def from_observation(cls, obs, m, n):
p = cls()
if m > 0:
sum_obs = np.sum(obs[0:m])
p.log_c = utils.gammaln(sum_obs + 1) - utils.gammaln(sum_obs + m)
p.alpha = np.asarray(obs[0:m]) + 1
if n > 0:
p.a = np.asarray(obs[m:]) + 1
p.b = np.ones(n)
return p
def from_observation(cls, obs, m, n):
p = cls()
if m > 0:
sum_obs = np.sum(obs[0:m])
p.log_c = utils.gammaln(sum_obs+1) - utils.gammaln(sum_obs+m)
p.alpha = np.asarray(obs[0:m]) + 1
if n > 0:
p.a = np.asarray(obs[m:]) +1
p.b = np.ones(n)
return p
def __mul__(self, other):
p = copy.deepcopy(self)
p.log_c += other.log_c
# Multiply Dirichlet component
if len(self.alpha) > 0:
p.alpha = self.alpha + other.alpha - 1
p.log_c += utils.gammaln(np.sum(self.alpha)) - np.sum(utils.gammaln(self.alpha))
p.log_c += utils.gammaln(np.sum(other.alpha)) - np.sum(utils.gammaln(other.alpha))
p.log_c += np.sum(utils.gammaln(p.alpha)) - utils.gammaln(np.sum(p.alpha))
# Multiply Gamma components
if len(self.a) > 0:
p.a = self.a + other.a - 1
p.b = (self.b * other.b) / (self.b + other.b)
p.log_c += np.sum(utils.gammaln(p.a) + p.a * np.log(p.b))
p.log_c -= np.sum(utils.gammaln(self.a) + self.a * np.log(self.b))
p.log_c -= np.sum(utils.gammaln(other.a) + other.a * np.log(other.b))
return p
def __mul__(self, other):
p = copy.deepcopy(self)
p.log_c += other.log_c
# Multiply Dirichlet component
if len(self.alpha) > 0:
p.alpha = self.alpha + other.alpha - 1
p.log_c += utils.gammaln(np.sum(self.alpha)) - np.sum(
utils.gammaln(self.alpha))
p.log_c += utils.gammaln(np.sum(other.alpha)) - np.sum(
utils.gammaln(other.alpha))
p.log_c += np.sum(utils.gammaln(p.alpha)) - utils.gammaln(
np.sum(p.alpha))
# Multiply Gamma components
if len(self.a) > 0:
p.a = self.a + other.a - 1
p.b = (self.b * other.b) / (self.b + other.b)
p.log_c += np.sum(utils.gammaln(p.a) + p.a * np.log(p.b))
p.log_c -= np.sum(utils.gammaln(self.a) + self.a * np.log(self.b))
p.log_c -= np.sum(
utils.gammaln(other.a) + other.a * np.log(other.b))
return p