I am creating a Forward Propagation In the feedforward step, an input pattern is propagated through the network to obtain an output. I have written this in pseudo code and currently attempting to implement this within MATLAB.
There are two errors I currently receive.
Patterns = x'; Desired = y; NHIDDENS = 1; prnout=Desired;
% Patterns become x so number of inputs becomes size of patterns
[NINPUTS,NPATS] = size(Patterns); [NOUTPUTS,NP] = size(Desired);
%apply the backprop here...
LearnRate = 0.15; Momentum = 0; DerivIncr = 0; deltaW1 = 0; deltaW2 = 0;
% Keeps the tan ordering of the examples of x
Inputs1= [Patterns;ones(1,NPATS)]; %Inputs1 = [ones(1,NPATS); Patterns];
% Weight initialisation
Weights1 = 0.5*(rand(NHIDDENS,1+NINPUTS)-0.5);
Weights2 = 0.5*(rand(1,1+NHIDDENS)-0.5);
TSS_Limit = 0.02;
for epoch = 1:10
% FORWARD LOOP
size(NOUTPUTS)
size(NPATS)
for ii = 0: ii < length(NINPUTS)
NOUTPUTS(ii+1) = NPATS(ii);
% Sets bias to 1
NOUTPUTS(1) = 1;
end
for ii = NHIDDENS: ii < NINPUTS
sum = 0;
for ij = 0: ij < ii
sum = sum + deltaW1(ii,ij) * NOUTPUTS(ij);
NOUTPUTS(ii) = tanh(sum);
end
end
Unable to perform assignment because the
left and right sides have a different
number of elements.
Error in mlpts (line 66)
NOUTPUTS(i+1) = NPATS(i);
i am still new to MATLAB and trying to become use to it.
After iterating through the loop
NOUTPUTS = 0 and the error is displayed. I am confused as I am trying to increment NOUTPUTS with ii by 1 through each loop.
I have been able to create the forward propagation with a loop.
for i =3:NNODES
summ = 0;
for j=1:i-1
summ = summ + weights(i,j) * Node_outputs(j);
end
if i == NNODES
Node_outputs(i) = summ
else
Node_outputs(i) = sigmoid(summ);
end
end
Out = Node_outputs(NNODES);
% BOut = ((Node_outputs(NNODES)) * (1 - Node_outputs));
BOut=zeros(1,6);
DeltaWeight = zeros(6,6);
I am programming a simple perceptron in matlab but it is not converging and I can't figure out why.
The goal is to binary classify 2D points.
%P1 Generate a dataset of two-dimensional points, and choose a random line in
%the plane as your target function f, where one side of the line maps to +1 and
%the other side to -1. Let the inputs xn 2 R2 be random points in the plane,
%and evaluate the target function f on each xn to get the corresponding output
%yn = f(xn).
clear all;
clc
clear
n = 20;
inputSize = 2; %number of inputs
dataset = generateRandom2DPointsDataset(n)';
[f , m , b] = targetFunction();
signs = classify(dataset,m,b);
weights=ones(1,2)*0.1;
threshold = 0;
fprintf('weights before:%d,%d\n',weights);
mistakes = 1;
numIterations = 0;
figure;
plotpv(dataset',(signs+1)/2);%mapping signs from -1:1 to 0:1 in order to use plotpv
hold on;
line(f(1,:),f(2,:));
pause(1)
while true
mistakes = 0;
for i = 1:n
if dataset(i,:)*weights' > threshold
result = 1;
else
result = -1;
end
error = signs(i) - result;
if error ~= 0
mistakes = mistakes + 1;
for j = 1:inputSize
weights(j) = weights(j) + error*dataset(i,j);
end
end
numIterations = numIterations + 1
end
if mistakes == 0
break
end
end
fprintf('weights after:%d,%d\n',weights);
random points and signs are fine since plotpv is working well
The code is based on that http://es.mathworks.com/matlabcentral/fileexchange/32949-a-perceptron-learns-to-perform-a-binary-nand-function?focused=5200056&tab=function.
When I pause the infinite loop, this is the status of my vairables:
I am not able to see why it is not converging.
Additional code( it is fine, just to avoid answers asking for that )
function [f,m,b] = targetFunction()
f = rand(2,2);
f(1,1) = 0;
f(1,2) = 1;
m = (f(2,2) - f(2,1));
b = f(2,1);
end
function dataset = generateRandom2DPointsDataset(n)
dataset = rand(2,n);
end
function values = classify(dataset,m,b)
for i=1:size(dataset,1)
y = m*dataset(i,1) + b;
if dataset(i,2) >= y, values(i) = 1;
else values(i) = -1;
end
end
end
I'm solving a tsp optimization problem with heuristics, and the following code works for all euclidian tsplib instances excluding the a280 instance that gives me the "Index exceeds matrix dimension" error and I can't figure out why?
I put comments so the code is understandable hopefully easily. The error happens at the before-last line of code i = mod(si(selected_edge)-1,n)+1;
n = size(a280,1); % n number of nodes
distances = dist(a280,a280');
savings = zeros(n);
lengths_obtained = [];
depot = 1 % depot is the central node
%% Test n number of solutions where each node is selected to be the depot
for depot = 1:n
%% Compute the matrix of savings of each pair of nodes
for i = 1:n
if i == depot
continue;
end
savings(i,(i+1):n)=distances(i,depot)+distances(depot,(i+1):n)-distances(i,(i+1):n);
end
%% Initialisation steps
minParent = 1:n;
% Rank the savings s(i,j) and list them in descending order of
% magnitude.
[~,si] = sort(savings(:),'descend');
si = si(1:fix(end/2));
% Setting cache
Nodes_depot = zeros(1,n); % binary vector, 1 if node i is disconnected from the depot, 0 otherwise
Nodes_depot(depot) = 1;
Nodes_depot_count = n-1;
degrees = zeros(1,n); % number of edges linking a node
selected_edge = 1;
pairs_of_nodes = zeros(n,2); % listing the couples (i,j) of nodes linked together
currentEdgeCount = 1;
%% Pair nodes as long as more than 2 nodes are still connected to the depot
while Nodes_depot_count>2
% Start with the edge (i,j) generating the topmost amount of
% savings.
i = mod(si(selected_edge)-1,n)+1;
j = floor((si(selected_edge)-1)/n)+1;
if Nodes_depot(i) == 0 && Nodes_depot(j)==0 && (minParent(i)~=minParent(j)) && i~=j && i~=depot && j~=depot
degrees(i) = degrees(i)+1;
degrees(j) = degrees(j)+1;
pairs_of_nodes(currentEdgeCount,:) = [i,j];
if minParent(i) < minParent(j)
minParent(minParent == minParent(j)) = minParent(i);
else
minParent(minParent == minParent(i)) = minParent(j);
end
currentEdgeCount = currentEdgeCount + 1;
% Removing i and/or j if they now are paired with two other
% nodes from partial tour.
if degrees(i) == 2
Nodes_depot(i) = 1;
Nodes_depot_count = Nodes_depot_count - 1;
end
if degrees(j) == 2
Nodes_depot(j) = 1;
Nodes_depot_count = Nodes_depot_count - 1;
end
end
selected_edge = selected_edge + 1;
end
If someone can push me in the right directions would be greatly appreciated. Thanks!!
I am trying to implement a wireless sensor network simulator on matlab and I need your help.
This is exactly what I want to do:
Deploy nodes randomly in a 2D plane.
Model a group leader election algorithm using two conditions:
a) Energy: generate random energy values associated with each of the sensors, the sensor node with the maximum energy has higher probability of being selected as leader.
b) Proximity: the sensor node that is mostly surrounded by neighboring nodes has higher probability of being selected.
So, for a random node that has maximum energy and more neighbors can be selected as leader and plotted with the rest of the nodes in a different color.
I have been working on this, trying to develop my code all to no avail. I am not really good with matlab coding, I am still learning.
Please guys, I need any help I can get on this, my deadline is imminent.
Thanks,
Ike
First of all building a wireless networks simulator using matlab is by far not the best choice to do. its better to use specialized simulators such as NS2 or Omnet++. however, I have developed once a simple m-file code to randomly deploy points in a square are and try to link between these points according to the distance between these points. the points are assumed to be sensor nodes.
try to modify on it to get what you need. please vote up if this answer was useful to you:
W=0;X=0;Y=0;Z=0;
nodedistance = zeros();
maxx = 400; maxy=400; maxn = 50;
q = zeros(maxn);
e = rand(maxn,1)*100;
nodeloc = rand(maxn, 2)* maxx;
node(maxn) = struct('NodeNum',zeros(maxn),'nEnergy',zeros(maxn),'Loc',[zeros(maxn,1), zeros(maxn,2)]);
rss(maxn,maxn) = struct('NodeNumber',zeros(maxn),'NodeDistance',zeros(maxn));
for a = 1:100,
m = 2;
node = struct();
rss = struct();
nodedistance = zeros();
maxx = 400; maxy=400; maxn = 50;
q = zeros(maxn);
e = rand(maxn,1)*100;
nodeloc = rand(maxn, 2)* maxx;
for i = 1: maxn,
node(i)=struct('NodeNum',i,'nEnergy',e(i),'Loc',[nodeloc(i, 1), nodeloc(i, 2)]);
end
for i = 1:maxn,
for j = 1:maxn,
rss(i,j) = struct('NodeNumber',i,'NodeDistance',sqrt((node(i).Loc(1)- node(j).Loc(1))^2+(node(i).Loc(2)-node(j).Loc(2))^2));
end
end
for i = 1:maxn,
for j = 1:maxn,
nodedistance(i,j)=rss(i,j).NodeDistance;
end
end
for i = 1:maxn,
for j = 1:maxn,
if (node(i).nEnergy > 0) && (node (j).nEnergy > 0) && (0 < nodedistance(i,j) && nodedistance(i,j) <= 30)
if (node(i).nEnergy < node (j).nEnergy) %|| ( (node(i).nEnergy == node (j).nEnergy )&& (0 < nodedistance(i,j) && nodedistance(i,j)) < 30) %|| (0 < nodedistance(i,j) && nodedistance(i,j) <= 30)
q(i,j) = 1;
elseif node(i).nEnergy == node (j).nEnergy && 0 < nodedistance(i,j) && nodedistance(i,j) < 30
q(i,j) = 1;
else if 0 < nodedistance(i,j) && nodedistance(i,j) <= 30
q(i,j) = 1;
else
q(i,j) = 0;
end
end
end
end
end
end
for i = 1:maxn,
for j = 1:maxn,
if q(i,j) == 1
q(j,i) = 1;
end
end
end
colordef white,
figure(1);
axis equal
for i = 1:maxn,
hold on
box on;
plot(nodeloc(i, 1), nodeloc(i, 2), 'k.', 'MarkerSize', 5);
lscatter(nodeloc(i, 1),nodeloc(i, 2),i);
grid on;
end
gplot(q,nodeloc,'r-');
c = q;
b = 0; zeros(maxn);
check = 1;
while (check)
for m = 2:30
p = size(b)
b = zeros(maxn,maxn);
for i = 1:maxn,
for j = 1:maxn,
if i ~= j
if c(i,j) == 0
for k = 1:maxn,
if c(i,k) >0 && q(k,j) >0
b(i,j) = m;
break
else b(i,j) = 0;
end
end
end
end
end
end
f = size(b)
c = c + b;
if b ==0
check = 0;
else b = 0;% while loop here the condition.
m = m + 1;
end
end
k = 0;
for i = 1:maxn,
for j = 1:maxn,
k = k + c(i,j);
end
end
average_hop = k/(maxn*(maxn-1));
o = 0;
for i = 1:maxn,
for j = 1:maxn,
if c(i,j) ~= 0
o = o + 1;
end
end
end
c(~c) = nan;
r=max(max(c));
t=min(min(c));
clear
end
formatSpec1 = 'The number of hops from node %d to node %d is %d \n';
formatSpec2 = 'The average number of hops is %.4f, the maximum hop-count is %d, the minimum hop-count is %d \n';
formatSpec3 = 'The number of created paths is %d \n';
fileID = fopen('Paths11.txt','w');
fprintf(fileID,formatSpec1,i,j,c(i,j));
fprintf(fileID,formatSpec2,average_hop,r,t);
fprintf(fileID,formatSpec3,o);
clear;
X = X+average_hop;
Y = Y+r;
z = Z+t;
W = W+o;
end;
formatSpec1 = 'The number of hops from node %d to node %d is %d \n';
formatSpec2 = 'The average number of hops is %.4f, the average maximum hop-count is %.2f, the minimum hop-count is %d \n';
formatSpec3 = 'The average number of created paths is %.2f \n';
fileID = fopen('Energy-50-AODV.txt','w');
fprintf(fileID,formatSpec1,i,j,c(i,j));
fprintf(fileID,formatSpec2,X/a,Y/a,t);
fprintf(fileID,formatSpec3,W/a);
clear;
This is not a get-your-homework-done-for-free site; you'll have to do most of the work yourself. Here are my tips:
Don't leave your assignments to the last minute.
Study the MATLAB code of other people working in your field, and refer to the MATLAB help files (hit F1) when you get stuck. Here's a fruitful search query to point you in the right direction: wsn OR "sensor network" clustering matlab code site:edu
I am a total beginner in Matlab and trying to write some Machine Learning Algorithms in Matlab. I would really appreciate it if someone can help me in debugging this code.
function y = KNNpredict(trX,trY,K,X)
% trX is NxD, trY is Nx1, K is 1x1 and X is 1xD
% we return a single value 'y' which is the predicted class
% TODO: write this function
% int[] distance = new int[N];
distances = zeroes(N, 1);
examples = zeroes(K, D+2);
i = 0;
% for(every row in trX) { // taking ONE example
for row=1:N,
examples(row,:) = trX(row,:);
%sum = 0.0;
%for(every col in this example) { // taking every feature of this example
for col=1:D,
% diff = compute squared difference between these points - (trX[row][col]-X[col])^2
diff =(trX(row,col)-X(col))^2;
sum += diff;
end % for
distances(row) = sqrt(sum);
examples(i:D+1) = distances(row);
examples(i:D+2) = trY(row:1);
end % for
% sort the examples based on their distances thus calculated
sortrows(examples, D+1);
% for(int i = 0; i < K; K++) {
% These are the nearest neighbors
pos = 0;
neg = 0;
res = 0;
for row=1:K,
if(examples(row,D+2 == -1))
neg = neg + 1;
else
pos = pos + 1;
%disp(distances(row));
end
end % for
if(pos > neg)
y = 1;
return;
else
y = -1;
return;
end
end
end
Thanks so much
When working with matrices in MATLAB, it is usually better to avoid excessive loops and instead use vectorized operations whenever possible. This will usually produce faster and shorter code.
In your case, the k-nearest neighbors algorithm is simple enough and can be well vectorized. Consider the following implementation:
function y = KNNpredict(trX, trY, K, x)
%# euclidean distance between instance x and every training instance
dist = sqrt( sum( bsxfun(#minus, trX, x).^2 , 2) );
%# sorting indices from smaller to larger distances
[~,ord] = sort(dist, 'ascend');
%# get the labels of the K nearest neighbors
kTrY = trY( ord(1:min(K,end)) );
%# majority class vote
y = mode(kTrY);
end
Here is an example to test it using the Fisher-Iris dataset:
%# load dataset (data + labels)
load fisheriris
X = meas;
Y = grp2idx(species);
%# partition the data into training/testing
c = cvpartition(Y, 'holdout',1/3);
trX = X(c.training,:);
trY = Y(c.training);
tsX = X(c.test,:);
tsY = Y(c.test);
%# prediction
K = 10;
pred = zeros(c.TestSize,1);
for i=1:c.TestSize
pred(i) = KNNpredict(trX, trY, K, tsX(i,:));
end
%# validation
C = confusionmat(tsY, pred)
The confusion matrix of the kNN prediction with K=10:
C =
17 0 0
0 16 0
0 1 16