http://www.perlmonks.org?node_id=935483


in reply to Naive Bayes Classifier Using Laplacian Smoothing

Let me know what you guys think.

The first thing that leapt of the page for me was:

sub size(){
    # setSize()
    # Returns the number of unique elements in bag.
    my $self = shift;
    my %dictionary = %{$self->{DICTIONARY}};
    # remove empty string key created by concat operation
    #delete  $self->{DICTIONARY}{''};
    
    return scalar keys (%dictionary);
    
}

[download]

Copying an entire hash from a reference into a local hash just to return how many keys it has is insanely wasteful. Especially as you are calling this over and over again in a sort comparator.

Coded as:

sub size {
    # setSize()
    # Returns the number of unique elements in bag.
    return scalar keys %{ shift() };   
}

[download]

Will speed up your sort by orders of magnitude.

Also, you should not be using -- and would be getting a messages telling you so if you were using strict & warnings -- an empty prototype sub size() { on a subroutine that takes parameters.

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Re^2: Naive Bayes Classifier Using Laplacian Smoothing
by talwyn (Monk) on Nov 02, 2011 at 21:02 UTC

    As I said this is a rough see-how /if it works implementation. That said. I do appreciate the input I was having lots of trouble with references. Making a 'real' hash was only thing that was working for me.

    I'll see if I can get your revision to work for me. I did not notice any hit at N < = 200 words in the dictionary so its probably something I'll run into when I implement persistence and actually save the learned data to become the next round of training data.

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In Section Cool Uses for Perl