def mcmc(self):
result = [0 for i in range(len(self.__space))]
random_ind = np.random.randint(len(self.__space))
random_state = self.__space[random_ind]
for iter in range(self.__m):
result[random_ind] += 1
neighbours = self.__get_neighbours(random_ind)
chosen = neighbours[np.random.randint(len(neighbours))]
c_E = self.__E(chosen)
r_E = self.__E(random_state)
dE = c_E - r_E
p = self.__get_p_accept_metropolis(
dE, 1 / len(neighbours),
1 / len(self.__get_neighbours(chosen)))
coin = np.random.binomial(1, p)
random_state = chosen if coin else random_state
conf = self.__space
if type(chosen) is not tuple:
chosen_ind = 0
else:
chosen_ind = conf[1:].index(chosen)
random_ind = chosen_ind if coin else random_ind
best_configuration = self.__space[np.argmax(np.asarray(result))]
bd = BD(10, self.__kt, self.__dt, best_configuration, 2,
self.__protein_dict, self.__protein_vec, self.__df)
return result, best_configuration, bd.BD_algorithm()
def __E(self, c):
"""
E function that used BD algorithm and calculate the loss of the result
:param c: the initial state
:return: the loss score
"""
bd = BD(10, self.__kt, self.__dt, c, 2, self.__protein_dict,
self.__protein_vec, self.__df)
res, bars = bd.BD_algorithm()
return self.__loss(bars)
