class BinomialColumns(StochasticParameter):
def __init__(self, p):
super(BinomialColumns, self).__init__()
if isinstance(p, StochasticParameter):
self.p = p
elif ia.is_single_number(p):
assert 0 <= p <= 1.0, "Expected probability p to be in range [0.0, 1.0], got %s." % (
p, )
self.p = Deterministic(float(p))
else:
raise Exception(
"Expected StochasticParameter or float/int value, got %s." %
(type(p), ))
def _draw_samples(self, size, random_state):
p = self.p.draw_sample(random_state=random_state)
assert 0 <= p <= 1.0, "Expected probability p to be in range [0.0, 1.0], got %s." % (
p, )
h, w, c = size
drops = random_state.binomial(1, p, (1, w, c))
drops_columns = np.tile(drops, (h, 1, 1))
return drops_columns
def __repr__(self):
return self.__str__()
def __str__(self):
if isinstance(self.p, float):
return "BinomialColumns(%.4f)" % (self.p, )
else:
return "BinomialColumns(%s)" % (self.p, )
class BinomialColumns(StochasticParameter):
def __init__(self, p):
super(BinomialColumns, self).__init__()
self.p = Deterministic(float(p))
def _draw_samples(self, size, random_state):
p = self.p.draw_sample(random_state=random_state)
h, w, c = size
drops = random_state.binomial(1, p, (1, w, c))
drops_columns = np.tile(drops, (h, 1, 1))
return drops_columns