def findposteriorfrequencies(x: int, tempdata: np.ndarray, maxMOI: int,
frequencies_RR, rand: np.random.RandomState):
nalleles = frequencies_RR.lengths[x]
# hard coded table() function from R
data = tempdata[:, x * maxMOI:(x + 1) * maxMOI].astype(int)
data_1d_array = data.flatten()
data_1d_array = data_1d_array[data_1d_array != 0]
data_1d_array = data_1d_array[data_1d_array <= nalleles]
data_unique, data_counts = np.unique(data_1d_array, return_counts=True)
# Start as ones for the frequency prior
counts_table = np.ones(nalleles)
counts_table[data_unique - 1] += data_counts
frequencies_RR.matrix[x, :nalleles] = rand.dirichlet(counts_table, 1)
def sample(self, k: int, rnd: np.random.RandomState):
return rnd.dirichlet(self.get_alphas(), k)
