Kernel: Anaconda (Python 3)
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import numpy as np import matplotlib.pyplot as plt %matplotlib notebook plt.style.use('ggplot') import random from random import randint import math
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food_amount = 100 pacman_amount = 2 board_size = 1000 default_health = 50 pacmen = [] food = [] crot = 1 cloc = .01 cbite = 1 turn_loss = 10 phi_prec = (80/255.) alpha_prec = 10/255. delta_prec = 10/255. pacman_radius = 1 food_health = 500
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class pacman: def __init__(self): #change dynamically self.x = random.uniform(49*board_size/100,51*board_size/100) self.y = random.uniform(49*board_size/100,51*board_size/100) #this is set so the prgression will be outward self.theta = randint(0,359) self.health = default_health self.active = 1 #dna self.phi = randint(0,255) self.alpha = randint(0,255) self.delta = randint(0,255) self.phi_val = self.phi*phi_prec self.alpha_val = self.alpha*alpha_prec self.delta_val = self.delta*delta_prec #self.f = closest food #self.dist = closest food distance def __bool__(self): return True def __repr__(self): return "{pacman, x:"+str(self.x)+", y:"+str(self.y)+", theta:"+str(self.theta)+"}" def find_dist(self,f): return np.sqrt((self.x-f.x)**2 + (self.y-f.y)**2) def find_angle(self,f): dx = f.x - self.x dy = f.y - self.y arc = math.degrees(math.atan((dy/dx))) theta1 = 1 if dx > 0: theta1 = 180 - arc #this used to be an if, but i think arctan returns a negative when a negative is inputted. #therefor the two would be equivalent else: if dy > 0: theta1 = np.absolute(arc) #this is absolute because the angle will be a negative else: theta1 = 360 - arc #theta1 is the angle the food is from the x axis #returned is the difference in the angles return np.absolute(theta1 - self.theta) def check_angle(self,f):#check if the food is in the range diff = (self.phi_val/2)#this creates virutal bounds f_angle = self.find_angle(f) if(f_angle < diff): return True return False def find_food(self): global food closest = False closest_dist = False for i in food: dist = self.find_dist(i) if((dist < closest_dist or not closest_dist) and (self.check_angle(i))): closest = i closest_dist = dist self.f = closest self.dist = closest_dist if(closest != False): return True return False def rotate_to_food(self):# returns (in radians!!!) the angle needed to get the food with genetic error #no cost to rotation dx = self.f.x - self.x dy = self.f.y - self.y arc = math.atan((dy/dx)) error = random.uniform(self.alpha_val/-2., self.alpha_val/2.) new_theta = arc + error return new_theta def get_food(self): theta = self.rotate_to_food() #warning this is in radians... #theta is not used... error = random.uniform(self.delta_val/-2., self.delta_val/2.) new_dist = self.dist + (error*self.dist) #the error for distance is relative to the distance, ie the greater the dist the greater the error dx = self.f.x - self.x dy = self.f.y - self.y distx = new_dist*(dx/self.dist)#equivalent to cos, error is not associted with the angle yet disty = new_dist*(dy/self.dist)#equivalent to sin self.x += distx self.y += disty cost = cloc*new_dist self.health -= cost #cost of rotate def bite(self): to_food = self.find_dist(self.f) self.health -= cbite if(to_food < pacman_radius):#means the bite was sucessful self.health += food_health self.f.reset() def random_rotate(self): self.theta = randint(0,359) #self.health += randint(-2,1) def step(self): self.health -= turn_loss if(self.find_food()): self.get_food() self.bite() self.random_rotate() def get_genes(self): return[self.phi,self.alpha,self.delta] def set_genes(self,genes): self.phi = genes[0] self.alpha = genes[1] self.delta = genes[2] def to_bits(self): genes = self.get_genes() split_genes = [] for i in genes: bin_str = format(i, '#010b') bin_str = bin_str[2:] split_genes.append([bin_str[:4],bin_str[4:]]) return split_genes def reproduce(self,other): son1 = pacman() son2 = pacman() son1_genes = [] son2_genes = [] mybits = self.to_bits() otherbits = other.to_bits() for i,v in enumerate(mybits): son1_gene = v[0]+otherbits[i][1] son2_gene = otherbits[i][0]+v[1] son1_gene = int('0b'+son1_gene,base=2) son2_gene = int('0b'+son2_gene,base=2) son1_genes.append(son1_gene) son2_genes.append(son2_gene) son1.set_genes(son1_genes) son2.set_genes(son2_genes) return [son1,son2] p = pacman() p2 = pacman() print(p.reproduce(p2))
[{pacman, x:493.32552888395134, y:490.18589634306005, theta:274}, {pacman, x:497.30320137676017, y:499.8184510839383, theta:180}]
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class dot: def __init__(self): self.x = random.uniform(0, board_size) self.y = random.uniform(0, board_size) self.active = 1 def __repr__(self): return "{dot, x:"+str(self.x)+", y:"+str(self.y)+"}" def reset(self): self.x = random.uniform(0, board_size) self.y = random.uniform(0, board_size)
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def gen1(): global pacmen pacmen = [] for i in range(0,pacman_amount): p = pacman() pacmen.append(p) pacmen = np.array(pacmen) print(pacmen[0]) return pacmen def food1(): global food food = [] for i in range(0,food_amount): f = dot() food.append(f) food = np.array(food) return food
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def driver(gen,life_span,generations): food = food1() dt = 1 t = [0] tracker = [] genetics = [[],[]] cycles = [] global pacmen for g in range(0,generations): #iterates through each generation tracker.append([]) genetics[0].append(pacmen[0].phi) genetics[1].append(pacmen[0].delta) for i in pacmen: tracker[g].append([]) cycles.append([]) t.append(t[-1]+dt) for q in range(0,life_span): #runs through time with current generation for i,v in enumerate(pacmen): tracker[g][i].append([v.x,v.y,v.theta]) v.step() cycles[g].append([])# note cycles starts after the first gen cycles[g][q].append([]) cycles[g][q].append([]) cycles[g][q].append([]) cycles[g][q].append([]) for w in pacmen: cycles[g][q][0].append(w.x) cycles[g][q][1].append(w.y) for f in food: cycles[g][q][2].append(f.x) cycles[g][q][3].append(f.y) #makes the next generation sons = [] healthy_pacmen = [] pacmen_ranked = [] avg_health = 0 for i in pacmen: pacmen_ranked.append(i.health) avg_health += i.health avg_health = avg_health/len(pacmen) pacmen_ranked = np.array(sorted(pacmen_ranked)) med_health = pacmen_ranked[10] print(med_health, avg_health) for i,v in enumerate(pacmen): #end of a generation mean reproduce if(v.health > med_health): healthy_pacmen.append(v) #this system kills off the bottom 5 pacmen for i,v in enumerate(healthy_pacmen): if( i % 2 == 1): temp = v.reproduce(healthy_pacmen[randint(0,len(healthy_pacmen)-1)]) sons.extend(temp) #print('generation over') pacmen = sons return t,tracker,genetics,cycles
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t,tracker,genetics,cycles = driver(gen1(),10,1)
{pacman, x:498.1968147534127, y:508.73615534198586, theta:77}
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-22-cda65b84592b> in <module>()
----> 1 t,tracker,genetics,cycles = driver(gen1(),10,1)
<ipython-input-15-17673b2464aa> in driver(gen, life_span, generations)
47 avg_health = avg_health/len(pacmen)
48 pacmen_ranked = np.array(sorted(pacmen_ranked))
---> 49 med_health = pacmen_ranked[10]
50 print(med_health, avg_health)
51
IndexError: index 10 is out of bounds for axis 0 with size 2
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print(genetics) plt.figure() plt.plot(genetics[0]) plt.plot(genetics[1])
[[100, 53, 140, 196, 175, 214, 217, 41, 130, 94, 210, 211, 70, 38, 243], [190, 6, 48, 37, 86, 123, 147, 227, 43, 58, 145, 145, 27, 235, 209]]
<IPython.core.display.Javascript object>
[<matplotlib.lines.Line2D at 0x7f6960722b38>]
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tracker = np.array(tracker) x = [] for i in tracker[0][0]: x.append(i[0]) plt.figure() plt.plot(x) figs = 2 gen = 1 f, axarr = plt.subplots(figs, sharex=True,sharey=True) for i in range(0,figs): axarr[i].scatter(cycles[gen][i][0],cycles[gen][i][1]) axarr[i].scatter(cycles[gen][i][2],cycles[gen][i][3], marker='^',c='red')
<IPython.core.display.Javascript object>
<IPython.core.display.Javascript object>
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