The linear regression model takes models built from some external
package and finds coefficients based on the features and weights. A
model consists of a list of features. The first should be the atom
Intercept plus a value. The following in the list should consist
of a feature (see Features) followed by a weight. An optional third
element may be a list of atomic values. If the result of the feature is
a member of this list the feature's value is treated as 1 else it is 0.
This third argument allows an efficient way to map categorical values
into numeric values. For example, from the F0 prediction model in
lib/f2bf0lr.scm. The first few parameters are
(set! f2b_f0_lr_start
'(
( Intercept 160.584956 )
( Word.Token.EMPH 36.0 )
( pp.tobi_accent 10.081770 (H*) )
( pp.tobi_accent 3.358613 (!H*) )
( pp.tobi_accent 4.144342 (*? X*? H*!H* * L+H* L+!H*) )
( pp.tobi_accent -1.111794 (L*) )
...
)
Note the feature pp.tobi_accent returns an atom, and is hence
tested with the map groups specified as third arguments.
Models may be built from feature data (in the same format as wagon using the ols program distributed with the speech tools library.