pathpy.MarkovSequence.MarkovSequence Class Reference

Public Member Functions
def	__init__
def	fitMarkovModel
def	getLikelihood
def	getBIC
def	getAIC
def	estimateOrder

Public Attributes
	sequence
	The sequence to be modeled.
	P
	The transition probabilities of higher-order Markov chains.
	states
	the set of states of higher-order Markov chains

Detailed Description

Instances of this class can be used to fit 
    standard higher-order Markov models for 
    sequences generated from concatenated paths 

Constructor & Destructor Documentation

def pathpy.MarkovSequence.MarkovSequence.__init__	(		self,
			sequence
	)

Generates a Markov model for a sequence, given 
as a single list of strings

Member Function Documentation

def pathpy.MarkovSequence.MarkovSequence.estimateOrder	(		self,
			maxOrder,
			method = 'BIC'
	)

Estimates the optimal order of a Markov model
    based on Likelihood, BIC or AIC 

def pathpy.MarkovSequence.MarkovSequence.fitMarkovModel	(		self,
			k = 1
	)

Generates a k-th order Markov model 
    for the underlying sequence

def pathpy.MarkovSequence.MarkovSequence.getAIC	(		self,
			k = 1,
			m = 1
	)

Returns the Aikake Information Criterion
    assuming a k-th order Markov model 

def pathpy.MarkovSequence.MarkovSequence.getBIC	(		self,
			k = 1,
			m = 1
	)

Returns the Bayesian Information Criterion
    assuming a k-th order Markov model 

def pathpy.MarkovSequence.MarkovSequence.getLikelihood	(		self,
			k = 1,
			log = True
	)

Returns the likelihood of the sequence 
assuming a k-th order Markov model 

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The documentation for this class was generated from the following file:

• /mnt/c/Users/ingos/Desktop/pathpy/pathpy/MarkovSequence.py