def mutate_brain(brain):
""" Add random mutations to a brain """
# For all synapses: shift in some direction with random chance
for s in brain.synapses:
if util.rand(0, 1) <= Agent._MUTATE_SYNAPSE_ODDS:
s.weight += Agent._MUTATE_SYNAPSE_SHIFT * util.rand(-1, 1)
s.weight = util.clamp(s.weight, -1, 1)
def mutate_brain(brain): """ Add random mutations to a brain """ # For all synapses: shift in some direction with random chance for s in brain.synapses: if util.rand(0, 1) <= Agent._MUTATE_SYNAPSE_ODDS: s.weight += Agent._MUTATE_SYNAPSE_SHIFT * util.rand(-1, 1) s.weight = util.clamp(s.weight, -1, 1)
def setup_scene():
# Create camera
target = util.ensure_target()
util.ensure_camera((util.rand(), util.rand(), 8 + util.rand()), target)
# Create light
util.ensure_light(origin=(0, 0, 10))
# Create backdrop
util.ensure_cube()
# Create paper
paperGen = paper.create_random_paper()
minHeight = util.ensure_paper(paperGen)
def adjust_height(world):
return mathutils.Vector((world[0], world[1], world[2] - minHeight))
# Return lambda that maps paper coordinates to world
return lambda coord: adjust_height(paperGen(*coord))
def breed_brain(parent_1_brain, parent_2_brain):
"""
Create a brain with mixed traits from two parents by starting with a
copy of parent_1_brain then swapping or mixing with parent_2_brain
"""
brain = parent_1_brain.make_copy()
# For all synapses: replace with parent 2, average, or leave the same
for i, s in enumerate(brain.synapses):
chance = util.rand(0, 1)
parent_2_weight = parent_2_brain.synapses[i].weight
if chance > 0.66:
s.weight = parent_2_weight
elif chance > 0.33:
s.weight = (s.weight + parent_2_weight) / 2.0
return brain
def breed_brain(parent_1_brain, parent_2_brain): """ Create a brain with mixed traits from two parents by starting with a copy of parent_1_brain then swapping or mixing with parent_2_brain """ brain = parent_1_brain.make_copy() # For all synapses: replace with parent 2, average, or leave the same for i, s in enumerate(brain.synapses): chance = util.rand(0, 1) parent_2_weight = parent_2_brain.synapses[i].weight if chance > 0.66: s.weight = parent_2_weight elif chance > 0.33: s.weight = (s.weight + parent_2_weight) / 2.0 return brain
#!/usr/bin/python
from util import rand, ls, load, promptint, promptstr
print("XKCD Password Generator.")
dicts = ls("/usr/share/dict")
print("{:d} files found in /usr/share/dict".format(len(dicts)))
print("Choose which one to use 0-{:d}".format(len(dicts)-1))
for i in range(len(dicts)):
print(str(i) + ": "+dicts[i].split("/")[-1])
d = load("/usr/share/dict/"+dicts[promptint("Choice (0-{:d}): ".format(len(dicts)-1))],
promptint("Minimum word length: "),
promptint("Maximum word length: "))
pw = ""
for i in range(promptint("Number of words: ")):
pw = pw + d[rand(len(d))][:-1] + " "
print(pw)
from flask import redirect
from flask import render_template
from flask import request
from flask.ext.socketio import SocketIO
from models import App
import traceback
import client
import config
import helpers
import util
client.set_validate_ssl(config.validate_ssl)
app = helpers.CustomFlask(__name__)
app.debug = os.getenv('APP_DEBUG', 'false') == 'true'
app.config['SECRET_KEY'] = os.getenv('SECRET_KEY', util.rand(16))
socketio = SocketIO(app)
routes_whitelist = [
'/signin',
]
def form_as_dict():
form = {}
for key, val in request.form.items():
if key not in form:
form[key] = val
elif not isinstance(form[key], list):
form[key] = [form[key], val]
def create_random_brain():
""" Create a brain with randomized parameters """
brain = nnet.NeuralNetwork()
# Logic neurons
logic_0 = nnet.Neuron("lgc_0")
logic_1 = nnet.Neuron("lgc_1")
logic_2 = nnet.Neuron("lgc_2")
logic_3 = nnet.Neuron("lgc_3")
# Input (hunger) neuron turns on as health goes down
hunger = nnet.Neuron("hunger", is_input=True)
# Input (smell) neurons for nearby Food
food_left = nnet.Neuron("fd_lft", is_input=True)
food_right = nnet.Neuron("fd_rght", is_input=True)
# Input (sight) neurons for nearby Agents
agent_left = nnet.Neuron("agnt_lft", is_input=True)
agent_right = nnet.Neuron("agnt_rght", is_input=True)
# Output (movement) neurons
move_left = nnet.Neuron("mv_lft")
move_right = nnet.Neuron("mv_rght")
# Output (attack) neuron
attack = nnet.Neuron("atk")
# Input layer
brain.add_neuron(agent_left)
brain.add_neuron(food_left)
brain.add_neuron(hunger)
brain.add_neuron(food_right)
brain.add_neuron(agent_right)
# Hidden layer
brain.add_neuron(logic_0)
brain.add_neuron(logic_1)
brain.add_neuron(logic_2)
brain.add_neuron(logic_3)
# Output layer
brain.add_neuron(move_left)
brain.add_neuron(attack)
brain.add_neuron(move_right)
# Input to hidden layer: left side
brain.add_synapse(
nnet.Synapse(agent_left, logic_0, util.rand(-0.25, 1)))
brain.add_synapse(nnet.Synapse(agent_left, logic_1, util.rand(-1, 1)))
brain.add_synapse(
nnet.Synapse(food_left, logic_0, util.rand(-0.25, 0.25)))
brain.add_synapse(nnet.Synapse(food_left, logic_1, util.rand(-0.25,
1)))
# Input to hidden layer: center
brain.add_synapse(nnet.Synapse(hunger, logic_1, util.rand(-0.25,
0.75)))
brain.add_synapse(nnet.Synapse(hunger, logic_2, util.rand(-0.25,
0.75)))
# Input to hidden layer: right side
brain.add_synapse(
nnet.Synapse(food_right, logic_2, util.rand(-0.25, 1)))
brain.add_synapse(
nnet.Synapse(food_right, logic_3, util.rand(-0.25, 0.25)))
brain.add_synapse(
nnet.Synapse(agent_right, logic_2, util.rand(-0.25, 0.25)))
brain.add_synapse(
nnet.Synapse(agent_right, logic_3, util.rand(-0.25, 1)))
# Hidden to output layer: left side
brain.add_synapse(
nnet.Synapse(logic_0, move_left, util.rand(-0.25, 0.25)))
brain.add_synapse(nnet.Synapse(logic_0, attack, util.rand(-0.5, 0.5)))
brain.add_synapse(nnet.Synapse(logic_1, move_left, util.rand(-0.25,
1)))
brain.add_synapse(nnet.Synapse(logic_1, attack, util.rand(-0.25,
0.25)))
# Hidden to output layer: right side
brain.add_synapse(nnet.Synapse(logic_2, attack, util.rand(-0.25,
0.25)))
brain.add_synapse(
nnet.Synapse(logic_2, move_right, util.rand(-0.25, 1)))
brain.add_synapse(nnet.Synapse(logic_3, attack, util.rand(-0.5, 0.5)))
brain.add_synapse(
nnet.Synapse(logic_3, move_right, util.rand(-0.25, 0.25)))
return brain
def move_to_random(self):
""" Move to a random location and heading within the world """
self.move_to(util.rand(0, Actor._WORLD_SIZE[0]),
util.rand(0, Actor._WORLD_SIZE[1]),
util.rand(0, 2 * math.pi))
def _will_attack(self):
""" Determine if this Agent will attack another in an interaction """
prob = self.brain.find_neuron("atk").get_activation()
prob = util.clamp(prob, 0, 1)
return util.rand(0, 1) <= prob
def create_random_brain():
""" Create a brain with randomized parameters """
brain = nnet.NeuralNetwork()
# Logic neurons
logic_0 = nnet.Neuron("lgc_0")
logic_1 = nnet.Neuron("lgc_1")
logic_2 = nnet.Neuron("lgc_2")
logic_3 = nnet.Neuron("lgc_3")
# Input (hunger) neuron turns on as health goes down
hunger = nnet.Neuron("hunger", is_input = True)
# Input (smell) neurons for nearby Food
food_left = nnet.Neuron("fd_lft", is_input = True)
food_right = nnet.Neuron("fd_rght", is_input = True)
# Input (sight) neurons for nearby Agents
agent_left = nnet.Neuron("agnt_lft", is_input = True)
agent_right = nnet.Neuron("agnt_rght", is_input = True)
# Output (movement) neurons
move_left = nnet.Neuron("mv_lft")
move_right = nnet.Neuron("mv_rght")
# Output (attack) neuron
attack = nnet.Neuron("atk")
# Input layer
brain.add_neuron(agent_left)
brain.add_neuron(food_left)
brain.add_neuron(hunger)
brain.add_neuron(food_right)
brain.add_neuron(agent_right)
# Hidden layer
brain.add_neuron(logic_0)
brain.add_neuron(logic_1)
brain.add_neuron(logic_2)
brain.add_neuron(logic_3)
# Output layer
brain.add_neuron(move_left)
brain.add_neuron(attack)
brain.add_neuron(move_right)
# Input to hidden layer: left side
brain.add_synapse(nnet.Synapse(agent_left, logic_0, util.rand(-0.25, 1)))
brain.add_synapse(nnet.Synapse(agent_left, logic_1, util.rand(-1, 1)))
brain.add_synapse(nnet.Synapse(food_left, logic_0, util.rand(-0.25, 0.25)))
brain.add_synapse(nnet.Synapse(food_left, logic_1, util.rand(-0.25, 1)))
# Input to hidden layer: center
brain.add_synapse(nnet.Synapse(hunger, logic_1, util.rand(-0.25, 0.75)))
brain.add_synapse(nnet.Synapse(hunger, logic_2, util.rand(-0.25, 0.75)))
# Input to hidden layer: right side
brain.add_synapse(nnet.Synapse(food_right, logic_2, util.rand(-0.25, 1)))
brain.add_synapse(nnet.Synapse(food_right, logic_3, util.rand(-0.25, 0.25)))
brain.add_synapse(nnet.Synapse(agent_right, logic_2, util.rand(-0.25, 0.25)))
brain.add_synapse(nnet.Synapse(agent_right, logic_3, util.rand(-0.25, 1)))
# Hidden to output layer: left side
brain.add_synapse(nnet.Synapse(logic_0, move_left, util.rand(-0.25, 0.25)))
brain.add_synapse(nnet.Synapse(logic_0, attack, util.rand(-0.5, 0.5)))
brain.add_synapse(nnet.Synapse(logic_1, move_left, util.rand(-0.25, 1)))
brain.add_synapse(nnet.Synapse(logic_1, attack, util.rand(-0.25, 0.25)))
# Hidden to output layer: right side
brain.add_synapse(nnet.Synapse(logic_2, attack, util.rand(-0.25, 0.25)))
brain.add_synapse(nnet.Synapse(logic_2, move_right, util.rand(-0.25, 1)))
brain.add_synapse(nnet.Synapse(logic_3, attack, util.rand(-0.5, 0.5)))
brain.add_synapse(nnet.Synapse(logic_3, move_right, util.rand(-0.25, 0.25)))
return brain
def move_to_random(self): """ Move to a random location and heading within the world """ self.move_to(util.rand(0, Actor._WORLD_SIZE[0]), util.rand(0, Actor._WORLD_SIZE[1]), util.rand(0, 2 * math.pi))
def _will_attack(self):
""" Determine if this Agent will attack another in an interaction """
prob = self.brain.find_neuron("atk").get_activation()
prob = util.clamp(prob, 0, 1)
return util.rand(0, 1) <= prob
