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
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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