de.jungblut.nlp

Class MarkovChain

java.lang.Object

de.jungblut.nlp.MarkovChain

public final class MarkovChain
extends Object

Markov chain, that can "learn" the state transition probabilities by a given input and returns the probability for a given sequence of states.

Author:

thomas.jungblut

Method Summary

Modifier and Type	Method and Description
double	averageTransitionProbability(int[] sequence)
int[]	completeStateSequence(com.google.common.base.Optional<Random> optionalRandom, int[] stateSequence, int... unsuppliedStateIndices) Completes the given state sequence by picking the best next state on the transition probabilities (so a transition with a high probability is picked more often).
static MarkovChain	create(int numStates) Creates a new markov chain with the supplied number of states.
static MarkovChain	create(int numStates, de.jungblut.math.DoubleMatrix mat) Creates a new markov chain with the supplied number of states and its predefined transition matrix.
int	getNumStates()
double	getProbabilityForSequence(int[] stateSequence) Calculates the probability that the given sequence occurs.
de.jungblut.math.DoubleMatrix	getTransitionProbabilities()
de.jungblut.math.DoubleVector	getTransitionProbabilities(int[] stateSequence)
void	train(java.util.stream.Stream<int[]> states) Trains the transition probabilities of the markov chain.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

train

public void train(java.util.stream.Stream<int[]> states)

Trains the transition probabilities of the markov chain.
Each list element contains a set of states. The values of the element-states are nominal and should be lower than the number of provided states (each nominal will be a index, so it's from 0 to numStates-1). So each element can be arbitrary sized, because in markov chains we are considering the transition between two states, thus it will measure the occurrence of each following two state pairs. e.G. [ 1, 2, 3, 4 ] will measure the probabilities of [1,2],[2,3],[3,4].

getProbabilityForSequence

public double getProbabilityForSequence(int[] stateSequence)

Calculates the probability that the given sequence occurs.

Returns:

value between 0d and 1d, where 1d is very likely that the sequence is happening.

averageTransitionProbability

public double averageTransitionProbability(int[] sequence)

Returns:

the average transition probability of the given sequence.

getTransitionProbabilities

public de.jungblut.math.DoubleVector getTransitionProbabilities(int[] stateSequence)

Returns:

the transition probabilities for the states.

completeStateSequence

public int[] completeStateSequence(com.google.common.base.Optional<Random> optionalRandom,
                                   int[] stateSequence,
                                   int... unsuppliedStateIndices)

Completes the given state sequence by picking the best next state on the transition probabilities (so a transition with a high probability is picked more often). If the optional random is not provided, it picks the next state by the highest transition probability between the states (it predicts the next state based on the previous).

getTransitionProbabilities

public de.jungblut.math.DoubleMatrix getTransitionProbabilities()

Returns:

the state transition probability matrix to export/serialize.

getNumStates

public int getNumStates()

Returns:

how many states were defined?

create

public static MarkovChain create(int numStates)

Creates a new markov chain with the supplied number of states.

create

public static MarkovChain create(int numStates,
                                 de.jungblut.math.DoubleMatrix mat)

Creates a new markov chain with the supplied number of states and its predefined transition matrix.