def __init__(self, states_df, score_type, max_num_mtries, alpha=0, num_starts=10, ess=1.0, verbose=False, vtx_to_states=None): """ Constructor Parameters ---------- alpha : float states_df : pandas.DataFrame score_type : str max_num_mtries : int num_starts : int ess : float Equivalent Sample Size, a parameter in BDEU scorer. Fudge factor that is supposed to grow as the amount of prior knowledge grows. verbose : bool vtx_to_states : dict[str, list[str]] A dictionary mapping each node name to a list of its state names. This information will be stored in self.bnet. If vtx_to_states=None, constructor will learn vtx_to_states from states_df Returns ------- None """ # this is a good default value self.alpha = 5 / len(states_df.index) lner = MB_MMPC_Lner(states_df, alpha, verbose, vtx_to_states, learn_later=True) lner.find_PC() self.vtx_to_nbors = lner.vtx_to_nbors HC_RandRestartLner.__init__(self, states_df, score_type, max_num_mtries, num_starts, ess, verbose, vtx_to_states)
def __init__(self, states_df, score_type, max_num_mtries, alpha=0, tabu_len=10, ess=1.0, verbose=False, vtx_to_states=None): """ Constructor Parameters ---------- tabu_len : int alpha : float states_df : pandas.DataFrame score_type : str max_num_mtries : int ess : float Equivalent Sample Size, a parameter in BDEU scorer. Fudge factor that is supposed to grow as the amount of prior knowledge grows. verbose : bool vtx_to_states : dict[str, list[str]] A dictionary mapping each node name to a list of its state names. This information will be stored in self.dag. If vtx_to_states=None, constructor will learn vtx_to_states from states_df Returns ------- None """ # this is a good default value self.alpha = 5/len(states_df.index) lner = MB_MMPC_Lner(states_df, alpha, verbose, vtx_to_states, learn_later=True) lner.find_PC() self.vtx_to_nbors = lner.vtx_to_nbors HC_TabuLner.__init__( self, states_df, score_type, max_num_mtries, tabu_len, ess, verbose, vtx_to_states)