is a non-null matrix, and G satisfy AGA = A, then G is called

Dear R-users, I would like to know whether there is a way to extract a diagonal of an inverse matrix without computing the inverse of the matrix itself. The size of my matrices are really huge and, also using sparse matrix, computing the inverse leads to storage problems and low speed. In other words, given a square matrix A, I aim to know diag(B), where B=solve(A), without computing solve(A).

I have so far used the following command glm(formula = s ~ age + gender + gemedu + gemhinc + es_gdppc + imf_pop + estbbo_m, family = binomial(link = "probit")) My question is 1. How do i discard the non significant selection variables (one out of the seven variables above is non-significant) and calculate the Inverse Mills Ratio of the significant variables 2. I need the inverse