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)
