def test_levels(self):
"""Test multiple level indexes"""
features = np.random.binomial(1, 0.01, size=(1000, 20000))
features = csr_matrix(features)
# build the search index!
data_to_return = range(1000)
# matrix size smaller - this forces the index to have multiple levels
cluster_index = cp.ClusterIndex(features, data_to_return,
matrix_size=10)
ret = cluster_index.search(features[0:10], k=1, k_clusters=1,
return_distance=False)
self.assertEqual([[x] for x in data_to_return[:10]], ret)
def test_euclidean(self):
"""Do a quick basic test for index/search functionality"""
data = [
'hello world',
'oh hello there',
'Play it',
'Play it again Sam',
]
features = [dict([(x, 1) for x in f.split()]) for f in data]
features = DictVectorizer().fit_transform(features)
cluster_index = cp.ClusterIndex(features, data, SlowEuclideanDistance)
ret = cluster_index.search(features, k=1, k_clusters=1,
return_distance=False)
self.assertEqual([[d] for d in data], ret)