How to save the results of an operation performed 1000 times in one vector?

Question

Deema42 2020-4-11

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/517166-how-to-save-the-results-of-an-operation-performed-1000-times-in-one-vector

评论： Image Analyst 2020-4-11

Hello,

When I run following code, each time would get different result (random), so I am trying to find the results (variable: total ) 1000 times and I want to save the reusults in a vector. How can I do this by only one run without having to run it 1000 times and save each value manually. I tried many things but every time I get an error. Hope someone can help me.

Thank you.

close all;
clear;
 
%Simulation window parameters :Eves
xMin=0;xMax=1;
yMin=0;yMax=1;
xDelta=xMax-xMin;yDelta=yMax-yMin; %rectangle dimensions
areaTotal=xDelta*yDelta;
PL=2;
lambda=1; %intensity (ie mean density) of the Poisson process, so we have three points we want to distribute randomly and now we will locate them in the rectangle of area=1.
%Simulate Poisson point process
numbPoints=poissrnd(areaTotal*lambda);%Poisson number of points
xx=xDelta*(rand(numbPoints,1))+xMin;%x coordinates of Poisson points. Generate values from the uniform distribution on the interval [xMax andxMin].
%matrix size generated by rand is: numbPoints * 1
yy=xDelta*(rand(numbPoints,1))+yMin;%y coordinates of Poisson points
%Plotting
% scatter(xx,yy,'LineWidth',2.0); 
% hold on;
 X=[xx , yy]; %matrix with eaves locations (as a pair)
%------------------------------------------------
%neareset one point to one source(P): Alice
xs=0;%
ys=0;
zs=[xs,ys ];
P=zs;
%compute Euclidean distances:
distances = sqrt(sum(bsxfun(@minus, P, X).^2,2));
%find the smallest distance and use that as an index into B:
closest  = X(find(distances==min(distances)),:); %location of the closest eave (the pair)
min_distance  =sqrt(closest(1).^2+closest(2).^2);%the min distance
total =min_distance  .^PL

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Answer 1

Image Analyst 2020-4-11

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/517166-how-to-save-the-results-of-an-operation-performed-1000-times-in-one-vector#answer_425402

在 MATLAB Online 中打开

Make it into a function

function  total = GetTheTotal( ...some inputs......)
    % code.....
    total = whatever......
end

Then call it in a loop 1000 different times storing the totals in a vector:

for k = 1 : 1000
    totals(k) = GetTheTotal( ...some inputs......);
end

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Deema42 2020-4-11

Sorry, I thought you wanted the origional code, here it is attached now.

Image Analyst 2020-4-11

在 MATLAB Online 中打开

No, you still didn't use my suggestion of putting it into a function and then calling it in a loop. Here is the fixed code. Just click the copy button and paste it into a new script in MATLAB and press the green run triangle.

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 18;
fprintf('Beginning to run %s.m ...\n', mfilename);
hFig = figure;
hFig.WindowState = 'maximized'
numberOfIterations = 1000;
totals = zeros(1, numberOfIterations);
for k = 1 : numberOfIterations
	totals(k) = GetTheTotal(2);
end
plot(totals, 'b.', 'MarkerSize', 14);
grid on;
title('Totals', 'FontSize', fontSize);
xlabel('Iteration Number', 'FontSize', fontSize);
ylabel('Total', 'FontSize', fontSize);
fprintf('Done running %s.m ...\n', mfilename);
function total = GetTheTotal(PL)
total = 0; % Initialize so we'll have a value in case of error.
%Simulation window parameters :Eves
xMin = 0;
xMax = 1;
yMin = 0;
yMax = 1;
xDelta = xMax-xMin;
yDelta = yMax-yMin; %rectangle dimensions
areaTotal = xDelta*yDelta;
% PL = 2;
lambda = 1; %intensity (ie mean density) of the Poisson process, so we have three points we want to distribute randomly and now we will locate them in the rectangle of area = 1.
%Simulate Poisson point process
numbPoints = poissrnd(areaTotal*lambda);%Poisson number of points
if numbPoints <= 0
	fprintf('Skipping this one because numbPoints = 0!\n');
	return;
else
	fprintf('Doing this one because numbPoints = %d!\n', numbPoints);
end
xx = xDelta*(rand(numbPoints,1))+xMin;%x coordinates of Poisson points. Generate values from the uniform distribution on the interval [xMax andxMin].
%matrix size generated by rand is: numbPoints * 1
yy = xDelta*(rand(numbPoints,1))+yMin;%y coordinates of Poisson points
%Plotting
% scatter(xx,yy,'LineWidth',2.0);
% hold on;
X = [xx , yy]; %matrix with eaves locations (as a pair)
%------------------------------------------------
%neareset one point to one source(P): Alice
xs = 0.0001;%
ys = 0.0001;
zs = [xs,ys ];
P = zs;
% scatter(xs,ys,'LineWidth',2.0); %we want this to be the soource
%compute Euclidean distances:
distances = sqrt(sum(bsxfun(@minus, P, X).^2,2));
%find the smallest distance and use that as an index into B:
closest = X(find(distances==min(distances)),:); %location of the closest eave (the pair)
min_distance = sqrt(closest(1).^2+closest(2).^2);%the min distance
total = min_distance .^PL
end

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Answer 2

Ameer Hamza 2020-4-11

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/517166-how-to-save-the-results-of-an-operation-performed-1000-times-in-one-vector#answer_425404

在 MATLAB Online 中打开

Your code gives an error when poissrnd(areaTotal*lambda) generates a zero because X is empty matrix and the line

min_distance =sqrt(closest(1).^2+closest(2).^2);%the min distance

fails. I added a condition in your code if 'closest' is empty, then the min_distance for that iteration will become infinity (change it according to your logic).

Check the following code, it uses a for loop to run your code 1000 time and save values in array

close all;
clear;
 
%Simulation window parameters :Eves
xMin=0;xMax=1;
yMin=0;yMax=1;
xDelta=xMax-xMin;yDelta=yMax-yMin; %rectangle dimensions
areaTotal=xDelta*yDelta;
PL=2;
total = zeros(1, 1000);
for i=1:1000
    lambda=1; %intensity (ie mean density) of the Poisson process, so we have three points we want to distribute randomly and now we will locate them in the rectangle of area=1.
    %Simulate Poisson point process
    numbPoints=poissrnd(areaTotal*lambda);%Poisson number of points
    xx=xDelta*(rand(numbPoints,1))+xMin;%x coordinates of Poisson points. Generate values from the uniform distribution on the interval [xMax andxMin].
    %matrix size generated by rand is: numbPoints * 1
    yy=xDelta*(rand(numbPoints,1))+yMin;%y coordinates of Poisson points
    %Plotting
    % scatter(xx,yy,'LineWidth',2.0); 
    % hold on;
     X=[xx , yy]; %matrix with eaves locations (as a pair)
    %------------------------------------------------
    %neareset one point to one source(P): Alice
    xs=0;%
    ys=0;
    zs=[xs,ys ];
    P=zs;
    %compute Euclidean distances:
    distances = sqrt(sum(bsxfun(@minus, P, X).^2,2));
    %find the smallest distance and use that as an index into B:
    closest  = X(distances==min(distances),:); %location of the closest eave (the pair)
    if isempty(closest)
        min_distance = inf;%the min distance
        total(i) = min_distance  .^PL;
    else
        min_distance  =sqrt(closest(1).^2+closest(2).^2);%the min distance
        total(i) = min_distance  .^PL;
    end
end