def derivative(self, input_vec, output_vec):
"""Return the derivative of this function.
We take both input and output vector because
some derivatives can be more efficiently calculated from
the output of this function.
"""
return calculate.dgaussian(input_vec, output_vec, self._variance)
def test_dgaussian_matrix():
tensor_shape = [random.randint(1, 10) for _ in range(2)]
helpers.check_gradient(
lambda X: calculate.gaussian(X),
lambda X: calculate.dgaussian(X, calculate.gaussian(X)),
f_arg_tensor=numpy.random.random(tensor_shape),
f_shape='lin')
def test_dgaussian():
helpers.check_gradient(
calculate.gaussian,
lambda x: calculate.dgaussian(x, calculate.gaussian(x)),
f_shape='lin')