def __init__(self, D:np.ndarray, secondary_distance_type:str,
metric:str='distance', classes:np.ndarray=None,
vectors:np.ndarray=None):
"""Initialize a hubness experiment"""
IO._check_distance_matrix_shape(D)
IO._check_valid_metric_parameter(metric)
if secondary_distance_type not in SEC_DIST.keys():
raise ValueError("Requested secondary distance type unknown.")
if classes is not None:
IO._check_distance_matrix_shape_fits_labels(D, classes)
if vectors is None:
self.embedding_dim = None
else: # got vectors
IO._check_distance_matrix_shape_fits_vectors(D, vectors)
self.embedding_dim = vectors.shape[1]
self.original_distance = D
self.secondary_distance_type = secondary_distance_type
self.classes = classes
self.vectors = vectors
self.metric = metric
self.n = D.shape[0]
# Obtained later through functions:
self.secondary_distance = None
self.hubness = dict()
self.anti_hubs = dict()
self.max_hub_k_occurence = dict()
self.knn_accuracy = dict()
self.gk_index = None
def test_check_dist_vs_vectors(self):
with self.assertRaises(TypeError):
D = np.zeros((5, 5))
vectors = np.zeros((4, 5))
IO._check_distance_matrix_shape_fits_vectors(D, vectors)
