FM / SQLite

/usr/lib64/perl5/vendor_perl/DBD/SQLite/

/usr/lib64/perl5/vendor_perl/DBD/SQLite

Name	Size	Mode	Actions
VirtualTable/	-	0755	rm
Constants.pm	16564	0644	edit dl rm
Cookbook.pod	3750	0644	edit dl rm
Fulltext_search.pod	19182	0644	edit dl rm
VirtualTable.pm	22361	0644	edit dl rm

Edit: /usr/lib64/perl5/vendor_perl/DBD/SQLite/Cookbook.pod (3750B)

=head1 NAME

DBD::SQLite::Cookbook - The DBD::SQLite Cookbook

=head1 DESCRIPTION

This is the L<DBD::SQLite> cookbook.

It is intended to provide a place to keep a variety of functions and
formals for use in callback APIs in L<DBD::SQLite>.

=head1 AGGREGATE FUNCTIONS

=head2 Variance

This is a simple aggregate function which returns a variance. It is
adapted from an example implementation in pysqlite.

package variance;
  
  sub new { bless [], shift; }
  
  sub step {
      my ( $self, $value ) = @_;
  
      push @$self, $value;
  }
  
  sub finalize {
      my $self = $_[0];
  
      my $n = @$self;
  
      # Variance is NULL unless there is more than one row
      return undef unless $n || $n == 1;
  
      my $mu = 0;
      foreach my $v ( @$self ) {
          $mu += $v;
      }
      $mu /= $n;
  
      my $sigma = 0;
      foreach my $v ( @$self ) {
          $sigma += ($v - $mu)**2;
      }
      $sigma = $sigma / ($n - 1);
  
      return $sigma;
  }
  
  # NOTE: If you use an older DBI (< 1.608),
  # use $dbh->func(..., "create_aggregate") instead.
  $dbh->sqlite_create_aggregate( "variance", 1, 'variance' );

The function can then be used as:

SELECT group_name, variance(score)
  FROM results
  GROUP BY group_name;

=head2 Variance (Memory Efficient)

A more efficient variance function, optimized for memory usage at the
expense of precision:

package variance2;
  
  sub new { bless {sum => 0, count=>0, hash=> {} }, shift; }
  
  sub step {
      my ( $self, $value ) = @_;
      my $hash = $self->{hash};
  
      # by truncating and hashing, we can comsume many more data points
      $value = int($value); # change depending on need for precision
                            # use sprintf for arbitrary fp precision
      if (exists $hash->{$value}) {
          $hash->{$value}++;
      } else {
          $hash->{$value} = 1;
      }
      $self->{sum} += $value;
      $self->{count}++;
  }
  
  sub finalize {
      my $self = $_[0];
  
      # Variance is NULL unless there is more than one row
      return undef unless $self->{count} > 1;
  
      # calculate avg
      my $mu = $self->{sum} / $self->{count};
  
      my $sigma = 0;
      while (my ($h, $v) = each %{$self->{hash}}) {
          $sigma += (($h - $mu)**2) * $v;
      }
      $sigma = $sigma / ($self->{count} - 1);
  
      return $sigma;
  }

The function can then be used as:

SELECT group_name, variance2(score)
  FROM results
  GROUP BY group_name;

=head2 Variance (Highly Scalable)

A third variable implementation, designed for arbitrarily large data sets:

package variance3;
  
  sub new { bless {mu=>0, count=>0, S=>0}, shift; }
  
  sub step {
      my ( $self, $value ) = @_;
      $self->{count}++;
      my $delta = $value - $self->{mu};
      $self->{mu} += $delta/$self->{count};
      $self->{S} += $delta*($value - $self->{mu});
  }
  
  sub finalize {
      my $self = $_[0];
      return $self->{S} / ($self->{count} - 1);
  }

The function can then be used as:

SELECT group_name, variance3(score)
  FROM results
  GROUP BY group_name;

=head1 SUPPORT

Bugs should be reported via the CPAN bug tracker at

L<http://rt.cpan.org/NoAuth/ReportBug.html?Queue=DBD-SQLite>

=head1 TO DO

=over

=item *

Add more and varied cookbook recipes, until we have enough to
turn them into a separate CPAN distribution.

=item *

Create a series of tests scripts that validate the cookbook recipes.

=back

=head1 AUTHOR

Adam Kennedy E<lt>adamk@cpan.orgE<gt>

=head1 COPYRIGHT

This program is free software; you can redistribute
it and/or modify it under the same terms as Perl itself.

The full text of the license can be found in the
LICENSE file included with this module.

cmd: