所以我使用了 scikit-learn 的 Gaussian mixture models
( 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?具体来说:
- 如何绘制概率密度分布?
- 如何计算拟合模型的均方误差?
这是您可能需要的代码:
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
更新: 我可以通过以下方式绘制分布:
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')
最佳答案
我认为结果是合理的,如果你稍微调整一下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))
关于python - scikit-learn:如何使用拟合概率模型?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/32419367/