第十二周作业（Matplotlib Exercise） python

题目总览
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1、Plotting a function 代码

import numpy as np import matplotlib.pyplot as plt X = np.arange(0,2,0.01) # 可以生成实数 # ~ X = np.linspace(0,0.001,2) # 生成整数 Y = np.sin(X-2)*np.sin(X-2)*np.exp(-X*X) fig, ax=plt.subplots() ax.plot(X,Y) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_xlim((0,2)) ax.set_ylim((0,1)) ax.set_title('Exercise 11.1:Plotting a function') plt.show()

运行结果

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2、Data 代码

import matplotlib.pyplot as plt import numpy as np def LinearLeastSquare(X,Y): N = len(X) sumx = sum(X) sumy = sum(Y) sumx2 = sum(X**2) sumxy = sum(X*Y) MA = [[sumx2,sumx],[sumx,N]] VB = [sumxy,sumx] eb,c = np.linalg.solve(MA,VB) return ebdef main(): # generate matrix X X = [] for i in range(0,20): X_item = np.random.random(10) X.append(X_item) X = np.array(X) X = X*10 # generate vector b b = np.random.normal(loc=2.,scale=8.,size=20) # generate noise vector z z = np.random.normal(loc=0.,scale=1.,size=10) # compute estimate vector b eb = [] for i in range(0,20): X_item = X[i] bi = b[i] Y = X_item * bi + z eb_item = LinearLeastSquare(X_item,Y) eb.append(eb_item) Index = list(range(1,21)) plt.plot(Index,b,'r*',label='$real b$') plt.plot(Index,eb,'g*',label='$estimate b$') plt.xlabel('index') plt.ylabel('value of b and eb') plt.title('b vs estimate b') plt.legend() plt.show()if __name__ == '__main__': main()

运行结果

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3、Histogram and density estimation 代码（使用stats）

import numpy as np import matplotlib.pyplot as plt from scipy import stats import matplotlibz=np.random.normal(loc=10,scale=20,size=10000) figure,ax=plt.subplots() ax.hist(z, bins=25,color='g',density=True) kernel=stats.gaussian_kde(z) x=np.linspace(-60,80,3000) y=kernel.pdf(x) ax.plot(x,y,'b-') ax.set_title('Exercise 11.3:Histogram and density estimation') plt.show()

运行结果（使用stats）

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代码（使用displot）

import seaborn import matplotlib.pyplot as plt import numpy as npdata = https://www.it610.com/article/np.random.normal(loc=10,scale=20,size=10000) seaborn.distplot(data,bins=25,hist=True) plt.show()

运行结果（使用displot） 【第十二周作业（Matplotlib Exercise）】