指数分布(Exponential Distribution): 生成方法: 使用numpy.random.exponential()函数生成。需要指定尺度参数(scale)。 特点: 指数分布用于描述连续随机事件的时间间隔。它具有单峰、右偏的形状,平均间隔时间由尺度参数决定。 常见场景: 信号传输延迟、设备故障时间、等待事件的时间间隔等。 import numpy as np import mat...
numpy.random.randint(low, high=None, size=None, dtype='l')Return random integers fromlow(inclusive) tohigh(exclusive). Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then r...
numpy.random.randint(low, high=None, size=None, dtype='l')Return random integers fromlow(inclusive) tohigh(exclusive). Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then r...
importnumpyasnp# 生成一个指数分布随机数组,scale=2,size=1000exponential_array=np.random.exponential(scale=2,size=1000)print("Exponential distribution array from numpyarray.com:",exponential_array) Python Copy Output: 这个示例生成了1000个符合指数分布的随机数,其中scale=2表示分布的尺度参数(1/λ)。 5...
plt.hist(random_numbers,bins=50,density=True)plt.xlabel('Random Numbers')plt.ylabel('Probability')plt.title('Exponential Distribution')plt.show() 1. 2. 3. 4. 5. 6. 7. 这里,我们使用plt.hist函数来绘制直方图,bins参数表示直方图的箱子数量,density=True表示将频率转换为概率。plt.xlabel和plt.ylab...
random.rand() This function returns random floats in a half-open interval[0.0, 1.0). The numbers are sampled from a uniform distribution over that range. You can also create multi-dimensional arrays of random floats by providing dimensions as arguments. ...
random_integers(low[, high, size]) 返回随机的整数,位于闭区间 [low, high]。 Notes To sample from N evenly spaced floating-point numbers between a and b, use: a + (b- a) * (np.random.random_integers(N) -1) / (N-1.) Examples ...
=np.random.poisson(5,1000)# 泊松分布exponential_data=np.random.exponential(1,1000)# 指数分布lognormal_data=np.random.lognormal(0,1,1000)# 对数正态分布beta_data=np.random.beta(0.5,0.5,1000)# 贝塔分布chisquare_data=np.random.chisquare(2,1000)# 卡方分布negative_binomial_data=np.random....
python-numpy-指数分布实例详解 如下所⽰：# Seed random number generator np.random.seed(42)# Compute mean no-hitter time: tau tau = np.mean(nohitter_times)# Draw out of an exponential distribution with parameter tau: inter_nohitter_time inter_nohitter_time = np.random.exponential(tau, ...