class KMeansHypothesisTest(unittest.TestCase):
def set_state_with_k_centroids(self, k, dim=3):
self._sub_h = [MockHypothesis(i) for i in range(k)]
self._hypothesis = KMeansHypothesis(dim, k, self._sub_h)
def tearDown(self):
self._sub_h = None
self._hypothesis = None
def test_update_and_get_guess_two_centroids(self):
"""Tests the update and get guess with two centroids"""
# Set up initial conditions
self.set_state_with_k_centroids(2)
# Set up the initial centroids
c1 = (25, 25, 25)
c2 = (75, 75, 75)
# Train the centroids
self.train_hypothesis_with_centroids(c1, c2, maximum_shift=15)
# Now lets get some guesses that should be near our
# centroids
# Now since the initial centroids are chosen randomly we cannot
# be certain which hypothesis will be chosen for them. As such
# we will have to work under the initial assumption that the
# system has foun the exactly specified centroids and then
# extrapolate from that
c1_index = self._hypothesis.get_guess(c1)
c2_index = self._hypothesis.get_guess(c2)
# Then we need to know that the system found two separrate centroids
# for each of the actual centroids
self.assertNotEqual(c1_index, c2_index)
# First we try to check the index of the vectors near the
# first centroid. We expect anything beneath (50, 50, 50)
# to, ideally, map to the first centroid
self.assertEqual(c1_index, self._hypothesis.get_guess((25, 25, 25)))
self.assertEqual(c1_index, self._hypothesis.get_guess((0, 0, 0)))
self.assertEqual(c1_index, self._hypothesis.get_guess((40, 40, 40)))
# Second we try to check the index of the vectors near the
# second centroid. We expect anything above (50, 50, 50)
# to, ideally, map to the second centroid
self.assertEqual(c2_index, self._hypothesis.get_guess((75, 75, 75)))
self.assertEqual(c2_index, self._hypothesis.get_guess((100, 100, 100)))
self.assertEqual(c2_index, self._hypothesis.get_guess((60, 60, 60)))
def test_update_and_get_guess_three_centroids(self):
"""Tests the update and get guess with two centroids"""
# Set up initial conditions
self.set_state_with_k_centroids(3)
# Set up the initial centroids
c1 = (-50, -50, -50)
c2 = (50, 50, 50)
c3 = (150, 150, 150)
# Train the centroids
self.train_hypothesis_with_centroids(c1, c2, c3, maximum_distance=25)
# Now lets get some guesses that should be near our
# centroids
# Now since the initial centroids are chosen randomly we cannot
# be certain which hypothesis will be chosen for them. As such
# we will have to work under the initial assumption that the
# system has found the exactly specified centroids and then
# extrapolate from that
c1_index = self._hypothesis.get_guess(c1)
c2_index = self._hypothesis.get_guess(c2)
c3_index = self._hypothesis.get_guess(c3)
# Assert that all of the found centroids have unique indices
for ci, cx in combinations([c1_index, c2_index, c3_index], 2):
self.assertNotEqual(ci, cx)
for centroid_index, centroid in zip([c1_index, c2_index, c3_index], [c1, c2, c3]):
for x in range(10):
vector = self.find_vector_close_to(centroid, maximum_distance=25)
self.assertEqual(centroid_index, self._hypothesis.get_guess(vector))
def train_hypothesis_with_centroids(self, *centroids, **kwargs):
for centroid in centroids:
for i in range(int(100 / len(centroids))):
self._hypothesis.update(self.find_vector_close_to(centroid, kwargs.get('maximum_distance', 10)), 1, 1)
def find_vector_close_to(self, centroid, maximum_distance=10):
result = []
for xi in centroid:
d = randint(0, maximum_distance)
result.append(xi + choice([-1, 1]) * d)
maximum_distance -= d
return tuple(result)
class KMeansHypothesisTest(unittest.TestCase):
def set_state_with_k_centroids(self, k, dim=3):
self._sub_h = [MockHypothesis(i) for i in range(k)]
self._hypothesis = KMeansHypothesis(dim, k, self._sub_h)
def tearDown(self):
self._sub_h = None
self._hypothesis = None
def test_update_and_get_guess_two_centroids(self):
"""Tests the update and get guess with two centroids"""
# Set up initial conditions
self.set_state_with_k_centroids(2)
# Set up the initial centroids
c1 = (25, 25, 25)
c2 = (75, 75, 75)
# Train the centroids
self.train_hypothesis_with_centroids(c1, c2, maximum_shift=15)
# Now lets get some guesses that should be near our
# centroids
# Now since the initial centroids are chosen randomly we cannot
# be certain which hypothesis will be chosen for them. As such
# we will have to work under the initial assumption that the
# system has foun the exactly specified centroids and then
# extrapolate from that
c1_index = self._hypothesis.get_guess(c1)
c2_index = self._hypothesis.get_guess(c2)
# Then we need to know that the system found two separrate centroids
# for each of the actual centroids
self.assertNotEqual(c1_index, c2_index)
# First we try to check the index of the vectors near the
# first centroid. We expect anything beneath (50, 50, 50)
# to, ideally, map to the first centroid
self.assertEqual(c1_index, self._hypothesis.get_guess((25, 25, 25)))
self.assertEqual(c1_index, self._hypothesis.get_guess((0, 0, 0)))
self.assertEqual(c1_index, self._hypothesis.get_guess((40, 40, 40)))
# Second we try to check the index of the vectors near the
# second centroid. We expect anything above (50, 50, 50)
# to, ideally, map to the second centroid
self.assertEqual(c2_index, self._hypothesis.get_guess((75, 75, 75)))
self.assertEqual(c2_index, self._hypothesis.get_guess((100, 100, 100)))
self.assertEqual(c2_index, self._hypothesis.get_guess((60, 60, 60)))
def test_update_and_get_guess_three_centroids(self):
"""Tests the update and get guess with two centroids"""
# Set up initial conditions
self.set_state_with_k_centroids(3)
# Set up the initial centroids
c1 = (-50, -50, -50)
c2 = (50, 50, 50)
c3 = (150, 150, 150)
# Train the centroids
self.train_hypothesis_with_centroids(c1, c2, c3, maximum_distance=25)
# Now lets get some guesses that should be near our
# centroids
# Now since the initial centroids are chosen randomly we cannot
# be certain which hypothesis will be chosen for them. As such
# we will have to work under the initial assumption that the
# system has found the exactly specified centroids and then
# extrapolate from that
c1_index = self._hypothesis.get_guess(c1)
c2_index = self._hypothesis.get_guess(c2)
c3_index = self._hypothesis.get_guess(c3)
# Assert that all of the found centroids have unique indices
for ci, cx in combinations([c1_index, c2_index, c3_index], 2):
self.assertNotEqual(ci, cx)
for centroid_index, centroid in zip([c1_index, c2_index, c3_index],
[c1, c2, c3]):
for x in range(10):
vector = self.find_vector_close_to(centroid,
maximum_distance=25)
self.assertEqual(centroid_index,
self._hypothesis.get_guess(vector))
def train_hypothesis_with_centroids(self, *centroids, **kwargs):
for centroid in centroids:
for i in range(int(100 / len(centroids))):
self._hypothesis.update(
self.find_vector_close_to(
centroid, kwargs.get('maximum_distance', 10)), 1, 1)
def find_vector_close_to(self, centroid, maximum_distance=10):
result = []
for xi in centroid:
d = randint(0, maximum_distance)
result.append(xi + choice([-1, 1]) * d)
maximum_distance -= d
return tuple(result)
def set_state_with_k_centroids(self, k, dim=3):
self._sub_h = [MockHypothesis(i) for i in range(k)]
self._hypothesis = KMeansHypothesis(dim, k, self._sub_h)
def set_state_with_k_centroids(self, k, dim=3):
self._sub_h = [MockHypothesis(i) for i in range(k)]
self._hypothesis = KMeansHypothesis(dim, k, self._sub_h)
