def _coeffs(self, k, n, sum_n):
alpha = array(self.alpha[k][1:])
sum_alpha = self.alpha[k][0]
q = self.mixture[k]
# Calculate the coefficient using logarithms
log_coeff = log_beta(n+alpha) - log_beta(alpha)
return q * exp(log_coeff)
def _pos_probs(self, counts):
n = array(counts)
# *** NEW CODE FROM HERE ****
sum_n = sum(n)
p = array([0.0]*len(counts))
log_coeffs = array([0.0]*self.num_distr)
q = array(self.mixture)
for j in xrange(self.num_distr):
alpha_j = array(self.alpha[j][1:])
log_coeffs[j] = log_beta(n+alpha_j) - log_beta(alpha_j)
log_coeffs = log_coeffs - max(log_coeffs)
coeffs = q * exp(log_coeffs)
for i in xrange(len(counts)):
a = array([self.alpha[j][i+1] for j in xrange(self.num_distr)])
sum_a_j = array([sum(self.alpha[j][1:]) for j in xrange(self.num_distr)])
N = (a+n[i])/(sum_a_j + sum(n))
p[i] = sum(coeffs*N)
return p / sum(p)
