def max_k(self, max_k):
if not isinstance(max_k, int):
raise e.TypeError('`max_k` should be an integer')
if max_k < 1:
raise e.ValueError('`max_k` should be >= 1')
self._max_k = max_k
def root(self, root):
if not isinstance(root, int):
raise e.TypeError('`root` should be an integer')
if root < 0:
raise e.ValueError('`root` should be >= 0')
self._root = root
def test_type_error():
new_exception = exception.TypeError('error')
try:
raise new_exception
except exception.TypeError:
pass
def n_plateaus(self, n_plateaus):
if not isinstance(n_plateaus, int):
raise e.TypeError('`n_plateaus` should be an integer')
if n_plateaus < 0:
raise e.ValueError('`n_plateaus` should be >= 0')
self._n_plateaus = n_plateaus
def label(self, label):
if not isinstance(label, int):
raise e.TypeError('`label` should be an integer')
if label < 0:
raise e.ValueError('`label` should be >= 0')
self._label = label
def cluster_label(self, cluster_label):
if not isinstance(cluster_label, int):
raise e.TypeError('`cluster_label` should be an integer')
if cluster_label < 0:
raise e.ValueError('`cluster_label` should be >= 0')
self._cluster_label = cluster_label
def n_clusters(self, n_clusters):
if not isinstance(n_clusters, int):
raise e.TypeError('`n_clusters` should be an integer')
if n_clusters < 0:
raise e.ValueError('`n_clusters` should be >= 0')
self._n_clusters = n_clusters
def best_k(self, best_k):
if not isinstance(best_k, int):
raise e.TypeError('`best_k` should be an integer')
if best_k < 0:
raise e.ValueError('`best_k` should be >= 0')
self._best_k = best_k
def size(self, size):
if not isinstance(size, int):
raise e.TypeError('`size` should be an integer')
if size < 1:
raise e.ValueError('`size` should be > 0')
self._size = size
def distance(self, distance):
if distance not in [
'additive_symmetric', 'average_euclidean', 'bhattacharyya',
'bray_curtis', 'canberra', 'chebyshev', 'chi_squared', 'chord',
'clark', 'cosine', 'dice', 'divergence', 'euclidean',
'gaussian', 'gower', 'hamming', 'hassanat', 'hellinger',
'jaccard', 'jeffreys', 'jensen', 'jensen_shannon',
'k_divergence', 'kulczynski', 'kullback_leibler',
'log_euclidean', 'log_squared_euclidean', 'lorentzian',
'manhattan', 'matusita', 'max_symmetric',
'mean_censored_euclidean', 'min_symmetric', 'neyman',
'non_intersection', 'pearson', 'sangvi', 'soergel', 'squared',
'squared_chord', 'squared_euclidean', 'statistic', 'topsoe',
'vicis_symmetric1', 'vicis_symmetric2', 'vicis_symmetric3',
'vicis_wave_hedges'
]:
raise e.TypeError(
'`distance` should be `additive_symmetric`, `average_euclidean`, `bhattacharyya`, '
'`bray_curtis`, `canberra`, `chebyshev`, `chi_squared`, `chord`, `clark`, `cosine`, '
'`dice`, `divergence`, `euclidean`, `gaussian`, `gower`, `hamming`, `hassanat`, `hellinger`, '
'`jaccard`, `jeffreys`, `jensen`, `jensen_shannon`, `k_divergence`, `kulczynski`, '
'`kullback_leibler`, `log_euclidean`, `log_squared_euclidean`, `lorentzian`, `manhattan`, '
'`matusita`, `max_symmetric`, `mean_censored_euclidean`, `min_symmetric`, `neyman`, '
'`non_intersection`, `pearson`, `sangvi`, `soergel`, `squared`, `squared_chord`, '
'`squared_euclidean`, `statistic`, `topsoe`, `vicis_symmetric1`, `vicis_symmetric2`, '
'`vicis_symmetric3` or `vicis_wave_hedges`')
self._distance = distance
def last(self, last):
if not isinstance(last, int):
raise e.TypeError('`last` should be an integer')
if last < -1:
raise e.ValueError('`last` should be > -1')
self._last = last
def pred(self, pred):
if not isinstance(pred, int):
raise e.TypeError('`pred` should be an integer')
if pred < c.NIL:
raise e.ValueError('`pred` should have a value larger than `NIL`, e.g., -1')
self._pred = pred
def constant(self, constant):
if not (isinstance(constant, float) or isinstance(constant, int)
or isinstance(constant, np.int32)
or isinstance(constant, np.int64)):
raise e.TypeError('`constant` should be a float or integer')
self._constant = constant
def max_density(self, max_density):
if not (isinstance(max_density, float) or isinstance(max_density, int)
or isinstance(max_density, np.int32)
or isinstance(max_density, np.int64)):
raise e.TypeError('`max_density` should be a float or integer')
self._max_density = max_density
def predicted_label(self, predicted_label):
if not isinstance(predicted_label, int):
raise e.TypeError('`predicted_label` should be an integer')
if predicted_label < 0:
raise e.ValueError('`predicted_label` should be >= 0')
self._predicted_label = predicted_label
def idx(self, idx):
if not isinstance(idx, int):
raise e.TypeError('`idx` should be an integer')
if idx < 0:
raise e.ValueError('`idx` should be >= 0')
self._idx = idx
def n_features(self, n_features):
if not isinstance(n_features, int):
raise e.TypeError('`n_features` should be an integer')
if n_features < 0:
raise e.ValueError('`n_features` should be >= 0')
self._n_features = n_features
def distance(self, distance):
if distance not in [
'bray_curtis', 'canberra', 'chi_squared', 'euclidean',
'gaussian', 'log_euclidean', 'log_squared_euclidean',
'manhattan', 'squared_chi_squared', 'squared_cord',
'squared_euclidean'
]:
raise e.TypeError(
'`distance` should be `bray_curtis`, `canberra`, `chi_squared`, `euclidean`, `gaussian`, `log_euclidean`, `log_squared_euclidean`, `manhattan`, `squared_chi_squared`, `squared_cord` or `squared_euclidean`'
)
self._distance = distance
def distance(self, distance):
if distance not in d.DISTANCES.keys():
raise e.TypeError('`distance` should be `additive_symmetric`, `average_euclidean`, `bhattacharyya`, '
'`bray_curtis`, `canberra`, `chebyshev`, `chi_squared`, `chord`, `clark`, `cosine`, '
'`dice`, `divergence`, `euclidean`, `gaussian`, `gower`, `hamming`, `hassanat`, `hellinger`, '
'`jaccard`, `jeffreys`, `jensen`, `jensen_shannon`, `k_divergence`, `kulczynski`, '
'`kullback_leibler`, `log_euclidean`, `log_squared_euclidean`, `lorentzian`, `manhattan`, '
'`matusita`, `max_symmetric`, `mean_censored_euclidean`, `min_symmetric`, `neyman`, '
'`non_intersection`, `pearson`, `sangvi`, `soergel`, `squared`, `squared_chord`, '
'`squared_euclidean`, `statistic`, `topsoe`, `vicis_symmetric1`, `vicis_symmetric2`, '
'`vicis_symmetric3` or `vicis_wave_hedges`')
self._distance = distance
def trained(self, trained):
if not isinstance(trained, bool):
raise e.TypeError('`trained` should be a boolean')
self._trained = trained
def status(self, status):
if status not in [c.STANDARD, c.PROTOTYPE]:
raise e.TypeError('`status` should be `STANDARD` or `PROTOTYPE`')
self._status = status
def nodes(self, nodes):
if not isinstance(nodes, list):
raise e.TypeError('`nodes` should be a list')
self._nodes = nodes
def idx_nodes(self, idx_nodes):
if not isinstance(idx_nodes, list):
raise e.TypeError('`idx_nodes` should be a list')
self._idx_nodes = idx_nodes
def adjacency(self, adjacency):
if not isinstance(adjacency, list):
raise e.TypeError('`adjacency` should be a list')
self._adjacency = adjacency
def radius(self, radius):
if not isinstance(radius, (float, int, np.int32, np.int64)):
raise e.TypeError('`radius` should be a float or integer')
self._radius = radius
def relevant(self, relevant):
if relevant not in [c.RELEVANT, c.IRRELEVANT]:
raise e.TypeError(
'`relevant` should be `RELEVANT` or `IRRELEVANT`')
self._relevant = relevant
def subgraph(self, subgraph):
if subgraph is not None:
if not isinstance(subgraph, Subgraph):
raise e.TypeError('`subgraph` should be a subgraph')
self._subgraph = subgraph
def pre_distances(self, pre_distances):
if pre_distances is not None:
if not isinstance(pre_distances, np.ndarray):
raise e.TypeError('`pre_distances` should be a numpy array')
self._pre_distances = pre_distances
def pre_computed_distance(self, pre_computed_distance):
if not isinstance(pre_computed_distance, bool):
raise e.TypeError('`pre_computed_distance` should be a boolean')
self._pre_computed_distance = pre_computed_distance
def distance_fn(self, distance_fn):
if not callable(distance_fn):
raise e.TypeError('`distance_fn` should be a callable')
self._distance_fn = distance_fn