vectorize a loop or two so that the program runs faster

Question

Osita Onyejekwe 2017-7-25

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/350170-vectorize-a-loop-or-two-so-that-the-program-runs-faster

评论： Jan 2017-7-25

can someone help me vectorize a loop or two so that my program runs faster? It runs but the results take AGES.. Here is the whole code. Thank you!!!

clear all
  clc
  noise_Levels     = 3;
  number_signals   = 4;
  MSE_CV           = zeros(number_signals,noise_Levels);
  MSE_plugIN       = zeros(number_signals,noise_Levels);
  MSE_Mean_Max_SNR = zeros(number_signals,noise_Levels);
  MSE_maxSNR       = zeros(number_signals,noise_Levels);
  MSE_Poly_SNR     = zeros(number_signals,noise_Levels);
%for j = 1:noise_Levels
 %   j
        kernelShape   = 'Tricube';
          %********************%COMPLEX SINE******************************
  %         w=[0.0:0.01:1-.01]';
  %         w=repmat(w,5,1); %repmat(w,10,1);
  % 
  %         t=[ones(100,1)];
  %         for i=2:5
  %             t=[t;i*ones(100,1)];
  %         end
  %         %stpFunc = abs(sin(2*pi*w.*t)); %+6*sigma;
  %         stpFunc = sin(2*pi*w.*t); %+6*sigma;
  %         n       = length(stpFunc);
  %         %plot(stpFunc)
  %         origFun = stpFunc';
          %****************************************************************
          %********************SINE****************************************
%         X = linspace(0,4*pi,100);
%         N=length(X);
%         ySignal=(X-5).^2+10; 
%         ySignal=sin(X);
%         origFun = ySignal;
%         y = origFun;
%         n = length(y);
        %****************************************************************
          %************** Polynomial function******************************
          cc = 1000;
          x = [1:cc]/cc;
          origFun =(x-0.5).*(x-0.25).*(x-0.75).*20+10;
          y = origFun;
          n = length(origFun);
          %****************************************************************
          bwStep = [0.001:0.001:0.004,0.005:0.01:0.2];  
          p      = 1;
          % x = [1:length(stpFunc)]/length(stpFunc);
          x      = [1:n]/n;
          X      = x;
          %x    = rand(n,1);
          spc   = 1/n;
          bwSpc = round( bwStep / spc );
for j = 1:noise_Levels
    j
    for  iLoop = 1:number_signals
         [ycnoise,bwpl,yHatPlugIn,yHatCV,yHatSNRAll,yHatSNR,yHatSNRfinalBW,yHatSNRmeanBW,sigma,cvScore,cvBW] = FCN_AR_1_2_W(X',origFun',p,bwStep,x',kernelShape);
                format short g
                MSE_CV(iLoop,j)                      = (1/length(ycnoise))*sum((yHatCV(j,:) - origFun).^2);
                MSE_plugIN(iLoop,j)                  = (1/length(ycnoise))*sum((yHatPlugIn(j,:) - origFun).^2);
                MSE_Mean_Max_SNR(iLoop,j)            = (1/length(ycnoise))*sum((yHatSNRmeanBW(j,:) - origFun).^2);
                MSE_maxSNR(iLoop,j)                  = (1/length(ycnoise))*sum((yHatSNRAll(j,:) - origFun).^2);
                MSE_Poly_SNR(iLoop,j)                = (1/length(ycnoise))*sum((yHatSNRfinalBW(j,:) - origFun).^2);
    end  
 end

The main part that needs to be vectorized is this part

for j = 1:noise_Levels
    j
    for  iLoop = 1:number_signals
         [ycnoise,bwpl,yHatPlugIn,yHatCV,yHatSNRAll,yHatSNR,yHatSNRfinalBW,yHatSNRmeanBW,sigma,cvScore,cvBW] = FCN_AR_1_2_W(X',origFun',p,bwStep,x',kernelShape);
                format short g
                MSE_CV(iLoop,j)                      = (1/length(ycnoise))*sum((yHatCV(j,:) - origFun).^2);
                MSE_plugIN(iLoop,j)                  = (1/length(ycnoise))*sum((yHatPlugIn(j,:) - origFun).^2);
                MSE_Mean_Max_SNR(iLoop,j)            = (1/length(ycnoise))*sum((yHatSNRmeanBW(j,:) - origFun).^2);
                MSE_maxSNR(iLoop,j)                  = (1/length(ycnoise))*sum((yHatSNRAll(j,:) - origFun).^2);
                MSE_Poly_SNR(iLoop,j)                = (1/length(ycnoise))*sum((yHatSNRfinalBW(j,:) - origFun).^2);
    end  
 end

2 个评论
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Stephen23 2017-7-25

"The main part that needs to be vectorized is this part"

How do you know that vectorization will help? What mlint messages do you get? Have you used the profiler? Have you tried any other ways to improve code running speed?:

https://www.mathworks.com/help/matlab/matlab_prog/techniques-for-improving-performance.html

Jan 2017-7-25

在 MATLAB Online 中打开

Omit the "format short g", because it is not useful here. This wil not acclerate the code remarkably, but keeping the loops clean is the point to start from.

It seems like

[ycnoise,bwpl,yHatPlugIn,yHatCV,yHatSNRAll,yHatSNR, ...
 yHatSNRfinalBW,yHatSNRmeanBW,sigma,cvScore,cvBW] = ...
   FCN_AR_1_2_W(X',origFun',p,bwStep,x',kernelShape);

does not depend on iLoop, then move it out of the for iLoop loop.

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