I need to generate some random 2D points (for example 30 points) near the y=x line, insert them in a matrix, plot it and then calculate the SVD of the matrix. But since I'm new to MATLAB I don't know how can I generate my desired matrix.
Since this looks like homework I'll just post some general ideas here.
randi can be used to get semi-random integers. Using that you can create a 2D matrix by duplicating the array and putting them together. Thus: generate a 30x1 column and duplicate it to a 30x2 column. All rows will have the same two entries, i.e. x=y.
Noise can be added to this by creating a 30x2 matrix of random numbers, use rand for that and simply add that to the previously created matrix.
Check the documentation on svd to see how the singular-value decomposition works, it's fairly straight-forward if you know your linear algebra.
Finally for plotting you can use various tools such as image, imagesc, plot, surf and scatter, try them and see which works best for you.
Here is a quick example I made: https://saturnapi.com/fullstack/2d-points-randomly-near-line
%// Welcome to Saturn's MATLAB-Octave API.
%// Delete the sample code below these comments and write your own!'
x = 13 + 6.*rand(20,1);
y = x*0.7 + 0.5*rand(20,1);
[X,Y] = meshgrid(x,y)
figure(1);
plot(x,y,'.');
%// Print plot as PNG with resultion of 60 pixels per inch
print("MyPNG.png", "-dpng", "-r60");
Related
I'm trying yo implement vlsh with the California ND Datastet, wich is composed by 701 photos.
10 subject wrote down in a txt file which photos are near duplicate for them, and we have also correlation matrix.
The images are RGB and i reduced them in 20x20. I created a 4-d array 20x20x3x701. So i tried to reshape and obtained a 1200x701 matrix, but the problem is that reshape can't maintain the order of the original matrix.
I tried to search online and most of suggestion is to use "Permute", but it seems to me that doesn't fit my situation.
I can post the matlab code:
`
path='C:\Users\franc\Desktop\stage\californiaND\prova*.jpg';
path2='C:\Users\franc\Desktop\stage\californiaND\prova';
d=dir(path);
a=[];
for m=1:length(d)
a=cat(4,a,imread(strcat(path2,d(m).name)));
end
r=reshape(a,[],701);
r=double(r);
L = lshConstruct( r, 10,4);`
I am assuming you need the 1D vector in RGBRGB format, as otherwise the solution would be trivial ( just (:)).
Assuming imread is reading 20x20x3 images one by one, this is how you directly make your 2D matrix:
for m=1:length(d) % this is 701, right?
im=imread(strcat(path2,d(m).name));
im=permute(im,[3 1 2]);
a=cat(2,im(:));
end
Part of an assignment of mine is to provide a 2D visualisation (plots) of some of my data points that are stored in a matrix. I'm slightly confused because the data is actually in 6D space (i.e. each row has 6 columns like 0 1 0 8 8 2).
Is there something I'm missing or does this genuinely not make sense? Is this something MATLAB can do?
Edit: Is something like this possible?
Though I wouldn't consider it visualizing 6D data, you can get the linked plot with a simple call to plot:
A = rand(6);
x = 1:6;
plot(x,A'); % Transpose A to plot rows since it's square, see plot documentation
Which produces the following:
From the documentation:
If one of X or Y is a vector and the other is a matrix, then the
matrix must have dimensions such that one of its dimensions equals the
vector length. If the number of matrix rows equals the vector length,
then the plot function plots each matrix column versus the vector. If
the number of matrix columns equals the vector length, then the
function plots each matrix row versus the vector. If the matrix is
square, then the function plots each column versus the vector.
Simply use:
surf(2Dmatrix)
You can read more here: http://uk.mathworks.com/help/matlab/ref/surf.html
If your matrix is 2D image, simply use
figure; imshow(2Dmatrix, [])
If you leave the square bracket empty, the limit will be automatic. When the figure is displayed you can change it to different colormap by Edit > Colormap.
I have a 100 x 100 matrix and i have to use plot3 in MATLAB environment to graph this data. I tried plot3(matrix name) but I faced this error "not enough input arguments". I think plot3 needs 3 input arguments, but I only have this matrix of data. could anyone help me to solve this problem? Is there any alternative for plot3 when we don't have enough arguments?
I need a graph like this:
I think you want to plot the values in a figure as a sort of surface element. What you can do then is:
[X,Y] = size(matrix);
figure;
surface(1:X,1:Y,matrix);
What this does is that it creates a vector for both X and Y indices, as possible in surface. The X and Y indices are obtained by setting them as integers from 1:size, so basically you assign the location of each matrix element to an index.
Note that you can strictly speaking use surface(matrix) as well, but the former approach allows you to use custom indexing, as long as the lengths of the vectors X and Y are the same as the size of your matrix.
For the waterfall use:
figure;
waterfall(matrix);
Sample code:
A=rand(100);
figure;
waterfall(1:100,1:100,A);
Gives:
where you can play around with the name-value pairs, see the documentation on that.
I think what you need is mesh or surf instead of plot3.
plot3 draws a line in 3d-space, so it will need three vectors of the same length (one for each dimension).
When you have a matrix, one reasonable way of displaying it is as a surface in 3d space, which is done by the functions mesh and surf.
Try it out! I hope i helps!
The figure shown above is the plot of cumulative distribution function (cdf) plot for relative error (attached together the code used to generate the plot). The relative error is defined as abs(measured-predicted)/(measured). May I know the possible error/interpretation as the plot is supposed to be a smooth curve.
X = load('measured.txt');
Xhat = load('predicted.txt');
idx = find(X>0);
x = X(idx);
xhat = Xhat(idx);
relativeError = abs(x-xhat)./(x);
cdfplot(relativeError);
The input data file is a 4x4 matrix with zeros on the diagonal and some unmeasured entries (represent with 0). Appreciate for your kind help. Thanks!
The plot should be a discontinuous one because you are using discrete data. You are not plotting an analytic function which has an explicit (or implicit) function that maps, say, x to y. Instead, all you have is at most 16 points that relates x and y.
The CDF only "grows" when new samples are counted; otherwise its value remains steady, just because there isn't any satisfying sample that could increase the "frequency".
You can check the example in Mathworks' `cdfplot1 documentation to understand the concept of "empirical cdf". Again, only when you observe a sample can you increase the cdf.
If you really want to "get" a smooth curve, either 1) add more points so that the discontinuous line looks smoother, or 2) find any statistical model of whatever you are working on, and plot the analytic function instead.
I have a Matlab figure I want to use in a paper. This figure contains multiple cdfplots.
Now the problem is that I cannot use the markers because the become very dense in the plot.
If i want to make the samples sparse I have to drop some samples from the cdfplot which will result in a different cdfplot line.
How can I add enough markers while maintaining the actual line?
One method is to get XData/YData properties from your curves follow solution (1) from #ephsmith and set it back. Here is an example for one curve.
y = evrnd(0,3,100,1); %# random data
%# original data
subplot(1,2,1)
h = cdfplot(y);
set(h,'Marker','*','MarkerSize',8,'MarkerEdgeColor','r','LineStyle','none')
%# reduced data
subplot(1,2,2)
h = cdfplot(y);
set(h,'Marker','*','MarkerSize',8,'MarkerEdgeColor','r','LineStyle','none')
xdata = get(h,'XData');
ydata = get(h,'YData');
set(h,'XData',xdata(1:5:end));
set(h,'YData',ydata(1:5:end));
Another method is to calculate empirical CDF separately using ECDF function, then reduce the results before plotting with PLOT.
y = evrnd(0,3,100,1); %# random data
[f, x] = ecdf(y);
%# original data
subplot(1,2,1)
plot(x,f,'*')
%# reduced data
subplot(1,2,2)
plot(x(1:5:end),f(1:5:end),'r*')
Result
I know this is potentially unnecessary given MATLAB's built-in functions (in the Statistics Toolbox anyway) but it may be of use to other viewers who do not have access to the toolbox.
The empirical CMF (CDF) is essentially the cumulative sum of the empirical PMF. The latter is attainable in MATLAB via the hist function. In order to get a nice approximation to the empirical PMF, the number of bins must be selected appropriately. In the following example, I assume that 64 bins is good enough for your data.
%# compute a histogram with 64 bins for the data points stored in y
[f,x]=hist(y,64);
%# convert the frequency points in f to proportions
f = f./sum(f);
%# compute the cumulative sum of the empirical PMF
cmf = cumsum(f);
Now you can choose how many points you'd like to plot by using the reduced data example given by yuk.
n=20 ; % number of total data markers in the curve graph
M_n = round(linspace(1,numel(y),n)) ; % indices of markers
% plot the whole line, and markers for selected data points
plot(x,y,'b-',y(M_n),y(M_n),'rs')
verry simple.....
try reducing the marker size.
x = rand(10000,1);
y = x + rand(10000,1);
plot(x,y,'b.','markersize',1);
For publishing purposes I tend to use the plot tools on the figure window. This allow you to tweak all of the plot parameters and immediately see the result.
If the problem is that you have too many data points, you can:
1). Plot using every nth sample of the data. Experiment to find an n that results in the look you want.
2). I typically fit curves to my data and add a few sparsely placed markers to plots of the fits to differentiate the curves.
Honestly, for publishing purposes I have always found that choosing different 'LineStyle' or 'LineWidth' properties for the lines gives much cleaner results than using different markers. This would also be a lot easier than trying to downsample your data, and for plots made with CDFPLOT I find that markers simply occlude the stairstep nature of the lines.