def test_kmeans_random():
data = np.random.randn( 1000, 3 )
data[500:] += np.array([ 10,10,10])
codebook1 = kmeans( data, 2)
codebook2 = kmeans( data, 2)
codebook3 = kmeans( data, 2)
allclose = (np.allclose( codebook1, codebook2 ) and
np.allclose( codebook2, codebook3))
assert not allclose
def test_kmeans_random():
data = np.random.randn(1000, 3)
data[500:] += np.array([10, 10, 10])
codebook1 = kmeans(data, 2)
codebook2 = kmeans(data, 2)
codebook3 = kmeans(data, 2)
allclose = (np.allclose(codebook1, codebook2)
and np.allclose(codebook2, codebook3))
assert not allclose
def test_kmeans():
data = np.array([
[0, 0],
[0, 1],
[1, 1],
[-1, 1],
[10, 10],
[11, 10],
[10, 11],
[10, 10.1],
[-100, -100],
[-101, -100],
[-102, -100],
],
dtype=np.float)
term = (
100,
0,
0,
0, # run for 1 stage (maxTotStage)
0.10, # min consec RDL
0.10, # min accum RDL
3, # max run stages
0.50, # init. prob. of acceptance
10, # temp. run length
0.95)
codebook = kmeans(data, 3, 'hybrid', term)
# XXX should do test here
return codebook
def call_kmeans(data, n, k, algorithm, term):
# data: nxd
# term = (100, 0, 0, 0, # run for 1 stage (maxTotStage)
# 0.10, # min consec RDL
# 0.10, # min accum RDL
# 3, # max run stages
# 0.50, # init. prob. of acceptance
# 10, # temp. run length
# 0.95)
data = data.reshape((n, -1))
codebook = kmeans(data, k, algorithm, term)
return codebook
def test_kmeans():
data = np.array([
[0, 0],
[0, 1],
[1, 1],
[-1, 1],
[10, 10],
[11, 10],
[10, 11],
[10, 10.1],
[-100, -100],
[-101, -100],
[-102, -100],
],
dtype=np.float)
codebook = kmeans(data, 3)
def test_kmeans():
data = np.array([[0, 0],
[0, 1],
[1, 1],
[-1, 1],
[10,10],
[11,10],
[10,11],
[10,10.1],
[-100, -100],
[-101, -100],
[-102, -100],
],
dtype=np.float)
codebook = kmeans( data, 3)