Quick answer (because you finally explained a workable example input=>desired output) before closing the question. x = [1 2 3 ; 4 5 6 ; 7 8 9] ; T = size(x,1) ; N = size(x,2) ; R = [x,x(:,1)] ; % copy the first column in last position (to be able to loop seamlessly) … Read more
The watt to dBm conversion follows the following rule: dBW = 10 * log10(P[w]) dBm = 10 * log10(1000 * P[w]) = 10 * log10(P[w]) + 10 * log10(1000) = 10 * log10(P[w]) + 10 * 3 = 10 * log10(P[w]) + 30 = dBW + 30 Hence, the inverse path will be: P[w] = … Read more
surf is a function which displays a quad-mesh, from your output I can only tell that you did not input the data in the right order or shape, it seems to randomly connect the wrong points. In the simplest case (a regular quad-mesh) means that each column of X contains the same value and each … Read more
You could do something like this: A = imread (‘tFKeD.jpg’); C=bwlabel(A); IM2 = imcomplement(C); % // invert the image so that your objects are 1 se = strel(‘diamond’,3); % // Create a morphological object BW2 = imdilate(IM2,se); L=bwlabel(BW2); % // Label your objects E = regionprops(L,’area’); % // Get the respective area Area = cell2mat(struct2cell(E)); … Read more
How about this x = linspace(-10, 10, 1000); fx = ( x > -0.5 ) & ( x < 0.5 ); figure; plot( x, fx );
If A is your matrix and n is your scalar: A = magic(5); n=2; Then to manipulate (e.g. multiply by 1000) the elements of A that are divisible by n, just do: idx = mod(A,n)==0; A(idx) = A(idx)*1000;
Suppose: err = rand(100,1); dt = logspace(0,4,100); ds = logspace(0,3,100); To plot those values on a log-log scale, simply use the loglog command loglog(dt, err) %% Plots error vs dt loglog(ds, err) %% Plots error vs. ds Or, if you only want a logarithmic x-axis use semi-log scale: semilogx(dt, err) semilogx(ds, err) If you want … Read more
It works as though everything on the right hand side happens before anything on the left hand side, effectively doing the “snapshot” thing but with probably better memory efficiency. In reality, it may loop through or it may take a “snapshot”, but due to abstraction you don’t really need to know or care about that. … Read more
Normally, if you are using a neural network, you should not have too different results between different runs on the same training set. So, first of all, check that your algorithm is working correctly using some standard benchmark problems (like iris/wisconsin from UCI repository) Regarding when to stop the training, there are two options: 1. … Read more
You can pass a vector as the first input to plot and a matrix (with a dimension which matches the size of the first vector) as the second input and it will create a plot for each pairing of the first vector and each row/column of the second input. plot(X, Y, ‘o’) This will automatically … Read more