def __init__(self, all_nodes, kind):
if len(all_nodes) > 0:
identifier_class = all_nodes[0].identifier.__class__
for node in all_nodes:
check.check_instance(node.identifier, identifier_class)
check.check_equal(node.finalized, True)
self.all_nodes = all_nodes
self.kind = check.check_one_of(kind,
[Graph.DIRECTED, Graph.UNDIRECTED])
self.log_len = math.log10(len(self.all_nodes) + 1)
self.indexes = {}
for node in self.all_nodes:
self.indexes[node.identifier] = node
self.clustering_coefficients = self._calculate_clustering_coefficients(
)
self._distances = {}
self._max_distances = {}
self._global_max_distance = None
self._background_calculations = threading.Thread(
target=self._submit_calculations)
self._background_calculations.daemon = True
self._background_calculations.start()
def __init__(self, name, default, plural=False, canonicalize=lambda v: v):
self.name = check.check_not_empty(name)
self.default = default
self.plural = check.check_one_of(plural, [True, False])
self.canonicalize = canonicalize
self.key = self.name if not self.plural else "%s[]" % self.name
def __init__(self, threshold, exclude_numbers):
self.threshold = check.check_gte(threshold, 1)
self.exclude_numbers = check.check_one_of(exclude_numbers, [True, False])
self.word_counts = {}
def __init__(self, improved, update_best):
self.improved = check.check_one_of(improved, [True, False])
self.update_best = check.check_one_of(update_best, [True, False])
def score(self, value=None):
if value is None:
return self._score
self._score = check.check_one_of(value, [True, False])
return self
def debug(self, value=None):
if value is None:
return self._debug
self._debug = check.check_one_of(value, [True, False])
return self
def convergence(self, value=None):
if value is None:
return self._convergence
self._convergence = check.check_one_of(value, [True, False])
return self