import numpy as np import matplotlib.pyplot as plt plt.rcParams[\'font.family\'] = [\'sans-serif\'] plt.rcParams[\'font.sans-serif\'] = [\'SimHei\']
def linear_regression(x, y): N = len(x) sumx = sum(x) sumy = sum(y) sumx2 = sum(x ** 2) sumxy = sum(x * y) A = np.mat([[N, sumx], [sumx, sumx2]]) b = np.array([sumy, sumxy]) return np.linalg.solve(A, b) #单臂 #修改数据1： X1=np.array([0,20,40,60,80,100,120,140,160,180,200]) Y1=np.array([0,0.02,0.06,0.1,0.13,0.16,0.19,0.22,0.245,0.278,0.3]) #半桥 #修改数据2： X2=np.array([0,20,40,60,80,100,120,140,160,180,200]) Y2=np.array([0,0.057,0.118,0.185,0.245,0.308,0.376,0.425,0.488,0.544,0.58]) a0, a1 = linear_regression(X1, Y1) # 生成拟合直线的绘制点 _X1 = [0, 200] _Y1 = [a0 + a1 * x for x in _X1] a0, a1 = linear_regression(X2, Y2) # 生成拟合直线的绘制点 _X2 = [0, 200] _Y2 = [a0 + a1 * x for x in _X1] #显示图像 plt.plot( X1, Y1, \'ro\', linewidth=2,label="单臂电桥") plt.plot(_X1, _Y1, \'b\',linewidth=2,label=\'单臂电桥\',color=\'C0\') plt.plot( X2, Y2, \'g^\', linewidth=2,label=\'半桥\') plt.plot(_X2, _Y2, \'b\', linewidth=2,label=\'半桥\',color=\'C1\') plt.xlabel(\'weight/g\') plt.ylabel(\'voltage/v\') plt.legend() plt.show()
