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 # 
 # @data
 # sunny,85,85,FALSE,no
 # sunny,80,90,TRUE,no
 # overcast,83,86,FALSE,yes
 # rainy,70,96,FALSE,yes
 # rainy,68,80,FALSE,yes
 # rainy,65,70,TRUE,no
 # overcast,64,65,TRUE,yes
 # sunny,72,95,FALSE,no
 # sunny,69,70,FALSE,yes
 # rainy,75,80,FALSE,yes
 # sunny,75,70,TRUE,yes
 # overcast,72,90,TRUE,yes
 # overcast,81,75,FALSE,yes
 # rainy,71,91,TRUE,no
 
 $nb->set_real('temperature', 'humidity');
 
 $nb->add_instance(attributes=>{outlook=>'sunny',temperature=>85,humidity=>85,windy=>'FALSE'},label=>'play=no');
 $nb->add_instance(attributes=>{outlook=>'sunny',temperature=>80,humidity=>90,windy=>'TRUE'},label=>'play=no');
 $nb->add_instance(attributes=>{outlook=>'overcast',temperature=>83,humidity=>86,windy=>'FALSE'},label=>'play=yes');
 $nb->add_instance(attributes=>{outlook=>'rainy',temperature=>70,humidity=>96,windy=>'FALSE'},label=>'play=yes');
 $nb->add_instance(attributes=>{outlook=>'rainy',temperature=>68,humidity=>80,windy=>'FALSE'},label=>'play=yes');
 $nb->add_instance(attributes=>{outlook=>'rainy',temperature=>65,humidity=>70,windy=>'TRUE'},label=>'play=no');
 $nb->add_instance(attributes=>{outlook=>'overcast',temperature=>64,humidity=>65,windy=>'TRUE'},label=>'play=yes');
 $nb->add_instance(attributes=>{outlook=>'sunny',temperature=>72,humidity=>95,windy=>'FALSE'},label=>'play=no');
 $nb->add_instance(attributes=>{outlook=>'sunny',temperature=>69,humidity=>70,windy=>'FALSE'},label=>'play=yes');
 $nb->add_instance(attributes=>{outlook=>'rainy',temperature=>75,humidity=>80,windy=>'FALSE'},label=>'play=yes');
 $nb->add_instance(attributes=>{outlook=>'sunny',temperature=>75,humidity=>70,windy=>'TRUE'},label=>'play=yes');
 $nb->add_instance(attributes=>{outlook=>'overcast',temperature=>72,humidity=>90,windy=>'TRUE'},label=>'play=yes');
 $nb->add_instance(attributes=>{outlook=>'overcast',temperature=>81,humidity=>75,windy=>'FALSE'},label=>'play=yes');
 $nb->add_instance(attributes=>{outlook=>'rainy',temperature=>71,humidity=>91,windy=>'TRUE'},label=>'play=no');
 
 $nb->train;
 
 my $printedmodel =  "Model:\n" . $nb->print_model;
 my $p = $nb->predict(attributes=>{outlook=>'sunny',temperature=>66,humidity=>90,windy=>'TRUE'});

 YAML::DumpFile('file', $p);
 die unless (abs($p->{'play=no'}  - 0.792) < 0.001);
 die unless(abs($p->{'play=yes'} - 0.208) < 0.001);

=head1 HISTORY

L<Algorithm::NaiveBayes> by Ken Williams was not what I needed so I
wrote this one.  L<Algorithm::NaiveBayes> is oriented towards text
categorization, it includes smoothing, and log probabilities.  This
module is a generic, basic Naive Bayes algorithm.

=head1 THANKS

I would like to thank Daniel Bohmer for documentation corrections,
Yung-chung Lin (cpan:xern) for the implementation of the Gaussian model
for continuous variables, and the following people for bug reports, support,
and comments (in no particular order):

Michael Stevens, Tom Dyson, Dan Von Kohorn, Craig Talbert,
Andrew Brian Clegg,

and CPAN-testers, including: Andreas Koenig, Alexandr Ciornii, jlatour,
Jost.Krieger, tvmaly, Matthew Musgrove, Michael Stevens, Nigel Horne,
Graham Crookham, David Cantrell (dcantrell).

=head1 AUTHOR

Copyright 2003-21 Vlado Keselj L<https://web.cs.dal.ca/~vlado>.
In 2004 Yung-chung Lin provided implementation of the Gaussian model for
continous variables.

This script is provided "as is" without expressed or implied warranty.
This is free software; you can redistribute it and/or modify it under
the same terms as Perl itself.

The module is available on CPAN (L<https://metacpan.org/author/VLADO>), and
L<https://web.cs.dal.ca/~vlado/srcperl/>.  The latter site is
updated more frequently.

=head1 SEE ALSO

L<Algorithm::NaiveBayes>, L<perl>.

=cut