pegasus.get_neighbors¶
- pegasus.get_neighbors(data, K=100, rep='pca', n_jobs=- 1, random_state=0, full_speed=False, use_cache=True)[source]¶
Find K nearest neighbors for each data point and return the indices and distances arrays.
- Parameters
data (pegasusio.MultimodalData) – An AnnData object.
K (int, optional (default: 100)) – Number of neighbors, including the data point itself.
rep (str, optional (default: ‘pca’)) – Representation used to calculate kNN. If None use data.X
n_jobs (int, optional (default: -1)) – Number of threads to use. -1 refers to using all physical CPU cores.
random_state (int, optional (default: 0)) – Random seed for random number generator.
full_speed (bool, optional (default: False)) – If full_speed, use multiple threads in constructing hnsw index. However, the kNN results are not reproducible. If not full_speed, use only one thread to make sure results are reproducible.
use_cache (bool, optional (default: True)) – If use_cache and found cached knn results, will not recompute.
- Returns
- Return type
kNN indices and distances arrays.
Examples
>>> indices, distances = tools.get_neighbors(data)