Python library for Hidden Markov Models
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==================================
Python Hidden Markov Model Library
==================================
This library is a pure Python implementation of Hidden
Markov Models (HMMs). The project structure is quite
simple::
Help on module Markov:
NAME
Markov  Library to implement hidden Markov Models
FILE
Markov.py
CLASSES
__builtin__.object
BayesianModel
HMM
Distribution
PoissonDistribution
Probability
class 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=1e07)
 Yields the PoissonDistribution's states, up to a cumulative
 probability of 1limit

 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)
Python Hidden Markov Model Library
==================================
This library is a pure Python implementation of Hidden
Markov Models (HMMs). The project structure is quite
simple::
Help on module Markov:
NAME
Markov  Library to implement hidden Markov Models
FILE
Markov.py
CLASSES
__builtin__.object
BayesianModel
HMM
Distribution
PoissonDistribution
Probability
class 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=1e07)
 Yields the PoissonDistribution's states, up to a cumulative
 probability of 1limit

 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)
Release History
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