def predictive_probability(self, M_c, X_L, X_D, 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 probability of the values specified by Q """ return su.predictive_probability(M_c, X_L, X_D, Y, Q)
def predictive_probability(self, M_c, X_L, X_D, 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: the latent variables associated with the latent state :type X_L: dict :param X_D: the particular cluster assignments of each row in each view :type X_D: 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 probability of the values specified by Q """ return su.predictive_probability(M_c, X_L, X_D, Y, Q)