similar to: multinomial regresion, nls

Hi Tobias and Sven, Thanks for your discussion and suggestion. @Sven: ISL actually allows users to have different identifiers with the same name. The problem that we have discussed is caused by incorrect usage of isl_space in Polly, so please do not worry about ISL library. You can skip the following information related to Polly implementation. @Tobias and Polly developers: I have attached

On 07/25/2013 09:01 PM, Star Tan wrote: > Hi Sebastian, > > > Recently, I found the "Polly - Calculate dependences" pass would lead to significant compile-time overhead when compiling some loop-intensive source code. Tobias told me you found similar problem as follows: > http://llvm.org/bugs/show_bug.cgi?id=14240 > > > My evaluation shows that "Polly -

At 2013-07-26 14:14:51,"Tobias Grosser" <tobias at grosser.es> wrote: >On 07/25/2013 09:01 PM, Star Tan wrote: >> Hi Sebastian, >> >> >> Recently, I found the "Polly - Calculate dependences" pass would lead to significant compile-time overhead when compiling some loop-intensive source code. Tobias told me you found similar problem as follows:

On 07/28/2013 06:52 AM, Star Tan wrote: > Hi Tobias, > > I tried to investigated the problem related to ScopInfo, but I need your > help on handling some problems about ISL and SCEV. I copied the list as the discussion may be helpful for others. @Sven, no need to read all. Just search for your name. [..] >>The interesting observation is, that Polly introduces three parameters

I am trying to make the following Kalman filter equations work and therefore produce their graphs. v_t=y_t - a_t a_t+1=a_t+K_t*v_t F_t=P_t+sigma.squared.epsilon P_t+1=P_t*(1-K_t)+sigma.squared.eta K_t=P_t/F_t Given: a_1=0,P_1=10^7,sigma.squared.epsilon=15099, sigma.squared.eta=1469.1 I have attached my code,which of course doesnt work.It produces NAs for the Fs,Ks and the a. Can somebody tell me

Full_Name: Manuel Luethi Version: 2.9.1 OS: Windows XP Submission from: (NULL) (129.132.128.136) Hi I created the following vectors: p_1=c(0.2,0.2,0.2,0.2,0.1,0.25,0.4,0.1,0.25,0.4,0.1,0.25,0.4,0.1,0.25,0.4,0.2,0.5,0.8,0.2,0.5,0.8,0.2,0.5,0.8,0.2,0.5,0.8) p_2=c(0,0,0,0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.4,0.25,0.1,0.4,0.25,0.1,0.4,0.25,0.1,0.4,0.25,0.1) As these are

Dear R List, I have a dataset with the following structure: """personal_id, p_0, p_1, p_2, .... , p_36, p_37 1, NA, 1, 4, .... , 1, NA 2, NA, NA, NA, .... , 4, NA . . . 6020, NA, 3, 3, ...., NA, NA 6021, NA, 2, 2, ...., 4, NA """ I used some made-up data. It is just meant to show the structure of the dataset. The variables of interest are p_0, ... p_37.

Saludo cordial para cada uno. Les pido ayuda para generar números aleatorios de una mixtura k-puntos. Sabemos que la función de distribución F es una mixtura k-puntos si es de la forma F(x) = p_1 F_1(x) + p_2 F_2(x) + … + p_k F_k(x), donde F_j es una función de distribución de probabilidad, p_j > 0 y suma(p_j) = 1, para j = 1, 2, …, k. En mi caso particular F es la suavización de la

Hola! I have a problem which is not strictly R, although R will be used for the analysis. We have data from a large investigation of drug abuse, initially analyzed by logistic regression. But the pupils are selected by first sampling schools, and as it happens the prevalence of use varies sharply from school to school, so there is over-dispersion. Now we are interested in comparing the

hi, i'm really in trouble to simulate some experiment. that is, it takes too much time to process the following code. following is short example, ------------------------------------------------------------------------------------------------------- p<-data.frame(a=rnorm(10),b=rnorm(10),c=rnorm(10),d=rnorm(10)) test<-data.frame(a=rnorm(1),b=rnorm(1),c=rnorm(1),d=rnorm(1))

Dear R users, suppose we have a random walk such as: v_t+1 = v_t + e_t+1 where e_t is a normal IID noise pocess with mean = m and standard deviation = sd and v_t is the fundamental value of a stock. Now suppose I want a trading strategy to be: x_t+1 = c(v_t – p_t) where c is a costant. I know, from the paper where this equations come from (Farmer and Joshi, The price dynamics of common

Dear all, I have a serious problem to solve my model. I study over exploitation of fish in the bay of biscay (france). I know only the level of catch and the fishing effort (see data below) by year. My model is composed by the following equations: * the growth function Gt(St) = r*St*(1-St/sbar) with Gt the growth of each period t r intrinsec growth of the stock sbar carriyng capacity of the

On 07/29/2013 09:15 AM, Sven Verdoolaege wrote: > On Mon, Jul 29, 2013 at 07:37:14AM -0700, Tobias Grosser wrote: >> On 07/29/2013 03:18 AM, Sven Verdoolaege wrote: >>> On Sun, Jul 28, 2013 at 04:42:25PM -0700, Tobias Grosser wrote: >>>> Sven: In terms of making the behaviour of isl easier to understand, >>>> it may make sense to fail/assert in case

Hi Sebastian, Recently, I found the "Polly - Calculate dependences" pass would lead to significant compile-time overhead when compiling some loop-intensive source code. Tobias told me you found similar problem as follows: http://llvm.org/bugs/show_bug.cgi?id=14240 My evaluation shows that "Polly - Calculate dependences" pass consumes 96.4% of total compile-time overhead

Folks, S_t = (x_t, y_t) is the state of a system at time t. There is an iterative function which converts S_t into S_{t+1}. I can easily write this in R like this: iterate <- function(S) { list(S$x+1, S$y+1) } So this function eats S_t and makes S_{t+1} and I can say S2 <- iterate(S1) My question: suppose I want to iterate from 1..10, what is the data structure

Dear R-users, I am trying to estimate a state space model of the form (1) b_t = G * b_t-1 + w_t w_t ~ N(0,W) (2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V) (Hamilton 1984: 372) In particular my estimation in state space form looks like (3) a3_t = 1 * a3_t-1 + w_t w_t ~ N(0,W) (4) g_t = (a1, a2) * (1, P_t)' + u_t * a3_t + v_t v_t ~ N(0,V) where g_t is the

Hi, I'm bit confused about ecdf (read the help files but still not sure about this). I have an analytical expression for the pdf, but want to get the empirical cdf. How do I use this analytical expression with ecdf? If this helps make it concrete, the pdf is: f(u) = \sum_{t = 1}^T 1/n_t \sum_{i = 1}^{n_t} 1/w K((u - u_{it})/w) where K = kernel density estimator, w = weights, and u_{it} =

Dear Users, I would like to simulate an AR(1) (y_t=ct1+y_t-1+e_t) model in R where the innovations are supposed to follow a t-GARCH(1,1) proccess. By t-GARCH I want to mean that: e_t=n_t*sqrt(h_t) and h_t=ct2+a*(e_t)^2+b*h_t-1. where n_t is a random variable with t-Student distribution. If someone could give some guidelines, I can going developing the model. I did it in matlab, but the loops

Dear Tobias and all LLVM/Polly developers, Thank you very much for all your help and advice. I have submitted my proposal to GSoC 2013 application system: http://www.google-melange.com/gsoc/proposal/review/google/gsoc2013/star/1. Some tables and paragraphs are simplified to make it more readable on GSoC official pages. Any suggestion or comment would be appreciated. At 2013-05-03

On 05/03/2013 11:39 AM, Star Tan wrote: > Dear Tobias, > > > Thank you very much for your very helpful advice. > > > Yes, -debug-pass and -time-passes are two very useful and powerful > options when evaluating the compile-time of each compiler pass. They > are exactly what I need! With these options, I can step into details > of the compile-time overhead of each pass.