Algorithm::MarkovChain - Object oriented Markov chain generator
use Algorithm::MarkovChain; my $chain = Algorithm::MarkovChain::->new(); # learn about things from @symbols $chain->seed(symbols => \@symbols, longest => 6); # attempt to tell me something about the sky my @newness = $chain->spew(length => 20, complete => [ qw( the sky is ) ]);
Algorithm::MarkovChain is an implementation of the Markov Chain algorithm within an object container.
It is implemented as a base class, Algorithm::MarkovChain::Base
, with storage implementations of a hash (Algorithm::MarkovChain
), and an fairly memory efficent implementation using glib
(Algorithm::MarkovChain::GHash
). DBI and MLDBM-friendly versions are planned.
Deriving alternate representations is intended to be straightforward.
Creates a new instance of the Algorithm::MarkovChain class.
Takes one optional parameter: recover_symbols
recover_symbols
has meaning if your symbols differ from their true values when stringifyed. With this option enabled steps are taken to ensure that the original values for symbols are returned by the spew method.
Seeds the markov chains from an example symbol stream.
Takes two parameters, one required symbols
, one optional longest
symbols
presents the symbols to seed from
longest
sets an upper limit on the longest chain to construct. (defaults to 4)
Uses the constructed chains to produce symbol streams
Takes four optional parameters complete
, length
, longest_subchain
, force_length
, stop_at_terminal
and strict_start
complete
provides a starting point for the generation of output. Note: the algorithm will discard elements of this list if it does not find a starting chain that matches it, this is infinite-loop avoidance.
length
specifies the minimum number of symbols desired (default is 30)
stop_at_terminal
directs the spew to stop chaining at the first terminal point reached
force_length
ensures you get exactly length
symbols returned (note this overrides the behaviour of stop_at_terminal
)
strict_start
makes the spew operation always take a known start state rather than selecting a sequence at random
Increments the seeness of a symbol following a sequence.
Recompute the probabilities for a branch of the tree. Called towards the end of the seed operation for 'dirty' sequences.
Returns possible next symbols and probablities as a hash.
I need to explain Markov Chains, and flesh out the examples some more.
Just email me <richardc@unixbeard.net> and I'll hit it with hammers...
Hopefully not, though if they probably arise from my not understanding Markov chaining as well as I thought I did when coding commenced.
That or they're jst stupid mistakes :)
Richard Clamp <richardc@unixbeard.net>
perl(1).