def test_AllPassiveMonsters_WithRando(self):
wimpy = WimpyHypothesis()
rando = RandoHypothesis()
self.setUpDecisioner(wimpy, rando)
# First we will start with the basic training case
# which is the first 100 in the range
for i in range(101):
monster = Monster(0, [randint(1, 100)], 'passive')
# Get the guess from the decisioner for the first 100,
# we expect every guess to be 1
self.assertTrue(self.decisioner.get_guess(monster.color))
# Then we will update from this guess
self.decisioner.update(monster.color, 1, monster.action(True))
for i in range(5000):
monster = Monster(0, [randint(1, 100)], 'aggressive')
self.decisioner.get_guess(monster.color)
# Then we will update from this guess
self.decisioner.update(monster.color, 1, 1)
# Flipping a coin is better than doing nothing
self.assertGreater(rando.fitness(), wimpy.fitness())
# Randomness should be near-even in distribution
self.assertGreater(rando.fitness(), 0.4)
self.assertGreater(0.6, rando.fitness())
def test_with_all_hypothesis(self):
brave = BraveHypothesis()
wimpy = WimpyHypothesis()
knn3 = KNearestNeighbors(3)
knn5 = KNearestNeighbors(5)
knn7 = KNearestNeighbors(7)
knn11 = KNearestNeighbors(11)
rando = RandoHypothesis()
prob = SimpleProbabilityHypothesis()
# drP3 = OptimusPerceptron(11)
# drP5 = DrPerceptron(5)
# drP7 = DrPerceptron(7)
# drP11 = DrPerceptron(11)
#
self.setUpDecisioner(brave,
wimpy,
knn3,
knn5,
knn7,
knn11,
rando,
prob,
# drP3,
# drP5,
# drP7,
# drP11,
)
def create_monster():
color = randint(1, 100)
if color < 70:
if random() >= 0.3:
return Monster(0, [color], 'passive')
return Monster(1, [color], 'aggressive')
else:
if random() >= 0.7:
return Monster(0, [color], 'passive')
return Monster(1, [color], 'aggressive')
for i in range(100):
monster = create_monster()
self.decisioner.update(monster.color, 1, monster.action(True))
maximum_value = 1000.0
actual_value = 0.0
for i in range(1000):
monster = create_monster()
guess = self.decisioner.get_guess(monster.color)
outcome = monster.action(guess)
self.decisioner.update(monster.color, guess, outcome)
maximum_value -= monster._aggressive
actual_value += outcome
BraveHypothesis(),
WimpyHypothesis(),
KNearestNeighbors(3),
KNearestNeighbors(5),
KNearestNeighbors(7),
KNearestNeighbors(11),
KNearestNeighbors(17),
KNearestNeighbors(23),
KNearestNeighbors(29),
KNearestNeighbors(31),
KNearestNeighbors(37),
KNearestNeighbors(41),
KNearestNeighbors(43),
KNearestNeighbors(47),
SimpleProbabilityHypothesis(),
RandoHypothesis(),
OptimusPerceptron(47), # mod 47 universe
OptimusPerceptron(43), # mod 43 universe
OptimusPerceptron(41), # mod 41 universe
OptimusPerceptron(37), # mod 37 universe
OptimusPerceptron(31), # mod 31 universe
OptimusPerceptron(29), # mod 29 universe
OptimusPerceptron(23), # mod 23 universe
OptimusPerceptron(17), # mod 17 universe
OptimusPerceptron(11), # mod 11 universe
OptimusPerceptron(7), # mod 7 universe
OptimusPerceptron(5), # mod 5 universe
OptimusPerceptron(3), # mod 3 universe
OptimusPerceptron(2), # even universe
OptimusPerceptron(
1), # lonliest number universe, interesting but not a good idea
def setUp(self):
self._hypothesis = RandoHypothesis()