return the probability of Multidimensional gaussian distribution, and there is a better implementation in scipy.stats 返回多维高斯分布的结果,该方法在scipi库中有更好的实现。 :param x: x :param mu: mean vector :param sigma: covariance matrix :return: the probability of Multidimensional gaussian distr...
Model 4: Multivariate Gaussian Distribution from scipy.stats import multivariate_normal from sklearn.metrics import f1_score,confusion_matrix def estimate_gaussian(dataset): mu = np.mean(dataset, axis=0) sigma = np.cov(dataset.T) return mu, sigma def multivariate_gaussian(dataset, mu, sigma): ...
4. LogNormalDistribution 5. ExponentialDistribution 6. BetaDistribution 7. GammaDistribution 8. DiscreteDistribution 9. PoissonDistribution 内核密度 1. GaussianKernelDensity 2. UniformKernelDensity 3. TriangleKernelDensity 多变量分布 1. IndependentComponentsDistribution 2. MultivariateGaussianDistribution 3. Dir...
importmatplotlib.pyplotasplt# 生成网格点x=np.linspace(-3,3,100)y=np.linspace(-3,3,100)X,Y=np.meshgrid(x,y)positions=np.vstack([X.ravel(),Y.ravel()])# 计算概率密度值Z=multivariate_normal(mu,sigma).pdf(positions.T).reshape(X.shape)# 绘制等高线图plt.contour(X,Y,Z)plt.scatter(data...
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...
sigma2 = np.var(X,0)#fits a multivariate Gaussian distribution to the data and#finds probablity distribution functionp = multivariate_normal.pdf(X, mu, sigma2)#finds predictedpdffor validation datapval = multivariate_normal.pdf(Xval, mu, sigma2) ...
# 需要导入模块: from torch import distributions [as 别名]# 或者: from torch.distributions importMultivariateNormal[as 别名]def_fitting_multivari(self, best_samples):""" Fit multivariate gaussian and sampling from it Parameters --- best_samples...
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: ...
# 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. ...
TSFresh(基于可扩展假设检验的时间序列特征提取)是一个专门用于时间序列数据特征自动提取的框架。该框架提取的特征可直接应用于分类、回归和异常检测等机器学习任务。TSFresh通过自动化特征工程流程,显著提升了时间序列分析的效率。 自动化特征提取过程涉及处理数百个统计特征,包括均值、方差、偏度和自相关性等,并通过统计检...