Matlab PLP 2006a Free Download
Saija Grzegorek
BTW, in this case, the reason I want this check is to know that I can use the function matlab.lang.makeUniqueStrings. If there were a robust, supported way to check for the availability of this function, I'd use it instead of testing that the current MATLAB is recent enough. Unfortunately, there doesn't seem to be such a check: exist returns false to every variant I can come up for the name of this function. Again, I can think of fragile hacks to mimic a proper test (e.g. which('matlab.lang.makeUniqueStrings')), but they're hardly better than the version-testing hacks I alluded to above.
The best solution I have found is to run the command using matlab.lang.makeUniqueStrings within a try-catch block. This is still a fragile hack, because MATLAB does not offer a robust, built-in way to catch specific exceptions!
If you only need to care about fairly recent versions, use the verLessThan command. However, verLessThan was introduced in about 2006a or so; if you need to support versions older than that, you will need to use the output of the version command.
Alternatively, you can robustly test for the existence of matlab.lang.makeUniqueStrings. Firstly, use m = meta.package.fromName('matlab.lang') to retrieve a meta.package object referring to the package. If m is empty, the package does not exist. Assuming m is not empty, check the FunctionList property of m to see whether makeUniqueStrings is present. (There's also a ClassList property as well).
If the memory required for the number of workspaces in each chunk is greater than that defined then the number of workspaces is reduced until it fits in the memory or a failure status is raised. These two values are defined in ./matlab/homer/default_homer_nchunk.m which needs to be edited to suit the memory needs of your computer. Tests have shown that LIBISIS will populate data fastest when it is chunked in groups of 100 workspaces, so this is the default value in the file.
Kayser, J., Tenke, C.E. (2006a). Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks. Clinical Neurophysiology, 117(2), 348-368. doi:10.1016/j.clinph.2005.08.034
ap172/code.html?fbclid=IwAR3OyYRaD81HJEXIxkilWjiy4sGtOWPjIpNWV8YmgtO-fioBsGP9m9CAa_o
This zip file contains a collection of Matlab functions that I wrote for my research on copulas for financial time series (Patton 2006a, Patton 2006b, Patton 2004, Granger et al. 2006, Patton 2007). Some simple example code is given in "copula_example_code.m". A table of contents is given in "contents.xls". Briefly, the toolbox contains CDFs, PDFs, log-likelihoods and random number generators for many common bivariate copulas, including the Clayton, Gumbel, Normal, Student's t, Frank, Plackett and symmetrised Joe-Clayton (SJC) copulas. Basic code for time-varying Normal, Gumbel and SJC copulas is included. Some helper functions are also included. If you find any bugs in this code please let me know: andrew...@economics.ox.ac.uk.
Numerical algorithm comparisons will be presented for test functions and for an environmentally based partial differential equation model that requires 1.5 hours to run for each simulation. This nonlinear model (based on fluid mechanics and chemical reactions) describes the fate and transport of water and pollutants in a groundwater aquifer. The optimization is used for calibration of the model by selecting the parameter values (decision variables) that best fit measured data from a military field site in Florida. The parameter surface is multi-modal so this is a global optimization problem. The results indicate that a Regis and Shoemaker (2006a) method generally gives better results for global optimization test problems and the environmental model than alternative methods when the number of model simulations is limited. It is especially effective at dimensions higher than 6. Related parallel algorithms will also be briefly discussed (Regis and Shoemaker, 2006b).