In this post, I briefly describe the idea of constructing a Gaussian mixture model using the EM algorithm and how to implement the model in Python. When I was learning EM, my biggest problem was the understanding of the equations, so I will try my best to explain the algorithm without man...
GMM概率密度形式如下: (1)p(x)=∑k=1KπkN(x|μk,Σk) 其中,πk、μk、Σk分别表示第k个高斯分布的权重、均值及协方差矩阵, 且∑k=1Kπk=1,∀πk≥0. 令样本集合为{x(1),x(2),⋯,x(n)}, 本文拟采用EM(Expectation-Maximization)算法求解上述优化变量{πk,μk,Σk}k=1∼K. step1...
然后,我们将使用高斯混合模型(GMM)来拟合这些数据点,估计原始的高斯分布参数。 我们可以使用Python的sklearn库中的GaussianMixture类来实现GMM。下面是相关代码: import numpy as np import matplotlib.pyplot as plt from sklearn.mixture import GaussianMixture # 设置随机种子以保证结果的可重复性 np.random.seed(0...
GaussianHMM是一个基于高斯混合模型(Gaussian Mixture Model,GMM)的隐马尔可夫模型(Hidden Markov Model,HMM)。下面是一个使用Python实现GaussianHMM的例子: 首先,导入必要的库: python import numpy as np import matplotlib.pyplot as plt from sklearn.mixture import GaussianMixture from sklearn.datasets import make...
二元高斯混合模型(Two-component Gaussian mixture model):数据点,以及模型的等概率平面(equi-probability surfaces)。 高斯混合模型是一种概率模型,它假定所有数据点都是由有限个参数未知的高斯分布的混合产生的。可以认为混合模型是k均值聚类的推广,它包含了关于数据的协方差结构以及潜在高斯分布的中心的信息。
概述 参考 sklearn.mixture: Gaussian Mixture Models 高斯混合模型(GMM)源代码实现(二) A Gaussian Mixture Model (GMM) is a parametric probability density function represe
Gaussian Mixture Models in Python Author: Jeremy Stober Contact: stober@gmail.com Version: 0.01 This is a standalone Pythonic implementation of Gaussian Mixture Models. Various initialization strategies are included along with a standard EM algorithm for determining the model parameters based on data. ...
代码实现:通过生成不同高斯分布的数据点(模拟不同种类的水果大小与重量),使用Python的sklearn库中的GaussianMixture类进行GMM拟合,估计原始分布参数。算法评价:优点:软聚类,为数据点分配每个类的概率;聚类形状灵活,适应不同形状;参数估计可用于生成模型。缺点:计算复杂性高,初始化敏感,需预先确定...
Specifically, the implementation of the Gaussian Mixture Model in Python can be foundhere. 5. Use Cases of Gaussian Mixture Models The applications of Gaussian Mixture Models include anomaly detection, image segmentation, and speech recognition. ...
Gaussian Mixture Models (GMM) are effective for multi model density representation. In this experiment GMM Parameters are estimated using Expectation Maximization(EM) algorithm results are shown for two datasets. The GMM algorithm and plotting functions are given in python code. Following are the requi...