本文整理汇总了Python中sklearn.neighbors.kd_tree.KDTree.kernel_density方法的典型用法代码示例。如果您正苦于以下问题：Python KDTree.kernel_density方法的具体用法？Python KDTree.kernel_density怎么用？Python KDTree.kernel_density使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.neighbors.kd_tree.KDTree的用法示例。
在下文中一共展示了KDTree.kernel_density方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_gaussian_kde
# 需要导入模块: from sklearn.neighbors.kd_tree import KDTree [as 别名]
# 或者: from sklearn.neighbors.kd_tree.KDTree import kernel_density [as 别名]
def test_gaussian_kde(n_samples=1000):
# Compare gaussian KDE results to scipy.stats.gaussian_kde
from scipy.stats import gaussian_kde
rng = check_random_state(0)
x_in = rng.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])
gkde = gaussian_kde(x_in, bw_method=h / np.std(x_in))
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)示例2: test_gaussian_kde
# 需要导入模块: from sklearn.neighbors.kd_tree import KDTree [as 别名]
# 或者: from sklearn.neighbors.kd_tree.KDTree import kernel_density [as 别名]
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)示例3: test_gaussian_kde
# 需要导入模块: from sklearn.neighbors.kd_tree import KDTree [as 别名]
# 或者: from sklearn.neighbors.kd_tree.KDTree import kernel_density [as 别名]
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:
# older versions of scipy don't accept explicit bandwidth
raise SkipTest
dens_kdt = kdt.kernel_density(x_out[:, None], h) / n_samples
dens_gkde = gkde.evaluate(x_out)
assert_allclose(dens_kdt, dens_gkde, rtol=1E-3, atol=1E-3)