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lib/Algorithm/CurveFit/Simple.pm  view on Meta::CPAN

our %STATS_H;  # side-products of fit() stored here for profiling purposes

BEGIN {
    require Exporter;
    our $VERSION = '1.03';
    our @ISA = qw(Exporter);
    our @EXPORT_OK = qw(fit %STATS_H);
}

# fit() - only public function for this distribution
# Given at least parameter "xy", generate a best-fit curve within a time limit.
# Output: max deviation, avg deviation, implementation source string (perl or C, for now).
# Optional parameters and their defaults:
#    terms       => 3      # number of terms in formula, max is 10
#    time_limit  => 3      # number of seconds to try for better fit
#    inv         => 1      # invert sense of curve-fit, from x->y to y->x
#    impl_lang   => 'perl' # programming language used for output implementation: perl, c
#    impl_name   => 'x2y'  # name given to output implementation function
sub fit {
    my %p = @_;

lib/Algorithm/CurveFit/Simple.pm  view on Meta::CPAN

=item * It allows for a time limit on the curve-fit instead of an iteration count,

=item * It implements the formula as source code (or as a perl coderef, if you want to use the formula immediately in your program).

=back

Additionally it returns a maximum deviation and average deviation of the formula vs the xydata, which is more useful (to me, at least) than L<Algorithm::CurveFit>'s square residual output.  Closer to 1.0 indicates a better fit.  Play with C<terms =E<...

=head1 SUBROUTINES

There is only one public subroutine, C<fit()>.  It B<must> be given either C<xydata> or C<xdata> and C<ydata> parameters.  All other paramters are optional.

It returns three values: A maximum deviation, the average deviation and the formula implementation.

=head2 Options

=over 4

=item C<fit(xdata =E<gt> \@xdata, ydata =E<gt> \@ydata)>

The data points the formula will fit.  Same as L<Algorithm::CurveFit> parameters of the same name.