Only 16 days separate us from OSCON and I am still polishing the material for my session ðŸ˜‰ I asked my fellow PerlMonks for feedback on a preliminary version of the presentation’s outline and as usual the comments were really useful. Based on the comments, I decided to reduce to two the number of case studies to be presented instead of the three I originally planned. I believe that in this way, I will have more time to clearly explain the techniques.

By the way, with this post, I will start a series of posts in which I show some of the snippets I will be presenting. Here are the first one:

**Description:**

A common practice in machine learning is to preprocess the data before building a model. One popular preprocessing technique is data normalization. Normalization puts the variables in a restricted range (with a zero mean and 1 standard deviation). This is important to achieve efficient and precise numerical computation.

In this snippet, I present how to do data normalization using the Perl Data Language. The input is a piddle (see comment below for a definition) in which each column represents a variable and each row represent a pattern. The output is a piddle (in which each variable is normalized to have a 0 mean and 1 standard deviation), and the mean and standard deviation of the input piddle.

What are Piddles?

They are a new data structure defined in the Perl Data Language. As indicated in RFC: Getting Started with PDL (the Perl Data Language):

Piddles are numerical arrays stored in column major order (meaning that the fastest varying dimension represent the columns following computational convention rather than the rows as mathematicians prefer). Even though, piddles look like Perl arrays, they are not. Unlike Perl arrays, piddles are stored in consecutive memory locations facilitating the passing of piddles to the C and FORTRAN code that handles the element by element arithmetic. One more thing to note about piddles is that they are referenced with a leading $

**Code:**

#!/usr/bin/perl

use warnings;

use strict;

use PDL;

use PDL::NiceSlice;

# ================================

# normalize

# ( $output_data, $mean_of_input, $stdev_of_input) =

# normalize( $input_data )

#

# processess $input_data so that $output_data

# has 0 mean and 1 stdev

#

# $output_data = ( $input_data – $mean_of_input ) / $stdev_of_input

# ================================

sub normalize {

my ( $input_data ) = @_;

my ( $mean, $stdev, $median, $min, $max, $adev )

= $input_data->xchg(0,1)->statsover();

my $idx = which( $stdev == 0 );

$stdev( $idx ) .= 1e-10;

my ( $number_of_dimensions, $number_of_patterns )

= $input_data->dims();

my $output_data

= ( $input_data – $mean->dummy(1, $number_of_patterns) )

/ $stdev->dummy(1, $number_of_patterns);

return ( $output_data, $mean, $stdev );

}

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