def _update_stick_weights(self, counts, alpha):
reverse_cumsum = counts[::-1].cumsum()[::-1]
a = 1 + counts[:-1]
b = alpha + reverse_cumsum[1:]
stick_weights, mixture_weights = stick_break_proc(a, b)
return stick_weights, mixture_weights
def _update_stick_weights(self, counts, alpha):
reverse_cumsum = counts[::-1].cumsum()[::-1]
a = 1 + counts[:-1]
b = alpha + reverse_cumsum[1:]
stick_weights, mixture_weights = stick_break_proc(a, b)
return stick_weights, mixture_weights
def _update_stick_weights(self, counts, beta, alpha0):
new_weights = np.zeros((self.ngroups, self.ncomp))
new_stick_weights = np.zeros((self.ngroups, self.ncomp - 1))
for j in xrange(self.ngroups):
reverse_cumsum = counts[j][::-1].cumsum()[::-1]
a = alpha0 * beta[:-1] + counts[j][:-1]
b = alpha0 * (1 - beta[:-1].cumsum()) + reverse_cumsum[1:]
sticksj, weightsj = stick_break_proc(a, b)
new_weights[j] = weightsj
new_stick_weights[j] = sticksj
return new_stick_weights, new_weights
def _update_stick_weights(self, counts, beta, alpha0):
new_weights = np.zeros((self.ngroups, self.ncomp))
new_stick_weights = np.zeros((self.ngroups, self.ncomp-1))
for j in xrange(self.ngroups):
reverse_cumsum = counts[j][::-1].cumsum()[::-1]
a = alpha0*beta[:-1] + counts[j][:-1]
b = alpha0*(1-beta[:-1].cumsum()) + reverse_cumsum[1:]
sticksj, weightsj = stick_break_proc(a, b)
new_weights[j] = weightsj
new_stick_weights[j] = sticksj
return new_stick_weights, new_weights
