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sandy4321 davan690 hal2001 sbgraves237 hsmohammed lnsongxf khusmann learner-python-r jpbm tatakof xdcccr qiustander maxclchen jangwoojin863bssm's Issues
Segfault due to R call inside parallel sampling
Apparently using pragma omp critical is not enough when calling R functions inside openMP, so there are occasional crashes with models using update_fn when trying to sample states in parallel (MCMC with ssm_ulg, ssm_ung, and their multivariate counterparts). I figured out a way to circumvent this issue, and it seems to work, but still needs some tidying up.
Add progress bar
It would be nice to have a progress bar or something for long MCMC runs. And I think it would also allow automatic way to interrupt C++ code without terminating R.
UBSAN on CRAN: nan is outside the range of representable values of type 'unsigned int'
R Under development (unstable) (2017-07-12 r72910) -- "Unsuffered Consequences"
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> library("testthat")
> test_check("bssm")
Loading required package: bssm
/data/gannet/ripley/R/test-clang/Rcpp/include/Rcpp/internal/caster.h:30:25: runtime error: value nan is outside the range of representable values of type 'unsigned int'
SUMMARY: AddressSanitizer: undefined-behavior /data/gannet/ripley/R/test-clang/Rcpp/include/Rcpp/internal/caster.h:30:25 in
OMP: Warning #96: Cannot form a team with 24 threads, using 2 instead.
OMP: Hint: Consider unsetting KMP_ALL_THREADS and OMP_THREAD_LIMIT (if either is set).
testthat results ================================================================
OK: 127 SKIPPED: 9 FAILED: 0
>
> proc.time()
user system elapsed
16.443 0.740 18.432
run_mcmc crashes with a simple linear gaussian model
Hi, I am trying to estimate a multivariate linear gaussian model. However, R crashes whenever I call the run_mcmc function. A toy example with simulated data is below.
The only output I get is the progress bar and after completion Rstudio tells me that the R Session aborted without further explanation.
Kalman filtering works fine.
library(bssm)
set.seed(1)
########### Simulating fake data
set.seed(1)
Tt <- 100
p<-2 #numero de observaveis
m<- 2 #numero de estados
y <- matrix(0,Tt,p) # observaveis
Z <- matrix(0,p,m) #matriz de observacao
Tm <- matrix(0,m,m)
H <- matrix(0,2,2)
R <- matrix(0,m,m)
R[1,1] <- 0.1
R[2,2] <- 0.01
eps_y1 <- rnorm(Tt,mean = 0, sd = 0.1)
eps_y2 <- rnorm(Tt,mean = 0, sd = 0.01)
W <- matrix(0,Tt,p)
W[,1] <- eps_y1
W[,2] <- eps_y2
for (i in 1:p){
Z[i,i] <- 1
}
Tm[1,1] <- 1
Tm[2,1] <- 0.1
for (t in 2:Tt){
y[t,] <- Tm %*% y[t-1,] + W[t,]
}
######### Estimation
#observation with NAs
y2 <- y
y2[97,1] <- NA
update_fn <- function(theta) {
Z <- matrix(c(1, 0, 0, 1), 2, 2)
R <- matrix(0,2,2)
R[1,1] <- 0.1
R[2,2] <- 0.01
H <- matrix(0,2,2)
# add third dimension so we have p x n x 1, p x m x 1, p x p x 1 arrays
dim(Z)[3] <- dim(R)[3] <- dim(H)[3] <- 1
Tm <- matrix(0,2,2)
Tm[1,1] <- theta
Tm[2,1] <- 0.1
dim(Z)[3] <- 1
dim(R)[3] <- 1
dim(H)[3] <- 1
dim(Tm)[3] <- 1
P1 <- matrix(10,m,m)
a1 <- c(0,0)
list(Z = Z, R = R,T = Tm, H = H,P1=P1, a1=a1)
}
init_theta <- 1
comps <- update_fn(init_theta)
prior_fn <- function(theta) {
sum(dnorm(theta, 1, 10, log = TRUE)) # log-jacobian
}
mod <- ssm_mlg(y2, Z = comps$Z, D = comps$D, H = comps$H, T=comps$T, R=comps$R,
a1 = comps$a1, P1=comps$P1,state_names = c("y1","y2"),update_fn = update_fn,
init_theta = init_theta,
prior_fn = prior_fn)
kf <- kfilter(mod)
fit <- run_mcmc(mod, iter = 1000, thin=2, burnin = 100,seed =5,verbose = TRUE)
Session Info
Dummy seasonal to trigonometric seasonal
Have to think about changing the dummy seasonal representation to trigonometric version, this makes few things easier as the noise covariance matrix is non-singular (unless one wants to use deterministic trend).
can't use all fixed sd params in bsm_ng
Hello again,
I was trying to set fixed params for sd_level and sd_seasonal instead of priors, but it doesn't look like fixing both is allowed. (I was hoping to just solve for beta). For example:
model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson",
sd_level = halfnormal(0.01, 1),
sd_seasonal = halfnormal(0.01, 1),
beta = normal(0, 0, 10),
xreg = Seatbelts[, "law"])
mcmc_bsm <- run_mcmc(model, iter = 5000, nsim=10)
works but:
model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson",
sd_level = 0.01,
sd_seasonal = 0.01,
beta = normal(0, 0, 10),
xreg = Seatbelts[, "law"])
mcmc_bsm <- run_mcmc(model, iter = 5000, nsim=10)
gives error:
Error in nongaussian_da_mcmc(model, output_type, nsim, iter, burnin, thin, : Col::subvec(): indices out of bounds or incorrectly used
Thanks!
as_prior_list function
I should add this at some point.
Psi-filter fails with semidefinite matrices
Psi-filter should be fixed to handle case where the variance of the conditional smoothed state is zero. This happens for example with constant drift term of seasonal component.
summary function for is-corrected mcmc
currently summary function for mcmc output does not work for weighted samples.
Error with Solaris
From CRAN:
* installing to library ‘/home/ripley/R/Lib32’
* installing *source* package ‘bssm’ ...
** package ‘bssm’ successfully unpacked and MD5 sums checked
** libs
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_approx.cpp -o R_approx.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_auxiliary_filter.cpp -o R_auxiliary_filter.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_bootstrap_filter.cpp -o R_bootstrap_filter.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_ekf.cpp -o R_ekf.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_ekpf.cpp -o R_ekpf.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_importance_sample.cpp -o R_importance_sample.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_kfilter.cpp -o R_kfilter.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_loglik.cpp -o R_loglik.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_mcmc.cpp -o R_mcmc.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_predict.cpp -o R_predict.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_psi.cpp -o R_psi.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_smoother.cpp -o R_smoother.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c R_ukf.cpp -o R_ukf.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c RcppExports.cpp -o RcppExports.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c conditional_dist.cpp -o conditional_dist.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c distr_consts.cpp -o distr_consts.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c dmvnorm.cpp -o dmvnorm.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c filter_smoother.cpp -o filter_smoother.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c interval.cpp -o interval.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c invlink.cpp -o invlink.o
/opt/csw//bin/g++ -std=gnu++11 -I/home/ripley/R/gcc/include -DNDEBUG -I"/home/ripley/R/Lib32/BH/include" -I"/home/ripley/R/Lib32/Rcpp/include" -I"/home/ripley/R/Lib32/RcppArmadillo/include" -I"/home/ripley/R/Lib32/ramcmc/include" -I/opt/csw/include -I/usr/local/include -fopenmp -fPIC -O2 -c mcmc.cpp -o mcmc.o
mcmc.cpp: In constructor ‘mcmc::mcmc(const uvec&, const mat&, unsigned int, unsigned int, unsigned int, unsigned int, unsigned int, double, double, const mat&, bool)’:
mcmc.cpp:23:47: error: call of overloaded ‘floor(unsigned int)’ is ambiguous
n_samples(floor((n_iter - n_burnin) / n_thin)),
^
In file included from /usr/include/math.h:15:0,
from /opt/csw/include/c++/5.2.0/cmath:44,
from /home/ripley/R/Lib32/Rcpp/include/Rcpp/platform/compiler.h:100,
from /home/ripley/R/Lib32/Rcpp/include/Rcpp/r/headers.h:48,
from /home/ripley/R/Lib32/Rcpp/include/RcppCommon.h:29,
from /home/ripley/R/Lib32/RcppArmadillo/include/RcppArmadilloForward.h:26,
from /home/ripley/R/Lib32/RcppArmadillo/include/RcppArmadillo.h:31,
from /home/ripley/R/Lib32/ramcmc/include/ramcmc.h:4,
from mcmc.cpp:4:
/opt/csw/lib/gcc/i386-pc-solaris2.10/5.2.0/include-fixed/iso/math_iso.h:193:21: note: candidate: long double std::floor(long double)
inline long double floor(long double __X) { return __floorl(__X); }
^
/opt/csw/lib/gcc/i386-pc-solaris2.10/5.2.0/include-fixed/iso/math_iso.h:164:15: note: candidate: float std::floor(float)
inline float floor(float __X) { return __floorf(__X); }
^
/opt/csw/lib/gcc/i386-pc-solaris2.10/5.2.0/include-fixed/iso/math_iso.h:77:15: note: candidate: double std::floor(double)
extern double floor __P((double));
^
mcmc.cpp: In member function ‘void mcmc::state_posterior(T, unsigned int)’:
mcmc.cpp:53:56: error: call of overloaded ‘floor(unsigned int)’ is ambiguous
unsigned thread_size = floor(n_stored / n_threads);
^
In file included from /usr/include/math.h:15:0,
from /opt/csw/include/c++/5.2.0/cmath:44,
from /home/ripley/R/Lib32/Rcpp/include/Rcpp/platform/compiler.h:100,
from /home/ripley/R/Lib32/Rcpp/include/Rcpp/r/headers.h:48,
from /home/ripley/R/Lib32/Rcpp/include/RcppCommon.h:29,
from /home/ripley/R/Lib32/RcppArmadillo/include/RcppArmadilloForward.h:26,
from /home/ripley/R/Lib32/RcppArmadillo/include/RcppArmadillo.h:31,
from /home/ripley/R/Lib32/ramcmc/include/ramcmc.h:4,
from mcmc.cpp:4:
/opt/csw/lib/gcc/i386-pc-solaris2.10/5.2.0/include-fixed/iso/math_iso.h:193:21: note: candidate: long double std::floor(long double)
inline long double floor(long double __X) { return __floorl(__X); }
^
/opt/csw/lib/gcc/i386-pc-solaris2.10/5.2.0/include-fixed/iso/math_iso.h:164:15: note: candidate: float std::floor(float)
inline float floor(float __X) { return __floorf(__X); }
^
/opt/csw/lib/gcc/i386-pc-solaris2.10/5.2.0/include-fixed/iso/math_iso.h:77:15: note: candidate: double std::floor(double)
extern double floor __P((double));
^
Cube index out of bound due to implicit casting of bool
From Travis (https://travis-ci.org/helske/bssm/jobs/160477908):
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: ng_bsm
> ### Title: Non-Gaussian Basic Structural (Time Series) Model
> ### Aliases: ng_bsm
>
> ### ** Examples
>
> model <- ng_bsm(Seatbelts[, "VanKilled"], distribution = "poisson",
+ sd_level = halfnormal(0.01, 1),
+ sd_seasonal = halfnormal(0.01, 1), slope = FALSE,
+ beta = normal(0, 0, 10),
+ xreg = Seatbelts[, "law"])
>
> set.seed(123)
> mcmc_out <- run_mcmc(model, n_iter = 5000, nsim = 10)
error: Cube::operator(): index out of bounds
Error in eval(substitute(expr), envir, enclos) :
Cube::operator(): index out of bounds
Calls: run_mcmc -> run_mcmc.ng_bsm -> ng_bsm_run_mcmc -> .Call
Weird thing is that this error happens only on Travis, not on my windows computers nor in a linux server where I have access. Valgrind results come clean as well. Next step address sanitizers?
NA values in MCMC for Gaussian model
y <- log(AirPassengers)
model <- bsm(y, sd_level = uniform(0.1, 0, 1000),
sd_slope = uniform(0.1, 0, 1000), sd_y = uniform(0.1, 0, 1000),
sd_seasonal = uniform(0.1, 0, 1000), period = 12)
out <- run_mcmc(model, n_iter = 2e5)
out
There seems to be a bug in the sampling of states as out$alpha is sometimes full of NaN:s...
Question about nlg_ar_exp
I checked ‘cpp_example_model’ function and found that ‘nlg_ar_exp’ does not apply to my model, I wonder whether my model is supported by bssm package , my model is as follows:
Q_{t}=exp(y_{t})+exp(y_{t-1})+exp(y_{t-2})
y_{t}=tau_{t}+c_{t}
tau_{t}=tau_{t-1}+d_{t-1}+eta_{t}
d_{t}=d_{t-1}+V_{t}
c_{t}=a_{1}c_{t-1}+a_{2}c_{t-2}+epsilon_{t}
Many thanks!
Multivariate missingness in nlg_ssm
Currently particle filters do not handle partially missing data properly.
Stray Stats Std BS7.7
@helske I'm trying to figure out ropensci-review-tools/srr#24, and note that you've got a standard BS7.7 in this line:
bssm/tests/testthat/test_mcmc.R
Line 3 in 8c52ea3
But Bayesian standards only go up to BS7.4.That results in your code reporting 124 standards from a total of 123 General + Bayesian. I'll implement a catch for any non-existent standards in srr, and could you please correct that one in your code? Thanks!
Installation from Github fails on Mac
Running os 10.12.6, latest Xcode, R3.4.2. This problem has arisen in other packages:
devtools::install_github("helske/bssm")
Downloading GitHub repo helske/bssm@master
from URL https://api.github.com/repos/helske/bssm/zipball/master
Installing bssm
'/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file --no-environ
--no-save --no-restore --quiet CMD INSTALL
'/private/var/folders/xw/mcmsdzzx4mgbttplylgs7ysh0000gp/T/RtmpoGVsEQ/devtoolsf4114663d465/helske-bssm-c86446d'
--library='/Users/rmendels/Library/R/3.4/library' --install-tests
- installing source package ‘bssm’ ...
** libs
clang++ -std=gnu++11 -I/Library/Frameworks/R.framework/Resources/include -DNDEBUG -I"/Users/rmendels/Library/R/3.4/library/BH/include" -I"/Users/rmendels/Library/R/3.4/library/Rcpp/include" -I"/Users/rmendels/Library/R/3.4/library/RcppArmadillo/include" -I"/Users/rmendels/Library/R/3.4/library/ramcmc/include" -I"/Users/rmendels/Library/R/3.4/library/sitmo/include" -I/usr/local/include -fopenmp -fPIC -Wall -g -O2 -c R_approx.cpp -o R_approx.o
clang: error: unsupported option '-fopenmp'
make: *** [R_approx.o] Error 1
ERROR: compilation failed for package ‘bssm’ - removing ‘/Users/rmendels/Library/R/3.4/library/bssm’
- restoring previous ‘/Users/rmendels/Library/R/3.4/library/bssm’
Installation failed: Command failed (1)
Thanks
Update vignette due to changes in phi/u
Vignette is now out of date due to the recent changes in model formulation.
Non-gaussian linear state model with continuous time
Hi, I have a model that is a linear state model with non-gaussian response with continuous time.
I found that function 'ssm_nlg' seems to be able to handle linear (also, non-linear) gaussian with continuous time, while 'ssm_mng' can handle linear state model with non-gaussian response but no continuous time.
I'm wondering whether it is possible to build a linear state model with non-gaussian response with continuous time. Is it possible to write C++ snippets in 'ssm_mng' function since the only difference is that I want continuous time.
Thank you!
Add gamma distribution
Will do at some point. If somebody needs it I'll do it faster...
CRAN complains about multithreading
Getting a note on CRAN's Debian pretest:
Examples with CPU time > 2.5 times elapsed time
user system elapsed ratio
exchange 1.196 0.04 0.159 7.774
Nothing in that example should use parallelisation as we are not doing MCMC. And we don't use data.table (Rdatatable/data.table#5658). Setting Sys.setenv("OMP_THREAD_LIMIT" = 2) doesn't help either.
UBSAN error
OMP: Warning #96: Cannot form a team with 24 threads, using 2 instead.
OMP: Hint: Consider unsetting KMP_ALL_THREADS and OMP_THREAD_LIMIT (if either is set).
In file included from growth_model_functions.cpp:1:
In file included from /data/gannet/ripley/R/test-clang/RcppArmadillo/include/RcppArmadillo.h:31:
In file included from /data/gannet/ripley/R/test-clang/RcppArmadillo/include/RcppArmadilloForward.h:26:
In file included from /data/gannet/ripley/R/test-clang/Rcpp/include/RcppCommon.h:70:
/data/gannet/ripley/R/test-clang/Rcpp/include/Rcpp/utils/tinyformat.h:1046:32: warning: no newline at end of file [-Wnewline-eof]
#endif // TINYFORMAT_H_INCLUDED
^
1 warning generated.
function_pointers.h:99:12: runtime error: call to function a1_fn(arma::Col<double> const&, arma::Col<double> const&) through pointer to incorrect function type 'arma::Col<double> (*)(const arma::Col<double> &, const arma::Col<double> &)'
/tmp/RtmprpYU6Z/sourceCpp-x86_64-pc-linux-gnu-0.12.11/sourcecpp_a8d276dc88da/growth_model_functions.cpp:7: note: a1_fn(arma::Col<double> const&, arma::Col<double> const&) defined here
SUMMARY: AddressSanitizer: undefined-behavior function_pointers.h:99:12 in
function_pointers.h:114:12: runtime error: call to function P1_fn(arma::Col<double> const&, arma::Col<double> const&) through pointer to incorrect function type 'arma::Mat<double> (*)(const arma::Col<double> &, const arma::Col<double> &)'
/tmp/RtmprpYU6Z/sourceCpp-x86_64-pc-linux-gnu-0.12.11/sourcecpp_a8d276dc88da/growth_model_functions.cpp:16: note: P1_fn(arma::Col<double> const&, arma::Col<double> const&) defined here
SUMMARY: AddressSanitizer: undefined-behavior function_pointers.h:114:12 in
function_pointers.h:65:12: runtime error: call to function Z_gn(unsigned int, arma::Col<double> const&, arma::Col<double> const&, arma::Col<double> const&, arma::Mat<double> const&) through pointer to incorrect function type 'arma::Mat<double> (*)(unsigned int, const arma::Col<double> &, const arma::Col<double> &, const arma::Col<double> &, const arma::Mat<double> &)'
/tmp/RtmprpYU6Z/sourceCpp-x86_64-pc-linux-gnu-0.12.11/sourcecpp_a8d276dc88da/growth_model_functions.cpp:55: note: Z_gn(unsigned int, arma::Col<double> const&, arma::Col<double> const&, arma::Col<double> const&, arma::Mat<double> const&) defined here
SUMMARY: AddressSanitizer: undefined-behavior function_pointers.h:65:12 in
function_pointers.h:81:12: runtime error: call to function H_fn(unsigned int, arma::Col<double> const&, arma::Col<double> const&, arma::Mat<double> const&) through pointer to incorrect function type 'arma::Mat<double> (*)(unsigned int, const arma::Col<double> &, const arma::Col<double> &, const arma::Mat<double> &)'
/tmp/RtmprpYU6Z/sourceCpp-x86_64-pc-linux-gnu-0.12.11/sourcecpp_a8d276dc88da/growth_model_functions.cpp:27: note: H_fn(unsigned int, arma::Col<double> const&, arma::Col<double> const&, arma::Mat<double> const&) defined here
SUMMARY: AddressSanitizer: undefined-behavior function_pointers.h:81:12 in
function_pointers.h:49:12: runtime error: call to function Z_fn(unsigned int, arma::Col<double> const&, arma::Col<double> const&, arma::Col<double> const&, arma::Mat<double> const&) through pointer to incorrect function type 'arma::Col<double> (*)(unsigned int, const arma::Col<double> &, const arma::Col<double> &, const arma::Col<double> &, const arma::Mat<double> &)'
/tmp/RtmprpYU6Z/sourceCpp-x86_64-pc-linux-gnu-0.12.11/sourcecpp_a8d276dc88da/growth_model_functions.cpp:47: note: Z_fn(unsigned int, arma::Col<double> const&, arma::Col<double> const&, arma::Col<double> const&, arma::Mat<double> const&) defined here
SUMMARY: AddressSanitizer: undefined-behavior function_pointers.h:49:12 in
function_pointers.h:33:12: runtime error: call to function log_prior_pdf(arma::Col<double> const&) through pointer to incorrect function type 'double (*)(const arma::Col<double> &)'
/tmp/RtmprpYU6Z/sourceCpp-x86_64-pc-linux-gnu-0.12.11/sourcecpp_a8d276dc88da/growth_model_functions.cpp:102: note: log_prior_pdf(arma::Col<double> const&) defined here
SUMMARY: AddressSanitizer: undefined-behavior function_pointers.h:33:12 in
function_pointers.h:33:12: runtime error: call to function log_prior_pdf(arma::Col<double> const&) through pointer to incorrect function type 'double (*)(const arma::Col<double> &)'
/tmp/RtmprpYU6Z/sourceCpp-x86_64-pc-linux-gnu-0.12.11/sourcecpp_a8d276dc88da/growth_model_functions.cpp:102: note: log_prior_pdf(arma::Col<double> const&) defined here
SUMMARY: AddressSanitizer: undefined-behavior function_pointers.h:33:12 in
trouble running filters / smoothers
Hello, thanks again for your work.
I am having trouble running filters and smoothers. Example:
model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson",
sd_level = halfnormal(0.01, 1),
sd_seasonal = halfnormal(0.01, 1),
beta = normal(0, 0, 10),
xreg = Seatbelts[, "law"])
filtered <- ekf(model)
gives error:
Error in ekf_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, :
Not compatible with requested type: [type=NULL; target=double].
Also occurs with ekf_smoother, ukf, etc.
Thank you!
add constant back to bootstrap filter likelihood
Just modified bootstrap filter for increased speed by dropping constants in density functions, but I should add the constant to final log-likelihood just for completeness. Low priority issue though...
Reparametrize theta for AR models for more efficient sampling
Sampling for ar1_lg and ar1_ng models is currently done in constrained (natural) space, but it would make sense to sample in transformed space for sigmas and phi as is already done for bsm models. This should improve the sampling efficiency especially if we are close to non-stationarity or zero variances.
To accomplish this, relatively small modifications to update_model and log_prior_pdf functions on the C++ side, and run_mcmc, ar1_lg, ar1_ng functions (and docs) as well as print and summary methods on the R side would be needed. This should be easy by just mimicking the bsm versions.
The same could be of course done to the SV model as well.
Installation from Github fails on Mac: clang: error: unsupported option '-fopenmp'
With R 3.6.0 under macOS 10.14.4, 'devtools::install_github("helske/bssm")' produced the following:
Downloading GitHub repo helske/bssm@master
✔ checking for file ‘/private/var/folders/mh/mrm_14nx19g13lsnj9zmvwjr0000gn/T/RtmpI8M1Qy/remotes57ff163932df/helske-bssm-70458ec/DESCRIPTION’ ...
─ preparing ‘bssm’:
✔ checking DESCRIPTION meta-information ...
─ cleaning src
─ checking for LF line-endings in source and make files and shell scripts
─ checking for empty or unneeded directories
─ looking to see if a ‘data/datalist’ file should be added
─ building ‘bssm_0.1.7.tar.gz’
- installing source package ‘bssm’ ...
** using staged installation
** libs
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.6/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/3.6/Resources/library/Rcpp/include" -I"/Library/Frameworks/R.framework/Versions/3.6/Resources/library/RcppArmadillo/include" -I"/Library/Frameworks/R.framework/Versions/3.6/Resources/library/ramcmc/include" -I"/Library/Frameworks/R.framework/Versions/3.6/Resources/library/sitmo/include" -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk -I/usr/local/include -fopenmp -fPIC -Wall -g -O2 -c R_approx.cpp -o R_approx.o
clang: error: unsupported option '-fopenmp'
make: *** [R_approx.o] Error 1
NOTE: This seemed quit similar to "Installation from Github fails on Mac #16", closed on 2017-11-13 with a solution posted. However, that solution seems out of date, and I'm afraid to try it for fear I might create more problems for myself than I solve. Thanks.
Particle filter for Gaussian models produces zero weights
It seems that I have broken bootstrap filter for Gaussian models at some point, fix it.
IS2 for ng_bsm does not give correct results
Trying to reproduce the Poisson example in Importance sampling type estimators based on approximate marginal MCMC does not give correct results for IS2 method. So at some point since those results I have introduced a bug somewhere...
Filtering equations incorrect in mgg_ssm::log_likelihood() (line 171)
See calculations in the photo attached. You wrote:
arma::mat K = Pt * Zt.t() * inv_cholF.t() * inv_cholF;
However, I think it should be:
arma::mat K = Pt * Zt.t() * inv_cholF * inv_cholF.t();
Thank you!
mv_gssm missing from package
Hi,
I just installed bssm from CRAN and the function mv_gssm is missing, despite being in the documentation. Should it be there?
I see it is in models.R, should I just download from github?
Thanks
Adam
General non-Gaussian model defined with C++ snippets
Like in case of non-linear models and linear-gaussian models, it would be nice to have an option to define non-Gaussian (poisson, binomial, SV model would work similarly) model via C++ snippets as well. Should be really straightforward to do by looking how those other models are defined, I just don't have time.
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