I have a 3-Dimensional matrix in Matlab (2 x 2 x 56 complex double).
I want to ignore/remove the 1st dimension (2) so the remaining part will be (2x56) and then use it to do some plotting. How can I do that?
I have come through squeeze (which actually removes the singleton) so it doesn't work here. Anything similar to it?
Related
I have got a matrix of AirFuelRatio values at certain engine speeds and throttlepositions. (eg. the AFR is 14 at 2500rpm and 60% throttle)
The matrix is now 25x10, and the engine speed ranges from 1200-6000rpm with interval 200rpm, the throttle range from 0.1-1 with interval 0.1.
Say i have measured new values, eg. an AFR of 13.5 at 2138rpm and 74,3% throttle, how do i merge that in the matrix? The matrix closest values are 2000 or 2200rpm and 70 or 80% throttle. Also i don't want new data to replace the older data. How can i make the matrix take this value in and adjust its values to take the new value in account?
Simplified i have the following x-axis values(top row) and 1x4 matrix(below):
2 4 6 8
14 16 18 20
I just measured an AFR value of 15.5 at 3 rpm. If you interpolate the AFR matrix you would've gotten a 15, so this value is out of the ordinary.
I want the matrix to take this data and adjust the other variables to it, ie. average everything so that the more data i put in the more reliable and accurate the matrix becomes. So in the simplified case the matrix would become something like:
2 4 6 8
14.3 16.3 18.2 20.1
So it averages between old and new data. I've read the documentation about concatenation but i believe my problem can't be solved with that function.
EDIT: To clarify my question, the following visual clarification.
The 'matrix' keeps the same size of 5 points whil a new data point is added. It takes the new data in account and adjusts the matrix accordingly. This is what i'm trying to achieve. The more scatterd data i get, the more accurate the matrix becomes. (and yes the green dot in this case would be an outlier, but it explains my case)
Cheers
This is not a matter of simple merge/average. I don't think there's a quick method to do this unless you have simplifying assumptions. What you want is a statistical inference of the underlying trend. I suggest using Gaussian process regression to solve this problem. There's a great MATLAB toolbox by Rasmussen and Williams called GPML. http://www.gaussianprocess.org/gpml/
This sounds more like a data fitting task to me. What you are suggesting is that you have a set of measurements for which you wish to get the best linear fit. Instead of producing a table of data, what you need is a table of values, and then find the best fit to those values. So, for example, I could create a matrix, A, which has all of the recorded values. Let's start with:
A=[2,14;3,15.5;4,16;6,18;8,20];
I now need a matrix of points for the inputs to my fitting curve (which, in this instance, lets assume it is linear, so is the set of values 1 and x)
B=[ones(size(A,1),1), A(:,1)];
We can find the linear fit parameters (where it cuts the y-axis and the gradient) using:
B\A(:,2)
Or, if you want the points that the line goes through for the values of x:
B*(B\A(:,2))
This results in the points:
2,14.1897 3,15.1552 4,16.1207 6,18.0517 8,19.9828
which represents the best fit line through these points.
You can manually extend this to polynomial fitting if you want, or you can use the Matlab function polyfit. To manually extend the process you should use a revised B matrix. You can also produce only a specified set of points in the last line. The complete code would then be:
% Original measurements - could be read in from a file,
% but for this example we will set it to a matrix
% Note that not all tabulated values need to be present
A=[2,14; 3,15.5; 4,16; 5,17; 8,20];
% Now create the polynomial values of x corresponding to
% the data points. Choosing a second order polynomial...
B=[ones(size(A,1),1), A(:,1), A(:,1).^2];
% Find the polynomial coefficients for the best fit curve
coeffs=B\A(:,2);
% Now generate a table of values at specific points
% First define the x-values
tabinds = 2:2:8;
% Then generate the polynomial values of x
tabpolys=[ones(length(tabinds),1), tabinds', (tabinds').^2];
% Finally, multiply by the coefficients found
curve_table = [tabinds', tabpolys*coeffs];
% and display the results
disp(curve_table);
I'm not getting the correct results from the Matlab function so maybe my data arrangement is wrong. I looked at the help file of the function I am using and the input, "X" that it takes must be in the form.
The rows of X correspond to observations, and columns correspond to
variables.
I am sorry if this is very basic but how exactly should my input matrix be arranged?
I have 5 writers, each have a feature vector of length 18 (for example for the sake of simplicity).
So I assumed that by observations it is meant the different features of the same writer and variables mean the writers, so I arranged the input matrix as [18 x 5] where each column is a writer.
This example is simple. What of in the case of SIFT features? where each writer will produce a feature matrix [128 x num. of keypoints] which usually becomes [128 x 70] for one image. So if I want to concatenate all of them into the input matrix my input matrix will become [128 x 350].
Will this just be the input matrix X? Then in the case of SIFT each variable in 70 columns wide.
Thank you in advance
If all of your writers data have different size, I suggest you to use cell() which is cell array. http://www.mathworks.com/help/matlab/cell-arrays.html - here is your reference. So for example if you need to calculate covariance you can do it for each matrix separately. Then your covariance matrices will be same size(128*128) so you can put them together and have your 3D matrix data.
Hope it will help you.
Preamble:
My framework is Matlab. I have a very large data matrix M (size(M) = 30 20 30 20 51 300 ) and I need to manipulate this matrix (to calculate some correlations, mean, shift it circularly, interpolate it and so on).
!Important! : most of the elements of this matrix are zeros or ones !!
My question: Since it is very time consuming to work with such a huge matrix, is it possible to perform the same manipulations, but on the sparse form of this matrix? Of course, one should not loose any information about zeros or ones (for example, for calculations of averages or correlations between different elements).
Is there any other way to handle such matrices? (huge and mostly 0's and 1's)
Thanks in advance!
You can use sparse matrices.
The only issue with sparse matrices is, that they only come in two dimensions, so the straight-forward way to represent your matrix would be to wrap it into a sparse matrix, of size [N 1] where N = prod([ 30 20 30 20 51 300]) in your case.
I've done this for N-dimension histograms (which sounds similar to your application) and it works fine.
You'll lose the possibility to use all the smart indexing though.
So using mean/sum etc. on single dimensions will become somewhat more complicated since you'll have to convert subscript-indices to linear indices and vice versa.
For that, you should have a look at sub2ind and ind2sub.
(Sounds like a fun project on wrapping the builtin sparse matrix into a n-dimensional sparse matrix...)
Inside a MATLAB function I have built a matrix A, whose dimensions M and N are set as parameters of the function. I would like to plot all the columns of this matrix, given a vector of indices B with length M. Hence, I use these lines:
figure
plot(B,A)
I specified figure as the MATLAB function returns more different plots.
My problem is that the program plots just two columns of the matrix with different colours (blue and violet). Where is my mistake?
Thank you for your attention.
go for
plot(repmat(B,1,N),A);
or
plot(repmat(B,N,1),A);
(depending on your rows/columns). You need to have same size matrices in plot.
Moreover, if B are just consecutive indexes, you may want to consider Plot(A) (or Plot(A')).
I noticed that there was an error which caused the overlap of the different curves, so the way which I used to plot the colums of a matrix is valid. However, the method proposed by Acorbe is a possibility, too.
I have seen plenty of answers regarding how to remove leading and/or trailing zeros, and how to remove all zeros from a vector or matrix. What I need to do, though, is only remove some of them. I have two matrices, and I only want to remove the entries where both of them are zero. They are two-dimensional x and y coordinates, solved using characteristics (I can give more detail if needed) and I just want to remove the values where both matrices contain zeros at the same indices. I can easily convert the matrices into vectors and work with vectors, so any help in either case would be greatly appreciated.
For the sake of simplicity, let's assume you're using vectors called X and Y (of the same length), and you want to remove only those entries where both vectors are zero. Here's how (not tested):
% Find the indexes where either X or Y is different from zero
% (these are the indexes of the components we want to keep)
I = find(X~=0 | Y~=0);
% Select the desired components from X and Y
X=X(I);
Y=Y(I);
Edit: As Oli has pointed out below (and stefano explained further), you should use logical indexing for better performance.