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(