Module Index : Topics : Keywords
 
 
structure_learning.approximators.greedy
index
/Users/165421/Documents/code/structure_learning/src/structure_learning/approximators/greedy.py

 
Modules
       
heapq
numpy
pandas
time

 
Classes
       
structure_learning.approximators.approximator.Approximator(abc.ABC)
GreedySearch

 
class GreedySearch(structure_learning.approximators.approximator.Approximator)
    GreedySearch(data: Union[structure_learning.data.data.Data, pandas.core.frame.DataFrame], incidence: Union[numpy.ndarray, structure_learning.data_structures.dag.DAG], blacklist=None, whitelist=None, seed=32, n_particles=1000000, max_evaluations=10000, score_type='bdeu', strategy='deterministic', include_reversal=False, max_unexplored=2000, retain_size=1000)
 

 
 
Method resolution order:
GreedySearch
structure_learning.approximators.approximator.Approximator
abc.ABC
builtins.object

Methods defined here:
__init__(self, data: Union[structure_learning.data.data.Data, pandas.core.frame.DataFrame], incidence: Union[numpy.ndarray, structure_learning.data_structures.dag.DAG], blacklist=None, whitelist=None, seed=32, n_particles=1000000, max_evaluations=10000, score_type='bdeu', strategy='deterministic', include_reversal=False, max_unexplored=2000, retain_size=1000)
Initialize self.  See help(type(self)) for accurate signature.
add_neighbours(self, neighbours)
config(self)
Returns the configuration of the HillClimb algorithm.
get_state_to_explore(self)
run(self, increment=None)

Data and other attributes defined here:
DETERMINISTIC_STRATEGY = 'deterministic'
PROBABILISTIC_PARTIAL_EXPLORATION_STRATEGY = 'probabilistic_partial'
PROBABILISTIC_STRATEGY = 'probabilistic'
__abstractmethods__ = frozenset()

Methods inherited from structure_learning.approximators.approximator.Approximator:
save(self, filename: str, compression='gzip')
Saves the Graph object to a file.
 
Parameters:
    filename (str): Path to the output file.

Class methods inherited from structure_learning.approximators.approximator.Approximator:
load(filename: str, compression='gzip')
Loads a Graph object from a file.
 
Parameters:
    filename (str): Path to the input file.
 
Returns:
    Graph: Loaded Graph object.

Data descriptors inherited from structure_learning.approximators.approximator.Approximator:
__dict__
dictionary for instance variables
__weakref__
list of weak references to the object

 
Data
        Union = typing.Union