ML之UliR：利用非线性回归，梯度下降法(迭代十万次)求出学习参数θ，进而求得Cost函数最优值

ML之UliR：利用非线性回归，梯度下降法(迭代十万次)求出学习参数θ，进而求得Cost函数最优值

目录

输出结果

代码设计

输出结果

更新……

代码设计

      import numpy as np
      import random
      def genData(numPoints,bias,variance):
       x = np.zeros(shape=(numPoints,2))
       y = np.zeros(shape=(numPoints))
      for i in range(0,numPoints):
       x[i][0]=1
       x[i][1]=i
       y[i]=(i+bias)+random.uniform(0,1)%variance
      return x,y
      def gradientDescent(x,y,theta,alpha,m,numIterations):
       xTran = np.transpose(x)
      for i in range(numIterations):
       hypothesis = np.dot(x,theta)
       loss = hypothesis-y
       cost = np.sum(loss**2)/(2*m)
       gradient=np.dot(xTran,loss)/m
       theta = theta-alpha*gradient
      print ("Iteration %d | cost :%f" %(i,cost))
      return theta
      x,y = genData(100, 25, 10)  #100行，
      print ("x:")
      print (x)
      print ("y:")
      print (y)
      m,n = np.shape(x)
      n_y = np.shape(y)
      print("m:"+str(m)+" n:"+str(n)+" n_y:"+str(n_y))
      numIterations = 100000
      alpha = 0.0005
      theta = np.ones(n)
      theta= gradientDescent(x, y, theta, alpha, m, numIterations)
      print(theta)
  
 

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ML之UliR：利用非线性回归，梯度下降法(迭代十万次)求出学习参数θ，进而求得Cost函数最优值

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原文链接：yunyaniu.blog.csdn.net/article/details/78993485