Paradram.m

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%>  \brief
%>  This is the ParaDRAM class for generating instances of serial and parallel
%>  Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo
%>  sampler of the ParaMonte MATLAB library.<br>
%>
%>  \details
%>  For more information, see the documentation of the class
%>  constructor [pm.sampling.Paradram::Paradram](@ref Paradram::Paradram).<br>
%>
%>  \note
%>  See the documentation of the class constructor for usage interface and examples.<br>
%>
%>  \final
%>
%>  \author
%>  \AmirShahmoradi, May 16 2016, 9:03 AM, Oden Institute for Computational Engineering and Sciences (ICES), UT Austin<br>
classdef Paradram < pm.sampling.Sampler
 
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    methods(Access = public)
 
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        %>  \brief
        %>  Generate and return an instance of the serial and parallel
        %>  Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo
        %>  sampler of the ParaMonte MATLAB library.<br>
        %>
        %>  \details
        %>  This function is the constructor of the [pm.sampling.Paradram](@ref Paradram) sampler class.<br>
        %>  Once you assign the desired simulation specifications to the corresponding attributes within the
        %>  component `spec` of an object of class [pm.sampling.Paradram](@ref Paradram), call the ParaDRAM
        %>  sampler via the object method [pm.sampling.Paradram.run()](@ref Paradram::run).<br>
        %>
        %>  While the constructor of this class does not take any input arguments,
        %>  all ParaDRAM simulation specifications can be set after creating the object.<br>
        %>
        %>  \return
        %>  ``sampler`` :   The output scalar object of class [pm.sampling.Paradram](@ref Paradram).<br>
        %>
        %>  \interface{Paradram}
        %>  \code{.m}
        %>
        %>      sampler = pm.sampling.Paradram();
        %>
        %>  \endcode
        %>
        %>  \warning
        %>  When using the ParaMonte MATLAB library functionalities, particularly ParaMonte samplers in parallel,
        %>  it would be best to close any such aggressive software/applications as **Dropbox**, **ZoneAlarm**, ...
        %>  that interfere with your ParaMonte MATLAB library output files, potentially causing the tasks
        %>  to fail and crash before successful completion.<br>
        %>  These situations scarcely happen.<br>
        %>
        %>  \note
        %>  On Windows systems, when restarting an old interrupted ParaDRAM simulation,
        %>  ensure your MATLAB session is also restarted before the simulation restart.<br>
        %>  This may be needed as **Windows frequently locks access to some or all simulation output files**.<br>
        %>
        %>  \note
        %>  To unset an already-set input simulation specification, simply set the
        %>  simulation attribute to empty double `[]` or re-instantiate an object
        %>  of class [pm.sampling.Paradram.run()](@ref Paradram).<br>
        %>
        %>  \see
        %>  [ParaDRAM simulation specifications listing](\pmdoc_usage_sampling/paradram/specifications/)<br>
        %>  [ParaDRAM simulation restart functionality](\pmdoc_usage_sampling/paradram/restart/)<br>
        %>  [ParaDRAM simulation output files](\pmdoc_usage_sampling/paradram/output/)<br>
        %>
        %>  ParaDRAM Simulation Specifications
        %>  ----------------------------------
        %>
        %>  The ParaDRAM simulation specifications have lengthy comprehensive descriptions
        %>  that appear in full in the output report files of every ParaDRAM simulation.<br>
        %>
        %>  The best way to learn about individual ParaDRAM simulation attributes
        %>  is to a run a minimal serial simulation as given in the above.<br>
        %>  You can also use the ``sampler.spec.doc()`` method:
        %>
        %>  \code{.m}
        %>      sampler = pm.sampling.Paradram();
        %>      sampler.spec.doc()
        %>  \endcode
        %>
        %>  Example Usage: Serial
        %>  ---------------------
        %>
        %>  First, ensure the ParaMonte ``+pm`` package (i.e., folder) is available in your MATLAB paths.
        %>
        %>  Here is a MATLAB script ``main.m`` for a serial ParaDRAM simulation.<br>
        %>  Copy and paste the following code into your MATLAB session:<br>
        %>
        %>  \code{.m}
        %>
        %>      ndim = 4;
        %>      sampler = pm.sampling.Paradram();
        %>      sampler.run ( @(x) - sum(x.^2)  ... getLogFunc: the natural log of the objective function.
        %>                  , ndim              ... ndim: the number of dimensions of the objective function.
        %>                  );
        %>      samples = sampler.readSample();
        %>      sample = samples{1};
        %>      tile = pm.vis.TileLine(sample.df);
        %>      tile.make("coly", sample.slfc + 1 : sample.slfc + ndim, "colc", "sampleLogFunc");
        %>
        %>  \endcode
        %>
        %>  The mathematical objective function in the above example is a
        %>  is a multivariate Normal distribution centered at the origin,
        %>  whose natural logarithm is returned by the lambda (``Anonymous``)
        %>  function defined as a function handle input to the ParaDRAM sampler.<br>
        %>
        %>  Running this code will generate a set of simulation output files (in the current working directory of MATLAB).<br>
        %>  Among these, the file suffixed with "_report.txt" contains the full description of all input specifications
        %>  of the ParaDRAM simulation as well as other information about the simulation results.<br>
        %>
        %>  Example Usage: Thread-Parallel
        %>  ------------------------------
        %>
        %>  First, ensure the ParaMonte ``+pm`` package (i.e., folder) is available in your MATLAB paths.<br>
        %>
        %>  Threading parallelism is possible as of ParaMonte MATLAB version ``3.0.0``.<br>
        %>  However, only ``singleChain`` ParaDRAM simulations are supported.<br>
        %>
        %>  Here is a MATLAB script ``main.m`` for a thread-parallel ParaDRAM simulation.<br>
        %>  Copy and paste the following code and paste into your MATLAB session:<br>
        %>
        %>  \code{.m}
        %>
        %>      sampler = pm.sampling.Paradram();
        %>      sampler.spec.parallelismNumThread = 0; % use all available threads.
        %>      sampler.run ( @(x) - sum(x.^2)  ... getLogFunc: the natural log of the objective function.
        %>                  , 4                 ... ndim:       the number of dimensions of the objective function.
        %>                  );
        %>      samples = sampler.readSample();
        %>      sample = samples{1};
        %>      pm.vis.tile(sample.contents)
        %>
        %>  \endcode
        %>
        %>  The mathematical objective function in the above example is a
        %>  is a multivariate Normal distribution centered at the origin,
        %>  whose natural logarithm is returned by the lambda (``Anonymous``)
        %>  function defined as a function handle input to the ParaDRAM sampler.<br>
        %>
        %>  Running this code will generate a set of simulation output files (in the current working directory of MATLAB).<br>
        %>  Among these, the file suffixed with ``"_report.txt"`` contains the full description of all input specifications
        %>  of the ParaDRAM simulation as well as other information about the simulation results.<br>
        %>
        %>  Specifying ``0`` as the number of threads will lead to using
        %>  all available CPU threads for thread-parallel ParaDRAM simulation.<br>
        %>
        %>  \note
        %>  **Benefits of thread-parallelism**<br>
        %>  Thread-parallel simulations offer a much more flexible
        %>  and easier approach to benefiting from parallelism without
        %>  going through the hassle of MPI-parallel simulations.<br>
        %>  But they can still potentially offer much faster speed than serial simulations.<br>
        %>  The actual speedup depends on a lot of factors.<br>
        %>  Moreover, the number of threads is limited to maximum
        %>  number of physical cores available on your system.<br>
        %>  As such, thread-parallel simulations are not scalable.<br>
        %>  If you need scalability, checkout MPI-parallelism below.<br>
        %>
        %>  Example Usage: MPI-Parallel
        %>  ---------------------------
        %>
        %>  First, ensure the ParaMonte ``+pm`` package (i.e., folder) is available in your MATLAB paths.<br>
        %>
        %>  MPI-parallel simulations can be slightly more cumbersome than thread-parallel simulations
        %>  described above because MPI-parallel simulations cannot be performed from within a MATLAB GUI
        %>  session and require launching MATLAB via a compatible ``mpiexec`` launcher.<br>
        %>
        %>  <ol>
        %>      <li>    Ensure you need and will get a speedup by running the an MPI-parallel simulation.<br>
        %>              Typically, your simulation may then benefit from parallelism only if a single
        %>              evaluation of the objective function takes longer than a few milliseconds.<br>
        %>
        %>      <li>    Ensure the required MPI libraries are installed on your system
        %>              (You can skip this step if you know that you already have
        %>              a compatible MPI library installed on your system).<br>
        %>              On the MATLAB command line type the following,<br>
        %>              \code{.m}
        %>                  pm.lib.verify();
        %>              \endcode
        %>              This will verify the existence of a valid MPI library on your system and,
        %>              if missing, will guide you to install the MPI library on your system.<br>
        %>
        %>      <li>    Once the MPI installation is verified, copy and paste the following
        %>              code into your MATLAB session:
        %>              \code{.m}
        %>
        %>                  fid = fopen("main_mpi.m", "w");
        %>                  sourceCode = ...
        %>                  "sampler = pm.sampling.Paradram();" + newline + ...
        %>                  "%sampler.mpiname = ''; % set this to an explicit MPI library name if needed." + newline + ...
        %>                  "sampler.run( @(x) - sum(x.^2)  ... getLogFunc: the natural log of the objective function." + newline + ...
        %>                  "           , 4                 ... ndim:       the number of dimensions of the objective function" + newline + ...
        %>                  "           );";
        %>                  fprintf(fid, "%s\n", sourceCode);
        %>                  fclose(fid);
        %>
        %>              \endcode
        %>
        %>      <li>    This will generate a ``main_mpi.m`` MATLAB script file in the current working directory of your MATLAB session.<br>
        %>              Now, you can execute this MATLAB script file (``main_mpi.m``) in parallel.<br>
        %>              To do so, you need to call MATLAB on a command-line, **out of MATLAB GUI**.<br>
        %>              <ol>
        %>                  <li>    **On Windows**:<br>
        %>                          From within command prompt that recognizes both MATLAB and ``mpiexec``,
        %>                          ideally, the Intel dedicated command-prompt that is shipped with Intel MPI library,
        %>                          type the following,
        %>                          \code{.m}
        %>
        %>                              mpiexec -localonly -n 3 matlab -batch "main_mpi"
        %>
        %>                          \endcode
        %>
        %>                          \note
        %>                          In the above MPI launcher command for Windows OS,
        %>                          we assumed that you would be using the Intel MPI library, hence,
        %>                          the reason for the extra flag ``-localonly``.<br>
        %>                          This flag runs the parallel code only on one node, but in doing so,
        %>                          it avoids the use of Hydra service and its registration.<br>
        %>                          If you are not on a Windows cluster, (e.g., you are using your personal device),
        %>                          then we recommend specifying this flag.<br>
        %>
        %>                  <li>    **On macOS/Linux**:<br>
        %>                          From within a Bash terminal that recognizes both MATLAB and ``mpiexec``,
        %>                          type the following,
        %>                          \code{.m}
        %>
        %>                              mpiexec -n 3 matlab -batch "main_mpi"
        %>
        %>                          \endcode
        %>
        %>                          \note
        %>                          In both cases in the above, the script ``main_mpi.m`` will run on 3 processors.<br>
        %>                          Feel free to change the number of processors to any number desired.<br>
        %>                          But do not request more than the available number of physical cores on your system.<br>
        %>              </ol>
        %>  </ol>
        %>
        %>  \warning
        %>  Do not add postprocessing codes (such as reading and plotting the output samples) in your MPI-parallel code.<br>
        %>  There is no point in doing so, since MATLAB will run in ``-batch`` mode for parallel simulations, disabling all plotting capabilities.<br>
        %>  Moreover, if you read and postprocess the output files in parallel mode, the task will be done
        %>  by all parallel processes, potentially overwriting external IO activities of each other.<br>
        %>  Only perform the sampling as described above in MPI-parallel mode.<br>
        %>
        %>  \example{himmelblau}
        %>  \include{lineno} example/sampling/Paradram/himmelblau/main.m
        %>  \output{himmelblau}
        %>  \include{lineno} example/sampling/Paradram/himmelblau/main.out.m
        %>  \vis{himmelblau}
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.domain.2d.png width=700
        %>  <br><br>
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.domain.3d.png width=700
        %>  <br><br>
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.triplex.lshc2.png width=700
        %>  <br><br>
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.triplex.lshc3.png width=700
        %>  <br><br>
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.triplex.lshcf.png width=700
        %>  <br><br>
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.traceplot.png width=700
        %>  <br><br>
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.proposalCov.png width=700
        %>  <br><br>
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.proposalAdaptation.png width=700
        %>  <br><br>
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.proposalAdaptation.line.png width=700
        %>  <br><br>
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.proposalAdaptation.scatter.png width=700
        %>  <br><br>
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.parallelism.speedup.scaling.strong.sameeff.png width=700
        %>  <br><br>
        %>  \image html example/sampling/Paradram/himmelblau/Paradram.himmelblau.parallelism.speedup.scaling.strong.zeroeff.png width=700
        %>
        %>  \final{Paradram}
        %>
        %>  \author
        %>  \AmirShahmoradi, May 16 2016, 9:03 AM, Oden Institute for Computational Engineering and Sciences (ICES), UT Austin<br>
        function self = Paradram()
            self = self@pm.sampling.Sampler("ParaDRAM")
        end
 
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        run(self, getLogFunc, ndim)
 
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        chainMarkovList = readChainMarkov(self, pattern, sep)
 
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    end
 
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    methods(Hidden)
 
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        ppm = getppm(self)
 
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    end
 
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end