d = bhatt(d,z)
Bhattacharyya-weighted distance for vector of distances, d, and pair-wise prior products, z.
d = dot_normed(A,B)
Return the normed dot-product distance between two matrices
Second dimension (num columns) of A and B must be the same
p,q,D = dtw(M)
Use dynamic programming to find a min-cost path through matrix M.
Return state sequence in p,q, and cost matrix in D
Ported from Matlab version by Dan Ellis, GPL v2
d = euc(A,B)
Return the Euclidean distance between two matrices.
Second dimension (num columns) of A and B must be the same.
d = euc2(A,B) , a faster implementation of Euclidean distance
Return the Euclidean distance between two matrices.
Second dimension (num columns) of A and B must be the same.
d = euc_normed(A,B)
Return the normed Euclidean distance between two matrices
Second dimension (num columns) of A and B must be the same
P = mds(D,[n, tol])
Multidimensional scaling of distance matrix D.
inputs:
D - a distance matrix (similarity=0, dissimilarity=1)
must be symmetric, positive, semidefinite,
i.e. all values >=0 and D[i,j] == D[j,i]
n - dimensionality of result [default (automatic)]
tol - tolerance [0-1] of auto selected dimensions [0.9]
outputs:
P - a matrix of points in n dimensions
s - Kruskal stress of solution