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...
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_...
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}") ...
现在我们已经计算了概率密度函数,可以使用matplotlib来绘制它。 # 绘制多元高斯分布plt.contourf(X,Y,Z,levels=100,cmap='viridis')# 绘制等高线填充图plt.colorbar()# 添加颜色条plt.title('Multivariate Gaussian Distribution')# 图形标题plt.xlabel('X-axis')# X轴标签plt.ylabel('Y-axis')# Y轴标签plt.a...
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...
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 ...
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 ...
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 ...
1. IndependentComponentsDistribution 2. MultivariateGaussianDistribution 3. DirichletDistribution 4. ConditionalProbabilityTable 5. JointProbabilityTable 模型可以从已知值中创建 模型也可以从数据直接学习 pomegranate 比 numpy 快 只需要一次数据集(适用于所有模型)。以下是正态分布统计示例: ...