variables - The subset of domain variables for the object.
domain - A domain for the model. If None the internal default domain is used. If the string 'new', a new empty domain is used.
new_domain_variables - A dictionary containing a mapping from any new variables to their values. domain is updated with these values
must_be_new - Whether domain variables in new_domain_variables have to be new
check - Whether to check that all variables exist in domain

Raises:

VariableError - If a variable in new_domain_variables already exists with values different from its values in new_domain_variables; Or if must_be_new is set and the variable already exists. Or if check is set and a variable in variables is not in the domain

Overrides: object.__init__

(inherited documentation)

Return a 'copy' of a subdomain

Parameters:

copy_domain - If true self's domain is copied, otherwise the copy shares self's domain

The copy

(inherited documentation)

str(x)

Return a marginal factor with variables

Unless variables is empty in which case return the sum of self's data

Parameters:

variables (Iterable) - Variables to project onto
check (Boolean) - Whether to bother with an initial check that every member of variables is in the CompactFactor. (If this check is omitted and there is an extra variable, then a Factor with the wrong number of values will be created.)

Returns: Factor object

The marginal factor

Raises:

KeyError - if variables contains a variable not contained in the CompactFactor.

Parameters:

prior - the Dirichlet prior parameter (the same parameter value is used for all instances!) Note there may be some problems with this method: a different prior is used by the BDeu score. However, in practice, for parameter estimation, this prior method seems to be ok. I was lazy and it was simple to implement (cb). If prior is zero, then the parameters are the maximum likelihood estimation solutions.

Return the number of nodes in the underlying ADTree

Class IncrementalCompactFactor