a point y and k
 points xi, then there is a probabilistic interpretation for softMin.  If
 we think of the k points representing equal variant gaussians each with
 mean xi and std sigma, then the softMin returns the relative probability
 of y being generated by each gaussian.

 USAGE
  M = softMin( D, sigma )

 INPUTS
  D       - NxK dissimilarity matrix
  sigma   - controls 'softness' of softMin

 OUTPUTS
  M       - the softMin (indexes into D)

 EXAMPLE - 1
  C = [0 0; 1 0; 0 1; 1 1]; x=[.7,.3; .1 .2];
 