def create(self, state):
""" Create the Markov chain itself. We use the parameter instead of the attribute so we can compute the matrix for different states """
# Separete the letters considering the letter and the symbol as a unique state:
# So from "88,a,b," we get: '8' '8,' 'a,' 'b,'
try:
# This is a first order markov model. Each individual object (letter, number, etc.) is a state
separated_letters = list(state)
except AttributeError:
print_error('There is no state yet')
return False
# Generate the MC
self.init_vector, self.matrix = mc.maximum_likelihood_probabilities(separated_letters, order=1)
def create(self, state):
""" Create the Markov chain itself. We use the parameter instead of the attribute so we can compute the matrix for different states """
# Separete the letters considering the letter and the symbol as a unique state:
# So from "88,a,b," we get: '8' '8,' 'a,' 'b,'
try:
# This is a first order markov model. Each individual object (letter, number, etc.) is a state
separated_letters = list(state)
except AttributeError:
print_error('There is no state yet')
return False
# Generate the MC
self.init_vector, self.matrix = mc.maximum_likelihood_probabilities(separated_letters, order=1)