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- def counts_given_parents(adj, X):
- n, d = X.shape
- states_list = [set(col) for col in X.T]
- pstates_list = [Counter(map(tuple, X[:, adj.T[i]])).keys() for i in range(d)]
- counts = {i: {j: {k: np.count_nonzero(X[:, i] == k)
- for k in states_list[i]}
- for j in pstates_list[i]}
- for i in range(d)}
- return counts
- def mle_discrete(adj, X):
- n, d = X.shape
- states_list = [set(col) for col in X.T]
- pstates_list = [Counter(map(tuple, X[:, adj.T[i]])).keys() for i in range(d)]
- n_ij = {i: {j: sum(n_ijk[i][j].values())
- for j in pstates_list[i]}
- for i in range(d)}
- cpt = {i: {j: {k: np.count_nonzero(X[:, i] == k) / n_ij[i][j]
- for k in states_list[i]}
- for j in pstates_list[i]}
- for i in range(d)}
- return cpt
- def score(adj, X)
- n_states_list = [len(set(col)) for col in X.T]
- pstates_list = [Counter(map(tuple, X[:, adj.T[i]])).keys() for i in range(d)]
- n_ijk = counts_given_parents(adj, X)
- n_ij = {i: {j: sum(n_ijk[i][j].values())
- for j in pstates_list[i]}
- for i in range(d)}
- score = np.sum([np.sum(np.log(np.arange(2, n + 1)))
- for n_jk in n_ijk.values()
- for n_k in n_jk.values()
- for n in n_k.values()])
- score += np.sum([np.sum(np.log(np.arange(2, n))) for n in n_states_list])
- score -= np.sum([np.sum(np.log(np.arange(2, n1 + n_states_list[i])))
- for i in range(d)
- for n1 in n_ij[i].values()])
- return score
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