def __init__(self,
radius_list=[0, 1],
thickness_list=[1, 2],
grammar=None,
core_interface_pair_remove_threshold=2,
interface_remove_threshold=2,
complexity=3,
vectorizer=Vectorizer(complexity=3),
estimator=estimator_wrapper.estimator_wrapper()):
self.complexity = complexity
self.feasibility_checker = FeasibilityChecker()
self.postprocessor = processing.PostProcessor()
self.vectorizer = vectorizer
# lists of int
self.radius_list = [int(2 * r) for r in radius_list]
self.thickness_list = [int(2 * t) for t in thickness_list]
# scikit classifier
self.estimatorobject = estimator
# grammar object
self.local_substitutable_graph_grammar = grammar
# cips hashes will be masked with this, this is unrelated to the vectorizer
self.hash_bitmask = pow(2, 20) - 1
# we will save current graph at every intervalth step of sampling and attach to graphinfos[graphs]
self.sampling_interval = None
# how many sampling steps are done
self.n_steps = None
# current step in sampling proces of a single graph
self.step = None
# how often do we try to get a cip from the current graph in sampling
self.select_cip_max_tries = None
# sample path
self.sample_path = None
self.local_substitutable_graph_grammar = LocalSubstitutableGraphGrammar(
self.radius_list,
self.thickness_list,
complexity=self.complexity,
cip_remove_threshold=core_interface_pair_remove_threshold,
interface_remove_threshold=interface_remove_threshold,
nbit=20)
