假设我们有一个序列的点,计算这些点在我们定义的高斯分布中的概率密度: # 示例数据点sample_points=np.array([[0,0],[1,1],[-1,-1],[0.5,0.5]])# 计算并输出各点的概率密度forpointinsample_points:pdf_value=multivariate_gaussian_pdf(point,mean,cov)print(f"Point:{point}, PDF:{pdf_value}") ...
X_train_MGD, X_test_MGD, y_train_MGD, y_test_MGD = train_test_split(principalDf2.iloc[:,0:5],principalDf2['anomaly'],test_size=0.20, random_state=42) mu, sigma = estimate_gaussian(X_train_MGD) p_tr = multivariate_gaussian(X_train_MGD,mu,sigma) p_ts = multivariate_gaussian(X_...
现在我们可以计算样本点的概率密度值了。 defmultivariate_gaussian(x,mu,sigma):k=len(mu)term1=(1/(np.power(2*np.pi,k/2)*np.sqrt(det_sigma)))term2=np.exp(-0.5*(x-mu).T @ inv_sigma @(x-mu))returnterm1*term2 prob_density=multivariate_gaussian(x,mu,sigma)print("概率密度:",prob_...
def multivariate_gaussian(x, mean, covariance): """多元高斯算法""" n = len(mean) norm_const = 1.0 / (np.power((2 * np.pi), n / 2.0) * np.power(np.linalg.det(covariance), 0.5)) x_mean = np.matrix(x - mean) inv_cov = np.linalg.inv(covariance) result = np.power(np.e...
def prior(theta): # evaluate the prior for the parameters on a multivariate gaussian. prior_out = sc.multivariate_normal.logpdf(theta[:2],mean=np.array([0,0]), cov=np.eye(2)*100) # this needs to be summed to the prior for the sigma, since I assumed independence. prio...
X,Y=np.meshgrid(x,y)# Pack X and Y into a single 3-dimensional arraypos=np.empty(X.shape+(2,))pos[:,:,0]=Xpos[:,:,1]=YZ=multivariate_gaussian(mu_v,sigma_v,pos)ax1=fig.add_subplot(122,projection='3d')ax1.plot_surface(X,Y,Z)plt.suptitle('Figure 2:Sampled and Ground ...
1. GaussianKernelDensity 2. UniformKernelDensity 3. TriangleKernelDensity 多变量分布 1. IndependentComponentsDistribution 2. MultivariateGaussianDistribution 3. DirichletDistribution 4. ConditionalProbabilityTable 5. JointProbabilityTable 模型可以从已知值中创建 ...
3. Problem: To use Gibbs Sampler to draw 10000 samples from a Bivariate Gaussian Distribution with μ=[5,5], μ=[5,5], and Σ=[10.90.91]. Σ=[10.90.91]. 4. Start up: Multivariate Gaussian Conditional distribution derivation can be found in the following links: ...
1. GaussianKernelDensity 2. UniformKernelDensity 3. TriangleKernelDensity 多变量分布 1. IndependentComponentsDistribution 2. MultivariateGaussianDistribution 3. DirichletDistribution 4. ConditionalProbabilityTable 5. JointProbabilityTable 模型可以从已知值中创建 ...
GMM与EM算法的Python实现 高斯混合模型(GMM)是一种常用的聚类模型,通常我们利用最大期望算法(EM)对高斯混合模型中的参数进行估计。 1. 高斯混合模型(Gaussian Mixture models, GMM) 高斯混合模型(Gaussian Mixture Model,GMM)是一种