I've been given a 1x16 matrix of temperature and a 1 x 21 matrix of humidity.
On top of that I have a 21x16 matrix of 0's and 1's where 0's are uncomfortable temp-humidity combinations and 1's are comfortable.
Any ideas on how I can go about plotting this graph and then finding a best-fit line to predict whether any temp-humidity pair is acceptable?
Related
I plotted 5 fold cross-validation data as a cell array to perfcurve function with positive class=1. Then it generated 3 curves as you can see in the diagram. I was expecting only one curve.
[X,Y,T,AUC,OPTROCPT,SUBY,SUBYNAMES] = perfcurve(Actual_label,Score,1);
plot(X,Y)
Here, Actual_label and Score are a cell array of size 5 X 1. Each cell array is of size 70 X 1. And 1 denotes positive class=1.
P.S: I am using One-class SVM and 'fitSVMPosterior' function is not appropriate for one-class learning (same has been mentioned in the documentation of MATLAB). Therefore posterior probability can't be used here.
When you compute the confidence bounds, X and Y are an m-by-3 array, where m is the number of fixed X values or thresholds (T values). The first column of Y contains the mean value. The second and third columns contain the lower bound and the upper bound, respectively, of the pointwise confidence bounds. AUC is also a row vector with three elements, following the same convention.
Above explanation is taken from MATLAB documentation.
That is expected because you are plotting the ROC curve for each of the 5 folds.
Now if you want to have only one ROC for your classifier, you can either use the 5 trained classifiers to predict the labels of an independent test set or you can average the posterior probabilities of the 5 folds and have one ROC.
How can one explain the principle of fft in a multidimensional array?
Y = fft(X)
If X is a multidimensional array, then fft(X) treats the values along the first array dimension whose size does not equal 1 as vectors and returns the Fourier transform of each vector.
I don't understand it very well.
X=[1 2 ; 3 4];
X(:,:,2)=[5 6 ; 7 8]
Let's take the 2D case for simplicity.
Given a 2D matrix of data, X, one can treat each row vector individually and perform a one-dimensional discrete Fourier transform. This will decompose each row into one dimensional frequency components. No attention is given to whether the rows of the matrix are correlated, and so every row of the output matrix will have some non-zero components.
Alternatively, using fft2, one can decompose X into its constituent two-dimensional frequency components. I find this easiest to think about in analogy with the DCT, where you can visualize the 2D basis easily
With the 2D FFT, correlations between rows would affect the output of the FFT. It's possible a single 2D frequency component (which is more like a surface than a wave) could contain all the energy of the 2D FFT, and so the output matrix could be much sparser than when treating the rows individually.
This analogy can be continued in to n-dimensions, with higher dimensional frequency components potentially capturing the higher dimensional structure in your data.
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.
My data matrix is X which is 4999*37152. Then I use this command in Matlab:
[coeff, score, latent, tsquared1, explained1] = pca(X);
The output: coeff is 37152*4998, score is 4999*4998, latent is 4998*1. According to http://www.mathworks.com/help/stats/pca.html, the coeff should be p*p. So what is wrong with my code ?
As Matlab documentation says, "Rows of X correspond to observations and columns correspond to variables". So you are feeding in a matrix with only 4999 observations for 37152 observations. Geometrically, you have 4999 points in a 37152-dimensional space. These points are contained in a 4998-dimensional affine subspace, so Matlab gets you 4998 directions there (each expressed as a vector with 37152 components).
For more, see the Statistics site:
Why are there only n-1 principal components for n data points if the number of dimensions is larger than n?
PCA when the dimensionality is greater than the number of samples
The MATLAB documentation is written under assumption that you have at least as many observations as variables, which is how people normally use PCA.
Of course, it's possible that your data actually has 37152 observations for 4999 variables, in which case you need to transpose X.
I am having difficulty producing a contour plot from two matrices;
I have one matrix A 9×8 with values (an amount of something), and another matrix 9×8 B where each entry corresponds to a location of an entry in A.
The issue is that the entries do not fall on a rectangular grid.
I'm trying to create a contour plot of the Y-Axis values (from matrix A), based on the X-axis values from matrix B.
I've tried meshgrid (based on the x and y ranges for B), however the values in matrix B are not evenly spaced;
Any help would be greatly appreciated!