数据分析python，线性回归

本节是python实现一元回归的代码部分，理论参考链接: link.
代码下载地址link.
代码可直接赋值运行，如有问题请留言

import numpy as np
import matplotlib.pyplot as pl
import matplotlib
matplotlib.rcParams['font.sans-serif']='SimHei'
matplotlib.rcParams['font.family']='sans-serif'
matplotlib.rcParams['axes.unicode_minus']=False

这些是需要的python组件和画图需要的包，matplotlib是画图的设置

def loadDataset(filename):
    X=[]
    Y=[]
    with open(filename,'rb') as f:
        for idx,line in enumerate(f):
            line=line.decode('utf-8').strip()
            if not line:
                continue
                
            eles=line.split(',')
            
            if idx==0:
                numFea=len(eles)
                
            eles=list(map(float,eles))#map返回一个迭代对象
            
            X.append(eles[:-1])
            Y.append([eles[-1]])
    return np.array(X),np.array(Y)

def h(theta,X):
return np.dot(X,theta)

def J(theta,X,Y):
return np.sum(np.dot((h(theta,X)-Y).T,(h(theta,X)-Y))/(2*m))

def bgd(alpha,maxloop,epsilon,X,Y):
    m,n=X.shape
    
    theta=np.zeros((2,1))
    
    count=0
    converged=False
    error=np.inf
    errors=[]
    thetas={0:[theta[0,0]],1:[theta[1,0]]}
    
    while count<=maxloop:
        if(converged):
            break
        
        count=count+1
        temp1=theta[0,0]-alpha/m*(h(theta,X)-Y).sum()
        temp2=theta[1,0]-alpha/m*(np.dot(X[:,1][:,np.newaxis].T,(h(theta,X)-Y))).sum()
        
        #同步更新
        theta[0,0]=temp1
        theta[1,0]=temp2
        thetas[0].append(temp1)
        thetas[1].append(temp2)
        
        error=J(theta,X,Y)
        errors.append(error)
        
        if(error

X,Y=loadDataset('./data/price_diff.csv')
print(X.shape)
print(Y.shape)

m,n=X.shape
X=np.concatenate((np.ones((m,1)),X),axis=1)
X.shape

xCopy=X.copy()
xCopy.sort(0)
yHat=h(theta,xCopy)
xCopy[:,1].shape,yHat.shape,theta.shape

pl.xlabel(u'1')
pl.ylabel(u'2')
pl.plot(xCopy[:,1],yHat,color='red')
pl.scatter(X[:,1].flatten(),Y.T.flatten())
pl.show()

pl.xlim(-1,3000)

pl.xlabel(u'迭代次数')
pl.ylabel(u'代价函数')
pl.plot(range(len(errors)),errors)
pl.show()