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