【第十二周】Matplotlib作业

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


f, ax = plt.subplots(1,1,figsize=(5,4))

x=np.linspace(0,10,1000)

#y=np.power(np.power( (x-2),np.power(math.e,-np.power(x,2))),2)
y=np.power( np.sin((x-2) * np.power(np.e,-np.power(x,2)) ) ,2)
#y=np.power(x,2)
ax.plot(x,y)
ax.set_xlim((0,2))
ax.set_ylim((0,1))
ax.set_xlabel("my x label")
ax.set_ylabel("my y label")
ax.set_title("Ex 11.1")

plt.tight_layout()
plt.savefig('ex11.1.pdf')

生成图像：

                                      

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0,10,10)
X = np.sin(x)
##true b = 0.75

X = 0.75*np.eye(10)@X+np.random.randn(10)

plt.plot(x,X,'ro',label='$True$ $coefficients$')

lam = 1

b = lam*np.eye(10)
z = data = np.random.randn(10)

sub_list=[]
lams=[0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5]

for l in lams:
    lam = l
    b = lam*np.eye(10)
    ty = b@X + z
    sub = sum(abs(ty[i]-X[i]) for i in range(0,10))
    sub_list.append(sub)
    
index=np.argmin(sub_list)
perfect_b = lams[index]
b = perfect_b*np.eye(10)
y = b@X+z
plt.plot(x,y,'bo',label='$Estimated$ $coefficients$')

"""
print(X)
print(y)
print(perfect_b)
"""
plt.xlim((0,10))
plt.ylim((-2,2))
plt.xlabel('index')
plt.ylabel('value')
plt.title('ex11.2:Data')
plt.legend()
plt.show()

生成图像：

                                    

import numpy as np

import matplotlib.pyplot as plt import matplotlib.mlab as mlab data = np.random.randn(10000) num_bins = 25 n, bins, patches = plt.hist(data, 25, normed=True, facecolor='b', alpha=0.5) y = mlab.normpdf(bins, 0, 1) plt.plot(bins, y, 'r--') plt.title(r'ex11.3: standard normal distribution') plt.show()

生成图：