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lib/Algorithm/SVMLight.pm  view on Meta::CPAN

which features are having the greatest impact on decision-making.

 my $arrayref = $m->get_linear_weights();

The first element (position 0) of the array will be the threshold
C<b>, and the rest of the elements will be the weights themselves.
Thus from 1 upward, the indices align with SVMLight's internal
indices.

If the model has not yet been trained, or if the kernel type is not
linear, an exception will be thrown.

=item feature_names()

Returns a list of feature names that have been fed to
C<add_instance()> as keys of the C<attribute> parameter, or in a
scalar context the number of such names.

=item num_features()

Returns the number of features known to this model.  Note that if you

lib/Algorithm/SVMLight.xs  view on Meta::CPAN

  double *labels;
} corpus;

double ranking_callback(DOC **docs, double *rankvalue, long i, long j, LEARN_PARM *learn_parm) {
  dSP;
  SV *callback = (SV *) learn_parm->costfunccustom;
  int count;
  double result;

  /* Don't bother checking the type of 'callback' - it could be a CODE
   * reference or a string, and perl will throw its own error otherwise.
   */

  ENTER;
  SAVETMPS;

  PUSHMARK(SP);
  XPUSHs(sv_2mortal(newSVnv(rankvalue[i])));
  XPUSHs(sv_2mortal(newSVnv(rankvalue[j])));
  XPUSHs(sv_2mortal(newSVnv(docs[i]->costfactor)));
  XPUSHs(sv_2mortal(newSVnv(docs[j]->costfactor)));