import math,pylab,matplotlib,numpy,statistics_functions,Binomial_distribution,normal_distribution def Draw_normal_distribution(u,q): x=numpy.arange(-4,4.1,0.1) #x取值范围可以随意更改 for value in x: y=normal_distribution.Normal_distribution(x,u,q) #x,y都要绘制出来 pylab.plot(x,y,'r') pyla...
def normal_dist_curve(x): return 10000*np.exp(-0.5*((x- mu)/sigma)**2)/(sigma*np.sqrt(2*np.pi)) 最后,我们在数据的直方图上绘制我们的预期分布: 代码语言:javascript 代码运行次数:0 运行 复制 x_range = np.linspace(-5, 15) y = normal_dist_curve(x_range) ax.plot(x_range, y, "...
# Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Draw Plot plt.figure(figsize=(13,10), dpi=80) sns.boxplot(x='class', y='hwy', data=df, notch=False) # Add N Obs inside boxplo...
normal Draw samples from a normal (Gaussian) distribution beta Draw samples from a beta distribution chisquare Draw samples from a chi-square distribution gamma Draw samples from a gamma distribution uniform Draw samples from a uniform [0, 1) distribution """ samples = np.random.normal(size=(4...
如下所示: 两个函数:Basemap.drawparallels ##纬度 Basemap.drawmeridians ##经度 from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # setup Lambert Conformal basemap. m = Basemap(width=12000000,height=9000000,projection='lcc' ...
A typical normal data distribution: importnumpy importmatplotlib.pyplotasplt x =numpy.random.normal(5.0,1.0,100000) plt.hist(x,100) plt.show() Result: Run example » Histogram Explained We use the array from thenumpy.random.normal()method, with 100000 values, to draw a histogram with 100...
import pymc as pm # Taking draws from a normal distribution seed = 42 x_dist = pm.Normal.dist(shape=(100, 3)) x_data = pm.draw(x_dist, random_seed=seed) # Independent Variables: # Sunlight Hours: Number of hours the plant is exposed to sunlight daily. # Water Amount: Daily water...
fromnumpy.randomimportdefault_rng rng = default_rng(12345) 像往常一样,我们导入了 Matplotlib 的pyplot模块并将其命名为plt,以及导入了 NumPy 并将其命名为np。 如何操作... 在接下来的步骤中,我们生成遵循正态分布的随机数据: 我们在Generator实例上使用normal方法来生成符合normal分布的随机数据。正态分布有两...
draw standard deviation normal distribution """mu =0.0# mean 均值sd =1.0# std 标准差x = np.linspace(mu -4* sd, mu +4* sd,100)# x rangey = stats.norm.pdf(x) plt.plot(x, y,"g", linewidth =2) plt.grid(True)# 显示网格线plt.show()if__name__ =='__main__': ...
当使用给定的函数绘制我的线性回归模型的结果时,我不确定是否定义的函数drawline不正确,或者我的建模过程中有其他错误。 这里是定义的功能 def drawline(model, x_test, y_test, title, r2):fig = plt.figure() ax = fig.add_subplot(111)ax.scatter(x_test, ... ...