def test_float_precision():
km = KMeansConstrained(n_init=1, random_state=30)
inertia = {}
X_new = {}
centers = {}
for dtype in [np.float64, np.float32]:
X_test = X.astype(dtype)
km.fit(X_test)
# dtype of cluster centers has to be the dtype of the input
# data
assert_equal(km.cluster_centers_.dtype, dtype)
inertia[dtype] = km.inertia_
X_new[dtype] = km.transform(X_test)
centers[dtype] = km.cluster_centers_
# ensure the extracted row is a 2d array
assert_equal(km.predict(X_test[:1]),
km.labels_[0])
if hasattr(km, 'partial_fit'):
km.partial_fit(X_test[0:3])
# dtype of cluster centers has to stay the same after
# partial_fit
assert_equal(km.cluster_centers_.dtype, dtype)
# compare arrays with low precision since the difference between
# 32 and 64 bit sometimes makes a difference up to the 4th decimal
# place
assert_array_almost_equal(inertia[np.float32], inertia[np.float64],
decimal=4)
assert_array_almost_equal(X_new[np.float32], X_new[np.float64],
decimal=4)
assert_array_almost_equal(centers[np.float32], centers[np.float64],
decimal=4)
def test_transform():
km = KMeansConstrained(n_clusters=n_clusters)
km.fit(X)
X_new = km.transform(km.cluster_centers_)
for c in range(n_clusters):
assert_equal(X_new[c, c], 0)
for c2 in range(n_clusters):
if c != c2:
assert_greater(X_new[c, c2], 0)
