Random.m

classdef Random < ParameterSampling.Interface
  % Parameter sampling class producing randomly distributed parameter samples
  % in sparameter space `{\cal M}`.
  %
  properties(SetAccess=private, Dependent)
    % @copybrief ParameterSampling.Interface.sample
    sample;
  end

  properties (Constant)
    % string specifying the random generator to be used
    method = 'twister';
  end

  properties
    % cell vector with min and max values for each parameter component
    ranges;

    % a random seed for initialization of the random generator (Default =
    % random value initialized with clock)
    seed;

    % number of parameters in the sample
    nparameters      = 100;

    % boolean value determining whether a logarithmic function shall be
    % applied on the uniform distribution of the parameter samples.
    log_distribution = false;

  end

  methods
    function rps = Random(nparameters, seed, log_distribution)
      % function rps = Random(nparameters, seed, log_distribution)
      % constructor
      %
      % Parameters:
      %   nparameters:      @copydoc Random.nparameters
      %   seed:             @copydoc Random.seed
      %   log_distribution: @copydoc Random.log_distribution
      if nargin >= 3
        rps.log_distribution = log_distribution;
      end
      if nargin >= 2
        rps.seed = seed;
      else
        rps.seed = sum(100*clock);
      end
      if nargin >= 1
        rps.nparameters = nparameters;
      end
    end

    function this = init_sample(this, dmodel)
      % function init_sample(this, dmodel)
      % @copybrief ParameterSampling.Interface.init_sample()
      %
      % @copydetails ParameterSampling.Interface.init_sample()
      %
      % This method initializes the random number generator with the given #seed.
      this.ranges    = dmodel.mu_ranges;
      rand(this.method, this.seed);
      this.init_done = true;
    end

    function size = size(this)
      % function size = size(this)
      % @copybrief ParameterSampling.Interface.size()
      %
      % @copydetails ParameterSampling.Interface.size()
      size = this.nparameters;
    end

    function M = get.sample(this)
      %
      %
      % copied from rand_uniform() written by
      % \author Bernard Haasdonk 29.3.2007
      % adapted by Martin Drohmann 31.3.2011

      assert(~isempty(this.ranges));
      rand(this.method, this.seed);

      mu_dim = length(this.ranges);
      M = rand(this.nparameters, mu_dim);
      for i=1:mu_dim
        mu_range = this.ranges{i};
        if this.log_distribution
          M(:,i) = exp(M(:,i)*(log(mu_range(2)-mu_range(1)))) + mu_range(1);
        else
          M(:,i) = M(:,i)*(mu_range(2)-mu_range(1)) + mu_range(1);
        end
      end

    end

  end


end