翻译 CleverAlgorithms 代码之 stochastic

最近开始看 Clever Algorithms: Nature-Inspired Programming Recipes，学习各种算法及其代码。作者使用Ruby来编写算法代码。这里为了加深理解和增强 Python 编码技能，建立了将代码翻译成 Python 的小项目，包含算法和测试代码。github 地址：https://github.com/fox000002/PyCleverAlgorithms。

翻译的第一部分是随机搜索算法，以直接随机搜索为例。

原始Ruby代码：

# Random Search in the Ruby Programming Language

# The Clever Algorithms Project: http://www.CleverAlgorithms.com
# (c) Copyright 2010 Jason Brownlee. Some Rights Reserved. 
# This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.5 Australia License.

def objective_function(vector)
  return vector.inject(0) {|sum, x| sum + (x ** 2.0)}
end

def random_vector(minmax)
  return Array.new(minmax.size) do |i|      
    minmax[i][0] + ((minmax[i][1] - minmax[i][0]) * rand())
  end
end

def search(search_space, max_iter)
  best = nil
  max_iter.times do |iter|
    candidate = {}
    candidate[:vector] = random_vector(search_space)
    candidate[:cost] = objective_function(candidate[:vector])
    best = candidate if best.nil? or candidate[:cost] < best[:cost]
    puts " > iteration=#{(iter+1)}, best=#{best[:cost]}"
  end
  return best
end

if __FILE__ == $0
  # problem configuration
  problem_size = 2
  search_space = Array.new(problem_size) {|i| [-5, +5]}
  # algorithm configuration
  max_iter = 100
  # execute the algorithm
  best = search(search_space, max_iter)
  puts "Done. Best Solution: c=#{best[:cost]}, v=#{best[:vector].inspect}"
end

翻译后Python代码：

#!/usr/bin/env python

def objective_function(v):
    return sum(map(lambda x : x**2, v))

def random_vector(minmax):
    import random
    return map(lambda x : x[0] + (x[1]-x[0]) * random.random(), minmax)

def search(search_space, max_iter):
    best = None
    for iter in range(0, max_iter):
        candidate = {}
        candidate['vector'] = random_vector(search_space)
        candidate['cost']   = objective_function(candidate['vector'])
        if best is None or candidate['cost'] < best['cost']:
            best = candidate
        print ' > iteration=%d, best=%f' % (iter+1, best['cost'])
    return best

def main():
    #
    problem_size = 2
    search_space = [[-5,5]] * problem_size
    #
    max_iter = 100
    #
    best = search(search_space, max_iter)
    print 'Done. Best Solution: c=%f, v=%s' % (best['cost'], str(best['vector']))

if __name__ == "__main__":
    main()

Python 代码采用lambda语法，利用map来操作list很方便，翻译起来还比较顺畅。

单元测试代码：

#!/usr/bin/env python

import unittest

import os

os.sys.path.append("..")

from random_search import objective_function, random_vector, search

class TestRandomSearch(unittest.TestCase):
    def setUp(self):
        self.data = [1,2]

    def test_objective_function(self):
        self.assertEqual(objective_function(self.data), 5)

    def test_random_vector(self):
        minmax = [ [1,2], [2,3] ]

        self.assertEqual(minmax[0][0], 1)

        rv = random_vector(minmax)

        self.assertEqual(len(rv), 2)
        self.assertTrue(rv[0] >= minmax[0][0] and rv[0] <= minmax[0][1])
        self.assertTrue(rv[1] >= minmax[1][0] and rv[1] <= minmax[1][1])

    def test_search(self):
        problem_size = 2
        search_space = [[-5,5]] * problem_size
        #
        max_iter = 100
        #
        best = search(search_space, max_iter)
        #
        self.assertIsNotNone(best)
        self.assertTrue(best['cost'] >= -5 and best['cost'] <= 5)


if __name__ == '__main__':
    unittest.main()