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Markov 0.4.0

==================================Python Hidden Markov Model Library==================================This library is a pure Python implementation of HiddenMarkov Models (HMMs). The project structure is quitesimple::Help on module Markov:NAME Markov - Library to implement hidden Markov ModelsFILE Markov.pyCLASSES __builtin__.object BayesianModel HMM Distribution PoissonDistribution Probabilityclass BayesianModel(__builtin__.object) | Represents a Bayesian probability model | | Methods defined here: | | MaximumLikelihoodOutcome(self, PriorProbs=None) | Returns the maximum likelihood outcome given PriorProbs | | MaximumLikelihoodState(self, Observations=None) | Returns the maximum likelihood of the internal state. If Observations | is None, defaults to the maximum likelihood of the Prior | | Outcomes(self) | Returns an iterator over the possible outcomes | | PriorProbs(self, Observations, PriorDist=None) | Returns a Distribution representing the probabilities of the prior | states, given a probability Distribution of Observations | | States(self) | Returns an iterator over the possible states | | __call__(self, PriorProbs=None) | Returns a Distribution representing the probabilities of the outcomes | given a particular distribution of the priors, which defaults to | self.Prior | | __iadd__(self, Model2) | Updates the BayesianModel with the data in another BayesianModel | | __init__(self, Prior, Conditionals) | Prior is a Distribution. Conditionals is a dictionary mapping | each state in Prior to a Distribution | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined)class Distribution(__builtin__.object) | Represents a probability distribution over a set of categories | | Methods defined here: | | MaximumLikelihoodState(self) | Returns the state with the greatest likelihood | | Sample(self) | Picks a random sample from the distribution | | States(self) | Yields the Distribution's states | | Update(self, categories) | Updates each category in the probability distiribution, according to | a dictionary of numerator and denominator values | | __call__(self, item) | Gives the probability of item | | __iadd__(self, Dist2) | Updates the Distribution given another Distribution with the same states | | __init__(self, categories, k=0) | The distribution may be initialised from a list of categories or a | dictionary of category frequencies. In the latter case, Laplacian | smoothing may be used | | __mul__(self, scalar) | Returns the probability of each item, multiplied by a scalar | | copy(self) | Returns a copy of the Distribution | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined)class HMM(BayesianModel) | Represents a Hidden Markov Model | | Method resolution order: | HMM | BayesianModel | __builtin__.object | | Methods defined here: | | Analyse(self, Sequence, MaximumLikelihood=False) | Yields the an estimate of the internal states that generated a Sequence | of observed values, either as the Maximum Likelihood state | (Maximumlikelihood=True) or as a Distribution (MaximumLikelihood=False) | | MaximumLikelihoodState(self, Observations=None) | Returns the maximum likelihood of the internal state. If Observations | is None, defaults to the maximum likelihood of the the Current state, or | the Prior if self.Current is None | | Outcomes(self) | | Predict(self) | Returns a Distribution representing the probabilities of the next | state given the current state | | PriorProbs(self, Observations) | Returns a Distribution the prior probabilities of the HMM's states | given a Distribution of Observations | | Train(self, Sequence) | Trains the HMM from a sequence of observations | | Update(self, Observations) | Updates the Prior probabilities, TransitionProbs | and Conditionals given Observations | | __call__(self, PriorProbs=None) | Returns a Distribution of outcomes given PriorProbs, which defaults | to self.Current if it is set, or self.Prior otherwise | | __init__(self, states, outcomes) | states is a list or dictionary of states, outcomes is a dictionary | mapping each state in states to a Distribution of the output states | | ---------------------------------------------------------------------- | Methods inherited from BayesianModel: | | MaximumLikelihoodOutcome(self, PriorProbs=None) | Returns the maximum likelihood outcome given PriorProbs | | States(self) | Returns an iterator over the possible states | | __iadd__(self, Model2) | Updates the BayesianModel with the data in another BayesianModel | | ---------------------------------------------------------------------- | Data descriptors inherited from BayesianModel: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined)class PoissonDistribution(Distribution) | Represents a Poisson distribution | | Method resolution order: | PoissonDistribution | Distribution | __builtin__.object | | Methods defined here: | | MaximumLikelihoodState(self) | | Mean(self) | Returns the Mean of the PoissonDistribution | | Sample(self) | Returns a random sample from the Poisson distribution | | States(self, limit=1e-07) | Yields the PoissonDistribution's states, up to a cumulative | probability of 1-limit | | Update(self, N, p=1.0) | Updates the distribution, given a value N that has a probability of P | of being drawn from this distribution | | __call__(self, N) | Returns the probability of N | | __init__(self, mean) | Initialises the distribution with a given mean | | copy(self) | Returns a copy of the PoissonDistribution | | ---------------------------------------------------------------------- | Methods inherited from Distribution: | | __iadd__(self, Dist2) | Updates the Distribution given another Distribution with the same states | | __mul__(self, scalar) | Returns the probability of each item, multiplied by a scalar | | ---------------------------------------------------------------------- | Data descriptors inherited from Distribution: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined)class Probability(__builtin__.object) | Represents a probability as a callable object | | Methods defined here: | | Update(self, deltaN, deltaD) | Updates the probability during Bayesian learning | | __call__(self) | Returns the value of the probability | | __iadd__(self, Prob2) | Updates the probability given another Probability object | | __init__(self, n, d) | Initialises the probability from a numerator and a denominator | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined)