def random(self, params):
a, b = self._get_standard_params(params)
a += 1
b += 1
return BetaData(beta_rvs(a, b))
def sample_atom(self, data, cell):
a = self.base_measure.params.a
b = self.base_measure.params.b
for item in cell.items:
a += data[item].x
b += data[item].n - data[item].x
return BetaData(beta_rvs(a, b))
def sample(self, old_value, num_clusters, num_data_points):
a = self.a
b = self.b
k = num_clusters
n = num_data_points
eta = beta_rvs(old_value + 1, n)
x = (a + k - 1) / (n * (b - log(eta)))
pi = x / (1 + x)
label = discrete_rvs([pi, 1 - pi])
scale = b - log(eta)
if label == 0:
new_value = gamma_rvs(a + k, scale)
else:
new_value = gamma_rvs(a + k - 1, scale)
return new_value
def random(self):
x = beta_rvs(self.params.a, self.params.b)
return BetaData(x)