26 #ifndef GUM_MULTI_DIM_NOISY_AND_H 27 #define GUM_MULTI_DIM_NOISY_AND_H 51 template <
typename GUM_SCALAR >
64 GUM_SCALAR default_weight = (GUM_SCALAR)1.0);
125 virtual const std::string&
name()
const;
131 #ifndef GUM_NO_EXTERN_TEMPLATE_CLASS 136 template <
typename GUM_SCALAR >
virtual const std::string & name() const
Returns the real name of the multiDimArray.
MultiDimNoisyAND(GUM_SCALAR external_weight, GUM_SCALAR default_weight=(GUM_SCALAR) 1.0)
Default constructor.
class for NoisyAND-net implementation as multiDim
gum is the global namespace for all aGrUM entities
Abstract base class for all multi dimensionnal containers.
Abstract base class for all multi dimensionnal Causal Independency models.
std::ostream & operator<<(std::ostream &output, const BayesNet< GUM_SCALAR > &bn)
Prints map's DAG in output using the Graphviz-dot format.
abstract class for Conditional Indepency Models
const std::string toString() const
Returns the real name of the multiDimArray.
virtual ~MultiDimNoisyAND()
Destructor.
Set of pairs of elements with fast search for both elements.
Noisy AND representation.
virtual MultiDimContainer< GUM_SCALAR > * newFactory() const
This method creates a clone of this object, withouth its content (including variable), you must use this method if you want to ensure that the generated object has the same type than the object containing the called newFactory()
Class for assigning/browsing values to tuples of discrete variables.