Finding maximum values of wave heights and wave lengths
dwcL01 though dwcL10 is arrays of <3001x2 double> with output from a numerical wave model.
Part of my script:
%% Plotting results from SWASH
% Examination of phase velocity on deep water with different number of layers
% Wave height 3 meters, wave peroid 8 sec on a depth of 30 meters
clear all; close all; clc;
T=8;
L0=1.56*T^2;
%% Loading results tabels.
load dwcL01.tbl; load dwcL02.tbl; load dwcL03.tbl; load dwcL04.tbl;
load dwcL05.tbl; load dwcL06.tbl; load dwcL07.tbl; load dwcL08.tbl;
load dwcL09.tbl; load dwcL10.tbl;
M(:,:,1) = dwcL01; M(:,:,2) = dwcL02; M(:,:,3) = dwcL03; M(:,:,4) = dwcL04;
M(:,:,5) = dwcL05; M(:,:,6) = dwcL06; M(:,:,7) = dwcL07; M(:,:,8) = dwcL08;
M(:,:,9) = dwcL09; M(:,:,10) = dwcL10;
%% Finding position of wave crest using diff and sign.
for ii=1:10
Tp(:,1,ii) = diff(sign(diff([M(1,2,ii);M(:,2,ii)]))) < 0;
Wc(:,:,ii) = M(Tp,1,ii);
L(:,ii) = diff(Wc(:,1,ii))
end
The loop
for ii=1:10
Tp(:,1,ii) = diff(sign(diff([M(1,2,ii);M(:,2,ii)]))) < 0;
Wc(:,:,ii) = M(Tp,1,ii);
L(:,ii) = diff(Wc(:,1,ii))
end
Works fine for ii = 1 Getting the following error for ii = 2
Index exceeds matrix dimensions.
Error in mkPlot (line 19)
Wc(:,:,i) = M(Tp,:,i);
Don't have the same number of wave crests for the different set ups, naturally M(Tp,1,ii) will have different dimensions. How do I work around this issue? Can it be done in a for loop? please feel free to email me or other wise ask for further information.
The problem is that Tp is a three dimensional array. I need to call the Tp(:,:,ii) corresponding to the present scenario. Together with this and defining Wc as a cell I solve my issue.
for ii = 1:10
Tp(:,1,ii) = diff(sign(diff([M(1,2,ii);M(:,2,ii)]))) < 0;
Wc{:,:,ii} = M(Tp(:,:,ii),1,ii);
L{:,ii} = diff(cell2mat(Wc(ii)));
end
Related
With using the PCA technique and the Yale database, I'm trying to work on face recognition within Matlab by randomly splitting the training process to 20% and the testing process to 80%. It is given an
Index in position 2 exceeds array bounds (must not exceed 29)
error. The following is the code, hoping to get help:
dataset = load('yale_FaceDataset.mat');
trainSz = round(dataset.samples*0.2);
testSz = round(dataset.samples*0.8);
trainSetCell = cell(1,trainSz*dataset.classes);
testSetCell = cell(1,testSz*dataset.classes);
j = 1;
k = 1;
m = 1;
for i = 1:dataset.classes
% training set
trainSetCell(k:k+trainSz-1) = dataset.images(j:j+trainSz-1);
trainLabels(k:k+trainSz-1) = dataset.labels(j:j+trainSz-1);
k = k+trainSz;
% test set
testSetCell(m:m+testSz-1) = dataset.images(j+trainSz:j+dataset.samples-1);
testLabels(m:m+testSz-1) = dataset.labels(j+trainSz:j+dataset.samples-1);
m = m+testSz;
j = j+dataset.samples;
end
% convert the data from a cell into a matrix format
numImgs = length(trainSetCell);
trainSet = zeros(numImgs,numel(trainSetCell{1}));
for i = 1:numImgs
trainSet(i,:) = reshape(trainSetCell{i},[],1);
end
numImgs = length(testSetCell);
testSet = zeros(numImgs,numel(testSetCell{1}));
for i = 1:numImgs
testSet(i,:) = reshape(testSetCell{i},[],1);
end
%% applying PCA
% compute the mean face
mu = mean(trainSet)';
% centre the training data
trainSet = trainSet - (repmat(mu,1,size(trainSet,1)))';
% generate the eigenfaces(features of the training set)
eigenfaces = pca(trainSet);
% set the number of principal components
Ncomponents = 100;
% Out of the generated components, we keep "Ncomponents"
eigenfaces = eigenfaces(:,1:Ncomponents);
% generate training features
trainFeatures = eigenfaces' * trainSet';
% Subspace projection
% centre features
testSet = testSet - (repmat(mu,1,size(testSet,1)))';
% subspace projection
testFeatures = inv(eigenfaces'*eigenfaces) * eigenfaces' * testSet';
mdl = fitcdiscr(trainFeatures',trainLabels);
labels = predict(mdl,testFeatures');
% find the images that were recognised and their respect. labels
correctRec = find(testLabels == labels');
correctLabels = labels(correctRec);
% find the images that were NOT recognised and their respect. labels
falseRec = find(testLabels ~= labels');
falseLabels = labels(falseRec);
% compute and display the recognition rate
result = length(correctRec)/length(testLabels)*100;
fprintf('The recognition rate is: %0.3f \n',result);
% divide the images into : recognised and unrecognised
correctTest = testSetCell(correctRec);
falseTest = testSetCell(falseRec);
% display some recognised samples and their respective labels
imgshow(correctTest(1:8),correctLabels(1:8));
% display all unrecognised samples and their respective labels
imgshow(falseTest(1:length(falseTest)), falseLabels(1:length(falseTest)));
it would be nice, if you provide also the line-number and the full message of the error and if you would strip your code to the essential. I guess, the PCA-stuff is not necessary here, as the error is raised probably in your loop. That is because you are incrementing j by j = j+dataset.samples; and take this in the next loop-set for indexing j:j+trainSz-1, which now must exceed dataset.samples...
Nevertheless, there is no randomness in the indexing. It is easiest if you use the built-in cvpartition-function:
% split data
cvp = cvpartition(Lbl,'HoldOut',.2);
lgTrn = cvp.training;
lgTst = cvp.test;
You may provide the number of classes as first input (Lbl in this case) or the actual class vector to let cvpartition pick random subsets that reflect the original distribution of the individual classes.
I have had zero luck finding this elsewhere on the site, so here's my problem. I loop through about a thousand mat files, each with about 10,000 points of data. I'm trying to create an overall histogram of this data, but it's not very feasible to concatenate all this data to give to hist.
I was hoping to be able to create an N and Bin variable each loop using hist (y), then N and Bin would be recalculated on the next loop iteration by using hist(y_new). And so on and so on. That way the source data doesn't grow and when the loop finally ends, I can just use bar(). If this method wouldn't work, then I am very open-minded to other solutions.
Also, it is probably not safe to assume that the x data will remain constant throughout each iteration. I'm using 2012a.
Thanks for any help!!
I think the best solution here is to loop through your files twice: once to set the bins and once to do the histogram. But, if this is impossible in your case, here's a one shot solution that requires you to set the bin width beforehand.
clear; close all;
rng('default') % for reproducibility
% make example data
N = 10; % number of data files
M = 5; % length of data files
xs = cell(1,N);
for i = 1:N
xs{i} = trnd(1,1,M);
end
% parameters
width = 2;
% main
for i = 1:length(xs)
x = xs{i}; % "load data"
range = [min(x) max(x)];
binsPos = 0:width:range(2)+width;
binsNeg = fliplr( 0:-width:range(1)-width );
newBins = [binsNeg(1:end-1) binsPos];
newCounts = histc(x, newBins);
newCounts(end) = []; % last bin should always be zero, see help histc
if i == 1
counts = newCounts;
bins = newBins;
else
% combine new and old counts
allBins = min(bins(1), newBins(1)) : width : max(bins(end), newBins(end));
allCounts = zeros(1,length(allBins)-1);
allCounts(find(allBins==bins(1)) : find(allBins==bins(end-1))) = counts;
allCounts(find(allBins==newBins(1)) : find(allBins==newBins(end-1))) = ...
allCounts(find(allBins==newBins(1)) : find(allBins==newBins(end-1))) + newCounts;
bins = allBins;
counts = allCounts;
end
end
% check
figure
bar(bins(1:end-1) + width/2, counts)
xFull = [xs{:}];
[fullCounts] = histc(xFull, bins);
fullCounts(end) = [];
figure
bar(bins(1:end-1) + width/2, fullCounts)
Hoping you may be able to assist me with this error. I am running some code to fit curves to ages using a cross validation regime. I iterate the curve fitting 1000 times to assess the best fit.
I define my models as:
linear_ft = fittype({'x', '1'});
monotonic_ft= fittype({'-1/x', '1'});
quadratic_ft = fittype('poly2');
I then run the following to iterate through different selections of data splitting, recording the residuals following the curve fit...
Data = randn(4,300,10,10);
Ages = randn(300,1);
for thisDim1 = 1:4
for thisDim2 = 1:10
for thisDim3 = 1:10
for nIts = 1:1000
RandomOrder = randperm(300,300);
Fit_Subs = RandomOrder(1:length(Ages)/2); % Take random subs to fit to
Test_Subs = RandomOrder(length(Ages)/2+1:300); % Take random subs to test fit to
Fit_Data = squeeze(Data(thisDim1,Fit_Subs,thisDim2,thisDim3)); % Take data to fit to
Test_Data = squeeze(Data(thisDim1,Test_Subs,thisDim2,thisDim3)); % Take data to test fit
Fit_Ages = Ages;
Fit_Ages(Fit_Subs) = []; %Take ages of Fit Subs only
Test_Ages = Ages;
Test_Ages(Test_Subs) = []; % Take ages of Test Subs only
Nsubs = (length(Ages)/2);
% Model Data using Curves
fFit_Lin = fit(Fit_Ages,Fit_Data',linear_ft);
fFit_Mon = fit(Fit_Ages,Fit_Data',monotonic_ft);
fFit_Quad = fit(Fit_Ages,Fit_Data',quadratic_ft);
% Fit Modelled Data to Test Data
tFit_Lin = fFit_Lin(Test_Ages);
tFit_Mon = fFit_Mon(Test_Ages);
tFit_Quad = fFit_Quad(Test_Ages);
% Calculate Median Residual
Lin_Med_Resid(nIts) = median(tFit_Lin - Test_Data');
Mon_Med_Resid(nIts) = median(tFit_Mon - Test_Data');
Quad_Med_Resid(nIts) = median(tFit_Quad - Test_Data');
end
end
end
end
If you run this with the fourth loop (nIts) as a for-loop it will run. If you run it as a parfor-loop it won't stating the error:
Error using fit>iFit (line 264)
The name 'lower' is not an accessible property for an instance of class
'llsqoptions'.
Error in fit (line 108) [fitobj, goodness, output, convmsg] = iFit(
xdatain, ydatain, fittypeobj, ...
Does anyone have any idea how to fix this? I would be most grateful for any advice!!
Thanks,
Ben
Try restarting MATLAB or typing clear all to see if it clears things up for you.
Your code works for me, but the parallel toolbox can be a bit finicky in my experience.
I have the following code, pasted below. I would like to change it to only average the 10 most recently filtered images and not the entire group of filtered images. The line I think I need to change is: Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;, but how do I do it?
j=1;
K = 1:3600;
window = zeros(1,10);
Yout = zeros(10,column,row);
figure;
y = 0; %# Preallocate memory for output
%Load one image
for i = 1:length(K)
disp(i)
str = int2str(i);
str1 = strcat(str,'.mat');
load(str1);
D{i}(:,:) = A(:,:);
%Go through the columns and rows
for p = 1:column
for q = 1:row
if(mean2(D{i}(p,q))==0)
x = 0;
else
if(i == 1)
meanvalue = mean2(D{i}(p,q));
end
%Calculate the temporal mean value based on previous ones.
meanvalue = (meanvalue+D{i}(p,q))/2;
x = double(D{i}(p,q)/meanvalue);
end
%Filtering for 10 bands, based on the previous state
for k = 1:10
[y, ZState{k}] = filter(bCoeff{k},aCoeff{k},x,ZState{k});
Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;
end
end
end
% for k = 2:10
% subplot(5,2,k)
% subimage(Yout(k)*5000, [0 100]);
% colormap jet
% end
% pause(0.01);
end
disp('Done Loading...')
The best way to do this (in my opinion) would be to use a circular-buffer to store your images. In a circular-, or ring-buffer, the oldest data element in the array is overwritten by the newest element pushed in to the array. The basics of making such a structure are described in the short Mathworks video Implementing a simple circular buffer.
For each iteration of you main loop that deals with a single image, just load a new image into the circular-buffer and then use MATLAB's built in mean function to take the average efficiently.
If you need to apply a window function to the data, then make a temporary copy of the frames multiplied by the window function and take the average of the copy at each iteration of the loop.
The line
Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;
calculates a kind of Moving Average for each of the 10 bands over all your images.
This line calculates a moving average of meanvalue over your images:
meanvalue=(meanvalue+D{i}(p,q))/2;
For both you will want to add a buffer structure that keeps only the last 10 images.
To simplify it, you can also just keep all in memory. Here is an example for Yout:
Change this line: (Add one dimension)
Yout = zeros(3600,10,column,row);
And change this:
for q = 1:row
[...]
%filtering for 10 bands, based on the previous state
for k = 1:10
[y, ZState{k}] = filter(bCoeff{k},aCoeff{k},x,ZState{k});
Yout(i,k,p,q) = y.^2;
end
YoutAvg = zeros(10,column,row);
start = max(0, i-10+1);
for avgImg = start:i
YoutAvg(k,p,q) = (YoutAvg(k,p,q) + Yout(avgImg,k,p,q))/2;
end
end
Then to display use
subimage(Yout(k)*5000, [0 100]);
You would do sth. similar for meanvalue
I am on a project thumb recognition system on matlab. I implemented Kmean Algorithm and I got results as well. Actually now I want to plot the results like here they done. I am trying but couldn't be able to do so. I am using the following code.
load training.mat; % loaded just to get trainingData variable
labelData = zeros(200,1);
labelData(1:100,:) = 0;
labelData(101:200,:) = 1;
k=2;
[trainCtr, traina] = kmeans(trainingData,k);
trainingResult1=[];
for i=1:k
trainingResult1 = [trainingResult1 sum(trainCtr(1:100)==i)];
end
trainingResult2=[];
for i=1:k
trainingResult2 = [trainingResult2 sum(trainCtr(101:200)==i)];
end
load testing.mat; % loaded just to get testingData variable
c1 = zeros(k,1054);
c1 = traina;
cluster = zeros(200,1);
for j=1:200
testTemp = repmat(testingData(j,1:1054),k,1);
difference = sum((c1 - testTemp).^2, 2);
[value index] = min(difference);
cluster(j,1) = index;
end
testingResult1 = [];
for i=1:k
testingResult1 = [testingResult1 sum(cluster(1:100)==i)];
end
testingResult2 = [];
for i=1:k
testingResult2 = [testingResult2 sum(cluster(101:200)==i)];
end
in above code trainingData is matrix of 200 X 1054 in which 200 are images of thumbs and 1054 are columns. actually each image is of 25 X 42. I reshaped each image in to row matrix (1 X 1050) and 4 other (some features) columns so total of 1054 columns are in each image. Similarly testingData I made it in the similar manner as I made testingData It is also the order of 200 X 1054. Now my Problem is just to plot the results as they did in here.
After selecting 2 features, you can just follow the example. Start a figure, use hold on, and use plot or scatter to plot the centroids and the data points. E.g.
selectedFeatures = [42,43];
plot(trainingData(trainCtr==1,selectedFeatures(1)),
trainingData(trainCtr==1,selectedFeatures(2)),
'r.','MarkerSize',12)
Would plot the selected feature values of the data points in cluster 1.