Python编程实现线性回归和批量梯度下降法代码实例

Python  /  管理员 发布于 7年前   167

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 npimport matplotlib.pyplot as pltimport randomclass 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          def show(self):    print 'yes'    if __name__ == "__main__":  c = dataMinning('c:\\city.txt','c:\\st.txt',np.ones(2),100000,0.000005)  c.genData(100,25,10)  c.gradientDescent()  cx = range(len(c.cost))  plt.figure(1)  plt.plot(cx,c.cost)  plt.ylim(0,25000)  plt.figure(2)  plt.plot(c.genx[:,1],c.geny,'b.')  x = np.arange(0,100,0.1)  y = x * c.theta[1] + c.theta[0]  plt.plot(x,y)  plt.margins(0.2)  plt.show()

图1. 迭代过程中的误差cost

图2. 数据散点图和解直线

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