def random_concept(num_instances=1, num_objects=10):
tree = TrestleTree()
for i in range(num_instances):
#print("Training concept with instance", i+1)
inst = random_instance(num_objects)
#pprint(inst)
tree.ifit(inst)
return tree.root
def random_concept(num_instances=1, num_objects=10):
tree = TrestleTree()
for i in range(num_instances):
# print("Training concept with instance", i+1)
inst = random_instance(num_objects)
# pprint(inst)
tree.ifit(inst)
return tree.root
class ScikitTrestle(object):
def __init__(self, params=None):
if params is None:
self.tree = TrestleTree()
else:
self.tree = TrestleTree(**params)
def ifit(self, x, y):
x = deepcopy(x)
x['_y_label'] = "%i" % y
self.tree.ifit(x)
def fit(self, X, y):
X = deepcopy(X)
for i, x in enumerate(X):
x['_y_label'] = "%i" % y[i]
self.tree.fit(X, randomize_first=False)
def predict(self, X):
return [int(self.tree.categorize(x).predict('_y_label')) for x in X]
class ScikitTrestle(object):
def __init__(self, **kwargs):
self.tree = TrestleTree(**kwargs)
self.state_format = "variablized_state"
def ifit(self, x, y):
x = deepcopy(x)
x['_y_label'] = float(y)
self.tree.ifit(x)
def fit(self, X, y):
X = deepcopy(X)
for i, x in enumerate(X):
x['_y_label'] = float(y)
self.tree.fit(X, randomize_first=False)
def skill_info(self, X):
raise NotImplementedError("Not implemented Erik H. says there is a way \
to serialize this -> TODO")
def predict(self, X):
return [self.tree.categorize(x).predict('_y_label') for x in X]