search for: laplacian

...usr/local/include -fpic -O3 -g -c interface.cpp -o interface.o gcc -I/usr/lib/R/include -I/usr/lib/R/include -g -O2 -I/usr/local/include -fpic -O3 -g -std=gnu99 -c jpegio.c -o jpegio.o gcc -I/usr/lib/R/include -I/usr/lib/R/include -g -O2 -I/usr/local/include -fpic -O3 -g -std=gnu99 -c laplacian.c -o laplacian.o laplacian.c: In function ?laplacian?: laplacian.c:14: warning: implicit declaration of function ?clearFrame? g++ -I/usr/lib/R/include -I/usr/lib/R/include -g -O2 -I/usr/local/include -fpic -O3 -g -c matrix.cpp -o matrix.o gcc -I/usr/lib/R/include -I/usr/lib/R/include -g -O2...

Hello, <http://tolstoy.newcastle.edu.au/R/help/04/07/0312.html#0313qlink1> Could anybody tell me how to generate discrete Laplacian distribution? I need to sample uma discretised Laplacian density like this: J( g -> g´) ~ exp (-lambda | g´ - g |) g in {0,…, gmax} Thanks, Nicolette [[alternative HTML version deleted]]

...the error Error in asMethod(object) : not a symmetric matrix; consider forceSymmetric() or symmpart() The output of dput(.traceback()) is pairlist("stop(\"not a symmetric matrix; consider forceSymmetric() or symmpart()\")", "asMethod(object)", "as(Laplacian,\"symmetricMatrix\")", ".Laplacian(x)", ".flowMap(x, ci, cj, tc)", ".local(x, origin, goal, ...)", c("passage(conduct_Tobler, origin =sites at coords[5, ], goal = sites at coords[3, ", " ])"), c("passage(conduct_Tobler, o...

.../R/include -g -O2 -I/usr/local/include -fpic -O2 -march=core2 -fomit-frame-pointer -fno-ident -fweb -pipe -msse3 -c jpegio.c -o jpegio.o x86_64-pc-linux-gnu-gcc -std=gnu99 -I/usr/lib64/R/include -g -O2 -I/usr/local/include -fpic -O2 -march=core2 -fomit-frame-pointer -fno-ident -fweb -pipe -msse3 -c laplacian.c -o laplacian.o laplacian.c: In function 'laplacian': laplacian.c:14: warning: implicit declaration of function 'clearFrame' x86_64-pc-linux-gnu-g++ -I/usr/lib64/R/include -g -O2 -I/usr/local/include -fpic -O2 -march=core2 -fomit-frame-pointer -fno-ident -fweb -pipe -msse3 -c matri...

...pegio.c: In function ?read_JPEG_file?: jpegio.c:108:5: warning: pointer targets in assignment differ in signedness gcc -std=gnu99 -I/usr/lib64/R/include -I/usr/local/include -fpic -fmessage-length=0 -O2 -Wall -D_FORTIFY_SOURCE=2 -fstack-protector -funwind-tables -fasynchronous-unwind-tables -c laplacian.c -o laplacian.o laplacian.c: In function ?laplacian?: laplacian.c:15:9: warning: unused variable ?val? g++ -I/usr/lib64/R/include -I/usr/local/include -fpic -fmessage-length=0 -O2 -Wall -D_FORTIFY_SOURCE=2 -fstack-protector -funwind-tables -fasynchronous-unwind-tables -c matrix.cpp -o matrix....

...ay' (the same dataset is analysed with GEE in the book 'Extending the linear model with R' by Julian Faraway). Basically, I've tried the 2 commands 'glmmPQL' and 'glmer' of R and the command 'xtmelogit' of Stata. If I'm not wrong, 'glmer' uses the Laplacian approximation as default, corresponding to adaptive Gauss-Hermite approximation with only 1 point, while 'xtmelogit' uses 7 points. In order to compare them, I tried also to change the corresponding parameters. This is the code for R: rm(list=ls()) library(faraway); library(lme4); libra...

I am using a 64 bit machine with CentOS5. I am getting the following errors when I try to install the rimage package g++ -shared -L/usr/local/lib64 -o rimage.so equalize.o fftw_access_func.o freqfilters.o interface.o jpegio.o laplacian.o matrix.o smooth.o sobel.o -ljpeg -lfftw /usr/bin/ld: /usr/local/lib/libfftw.a(fftwnd.o): relocation R_X86_64_32 against `a local symbol' can not be used when making a shared object; recompile with -fPIC /usr/local/lib/libfftw.a: could not read symbols: Bad value collect2: ld returned 1 exit s...

...them and are highly optimized, so time/speed comparison will not be fair... Then there are innovations in the NHW codec: like a new fast wavelet transform, a multistage residual coding, 3 new entropy coding schemes and other things like for example interesting pre- and post- processing based on a laplacian kernel, a feedback correction,etc... Monty told me :"If you don't have access to web space you can use for documenting and advertising the project, we can certainly offer that." Are you interested in documentation of the innovations of the NHW codec? As I am still very busy (but thi...

...to have got mangled. Let me try to ask my question on this forum. any help is very much appreciated. While trying to download the rimage package I am checking at the following step "g++ -shared -L/usr/local/lib64 -o rimage.so equalize.o fftw_access_func.o freqfilters.o interface.o jpegio.o laplacian.o matrix.o smooth.o sobel.o -ljpeg -lfftw /usr/bin/ld: /usr/local/lib/libfftw.a(fftwnd.o): relocation R_X86_64_32 against `a local symbol' can not be used when making a shared object; recompile with -fPIC /usr/local/lib/libfftw.a: could not read symbols: Bad value collect2: ld returned 1 exit s...

...mcmcsamp does not work with Do we rely exclusively on z values of model parameters, or could we use anova() with likelihood ratios, AIC and BIC, with (or without) method="ML" (with didn't seem right to me)? I also received an error using adaptive Gaussian quadrature (but not Laplacian approximations): > mod2 <- lmer(yb ~ reg*nutrient*amd + + (1|rack) + (1|status) + + (1|popu) + (1|popu:amd) + + (1|gen) + (1|gen:nutrient) + (1|gen:amd) + + (1|gen:nutrient:amd), + data=datnm, family=binomial, method="AGQ&quo...

...ocal/include -fPIC -g -O2 -c sobel.c -o sobel.o g++ -arch ppc -dynamiclib -Wl,-headerpad_max_install_names -mmacosx-version-min=10.4 -undefined dynamic_lookup -single_module -multiply_defined suppress -L/usr/local/lib -o rimage.so equalize.o fftw_access_func.o freqfilters.o interface.o jpegio.o laplacian.o matrix.o smooth.o sobel.o -L/sw/lib -ljpeg -lfftw -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation ld: /Library/Frameworks/R.framework/../R.framework/R load command 14 unknown cmd field /usr/bin/libtool: internal link edit command failed make: *** [rimage.so] Er...

...k there is new things. > For the multistage residual coding, this is not new, but I have never seen > it applied to an image codec... > Feedback correction is not new, but I think it's old enough to be > patent-free.I also did not see it in an image codec. > Preprocessing (with a laplacian kernel) is interesting in the NHW codec, > because it retains the details, grain that would be normally washed out by > increased wavelet quantization. > > There are also other processing in the NHW codec (all selected and > optimized for speed), so I will try to make a technology de...

...t calculation, see functions graph.maxflow(), graph.mincut(), stCuts(), stMincuts(). - Check whether a directed graph is a DAG, is.dag(). - has.multiple() to decide whether a graph has multiple edges. - Added a function to calculate a diversity score for the vertices, graph.diversity(). - Graph Laplacian calculation (graph.laplacian()) supports edge weights now. - Biconnected component calculation, biconnected.components() now returns the components themselves. - bipartite.projection() calculates multiplicity of edges. - Maximum cardinality search: maximum.cardinality.search() and chordality...

...t calculation, see functions graph.maxflow(), graph.mincut(), stCuts(), stMincuts(). - Check whether a directed graph is a DAG, is.dag(). - has.multiple() to decide whether a graph has multiple edges. - Added a function to calculate a diversity score for the vertices, graph.diversity(). - Graph Laplacian calculation (graph.laplacian()) supports edge weights now. - Biconnected component calculation, biconnected.components() now returns the components themselves. - bipartite.projection() calculates multiplicity of edges. - Maximum cardinality search: maximum.cardinality.search() and chordality...

...near future, I would like to create a post-processing function that removes aliasing on the reconstructed Y comp of the NHW decoder.Starts to become imperative.Don't know if it is the right approach, but for now I am interested in trying to design such a post-processing, (I will maybe reuse the Laplacian kernel) but I will have to detect blurred edges, parts.If you have such a aliasing detector, please do not hesitate to contact me, and any contribution from the community is also welcome! Again, if you care about the NHW Project and you think there are things that really don't go in the right...

...rix methods - in particular, Tim Davis' LDL package - we are able to fit models with crossed random effects quickly and effectively. This package also contains an implementation of GLMM for Generalized Linear Mixed Models using either Penalized Quasi-Likelihood (PQL) or ML estimation using the Laplacian approximation to the marginal likelihood. At present method = 'Laplace' is considerably slower than method = 'PQL'. We would recommend using PQL for model building and determining final parameter estimates using method = 'Laplace'. Feedback is welcome. -- Douglas Bates...

...of these two packages are interdependent. The lme4 package requires Matrix_0.96-1 or later but we cannot enforce the other dependency. Please remember that if you upgrade the Matrix package you should also upgrade the lme4 package. The method for fitting generalized linear mixed models using the Laplacian approximation is considerably faster in this version. Also, the packages have been reorganized so the interdependence will not be as strong in the future. _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/...

...ed Online Learning algorithms for classification, novelty detection and regression o kpca() : Kernel Pricipal Components Analysis o kcca() : Kernel Canonical Correlation Analysis o kfa() : Kernel Feature Analysis o sigest() : Hyperparameter estimation for the Gaussian and the Laplacian kernels o inchol() : Incomplete Cholesky decomposition method o csi() : Cholesky decomposition with side information o ipop() : Interior point-based Quadratic Optimizer Kernlab also includes a range of functions enabling the easy implementation of new kernel methods including functions for c...

To whom it may concern: I just would like to know how can one generate a set of random values from the double exponential distribution. Thanks. Jason Parcon

...rix methods - in particular, Tim Davis' LDL package - we are able to fit models with crossed random effects quickly and effectively. This package also contains an implementation of GLMM for Generalized Linear Mixed Models using either Penalized Quasi-Likelihood (PQL) or ML estimation using the Laplacian approximation to the marginal likelihood. At present method = 'Laplace' is considerably slower than method = 'PQL'. We would recommend using PQL for model building and determining final parameter estimates using method = 'Laplace'. Feedback is welcome. -- Douglas Bates...