antiyoy.js/NeuralNetwork.js

class GeneticAlgorithm {
    constructor(maxUnits, topUnits, game) {
        this.maxUnits = maxUnits;
        this.topUnits = topUnits;

        if(this.maxUnits < this.topUnits)
            this.topUnits = maxUnits;

        this.population = [];

        this.SCALE_FACTOR = 200;

        this.game = game;
    }

    reset() {
        this.iteration = 1;
        this.mutateRate = 1;

        this.bestPopulation = 0;
        this.bestFitness = 0;
        this.bestScore = 0;
    }

    createPopulation() {
        this.population.slice(0, this.population.length);

        for(let i = 0; i < this.maxUnits; i++) {
            const newUnit = new synaptic.Architect.Perceptron(6 * this.game.fieldSize.x * this.game.fieldSize.y, 35, 5);
            newUnit.index = i;
            newUnit.fitness = 0;
            newUnit.score = 0;
            newUnit.isWinner = false;

            this.population.push(newUnit);
        }
    }

    activateBrain(civilization) {
        // TODO: Inputs
        const inputs = [civilization.balance, civilization.getBalanceChange()];
        for(let x = 0; x < this.game.fieldSize.x; x++) {
            for(let y = 0; y < this.game.fieldSize.y; y++) {
                const tile = this.game.field[x][y];

                const isFriendly = tile.claimedBy === civilization ? 1 : 0;
                const hasObject = !!tile.object ? 1 : 0;
                const canAttack = tile.object instanceof Warrior ? 1 : 0;
                const rank = !!tile.object ? tile.object.rank / 4 : 0;

                inputs.push(x, y, isFriendly, hasObject, canAttack, rank);
            }
        }

        console.log(civilization.index);
        const outputs = this.population[civilization.index].activate(inputs);

        console.log(outputs);
        const type = outputs[0];
        const x = Math.round(outputs[1] * (this.game.fieldSize.x - 1));
        const y = Math.round(outputs[2] * (this.game.fieldSize.y - 1));
        const tile = this.game.field[x][y];

        if(type < .5) {
            if(tile.object && tile.claimedBy === civilization && tile.object instanceof Warrior) {
                const deltaX = Math.round(outputs[3] * 4 - 2) / 4;
                const deltaY = Math.round(outputs[4] * 4 - 2) / 4;

                const targetX = x + deltaX;
                const targetY = y + deltaY;

                tile.object.move(targetX, targetY);

                console.log(deltaX, deltaY);
            }
        } else {
            const itemType = outputs[3];
            console.log('hey');

            if(itemType > .5) {
                console.log('ho');
                if(!tile.getAdjacentTiles().find(tile => tile.claimedBy === civilization))
                    return;

                const troopType = Math.round(outputs[4] * 3);
                const troopTypes = [Farmer, Hoplite, Servant, Knight];
                this.game.addWarrior(x, y, civilization, troopTypes[troopType]);
            } else {
                if(tile.claimedBy !== civilization)
                    return;

                const buildingType = Math.round(outputs[4] * 2);
                const buildingTypes = [Farm, SmallTower, BigTower];
                console.log('ho2', buildingType, tile);
                this.game.addBuilding(x, y, civilization, buildingTypes[buildingType]);
            }
        }
    }

    evolvePopulation() {
        const winners = this.selection();

        if(this.mutateRate === 1 && winners[0].fitness < 0) {
            this.createPopulation();
        } else {
            this.mutateRate = .2;
        }

        for(let i = this.topUnits; i < this.maxUnits; i++) {
            let parentA, parentB, offspring;

            if(i === this.topUnits) {
                parentA = winners[0].toJSON();
                parentB = winners[1].toJSON();
                offspring = this.crossOver(parentA, parentB);
            } else if(i < this.maxUnits - 2) {
                parentA = this.getRandomUnit(winners).toJSON();
                parentB = this.getRandomUnit(winners).toJSON();
                offspring = this.crossOver(parentA, parentB);
            } else {
                offspring = this.getRandomUnit(winners).toJSON();
            }

            offspring = this.mutation(offspring);

            const newUnit = synaptic.Network.fromJSON(offspring);
            newUnit.index = this.population[i].index;
            newUnit.fitness = 0;
            newUnit.score = 0;
            newUnit.isWinner = false;

            this.population[i] = newUnit;
        }

        if(winners[0].fitness > this.bestFitness) {
            this.bestPopulation = this.iteration;
            this.bestFitness = winners[0].fitness;
            this.bestScore = winners[0].score;
        }

        this.population.sort((unitA, unitB) => unitA.index - unitB.index);
    }

    selection() {
        const sortedPopulation = this.population.sort((unitA, unitB) => unitB.fitness - unitA.fitness);

        for(let i = 0; i < this.topUnits; i++) {
            this.population[i].isWinner = true;
        }

        return sortedPopulation.slice(0, this.topUnits);
    }

    crossOver(parentA, parentB) {
        const cutPoint = this.random(0, parentA.neurons.length - 19);

        for(let i = cutPoint; i < parentA.neurons.length; i++) {
            const biasFromParentA = parentA.neurons[i]['bias'];
            parentA.neurons[i]['bias'] = parentB.neurons[i]['bias'];
            parentB.neurons[i]['bias'] = biasFromParentA;
        }

        return this.random(0, 1) === 1 ? parentA : parentB;
    }

    mutation(offspring) {
        for(let i = 0; i < offspring.neurons.length; i++) {
            offspring.neurons[i]['bias'] = this.mutate(offspring.neurons[i]['bias']);
        }

        for(let i = 0; i < offspring.connections.length; i++) {
            offspring.connections[i]['weight'] = this.mutate(offspring.connections[i]['weight']);
        }

        return offspring;
    }

    mutate(gene) {
        if(Math.random() < this.mutateRate) {
            gene *= 1 + ((Math.random() - .5) * 3 + (Math.random() - .5));
        }

        return gene;
    }

    random(min, max) {
        return Math.floor(Math.random() * (max - min + 1) + min);
    }

    getRandomUnit(array) {
        return array[this.random(0, array.length - 1)];
    }

    normalize(value, max) {
        if(value < -max)
            value = -max;
        else if(value > max)
            value = max;

        return value / max;
    }
}