之前一直用Python来写优化算法，为了增强 JS 的熟练程度，开始将原有的代码改写成 JS。采用的工具包括 node.js + Grunt + nodeunit + github + npm + travis-ci。
最初的版本采用过程式的方式实现，没有采用面向对象或事件驱动的模式。
#!/usr/bin/env node --harmony
// Random Search
"use strict";
var util = require("util");
function objective_function(v) {
return v.map(function(x) {
return x*x;
}).reduce(function(a, b) {
return a+b;
});
}
function random_vector(min_max) {
return min_max.map(function(x) {
return x[0] + (x[1] - x[0]) * Math.random();
});
}
function search(search_space, max_iteration) {
var best = {};
for (var iteration = 0; iteration < max_iteration; iteration++) {
var candidate = {
'vector': random_vector(search_space)
};
candidate['cost'] = objective_function(candidate['vector']);
//console.log(candidate);
if (iteration === 0 || candidate['cost'] < best['cost']) {
best = candidate;
}
console.log(' > iteration=' + (iteration+1) + ', best=' + best['cost']);
}
return best;
}
function generate_array(element, repeat) {
return new Array(repeat+1).join(1).split('').map(function(){return element;});
}
function run () {
var problem_size = 2;
var search_space = generate_array([-5, 5], problem_size);
var max_iteration = 100;
var best = search(search_space, max_iteration);
console.log("Done. Best Solution: " + util.inspect(best));
}
exports.objective_function = objective_function;
exports.random_vector = random_vector;
exports.generate_array = generate_array;
exports.search = search;
exports.run = run;
调用方式很简单。
var rs = require('clever_algorithms_js').random_search;
rs.run();
单元测试：
var random_search = require('../../lib/stochastic/random_search');
exports['objective'] = function (test) {
test.equal(random_search.objective_function([1, 2]), 5);
test.done();
};
exports['random_vector'] = function (test) {
var rv = random_search.random_vector([[1, 2], [2, 3]]);
test.equal(rv.length, 2);
test.ok(1 <= rv[0] && rv[0] <= 2);
test.ok(2 <= rv[1] && rv[1] <= 3);
test.done();
};
exports['generate_array'] = function (test) {
var a = random_search.generate_array([-5, 5], 2);
test.equal(a.length, 2);
test.deepEqual(a, [[-5,5], [-5,5]]);
test.done();
};
exports['search'] = function (test) {
var problem_size = 2,
search_space = random_search.generate_array([-5, 5], problem_size),
max_iter = 100;
var best = random_search.search(search_space, max_iter);
test.notEqual(best, {});
test.ok(-5 <= best['cost'] && best['cost'] <= 5);
test.done();
};
如果采用CoffeeScript进行改写的话，代码会更简洁一些：
# Random Search
util = require("util");
objective_function = (v) ->
v.reduce (x,y) -> x*x + y*y
random_vector = (min_max) ->
min_max.map (rx) -> rx[0] + (rx[1] - rx[0]) * Math.random()
generate_array = (element, repeat) ->
(element for [1..repeat])
search = (search_space, max_iteration) ->
best = {}
for iteration in [0..max_iteration-1]
candidate = {
'vector': random_vector(search_space)
}
candidate['cost'] = objective_function(candidate['vector'])
best = candidate if iteration == 0 || candidate['cost'] < best['cost']
console.log ' > iteration=' + (iteration+1) + ' best=' + best['cost'];
best
run = () ->
problem_size = 2
search_space = generate_array([-5, 5], problem_size)
max_iteration = 100
best = search(search_space, max_iteration)
console.log "Done. Best Solution: " + util.inspect(best);
return
exports.objective_function = objective_function;
exports.random_vector = random_vector;
exports.generate_array = generate_array;
exports.search = search;
exports.run = run;
编译出的JavaScript代码，看起来是这个样子：
(function() {
var generate_array, objective_function, random_vector, run, search, util;
util = require("util");
objective_function = function(v) {
return v.reduce(function(x, y) {
return x * x + y * y;
});
};
random_vector = function(min_max) {
return min_max.map(function(rx) {
return rx[0] + (rx[1] - rx[0]) * Math.random();
});
};
generate_array = function(element, repeat) {
var _i, _results;
_results = [];
for (_i = 1; 1 <= repeat ? _i <= repeat : _i >= repeat; 1 <= repeat ? _i++ : _i--) {
_results.push(element);
}
return _results;
};
search = function(search_space, max_iteration) {
var best, candidate, iteration, _i, _ref;
best = {};
for (iteration = _i = 0, _ref = max_iteration - 1; 0 <= _ref ? _i <= _ref : _i >= _ref; iteration = 0 <= _ref ? ++_i : --_i) {
candidate = {
'vector': random_vector(search_space)
};
candidate['cost'] = objective_function(candidate['vector']);
if (iteration === 0 || candidate['cost'] < best['cost']) {
best = candidate;
}
console.log(' > iteration=' + (iteration + 1) + ' best=' + best['cost']);
}
return best;
};
run = function() {
var best, max_iteration, problem_size, search_space;
problem_size = 2;
search_space = generate_array([-5, 5], problem_size);
max_iteration = 100;
best = search(search_space, max_iteration);
console.log("Done. Best Solution: " + util.inspect(best));
};
exports.objective_function = objective_function;
exports.random_vector = random_vector;
exports.generate_array = generate_array;
exports.search = search;
exports.run = run;
}).call(this);