def check_forward(self, x_data):
xp = cuda.get_array_module(x_data)
y = maximum_entropy_mellowmax(x_data)
self.assertEqual(y.data.dtype, self.dtype)
print('y', y.data)
# Outputs must be positive
xp.testing.assert_array_less(xp.zeros_like(y.data), y.data)
# Sums must be ones
sums = xp.sum(y.data, axis=1)
testing.assert_allclose(sums, xp.ones_like(sums))
# Expectations must be equal to memllowmax's outputs
testing.assert_allclose(xp.sum(y.data * x_data, axis=1),
mellowmax(x_data, axis=1).data)
def check_forward(self, x_data):
xp = cuda.get_array_module(x_data)
y = mellowmax(x_data, axis=self.axis, omega=self.omega)
self.assertEqual(y.data.dtype, self.dtype)
x_min = xp.min(x_data, axis=self.axis)
x_max = xp.max(x_data, axis=self.axis)
x_mean = xp.mean(x_data, axis=self.axis)
print('x_min', x_min)
print('y.data', y.data)
# min <= mellowmax <= max
eps = 1e-5
self.assertTrue(xp.all(x_min <= y.data + eps))
self.assertTrue(xp.all(x_max >= y.data - eps))
# omega > 0 -> mellowmax is more like max
if self.omega > 0:
self.assertTrue(xp.all(x_mean <= y.data + eps))
# omega < 0 -> mellowmax is more like min
if self.omega < 0:
self.assertTrue(xp.all(x_mean >= y.data - eps))
