Classic (SVD-based) Multidimensional Scaling, as proposed in:
Torgerson, W.S. Multidimensional scaling: I. Theory and method. Psychometrika 17, 401–419 (1952).
Thanks to Francis Song, from whom this implementation has borrowed. Source: http://www.nervouscomputer.com/hfs/cmdscale-in-python/
- class evomap.mapping._cmds.CMDS(n_dims=2)[source]#
- static _cmdscale(D, n_dims)[source]#
Classical multidimensional scaling (MDS)
D ((n, n) array) – Symmetric distance matrix.
Y ((n, p) array) – Configuration matrix. Each column represents a dimension. Only the p dimensions corresponding to positive eigenvalues of B are returned. Note that each dimension is only determined up to an overall sign, corresponding to a reflection.
e ((n,) array) – Eigenvalues of B.