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:
datasets = []
labelsets = []
addressD = '' #Data folder
addressL = '' #Label folder
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):
if datasets is None:
self.datasets = []
else:
self.datasets = datasets
self.addressD = addressD
self.addressL = addressL
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()
self.npDatasets = np.array(self.datasets)
fl = open(self.addressL,'r')
for line in fl:
tmp = line[:-1].split()
self.labelsets.append([int(i) for i in tmp])
fl.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
self.genx[i][1] = i
self.geny[i] = (i + bias) + random.uniform(0,1) * variance
def gradientDescent(self):
xTrans = self.genx.transpose() #
i = 0
while i < self.numIterations:
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
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