I am making with an implementation of K-means clustering in Julia.
Figure out, and implement a modification of k-means that alternatively measure similarity by the angle between vectors.
So I assumed that one could use Cosine Similarity for this, I have made the code work with regular K-means by calculating th squared Euclidian Distance, by this:
Distances[:,i] = sum((X.-C[[i],:]).^2, dims=2) # Where C is center, Distances are added using the i-th center
I tried to do this by using cosine similarity such as this:
Distances[:, i] = sum(1 .- ((X*C[[i], :]).^2 /(sum(X.^2, dims=2).*(C[[i],:]'*C[[i],:]))))
But this seems to not be working.
Alternatively I tried:
clust_center = C[[i], :] curr_dist = 0 for currx = 1:size(X) curr_dist = curr_dist+ evaluate(CosineDist(), X[currx, :], clust_center) end Distances[:,i] = curr_dist
But then I get the error:
ArgumentError: indexed assignment with a single value to many locations is not supported; perhaps use broadcasting `.=` instead?
Have I misunderstood the question or am I implementing it wrong?