我正在尝试将牛顿方法应用于具有回溯的梯度下降算法。 梯度下降算法: ? 使用回溯算法的梯度下降: ? 牛顿方法: ? import numpy as np
from scipy import optimize as opt
def newton_gd_backtracking(w,itmax,tol):
# You may set bounds of "learnrate"
max_learnrate =0.1
min_learnrate =0.001
for i in range(itmax):
gra
我正在尝试为多变量梯度下降算法找出python代码,并发现了几个类似于以下几种实现:
import numpy as np
# m denotes the number of examples here, not the number of features
def gradientDescent(x, y, theta, alpha, m, numIterations):
xTrans = x.transpose()
for i in range(0, numIterations):
hypothesis = np.dot(x, theta)