search for: linpr

...t didn't really enumerate all the models for me (that's not its purpose), so I went through and enumerated by hand. For example; library(glmmML) set.seed(1001) a <- runif(100) b <- runif(100) c <- runif(100) x <- runif(100) n <- rep(20,100) cluster <- factor(rep(1:5,20)) linpred <- a+b+c+x-2 y <- rbinom(100,prob=plogis(linpred),size=n) data <- data.frame(y,a,b,c,x,n) m <- list() ## full model m[[1]] <- glmmML(cbind (y, n-y)~ x+a+b+c, family = binomial, data, cluster) ## 3-term models m[[2]] <- update(m[[1]],.~.-a) ## xbc m[[3]] <- update(m[[1]]...

...overflows for 48kHz analysis, hence the +1. - */ -#define MAX_SAMPLES_PER_WINDOW (size_t) (MAX_SAMP_FREQ * RMS_WINDOW_TIME + 1.) /* max. Samples per Time slice */ -#define PINK_REF 64.82 /* 298640883795 */ /* calibration value */ - -static Float_t linprebuf [MAX_ORDER * 2]; -static Float_t* linpre; /* left input samples, with pre-buffer */ -static Float_t lstepbuf [MAX_SAMPLES_PER_WINDOW + MAX_ORDER]; -static Float_t* lstep; /* left "f...

The replay gain code has dos line endings in CVS, which causes problems for the Sun compiler, among others. Attached is a patch for the lazy, but it's probably easier to fix locally and commit. -r

.... I found sources using samples from within the time series to construct a linear system: http://practicalcryptography.com/miscellaneous/machine-learning/linear-prediction-tutorial/ However, talkbox, a scikit for signal proccessing ( https://github.com/cournape/talkbox/tree/master/scikits/talkbox/linpred ), first autocorrolates the signal using discrete inverse Fourier transforms which then passes it to the levinson durbin algorithm. I would really appriciate an explanation or information on a good resource to learn more about how the prediction coefficients are solved for. Once the lpc coeffic...

...4] 512.6 -20.5 -24.2 31.7 .. ..$ delu : num [1:4] 3.97 -12.4 -6.62 15.04 .. ..$ theta : num 0.499 .. ..$ u0 : num [1:4] 0 0 0 0 .. ..and 42 methods, of which 30 are possibly relevant: .. .. b, beta, CcNumer, copy#envRefClass, initialize, initializePtr, installPars, L, ldL2, ldRX2, linPred, P, ptr, RX, RXdiag, RXi, .. .. setBeta0, setDelb, setDelu, setTheta, solve, solveU, sqrL, u, unsc, updateDecomp, updateL, updateLamtUt, updateRes, updateXwts ..@ optinfo:List of 4 .. ..$ optimizer: chr "Nelder_Mead" .. ..$ control :List of 1 .. .. ..$ verbose: int 0 .. .....

...t didn't really enumerate all the models for me (that's not its purpose), so I went through and enumerated by hand. For example; library(glmmML) set.seed(1001) a <- runif(100) b <- runif(100) c <- runif(100) x <- runif(100) n <- rep(20,100) cluster <- factor(rep(1:5,20)) linpred <- a+b+c+x-2 y <- rbinom(100,prob=plogis(linpred),size=n) data <- data.frame(y,a,b,c,x,n) m <- list() ## full model m[[1]] <- glmmML(cbind (y, n-y)~ x+a+b+c, family = binomial, data, cluster) ## 3-term models m[[2]] <- update(m[[1]],.~.-a) ## xbc m[[3]] <- update(m[[1]]...

Javier, Creo que aquí aplica la ley de Linus que dice: "Dado un número suficientemente elevado de ojos, todos los errores se convierten en obvios". La persona que revisa y encuentra un error no necesariamente tiene que ser la misma que la que lo escribe. Una motivación muy importante al compartir un código es la de recibir los beneficios del control de calidad por parte de tus pares.

...> .. ..$ delu : num [1:4] 3.97 -12.4 -6.62 15.04 > .. ..$ theta : num 0.499 > .. ..$ u0 : num [1:4] 0 0 0 0 > .. ..and 42 methods, of which 30 are possibly relevant: > .. .. b, beta, CcNumer, copy#envRefClass, initialize, initializePtr, installPars, L, ldL2, ldRX2, linPred, P, ptr, RX, RXdiag, RXi, > .. .. setBeta0, setDelb, setDelu, setTheta, solve, solveU, sqrL, u, unsc, updateDecomp, updateL, updateLamtUt, updateRes, updateXwts > ..@ optinfo:List of 4 > .. ..$ optimizer: chr "Nelder_Mead" > .. ..$ control :List of 1 > .. .. ....