def test_kmeans(self):
""" with k=1 always get one cluster centered on the mean"""
data = self.sc.parallelize(array([
array([1.0, 2.0, 6.0]),
array([1.0, 3.0, 0.0]),
array([1.0, 4.0, 6.0])]
))
labels, centers = kmeans(data, k=1, maxiter=20, tol=0.001)
assert array_equal(centers[0], array([1.0, 3.0, 4.0]))
assert array_equal(labels.collect(), array([0, 0, 0]))
def test_kmeans(self):
""" With k=1 always get one cluster centered on the mean"""
data_local = [
array([1.0, 2.0, 6.0]),
array([1.0, 3.0, 0.0]),
array([1.0, 4.0, 6.0])]
data = self.sc.parallelize(zip(range(1, 4), data_local))
labels, centers = kmeans(data, k=1, maxiter=20, tol=0.001)
assert array_equal(centers[0], array([1.0, 3.0, 4.0]))
assert array_equal(labels.map(lambda (_, v): v).collect(), array([0, 0, 0]))
def test_kmeans(self):
""" With k=1 always get one cluster centered on the mean"""
data_local = [
array([1.0, 2.0, 6.0]),
array([1.0, 3.0, 0.0]),
array([1.0, 4.0, 6.0])
]
data = self.sc.parallelize(zip(range(1, 4), data_local))
labels, centers = kmeans(data, k=1, maxiter=20, tol=0.001)
assert array_equal(centers[0], array([1.0, 3.0, 4.0]))
assert array_equal(
labels.map(lambda (_, v): v).collect(), array([0, 0, 0]))
def test_kmeans(self):
data = get_data_kmeans(self)
labels, centers, dists, normDists = kmeans(data, 5, "euclidean")
labels.collect()
dists.collect()
normDists.collect()