How to fix: Index Exceeds Matrix Dimensions

Hi,

Im new to MATLAB and am trying to extract data from a sine wave. I've set the threshold to -1 SD and want all of the data below it to stack into one matrix. In other words, M has to be a stacked product of P. However, at the last part of the code it gives the error: Index Exceeds Matrix Dimensions. From what I understand, this means that length(P)+P(i)>length(noise). So the logical thing to do would be to make 'noise' bigger or P(i) smaller. However, I can't seem to get rid of the error. I was told not to index so far into P, but i have no idea how to do that. Hopefully someone can help me with this!

clear all
hold off

% parameters
srate=1000;
t=1:99/srate:10;
noiseAmplitude=2;
a=4;
f=4;
%signal
signal=a*sin(2*pi*f*t);
noise= signal + noiseAmplitude*randn(1,length(signal));

standdev=std(noise);

P=find(diff(noise<-standdev)==1);

for i=1:length(P)
    M(i,:)=noise(P(i):P(i)+10); (<-- ERROR: index exceeds matrix dimensions)
end

plot(M)

Answer 1

[Alexandra Harkai]

P =
     2    27    32    34    37    50    52    60    65    68    70    75    90

P+10
ans =
    12    37    42    44    47    60    62    70    75    78    80    85   100

Therefore P+10 exceeds the size of noise, and is giving you the error.

How you go about fixing it depends what you want to achieve in those cases. This one would fill in the rightmost part of array M:

for i=1:length(P)
  offset = min(10, size(noise,2)-P(i));
  M(i,(end-offset):end)=noise(P(i):P(i)+offset);
end

[Wouter Mattheussens]

Thanks! This seems to be working for me. Although I also had to put M=zeros(1,100) before the for-loop.

Answer 2

[Cam Salzberger]

I don't fully understand what you are trying to do here, but I can tell you how to examine the issue. With any kind of error, it's often easiest to set a breakpoint at the line the error occurs. With an error in a loop, however, it could be many iterations before you actually hit the parameter combination that will cause the error. In that case, it's probably easiest to just run this at the command line:

dbstop if error

Now MATLAB will automatically go into debug mode as soon as it encounters an error. Once in debug mode, you can examine the variables and see where the issue is. Of course, in your particular code, the exact iteration may change every time because of the randomized noise.

I suspect that you don't actually want to index into "P" using "10" as an offset to the next index value, when "P" already contains index values itself. But I don't fully understand your workflow, so I can't be sure.

-Cam

Answer 3

[dawa lepcha]

Hi , i have an error in line 18,

kindly need helps

>> guidedfilter_color

Index exceeds matrix dimensions.

Error in guidedfilter_color (line 18)

mean_I_g = boxfilter(I(:, :, 2), r) ./ N;

function q = guidedfilter_color(I, p, r, eps)
%   GUIDEDFILTER_COLOR   O(1) time implementation of guided filter using a color image as the guidance.
%
%   - guidance image: I (should be a color (RGB) image)
%   - filtering input image: p (should be a gray-scale/single channel image)
%   - local window radius: r
%   - regularization parameter: eps

I = double(imread('.\img_smoothing\cat.bmp')) / 255;
p = I;
r = 4; % try r=2, 4, or 8
eps = 0.2^2; % try eps=0.1^2, 0.2^2, 0.4^2

[hei, wid] = size(p);
N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.

mean_I_r = boxfilter(I(:, :, 1), r) ./ N;
mean_I_g = boxfilter(I(:, :, 2), r) ./ N;
mean_I_b = boxfilter(I(:, :, 3), r) ./ N;

mean_p = boxfilter(p, r) ./ N;

mean_Ip_r = boxfilter(I(:, :, 1).*p, r) ./ N;
mean_Ip_g = boxfilter(I(:, :, 2).*p, r) ./ N;
mean_Ip_b = boxfilter(I(:, :, 3).*p, r) ./ N;

% covariance of (I, p) in each local patch.
cov_Ip_r = mean_Ip_r - mean_I_r .* mean_p;
cov_Ip_g = mean_Ip_g - mean_I_g .* mean_p;
cov_Ip_b = mean_Ip_b - mean_I_b .* mean_p;

% variance of I in each local patch: the matrix Sigma in Eqn (14).
% Note the variance in each local patch is a 3x3 symmetric matrix:
%           rr, rg, rb
%   Sigma = rg, gg, gb
%           rb, gb, bb
var_I_rr = boxfilter(I(:, :, 1).*I(:, :, 1), r) ./ N - mean_I_r .*  mean_I_r; 
var_I_rg = boxfilter(I(:, :, 1).*I(:, :, 2), r) ./ N - mean_I_r .*  mean_I_g; 
var_I_rb = boxfilter(I(:, :, 1).*I(:, :, 3), r) ./ N - mean_I_r .*  mean_I_b; 
var_I_gg = boxfilter(I(:, :, 2).*I(:, :, 2), r) ./ N - mean_I_g .*  mean_I_g; 
var_I_gb = boxfilter(I(:, :, 2).*I(:, :, 3), r) ./ N - mean_I_g .*  mean_I_b; 
var_I_bb = boxfilter(I(:, :, 3).*I(:, :, 3), r) ./ N - mean_I_b .*  mean_I_b; 

a = zeros(hei, wid, 3);
for y=1:hei
    for x=1:wid        
        Sigma = [var_I_rr(y, x), var_I_rg(y, x), var_I_rb(y, x);
            var_I_rg(y, x), var_I_gg(y, x), var_I_gb(y, x);
            var_I_rb(y, x), var_I_gb(y, x), var_I_bb(y, x)];
        %Sigma = Sigma + eps * eye(3);
        
        cov_Ip = [cov_Ip_r(y, x), cov_Ip_g(y, x), cov_Ip_b(y, x)];        
        
        a(y, x, :) = cov_Ip * inv(Sigma + eps * eye(3)); % Eqn. (14) in the paper;
    end
end

b = mean_p - a(:, :, 1) .* mean_I_r - a(:, :, 2) .* mean_I_g - a(:, :, 3) .* mean_I_b; % Eqn. (15) in the paper;

q = (boxfilter(a(:, :, 1), r).* I(:, :, 1)...
+ boxfilter(a(:, :, 2), r).* I(:, :, 2)...
+ boxfilter(a(:, :, 3), r).* I(:, :, 3)...
+ boxfilter(b, r)) ./ N;  % Eqn. (16) in the paper;
figure();imshow(q)
end

Answer 4

[Walter Roberson]

Bmp files are not always rgb.

And when they are rgb then

[hei, wid] = size(p);

is always wrong.

[hei, wid, panes] = size(p);

Answer 5

[Getnet Belie]

Index exceeds matrix dimensions.

Error in feasmbll (line 21)

kk(ii,jj)=kk(ii,jj)+k(i,j)

function [kk]=feasmbll(kk,k,index)
%..................
%purpose;
%assembly of element matrices into the system matricx
%
% Synopsis;
%[kk]=feasmbll(kk,k,index)
%
%Variable Description;
%kk-system matrix
%k-element matrix
%index-d.o.f.vector associated with an element
%.................
%
edof=length(index);
%
for i=1:edof
    ii=index(i);
    for j=1:edof
        jj=index(j);
26.      kk(ii,jj)=kk(ii,jj)+k(i,j)
    end
end
end

could you help me

kindly

[Walter Roberson]

We do not know what size(kk) or size(k) are.

Is the 26. part of the code?

Answer 6

[Josh Meyer]

The issue is with the line:

M(i,:)=noise(P(i):P(i)+10);

You want to take the index P(i) and grab the next 10 elements after that point, then put them into a row in M. This works fine for the first several iterations where P is picking out relatively small indices. However, some of the elements of P are picking out array elements near the end of noise, so there aren't 10 elements after those points to grab.

To put it more concretely, here is what P contains when I ran the code:

>> P
P =
   2    12    14    20    27    30    32    40    55    65    70    75    78    80    88

So, when you get to the last element of P, 88, you will want to grab elements 88-98 from noise and put them into M. However, noise only has 91 elements:

>> size(noise)
ans =
   1    91

So to fix this error you need to replace the +10 with something else. You can use min(diff(P)) to see the minimum distance between consecutive indices in P, or min(abs(P-length(noise))) to see how close the last element in P is to the end of noise, or maybe you could just loop to length(P)-1 or length(P)-2, avoiding the last few large indices in P.

Answer 7

[yuvarani divakaramoorthy]

for i=1:s/k sum=0; for j=1:k sum=m((i-1)*k+j)+sum; <--(ERROR: index exceeds matrix dimensions) end M(i)=sum/k;

what can be done here?

Answer 8

[rubina naz]

Rmin = 60; 
Rmax = 100; 
[center, radius] = imfindcircles(RGB,[Rmin Rmax],'Sensitivity',0.9); % Display the circle 
viscircles(center,radius); % Display the calculated center
 hold on;
 plot(center(:,1),center(:,2),'yx','LineWidth',2);%index axceed matrix problems 
hold off;

Answer 9

[MathWorks Support Team]

This error is returned when MATLAB tries to access elements of an array that do not exist. In this case, since “noise” only has 91 elements, MATLAB errors when the loop reaches an element of “P” such that “P(i) + 10” exceeds 91. Starting in R2018b, you will see the following error instead:

Index exceeds the number of array elements (91).

To fix this error, you will need to replace the index “P(i)+10” with something that doesn’t exceed 91, or end your loop earlier. For example, you could stop the loop at “length(P)-2”, and concatenate the remaining elements of “M” outside of the loop.

Answer 10

[Nazish Iqbal]

kindly help me regarding this error..

Answer 11

[Abdul Basit]

clc;
clear
close all;

%% Phase 1 - Define Objective Function


%% Phase 2 - PSO Parameters

LB=[0 0 0];
UB=[10 10 10];
m=3;
n=500;
wmax=0.9;
wmin=0.4;
c1=2.05;
c2=2.05;
Maxiter=100;


% Loop for maximum run start

for run=1:20

%% Phase 3 - Initialization of Position & Velocity

for i=1:n
    for j=1:m        
        pos(i,j)=(LB(i,j)+rand()).*(UB(i,j)-LB(i,j)); <===Index exceeds matrix dimensions

Please some one help as at this point i see the error: Index exceeds matrix dimension

    end
end
vel=(0.1).*pos;


%% Phase 4 - Function Evaluation

for i=1:n
    out(i,1)=fun(pos(i,:));
end
pbestval=out;
pbest=pos;
[fminval, index]=min(out);
gbest=pbest(index,:);

%PSO algorithm start here

iter=1;
while iter<=Maxiter
    w=wmax-(iter/Maxiter).*(wmax-wmin);

%% Phase 5 - Compute PBest and GBest

X=pos;
Out=fun(X);

Har=find(out<=pbestval);
pbest(Har,:)=X(Har,:);
pbestval=out(Har);

[fbestval, ind1]=min(pbestval);
if fbestval<=fminval
    fminvalue=fbestval;
    gbest=pbest(ind1,:);
end


%% Phase 6 - Update Velocity & Position (Handling Boundary Constrains)

for i=1:n
    for j=1:m
        
        vel(i,j)=w.*vel(i,j)+c1.*rand().*(pbest(i,j)-pos(i,j))...
        +c2.*rand().*(gbeat(1,j)-pos(i,j));
        
    pos(i,j)=vel(i,j)+pos(i,j);

    if pos(i,j)<LB(j)
        pos(i,j)=LB(j);
    elseif pos(i,j)>UB(j)
        pos(i,j)=UB(j);
    end
    end
end

iter=iter+1;
end

%% Phase 7 - Store Best Value

F_ans(run)=fun(gbest);
F_gbest(run,:)=gbest;


% Loop for maximum run end

end

[bestFUN, bestRUN]=min(F_ans);
Best_X=F_gbest(bestRUN,:);

plot(F_ans)


function output = fun(X)


fx=10*(x1-1)^2+20.*x1.*x2+(x3-3)^2;

Con=[];
% Con(1)=x1+x2+x3-5;
% Con(2)=x1^2+x2^2-x3;
% 
% for i=1:length(Con)
%     if Con(i)>0
%         Pen(i)=1;
%     else
%         Pen(i)=0;
%     end
% end
% penalty=1000;
%output=fx+penalty*sum(pen);
output=fx;

end

[Walter Roberson]

LB=[0 0 0];
UB=[10 10 10];

LB and UB are each 1 x 3

m=3;
n=500;

m goes to 3, n goes to 500

for i=1:n

i goes to n which is 500

    for j=1:m

j goes to m which is 3

        pos(i,j)=(LB(i,j)+rand()).*(UB(i,j)-LB(i,j));

j is at most 3 and LB and UB are 1 x 3, so the j indexing is okay.

i is at most 500, and LB and UB are 1 x 3, so the i indexing will fail as soon as i reaches 2.

    end

You did not initialize LB and UB to the full 500 x 3; and you also do not store new LB and UB inside the loop ready to be used in the next i iteration. Your UB and LB needs to have at least K rows by the time the for i loop reaches i = K so that the for j loop can access the items.

[Abdul Basit]

Utmost thank you for quick response, I want to run Pair Swarm Optimization algorithm, I am confused at this simulation, every time new error appears. Can you please let me know how can it be resolved? I want to look for minimum value with respect to three variables.

clc;
clear
close all;

%% Phase 1 - Objective Function

%fun

%% Phase 2 - PSO Parameters

LB=zeros(500, 3);    %% How can I set upper bounds and lowers bounds that will go accordingly with array size.
UB=zeros(500, 3);

m=3;
n=500;
wmax=0.9;
wmin=0.4;
c1=2.05;
c2=2.05;
Maxiter=100;


% Loop for maximum run start

for run=1:20

%% Phase 3 - Initialization of Position & Velocity

for i=1:n
    for j=1:m        
        pos(i,j)=(LB(i,j)+rand()).*(UB(i,j)-LB(i,j));  
    end
end
vel=(0.1).*pos;


%% Phase 4 - Function Evaluation

for i=1:n
    out(i,1)=fun(pos(i,:));
end
pbestval=out;
pbest=pos;
[fminval, index]=min(out);
gbest=pbest(index,:);

%PSO algorithm start here

iter=1;
while iter<=Maxiter
    w=wmax-(iter/Maxiter).*(wmax-wmin);

%% Phase 5 - Compute PBest and GBest

X=pos;
Out=fun(X);

Har=find(out<=pbestval);
pbest(Har,:)=X(Har,:);
pbestval=out(Har);

[fbestval, ind1]=min(pbestval);
if fbestval<=fminval
    fminvalue=fbestval;
    gbest=pbest(ind1,:);
end


%% Phase 6 - Update Velocity & Position (Handling Boundary Constrains)

for i=1:n
    for j=1:m
        
        vel(i,j)=w.*vel(i,j)+c1.*rand().*(pbest(i,j)-pos(i,j))...
        +c2.*rand().*(gbest(1,j)-pos(i,j));
        
    pos(i,j)=vel(i,j)+pos(i,j);

    if pos(i,j)<LB(j)
        pos(i,j)=LB(j);
    elseif pos(i,j)>UB(j)
        pos(i,j)=UB(j);
    end
    end
end

iter=iter+1;
end

%% Phase 7 - Store Best Value

F_ans(run)=fun(gbest);
F_gbest(run,:)=gbest;


% Loop for maximum run end

end

[bestFUN, bestRUN]=min(F_ans);
Best_X=F_gbest(bestRUN,:);

plot(F_ans)


%% Function Starts Here

function output = fun(X)

x=X(:,1);
R=X(:,2);
H=X(:,3);


vc=3;       %Cut in Speed(m/s)
vf=25;      %Cut Out Speed(m/s)
vr=12;      %Rated Wind Speed(m/s)
p=1.2754;   %value of air density i.e.:1.2754kg/m^3
% R=36;       %Value of Rotor Radius (m)
% H=65;       %Value of Hub Height (m)
Pr=1194.7;  %Value of Rated Power(kW)

k=2;        %Shape Factor:
c=7.5;      %Scale Factor

fun1 = @(v)(v.^3).*(k/c).*((v/c).^(k-1)).*exp(-(v/c).^k);
fun2 = @(v)(vr.^3).*(k/c).*((v/c).^(k-1)).*exp(-(v/c).^k);
f1 = integral(fun1,vc,vr);
f2 = integral(fun2,vr,vf);

AEP=((1744.992.*p.*(pi).*(R.^2))).*(f1+f2);

a=(0.00622.*(R.^3.5))+(0.07756.*(R.^3))+(0.0612.*(R.^2.946))+(0.009426.*(R.^2.887))+(0.3508.*(R.^2.6578));

b=(1.3377.*(R.^2.53))+(0.5297.*(R.^2.5025))-(0.00771.*(R.^2.5))+(0.06901.*(R.^2).*H.*pi)+(5.343.*(R.^1.953));

c=(23.9347.*(R))+(55.7435.*(H.^0.4037).*(R.^0.8074))+((4.041.*(10.^-7)).*(Pr.^3))-(0.00244.*(Pr.^2))+(2.096.*(Pr.^1.249));

d=(15.0656.*(Pr))+(6.8004.*(Pr))+(((0.00108).*(AEP)).*(1-x/15000))+(8265.6416);

e=((0.2082.*(Pr.^3))-(0.006.*(Pr.^3))+(49.4518.*(Pr))+(0.5133.*(H.^1.1736).*(R.^1.1736))).*(1+x/15000);

f=(0.007.*(1+(x/15000)));

z=((a/AEP)+(b/AEP)+(c/AEP)+(d/AEP)+(e/AEP)+f); %% Error at this point occurs

output=z;

end

[Walter Roberson]

Your upper and lower bounds are the same for all particles, so just index UB and LB by j instead of by (i,j)

[Abdul Basit]

Can you please let me know lines of code.

[Abdul Basit]

Please run this code and set any UB and LB, I really appreciate your help.

[Abdul Basit]

https://www.mathworks.com/matlabcentral/answers/356711-how-to-fix-index-exceeds-matrix-dimensions#comment_898431

z=((a/AEP)+(b/AEP)+(c/AEP)+(d/AEP)+(e/AEP)+f); %% Error at this point occurs as well

[Walter Roberson]

In terms of the initial code,

pos(i,j)=(LB(j)+rand()).*(UB(j)-LB(j))

[Walter Roberson]

function output = fun(X)
fx=10*(x1-1)^2+20.*x1.*x2+(x3-3)^2;

That code attempts to use x1, x2, and x3, but those do not exist in that context: there is just X .

The code should probably be using X(1), X(2), X(3)

[Abdul Basit]

Brilliant, Many thanks. I look forward to disscuss more with you. Words do not seems enough to say thank you.

[Abdul Basit]

Sorry to be pain, Can you please help here with the last code error:

clc;
clear
close all;

%% Phase 1 - Objective Function

%fun

%% Phase 2 - PSO Parameters

LB=[0 0 0];
UB=[10 10 10];

m=3;
n=500;
wmax=0.9;
wmin=0.4;
c1=2.05;
c2=2.05;
Maxiter=100;


% Loop for maximum run start

for run=1:20

%% Phase 3 - Initialization of Position & Velocity

for i=1:n
    for j=1:m        
        pos(i,j)=(LB(j)+rand()).*(UB(j)-LB(j)); 
    end
end
vel=(0.1).*pos;


%% Phase 4 - Function Evaluation

for i=1:n
    out(i,1)=fun(pos(i,:));
end
pbestval=out;
pbest=pos;
[fminval, index]=min(out);
gbest=pbest(index,:);

%PSO algorithm start here

iter=1;
while iter<=Maxiter
    w=wmax-(iter/Maxiter).*(wmax-wmin);

%% Phase 5 - Compute PBest and GBest

X=pos;
Out=fun(X);

Har=find(out<=pbestval);
pbest(Har,:)=X(Har,:);
pbestval=out(Har);

[fbestval, ind1]=min(pbestval);
if fbestval<=fminval
    fminvalue=fbestval;
    gbest=pbest(ind1,:);
end


%% Phase 6 - Update Velocity & Position (Handling Boundary Constrains)

for i=1:n
    for j=1:m
        
        vel(i,j)=w.*vel(i,j)+c1.*rand().*(pbest(i,j)-pos(i,j))...
        +c2.*rand().*(gbest(1,j)-pos(i,j));
        
    pos(i,j)=vel(i,j)+pos(i,j);

    if pos(i,j)<LB(j)
        pos(i,j)=LB(j);
    elseif pos(i,j)>UB(j)
        pos(i,j)=UB(j);
    end
    end
end

iter=iter+1;
end

%% Phase 7 - Store Best Value

F_ans(run)=fun(gbest);
F_gbest(run,:)=gbest;


% Loop for maximum run end

end

[bestFUN, bestRUN]=min(F_ans);
Best_X=F_gbest(bestRUN,:);

plot(F_ans)

Function

function output = fun(X)

x=X(:,1);
R=X(:,2);
H=X(:,3);

vc=3;       %Cut in Speed(m/s)
vf=25;      %Cut Out Speed(m/s)
vr=12;      %Rated Wind Speed(m/s)
p=1.2754;   %value of air density i.e.:1.2754kg/m^3
% R=36;       %Value of Rotor Radius (m)
% H=65;       %Value of Hub Height (m)
Pr=1194.7;  %Value of Rated Power(kW)

k=2;        %Shape Factor:
c=7.5;      %Scale Factor

fun1 = @(v)(v.^3).*(k/c).*((v/c).^(k-1)).*exp(-(v/c).^k);
fun2 = @(v)(vr.^3).*(k/c).*((v/c).^(k-1)).*exp(-(v/c).^k);
f1 = integral(fun1,vc,vr);
f2 = integral(fun2,vr,vf);

AEP=((1744.992.*p.*(pi).*(R.^2))).*(f1+f2);

a=(0.00622.*(R.^3.5))+(0.07756.*(R.^3))+(0.0612.*(R.^2.946))+(0.009426.*(R.^2.887))+(0.3508.*(R.^2.6578));

b=(1.3377.*(R.^2.53))+(0.5297.*(R.^2.5025))-(0.00771.*(R.^2.5))+(0.06901.*(R.^2).*H.*pi)+(5.343.*(R.^1.953));

c=(23.9347.*(R))+(55.7435.*(H.^0.4037).*(R.^0.8074))+((4.041.*(10.^-7)).*(Pr.^3))-(0.00244.*(Pr.^2))+(2.096.*(Pr.^1.249));

d=(15.0656.*(Pr))+(6.8004.*(Pr))+(((0.00108).*(AEP)).*(1-x/15000))+(8265.6416);

e=((0.2082.*(Pr.^3))-(0.006.*(Pr.^3))+(49.4518.*(Pr))+(0.5133.*(H.^1.1736).*(R.^1.1736))).*(1+x/15000);

f=(0.007.*(1+(x/15000)));

output=((a/AEP)+(b/AEP)+(c/AEP)+(d/AEP)+(e/AEP)+f); <== Error Here



end

Error using +

Matrix dimensions must agree.

Error in fun (line 58)

output=((a/AEP)+(b/AEP)+(c/AEP)+(d/AEP)+(e/AEP)+f);

Error in PSOnew (line 56)

Out=fun(X);

[aaliyan javaid]

hello brother have you implemented pso suceesfully i facing some issues can you help me

thanks

[Walter Roberson]

@Abdul Basit

The code executed for me without error (other than commenting out the "<== Error Here" phrase)

Answer 12

[Mirghni Mohammed]

The program you will prepare must have an interface,

 · Inputs (inputs.xlsx) and outputs (targets.xlsx) will be read from the files given to you in the program.  The program will automatically determine the number of inputs and outputs according to the data read.

 · There will be only one hidden layer in the program,

 · The user will be able to determine the number of neurons in the hidden layer, the training rate, the number of epochs, and the data rates to be used for training and testing,

 · Training and test data will be randomly selected according to the determined rate,

 · The neurons in the hidden layer will use the sigmoid activation function, and the neuron in the output layer will use the linear activation (k=1) function,

 · When the program starts, the initial weight values of the artificial neural network will be determined as random real numbers between -5 and 5.

 · Backpropagation algorithm will be used for training.

 · After the user-specified amount of training data and the desired epoch number are completed, the weights of the trained network should be displayed in the program.

 · After the training is completed, the success of the test data will be graphically displayed using the trained network and test data.  The metric to be used to determine success will be the Mean squared error.

[Walter Roberson]

I do not understand how this will solve the problem that the original poster had.