1用图表画一个函数
import numpy as np
import matplotlib.pyplot as plt
import math
from pylab import *
x = np.arange(0,2, 0.02)
y = np.sin(x)*np.sin(x)*(x-2)*exp(-x*x) #函数
plt.figure(1)
plt.subplot(211)
plt.annotate('local min', xy=(0.75, -0.3), xytext=(0.7, -0.1),
arrowprops=dict(facecolor='black', shrink=0.05),
) #标签
plt.plot(x, y)
plt.title("plotting a function") #名字
plt.show() #画图
2.数据
import numpy as np
import matplotlib.pyplot as plt
x = np.random.normal(0, 1, 20)
y = np.random.normal(0, 1, 20)
b = np.random.normal(0, 1, 20)
plt.scatter(x, x*b-y, s = 75,marker='x') #画图
plt.xlim((-2, 2))
plt.ylim((-2, 2))
plt.show()
可见b = argmin||Xb − y||2 为(-1.1,-0.5)。
3.柱状图和密度估计
from scipy import stats #核密度估计
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
mu, sigma = 100, 15
x = mu + sigma*np.random.randn(10000)
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='blue', alpha=0.75)
y = mlab.normpdf( bins, mu, sigma)
l = plt.plot(bins, y, 'r-', linewidth=1) #制图
plt.axis([40, 160, 0, 0.03])
plt.show()