コード例 #1
0
ファイル: LocalEngine.py プロジェクト: avinson/crosscat
    def predictive_probability_multistate(self, M_c, X_L_list, X_D_list, Y, Q):
        """Calculate probability of cells jointly taking values given a
        latent state.

        :param Y: A list of constraints to apply when querying.  Each constraint
            is a triplet of (r,d,v): r is the row index, d is the column
            index and v is the value of the constraint
        :type Y: list of lists
        :param Q: A list of values to query.  Each value is triplet of (r,d,v):
            r is the row index, d is the column index, and v is the value at
            which the density is evaluated.
        :type Q: list of lists

        :returns: float -- joint log probabilities of the values specified by Q
        """
        return su.predictive_probability_multistate(
            M_c, X_L_list, X_D_list, Y, Q)
コード例 #2
0
ファイル: LocalEngine.py プロジェクト: joshnr13/crosscat
    def predictive_probability_multistate(self, M_c, X_L_list, X_D_list, Y, Q):
        """Calculate the probability of cellS jointly taking values given a latent state

        :param M_c: The column metadata
        :type M_c: dict
        :param X_L_list: list of the latent variables associated with the latent state
        :type X_L_list: list of dict
        :param X_D_list: list of the particular cluster assignments of each row in each view
        :type X_D_list: list of list of lists
        :param Y: A list of constraints to apply when querying.  Each constraint
                  is a triplet of (r,d,v): r is the row index, d is the column
                  index and v is the value of the constraint
        :type Y: list of lists
        :param Q: A list of values to query.  Each value is triplet of (r,d,v):
                  r is the row index, d is the column index, and v is the value at
                  which the density is evaluated.
        :type Q: list of lists
        :returns: float -- joint log probabilities of the values specified by Q

        """
        return su.predictive_probability_multistate(M_c, X_L_list, X_D_list, Y, Q)