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 density(self, density):
if not (isinstance(density, float) or isinstance(density, int)
or isinstance(density, np.int32)
or isinstance(density, np.int64)):
raise e.TypeError('`density` should be a float or integer')
self._density = density
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 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_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 label(self, label):
if not isinstance(label, int):
raise e.TypeError('`label` should be an integer')
if label < 1:
raise e.ValueError('`label` should be >= 1')
self._label = 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 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 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 idx_sample(self, idx_sample):
if not isinstance(idx_sample, int):
raise e.TypeError('`idx` should be an integer')
if idx_sample < 0:
raise e.ValueError('`idx_sample` should be >= 0')
self._idx_sample = idx_sample
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 idx_sample_conqueror(self, idx_sample_conqueror):
if not isinstance(idx_sample_conqueror, int):
raise e.TypeError('`idx_sample_conqueror` should be an integer')
if idx_sample_conqueror < 0:
raise e.ValueError('`idx_sample_conqueror` should be >= 0')
self._idx_sample_conqueror = idx_sample_conqueror
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 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 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 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 trained(self, trained):
if not isinstance(trained, bool):
raise e.TypeError('`trained` should be a boolean')
self._trained = trained
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 nodes(self, nodes):
if not isinstance(nodes, list):
raise e.TypeError('`nodes` should be a list')
self._nodes = nodes
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
def color(self, color):
if not isinstance(color, list):
raise e.TypeError('`color` should be a list')
self._color = color
def cost(self, cost):
if not isinstance(cost, list):
raise e.TypeError('`cost` should be a list')
self._cost = cost
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 pos(self, pos):
if not isinstance(pos, list):
raise e.TypeError('`pos` should be a list')
self._pos = pos
def p(self, p):
if not isinstance(p, list):
raise e.TypeError('`p` should be a list')
self._p = p
