Mahalanobis distance

`d = mahal(Y,X)`

`d = mahal(Y,X)`

computes the Mahalanobis distance (in squared units) of each observation
in `Y`

from the reference sample in matrix `X`

.
If `Y`

is *n*-by-*m*,
where *n* is the number of observations and *m* is
the dimension of the data, `d`

is *n*-by-1. `X`

and `Y`

must
have the same number of columns, but can have different numbers of
rows. `X`

must have more rows than columns.

For observation `I`

, the Mahalanobis distance
is defined by `d(I) = (Y(I,:)-mu)*inv(SIGMA)*(Y(I,:)-mu)'`

,
where `mu`

and `SIGMA`

are the sample
mean and covariance of the data in `X`

. `mahal`

performs
an equivalent, but more efficient, computation.

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