model.log_probability(X)/model.probability(X)model.sample()model.fit(X,weights,inertia)model.summarize(X,weights)model.from_summaries(inertia)model.predict(X)model.predict_proba(X)model.predict_log_proba(X)model.from_samples(X,weights) 支持很多分布函数 单变量分布 1. UniformDistribution 2. Bernou...
distributions=['gamma','rayleigh','uniform'], timeout =100)即可。2
单个指数分布不能很好的数据进行建模 model = ExponentialDistribution.from_samples(X) 两个指数混合使数据更好的模拟 model = GeneralMixtureModel.from_samples(ExponentialDistribution, 2, X) Heterogeneous mixtures are natively supported model = GeneralMixtureModel.from_samples([ExponentialDistribution, UniformDist...
rayleigh Rayleigh distribution. triangular Triangular distribution. uniform Uniform distribution. vonmises Von Mises circular distribution. wald Wald (inverse Gaussian) distribution. weibull Weibull distribution. zipf Zipf's distribution over ranked data.=== === === ===Multivariate distributions===...
n_draw = 20000prior_ni = pd.Series(np.random.uniform(0, 1, size = n_draw))plt.figure(figsize=(8,5))plt.hist(prior_ni)plt.title('Uniform distribution(0,1)')plt.xlabel('Prior on AVG')plt.ylabel('Frequency')先验概率代表了我们在得到具体数据之前对某事物的普遍看法。在上述分布中，所有...
numpy.random.randn(d0, d1, ..., dn)Return a sample (or samples) from the “standard normal” distribution. 【例】根据指定大小产生满足标准正态分布的数组(均值为0,标准差为1)。 import numpy as np import matplotlib.pyplot as plt from scipy import stats ...
uniform within range sequences --- pick random element pick random sample pick weighted random sample generate random permutation distributions on the real line: --- uniform triangular normal (Gaussian) lognormal negative exponential gamma beta pareto Weibull...
使用uniform(min, max) 函数可以生成在 [min, max) 之间的浮点数,该函数的返回值也是基于 random() 的返回值进行调整取得。 import random for i in xrange(5): print '%04.3f' % random.uniform(1, 100), print 85.481 89.581 34.903 56.218 20.251 ...
()trace=pm.sample(18000, step=step)burned_trace1=trace[1000:]#plot the posterior distribution of theta.p_true=0.5figsize(12.5,4)plt.title(r"Posterior distribution of $\theta for sample sizeN=1000$")plt.vlines(p_true,0,25, linestyl...
(value)return random_integers # return the resultX = random_uniform_sample(num_iterations, [0, 99])# print(X)fig = plt.figure()plt.hist(X)plt.title(f"Check Uniform Distribution of {num_iterations} iterations")plt.xlabel("Numbers")plt.ylabel("N of each number")plt.xlim([0, 99])...