Let's say, I have following template.
Hello, {I'm|he is} a {notable|famous} person.
Result should be
Hello, I'm a notable person.
Hello, I'm a famous person.
Hello, he is a notable person.
Hello, he is a famous person.
The only possible solution I have in mind - full search, but it is not effective.
May be there is a good algorithm for such kind of job but I do not know what task about. All permutations in array is very close to this but I have no idea how to use it here.
Here is working solution (it's part of object, so here is only relevant part).
generateText() parses string and converts 'Hello, {1|2}, here {3,4}' into ['Hello', ['1', '2'], 'here', ['3', '4']]]
extractText() takes this multidimensional array and creates all possible strings
STATE_TEXT: 'TEXT',
STATE_INSIDE_BRACKETS: 'INSIDE_BRACKETS',
generateText: function(text) {
var result = [];
var state = this.STATE_TEXT;
var length = text.length;
var simpleText = '';
var options = [];
var singleOption = '';
var i = 0;
while (i < length) {
var symbol = text[i];
switch(symbol) {
case '{':
if (state === this.STATE_TEXT) {
simpleText = simpleText.trim();
if (simpleText.length) {
result.push(simpleText);
simpleText = '';
}
state = this.STATE_INSIDE_BRACKETS;
}
break;
case '}':
if (state === this.STATE_INSIDE_BRACKETS) {
singleOption = singleOption.trim();
if (singleOption.length) {
options.push(singleOption);
singleOption = '';
}
if (options.length) {
result.push(options);
options = [];
}
state = this.STATE_TEXT;
}
break;
case '|':
if (state === this.STATE_INSIDE_BRACKETS) {
singleOption = singleOption.trim();
if (singleOption.length) {
options.push(singleOption);
singleOption = '';
}
}
break;
default:
if (state === this.STATE_TEXT) {
simpleText += symbol;
} else if (state === this.STATE_INSIDE_BRACKETS) {
singleOption += symbol;
}
break;
}
i++;
}
return result;
},
extractStrings(generated) {
var lengths = {};
var currents = {};
var permutations = 0;
var length = generated.length;
for (var i = 0; i < length; i++) {
if ($.isArray(generated[i])) {
lengths[i] = generated[i].length;
currents[i] = lengths[i];
permutations += lengths[i];
}
}
var strings = [];
for (var i = 0; i < permutations; i++) {
var string = [];
for (var k = 0; k < length; k++) {
if (typeof lengths[k] === 'undefined') {
string.push(generated[k]);
continue;
}
currents[k] -= 1;
if (currents[k] < 0) {
currents[k] = lengths[k] - 1;
}
string.push(generated[k][currents[k]]);
}
strings.push(string.join(' '));
}
return strings;
},
The only possible solution I have in mind - full search, but it is not effective.
If you must provide full results, you must run full search. There is simply no way around it. You don't need all permutations, though: the number of results is equal to the product of the number of alternatives in each template.
Although there are multiple ways to implement this, recursion is among the most popular approaches. Here is some pseudo-code to get you started:
string[][] templates = {{"I'm", "he is"}, {"notable", "famous", "boring"}}
int[] pos = new int[templates.Length]
string[] fills = new string[templates.Length]
recurse(templates, fills, 0)
...
void recurse(string[][] templates, string[] fills, int pos) {
if (pos == fills.Length) {
formatResult(fills);
} else {
foreach option in templates[pos] {
fills[pos] = option
recurse(templates, fills, pos+1);
}
}
}
It seems like the best solution here is going to be n*m where n=the first array and m= the second array . There are nm required lines of output, which means that as long as you are only doing nm you aren't doing any extra work
The generic running time for this is where there is more than 2 arrays with options, it would be
n1*n2...*nm where each of those is equal to the size of the respective list
A nested loop where you just print out the value for the current index of the outer loop along with the current value for the index of the inner loop should do this properly
I am trying to code an algorithm so that it can start from any "start" cell of a grid ( eg.cell no. 4 in the pic) and parse through each cell of a grid once. The grid can be of any size 3x3, 4x4, 8x8 etc.
The following codes generates such paths. And works fine for many cells in 8x8 grid. But for many cells, it takes forever to search a path.
I was wondering if there is some better solution that can be referenced, so that the solution can be optimized.
package
{
import flash.display.MovieClip;
public class Main extends MovieClip
{
private var rand_Num;
public var node0_Mc:MovieClip ,node1_Mc:MovieClip,node2_Mc:MovieClip,node3_Mc:MovieClip,node4_Mc:MovieClip,node5_Mc:MovieClip,node6_Mc:MovieClip,node7_Mc:MovieClip,node8_Mc:MovieClip,node9_Mc:MovieClip,
node10_Mc:MovieClip,node11_Mc:MovieClip,node12_Mc:MovieClip,node13_Mc:MovieClip,node14_Mc:MovieClip,node15_Mc:MovieClip,node16_Mc:MovieClip,node17_Mc:MovieClip,node18_Mc:MovieClip,node19_Mc:MovieClip,
node20_Mc:MovieClip,node21_Mc:MovieClip,node22_Mc:MovieClip,node23_Mc:MovieClip,node24_Mc:MovieClip,node25_Mc:MovieClip,node26_Mc:MovieClip,node27_Mc:MovieClip,node28_Mc:MovieClip,node29_Mc:MovieClip,
node30_Mc:MovieClip,node31_Mc:MovieClip,node32_Mc:MovieClip,node33_Mc:MovieClip,node34_Mc:MovieClip,node35_Mc:MovieClip,node36_Mc:MovieClip,node37_Mc:MovieClip,node38_Mc:MovieClip,node39_Mc:MovieClip,
node40_Mc:MovieClip,node41_Mc:MovieClip,node42_Mc:MovieClip,node43_Mc:MovieClip,node44_Mc:MovieClip,node45_Mc:MovieClip,node46_Mc:MovieClip,node47_Mc:MovieClip,node48_Mc:MovieClip,node49_Mc:MovieClip,
node50_Mc:MovieClip,node51_Mc:MovieClip,node52_Mc:MovieClip,node53_Mc:MovieClip,node54_Mc:MovieClip,node55_Mc:MovieClip,node56_Mc:MovieClip,node57_Mc:MovieClip,node58_Mc:MovieClip,node59_Mc:MovieClip,
node60_Mc:MovieClip, node61_Mc:MovieClip, node62_Mc:MovieClip, node63_Mc:MovieClip;
public const NUM_COLS:Number = 8;
// 3 ;// 4 ;
public const NUM_ROWS:Number = 8;// 3 ;// 4 ;
var chain_Arr:Array = [];
var blockIndex_Arr:Array = [];
var nodearr:Array;
var adjacentNodeArray_Arr:Array = [];
var parsedNodeIndex_Arr:Array = [];
var validNextAdjacentNodeIndexArray_Arr:Array = [];
var validPreviousAdjacentNodeIndexArray_Arr:Array = [];
var savePair_Arr:Array = [];
var countChain_Num:Number = 0;
var saveParent_Arr:Array = [];
public function Main()
{
// constructor code
nodearr = [node0_Mc,node1_Mc,node2_Mc,node3_Mc,node4_Mc,node5_Mc,node6_Mc,node7_Mc,node8_Mc,node9_Mc,node10_Mc,node11_Mc,node12_Mc,node13_Mc,node14_Mc,node15_Mc,node16_Mc,node17_Mc,node18_Mc,node19_Mc,node20_Mc,node21_Mc,node22_Mc,node23_Mc,node24_Mc,node25_Mc,node26_Mc,node27_Mc,node28_Mc,node29_Mc,node30_Mc,node31_Mc,node32_Mc,node33_Mc,node34_Mc,node35_Mc,node36_Mc,node37_Mc,node38_Mc,node39_Mc,node40_Mc,node41_Mc,node42_Mc,node43_Mc,node44_Mc,node45_Mc,node46_Mc,node47_Mc,node48_Mc,node49_Mc,node50_Mc,node51_Mc,node52_Mc,node53_Mc,node54_Mc,node55_Mc,node56_Mc,node57_Mc,node58_Mc,node59_Mc,node60_Mc,node61_Mc,node62_Mc,node63_Mc];
var possibleAdjacentNodeIndex_Arr:Array = [];
initValidNextAdjacentNodeIndexArray();
initValidPreviousAdjacentNodeIndexArray();
savePair_Arr = [];
var startIndex_num:Number = 45;// 0 ;
rand_Num = 62;// randomRange(10, (NUM_COLS * NUM_ROWS) - 1);
getAllChainsFromParamNodeIndexParamParentChain(startIndex_num,[startIndex_num]);
}
function initValidNextAdjacentNodeIndexArray():void
{
for (var index_num = 0; index_num < NUM_COLS*NUM_ROWS; index_num++)
{
validNextAdjacentNodeIndexArray_Arr[index_num] = getValidNodeIndexArrayAdjacentToParamNodeIndex(index_num);
//trace(validNextAdjacentNodeIndexArray_Arr[index_num]);
}
}
function initValidPreviousAdjacentNodeIndexArray():void
{
for (var index_num = 0; index_num < NUM_COLS*NUM_ROWS; index_num++)
{
validPreviousAdjacentNodeIndexArray_Arr[index_num] = getValidNodeIndexArrayAdjacentToParamNodeIndex(index_num);
//trace(validNextAdjacentNodeIndexArray_Arr[index_num]);
}
}
//function getAllChainsFromParamNodeIndexParamParentChain( path_arr:Array,index_param_num:Number ):void
function getAllChainsFromParamNodeIndexParamParentChain( index_param_num:Number, parent_arr:Array ):void
{
var i;
if ( countChain_Num > 15 )
{
return;
}
reinitPath();
var adjacent_arr = getValidNodeIndexArrayAdjacentToParamNodeIndex(index_param_num);
for (i = 0; i < adjacent_arr.length; i++)
{
reinitPath();
if ( ( checkRepeat(chain_Arr)) )
{
continue;
}
chain_Arr.push(adjacent_arr[i]);
//trace("chain starts from", chain_Arr);
var nodeIndex_num:Number = adjacent_arr[i];
var nodeIndexExist_num = 0;
var parentNode_num:Number = parent_arr[parent_arr.length - 1];
var bool:Boolean;
while ( isNaN(nodeIndex_num) == false)
{
//var childNodeIndex_num = manageValidAdjacentIndexArray(parent_arr[parent_arr.length-1], parentNode_num , nodeIndex_num );
var childNodeIndex_num = manageValidAdjacentIndexArray(adjacent_arr[i],parentNode_num,nodeIndex_num);
//var childNodeIndex_num = manageValidAdjacentIndexArray(parentNode_num, parentNode_num , nodeIndex_num );
parentNode_num = nodeIndex_num;
nodeIndex_num = childNodeIndex_num;
if ( ( checkRepeat(chain_Arr)) )
{
break;
}
if ( isNaN(nodeIndex_num) == false)
{
chain_Arr.push(nodeIndex_num);
}
}
if (chain_Arr.length > NUM_COLS * NUM_ROWS - 1)
{
//if ( !( checkRepeat(chain_Arr)) )
{
trace(chain_Arr);
saveParent_Arr[saveParent_Arr.length ] = new Array();
for (var k = 0; k < rand_Num; k++)
{
saveParent_Arr[saveParent_Arr.length - 1].push( chain_Arr[k]);
}
countChain_Num++;
}
}
};
for (i = 0; i < adjacent_arr.length; i++)
{
var arr2:Array = parent_arr.slice();
arr2.push(adjacent_arr[i]);
getAllChainsFromParamNodeIndexParamParentChain( adjacent_arr[i], arr2 );
}
function reinitPath():void
{
chain_Arr = [];
chain_Arr = chain_Arr.concat(parent_arr);
}
}
private function checkRepeat(chain_arr:Array):Boolean
{
var bool:Boolean;
var num = rand_Num;
if (chain_arr.length >= num - 1)
{
for (var i = 0; i < saveParent_Arr.length; i++)
{
//trace( saveParent_Arr[i][0], saveParent_Arr[i][1]);
if (saveParent_Arr[i] != undefined)
{
var z = 0;
for (var j = 0; j < num; j++)
{
if (saveParent_Arr[i][j] == chain_arr[j])
{
z++;
if ( z >= num)
{
bool = true;
break;
}
}
else
{
break;
}
}
if (bool)
{
break;
}
}
}
}
return bool;
}
function randomRange( minNum:Number, maxNum:Number):Number
{
return (Math.floor(Math.random() * (maxNum - minNum + 1)) + minNum);
}
function randomizeArray(arr:Array ):void
{
for (var i = 0; i < arr.length; i++)
{
var random = randomRange(0,arr.length - 1);
var temp = arr[i];
arr[i] = arr[random];
arr[random] = temp;
}
}
//will send NaN if no valid adjacent index array is possible
private function manageValidAdjacentIndexArray(chainStartIndex_num:Number, parentNodeIndex_param_num:Number, nodeIndex_num:Number):Number
{
var num_nan:Number;
var ret_num:Number;
var j;
//var tot:Number = validNextAdjacentNodeIndexArray_Arr[nodeIndex_num].length ;
j = 0;
var arr:Array = validNextAdjacentNodeIndexArray_Arr[nodeIndex_num];// getValidNodeIndexArrayAdjacentToParamNodeIndex(nodeIndex_num) ;
randomizeArray(arr);
while (arr.length > 0 )// && isNaN(ret_num))
{
ret_num = arr[j];//validNextAdjacentNodeIndexArray_Arr[nodeIndex_num][j] ;
//if this index is present in chain then remove it off
if (chain_Arr.indexOf(ret_num) >= 0)
{
//j++ ;
ret_num = num_nan;
}
j++;
if ( j >= arr.length )
{
ret_num = num_nan;
break;
}
}
return ret_num;
}
private function getValidAdjacentIndexToParamNodeIndex(nodeIndex_param_num:Number):Number
{
var adjacentNode_arr:Array = [];
var adjacentRow_arr:Array = [];
var adjacentCol_arr:Array = [];
var allIndex_arr:Array = [];
var r:Number;
var c:Number;
var row_num:Number = int(nodeIndex_param_num / NUM_COLS);
var col_num:Number = (nodeIndex_param_num % NUM_COLS);
allIndex_arr = getAllAdjacentNodeIndexArray(row_num,col_num);
validateIndices(allIndex_arr);
var ret_num:Number;
ret_num = allIndex_arr[0];
return ret_num;
}
function getValidNodeIndexArrayAdjacentToParamNodeIndex(nodeIndex_param_num:Number ):Array
{
var adjacentNode_arr = [];
for (var positionId_num = 0; positionId_num < 8; positionId_num++)
{
var num:Number = getNodeIndexAtParamPositionIdOfParamIndex(positionId_num,nodeIndex_param_num);
if (isNaN(num))
{
}
else
{
adjacentNode_arr.push(num);
}
}
return adjacentNode_arr;
}
private function getAllAdjacentNodeIndexArray(row_num:Number, col_num:Number,within_arr:Array=null):Array
{
var num:Number;
var index_arr:Array = [num,num,num,num,num,num,num,num];
var r:Number;
var c:Number;
if ( row_num > 0 )
{
r = row_num - 1;
c = col_num;
index_arr[0] = r * NUM_COLS + c;
}
if ( col_num < NUM_COLS-1)
{
r = row_num;
c = col_num + 1;
index_arr[1] = r * NUM_COLS + c;
}
if ( row_num < NUM_ROWS-1)
{
r = row_num + 1;
c = col_num;
index_arr[2] = r * NUM_COLS + c;
}
if ( col_num > 0 )
{
r = row_num;
c = col_num - 1;
index_arr[3] = r * NUM_COLS + c;
}
///////////////////////////////////////////
if ( row_num > 0 && col_num > 0)
{
r = row_num - 1;
c = col_num - 1;
index_arr[4] = r * NUM_COLS + c;
}
if ( row_num > 0 && col_num < NUM_COLS-1)
{
r = row_num - 1;
c = col_num + 1;
index_arr[5] = r * NUM_COLS + c;
}
if ( row_num < NUM_ROWS-1 && col_num < NUM_COLS-1)
{
r = row_num + 1;
c = col_num + 1;
index_arr[6] = r * NUM_COLS + c;
}
if ( row_num < NUM_ROWS-1 && col_num > 0 )
{
r = row_num + 1;
c = col_num - 1;
index_arr[7] = r * NUM_COLS + c;
}
return index_arr;
}
//the adjacent node must be present in within_arr, which we splice, one time for variation
function getNodeIndexAtParamPositionIdOfParamIndex( n:Number , nodeIndex_param_num:Number):Number
{
var adjacentNode_arr:Array = [];
var adjacentRow_arr:Array = [];
var adjacentCol_arr:Array = [];
var index_arr:Array = [];
var r:Number;
var c:Number;
var index_num:Number;
var row_num:Number = int(nodeIndex_param_num / NUM_COLS);
var col_num:Number = (nodeIndex_param_num % NUM_COLS);
index_arr = getAllAdjacentNodeIndexArray(row_num,col_num);
validateIndices(index_arr);
var ret_num:Number;
ret_num = index_arr[n];
return ret_num;
}
private function validateIndices(index_arr:Array):void
{
for (var i = 0; i < index_arr.length; i++)
{
if (chain_Arr.indexOf(index_arr[i]) == -1)
{
}
else
{
var num:Number;
index_arr[i] = num;//pushing NaN
}
}
}
}
}
It's a Hamiltonian path problem. It's NP complete and afaik there is still no efficient solution.
check this out: hamilton paths in grid graphs (pdf)
short randomized algorithm explanation: Princeton Hamiltonian path problem
or wikipedia: Hamiltonian path problem
edit:
I did some more research and for your special case of strongly connected square grids (nxn chessboards) a variation of the warndoffs rule for finding a knights tour might give you a solution in near linear time. See here: A method for finding hamiltonian paths and knight tours (pdf)
I wrote a quick experiment with a genetic algorithm. It simply takes a grid of squares and tries to mutate their color to make them all yellow. It fails miserably and I can't seem to figure out why. I've included a link to JSFiddle that demonstrates working code, as well as a copy of the code in its entirety.
http://jsfiddle.net/mankyd/X6x9L/
<!DOCTYPE html>
<html lang="en">
<head>
</head>
<body>
<div class="container">
<h1>The randomly flashing squares <i>should</i> be turning yellow</h1>
<div class="row">
<canvas id="input_canvas" width="100" height="100"></canvas>
<canvas id="output_canvas" width="100" height="100"></canvas>
</div>
<div class="row">
<span id="generation"></span>
<span id="best_fitness"></span>
<span id="avg_fitness"></span>
</div>
</div>
</body>
</html>
Note that the below javascript relies on jquery in a few places.
// A bit of code that draws several squares in a canvas
// and then attempts to use a genetic algorithm to slowly
// make those squares all yellow.
// Knobs that can be tweaked
var mutation_rate = 0.1; // how often should we mutate something
var crossover_rate = 0.6; // how often should we crossover two parents
var fitness_influence = 1; // affects the fitness's influence over mutation
var elitism = 1; // how many of the parent's generation to carry over
var num_offspring = 20; // how many spawn's per generation
var use_rank_selection = true; // false == roulette_selection
// Global variables for easy tracking
var children = []; // current generation
var best_spawn = null; // keeps track of our best so far
var best_fitness = null; // keeps track of our best so far
var generation = 0; // global generation counter
var clear_color = 'rgb(0,0,0)';
// used for output
var $gen_span = $('#generation');
var $best_fit = $('#best_fitness');
var $avg_fit = $('#avg_fitness');
var $input_canvas = $('#input_canvas');
var input_ctx = $input_canvas[0].getContext('2d');
var $output_canvas = $('#output_canvas');
var output_ctx = $output_canvas[0].getContext('2d');
// A spawn represents a genome - a collection of colored
// squares.
var Spawn = function(nodes) {
var _fitness = null; // a cache of our fitness
this.nodes = nodes; // the squares that make up our image
this.fitness = function() {
// fitness is simply a function of how close to yellow we are.
// This is defined through euclidian distance. Smaller fitnesses
// are better.
if (_fitness === null) {
_fitness = 0;
for (var i = 0; i < nodes.length; i++) {
_fitness += Math.pow(-nodes[i].color[0], 2) +
Math.pow(255 - nodes[i].color[1], 2) +
Math.pow(255 - nodes[i].color[2], 2);
}
_fitness /= 255*255*3*nodes.length; // divide by the worst possible distance
}
return _fitness;
};
this.mutate = function() {
// reset our cached fitness to unknown
_fitness = null;
var health = this.fitness() * fitness_influence;
var width = $output_canvas[0].width;
var height = $output_canvas[0].height;
for (var i = 0; i < nodes.length; i++) {
// Sometimes (most times) we don't mutate
if (Math.random() > mutation_rate) {
continue;
}
// Mutate the colors.
for (var j = 0; j < 3; j++) {
// colors can move by up to 32 in either direction
nodes[i].color[j] += 64 * (.5 - Math.random()) * health;
// make sure that our colors stay between 0 and 255
nodes[i].color[j] = Math.max(0, Math.min(255, nodes[i].color[j]));
}
}
};
this.draw = function(ctx) {
// This draw function is a little overly generic in that it supports
// arbitrary polygons.
ctx.save();
ctx.fillStyle = clear_color;
ctx.fillRect(0, 0, ctx.canvas.width, ctx.canvas.height);
for (var i = 0; i < nodes.length; i++) {
ctx.fillStyle = 'rgba(' + Math.floor(nodes[i].color[0]) + ',' + Math.floor(nodes[i].color[1]) + ',' + Math.floor(nodes[i].color[2]) + ',' + nodes[i].color[3] + ')';
ctx.beginPath();
ctx.moveTo(nodes[i].points[0][0], nodes[i].points[0][1]);
for (var j = 1; j < nodes[i].points.length; j++) {
ctx.lineTo(nodes[i].points[j][0], nodes[i].points[j][1]);
}
ctx.fill();
ctx.closePath();
}
ctx.restore();
};
};
Spawn.from_parents = function(parents) {
// Given two parents, mix them together to get another spawn
var nodes = [];
for (var i = 0; i < parents[0].nodes.length; i++) {
if (Math.random() > 0.5) {
nodes.push($.extend({}, parents[0].nodes[i]));
}
else {
nodes.push($.extend({}, parents[1].nodes[i]));
}
}
var s = new Spawn(nodes);
s.mutate();
return s;
};
Spawn.random = function(width, height) {
// Return a complete random spawn.
var nodes = [];
for (var i = 0; i < width * height; i += 10) {
var n = {
color: [Math.random() * 256, Math.random() * 256, Math.random() * 256, 1],
points: [
[i % width, Math.floor(i / width) * 10],
[(i % width) + 10, Math.floor(i / width) * 10],
[(i % width) + 10, Math.floor(i / width + 1) * 10],
[i % width, Math.floor(i / width + 1) * 10],
]
};
nodes.push(n);
}
return new Spawn(nodes);
};
var select_parents = function(gene_pool) {
if (use_rank_selection) {
return rank_selection(gene_pool);
}
return roulette_selection(gene_pool);
};
var roulette_selection = function(gene_pool) {
var mother = null;
var father = null;
gene_pool = gene_pool.slice(0);
var sum_fitness = 0;
var i = 0;
for (i = 0; i < gene_pool.length; i++) {
sum_fitness += gene_pool[i].fitness();
}
var choose = Math.floor(Math.random() * sum_fitness);
for (i = 0; i < gene_pool.length; i++) {
if (choose <= gene_pool[i].fitness()) {
mother = gene_pool[i];
break;
}
choose -= gene_pool[i].fitness();
}
// now remove the mother and repeat for the father
sum_fitness -= mother.fitness();
gene_pool.splice(i, 1);
choose = Math.floor(Math.random() * sum_fitness);
for (i = 0; i < gene_pool.length; i++) {
if (choose <= gene_pool[i].fitness()) {
father = gene_pool[i];
break;
}
choose -= gene_pool[i].fitness();
}
return [mother, father];
};
var rank_selection = function(gene_pool) {
gene_pool = gene_pool.slice(0);
gene_pool.sort(function(a, b) {
return b.fitness() - a.fitness();
});
var choose_one = function() {
var sum_fitness = (gene_pool.length + 1) * (gene_pool.length / 2);
var choose = Math.floor(Math.random() * sum_fitness);
for (var i = 0; i < gene_pool.length; i++) {
// figure out the sume of the records up to this point. if we exceed
// our chosen spot, we've found our spawn.
if ((i + 1) * (i / 2) >= choose) {
return gene_pool.splice(i, 1)[0];
}
}
return gene_pool.pop(); // last element, if for some reason we get here
};
var mother = choose_one();
var father = choose_one();
return [mother, father];
};
var start = function() {
// Initialize our first generation
var width = $output_canvas[0].width;
var height = $output_canvas[0].height;
generation = 0;
children = [];
for (var j = 0; j < num_offspring; j++) {
children.push(Spawn.random(width, height));
}
// sort by fitness so that our best comes first
children.sort(function(a, b) {
return a.fitness() - b.fitness();
});
best_spawn = children[0];
best_fitness = best_spawn.fitness();
best_spawn.draw(output_ctx);
};
var generate = function(spawn_pool) {
// generate a new set of offspring
var offspring = [];
for (var i = 0; i < num_offspring; i++) {
var parents = select_parents(spawn_pool);
// odds of crossover decrease as we get closer
if (Math.random() * best_fitness < crossover_rate) {
var s = Spawn.from_parents(parents);
}
else {
// quick hack to copy our mother, with possible mutation
var s = Spawn.from_parents([parents[0], parents[0]]);
}
offspring.push(s);
}
// select a number of best from the parent pool (elitism)
for (var i = 0; i < elitism; i++) {
offspring.push(spawn_pool[i]);
}
// sort our offspring by fitness (this includes the parents from elitism). Fittest first.
offspring.sort(function(a, b) {
return a.fitness() - b.fitness();
});
// pick off the number that we want
offspring = offspring.slice(0, num_offspring);
best_spawn = offspring[0];
best_fitness = best_spawn.fitness();
best_spawn.draw(output_ctx);
generation++;
return offspring;
};
var average_fitness = function(generation) {
debugger;
var a = 0;
for (var i = 0; i < generation.length; i++) {
a += generation[i].fitness();
}
return a / generation.length;
};
//Draw yellow and then initialize our first generation
input_ctx.fillStyle = 'yellow';
input_ctx.fillRect(0, 0, input_ctx.canvas.width, input_ctx.canvas.height);
start();
// Our loop function. Use setTimeout to prevent things from freezing
var gen = function() {
children = generate(children);
$gen_span.text('Generation: ' + generation);
$best_fit.text('Best Fitness: ' + best_fitness);
$avg_fit.text('Avg. Fitness: ' + average_fitness(children));
if (generation % 100 === 0) {
console.log('Generation', generation);
console.log('Fitness', best_fitness);
}
setTimeout(gen, 1);
};
gen();
I've commented the code to try to make parsing it easy. The basic idea is quite simple:
Select 1 or 2 parents from the current generation
Mix those one or two parents together
Mutate the result slightly and add it to the next generation
Select the best few parents (1 in the example) and add them to the next generation
Sort and slice off N results and use them for the next generation (potentially a mix of parents and offspring)
Rinse and repeat
The output never gets anywhere near yellow. It quickly falls into a steady state of a sort that looks awful. Where have I gone wrong?
Solved it. It was in the "from_parents" method:
if (Math.random() > 0.5) {
nodes.push($.extend({}, parents[0].nodes[i]));
}
else {
nodes.push($.extend({}, parents[1].nodes[i]));
}
The $.extend() was doing a shallow copy. The obvious solution was to either put true as the first argument which causes a deep copy. This, however, is incredibly slow performance-wise. The better solution was to remove the $.extend() from that chunk of code entirely and instead to move it up to the mutate() method, where I call $.extend() only if a node is actually about to be changed. In other words, it becomes a copy-on-write.
Also, the color I put in the fitness function was wrong :P
Trying to toggle the visibility of MarkerClusterer (V3):
var hydrantsShowing = true;
function ToggleHydrants() {
var markers = hydrantsClusterer.getMarkers();
for (var i = 0; i < markers.length; i++) {
markers[i].setVisible(!hydrantsShowing);
}
hydrantsShowing = !hydrantsShowing;
}
The markers do toggle but with two problems:
1. The map must be panned a bit to the change can take place.
2. The MarkerClusterer icons (with the numbers) are always there, even after the markers are not visible.
I've also tried using the setMap approach, but with similar behavior:
var hydrantsShowing = true;
function ToggleHydrants() {
var markers = hydrantsClusterer.getMarkers();
if (hydrantsShowing) {
for (var i = 0; i < markers.length; i++) {
markers[i].setMap(null);
}
}
else {
for (var i = 0; i < markers.length; i++) {
markers[i].setMap(gmap);
}
}
hydrantsShowing = !hydrantsShowing;
}
Solved it by using MarkerClustererPlus instead.
var hydrantsShowing = true;
function ToggleHydrants() {
var markers = hydrantsClusterer.getMarkers();
for (var i = 0; i < markers.length; i++) {
markers[i].setVisible(!hydrantsShowing);
}
hydrantsClusterer.repaint();
hydrantsShowing = !hydrantsShowing;
}
Calling repaint() after setting visibility sorted all issues.
The original MarkerClusterer don't have such a function.