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使用Golang编写优化算法 (1)

动手写点东西是学习新知识很重要的一个阶段。之前用 Python 和 JavaScript 实现优化算法,现在用 Golang 来实现。语法上略有不爽,某些C语言的思维又回来了。

- Golang 用 package 来组织代码,同一 package 下不同文件之间的标识符是共享的,不能包含两个相同名称的函数。而且只有 package main 能够包含 main 函数。所以将公用的函数提取出来,放在package common。同时,每种例子程序移动到 examples 目录下。

- 在 CleverAlgorithms 中都是随机优化算法,最常用的是随机数或向量的生成函数。因为默认采用Fixed Seed,所以需要自行设置成运行时刻的纳秒值作为种子。

- 在缺乏灵活的dict类型之后,需要定义struct组合类型来满足数组单元中存储不同类型值的需求。

 

package commonimport (	"math/rand"	"time")// InitSeed set random seed with current time valuefunc InitSeed() {	rand.Seed(time.Now().UnixNano())}// RandomVector generates a random vector from min_max bound.// It returns the generated random vector.func RandomVector(min_max [][2]float64) []float64 {	var v = make([]float64, len(min_max))	for i, mm := range min_max {		v[i] = mm[0] + (mm[1]-mm[0])*rand.Float64()	}	return v}// RandomBound generates a random value from the bound.// It returns the random value.func RandomBound(bound [2]float64) float64 {	return bound[0] + (bound[1]-bound[0])*rand.Float64()}// FRange simulates range in python for float64.// It yields values in the range.func FRange(start float64, stop float64, step float64) (c chan float64) {	c = make(chan float64)	go func() {		for x := start; x<stop;	x += step {			c <- x		}		close(c)	}()	return}// Entity stores cost and vector.type Entity struct {	Cost   float64	Vector []float64}

 

然后,随机搜索的代码变成:

//// Random Search//package stochasticimport (	"clever_algorithms/common"	"fmt")func objective_function(v []float64) float64 {	return common.SphereFunction(v)}func RandomSearch(search_space [][2]float64, max_iteration int) common.Entity {	var best common.Entity	common.InitSeed()	for i := 0; i < max_iteration; i++ {		candidate := common.Entity{			0.0,			common.RandomVector(search_space),		}		candidate.Cost = objective_function(candidate.Vector)		if best.Vector == nil || best.Cost > candidate.Cost {			best = candidate		}		fmt.Println("Iteration ", i+1, ", best=", best.Cost)	}	return best}

 添加简单的单元测试:

package stochasticimport (	"fmt"	"testing")func TestObjectiveFunction(t *testing.T) {	if 5 != objective_function([]float64{1, 2}) {		t.Error("Objetive function failed")	}}func TestSearch(t *testing.T) {	//	var problem_size = 2	var search_space = make([][2]float64, problem_size)	for i, _ := range search_space {		search_space[i] = [2]float64{-5, 5}	}	//	const max_iteration = 100	//	var best = RandomSearch(search_space, max_iteration)	if best.Vector == nil {		t.Error("Search result should not be nil.")	}	fmt.Println("Done. Best Solution: c=", best.Cost, ", v= [")	for i, v := range best.Vector {		fmt.Print("  ", v)		if v < search_space[i][0] || v > search_space[i][1] {			t.Error("vector values should be in the search space.")		}	}	fmt.Println("]")}

 

[1]https://coding.net/u/huys03/p/clever_algorithms_go/git

 

使用Golang编写优化算法 (1)