search for: outtypes

Hello, Earlier I posted a question about memory usage, and the community's input was very helpful. However, I'm now extending my dataset (which I use when running a regression using lm). As a result, I am continuing to run into problems with memory usage, and I believe I need to shift to implementing the analysis on a different system.. I know that R supports R servers through

...;Additive Outliers","Levels Shifts","Seasonal Level Shifts","Innovation Outlier","Temporary Change") tsFrequency <- 52 params <- list("outlier.types" = outlier.types,"tsFrequency" = tsFrequency) tsFrequency = params$tsFrequency outTypes <- unlist(params$outlier.types) ts.var <- ts(inputData[,1], frequency = tsFrequency) ts_outlier <- tso(ts.var, types = ?outlier.types ? , maxit = 1) ? This piece of code runs in about 60 seconds on my laptop but take about upward of 13 mins on a decent AWS node with nothing else running...

Hi Gang, I'm testing 1.8.0 on one of my machines and this snippet "chokes" on line 7 (works fine with 1.4.30) [tb-account-balance] exten => s,1,Set(BALCOUNT=0) exten => s,n,NoOp(Verbose(acct ${digitacc} pwd ${digitpwd} )) exten => s,n(runagi),Set(TEST_RETURN="NONE") exten =>

...;Additive Outliers","Levels Shifts","Seasonal Level Shifts","Innovation Outlier","Temporary Change") tsFrequency <- 52 params <- list("outlier.types" = outlier.types,"tsFrequency" = tsFrequency) tsFrequency = params$tsFrequency outTypes <- unlist(params$outlier.types) ts.var <- ts(inputData[,1], frequency = tsFrequency) ts_outlier <- tso(ts.var, types = ?outlier.types ? , maxit = 1) ? This piece of code runs in about 60 seconds on my laptop but take about upward of 13 mins on a decent AWS node with nothing else running...

Hello all, Here is my GSoC 2012 proposal: Python bindings for LLVM. Any feedback are welcome! *Title: Python bindings for LLVM* *Abstract: * llvm-py provides Python bindings for LLVM. The latest llvm-py supports bindings with Python 2.x version for LLVM 2.x. This project is to improve llvm-py to make it compatible with both Python 2.x and Python 3 for LLVM 3. *Motivation* LLVM is used as a

Add an optional argument for --machine-readable to select the output, adding a new function to specifically write data to that output stream. The possible choices are: * --machine-readable: to stdout, like before * --machine-readable=file:name-of-file: to the specified file * --machine-readable=stream:stdout: explicitly to stdout * --machine-readable=stream:stderr: explicitly to stderr Adapt all

Hi, this is a first approach (hence RFC, since it misses tests & documentation) in selecting the output for --machine-readable. The possible choices are: * --machine-readable: to stdout, like before * --machine-readable=file:name-of-file: to the specified file * --machine-readable=stream:stdout: explicitly to stdout * --machine-readable=stream:stderr: explicitly to stderr This makes it

Add an optional argument for --machine-readable to select the output, adding a new function to specifically write data to that output stream. The possible choices are: * --machine-readable: to stdout, like before * --machine-readable=file:name-of-file: to the specified file * --machine-readable=stream:stdout: explicitly to stdout * --machine-readable=stream:stderr: explicitly to stderr Adapt all

Hi, this adds the possibility to select the output for --machine-readable in OCaml tools. The possible choices are: * --machine-readable: to stdout, like before * --machine-readable=file:name-of-file: to the specified file * --machine-readable=stream:stdout: explicitly to stdout * --machine-readable=stream:stderr: explicitly to stderr This makes it possible to add additional output for