def sample(self, size=None, replace=True):
value_indices = np.random.choice(range(len(self)), size, replace,
self.normalize())
if isinstance(value_indices, Iterable):
to_value = np.vectorize(lambda idx: data.Value(self.variable, idx))
return to_value(value_indices)
return data.Value(self.variable, value_indices)
def random(self):
v = random.random() * np.sum(self)
s = i = 0
for i, e in enumerate(self):
s += e
if s > v:
break
return data.Value(self.variable, i) if self.variable is not None else i
def modus(self):
val = np.argmax(self)
return data.Value(self.variable,
val) if self.variable is not None else val