def test_GroupedAggroByColor_WithWimpyBraveAndKNN(self):
brave = BraveHypothesis()
wimpy = WimpyHypothesis()
knn = KNearestNeighbors(3)
self.setUpDecisioner(brave, wimpy, knn)
def create_monster():
color = randint(1, 100)
# Every monster below 50 is aggressive
if color < 50:
return Monster(1, [color], 'aggressive')
# Otherwise if they are above 50 they are
# passive
else:
return Monster(0, [color], 'passive')
# Next we load up the training data
for i in range(100):
# Create the monster and generate all the data, by default
# we expect everything in the training period to be true
# meaning attack for data
monster = create_monster()
self.assertTrue(self.decisioner.get_guess(monster.color))
self.decisioner.update(monster.color, 1, monster.action(True))
# Finally we need to know that normal KNN was matched as the best
# fitness for the data set
self.assertGreater(knn.fitness(), brave.fitness())
self.assertGreater(knn.fitness(), wimpy.fitness())
# Then we are going to run over all of the data sets
# We want to also track the actual value and the maximum
# value for comparison
maximum_value = 5000.0
actual_value = 0.0
for i in range(5000):
# Create the monster, guess on it and grab the outcome
# which is all required information for the loop
monster = create_monster()
guess = self.decisioner.get_guess(monster.color)
outcome = monster.action(guess)
# update values for comparison after loop
maximum_value -= monster._aggressive
actual_value += outcome
#
self.decisioner.update(monster.color, guess, outcome)
# We need to know that KNN is still our best fit for this
# data set
self.assertGreater(knn.fitness(), brave.fitness())
self.assertGreater(knn.fitness(), wimpy.fitness())
# Finally we expect that the standard KNN process will obtain
# within 10 percent margin of the best possible solution
self.assertGreater(actual_value / maximum_value, 0.9)
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
def test_update_ratio_matches_expected(self):
"""Test the KNN algorithm with our built in rolling window"""
knn = KNearestNeighbors(1)
# First we define the method for generating our two
# categories in a single dimension
def generate_monster():
color = randint(1, 100)
# All colors less than 50 are passive
if color < 50:
return Monster(0, [color], 'passive')
# Otherwise the monster is aggresive
return Monster(1, [color], 'aggressive')
# Run the inital training phase
for i in range(100):
monster = generate_monster()
knn.update(monster.color, 1, monster.action(True))
# Then we need to run over a set of values, we should see
# a relatively high ratio of actual value vs maximum value
actual_value = 0.0
maximum_value = 5000.0
for i in range(5000):
monster = generate_monster()
guess = knn.get_guess(monster.color)
outcome = monster.action(guess)
maximum_value -= monster._aggressive
actual_value += outcome
knn.update(monster.color, guess, outcome)
self.assertGreaterEqual(actual_value / maximum_value, 0.9)
def test_update_ratio_matches_expected(self):
"""Test the KNN algorithm with our built in rolling window"""
knn = KNearestNeighbors(1)
# First we define the method for generating our two
# categories in a single dimension
def generate_monster():
color = randint(1, 100)
# All colors less than 50 are passive
if color < 50:
return Monster(0, [color], 'passive')
# Otherwise the monster is aggresive
return Monster(1, [color], 'aggressive')
# Run the inital training phase
for i in range(100):
monster = generate_monster()
knn.update(monster.color, 1, monster.action(True))
# Then we need to run over a set of values, we should see
# a relatively high ratio of actual value vs maximum value
actual_value = 0.0
maximum_value = 5000.0
for i in range(5000):
monster = generate_monster()
guess = knn.get_guess(monster.color)
outcome = monster.action(guess)
maximum_value -= monster._aggressive
actual_value += outcome
knn.update(monster.color, guess, outcome)
self.assertGreaterEqual(actual_value/ maximum_value, 0.9)
def create_monster():
color = randint(1, 100)
if color < 70:
if random() < 0.3:
return Monster(1, [color], 'aggressive')
return Monster(0, [color], 'passive')
else:
if random() < 0.7:
return Monster(1, [color], 'aggressive')
return Monster(0, [color], 'passive')
hyps = [
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
def setUp(self):
# We set the window to 99 to allow the first set to key
# on 100 values updated
self._hypothesis = KNearestNeighbors(3, window=99)
class KNearestNeighborsTest(unittest.TestCase):
def setUp(self):
# We set the window to 99 to allow the first set to key
# on 100 values updated
self._hypothesis = KNearestNeighbors(3, window=99)
def tearDown(self):
self._hypothesis = None
def test_update_1dimension(self):
"""Test the KNN algorithm in one dimension at multiple points"""
# Set up cluster of two distant groups in one dimension
for i in range(50):
self._hypothesis.update([1], 1, -1)
for i in range(50):
self._hypothesis.update([50], 1, 1)
self.assertFalse(self._hypothesis.get_guess([-1]))
self.assertFalse(self._hypothesis.get_guess([1]))
self.assertFalse(self._hypothesis.get_guess([-15]))
self.assertFalse(self._hypothesis.get_guess([15]))
self.assertFalse(self._hypothesis.get_guess([-24]))
self.assertFalse(self._hypothesis.get_guess([24]))
self.assertTrue(self._hypothesis.get_guess([50]))
self.assertTrue(self._hypothesis.get_guess([49]))
self.assertTrue(self._hypothesis.get_guess([51]))
self.assertTrue(self._hypothesis.get_guess([26]))
self.assertTrue(self._hypothesis.get_guess([74]))
def test_update_2dimension(self):
"""Test the KNN algorithm in two dimensions at multiple points"""
# Set up cluster of two distance groups in two dimensions
for i in range(50):
self._hypothesis.update([1, 1], 1, -1)
for i in range(50):
self._hypothesis.update([50, 50], 1, 1)
self.assertFalse(self._hypothesis.get_guess([1, 1]))
self.assertFalse(self._hypothesis.get_guess([-1, -1]))
self.assertFalse(self._hypothesis.get_guess([15, 15]))
self.assertFalse(self._hypothesis.get_guess([-15, -15]))
self.assertFalse(self._hypothesis.get_guess([24, 24]))
self.assertFalse(self._hypothesis.get_guess([-24, -24]))
self.assertTrue(self._hypothesis.get_guess([50, 50]))
self.assertTrue(self._hypothesis.get_guess([49, 49]))
self.assertTrue(self._hypothesis.get_guess([51, 51]))
self.assertTrue(self._hypothesis.get_guess([26, 26]))
self.assertTrue(self._hypothesis.get_guess([74, 74]))
def test_update_over_Ndimensions(self):
"""Test the KNN algorithm in between 2 and 10 dimensions"""
for dimension in range(2, 10):
# first set the aggressive monsters at [10, ..., 10]
for i in range(50):
self._hypothesis.update([10] * dimension, 1, -1)
# Next set the non-aggresive monsters at [90, ..., 90]
for i in range(50):
self._hypothesis.update([90] * dimension, 1, 1)
self.assertFalse(self._hypothesis.get_guess([0] * dimension))
self.assertFalse(self._hypothesis.get_guess([40] * dimension))
self.assertTrue(self._hypothesis.get_guess([100] * dimension))
self.assertTrue(self._hypothesis.get_guess([55] * dimension))
def test_update_ratio_matches_expected(self):
"""Test the KNN algorithm with our built in rolling window"""
knn = KNearestNeighbors(1)
# First we define the method for generating our two
# categories in a single dimension
def generate_monster():
color = randint(1, 100)
# All colors less than 50 are passive
if color < 50:
return Monster(0, [color], 'passive')
# Otherwise the monster is aggresive
return Monster(1, [color], 'aggressive')
# Run the inital training phase
for i in range(100):
monster = generate_monster()
knn.update(monster.color, 1, monster.action(True))
# Then we need to run over a set of values, we should see
# a relatively high ratio of actual value vs maximum value
actual_value = 0.0
maximum_value = 5000.0
for i in range(5000):
monster = generate_monster()
guess = knn.get_guess(monster.color)
outcome = monster.action(guess)
maximum_value -= monster._aggressive
actual_value += outcome
knn.update(monster.color, guess, outcome)
self.assertGreaterEqual(actual_value/ maximum_value, 0.9)
def setUp(self):
# We set the window to 99 to allow the first set to key
# on 100 values updated
self._hypothesis = KNearestNeighbors(3, window=99)
class KNearestNeighborsTest(unittest.TestCase):
def setUp(self):
# We set the window to 99 to allow the first set to key
# on 100 values updated
self._hypothesis = KNearestNeighbors(3, window=99)
def tearDown(self):
self._hypothesis = None
def test_update_1dimension(self):
"""Test the KNN algorithm in one dimension at multiple points"""
# Set up cluster of two distant groups in one dimension
for i in range(50):
self._hypothesis.update([1], 1, -1)
for i in range(50):
self._hypothesis.update([50], 1, 1)
self.assertFalse(self._hypothesis.get_guess([-1]))
self.assertFalse(self._hypothesis.get_guess([1]))
self.assertFalse(self._hypothesis.get_guess([-15]))
self.assertFalse(self._hypothesis.get_guess([15]))
self.assertFalse(self._hypothesis.get_guess([-24]))
self.assertFalse(self._hypothesis.get_guess([24]))
self.assertTrue(self._hypothesis.get_guess([50]))
self.assertTrue(self._hypothesis.get_guess([49]))
self.assertTrue(self._hypothesis.get_guess([51]))
self.assertTrue(self._hypothesis.get_guess([26]))
self.assertTrue(self._hypothesis.get_guess([74]))
def test_update_2dimension(self):
"""Test the KNN algorithm in two dimensions at multiple points"""
# Set up cluster of two distance groups in two dimensions
for i in range(50):
self._hypothesis.update([1, 1], 1, -1)
for i in range(50):
self._hypothesis.update([50, 50], 1, 1)
self.assertFalse(self._hypothesis.get_guess([1, 1]))
self.assertFalse(self._hypothesis.get_guess([-1, -1]))
self.assertFalse(self._hypothesis.get_guess([15, 15]))
self.assertFalse(self._hypothesis.get_guess([-15, -15]))
self.assertFalse(self._hypothesis.get_guess([24, 24]))
self.assertFalse(self._hypothesis.get_guess([-24, -24]))
self.assertTrue(self._hypothesis.get_guess([50, 50]))
self.assertTrue(self._hypothesis.get_guess([49, 49]))
self.assertTrue(self._hypothesis.get_guess([51, 51]))
self.assertTrue(self._hypothesis.get_guess([26, 26]))
self.assertTrue(self._hypothesis.get_guess([74, 74]))
def test_update_over_Ndimensions(self):
"""Test the KNN algorithm in between 2 and 10 dimensions"""
for dimension in range(2, 10):
# first set the aggressive monsters at [10, ..., 10]
for i in range(50):
self._hypothesis.update([10] * dimension, 1, -1)
# Next set the non-aggresive monsters at [90, ..., 90]
for i in range(50):
self._hypothesis.update([90] * dimension, 1, 1)
self.assertFalse(self._hypothesis.get_guess([0] * dimension))
self.assertFalse(self._hypothesis.get_guess([40] * dimension))
self.assertTrue(self._hypothesis.get_guess([100] * dimension))
self.assertTrue(self._hypothesis.get_guess([55] * dimension))
def test_update_ratio_matches_expected(self):
"""Test the KNN algorithm with our built in rolling window"""
knn = KNearestNeighbors(1)
# First we define the method for generating our two
# categories in a single dimension
def generate_monster():
color = randint(1, 100)
# All colors less than 50 are passive
if color < 50:
return Monster(0, [color], 'passive')
# Otherwise the monster is aggresive
return Monster(1, [color], 'aggressive')
# Run the inital training phase
for i in range(100):
monster = generate_monster()
knn.update(monster.color, 1, monster.action(True))
# Then we need to run over a set of values, we should see
# a relatively high ratio of actual value vs maximum value
actual_value = 0.0
maximum_value = 5000.0
for i in range(5000):
monster = generate_monster()
guess = knn.get_guess(monster.color)
outcome = monster.action(guess)
maximum_value -= monster._aggressive
actual_value += outcome
knn.update(monster.color, guess, outcome)
self.assertGreaterEqual(actual_value / maximum_value, 0.9)