Time for action – drawing a normal distribution 刚刚发生了什么? 我们使用来自随机 NumPy 模块的normal()函数可视化正态分布。 为此,我们绘制了钟形曲线和随机生成的值的直方图(请参见normaldist.py): 代码语言:javascript 复制 import numpy as np import matplotlib.pyplot as plt N=10000 np.random.seed(27...
还可以使用normal distribution方法来初始化数组,均值为0、方差为1 >>np.random.randn(3,4) array([[ 0.05971094, 1.57336808, -0.56372917, 1.58623654], [-0.82797012, 0.19435163, 1.64495295, 0.07486049], [-0.97815692, 1.4891987 , 0.65185811, -0.53984805]]) 为了更加直观的表示上面的函数,将使用matplotlib将...
rand Draw(抽出) samples from a uniform distribution randint Draw random integers from a given low-to-high range randn Draw samples from a normal distribution with mean 0 and standard deviation 1 (MATLAB-like interface) binomial Draw samples from a binomial distribution normal Draw samples from a ...
price_std=diamonds["price"].std() #Drawfromaperfectnormaldistribution perfect_norm=np.random.normal(price_mean,price_std,size=1000000) sns.kdeplot(diamonds["price"],ax=ax) sns.kdeplot(perfect_norm,ax=ax) plt.legend(["Price","PerfectNormalDistribution"]); 这可以通过在完美正态分布之上绘制钻...
plt.legend(["Price","Perfect Normal Distribution"]); 这可以通过在完美正态分布之上绘制钻石价格的 KDE 来实现,以使差异可见。 np.rint 如果你想将数组的每个元素四舍五入到最接近的整数, rint 是一个漂亮的小函数。当你想将类概率转换为二进制分类中的类标...
Help on built-in function randint: randint(...) method of numpy.random.mtrand.RandomState instance randint(low, high=None, size=None, dtype='l') Return random integers from `low` (inclusive) to `high` (exclusive). Return random integers from the "discrete uniform" distribution of ...
fig, ax = plt.subplots(figsize=(6, 8))price_mean = diamonds["price"].mean()price_std = diamonds["price"].std()# Drawfroma perfect normal distributionperfect_norm = np.random.normal(price_mean, price_std,size=1000000)sns.kdeplot(diamonds["price"], ax=ax)sns.kdeplot(perfect_norm, ...
doxygenfunction 这个指令生成单个函数的适当输出。函数名在项目中必须是唯一的。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 .. doxygenfunction:: <function name> :outline: :no-link: 查看示例 以查看它的工作原理。 doxygenclass 这个指令生成单个类的适当输出。它接受标准项目、路径、大纲和无链接选...
perfect_norm=np.random.normal(price_mean,price_std,size=1000000) sns.kdeplot(diamonds["price"],ax=ax) sns.kdeplot(perfect_norm,ax=ax) plt.legend(["Price","PerfectNormalDistribution"]); 这可以通过在完美正态分布之上绘制钻石价格的 KDE 来实现,以使差异可见。
5:normal normal(loc=0.0, scale=1.0, size=None) Draw random samples from a normal (Gaussian) distribution. The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and Laplace ...