 read_basis - get basis vectors, typically eigenvectors, from file
              whose format is also used for covariance matrices.
 write_basis - write basis vectors to a file.

 one_squared_euclid_dist - calculate the euclidean distances of one
                       unknown feature vectors to many known ones
 kl_premult - does necessary premultiplication ahead of the KL_transform
              call. Gives efficiency.
 kl_transform - calculate the KL transform of 32x32 binary images

 kl_transform_mis - takes an mis structure of spatially normalized
                    (32 X 32) characters and computes a kl-feature
                    for each one.
 readmedianfile - read the vectors that are the median of
                  of features for each class
 writemedianfile - write a median file as above. The file generally
                   stores L text vectors each of N elements.
 readpatstreefile - read features, classes, and their NN tree from a
                    file that allows for fast binary block io.
 writepatstreefile - write features, classes, and their NN tree to a
                     file for fast binary io.
 treepnnhypscons - PNN classifier with tree indexed prototypes. Returns
 	          hypotheses and confidences.
 pnn_hypscons - returns the value the maximum normalized PNN activation
                and its position.
 pnn_normedacts - returns the normalized PNN activations. ie. the values
                  divided by their sum so that they add to 1.0;
 pnn_fastacts - returns the unweighted unnormalized PNN activations. ie.
                the raw exp(-d squared / 2 sigma squared) values
