I have a vector named signal consisting of 300001 values. In each iteration of the for loop, I want to pick up 2000 consecutive values from this vector and store it in another vector X (X is 1*2000 vector)
The code is as follows:
D = 1:300001;
A = zeros(1,2000);
r=1;
n=0;
m=1;
for i=1:300001
for p = (1+(2000*n)):(r*2000)
while m<2000
A(1,m)= signal(1,p);
%disp (m);
m = m+1;
end
end
r = r+1;
n = n+1;
m = 1;
end
But it gives me the error "Index exceeds matrix dimensions.
Can somebody help me out with a better way to do it?
this would work
signal = ones(1,30000);
index1= 1:2000:length(signal);
index2= 2000:2000:length(signal);
for i=1:length(index1)
A = signal(index1(i):index2(i));
end
or this
signal = ones(1,30000);
temp = reshape(signal,2000,[]);
for i = 1:size(temp,2)
A=temp(:,i);
end
Related
I am trying to convert my code over to run with parfor, since as it is it takes a long time to run on its own. However I keep getting this error. I have search around on the website and have read people with similar problems, but none of those answers seem to fix my problem. This is my code:
r = 5;
Mu = 12.57e-9;
Nu = 12e6;
I = 1.8;
const = pi*Nu*Mu*r*I;
a = 55;
b = 69;
c = 206;
[m,n,p] = size(Lesion_Visible);
A = zeros(m,n,p);
parpool(2)
syms k
parfor J = 1:m
for I = 1:n
for K = 1:p
if Lesion_Visible(J,I,K) ~= 0
Theta = atand((J-b)/(I-a));
Rho = abs((I-a)/cosd(Theta))*0.05;
Z = abs(c-K)*0.05;
E = vpa(const*int(abs(besselj(0,Rho*k)*exp(-Z*k)*besselj(0,r*k)),0,20),5);
A (J,I,K) = E;
end
end
end
end
I'm trying to calculate the electric field in specific position on an array and matlab give me the error "The variable A in a parfor cannot be classified". I need help. Thanks.
As classification of variables in parfor loop is not permitted, you should try to save the output of each loop in a variable & then save the final output into the desired variable, A in your case!
This should do the job-
parfor J = 1:m
B=zeros(n,p); %create a padding matrix of two dimension
for I = 1:n
C=zeros(p); %create a padding matrix of one dimension
for K = 1:p
if Lesion_Visible(J,I,K) ~= 0
Theta = atand((J-b)./(I-a));
Rho = abs((I-a)./cosd(Theta))*0.05;
Z = abs(c-K).*0.05;
E = vpa(const.*int(abs(besselj(0,Rho.*k).*exp(-Z.*k).*besselj(0,r.*k)),0,20),5);
C(K) = E; %save output of innnermost loop to the padded matrix C
end
end
B(I,:)=C; % save the output to dim1 I of matrix B
end
A(J,:,:)=B; save the output to dim1 J of final matrix A
end
Go through the following for better understanding-
http://www.mathworks.com/help/distcomp/classification-of-variables-in-parfor-loops.html
http://in.mathworks.com/help/distcomp/sliced-variable.html
This is a part of my code in Matlab. I tried to make it parallel but there is an error:
The variable gax in a parfor cannot be classified.
I know why the error occurs. because I should tell Matlab that v is an incresing vector which doesn't contain repeated elements. Could anyone help me to use this information to parallelize the code?
v=[1,3,6,8];
ggx=5.*ones(15,14);
gax=ones(15,14);
for m=v
if m > 1
parfor j=1:m-1
gax(j,m-1) = ggx(j,m-1);
end
end
if m<nn
parfor jo=m+1:15
gax(jo,m) = ggx(jo,m);
end
end
end
Optimizing a code should be closely related to its purpose, especially when you use parfor. The code you wrote in the question can be written in a much more efficient way, and definitely, do not need to be parallelized.
However, I understand that you tried to simplify the problem, just to get the idea of how to slice your variables, so here is a fixed version the can run with parfor. But this is surely not the way to write this code:
v = [1,3,6,8];
ggx = 5.*ones(15,14);
gax = ones(15,14);
nn = 5;
for m = v
if m > 1
temp_end = m-1;
temp = ggx(:,temp_end);
parfor ja = 1:temp_end
gax(ja,temp_end) = temp(ja);
end
end
if m < nn
temp = ggx(:,m);
parfor jo = m+1:15
gax(jo,m) = temp(jo);
end
end
end
A vectorized implementation will look like this:
v = [1,3,6,8];
ggx = 5.*ones(15,14);
gax = ones(15,14);
nn = 5;
m1 = v>1; % first condition with logical indexing
temp = v(m1)-1; % get the values from v
r = ones(1,sum(temp)); % generate a vector of indicies
r(cumsum(temp)) = -temp+1; % place the reseting locations
r = cumsum(r); % calculate the indecies
r(cumsum(temp)) = temp; % place the ending points
c = repelem(temp,temp); % create an indecies vector for the columns
inds1 = sub2ind(size(gax),r,c); % convert the indecies to linear
mnn = v<nn; % second condition with logical indexing
temp = v(mnn)+1; % get the values from v
r_max = size(gax,1); % get the height of gax
r_count = r_max-temp+1; % calculate no. of rows per value in v
r = ones(1,sum(r_count)); % generate a vector of indicies
r([1 r_count(1:end-1)+1]) = temp; % set the t indicies
r(cumsum(r_count)+1) = -(r_count-temp)+1; % place the reseting locations
r = cumsum(r(1:end-1)); % calculate the indecies
c = repelem(temp-1,r_count); % create an indecies vector for the columns
inds2 = sub2ind(size(gax),r,c); % convert the indecies to linear
gax([inds1 inds2]) = ggx([inds1 inds2]); % assgin the relevant values
This is indeed quite complicated, and not always necessary. A good thing to remember, though, is that nested for loop are much slower than a single loop, so in some cases (depend on the size of the output), this will may be the fastest solution:
for m = v
if m > 1
gax(1:m-1,m-1) = ggx(1:m-1,m-1);
end
if m<nn
gax(m+1:15,m) = ggx(m+1:15,m);
end
end
I have x and y data that has n number of points in each of the arrays.
I want to use polyfit on portions of the data.
I want to divide the data into a certain number of divisions(numDivisions).
My idea would be to do something along the lines of
n= size(x)%number of data points
numDivisions = 4;%number of times to divide the data
div = zeros(numDivisions,1)%number of points per division
p = zeros(numDivisions,4);% second number is degree of polynomial+1
S = zeros(numDivisions,1);
mu = zeros(numDivisions,1);
E = zeros(numDivisions,1);
for i = 1:numDivisions
div(i) = round(n(1,1)*i/numDivisions) %assign markers for divisions of points
end
for i = 1:size(div)
if i == 1
start = 1;
endpoint = div(i);
[p(i), S(i), mu(i)] = polyfit(x(start:endpoint), y(start:endpoint), 3);
else
[p(i), S(i), mu(i)] = polyfit(x(div(i-1):div(i)), y(div(i-1):div(i)), 3);
end
end
The goal would be to have an array of p values from the polyfits.
However, when I run it I get this error:
In an assignment A(I) = B, the number of elements in B
and I must be the same.
Error in (line 33)
[p(i), S(i), mu(i)] =
polyfit(x(start:endpoint),
y(start:endpoint), 3);
I am writing a graphical representation of numerical stability of differential operators and I am having trouble removing a nested for loop. The code loops through all entries in the X,Y, plane and calculates the stability value for each point. This is done by finding the roots of a polynomial of a size dependent on an input variable (length of input vector results in a polynomial 3d matrix of size(m,n,(lenght of input vector)). The main nested for loop is as follows.
for m = 1:length(z2)
for n = 1:length(z1)
pointpoly(1,:) = p(m,n,:);
r = roots(pointpoly);
if isempty(r),r=1e10;end
z(m,n) = max(abs(r));
end
end
The full code of an example numerical method (Trapezoidal Rule) is as follows. Any and all help is appreciated.
alpha = [-1 1];
beta = [.5 .5];
Wind = 2;
Wsize = 500;
if numel(Wind) == 1
Wind(4) = Wind(1);
Wind(3) = -Wind(1);
Wind(2) = Wind(4);
Wind(1) = Wind(3);
end
if numel(Wsize) == 1
Wsize(2) = Wsize;
end
z1 = linspace(Wind(1),Wind(2),Wsize(1));
z2 = linspace(Wind(3),Wind(4),Wsize(2));
[Z1,Z2] = meshgrid(z1,z2);
z = Z1+1i*Z2;
p = zeros(Wsize(2),Wsize(1),length(alpha));
for n = length(alpha):-1:1
p(:,:,(length(alpha)-n+1)) = alpha(n)-z*beta(n);
end
for m = 1:length(z2)
for n = 1:length(z1)
pointpoly(1,:) = p(m,n,:);
r = roots(pointpoly);
if isempty(r),r=1e10;end
z(m,n) = max(abs(r));
end
end
figure()
surf(Z1,Z2,z,'EdgeColor','None');
caxis([0 2])
cmap = jet(255);
cmap((127:129),:) = 0;
colormap(cmap)
view(2);
title(['Alpha Values (',num2str(alpha),') Beta Values (',num2str(beta),')'])
EDIT::
I was able to remove one of the for loops using the reshape command. So;
for m = 1:length(z2)
for n = 1:length(z1)
pointpoly(1,:) = p(m,n,:);
r = roots(pointpoly);
if isempty(r),r=1e10;end
z(m,n) = max(abs(r));
end
end
has now become
gg = reshape(p,[numel(p)/length(alpha) length(alpha)]);
r = zeros(numel(p)/length(alpha),1);
for n = 1:numel(p)/length(alpha)
temp = roots(gg(n,:));
if isempty(temp),temp = 0;end
r(n,1) = max(abs(temp));
end
z = reshape(r,[Wsize(2),Wsize(1)]);
This might be one for loop, but I am still going through the same number of elements. Is there a way to use the roots command on all of my rows at the same time?
I am trying to compute the pairwise distances between two struct objects. This distance is symmetric. I have about N = 8000, such objects in an array.
So I need to compute N * (N+1)/2 distances only. How can I parallelize this computation, since each computation is independent ?
Here my objects are stored in Array X and I want to store the distances in Array A which is of size N*(N+1)/2. BDHMM() is a function which returns the distance between two objects.
I have tried the following Matlab Code.
N = 8000;
load inithmm.mat
size = N*(N+1)/2;
A = zeros(size,1);
matlabpool open local 4
parfor i = 1:N-1
i
T = [];
for j = i:N
if(j == i)
temp = 0;
else
temp = BDHMM(X(i),X(j));
end
T = [T; temp];
end
beg = size - (N + 1 - i)*(N + 2 - i)/2 + 1;
l = length(T);
A(beg:beg+l-1, 1) = T;
end
matlabpool close
I am getting the following error:
Error: The variable A in a parfor cannot be classified.
Please help.
You cannot assassin to indexes you calculate withing the parfor, Matlab needs to know in advance what sections of the matrix will be assassin by witch iteration. this makes sense if you think about it.
this should solve it:
N = 800;
size = N*(N+1)/2;
A = cell(N,1);
matlabpool open local 4
parfor i = 1:N-1
i
T = zeros(N-i+1,1);
for j = i:N
if(j == i)
T(j-i+1) = 0;
else
T(j-i+1) = BDHMM(X(i),X(j));
end
end
A{i, 1} = T;
end
matlabpool close
B=vertcat(A{:})