用最小二乘法对多项式进行拟合并可视化

import xlrd
import numpy
from matplotlib import pyplot as pl
data=xlrd.open_workbook(r'C:\Users\admin\Desktop\1.xlsx')
table=data.sheet_by_name('sheet1')
a=table.col_values()[:]
b=table.col_values()[:]
x=numpy.array(a)
y=numpy.array(b)
class fitting:
    def __init__(self,X,Y):
        self.x=numpy.array(X)
        self.y=numpy.array(Y)
    def fitting(self,n):
        self.z=numpy.polyfit(self.x,self.y,n)
        self.p=numpy.poly1d(self.z)
        self.error=numpy.abs(self.y-numpy.polyval(self.p,self.x))
        self.ER2=numpy.sum(numpy.power(self.error,))/len(self.x)
        return self.z,self.p
    def geterror(self):
        return self.error,self.ER2
    def show(self):
        figure1=pl.figure()
        pl.plot(self.x,self.y,'ro-',markersize=.,figure=figure1,label='origin data')
        pl.plot(self.x,numpy.polyval(self.p,self.x),markersize=,figure=figure1,label='fitting data')
        pl.legend()


        pl.show()
    def predict(self,x):
        return numpy.polyval(self.p,x)
F=fitting(x,y)
z,p=F.fitting()
e,E=F.geterror()
print ('系数：',z)
print ('拟合函数：',p)
print ('最小平方误差：',E)
a=#通过改变a的值来进行预测
print ('F({})的预测值'.format(a),F.predict(a))
F.show()