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package Hailo::Engine::Scored; |
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our $AUTHORITY = 'cpan:AVAR'; |
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3
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$Hailo::Engine::Scored::VERSION = '0.75'; |
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1
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1
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1652
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use v5.10.0; |
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1
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4
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5
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1
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1
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5
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use Moose; |
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2
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1
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7
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6
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1
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1
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6999
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use List::Util qw<sum>; |
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1
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2
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1
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72
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7
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1
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1
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6
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use List::MoreUtils qw<any>; |
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1
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2
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1
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9
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8
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1
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1
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708
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use Time::HiRes qw<gettimeofday tv_interval>; |
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1
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3
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1
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10
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9
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10
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extends 'Hailo::Engine::Default'; |
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12
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after BUILD => sub { |
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13
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my ($self) = @_; |
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14
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my %args = $self->arguments; |
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15
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16
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if (defined $args{iterations} && defined $args{interval}) { |
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die __PACKAGE__.": You can only specify one of 'iterations' and 'interval'\n"; |
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} |
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return; |
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}; |
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22
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sub reply { |
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23
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0
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0
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0
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my $self = shift; |
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24
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0
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0
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my $tokens = shift // []; |
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25
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26
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# see if we recognize any of the input tokens |
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27
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0
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my $token_cache = $self->_resolve_input_tokens($tokens); |
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28
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0
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my @input_token_ids = keys %$token_cache; |
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29
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0
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my @token_counts; |
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30
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31
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# let's select potential pivot tokens from the input |
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32
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0
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0
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if (keys %$token_cache) { |
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33
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# we only want the ones with normal spacing (usually normal words) |
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34
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@token_counts = map { |
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35
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0
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0
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$token_cache->{$_}[0] == 0 ? [$_, $token_cache->{$_}[2]] : () |
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0
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36
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} keys %$token_cache; |
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37
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} |
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38
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39
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0
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my $token_probs = $self->_get_pivot_probabilites(\@token_counts); |
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40
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0
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my @started = gettimeofday(); |
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41
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0
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my $iterations = 0; |
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42
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43
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0
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my $done; |
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44
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0
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my %args = $self->arguments; |
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45
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0
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0
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0
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if (!defined $args{iterations} && !defined $args{interval}) { |
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46
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# construct replies for half a second by default |
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47
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0
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$args{interval} = 0.5; |
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48
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} |
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49
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50
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0
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0
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if (defined $args{iterations}) { |
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51
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$done = sub { |
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52
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0
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0
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0
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return 1 if $iterations == $args{iterations}; |
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53
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0
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}; |
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54
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} |
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55
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else { |
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56
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$done = sub { |
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57
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0
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0
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my $elapsed = tv_interval(\@started, [gettimeofday]); |
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58
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0
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0
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return 1 if $elapsed >= $args{interval}; |
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59
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0
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}; |
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60
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} |
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61
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62
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0
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my (%link_cache, %expr_cache, $best_score, $best_reply); |
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63
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0
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while (1) { |
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64
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0
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$iterations++; |
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65
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0
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my $reply = $self->_generate_reply($token_probs, \%expr_cache); |
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66
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0
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0
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return if !defined $reply; # we don't know any expressions yet |
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67
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68
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0
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my $score = $self->_evaluate_reply(\@input_token_ids, $reply, \%link_cache); |
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69
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70
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0
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0
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0
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if (defined $best_reply && $self->_too_similar(\@input_token_ids, $reply)) { |
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71
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0
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0
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last if $done->(); |
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72
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0
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next; |
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73
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} |
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74
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75
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0
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0
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0
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if (!defined $best_score || $score > $best_score) { |
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76
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0
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$best_score = $score; |
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77
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0
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$best_reply = $reply; |
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78
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} |
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79
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80
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0
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0
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last if $done->(); |
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81
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} |
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82
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83
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# translate token ids to token spacing/text |
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84
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my @output = map { |
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85
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0
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0
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$token_cache->{$_} // ($token_cache->{$_} = $self->_token_info($_)) |
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0
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86
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} @$best_reply; |
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87
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0
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return \@output; |
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88
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} |
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89
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90
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# Calculate the probability we wish to pick each token as the pivot. |
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91
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# This uses -log2(p) as a method for inverting token probability, |
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92
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# ensuring that our rarer tokens are picked more often. |
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93
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sub _get_pivot_probabilites { |
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94
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0
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0
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my ($self, $token_counts) = @_; |
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95
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96
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0
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0
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return [] if !@$token_counts; |
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97
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0
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0
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return [[$token_counts->[0], 1]] if @$token_counts == 1; |
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98
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99
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# calculate the (non-normalized) probability we want each to occur |
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100
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0
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my $count_sum = sum(map { $_->[1] } @$token_counts); |
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0
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101
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0
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my $p = []; |
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102
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0
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my $p_sum = 0; |
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103
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0
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for my $token_count (map { $_->[1] } @$token_counts) { |
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0
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104
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0
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my $token_p = -log(($token_count/$count_sum))/log(2); |
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105
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0
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push @$p, $token_p; |
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106
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0
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$p_sum += $token_p; |
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107
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} |
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108
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109
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# normalize the probabilities |
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110
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my @probs = map { |
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111
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0
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[$token_counts->[$_], $p->[$_] / $p_sum]; |
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112
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0
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} 0..$#{ $token_counts }; |
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0
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113
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114
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0
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return \@probs; |
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115
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} |
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116
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117
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sub _generate_reply { |
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118
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0
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0
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my ($self, $token_probs, $expr_cache) = @_; |
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119
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120
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0
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my ($pivot_expr_id, @token_ids) = @_; |
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121
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0
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0
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if (@$token_probs) { |
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122
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0
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my $pivot_token_id = $self->_choose_pivot($token_probs); |
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123
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0
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($pivot_expr_id, @token_ids) = $self->_random_expr($pivot_token_id); |
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124
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} |
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125
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else { |
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126
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0
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($pivot_expr_id, @token_ids) = $self->_random_expr(); |
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127
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0
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0
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return if !defined $pivot_expr_id; # no expressions in the database |
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128
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} |
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129
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130
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# construct the end of the reply |
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131
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0
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$self->_construct_reply('next', $pivot_expr_id, \@token_ids, $expr_cache); |
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132
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133
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# construct the beginning of the reply |
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134
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0
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$self->_construct_reply('prev', $pivot_expr_id, \@token_ids, $expr_cache); |
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135
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136
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0
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return \@token_ids; |
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137
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} |
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138
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139
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sub _evaluate_reply { |
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140
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0
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0
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my ($self, $input_token_ids, $reply_token_ids, $cache) = @_; |
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141
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0
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my $order = $self->order; |
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142
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0
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my $score = 0; |
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143
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144
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0
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for my $idx (0 .. $#{ $reply_token_ids } - $order) { |
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0
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145
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0
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my $next_token_id = $reply_token_ids->[$idx]; |
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146
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147
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0
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0
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0
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if (any { $_ == $next_token_id } @$input_token_ids) { |
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0
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148
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0
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my @expr = @$reply_token_ids[$idx .. $idx+$order-1]; |
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149
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0
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my $key = join('_', @expr)."-$next_token_id"; |
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150
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151
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0
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0
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if (!defined $cache->{$key}) { |
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152
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0
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$cache->{$key} = $self->_expr_token_probability('next', \@expr, $next_token_id); |
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153
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} |
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154
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0
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0
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if ($cache->{$key} > 0) { |
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155
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0
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$score -= log($cache->{$key})/log(2); |
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156
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} |
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157
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} |
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158
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} |
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159
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160
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0
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for my $idx (0 .. $#{ $reply_token_ids } - $order) { |
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0
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161
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0
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my $prev_token_id = $reply_token_ids->[$idx]; |
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162
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163
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0
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0
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0
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if (any { $_ == $prev_token_id } @$input_token_ids) { |
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0
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164
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0
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my @expr = @$reply_token_ids[$idx+1 .. $idx+$order]; |
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165
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0
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my $key = "$prev_token_id-".join('_', @expr); |
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166
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167
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0
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0
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if (!defined $cache->{$key}) { |
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168
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0
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$cache->{$key} = $self->_expr_token_probability('prev', \@expr, $prev_token_id); |
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169
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} |
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170
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0
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0
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if ($cache->{$key} > 0) { |
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171
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0
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$score -= log($cache->{$key})/log(2); |
|
172
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} |
|
173
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} |
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174
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} |
|
175
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176
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# Prefer shorter replies. This behavior is present but not |
|
177
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# documented in recent MegaHAL. |
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178
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0
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my $score_divider = 1; |
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179
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0
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0
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if (@$reply_token_ids >= 8) { |
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0
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180
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0
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|
$score /= sqrt(@$reply_token_ids - 1); |
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181
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} |
|
182
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|
elsif (@$reply_token_ids >= 16) { |
|
183
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0
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|
$score /= @$reply_token_ids; |
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184
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} |
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185
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186
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0
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return $score; |
|
187
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} |
|
188
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189
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sub _expr_token_probability { |
|
190
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0
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0
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my ($self, $pos, $expr, $token_id) = @_; |
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191
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0
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my $order = $self->order; |
|
192
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193
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0
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my $expr_id = $self->_expr_id_add($expr); |
|
194
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195
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0
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$self->{"_sth_${pos}_token_count"}->execute($expr_id, $token_id); |
|
196
|
0
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my $expr2token = $self->{"_sth_${pos}_token_count"}->fetchrow_array(); |
|
197
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0
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0
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return 0 if !$expr2token; |
|
198
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199
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0
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$self->{"_sth_${pos}_token_links"}->execute($expr_id); |
|
200
|
0
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my $expr2all = $self->{"_sth_${pos}_token_links"}->fetchrow_array(); |
|
201
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0
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return $expr2token / $expr2all; |
|
202
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} |
|
203
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204
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sub _choose_pivot { |
|
205
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0
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0
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|
my ($self, $token_probs) = @_; |
|
206
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207
|
0
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my $random = rand; |
|
208
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0
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|
my $p = 0; |
|
209
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0
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for my $token (@$token_probs) { |
|
210
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0
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|
$p += $token->[1]; |
|
211
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0
|
0
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|
|
return $token->[0][0] if $p > $random; |
|
212
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|
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|
} |
|
213
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214
|
0
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|
return; |
|
215
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|
} |
|
216
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217
|
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|
sub _too_similar { |
|
218
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0
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0
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|
|
my ($self, $input_token_ids, $reply_token_ids) = @_; |
|
219
|
|
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|
220
|
0
|
|
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|
|
my %input_token_ids = map { +$_ => 1 } @$input_token_ids; |
|
|
0
|
|
|
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|
|
|
|
221
|
|
|
|
|
|
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|
|
222
|
0
|
|
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|
|
|
for my $reply_token_id (@$reply_token_ids) { |
|
223
|
0
|
0
|
|
|
|
|
return if !$input_token_ids{$reply_token_id}; |
|
224
|
|
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|
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|
|
} |
|
225
|
0
|
|
|
|
|
|
return 1; |
|
226
|
|
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|
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|
|
} |
|
227
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|
228
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|
|
__PACKAGE__->meta->make_immutable; |
|
229
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|
230
|
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|
|
=encoding utf8 |
|
231
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|
232
|
|
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|
|
|
=head1 NAME |
|
233
|
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|
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|
|
234
|
|
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|
|
|
Hailo::Engine::Scored - MegaHAL-style reply scoring for L<Hailo|Hailo> |
|
235
|
|
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|
236
|
|
|
|
|
|
|
=head1 DESCRIPTION |
|
237
|
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|
|
238
|
|
|
|
|
|
|
This backend implements the logic of replying to and learning from |
|
239
|
|
|
|
|
|
|
input using the resources given to the L<engine |
|
240
|
|
|
|
|
|
|
roles|Hailo::Role::Engine>. It is inherits from |
|
241
|
|
|
|
|
|
|
L<Hailo::Engine::Default|Hailo::Engine::Default> and only overrides its |
|
242
|
|
|
|
|
|
|
C<reply> method. |
|
243
|
|
|
|
|
|
|
|
|
244
|
|
|
|
|
|
|
It generates multiple replies and applies a scoring algorithm to them, then |
|
245
|
|
|
|
|
|
|
returns the best one, similar to MegaHAL. |
|
246
|
|
|
|
|
|
|
|
|
247
|
|
|
|
|
|
|
=head1 ATTRIBUTES |
|
248
|
|
|
|
|
|
|
|
|
249
|
|
|
|
|
|
|
=head2 C<engine_args> |
|
250
|
|
|
|
|
|
|
|
|
251
|
|
|
|
|
|
|
This is a hash reference which can have the following keys: |
|
252
|
|
|
|
|
|
|
|
|
253
|
|
|
|
|
|
|
=head3 C<iterations> |
|
254
|
|
|
|
|
|
|
|
|
255
|
|
|
|
|
|
|
The number of replies to generate before returning the best one. |
|
256
|
|
|
|
|
|
|
|
|
257
|
|
|
|
|
|
|
=head3 C<interval> |
|
258
|
|
|
|
|
|
|
|
|
259
|
|
|
|
|
|
|
The time (in seconds) to spend on generating replies before returning the |
|
260
|
|
|
|
|
|
|
best one. |
|
261
|
|
|
|
|
|
|
|
|
262
|
|
|
|
|
|
|
You can not specify both C<iterations> and C<interval> at the same time. If |
|
263
|
|
|
|
|
|
|
neither is specified, a default C<interval> of 0.5 seconds will be used. |
|
264
|
|
|
|
|
|
|
|
|
265
|
|
|
|
|
|
|
=head1 AUTHORS |
|
266
|
|
|
|
|
|
|
|
|
267
|
|
|
|
|
|
|
Hinrik E<Ouml>rn SigurE<eth>sson, hinrik.sig@gmail.com |
|
268
|
|
|
|
|
|
|
|
|
269
|
|
|
|
|
|
|
This module was based on code from Peter Teichman's Cobe project. |
|
270
|
|
|
|
|
|
|
|
|
271
|
|
|
|
|
|
|
=head1 LICENSE AND COPYRIGHT |
|
272
|
|
|
|
|
|
|
|
|
273
|
|
|
|
|
|
|
Copyright 2010 Hinrik E<Ouml>rn SigurE<eth>sson and |
|
274
|
|
|
|
|
|
|
E<AElig>var ArnfjE<ouml>rE<eth> Bjarmason <avar@cpan.org> |
|
275
|
|
|
|
|
|
|
|
|
276
|
|
|
|
|
|
|
This program is free software, you can redistribute it and/or modify |
|
277
|
|
|
|
|
|
|
it under the same terms as Perl itself. |
|
278
|
|
|
|
|
|
|
|
|
279
|
|
|
|
|
|
|
=cut |