Python编程实现线性回归和批量梯度下降法代码实例

Python编程实现线性回归和批量梯度下降法代码实例编程实现线性回归和批量梯度下降法代码实例

主要介绍了Python编程实现线性回归和批量梯度下降法代码实例，具有一定借鉴价值,需要的朋友可以参考下

通过学习斯坦福公开课的线性规划和梯度下降，参考他人代码自己做了测试，写了个类以后有时间再去扩展，代码注释以后再

加，作业好多：

import numpy as np

import matplotlib.pyplot as plt

import random

class dataMinning:

npDatasets = np.zeros(1)

npLabelsets = np.zeros(1)

cost = []

numIterations = 0

alpha = 0

theta = np.ones(2)

#pCols = 0

#dRows = 0

def __init__(self,addressD,addressL,theta,numIterations,alpha,datasets=None):

self.theta = theta

self.numIterations = numIterations

self.alpha = alpha

def readFrom(self):

fd = open(self.addressD,'r')

for line in fd:

tmp = line[:-1].split()

self.datasets.append([int(i) for i in tmp])

fd.close()

tm = []

for item in self.labelsets:

tm = tm + item

self.npLabelsets = np.array(tm)

def genData(self,numPoints,bias,variance):

self.genx = np.zeros(shape = (numPoints,2))

self.geny = np.zeros(shape = numPoints)

for i in range(0,numPoints):

self.genx[i][0] = 1

hypothesis = np.dot(self.genx,self.theta)

loss = hypothesis - self.geny

#record the cost

self.cost.append(np.sum(loss ** 2))

#calculate the gradient

gradient = np.dot(xTrans,loss)

#updata, gradientDescent

self.theta = self.theta - self.alpha * gradient

i = i + 1