2. 选择了不合适的 n_components n_components 的值决定了聚类的数量。如果选择的值过小,则聚类可能...
returnsegments # Go over 1 and 2 components and calculate statistics best_fitting_score=np.Inf self.logger.info("Begin to fit GMMs with 1 and 2 components.") foriin [1, 2]: # For each number of component (1 or 2), fit GMM gmm=GaussianMixture(n_components=i, random_state=0, tol=...
n_components = np.arange(1, 11) models = [GMM(n, covariance_type='full', random_state=0).fit(features_std) for n in n_components] plt.plot(n_components, [m.aic(features_std) for m in models], label='AIC') plt.plot(n_components, [m.bic(features_std) for m in models], lab...
self.logger.info("Begin to fit GMMs with 1 and 2 components.") for i in [1, 2]: # For each number of component (1 or 2), fit GMM gmm = GaussianMixture(n_components=i, random_state=0, tol=10 ** -6).fit(X) # Calculate AIC and BIC and the average between them aic, bic ...
理解了数学原理,GMM的代码也不复杂,基本上上面的每一个公式使用1-2行就可以完成 2、E stepdefstep_expectation(X,n_components,means,variance…
n_components : int The number of clusters means : array-like, shape (n_components,) The means of each mixture component. variances : array-like, shape (n_components,) The variances of each mixture component. Returns --- weights : array-like, shape (n_components,n_samples) """ ...
n_components : int The number of clusters means : array-like, shape (n_components,) The means of each mixture component. variances : array-like, shape (n_components,) The variances of each mixture component. Returns --- weights : array-like, shape (n_components,n_samples) """ ...
n_components : int The number of clusters means : array-like, shape (n_components,) The means of each mixture component. variances : array-like, shape (n_components,) The variances of each mixture component. Returns --- weights : array-like, shape (n_components,n_samples) """ ...
1. n_components: 混合高斯模型个数,默认为 1 2. covariance_type: 协方差类型,包括 {‘full’,‘tied’, ‘diag’, ‘spherical’} 四种,full 指每个分量有各自不同的标准协方差矩阵,完全协方差矩阵(元素都不为零), tied 指所有分量有相同的标准协方差矩阵(HMM 会用到),diag 指每个分量有各自不同对角协...
gmm=GaussianMixture(n_components=2,covariance_type='full',random_state=28)gmm.fit(x,y) 查看一下训练出来的模型: 5.理模型的相关参数的输出 代码语言:javascript 复制 ## 模型相关参数输出print('均值 = \n',gmm.means_)print('方差 = \n',gmm.covariances_) ...