test_kd_tree.py 文件源码

def test_gaussian_kde(n_samples=1000):
    # Compare gaussian KDE results to scipy.stats.gaussian_kde
    from scipy.stats import gaussian_kde
    np.random.seed(0)
    x_in = np.random.normal(0, 1, n_samples)
    x_out = np.linspace(-5, 5, 30)

    for h in [0.01, 0.1, 1]:
        kdt = KDTree(x_in[:, None])
        try:
            gkde = gaussian_kde(x_in, bw_method=h / np.std(x_in))
        except TypeError:
            raise SkipTest("Old scipy, does not accept explicit bandwidth.")

        dens_kdt = kdt.kernel_density(x_out[:, None], h) / n_samples
        dens_gkde = gkde.evaluate(x_out)

        assert_array_almost_equal(dens_kdt, dens_gkde, decimal=3)