第十二周作业：Matplotlib Exercise

题目总览

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

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 eb

def 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

3、Histogram and density estimation

代码（使用stats）

import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import matplotlib

z=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）

代码（使用displot）

import seaborn
import matplotlib.pyplot as plt
import numpy as np

data = 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）