实例1:

需求: 一元线性回归+描点绘制+方程输出

效果:

code:

# encoding = utf-8 import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 #n=int(input()) x = [[8.214], [7.408], [6.879], [5.490], [5.196]] y = [[-1.756], [-1.442], [-1.194], [-0.606], [-0.476]] # for i in range (1, n): # print('x' ,i, " = ") # tmp = float(input()) # x.append(tmp) # for i in range (1, n): # print('x\y' ,i, " = ") # tmp = float(input()) # y.append(tmp) plt.title('$U_0-v$') plt.xlabel(r'频率$v/10^{14} HZ$') plt.ylabel(r'截止电压$U_0/1 Volt$') plt.grid(True) plt.plot(x, y, 'r^') regr = linear_model.LinearRegression(); regr.fit(x, y) print(type(float(regr.coef_))) #plt.text(7.5, 1.2, r'y='.join(str(float(regr.coef_))).join('x+').join(str(float(regr.intercept_))).join('$')) plt.plot(x, regr.predict(x), 'g--', label = '$y = '+ str(float(regr.coef_))+"x +" +str(float(regr.intercept_))+'$') plt.legend() plt.show() print(x), print(y)

实例2:

需求: 曲线描点

效果:

code:

# encoding = utf-8 import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 #n=int(input()) x = [list(range(-1, 49, 2))] x = np.array(x); x = x.reshape(25, 1) print(x) y = [0.1, 5.1, 10.5, 14.6, 17.1, 19.2, 20.9, 22.4, 23.6, 24.7, 25.5, 26.3, 26.8, 27.2, 27.6, 27.9, 28.3, 28.9, 29.2, 29.6, 29.9, 30.2, 30.7, 31.2, 31.5] y = np.array(y); #y = y.reshape(25, 1) # for i in range (1, n): # print('x' ,i, " = ") # tmp = float(input()) # x.append(tmp) # for i in range (1, n): # print('x\y' ,i, " = ") # tmp = float(input()) # y.append(tmp) plt.title('伏安特性数据') plt.xlabel(r'$U_{AK} / V$') plt.ylabel(r'$I / 10^{-10} A$') plt.grid(True) plt.plot(x, y, 'r--',label = r'伏安特性曲线') plt.plot(x, y, 'k^') # regr = linear_model.LinearRegression(); # regr.fit(x, y) # print(type(float(regr.coef_))) #plt.text(7.5, 1.2, r'y='.join(str(float(regr.coef_))).join('x+').join(str(float(regr.intercept_))).join('$')) #plt.plot(x, regr.predict(x), 'g--', label = '$y = '+ str(float(regr.coef_))+"x +" +str(float(regr.intercept_))+'$') plt.legend() plt.show() #print(x), print(y)

实例3 简单描点:

效果:

两个差不多的,放第一个代码

# encoding = utf-8 import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 #n=int(input()) x = [2, 4, 8] x = np.array(x); print(x) y = [7.9, 31.5, 110.1] y = np.array(y); #y = y.reshape(25, 1) # for i in range (1, n): # print('x' ,i, " = ") # tmp = float(input()) # x.append(tmp) # for i in range (1, n): # print('x\y' ,i, " = ") # tmp = float(input()) # y.append(tmp) plt.title('$I_m - \phi$') plt.xlabel(r'光阑孔径$\phi / mm$') plt.ylabel(r'$I / 10^{-10} A$') plt.grid(True) plt.plot(x, y, 'r--',label = r'$I_m - \phi$数据曲线') plt.plot(x, y, 'k^') # regr = linear_model.LinearRegression(); # regr.fit(x, y) # print(type(float(regr.coef_))) #plt.text(7.5, 1.2, r'y='.join(str(float(regr.coef_))).join('x+').join(str(float(regr.intercept_))).join('$')) #plt.plot(x, regr.predict(x), 'g--', label = '$y = '+ str(float(regr.coef_))+"x +" +str(float(regr.intercept_))+'$') plt.legend() plt.show() #print(x), print(y) egr.predict(x), 'g--', label = '$y = '+ str(float(regr.coef_))+"x +" +str(float(regr.intercept_))+'$') plt.legend() plt.show() #print(x), print(y)