Out of memory. The likely cause is an infinite recursion within the program.
4 views (last 30 days)
Show older comments
% Bus Admittance Matrix
% Copyright (c) 1998 by H. Saadat.
function[Ybus] = ybus(zdata)
nl=zdata(:,1); nr=zdata(:,2); R=zdata(:,3); X=zdata(:,4);
nbr=length(zdata(:,1)); nbus = max(max(nl), max(nr));
Z = R + j*X; %branch impedance
y= ones(nbr,1)./Z; %branch admittance
Ybus=zeros(nbus,nbus); % initialize Ybus to zero
for k = 1:nbr; % formation of the off diagonal elements
if nl(k) > 0 & nr(k) > 0
Ybus(nl(k),nr(k)) = Ybus(nl(k),nr(k)) - y(k);
Ybus(nr(k),nl(k)) = Ybus(nl(k),nr(k));
end
end
for n = 1:nbus % formation of the diagonal elements
for k = 1:nbr
if nl(k) == n | nr(k) == n
Ybus(n,n) = Ybus(n,n) + y(k);
else, end
end
end
i want to run this code but i get this error Out of memory. The likely cause is an infinite recursion within the program.
% From To R X
zdata = [ 0 1 0 1.0
0 2 0 0.8
1 2 0 0.4
1 3 0 0.2
2 3 0 0.2
3 4 0 0.08];
[Ybus] = ybus(zdata) % bus admittance matrix
Ibus = [-j*1.1; -j*1.25; 0; 0]; % vector of injected bus currents
Zbus = inv(Ybus) % bus impedance matrix
Vbus = Zbus*Ibus
%Vbus = Ybus\Ibus
0 Comments
Answers (2)
essrra
on 4 Mar 2023
function [neighborIds, neighborDistances] = kNearestNeighbors(dataMatrix, queryMatrix, k)
%--------------------------------------------------------------------------
% Program to find the k - nearest neighbors (kNN) within a set of points.
% Distance metric used: Euclidean distance
%
% Usage:
% [neighbors distances] = kNearestNeighbors(dataMatrix, queryMatrix, k);
% dataMatrix (N x D) - N vectors with dimensionality D (within which we search for the nearest neighbors)
% queryMatrix (M x D) - M query vectors with dimensionality D
% k (1 x 1) - Number of nearest neighbors desired
%
% Example:
% a = [1 1; 2 2; 3 2; 4 4; 5 6];
% b = [1 1; 2 1; 6 2];
% [neighbors distances] = kNearestNeighbors(a,b,2);
%
% Output:
% neighbors =
% 1 2
% 1 2
% 4 3
%
% distances =
% 0 1.4142
% 1.0000 1.0000
% 2.8284 3.0000
%--------------------------------------------------------------------------
neighborIds = zeros(size(queryMatrix,1),k);
neighborDistances = neighborIds;
numDataVectors = size(dataMatrix,1);
numQueryVectors = size(queryMatrix,1);
for i=1:numQueryVectors
dist = sum((repmat(queryMatrix(i,:),numDataVectors,1)-dataMatrix).^2,2);
[sortval, sortpos] = sort(dist,'ascend');
neighborIds(i,:) = sortpos(1:k);
neighborDistances(i,:) = sqrt(sortval(1:k));
endfunction [neighborIds, neighborDistances] = kNearestNeighbors(dataMatrix, queryMatrix, k)
%--------------------------------------------------------------------------
% Program to find the k - nearest neighbors (kNN) within a set of points.
% Distance metric used: Euclidean distance
%
% Usage:
% [neighbors distances] = kNearestNeighbors(dataMatrix, queryMatrix, k);
% dataMatrix (N x D) - N vectors with dimensionality D (within which we search for the nearest neighbors)
% queryMatrix (M x D) - M query vectors with dimensionality D
% k (1 x 1) - Number of nearest neighbors desired
%
% Example:
% a = [1 1; 2 2; 3 2; 4 4; 5 6];
% b = [1 1; 2 1; 6 2];
% [neighbors distances] = kNearestNeighbors(a,b,2);
%
% Output:
% neighbors =
% 1 2
% 1 2
% 4 3
%
% distances =
% 0 1.4142
% 1.0000 1.0000
% 2.8284 3.0000
%--------------------------------------------------------------------------
neighborIds = zeros(size(queryMatrix,1),k);
neighborDistances = neighborIds;
numDataVectors = size(dataMatrix,1);
numQueryVectors = size(queryMatrix,1);
for i=1:numQueryVectors
dist = sum((repmat(queryMatrix(i,:),numDataVectors,1)-dataMatrix).^2,2);
[sortval, sortpos] = sort(dist,'ascend');
neighborIds(i,:) = sortpos(1:k);
neighborDistances(i,:) = sqrt(sortval(1:k));
endfunction [neighborIds, neighborDistances] = kNearestNeighbors(dataMatrix, queryMatrix, k)
%--------------------------------------------------------------------------
% Program to find the k - nearest neighbors (kNN) within a set of points.
% Distance metric used: Euclidean distance
%
% Usage:
% [neighbors distances] = kNearestNeighbors(dataMatrix, queryMatrix, k);
% dataMatrix (N x D) - N vectors with dimensionality D (within which we search for the nearest neighbors)
% queryMatrix (M x D) - M query vectors with dimensionality D
% k (1 x 1) - Number of nearest neighbors desired
%
% Example:
% a = [1 1; 2 2; 3 2; 4 4; 5 6];
% b = [1 1; 2 1; 6 2];
% [neighbors distances] = kNearestNeighbors(a,b,2);
%
% Output:
% neighbors =
% 1 2
% 1 2
% 4 3
%
% distances =
% 0 1.4142
% 1.0000 1.0000
% 2.8284 3.0000
%--------------------------------------------------------------------------
neighborIds = zeros(size(queryMatrix,1),k);
neighborDistances = neighborIds;
numDataVectors = size(dataMatrix,1);
numQueryVectors = size(queryMatrix,1);
for i=1:numQueryVectors
dist = sum((repmat(queryMatrix(i,:),numDataVectors,1)-dataMatrix).^2,2);
[sortval, sortpos] = sort(dist,'ascend');
neighborIds(i,:) = sortpos(1:k);
neighborDistances(i,:) = sqrt(sortval(1:k));
endV
i want to run this code but i get this error Out of memory. The likely cause is an infinite recursion within the program.
0 Comments
Walter Roberson
on 22 Dec 2021
Put the function after the script.
% From To R X
zdata = [ 0 1 0 1.0
0 2 0 0.8
1 2 0 0.4
1 3 0 0.2
2 3 0 0.2
3 4 0 0.08];
[Ybus] = ybus(zdata) % bus admittance matrix
Ibus = [-j*1.1; -j*1.25; 0; 0]; % vector of injected bus currents
Zbus = inv(Ybus) % bus impedance matrix
Vbus = Zbus*Ibus
%Vbus = Ybus\Ibus
% Bus Admittance Matrix
% Copyright (c) 1998 by H. Saadat.
function[Ybus] = ybus(zdata)
nl=zdata(:,1); nr=zdata(:,2); R=zdata(:,3); X=zdata(:,4);
nbr=length(zdata(:,1)); nbus = max(max(nl), max(nr));
Z = R + j*X; %branch impedance
y= ones(nbr,1)./Z; %branch admittance
Ybus=zeros(nbus,nbus); % initialize Ybus to zero
for k = 1:nbr; % formation of the off diagonal elements
if nl(k) > 0 & nr(k) > 0
Ybus(nl(k),nr(k)) = Ybus(nl(k),nr(k)) - y(k);
Ybus(nr(k),nl(k)) = Ybus(nl(k),nr(k));
end
end
for n = 1:nbus % formation of the diagonal elements
for k = 1:nbr
if nl(k) == n | nr(k) == n
Ybus(n,n) = Ybus(n,n) + y(k);
else, end
end
end
end
0 Comments
See Also
Categories
Find more on Statistics and Machine Learning Toolbox in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!