Hill-climbing 其實(shí)也不是很復(fù)雜，在這個(gè)博文里面，我假定一個(gè)512維度的空間中存在一個(gè)點(diǎn)point1 ，我的目標(biāo)是隨機(jī)初始化一個(gè)點(diǎn)，通過(guò)Hill-climbing找到這個(gè)目標(biāo)點(diǎn)point1。

站在當(dāng)前點(diǎn)，通過(guò)探索所有可能的走法去判斷下一步哪一個(gè)走法是能夠朝著目標(biāo)靠近的。過(guò)程其實(shí)也不復(fù)雜，在512里面的某一個(gè)維度，走起來(lái)只有兩種選擇，加一個(gè)步長(zhǎng)或者減去一個(gè)步長(zhǎng)。如果下一步找不到比較好的選擇，都不能靠近目標(biāo)點(diǎn)，那么算法結(jié)束。

具體代碼可以看下面：

__author__ = 'xingbo, it is based on inital version of sidharthgoyal' import math from random import * import random import numpy import copyincrement = 0.05 startingPoint = numpy.random.random(512) point1 = numpy.random.random(512) print('target:',point1) # point2 = [6,4] # point3 = [5,2] # point4 = [2,1]def distance(coords1, coords2):""" Calculates the euclidean distance between 2 lists of coordinates. """# print('coords1',coords1)# print('coords2',coords2)return numpy.sqrt(numpy.sum((coords1 - coords2)**2))def sumOfDistances(x, px):d1 = distance(x, px)return d1def newDistance(d1, point1):d1temp = sumOfDistances(d1, point1 )d1 = numpy.append(d1,d1temp) return d1minDistance = sumOfDistances(startingPoint, point1 ) flag = Truethreshold = 0.4 i = 1 lastFitness = 99 while lastFitness > threshold:d = []old_point = startingPointfor index in range(512):increment_arr = numpy.zeros(512)increment_arr[index] = incrementnewpoint = startingPoint + increment_arrd1 = newDistance(newpoint, point1)newpoint = startingPoint - increment_arrd2 = newDistance(newpoint, point1)d.append(d1)d.append(d2)# print(i,' ', startingPoint[:4])d = numpy.array(d)minimum = min(d[:,512])if minimum < minDistance:minindex = numpy.argmin(d[:,512])startingPoint = d[minindex,:512]minDistance = minimumprint('found ',i,' ', startingPoint[:4],'score',d[minindex,512])lastFitness = d[minindex,512]else:flag = False# print('new start poiny ',i)startingPoint = startingPoint + numpy.random.random(512) * 0.1i+=1print('target:',point1[:10]) print('result:',startingPoint[:10])

運(yùn)行結(jié)果：

found 3319 [0.54880071 0.14943408 0.16443942 0.67552999] score 0.40665809714898343 found 3320 [0.54880071 0.14943408 0.16443942 0.67552999] score 0.4050587747707967 found 3321 [0.54880071 0.14943408 0.16443942 0.67552999] score 0.4034618667141106 found 3322 [0.54880071 0.14943408 0.16443942 0.67552999] score 0.4018617552269553 found 3323 [0.54880071 0.14943408 0.16443942 0.67552999] score 0.4002772234555598 found 3324 [0.54880071 0.14943408 0.16443942 0.67552999] score 0.3986881730615146 target: [0.54391242 0.16028716 0.15798658 0.6986903 0.74380195 0.258413040.40675962 0.2248158 0.76813796 0.7937874 ] result: [0.54880071 0.14943408 0.16443942 0.67552999 0.73628524 0.25467860.37020921 0.2397613 0.75941863 0.75618421] import numpy as np from scipy.optimize import minimizext = np.random.rand(512) * 2 print('x terget:',xt[:10])def rosen(x):"""The Rosenbrock function"""return np.sqrt(np.sum((x - xt)**2))# 初始迭代點(diǎn) x0 = np.random.rand(512) * 2res = minimize(rosen, x0, method='BFGS', jac=[],options={'disp': True})print('res',res.x[:10])

總結(jié)

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