InputParser vs exist(...，'var')vs nargin性能 [英] InputParser vs exist(...,'var') vs nargin performance

问题描述

这三个变体之间在输入检查/默认初始化方面是否有性能比较?

Is there any performance comparison among those three variants for input checking/default initializaion?

将其与最新版本进行比较会很有用，例如R2014b和更旧的R2012b.

It would be useful a comparison on a recent version e.g. R2014b and and older one R2012b.

一个例子:

function foo(a,b)
  if nargin < 1, a = 1; end
  if nargin < 2, b = 2; end
end

versus

function foo(a,b)
  if exist('a','var'), a = 1; end
  if exist('b','var'), b = 2; end
end

versus 

function foo(varargin)
  p = inputParser;
  addOptional(p,'a',1)
  addOptional(p,'b',2)
  parse(p,varargin{:})
end

在R2014b上使用Amro的测试套件:

Using Amro's testing suite, on R2014b:

  func           nargs       time   
_________________    _____    __________
'foo_nargin'         0        2.3674e-05
'foo_exist'          0        3.1339e-05
'foo_inputparser'    0        9.6934e-05
'foo_nargin'         1        2.4437e-05
'foo_exist'          1        3.2157e-05
'foo_inputparser'    1         0.0001307
'foo_nargin'         2        2.3838e-05
'foo_exist'          2        3.0492e-05
'foo_inputparser'    2        0.00015775

推荐答案

下面是一些测试这三种方法的代码:

Here is some code to test the three approaches:

function t = testArgParsing()
    args = {1, 2};
    fcns = {
        @foo_nargin ;
        @foo_exist ;
        @foo_inputparser
    };

    % parameters sweep
    [f,k] = ndgrid(1:numel(fcns), 0:numel(args));
    f = f(:); k = k(:);

    % test combinations of functions and number of input args
    t = cell(numel(f), 3);
    for i=1:size(t,1)
        t{i,1} = func2str(fcns{f(i)});
        t{i,2} = k(i);
        t{i,3} = timeit(@() feval(fcns{f(i)}, args{1:k(i)}), 2);
    end

    % format results in table
    t = cell2table(t, 'VariableNames',{'func','nargs','time'});
end

function [aa,bb] = foo_nargin(a,b)
  if nargin < 1, a = 1; end
  if nargin < 2, b = 2; end
  aa = a;
  bb = b;
end

function [aa,bb] = foo_exist(a,b)
  if ~exist('a','var'), a = 1; end
  if ~exist('b','var'), b = 2; end
  aa = a;
  bb = b;
end

function [aa,bb] = foo_inputparser(varargin)
  p = inputParser;
  addOptional(p,'a',1);
  addOptional(p,'b',2);
  parse(p, varargin{:});
  aa = p.Results.a;
  bb = p.Results.b;
end

这是我在计算机上获得的R2014a:

Here is what I get in R2014a on my machine:

>> t = testArgParsing
t = 
          func           nargs       time   
    _________________    _____    __________
    'foo_nargin'         0        3.4556e-05
    'foo_exist'          0        5.2901e-05
    'foo_inputparser'    0        0.00010254
    'foo_nargin'         1        2.5531e-05
    'foo_exist'          1        3.7105e-05
    'foo_inputparser'    1         0.0001263
    'foo_nargin'         2        2.4991e-05
    'foo_exist'          2        3.6772e-05
    'foo_inputparser'    2        0.00015148

还有一个漂亮的图来查看结果:

And a pretty plot to view the results:

tt = unstack(t, 'time', 'func');
names = tt.Properties.VariableNames(2:end);
bar(tt{:,2:end}.')
set(gca, 'XTick',1:numel(names), 'XTickLabel',names, 'YGrid','on')
legend(num2str(tt{:,1}, 'nargin=%d'))
ylabel('Time [sec]'), xlabel('Functions')

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