Soh*_*ian 3 python gaussian matplotlib surface
如何创建多元偏斜法线函数,然后通过输入 x 和 y 点,我们可以创建 3d 曲面图(x、y 和 z 坐标)
我写了一篇关于此的博客文章,但这里是完整的工作代码:
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import (multivariate_normal as mvn,
norm)
class multivariate_skewnorm:
def __init__(self, a, cov=None):
self.dim = len(a)
self.a = np.asarray(a)
self.mean = np.zeros(self.dim)
self.cov = np.eye(self.dim) if cov is None else np.asarray(cov)
def pdf(self, x):
return np.exp(self.logpdf(x))
def logpdf(self, x):
x = mvn._process_quantiles(x, self.dim)
pdf = mvn(self.mean, self.cov).logpdf(x)
cdf = norm(0, 1).logcdf(np.dot(x, self.a))
return np.log(2) + pdf + cdf
xx = np.linspace(-2, 2, 100)
yy = np.linspace(-2, 2, 100)
X, Y = np.meshgrid(xx, yy)
pos = np.dstack((X, Y))
fig = plt.figure(figsize=(10, 10), dpi=150)
axes = [
fig.add_subplot(1, 3, 1, projection='3d'),
fig.add_subplot(1, 3, 2, projection='3d'),
fig.add_subplot(1, 3, 3, projection='3d')
]
for a, ax in zip([[0, 0], [5, 1], [1, 5]], axes):
Z = multivariate_skewnorm(a=a).pdf(pos)
ax.plot_surface(X, Y, Z, cmap=cm.viridis)
ax.set_title(r'$\alpha$ = %s, cov = $\mathbf{I}$' % str(a), fontsize=18)
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该代码将生成此图:
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