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
更新……
- 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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