%AK Specify parameters in a vector that can be read in separately to avoid
%updating code every optimization
%Can enter this vector differently if pulling in from other files, etc.
% p = [1500 85.8 0.4477 1.805 0.1269 0.003 3.1046 2.5E-7 195.73 1.626 ... %10 values
%     0.0467 0.015 0.106 0.16 0.258 0.0208 0.0396 794.07 1.24 484.44 ...  %10 values
%     0.0917 0.08 0.716 394 8.1285 996.83 0.1678 1 0.771 0.0045];         %10 values

% lowbounds = [1500,0.2,0.15,0.01,0.1,0.0005,0.01,2.5e-07,100,0.05,...    %10 values
%     0.001,0.015,0.1,0.05,0.1,0.001,0.01,100,0.5,5,...                   %10 values
%     0.01,0.08,0.001,150,1,100,0.001,1,0.5,0.001];                       %10 values
% upbounds = [1500,250,0.8,50,0.5,0.05,10,3.5e-05,300,10,...              %10 values
%     1,0.015,5,0.5,10,0.1,0.1,800,1.5,500,...                            %10 values
%     0.5,0.08,1,450,10,1000,1,1,1.5,0.01];                               %10 values
% 
% %Bounds as specified by code
% lowbounds = [1500,0.2,0.15,0.01,0.1,0.0005,0.01,2.5e-07,150,0.05,...      %10 values
%     0.001,0.015,0.1,0.05,0.1,0.001,0.01,100,0.5,50,...                    %10 values
%     0.01,0.08,0.001,5,1,50,0.001,1,0.5,0.001,...                          %10 values
%     0.1];                            
% upbounds = [1500,250,0.8,50,0.5,0.05,10,3.5e-05,250,80,...                %10 values
%     1,0.015,1,0.5,1,0.1,0.1,800,1.5,1000,...                              %10 values
%     0.5,0.08,1,450,10,1000,1,1,1.5,0.01,...                               %10 values
%     0.5];     


%Bounds as specified by code slight alteration to p(16) and p(20) to see if
%the drop out of the mito can be reduced
lowbounds = [1500,0.2,0.15,0.01,0.1,0.0005,0.01,2.5e-07,150,0.05,...      %10 values
    0.001,0.015,0.1,0.05,0.1,0.01,0.01,100,0.5,50,...                    %10 values
    0.01,0.08,0.001,5,1,50,0.001,1,0.5,0.001,...                          %10 values
    0.1];                            
upbounds = [1500,250,0.8,50,0.5,0.05,10,3.5e-05,250,80,...                %10 values
    1,0.015,1,0.5,1,0.1,0.1,800,1.5,500,...                              %10 values
    0.5,0.08,1,450,10,1000,1,1,1.5,0.01,...                               %10 values
    0.5]; 

%specify the size of the population
popsize = 8500;
%Develop the initial population
M = zeros(popsize,length(lowbounds));
for i = 1:popsize
    for j = 1:length(lowbounds)
        M(i,j) = 10^(log10(lowbounds(j)) + rand*(log10(upbounds(j)) ...
            - log10(lowbounds(j)))); 
    end
end


%Need to perform minimization of parameter sets

%*******Sets options for running the genetic algorithm
%--------------------------------------------------------------------------
%Added output into the output of ga on March 2, 2015
%PopulationSize specifies the size of the population
%IniialPopulation is used to seed the genetic algorithm
%PlotFcns is an array of handles to functions that plot data computed by
% the algorithm
% options = gaoptimset('PopulationSize',popsize,'Generations',100, ...
%     'InitialPopulation',M,'PlotFcns', ...
%     {@gaplotbestf,@gaplotscores,@gaplotstopping,@gaplotdistance});
% 
% [pmin,Smin,exitflag,output] = ga(@AK_Optimization_ToMinimize_20151021_Revised10_PMCA_PeakHeight,...
%     length(lowbounds),[],[],[],[],lowbounds,upbounds,[],options);


%*******Sets options for running the fmincon
%--------------------------------------------------------------------------
%This enables plotting of the results
% options = optimset('PlotFcns',{@optimplotfval,@optimplotstepsize});
% % Try running fmincon: find minimum of constrained nonlinear multivariable
% % function
% 
% [pminf,Smin,exitflag,output]=fmincon(@AK_Optimization_ToMinimize_20151021_Revised10_PMCA_PeakHeight,...
%     pmin,[],[],[],[],lowbounds,upbounds,[],options);
% 


%********Sets options for running the patternsearch
%--------------------------------------------------------------------------
%This enables plotting of the results
options = psoptimset('PlotFcns',{@psplotbestf,@psplotmeshsize});

[pminp,Smin,exitflag,output] = ...
    patternsearch(@AK_Optimization_ToMinimize_20151021_Revised10_PMCA_PeakHeight, ...
    pminf,[],[],[],[],lowbounds,upbounds,[],options);


% %  
%*******Sets options for running the simulannealbnd
%--------------------------------------------------------------------------
%Find minimum of function using simulated annealing algorithm
%This enables plotting of the results
% options = saoptimset('PlotFcns',...
%     {@saplotbestf,@saplotbestx,@saplotf,@saplottemperature});
% 
% [pmin,Smin,exitflag,output] = simulannealbnd(@AK_Optimization_ToMinimize_20150319_All3,...
%     pmin,lowbounds,upbounds,options);