#! /usr/bin/perl

use strict ;
use warnings ;

my @complaints = (
                   {
                     'complaint_id' => 'a123',
                     'x'            => '45.2',
                     'y'            => '39.7'
                   },
                   {
                     'complaint_id' => 'b456',
                     'x'            => '79.3',
                     'y'            => '42.0'
                   },
                   {
                     'complaint_id' => 'c789',
                     'x'            => '11.9',
                     'y'            => '29.8'
                   },
                   {
                     'complaint_id' => 'd863',
                     'x'            => '95.3',
                     'y'            => '17.2'
                   },
                   {
                     'complaint_id' => 'e635',
                     'x'            => '65.5',
                     'y'            => '33.3'
                   },
                 ) ;

my ( $curr_x, $curr_y ) = ( 36.2, 47.3 ) ;

my @sorted_by_proximity = map { $_->[1]->{'dist'} = $_->[0]; $_->[1] }
                          sort { $a->[0] <=> $b->[0] }
                          map { [ distance( $curr_x, $curr_y,
                                            $_->{'x'}, $_->{'y'}
                                          ), $_
                                ]
                              } @complaints ;

foreach ( @sorted_by_proximity )
{
    print $_->{'complaint_id'} . " is " . $_->{'dist'} . " away.\n" ;
}

sub distance
{
    my ($x1, $y1, $x2, $y2 ) = @_ ;
    return sqrt( abs( $x2 - $x1 )**2 + abs( $y2 - $y1 )**2 ) ;
}

##</code><code>##

#! /usr/bin/perl

use strict ;
use warnings ;

# Define some rules and constants.
my $CLUSTER_RADIUS =  10 ; # Max distance to be included in cluster.
my $CENTER_SKIP    =   5 ; # Distance between centers on cluster scans.
my $CLUSTER_COUNT  =   4 ; # Minimum no. of complaints to make a cluster.
my $START_X        =   0 ;
my $END_X          = 100 ;
my $START_Y        =   0 ;
my $END_Y          = 100 ;

# Load the complaints.
my @complaints = () ;
while ( <DATA> )
{
    next if /^$/ ;
    my $hashref = {} ;
    ( $hashref->{'id'}, $hashref->{'x'}, $hashref->{'y'} ) = split ;
    push @complaints, $hashref ;
}

for ( my $curr_x = $START_X ;
      $curr_x <= $END_X ;
      $curr_x += $CENTER_SKIP )
{
    for ( my $curr_y = $START_Y ;
          $curr_y <= $END_Y ;
          $curr_y += $CENTER_SKIP )
    {
        my @near = map  { $_->[1]->{'dist'} = $_->[0] ; $_->[1] }
                   grep { $_->[0] <= $CLUSTER_RADIUS }
                   map  { [ distance( $curr_x, $curr_y,
                                      $_->{'x'}, $_->{'y'}
                                    ), $_
                          ]
                        } @complaints ;

        my $complaint_count = @near ;
        if ( $complaint_count >= $CLUSTER_COUNT )
        {
            printf "%d complaints near (%d,%d).\n",
            $complaint_count, $curr_x, $curr_y ;
        }
    }
}
exit 0 ;

#-------------------------------------------------------

sub distance
{
    my ($x1, $y1, $x2, $y2 ) = @_ ;
    return sqrt( ( $x2 - $x1 )**2 + ( $y2 - $y1 )**2 ) ;
}

__DATA__
a123 10.1 12.5
b124 12.5 8.6
c125 9.8  10.2
d213 24.6 19.5
e753 35.6 2.2
f854 76.5 46.7
g354 8.7  8.6