def test_truncnorm_all_values_inside_boundaries():
mean = 0
std = 0.01
layer = nn.Dense(in_units=1, units=1000)
layer.initialize(init=initializer.TruncNorm(mean, std))
assert (layer.weight.data() <= 2 * std).asnumpy().all()
assert (layer.weight.data() >= -2 * std).asnumpy().all()
def test_truncnorm_all_values_inside_boundaries():
mean = 0
std = 0.01
layer = nn.Dense(prefix="test_layer", in_units=1, units=1000)
layer.initialize(init=initializer.TruncNorm(mean, std))
assert ((layer.weight.data() > 2 * std).sum() +
(layer.weight.data() < -2 * std).sum()).sum().asscalar() == 0
def test_truncnorm_generates_values_with_defined_mean_and_std():
from scipy import stats
mean = 10
std = 5
layer = nn.Dense(in_units=1, units=100000)
layer.initialize(init=initializer.TruncNorm(mean, std))
samples = layer.weight.data().reshape((-1, )).asnumpy()
p_value = stats.kstest(samples, 'truncnorm', args=(-2, 2, mean, std)).pvalue
assert p_value > 0.0001