I'm attempting to create a loop that reads through a matrix (A) and stores the non-zero values into a new matrix (w). I'm not sure what is wrong with my code.
function [d,w] = matrix_check(A)
[nrow ncol] = size(A);
total = 0;
for i = 1:nrow
for j = 1:ncol
if A(i,j) ~= 0
total = total + 1;
end
end
end
d = total;
w = [];
for i = 1:nrow
for j = 1:ncol
if A(i,j) ~= 0
w = [A(i,j);w];
end
end
end
The second loop is not working (at at least it is not printing out the results of w).
You can use nonzeros and nnz:
w = flipud(nonzeros(A)); %// flipud to achieve the same order as in your code
d = nnz(A);
The second loop is working. I'm guessing you're doing:
>> matrix_check(A)
And not:
>> [d, w] = matrix_check(A)
MATLAB will only return the first output unless otherwise specified.
As an aside, you can accomplish your task utilizing MATLAB's logical indexing and take advantage of the (much faster, usually) array operations rather than loops.
d = sum(sum(A ~= 0));
w = A(A ~= 0);
Related
I am sending a matrix to my function modifikuj, where I want to replace the elements of the matrix with:
1 if element is a prime number
0 if element is a composite number
0.5 if element is 1
I dont understand why it is not working. I just started with MATLAB, and I created this function:
function B = modifikuj(A)
[n,m] = size(A);
for i = 1:n
for j = 1:m
prost=1;
if (A(i,j) == 1)
A(i,j) = 0.5;
else
for k = 2:(A(i,j))
if(mod(A(i,j),k) == 0)
prost=0;
end
end
if(prost==1)
A(i,j)=1;
else
A(i,j)=0;
end
end
end
end
With
A = [1,2;3,4];
D = modifikuj(A);
D should be:
D=[0.5, 1; 1 0];
In MATLAB you'll find you can often avoid loops, and there's plenty of built in functions to ease your path. Unless this is a coding exercise where you have to use a prescribed method, I'd do the following one-liner to get your desired result:
D = isprime( A ) + 0.5*( A == 1 );
This relies on two simple tests:
isprime( A ) % 1 if prime, 0 if not prime
A == 1 % 1 if == 1, 0 otherwise
Multiplying the 2nd test by 0.5 gives your desired condition for when the value is 1, since it will also return 0 for the isprime test.
You are not returning anything from the function. The return value is supposed to be 'B' according to your code but this is not set. Change it to A.
You are looping k until A(i,j) which is always divisible by itself, loop to A(i,j)-1
With the code below I get [0.5,1;1,0].
function A = modifikuj(A)
[n,m] = size(A);
for i = 1:n
for j = 1:m
prost=1;
if (A(i,j) == 1)
A(i,j) = 0.5;
else
for k = 2:(A(i,j)-1)
if(mod(A(i,j),k) == 0)
prost=0;
end
end
if(prost==1)
A(i,j)=1;
else
A(i,j)=0;
end
end
end
end
In addition to #EuanSmith's answer. You can also use the in built matlab function in order to determine if a number is prime or not.
The following code will give you the desired output:
A = [1,2;3,4];
A(A==1) = 0.5; %replace 1 number with 0.5
A(isprime(A)) = 1; %replace prime number with 1
A(~ismember(A,[0.5,1])) = 0; %replace composite number with 0
I've made the assumption that the matrice contains only integer.
If you only want to learn, you can also preserve the for loop with some improvement since the function mod can take more than 1 divisor as input:
function A = modifikuj(A)
[n,m] = size(A);
for i = 1:n
for j = 1:m
k = A(i,j);
if (k == 1)
A(i,j) = 0.5;
else
if all(mod(k,2:k-1)) %check each modulo at the same time.
A(i,j)=1;
else
A(i,j)=0;
end
end
end
end
And you can still improve the prime detection:
2 is the only even number to test.
number bigger than A(i,j)/2 are useless
so instead of all(mod(k,2:k-1)) you can use all(mod(k,[2,3:2:k/2]))
Note also that the function isprime is a way more efficient primality test since it use the probabilistic Miller-Rabin algorithme.
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
I would like to generate all the possible adjacency matrices (zero diagonale) of an undirected graph of n nodes.
For example, with no relabeling for n=3 we get 23(3-1)/2 = 8 possible network configurations (or adjacency matrices).
One solution that works for n = 3 (and which I think is quite stupid) would be the following:
n = 3;
A = [];
for k = 0:1
for j = 0:1
for i = 0:1
m = [0 , i , j ; i , 0 , k ; j , k , 0 ];
A = [A, m];
end
end
end
Also I though of the following which seems to be faster but something is wrong with my indexing since 2 matrices are missing:
n = 3
C = [];
E = [];
A = zeros(n);
for i = 1:n
for j = i+1:n
A(i,j) = 1;
A(j,i) = 1;
C = [C,A];
end
end
B = ones(n);
B = B- diag(diag(ones(n)));
for i = 1:n
for j = i+1:n
B(i,j) = 0;
B(j,i) = 0;
E = [E,B];
end
end
D = [C,E]
Is there a faster way of doing this?
I would definitely generate the off-diagonal elements of the adjacency matrices with binary encoding:
n = 4; %// number of nodes
m = n*(n-1)/2;
offdiags = dec2bin(0:2^m-1,m)-48; %//every 2^m-1 possible configurations
If you have the Statistics and Machine Learning Toolbox, then squareform will easily create the matrices for you, one by one:
%// this is basically a for loop
tmpcell = arrayfun(#(k) squareform(offdiags(k,:)),1:size(offdiags,1),...
'uniformoutput',false);
A = cat(2,tmpcell{:}); %// concatenate the matrices in tmpcell
Although I'd consider concatenating along dimension 3, then you can see each matrix individually and conveniently.
Alternatively, you can do the array synthesis yourself in a vectorized way, it's probably even quicker (at the cost of more memory):
A = zeros(n,n,2^m);
%// lazy person's indexing scheme:
[ind_i,ind_j,ind_k] = meshgrid(1:n,1:n,1:2^m);
A(ind_i>ind_j) = offdiags.'; %'// watch out for the transpose
%// copy to upper diagonal:
A = A + permute(A,[2 1 3]); %// n x n x 2^m matrix
%// reshape to n*[] matrix if you wish
A = reshape(A,n,[]); %// n x (n*2^m) matrix
I can't seem to find a fix to my infinite loop. I have coded a Jacobi solver to solve a system of linear equations.
Here is my code:
function [x, i] = Jacobi(A, b, x0, TOL)
[m n] = size(A);
i = 0;
x = [0;0;0];
while (true)
i =1;
for r=1:m
sum = 0;
for c=1:n
if r~=c
sum = sum + A(r,c)*x(c);
else
x(r) = (-sum + b(r))/A(r,c);
end
x(r) = (-sum + b(r))/A(r,c);
xxx end xxx
end
if abs(norm(x) - norm(x0)) < TOL;
break
end
x0 = x;
i = i + 1;
end
When I terminate the code it ends at the line with xxx
The reason why your code isn't working is due to the logic of your if statements inside your for loops. Specifically, you need to accumulate all values for a particular row that don't belong to the diagonal of that row first. Once that's done, you then perform the division. You also need to make sure that you're dividing by the diagonal coefficient of A for that row you're concentrating on, which corresponds to the component of x you're trying to solve for. You also need to remove the i=1 statement at the beginning of your loop. You're resetting i each time.
In other words:
function [x, i] = Jacobi(A, b, x0, TOL)
[m n] = size(A);
i = 0;
x = [0;0;0];
while (true)
for r=1:m
sum = 0;
for c=1:n
if r==c %// NEW
continue;
end
sum = sum + A(r,c)*x(c); %// NEW
end
x(r) = (-sum + b(r))/A(r,r); %// CHANGE
end
if abs(norm(x) - norm(x0)) < TOL;
break
end
x0 = x;
i = i + 1;
end
Example use:
A = [6 1 1; 1 5 3; 0 2 4]
b = [1 2 3].';
[x,i] = Jacobi(A, b, [0;0;0], 1e-10)
x =
0.048780487792648
-0.085365853612062
0.792682926806031
i =
20
This means it took 20 iterations to achieve a solution with tolerance 1e-10. Compare this with MATLAB's built-in inverse:
x2 = A \ b
x2 =
0.048780487804878
-0.085365853658537
0.792682926829268
As you can see, I specified a tolerance of 1e-10, which means we are guaranteed to have 10 decimal places of accuracy. We can certainly see 10 decimal places of accuracy between what Jacobi gives us with what MATLAB gives us built-in.
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{:})