def createModel(self): self.decisionTreeModel = np.empty([self.numBins, self.numBins]) for i in range(0, self.numBins): for j in range(0, self.numBins): r = Resolver() query = "Feature1/bin " + str(i + 1) + "/Feature2/bin " + str( j + 1) + "/*" try: prediction = r.glob(self.decisionTree, query)[0].name except: try: query = "Feature2/bin " + str( j + 1) + "/Feature1/bin " + str(i + 1) + "/*" prediction = r.glob(self.decisionTree, query)[0].name except: try: query = "Feature2/bin " + str(j + 1) + "/*" prediction = r.glob(self.decisionTree, query)[0].name except: query = "Feature1/bin " + str(i + 1) + "/*" prediction = r.glob(self.decisionTree, query)[0].name if (prediction == "Label = 1"): prediction = 1 elif (prediction == "Label = 0"): prediction = 0 self.decisionTreeModel[i][j] = prediction
def test_enum(): class Animals(IntEnum): Mammal = 1 Cat = 2 Dog = 3 root = Node("ANIMAL") mammal = Node(Animals.Mammal, parent=root) cat = Node(Animals.Cat, parent=mammal) dog = Node(Animals.Dog, parent=mammal) r = Resolver() eq_(r.glob(root, "/ANIMAL/*"), [mammal]) eq_(r.glob(root, "/ANIMAL/*/*"), [cat, dog])
def node_search(tree, cible): s = cible if s == "": return [] if s[0] == '/': s = '/' + tree.root.name + s r = Resolver('name') return r.glob(tree, s + '*')
def resolve_rels(self, patt) -> List['AnalNode']: """ >>> f.resolve_rels("*/obj") :param patt: :return: """ r = Resolver('dependency_relation') return r.glob(self, patt)
def node_path_length(tree): resolver = Resolver('name') start = '*' for i in range(tree.height): node = resolver.glob(tree, start + '{' + sys.argv[2] + '}') if node != []: return node[0].depth else: start += '/*'
def distance_to_interface(tree, node_name): resolver = Resolver('name') start = '*' for i in range(tree.height): node = resolver.glob(tree, start + node_name) if node != []: depth = node[0].depth return depth else: if node_name == '{external node}': return 0 else: start += '/*' # When a node is not connected return 999
def __floordiv__(self, patt): """ >>> from sagas.nlu.warehouse import warehouse as wh >>> wh//'find*' >>> wh//'*Person*' >>> [(el, el.words) for el in wh//'*Person*'] :param patt: :return: """ if isinstance(patt, str): r = Resolver('name') return r.glob(self, patt) elif isinstance(patt, ref_): val = self.resolve_entity(patt.val) return [val] else: return []
def node_path_length(tree, node_name): resolver = Resolver('name') total = 0 children_number = 0 start = '*' for i in range(tree.height): node = resolver.glob(tree, start + node_name) if node: node[0].parent = None result = get_all_children(node[0], total, children_number, True) total += result[0] children_number += result[1] else: start += '/*' if children_number != 0: total = total / children_number return total
r = Resolver() reader = csv.DictReader(open('t.tab', 'r'), delimiter='\t') for item in reader: ec = item.get('ec') iturl = item.get('p') it = iturl[iturl.rfind('/') + 1:] if ec == '-.-.-.-': continue ec = ec.split('.') if len(ec) != 4: print(l) p = 3 for i in [3, 2]: if ec[i] == '-': p = i - 1 for i in range(p, -1, -1): t = '/'.join(ec[0:i]) if i == 0: t = '' try: res = r.glob(tree, '/Root/' + t) except ChildResolverError: continue if len(res[0].q) == 0: continue print( '{}|P680|{}|P4390|Q39894595|S854|"ftp://ftp.expasy.org/databases/enzyme/enzclass.txt"|S813|+2019-12-09T00:00:00Z/11' .format(it, res[0].q)) break
s = superc.get((q, tc)) if s != None: s.add(qq) else: superc[(q, tc)] = set([qq]) for q, tc in superc.keys(): origtc = tc found = False mr = print_subc_tpfam if mr: p = tc.rfind('.') tc = tc[:p] while not found and tc.rfind('.') >= 0: try: res = r.glob(tree, '/Root/' + tc.replace('.', '/')) except ChildResolverError: m = missing.get(tc) if m is None: missing[tc] = 1 else: missing[tc] = m + 1 p = tc.rfind('.') tc = tc[:p] continue if len(res[0].q) > 0: tpsuperfam = res[0].q if print_partof_tpfam: s = superc.get((q, origtc)) if s is None or tpsuperfam not in s: print(