def init_emission_probablity_matrix_row(self, row_index):
if(self.init_method==SpecializedHMM.InitMethod.random):
row = [0] + random_list_with_sum(self.number_of_emissions-2, 1) + [0]
return row
elif(self.init_method==SpecializedHMM.InitMethod.count_based):
nr_of_training_examples = len(self.training_examples)
alphabet = self.alphabet
alphabet_size = len(alphabet)
def count_position(position):
#pseudocount
count_list = random_list_with_sum(alphabet_size,
nr_of_training_examples*0.1)
#Do the counting
for e in self.training_examples:
if position < len(e):
character_index = alphabet.index(e[position])
count_list[character_index] = count_list[character_index] + 1
return count_list
count_list = count_position(row_index-1)
total_count = sum(count_list)
def normalize_element(element):
return element / total_count
row = map(normalize_element, count_list)
return [0] + row + [0]
else:
raise "Init Method Not Supported"
def count_position(position):
#pseudocount
count_list = random_list_with_sum(alphabet_size,
nr_of_training_examples*0.1)
#Do the counting
for e in self.training_examples:
if position < len(e):
character_index = alphabet.index(e[position])
count_list[character_index] = count_list[character_index] + 1
return count_list
def init_emission_probablity_matrix_row(self, row_index):
if (self.init_method == SpecializedHMM.InitMethod.random):
row = [0] + random_list_with_sum(self.number_of_emissions - 2,
1) + [0]
return row
elif (self.init_method == SpecializedHMM.InitMethod.count_based):
nr_of_training_examples = len(self.training_examples)
alphabet = self.alphabet
alphabet_size = len(alphabet)
def count_position(position):
#pseudocount
use_pseudocount = True
uniform_pseudocount = False
if use_pseudocount:
if uniform_pseudocount:
count_list = zeros(alphabet_size)
for i in range(0, alphabet_size):
count_list[i] = (nr_of_training_examples *
0.1) / alphabet_size
else:
count_list = random_list_with_sum(
alphabet_size, nr_of_training_examples * 0.1)
else:
count_list = zeros(alphabet_size)
#Do the counting
for e in self.training_examples:
if position < len(e):
character_index = alphabet.index(e[position])
count_list[
character_index] = count_list[character_index] + 1
return count_list
count_list = count_position(row_index - 1)
total_count = sum(count_list)
def normalize_element(element):
return element / total_count
row = map(normalize_element, count_list)
return [0] + row + [0]
else:
raise "Init Method Not Supported"
def count_position(position):
#pseudocount
use_pseudocount = True
uniform_pseudocount = False
if use_pseudocount:
if uniform_pseudocount:
count_list = zeros(alphabet_size)
for i in range(0,alphabet_size):
count_list[i]= (nr_of_training_examples*0.1)/alphabet_size
else:
count_list = random_list_with_sum(alphabet_size,
nr_of_training_examples*0.1)
else:
count_list = zeros(alphabet_size)
#Do the counting
for e in self.training_examples:
if position < len(e):
character_index = alphabet.index(e[position])
count_list[character_index] = count_list[character_index] + 1
return count_list
def count_position(position):
#pseudocount
use_pseudocount = True
uniform_pseudocount = False
if use_pseudocount:
if uniform_pseudocount:
count_list = zeros(alphabet_size)
for i in range(0,alphabet_size):
count_list[i]= (nr_of_training_examples*0.1)/alphabet_size
else:
count_list = random_list_with_sum(alphabet_size,
nr_of_training_examples*0.1)
else:
count_list = zeros(alphabet_size)
#Do the counting
for e in self.training_examples:
if position < len(e):
character_index = alphabet.index(e[position])
count_list[character_index] = count_list[character_index] + 1
return count_list