pythonbiopython
1.60

Classes  
class  MarkovModelBuilder 
class  HiddenMarkovModel 
Functions  
def  _gen_random_array 
def  _calculate_emissions 
def  _calculate_from_transitions 
def  _calculate_to_transitions 
Deal with representations of Markov Models.
def Bio.HMM.MarkovModel._calculate_emissions  (  emission_probs  )  [private] 
Calculate which symbols can be emitted in each state
Definition at line 23 of file MarkovModel.py.
00023 00024 def _calculate_emissions(emission_probs): 00025 """Calculate which symbols can be emitted in each state 00026 """ 00027 # loop over all of the statesymbol duples, mapping states to 00028 # lists of emitted symbols 00029 emissions = dict() 00030 for state, symbol in emission_probs: 00031 try: 00032 emissions[state].append(symbol) 00033 except KeyError: 00034 emissions[state] = [symbol] 00035 00036 return emissions
def Bio.HMM.MarkovModel._calculate_from_transitions  (  trans_probs  )  [private] 
Calculate which 'from transitions' are allowed for each state This looks through all of the trans_probs, and uses this dictionary to determine allowed transitions. It converts this information into a dictionary, whose keys are source states and whose values are lists of destination states reachable from the source state via a transition.
Definition at line 37 of file MarkovModel.py.
00037 00038 def _calculate_from_transitions(trans_probs): 00039 """Calculate which 'from transitions' are allowed for each state 00040 00041 This looks through all of the trans_probs, and uses this dictionary 00042 to determine allowed transitions. It converts this information into 00043 a dictionary, whose keys are source states and whose values are 00044 lists of destination states reachable from the source state via a 00045 transition. 00046 """ 00047 transitions = dict() 00048 for from_state, to_state in trans_probs: 00049 try: 00050 transitions[from_state].append(to_state) 00051 except KeyError: 00052 transitions[from_state] = [to_state] 00053 00054 return transitions
def Bio.HMM.MarkovModel._calculate_to_transitions  (  trans_probs  )  [private] 
Calculate which 'to transitions' are allowed for each state This looks through all of the trans_probs, and uses this dictionary to determine allowed transitions. It converts this information into a dictionary, whose keys are destination states and whose values are lists of source states from which the destination is reachable via a transition.
Definition at line 55 of file MarkovModel.py.
00055 00056 def _calculate_to_transitions(trans_probs): 00057 """Calculate which 'to transitions' are allowed for each state 00058 00059 This looks through all of the trans_probs, and uses this dictionary 00060 to determine allowed transitions. It converts this information into 00061 a dictionary, whose keys are destination states and whose values are 00062 lists of source states from which the destination is reachable via a 00063 transition. 00064 """ 00065 transitions = dict() 00066 for from_state, to_state in trans_probs: 00067 try: 00068 transitions[to_state].append(from_state) 00069 except KeyError: 00070 transitions[to_state] = [from_state] 00071 00072 return transitions
def Bio.HMM.MarkovModel._gen_random_array  (  n  )  [private] 
Return an array of n random numbers, where the elements of the array sum to 1.0
Definition at line 14 of file MarkovModel.py.
00014 00015 def _gen_random_array(n): 00016 """ Return an array of n random numbers, where the elements of the array sum 00017 to 1.0""" 00018 randArray = [random.random() for i in range(n)] 00019 total = sum(randArray) 00020 normalizedRandArray = [x/total for x in randArray] 00021 00022 return normalizedRandArray