def deviation_from_centroid(agent,coord,agents):
# doesn't count the agent themselves in the centroid
agents_in_tile=agents_in_cell(coord,agents)
if len (agents_in_tile)==1:return 0
list_of_phenotypes=[other_agent.phenotype for other_agent in agents_in_tile if other_agent!=agent]
centroid=centroidnp(list_of_phenotypes)
return euclidean_distance(centroid,agent.phenotype)
def deviation_from_centroid(agent, coord, agents):
# doesn't count the agent themselves in the centroid
agents_in_tile = agents_in_cell(coord, agents)
if len(agents_in_tile) == 1: return 0
list_of_phenotypes = [
other_agent.phenotype for other_agent in agents_in_tile
if other_agent != agent
]
centroid = centroidnp(list_of_phenotypes)
return euclidean_distance(centroid, agent.phenotype)
def run_simulation(starting_pop=25):
if GRAPHICS_ON: initialize_screen()
initial_strategy=np.zeros((7,13))
initial_strategy[3][5]=100
initial_strategy[4][6]=70
initial_strategy[6][0]=20
initial_phenotype=(WMAX/4*3,WMAX/4*3,WMAX/4*3,WMAX/4*3,WMAX/4*3,WMAX/4*3,WMAX/4*3,WMAX/4*3,WMAX/4*3)
print initial_strategy
#generate agents
for i in range(starting_pop):
spawn_location=r.choice(COORDINATES)
initial_direction=r.choice(DIRECTIONS)
AGENTS.append(Agent(parent_genes=initial_strategy,
initial_location=spawn_location,
initial_energy=INITIAL_ENERGY, #p0007
phenotype=initial_phenotype,
parent_direction=initial_direction))
#spawn initial food supply
for coord in COORDINATES:
#TODO: determine how to distribute the initial supply
RESOURCES[coord]=FOOD_SIZE
tick=1
print "total energy: ", tally_energy(AGENTS,RESOURCES,COORDINATES)
print "total agents: ", len(AGENTS)
while tick :
#graphics
if GRAPHICS_ON:
pygame.display.update()
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit(); sys.exit();
tick+=1
if tick%FOOD_FREQUENCY==0: #p0004
for i in range(FOOD_COUNT): #p0005
coord=r.choice(COORDINATES)
RESOURCES[coord]+=FOOD_SIZE #p0006
if GRAPHICS_ON: draw_food(coord)
for agent in AGENTS:
if count_agents(agent.location,AGENTS)>2:
candidates=agents_in_cell(agent.location,AGENTS)
targets= [target for target in candidates if target != agent]
partner=r.choice(targets)
kinship_to_partner=determine_kinship_to_partner(agent,partner,WMAX,K)
elif count_agents(agent.location,AGENTS)>1:
candidates=agents_in_cell(agent.location,AGENTS)
targets= [target for target in candidates if target != agent]
partner=targets[0]
else:
partner=agent
kinship_to_partner=0 #0 is high
directional_resources= [bool(RESOURCES[agent.location])]+[ bool(RESOURCES[agent.forwards]),bool(RESOURCES[agent.right]),bool(RESOURCES[agent.left])]
directional_agents= [count_agents(agent.location,AGENTS)]+[ count_agents(agent.forwards,AGENTS),count_agents(agent.right,AGENTS),count_agents(agent.left,AGENTS) ]
kinship_with_locals=deviation_from_centroid(agent,agent.location,AGENTS)
# p0001 , p0003
input=np.transpose(np.matrix([K, float(agent.energy)/ENERGY_MAX,float(ENERGY_MAX-agent.energy)/ENERGY_MAX , kinship_with_locals ,kinship_to_partner] +directional_resources+directional_agents))
#print input
output=agent.genes*input
#print output
top_choice=max(output)
indices = [i for i, x in enumerate(output) if x==top_choice]
action=r.choice(indices)
#print indices,action
if action ==0: agent.rest()
elif action ==1: agent.turn(right=True)
elif action ==2: agent.turn(right=False)
elif action==3: agent.feed()
elif action==4: agent.move_forward()
elif action==5: agent.attack(partner)
elif action==6: agent.divide()
else:print "error"
if tick%50==0:
if GRAPHICS_ON or LOG_TO_CONSOLE>1:
agent_energy, available_energy=tally_energy(AGENTS,RESOURCES,COORDINATES)
if GRAPHICS_ON: draw_world_stats((len(AGENTS),round(agent_energy,2),round(available_energy,2),tick/100.0)) #drawing
print "total agent energy: ", agent_energy
print "total agents: ", len(AGENTS)
print "available energy: ", available_energy
if GRAPHICS_ON or LOG_TO_CONSOLE>0:
print tick
if len(AGENTS)==0:tick=False
