fs_profiler_tester.m

%% Matlab tic toc timeit Timers and Profiler Save to HTML, Testing to Improve Code Speed
% *back to* <https://fanwangecon.github.io *Fan*>*'s* <https://fanwangecon.github.io/Math4Econ/ 
% *Intro Math for Econ*>*,*  <https://fanwangecon.github.io/M4Econ/ *Matlab Examples*>*, 
% or* <https://fanwangecon.github.io/MEconTools/ *MEconTools*> *Repositories*
%% Tic and Toc and Timeit
% Simple timing example. In the following example, we want to simulate many 
% random normal draws with different means.  Calling Method B which uses mean 
% and standard deviation as parameters for normrnd is similar in speed to using 
% the z-score inverse function to transform from standard normal to normal as 
% in method A. Using tic and toc, it is much faster to use method A when we want 
% to draw from many normal distributions with different means.

% Two method to generate random normal draws
f_replace_method_a = @() normrnd(0, 1)*10+10;
f_replace_method_b = @() normrnd(10, 10);

% Time replacing one value
fl_speed_method_a = timeit(f_replace_method_a);
fl_speed_method_b = timeit(f_replace_method_b);
fl_speed_a_b_ratio = fl_speed_method_a/fl_speed_method_b;
disp(['Load table cell time, fl_speed_a_b_ratio=' num2str(fl_speed_a_b_ratio)]);

% Timing assignment with Method A
ar_rand = rand([1,1e4]);
fl_time_start = tic;
ar_z = normrnd(0, 1)*10+ar_rand;
fl_time_end = toc(fl_time_start);
disp(['Method A fl_time_end = ' num2str(fl_time_end)]);

% Timing assignment with Method A
fl_time_start = tic;
for (it_rand_ctr=1:length(ar_rand))
    ar_z(it_rand_ctr) = normrnd(ar_rand(it_rand_ctr), 10);
end
fl_time_end = toc(fl_time_start);
disp(['Method B fl_time_end = ' num2str(fl_time_end)]);
%% Profiling Lines of Code
% There is a program that we have written. We want to know which lines of code 
% is taking more or less time, and identify opportunities for speed improvement. 
% Use Matlab Profiler to run through code and generate performance report line 
% by line. This can be achieved by clicking on the Run and Time button under the 
% Editor Pane. Below, we start the profiler inline, run a function, and save profiling 
% results to a HTML file.
% 
% The function below finds the file to the current m file where the profiler 
% is running at, and generates a subfolder which will contain a subfolder that 
% contains all HTML results from a particular profiler timing run.

% Define profiling folder and file names
% Find current path to m file and generates profiling subfolder
srn_profile_folder = '_profile';
% Name of the profling output folder (including HTML files)
srn_mprofile_subfolder = 'fs_profiler_tester';

% Turn profiler on
profile on

% OLS Regression
fci_ols_lin = @(y, x) (x'*x)^(-1)*(x'*y);
% Regression inputs
it_obs_n = 10000;
it_k_n = 5;
rng(123);
ar_y = rand([it_obs_n,1]);
mt_x = rand([it_obs_n, it_k_n]);
% Regression
ar_esti = fci_ols_lin(ar_y, mt_x);

% Turn Profiler off
profile off;

% Save profiling results to file
% Find current file folder and generate a profiling subfolder
spn_path2file = matlab.desktop.editor.getActiveFilename;
[spt_path_folder_root, ~, ~] = fileparts(spn_path2file);
spn_profiler = fullfile(spt_path_folder_root, srn_profile_folder);
if ~exist(spn_profiler, 'dir')
    mkdir(spn_profiler);
end

% Store results to file
spn_profiler_results = fullfile(spn_profiler, srn_mprofile_subfolder);
profsave(profile('info'), spn_profiler_results);
%%