def test_gaussian_from_dict(self):
width = 2.0
kernel = PyGaussianKernel(width)
dict_repr = kernel.to_dict()
x0 = -0.34
k0 = kernel.evaluate(x0)
kernel.from_dict(dict_repr)
self.assertAlmostEqual(k0, kernel.evaluate(x0))
def test_gaussian_kernel(self):
width = 2.0
kernel = PyGaussianKernel(width)
# Confirm that the integral is 1
w = np.linspace(-10 * width, 10 * width, 20000).tolist()
values = [kernel.evaluate(x) for x in w]
integral = np.trapz(values, dx=w[1] - w[0])
self.assertAlmostEqual(integral, 1.0, places=4)
# Check some values
expected = np.exp(-0.5) * 0.5 / np.sqrt(2 * np.pi)
self.assertAlmostEqual(kernel.evaluate(width), expected)
self.assertAlmostEqual(kernel.evaluate(-width), expected)
# Check derivatives
self.assertAlmostEqual(kernel.deriv(0.0), 0.0)
self.assertAlmostEqual(kernel.deriv(width), -expected / width)