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')