def __init__(self, dimension): """initialize a new problem""" Notifier.__init__(self) Listener.__init__(self) self.dimension = dimension self.prototype = {} self.cg = ConstraintGraph()
def __init__(self, methodclasses): """Create a new solver, using the given subclasses of ClusterMethod.""" # init superclasses Notifier.__init__(self) # store arguments self._methodclasses = methodclasses self._pattern_methods = filter(lambda m: hasattr(m,"patterngraph"),self._methodclasses) self._handcoded_methods = filter(lambda m: hasattr(m,"handcoded_match"),self._methodclasses) self._incremental_methods = filter(lambda m: hasattr(m,"incremental_matcher"),self._methodclasses) # init instance vars self._graph = Graph() #self._graph.add_vertex("_root") # self._graph.add_vertex("_toplevel") self._graph.add_vertex("_variables") self._graph.add_vertex("_clusters") self._graph.add_vertex("_methods") self._new = [] self._mg = MethodGraph() # add prototype_selection boolean var to method graph self._prototype_selection_var = "_prototype_selection_enabled" self._mg.add_variable(self._prototype_selection_var) self._mg.set(self._prototype_selection_var, True) # store map of selection_constraints to SelectionMethod (or None) self._selection_method = {} # store root cluster (will be assigned when first cluster added) self._rootcluster = None # an incrementally updated toplevel set self._toplevel = MutableSet() # incrementally updated set of applicable methods self._incremental_matchers = map(lambda method: method.incremental_matcher(self), self._incremental_methods) #print "incremental matchers:",self._incremental_matchers self._applicable_methods = Union(*self._incremental_matchers)
def __init__(self, client, email_info, owner): Notifier.__init__(self, client, email_info, owner) self.frm = email_info.get("from", None) self.name = email_info.get("name", None) self.smtp = email_info.get("smtp", None) self.username = email_info.get("username", None) self.password = email_info.get("password", None)
def __init__(self): """Create a new, empty ConstraintGraph""" Notifier.__init__(self) self._variables = {} """A set of variables""" self._constraints = {} """A set of constraints""" self._graph = Graph() """A graph for fast navigation. The graph contains an
def __init__(self, graph=None): Notifier.__init__(self) self._dict = {} """the edges are stored in a dictionary of dictionaries""" self._reverse = {} """the reverse graph is stored here""" # copy input graph if graph: for v in graph.vertices(): self.add_vertex(v) for e in graph.edges(): (v,w) = e self.set(v,w,graph.get(v,w))
def __init__(self, dimension): """Create a new empty solver""" Notifier.__init__(self) self.dimension = dimension self._graph = Graph() self._graph.add_vertex("_root") self._graph.add_vertex("_toplevel") self._graph.add_vertex("_variables") self._graph.add_vertex("_distances") self._graph.add_vertex("_angles") self._graph.add_vertex("_rigids") self._graph.add_vertex("_hedgehogs") self._graph.add_vertex("_balloons") self._graph.add_vertex("_methods") # queue of new objects to process self._new = [] # methodgraph self._mg = MethodGraph()
def __init__(self, graph=None): Notifier.__init__(self) self._dict = {} """the edges are stored in a dictionary of dictionaries""" self._reverse = {} """the reverse graph is stored here""" self._fanin = {} """map from vertices to fan-in number""" self._fanout = {} """map from vertices to fan-out number""" self._infan = {} """map from fan-in numbers to vertices with that fan-in""" self._outfan = {} """map from fan-out numbers to vertices with that fan-out""" # copy input graph if graph: for v in graph.vertices(): self.add_vertex(v) for e in graph.edges(): (v,w) = e self.set(v,w,graph.get(v,w))
def __init__(self, methodclasses): """Create a new solver, using the given subclasses of ClusterMethod.""" # init superclasses Notifier.__init__(self) # store arguments self._methodclasses = methodclasses self._pattern_methods = filter(lambda m: hasattr(m, "patterngraph"), self._methodclasses) self._handcoded_methods = filter( lambda m: hasattr(m, "handcoded_match"), self._methodclasses) self._incremental_methods = filter( lambda m: hasattr(m, "incremental_matcher"), self._methodclasses) # init instance vars self._graph = Graph() #self._graph.add_vertex("_root") # self._graph.add_vertex("_toplevel") self._graph.add_vertex("_variables") self._graph.add_vertex("_clusters") self._graph.add_vertex("_methods") self._new = [] self._mg = MethodGraph() # add prototype_selection boolean var to method graph self._prototype_selection_var = "_prototype_selection_enabled" self._mg.add_variable(self._prototype_selection_var) self._mg.set(self._prototype_selection_var, True) # store map of selection_constraints to SelectionMethod (or None) self._selection_method = {} # store root cluster (will be assigned when first cluster added) self._rootcluster = None # an incrementally updated toplevel set self._toplevel = MutableSet() # incrementally updated set of applicable methods self._incremental_matchers = map( lambda method: method.incremental_matcher(self), self._incremental_methods) #print "incremental matchers:",self._incremental_matchers self._applicable_methods = Union(*self._incremental_matchers)
def __init__(self): """initialize ParametricConstraint""" Notifier.__init__(self) self._value = None