scikit-learn：如何使用拟合概率模型？

Question

所以我使用scikit-learn import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LogNorm from sklearn import mixture import matplotlib as mpl from matplotlib.patches import Ellipse %matplotlib inline n_samples = 300 # generate random sample, two components np.random.seed(0) shifted_gaussian = np.random.randn(n_samples, 2) + np.array([20, 5]) sample= shifted_gaussian # fit a Gaussian Mixture Model with two components clf = mixture.GMM(n_components=2, covariance_type='full') clf.fit(sample) # plot sample scatter plt.scatter(sample[:, 0], sample[:, 1]) # 1. Plot the probobility density distribution # 2. Calculate the mean square error of the fitting model （http://scikit-learn.org/stable/modules/mixture.html）来拟合我的数据，现在我想使用该模型，我该怎么做？具体做法是：

1. 如何绘制概率密度分布？
2. 如何计算拟合模型的均方误差？

以下是您可能需要的代码：

x = np.linspace(-20.0, 30.0)
y = np.linspace(-20.0, 40.0)
X, Y = np.meshgrid(x, y)
XX = np.array([X.ravel(), Y.ravel()]).T
Z = -clf.score_samples(XX)[0]
Z = Z.reshape(X.shape)

CS = plt.contour(X, Y, Z, norm=LogNorm(vmin=1.0, vmax=1000.0),
                 levels=np.logspace(0, 3, 10))
CB = plt.colorbar(CS, shrink=0.8, extend='both')

更新： 我可以通过以下方式绘制分布图：

{{1}}

但这不是很奇怪吗？有没有更好的方法呢？我可以画这样的东西吗？

Answer 1

我认为结果是合理的，如果你稍微调整xlim和ylim：

# plot sample scatter
plt.scatter(sample[:, 0], sample[:, 1], marker='+', alpha=0.5)

# 1. Plot the probobility density distribution
# 2. Calculate the mean square error of the fitting model
x = np.linspace(-20.0, 30.0, 100)
y = np.linspace(-20.0, 40.0, 100)
X, Y = np.meshgrid(x, y)
XX = np.array([X.ravel(), Y.ravel()]).T
Z = -clf.score_samples(XX)[0]
Z = Z.reshape(X.shape)

CS = plt.contour(X, Y, Z, norm=LogNorm(vmin=1.0, vmax=10.0),
                 levels=np.logspace(0, 1, 10))
CB = plt.colorbar(CS, shrink=0.8, extend='both')
plt.xlim((10,30))
plt.ylim((-5, 15))