"""
(c) 2011, 2012 Georgia Tech Research Corporation
This source code is released under the New BSD license.  Please see
http://wiki.quantsoftware.org/index.php?title=QSTK_License
for license details.

This package includes code for representing and learning HMM's.

Most of the code in this package was derived from the descriptions provided in
'A Tutorial on Hidden Markov Models and Selected Applications in Speach
Recognition' by <NAME>.

Conventions:
The keyword argument elem_size will be passed in when creating numpy array 
objects.
"""
import math,random,sys
import numpy

def calcalpha(stateprior,transition,emission,observations,numstates,elem_size=numpy.longdouble):
	"""
	Calculates 'alpha' the forward variable.
	
	The alpha variable is a numpy array indexed by time, then state (TxN).
	alpha[t][i] = the probability of being in state 'i' after observing the 
	first t symbols.
	"""
	alpha = numpy.zeros((len(observations),numstates),dtype=elem_size)
	for x in xrange(numstates):
		alpha[0][x] = stateprior[x]*emission[x][observations[0]]
	for t in xrange(1,len(observations)):
		for j in xrange(numstates):
			for i in xrange(numstates):
				alpha[t][j] += alpha[t-1][i]*transition[i][j]
			alpha[t][j] *= emission[j][observations[t]]
	return alpha

def forwardbackward(stateprior,transition,emission,observations,numstates,elem_size=numpy.longdouble):
	"""
	Calculates the probability of a sequence given the HMM.
	"""
	alpha = calcalpha(stateprior,transition,emission,observations,numstates,elem_size)
	return sum(alpha[-1])

def calcbeta(transition,emission,observations,numstates,elem_size=numpy.longdouble):
	"""
	Calculates 'beta' the backward variable.
	
	The beta variable is a numpy array indexed by time, then state (TxN).
	beta[t][i] = the probability of being in state 'i' and then observing the
	symbols from t+1 to the end (T).
	"""
	beta = numpy.zeros((len(observations),numstates),dtype=elem_size)
	for s in xrange(numstates):
		beta[len(observations)-1][s] = 1.
	for t in xrange(len(observations)-2,-1,-1):
		for i in xrange(numstates):
			for j in xrange(numstates):
				beta[t][i] += transition[i][j]*emission[j][observations[t+1]]*beta[t+1][j]
	return beta

def calcxi(stateprior,transition,emission,observations,numstates,alpha=None,beta=None,elem_size=numpy.longdouble):
	"""
	Calculates 'xi', a joint probability from the 'alpha' and 'beta' variables.
	
	The xi variable is a numpy array indexed by time, state, and state (TxNxN).
	xi[t][i][j] = the probability of being in state 'i' at time 't', and 'j' at
	time 't+1' given the entire observation sequence.
	"""
	if alpha is None:
		alpha = calcalpha(stateprior,transition,emission,observations,numstates,elem_size)
	if beta is None:
		beta = calcbeta(transition,emission,observations,numstates,elem_size)
	xi = numpy.zeros((len(observations),numstates,numstates),dtype=elem_size)
	for t in xrange(len(observations)-1):
		denom = 0.0
		for i in xrange(numstates):
			for j in xrange(numstates):
				thing = 1.0
				thing *= alpha[t][i]
				thing *= transition[i][j]
				thing *= emission[j][observations[t+1]]
				thing *= beta[t+1][j]
				denom += thing
		for i in xrange(numstates):
			for j in xrange(numstates):
				numer = 1.0
				numer *= alpha[t][i]
				numer *= transition[i][j]
				numer *= emission[j][observations[t+1]]
				numer *= beta[t+1][j]
				xi[t][i][j] = numer/denom
	return xi

def calcgamma(xi,seqlen,numstates, elem_size=numpy.longdouble):
	"""
	Calculates 'gamma' from xi.
	
	Gamma is a (TxN) numpy array, where gamma[t][i] = the probability of being
	in state 'i' at time 't' given the full observation sequence.
	"""
	gamma = numpy.zeros((seqlen,numstates),dtype=elem_size)
	for t in xrange(seqlen):
		for i in xrange(numstates):
			gamma[t][i] = sum(xi[t][i])
	return gamma

def baumwelchstep(stateprior,transition,emission,observations,numstates,numsym,elem_size=numpy.longdouble):
	"""
	Given an HMM model and a sequence of observations, computes the Baum-Welch 
	update to the parameters using gamma and xi. 
	"""
	xi = calcxi(stateprior,transition,emission,observations,numstates,elem_size=elem_size)
	gamma = calcgamma(xi,len(observations),numstates,elem_size)
	newprior = gamma[0]
	newtrans = numpy.zeros((numstates,numstates),dtype=elem_size)
	for i in xrange(numstates):
		for j in xrange(numstates):
			numer = 0.0
			denom = 0.0
			for t in xrange(len(observations)-1):
				numer += xi[t][i][j]
				denom += gamma[t][i]
			newtrans[i][j] = numer/denom
	newemiss = numpy.zeros( (numstates,numsym) ,dtype=elem_size)
	for j in xrange(numstates):
		for k in xrange(numsym):
			numer = 0.0
			denom = 0.0
			for t in xrange(len(observations)):
				if observations[t] == k:
					numer += gamma[t][j]
				denom += gamma[t][j]
			newemiss[j][k] = numer/denom
	return newprior,newtrans,newemiss

class HMMLearner:
	"""
	A class for modeling and learning HMMs.
	
	This class conveniently wraps the module level functions. Class objects hold 6
	data members:
	- num_states       		number of hidden states in the HMM
	- num_symbols      		number of possible symbols in the observation 
	                   		sequence
	- precision        		precision of the numpy.array elements (defaults to
	                   		longdouble)
	- prior            		The prior probability of starting in each state
	                   		(Nx1 array)
	- transition_matrix		The probability of transitioning between each state
	                  		(NxN matrix)
	- emission_matrix  		The probability of each symbol in each state
	                   		(NxO matrix)
	You can set the 3 matrix parameters as you wish, but make sure the shape of
	the arrays matches num_states and num_symbols, as these are used internally
	
	Typical usage of this class is to create an HMM with a set number of states
	and external symbols, train the HMM using addEvidence(...), and then use
	the sequenceProb(...) method to see how well a specific sequence matches
	the trained HMM.
	"""
	def __init__(self,num_states,num_symbols,init_type='uniform',precision=numpy.longdouble):
		"""
		Creates a new HMMLearner object with the given number of internal
		states, and external symbols.
		
		calls self.reset(init_type=init_type)
		"""
		self.num_states = num_states
		self.num_symbols = num_symbols
		self.precision = precision
		self.reset(init_type=init_type)
	
	def reset(self, init_type='uniform'):
		"""
		Resets the 3 arrays using the given initialization method.
		
		Wipes out the old arrays. You can use this method to change the shape
		of the arrays by first changing num_states and/or num_symbols, and then
		calling this method.
		
		Currently supported initialization methods:
		uniform		prior, transition, and emission probabilities are all 
					uniform (default)
		"""
		if init_type == 'uniform':
			self.prior = numpy.ones( (self.num_states), dtype=self.precision) *(1.0/self.num_states)
			self.transition_matrix = numpy.ones( (self.num_states,self.num_states), dtype=self.precision)*(1.0/self.num_states)
			self.emission_matrix = numpy.ones( (self.num_states,self.num_symbols), dtype=self.precision)*(1.0/self.num_symbols)
	
	def sequenceProb(self, newData):
		"""
		Returns the probability that this HMM generated the given sequence.
		
		Uses the forward-backward algorithm.  If given an array of
		sequences, returns a 1D array of probabilities.
		"""
		if len(newData.shape) == 1:
			return forwardbackward(	self.prior,\
									self.transition_matrix,\
									self.emission_matrix,\
									newData,\
									self.num_states,\
									self.precision)
		elif len(newData.shape) == 2:
			return numpy.array([forwardbackward(self.prior,self.transition_matrix,self.emission_matrix,newSeq,self.num_states,self.precision) for newSeq in newData])

	def addEvidence(self, newData, iterations=1,epsilon=0.0):
		"""
		Updates this HMMs parameters given a new set of observed sequences
		using the Baum-Welch algorithm.
		
		newData can either be a single (1D) array of observed symbols, or a 2D
		matrix, each row of which is a seperate sequence. The Baum-Welch update
		is repeated 'iterations' times, or until the sum absolute change in
		each matrix is less than the given epsilon.  If given multiple
		sequences, each sequence is used to update the parameters in order, and
		the sum absolute change is calculated once after all the sequences are
		processed.
		"""
		if len(newData.shape) == 1:
			for i in xrange(iterations):
				newp,newt,newe = baumwelchstep(	self.prior, \
												self.transition_matrix, \
												self.emission_matrix, \
												newData, \
												self.num_states, \
												self.num_symbols,\
												self.precision)
				pdiff = sum([abs(np-op) for np in newp for op in self.prior])
				tdiff = sum([sum([abs(nt-ot) for nt in newti for ot in oldt]) for newti in newt for oldt in self.transition_matrix])
				ediff = sum([sum([abs(ne-oe) for ne in newei for oe in olde]) for newei in newe for olde in self.emission_matrix])
				if(pdiff < epsilon) and (tdiff < epsilon) and (ediff < epsilon):
					break
				self.prior = newp
				self.transition_matrix = newt
				self.emission_matrix = newe
		else:
			for i in xrange(iterations):
				for sequence in newData:
					newp,newt,newe = baumwelchstep(	self.prior, \
													self.transition_matrix, \
													self.emission_matrix, \
													sequence, \
													self.num_states, \
													self.num_symbols,\
													self.precision)
					self.prior = newp
					self.transition_matrix = newt
					self.emission_matrix = newe
				pdiff = sum([abs(np-op) for np in newp for op in self.prior])
				tdiff = sum([sum([abs(nt-ot) for nt in newti for ot in oldt]) for newti in newt for oldt in self.transition_matrix])
				ediff = sum([sum([abs(ne-oe) for ne in newei for oe in olde]) for newei in newe for olde in self.emission_matrix])
				if(pdiff < eps) and (tdiff < eps) and (ediff < eps):
					break
				self.prior = newp
				self.transition_matrix = newt
				self.emission_matrix = newe

#def test():
#	#an unfair coinflip, simpler than unfair casino
#	#symbols: 0 = heads, 1 = tails
#	#states: 0 = fair coin, 1 = heads weighted coin
#	stateprior = (0.8,0.2)
#	transition = ((0.9,0.1),(0.3,0.7))
#	emission = ((0.5,0.5),(0.9,0.1))
#	ob1 = (0,1,0,1,0,1,0,1,0,1,0,1)
#	ob2 = (0,0,0,0,0,0,1,1,1,1,1,1)
#	ob3 = (1,1,1,1,1,1,1,1,1,1,1,1)
#	ob4 = (0,0,0,0,0,0,0,0,0,0,0,0)
#	print "Pr(",ob1,") = ",forwardbackward(stateprior,transition,emission,ob1,2)
#	print "Pr(",ob2,") = ",forwardbackward(stateprior,transition,emission,ob2,2)
#	print "Pr(",ob3,") = ",forwardbackward(stateprior,transition,emission,ob3,2)
#	print "Pr(",ob4,") = ",forwardbackward(stateprior,transition,emission,ob4,2)
#	print
#	xi = calcxi(stateprior,transition,emission,ob2,2)
#	gamma = calcgamma(xi,len(ob2),2)
#	for t in xrange(len(ob2)):
#		print gamma[t]
#	print
#	ob5 = (0,1,2,3,0,1,2,3,0,1,2,3)
#	print "Doing Baum-welch"
#	my_hmm = HMMLearner(2,2)
#	my_hmm.addEvidence(numpy.array(ob3*10),100)
#	print "Prior",my_hmm.prior
#	print "Transition",my_hmm.transition_matrix
#	print "Emission", my_hmm.emission_matrix
#	print "Probability that the above HMM generated", (ob3*10)
#	print my_hmm.sequenceProb(numpy.array(ob3*10))
#	print "Probability that the above HMM generated", (ob1*10)
#	print my_hmm.sequenceProb(numpy.array(ob1*10))
#	
#if __name__=="__main__":
#	test()

"""
(c) 2011, 2012 Georgia Tech Research Corporation
This source code is released under the New BSD license.  Please see
http://wiki.quantsoftware.org/index.php?title=QSTK_License
for license details.

This package includes code for representing and learning HMM's.

Most of the code in this package was derived from the descriptions provided in
'A Tutorial on Hidden Markov Models and Selected Applications in Speach
Recognition' by <NAME>.

Conventions:
The keyword argument elem_size will be passed in when creating numpy array 
objects.
"""
import math,random,sys
import numpy

def calcalpha(stateprior,transition,emission,observations,numstates,elem_size=numpy.longdouble):
	"""
	Calculates 'alpha' the forward variable.
	
	The alpha variable is a numpy array indexed by time, then state (TxN).
	alpha[t][i] = the probability of being in state 'i' after observing the 
	first t symbols.
	"""
	alpha = numpy.zeros((len(observations),numstates),dtype=elem_size)
	for x in xrange(numstates):
		alpha[0][x] = stateprior[x]*emission[x][observations[0]]
	for t in xrange(1,len(observations)):
		for j in xrange(numstates):
			for i in xrange(numstates):
				alpha[t][j] += alpha[t-1][i]*transition[i][j]
			alpha[t][j] *= emission[j][observations[t]]
	return alpha

def forwardbackward(stateprior,transition,emission,observations,numstates,elem_size=numpy.longdouble):
	"""
	Calculates the probability of a sequence given the HMM.
	"""
	alpha = calcalpha(stateprior,transition,emission,observations,numstates,elem_size)
	return sum(alpha[-1])

def calcbeta(transition,emission,observations,numstates,elem_size=numpy.longdouble):
	"""
	Calculates 'beta' the backward variable.
	
	The beta variable is a numpy array indexed by time, then state (TxN).
	beta[t][i] = the probability of being in state 'i' and then observing the
	symbols from t+1 to the end (T).
	"""
	beta = numpy.zeros((len(observations),numstates),dtype=elem_size)
	for s in xrange(numstates):
		beta[len(observations)-1][s] = 1.
	for t in xrange(len(observations)-2,-1,-1):
		for i in xrange(numstates):
			for j in xrange(numstates):
				beta[t][i] += transition[i][j]*emission[j][observations[t+1]]*beta[t+1][j]
	return beta

def calcxi(stateprior,transition,emission,observations,numstates,alpha=None,beta=None,elem_size=numpy.longdouble):
	"""
	Calculates 'xi', a joint probability from the 'alpha' and 'beta' variables.
	
	The xi variable is a numpy array indexed by time, state, and state (TxNxN).
	xi[t][i][j] = the probability of being in state 'i' at time 't', and 'j' at
	time 't+1' given the entire observation sequence.
	"""
	if alpha is None:
		alpha = calcalpha(stateprior,transition,emission,observations,numstates,elem_size)
	if beta is None:
		beta = calcbeta(transition,emission,observations,numstates,elem_size)
	xi = numpy.zeros((len(observations),numstates,numstates),dtype=elem_size)
	for t in xrange(len(observations)-1):
		denom = 0.0
		for i in xrange(numstates):
			for j in xrange(numstates):
				thing = 1.0
				thing *= alpha[t][i]
				thing *= transition[i][j]
				thing *= emission[j][observations[t+1]]
				thing *= beta[t+1][j]
				denom += thing
		for i in xrange(numstates):
			for j in xrange(numstates):
				numer = 1.0
				numer *= alpha[t][i]
				numer *= transition[i][j]
				numer *= emission[j][observations[t+1]]
				numer *= beta[t+1][j]
				xi[t][i][j] = numer/denom
	return xi

def calcgamma(xi,seqlen,numstates, elem_size=numpy.longdouble):
	"""
	Calculates 'gamma' from xi.
	
	Gamma is a (TxN) numpy array, where gamma[t][i] = the probability of being
	in state 'i' at time 't' given the full observation sequence.
	"""
	gamma = numpy.zeros((seqlen,numstates),dtype=elem_size)
	for t in xrange(seqlen):
		for i in xrange(numstates):
			gamma[t][i] = sum(xi[t][i])
	return gamma

def baumwelchstep(stateprior,transition,emission,observations,numstates,numsym,elem_size=numpy.longdouble):
	"""
	Given an HMM model and a sequence of observations, computes the Baum-Welch 
	update to the parameters using gamma and xi. 
	"""
	xi = calcxi(stateprior,transition,emission,observations,numstates,elem_size=elem_size)
	gamma = calcgamma(xi,len(observations),numstates,elem_size)
	newprior = gamma[0]
	newtrans = numpy.zeros((numstates,numstates),dtype=elem_size)
	for i in xrange(numstates):
		for j in xrange(numstates):
			numer = 0.0
			denom = 0.0
			for t in xrange(len(observations)-1):
				numer += xi[t][i][j]
				denom += gamma[t][i]
			newtrans[i][j] = numer/denom
	newemiss = numpy.zeros( (numstates,numsym) ,dtype=elem_size)
	for j in xrange(numstates):
		for k in xrange(numsym):
			numer = 0.0
			denom = 0.0
			for t in xrange(len(observations)):
				if observations[t] == k:
					numer += gamma[t][j]
				denom += gamma[t][j]
			newemiss[j][k] = numer/denom
	return newprior,newtrans,newemiss

class HMMLearner:
	"""
	A class for modeling and learning HMMs.
	
	This class conveniently wraps the module level functions. Class objects hold 6
	data members:
	- num_states       		number of hidden states in the HMM
	- num_symbols      		number of possible symbols in the observation 
	                   		sequence
	- precision        		precision of the numpy.array elements (defaults to
	                   		longdouble)
	- prior            		The prior probability of starting in each state
	                   		(Nx1 array)
	- transition_matrix		The probability of transitioning between each state
	                  		(NxN matrix)
	- emission_matrix  		The probability of each symbol in each state
	                   		(NxO matrix)
	You can set the 3 matrix parameters as you wish, but make sure the shape of
	the arrays matches num_states and num_symbols, as these are used internally
	
	Typical usage of this class is to create an HMM with a set number of states
	and external symbols, train the HMM using addEvidence(...), and then use
	the sequenceProb(...) method to see how well a specific sequence matches
	the trained HMM.
	"""
	def __init__(self,num_states,num_symbols,init_type='uniform',precision=numpy.longdouble):
		"""
		Creates a new HMMLearner object with the given number of internal
		states, and external symbols.
		
		calls self.reset(init_type=init_type)
		"""
		self.num_states = num_states
		self.num_symbols = num_symbols
		self.precision = precision
		self.reset(init_type=init_type)
	
	def reset(self, init_type='uniform'):
		"""
		Resets the 3 arrays using the given initialization method.
		
		Wipes out the old arrays. You can use this method to change the shape
		of the arrays by first changing num_states and/or num_symbols, and then
		calling this method.
		
		Currently supported initialization methods:
		uniform		prior, transition, and emission probabilities are all 
					uniform (default)
		"""
		if init_type == 'uniform':
			self.prior = numpy.ones( (self.num_states), dtype=self.precision) *(1.0/self.num_states)
			self.transition_matrix = numpy.ones( (self.num_states,self.num_states), dtype=self.precision)*(1.0/self.num_states)
			self.emission_matrix = numpy.ones( (self.num_states,self.num_symbols), dtype=self.precision)*(1.0/self.num_symbols)
	
	def sequenceProb(self, newData):
		"""
		Returns the probability that this HMM generated the given sequence.
		
		Uses the forward-backward algorithm.  If given an array of
		sequences, returns a 1D array of probabilities.
		"""
		if len(newData.shape) == 1:
			return forwardbackward(	self.prior,\
									self.transition_matrix,\
									self.emission_matrix,\
									newData,\
									self.num_states,\
									self.precision)
		elif len(newData.shape) == 2:
			return numpy.array([forwardbackward(self.prior,self.transition_matrix,self.emission_matrix,newSeq,self.num_states,self.precision) for newSeq in newData])

	def addEvidence(self, newData, iterations=1,epsilon=0.0):
		"""
		Updates this HMMs parameters given a new set of observed sequences
		using the Baum-Welch algorithm.
		
		newData can either be a single (1D) array of observed symbols, or a 2D
		matrix, each row of which is a seperate sequence. The Baum-Welch update
		is repeated 'iterations' times, or until the sum absolute change in
		each matrix is less than the given epsilon.  If given multiple
		sequences, each sequence is used to update the parameters in order, and
		the sum absolute change is calculated once after all the sequences are
		processed.
		"""
		if len(newData.shape) == 1:
			for i in xrange(iterations):
				newp,newt,newe = baumwelchstep(	self.prior, \
												self.transition_matrix, \
												self.emission_matrix, \
												newData, \
												self.num_states, \
												self.num_symbols,\
												self.precision)
				pdiff = sum([abs(np-op) for np in newp for op in self.prior])
				tdiff = sum([sum([abs(nt-ot) for nt in newti for ot in oldt]) for newti in newt for oldt in self.transition_matrix])
				ediff = sum([sum([abs(ne-oe) for ne in newei for oe in olde]) for newei in newe for olde in self.emission_matrix])
				if(pdiff < epsilon) and (tdiff < epsilon) and (ediff < epsilon):
					break
				self.prior = newp
				self.transition_matrix = newt
				self.emission_matrix = newe
		else:
			for i in xrange(iterations):
				for sequence in newData:
					newp,newt,newe = baumwelchstep(	self.prior, \
													self.transition_matrix, \
													self.emission_matrix, \
													sequence, \
													self.num_states, \
													self.num_symbols,\
													self.precision)
					self.prior = newp
					self.transition_matrix = newt
					self.emission_matrix = newe
				pdiff = sum([abs(np-op) for np in newp for op in self.prior])
				tdiff = sum([sum([abs(nt-ot) for nt in newti for ot in oldt]) for newti in newt for oldt in self.transition_matrix])
				ediff = sum([sum([abs(ne-oe) for ne in newei for oe in olde]) for newei in newe for olde in self.emission_matrix])
				if(pdiff < eps) and (tdiff < eps) and (ediff < eps):
					break
				self.prior = newp
				self.transition_matrix = newt
				self.emission_matrix = newe

#def test():
#	#an unfair coinflip, simpler than unfair casino
#	#symbols: 0 = heads, 1 = tails
#	#states: 0 = fair coin, 1 = heads weighted coin
#	stateprior = (0.8,0.2)
#	transition = ((0.9,0.1),(0.3,0.7))
#	emission = ((0.5,0.5),(0.9,0.1))
#	ob1 = (0,1,0,1,0,1,0,1,0,1,0,1)
#	ob2 = (0,0,0,0,0,0,1,1,1,1,1,1)
#	ob3 = (1,1,1,1,1,1,1,1,1,1,1,1)
#	ob4 = (0,0,0,0,0,0,0,0,0,0,0,0)
#	print "Pr(",ob1,") = ",forwardbackward(stateprior,transition,emission,ob1,2)
#	print "Pr(",ob2,") = ",forwardbackward(stateprior,transition,emission,ob2,2)
#	print "Pr(",ob3,") = ",forwardbackward(stateprior,transition,emission,ob3,2)
#	print "Pr(",ob4,") = ",forwardbackward(stateprior,transition,emission,ob4,2)
#	print
#	xi = calcxi(stateprior,transition,emission,ob2,2)
#	gamma = calcgamma(xi,len(ob2),2)
#	for t in xrange(len(ob2)):
#		print gamma[t]
#	print
#	ob5 = (0,1,2,3,0,1,2,3,0,1,2,3)
#	print "Doing Baum-welch"
#	my_hmm = HMMLearner(2,2)
#	my_hmm.addEvidence(numpy.array(ob3*10),100)
#	print "Prior",my_hmm.prior
#	print "Transition",my_hmm.transition_matrix
#	print "Emission", my_hmm.emission_matrix
#	print "Probability that the above HMM generated", (ob3*10)
#	print my_hmm.sequenceProb(numpy.array(ob3*10))
#	print "Probability that the above HMM generated", (ob1*10)
#	print my_hmm.sequenceProb(numpy.array(ob1*10))
#	
#if __name__=="__main__":
#	test()

(c) 2011, 2012 Georgia Tech Research Corporation This source code is released under the New BSD license.  Please see http://wiki.quantsoftware.org/index.php?title=QSTK_License for license details.  This package includes code for representing and learning HMM's.  Most of the code in this package was derived from the descriptions provided in 'A Tutorial on Hidden Markov Models and Selected Applications in Speach Recognition' by <NAME>.  Conventions: The keyword argument elem_size will be passed in when creating numpy array  objects. Calculates 'alpha' the forward variable. 	 	The alpha variable is a numpy array indexed by time, then state (TxN). 	alpha[t][i] = the probability of being in state 'i' after observing the  	first t symbols. Calculates the probability of a sequence given the HMM. Calculates 'beta' the backward variable. 	 	The beta variable is a numpy array indexed by time, then state (TxN). 	beta[t][i] = the probability of being in state 'i' and then observing the 	symbols from t+1 to the end (T). Calculates 'xi', a joint probability from the 'alpha' and 'beta' variables. 	 	The xi variable is a numpy array indexed by time, state, and state (TxNxN). 	xi[t][i][j] = the probability of being in state 'i' at time 't', and 'j' at 	time 't+1' given the entire observation sequence. Calculates 'gamma' from xi. 	 	Gamma is a (TxN) numpy array, where gamma[t][i] = the probability of being 	in state 'i' at time 't' given the full observation sequence. Given an HMM model and a sequence of observations, computes the Baum-Welch  	update to the parameters using gamma and xi. A class for modeling and learning HMMs. 	 	This class conveniently wraps the module level functions. Class objects hold 6 	data members: 	- num_states       		number of hidden states in the HMM 	- num_symbols      		number of possible symbols in the observation  	                   		sequence 	- precision        		precision of the numpy.array elements (defaults to 	                   		longdouble) 	- prior            		The prior probability of starting in each state 	                   		(Nx1 array) 	- transition_matrix		The probability of transitioning between each state 	                  		(NxN matrix) 	- emission_matrix  		The probability of each symbol in each state 	                   		(NxO matrix) 	You can set the 3 matrix parameters as you wish, but make sure the shape of 	the arrays matches num_states and num_symbols, as these are used internally 	 	Typical usage of this class is to create an HMM with a set number of states 	and external symbols, train the HMM using addEvidence(...), and then use 	the sequenceProb(...) method to see how well a specific sequence matches 	the trained HMM. Creates a new HMMLearner object with the given number of internal 		states, and external symbols. 		 		calls self.reset(init_type=init_type) Resets the 3 arrays using the given initialization method. 		 		Wipes out the old arrays. You can use this method to change the shape 		of the arrays by first changing num_states and/or num_symbols, and then 		calling this method. 		 		Currently supported initialization methods: 		uniform		prior, transition, and emission probabilities are all  					uniform (default) Returns the probability that this HMM generated the given sequence. 		 		Uses the forward-backward algorithm.  If given an array of 		sequences, returns a 1D array of probabilities. Updates this HMMs parameters given a new set of observed sequences 		using the Baum-Welch algorithm. 		 		newData can either be a single (1D) array of observed symbols, or a 2D 		matrix, each row of which is a seperate sequence. The Baum-Welch update 		is repeated 'iterations' times, or until the sum absolute change in 		each matrix is less than the given epsilon.  If given multiple 		sequences, each sequence is used to update the parameters in order, and 		the sum absolute change is calculated once after all the sequences are 		processed. #def test(): #	#an unfair coinflip, simpler than unfair casino #	#symbols: 0 = heads, 1 = tails #	#states: 0 = fair coin, 1 = heads weighted coin #	stateprior = (0.8,0.2) #	transition = ((0.9,0.1),(0.3,0.7)) #	emission = ((0.5,0.5),(0.9,0.1)) #	ob1 = (0,1,0,1,0,1,0,1,0,1,0,1) #	ob2 = (0,0,0,0,0,0,1,1,1,1,1,1) #	ob3 = (1,1,1,1,1,1,1,1,1,1,1,1) #	ob4 = (0,0,0,0,0,0,0,0,0,0,0,0) #	print "Pr(",ob1,") = ",forwardbackward(stateprior,transition,emission,ob1,2) #	print "Pr(",ob2,") = ",forwardbackward(stateprior,transition,emission,ob2,2) #	print "Pr(",ob3,") = ",forwardbackward(stateprior,transition,emission,ob3,2) #	print "Pr(",ob4,") = ",forwardbackward(stateprior,transition,emission,ob4,2) #	print #	xi = calcxi(stateprior,transition,emission,ob2,2) #	gamma = calcgamma(xi,len(ob2),2) #	for t in xrange(len(ob2)): #		print gamma[t] #	print #	ob5 = (0,1,2,3,0,1,2,3,0,1,2,3) #	print "Doing Baum-welch" #	my_hmm = HMMLearner(2,2) #	my_hmm.addEvidence(numpy.array(ob3*10),100) #	print "Prior",my_hmm.prior #	print "Transition",my_hmm.transition_matrix #	print "Emission", my_hmm.emission_matrix #	print "Probability that the above HMM generated", (ob3*10) #	print my_hmm.sequenceProb(numpy.array(ob3*10)) #	print "Probability that the above HMM generated", (ob1*10) #	print my_hmm.sequenceProb(numpy.array(ob1*10)) # #if __name__=="__main__": #	test()