def test_KMeansMachine():
# Test a KMeansMachine
means = np.array([[3, 70, 0], [4, 72, 0]], "float64")
test_val = np.array([3, 70, 1], "float64")
test_arr = np.array([[3, 70, 1], [5, 72, 0]], "float64")
for transform in (to_numpy, to_dask_array):
means, test_val, test_arr = transform(means, test_val, test_arr)
# Initializes a KMeansMachine
km = KMeansMachine(2)
km.centroids_ = means
# Distance and closest mean
np.testing.assert_equal(km.transform(test_val)[0], np.array([1]))
np.testing.assert_equal(km.transform(test_val)[1], np.array([6]))
index = km.predict(test_val)
assert index == 0
indices = km.predict(test_arr)
np.testing.assert_equal(indices, np.array([0, 1]))
# Check __eq__ and is_similar_to
km2 = KMeansMachine(2)
assert km != km2
assert not km.is_similar_to(km2)
km2 = copy.deepcopy(km)
assert km == km2
assert km.is_similar_to(km2)
km2.centroids_[0, 0] += 1
assert km != km2
assert not km.is_similar_to(km2)
def test_KMeansMachine():
# Test a KMeansMachine
means = numpy.array([[3, 70, 0], [4, 72, 0]], 'float64')
mean = numpy.array([3,70,1], 'float64')
# Initializes a KMeansMachine
km = KMeansMachine(2,3)
km.means = means
assert km.shape == (2,3)
# Sets and gets
assert (km.means == means).all()
assert (km.get_mean(0) == means[0,:]).all()
assert (km.get_mean(1) == means[1,:]).all()
km.set_mean(0, mean)
assert (km.get_mean(0) == mean).all()
# Distance and closest mean
eps = 1e-10
assert equals( km.get_distance_from_mean(mean, 0), 0, eps)
assert equals( km.get_distance_from_mean(mean, 1), 6, eps)
(index, dist) = km.get_closest_mean(mean)
assert index == 0
assert equals( dist, 0, eps)
assert equals( km.get_min_distance(mean), 0, eps)
# Loads and saves
filename = str(tempfile.mkstemp(".hdf5")[1])
km.save(bob.io.base.HDF5File(filename, 'w'))
km_loaded = KMeansMachine(bob.io.base.HDF5File(filename))
assert km == km_loaded
# Resize
km.resize(4,5)
assert km.shape == (4,5)
# Copy constructor and comparison operators
km.resize(2,3)
km2 = KMeansMachine(km)
assert km2 == km
assert (km2 != km) is False
assert km2.is_similar_to(km)
means2 = numpy.array([[3, 70, 0], [4, 72, 2]], 'float64')
km2.means = means2
assert (km2 == km) is False
assert km2 != km
assert (km2.is_similar_to(km)) is False
# Clean-up
os.unlink(filename)
def test_KMeansMachine():
# Test a KMeansMachine
means = numpy.array([[3, 70, 0], [4, 72, 0]], 'float64')
mean = numpy.array([3, 70, 1], 'float64')
# Initializes a KMeansMachine
km = KMeansMachine(2, 3)
km.means = means
assert km.shape == (2, 3)
# Sets and gets
assert (km.means == means).all()
assert (km.get_mean(0) == means[0, :]).all()
assert (km.get_mean(1) == means[1, :]).all()
km.set_mean(0, mean)
assert (km.get_mean(0) == mean).all()
# Distance and closest mean
eps = 1e-10
assert equals(km.get_distance_from_mean(mean, 0), 0, eps)
assert equals(km.get_distance_from_mean(mean, 1), 6, eps)
(index, dist) = km.get_closest_mean(mean)
assert index == 0
assert equals(dist, 0, eps)
assert equals(km.get_min_distance(mean), 0, eps)
# Loads and saves
filename = str(tempfile.mkstemp(".hdf5")[1])
km.save(bob.io.base.HDF5File(filename, 'w'))
km_loaded = KMeansMachine(bob.io.base.HDF5File(filename))
assert km == km_loaded
# Resize
km.resize(4, 5)
assert km.shape == (4, 5)
# Copy constructor and comparison operators
km.resize(2, 3)
km2 = KMeansMachine(km)
assert km2 == km
assert (km2 != km) is False
assert km2.is_similar_to(km)
means2 = numpy.array([[3, 70, 0], [4, 72, 2]], 'float64')
km2.means = means2
assert (km2 == km) is False
assert km2 != km
assert (km2.is_similar_to(km)) is False
# Clean-up
os.unlink(filename)