第十二周作业 matplotlib

1、

代码如下：

import matplotlib.pyplot as plt
from math import *

input_values = [i/10000 for i in range(1, 20000)]
y = [pow(sin(x-2), 2)*pow(e, -pow(x, 2)) for x in input_values]

plt.plot(input_values, y, linewidth=5)

plt.title("function", fontsize=24)
plt.xlabel("Value", fontsize=14)
plt.ylabel("f_value", fontsize=14)

plt.tick_params(axis="both", labelsize=14)
plt.show()

运行结果：

2、

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import leastsq


X = np.random.randint(0,10,(20,10))
b = (np.random.randint(-5,5,(1,10))).T
z = np.random.randn(20,1)
y = np.dot(X,b)+z
est_b = (np.linalg.lstsq(X, y, rcond=None)[0]).T

x =range(0,10)
plt.scatter(x, b, c='b', marker='o', label='true parameters')
plt.scatter(x, est_b, c='r', marker='x', label='estimated parameters')
plt.legend()
plt.xlabel('index')
plt.ylabel('value')
plt.show()

运行结果：

3、

代码如下：

import numpy as np  
import matplotlib.pyplot as plt   
import matplotlib.mlab as mlab   
  
data = np.random.randn(10000)     
n, bins, patches = plt.hist(data, 25,  normed=True, facecolor='r')   
y = mlab.normpdf(bins, 0, 1)   
plt.plot(bins, y, 'b')   
plt.title('Normal Distribution')   
plt.show()  

运行结果：