def test1(self):
print "TEST 1:----------------------------------------------------------------"
features = np.array([[1.9, 2.3],
[1.5, 2.5],
[0.8, 0.6],
[0.4, 1.8],
[0.1, 0.1],
[0.2, 1.8],
[2.0, 0.5],
[0.3, 1.5],
[1.0, 1.0]])
whitened = whiten(features)
book = np.array((whitened[0], whitened[2]))
numpy_result = kmeans(whitened, book)[0]
print numpy_result
print ""
features2 = np.array([[1.9, 2.3,0],
[1.5, 2.5,0],
[0.8, 0.6,0],
[0.4, 1.8,0],
[0.1, 0.1,0],
[0.2, 1.8,0],
[2.0, 0.5,0],
[0.3, 1.5,0],
[1.0, 1.0,0]])
whitened2 = whiten(features2)
book2 = [whitened[0], whitened[2]]
our_result = np.array(KMeans.k_means2(whitened2.tolist(), 2, book2).centroids)[:, :-1]
print our_result
def test2(self):
print "TEST 2:----------------------------------------------------------------"
rand.seed(777)
sampler = swr.SampleWithoutReplacement('datasets/adjusted-abalone.csv', .10)
sampler.z_scale()
training_set = sampler.get_training_set()
test_set = sampler.get_test_set()
indices_selected = list()
centroids = [None]*4
for i in range(4):
while True:
index_selected = np.random.randint(0, len(training_set))
if index_selected not in indices_selected:
centroids[i] = training_set[index_selected]
indices_selected.append(index_selected)
break
numpy_result=kmeans(np.array(training_set)[:, :-1], np.array(centroids)[:, :-1])[0]
our_result=np.array(KMeans.k_means2(training_set, 4, centroids).centroids)[:, :-1]
print numpy_result
print ""
print our_result