nearest doc

README.md

@@ -20,6 +20,7 @@ The package consists of the following clustering algorithms:
 * **[Voxel Grid Pooling](#voxelgrid)** from, *e.g.*, Simonovsky and Komodakis: [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://arxiv.org/abs/1704.02901) (CVPR 2017)
 * **[Iterative Farthest Point Sampling](#farthestpointsampling)** from, *e.g.* Qi *et al.*: [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://arxiv.org/abs/1706.02413) (NIPS 2017)
 * **[k-NN](#knn-graph)** and **[Radius](#radius-graph)** graph generation
+* Clustering based on **[Nearest](#nearest)** points
 
 All included operations work on varying data types and are implemented both for CPU and GPU.
 
@@ -143,6 +144,26 @@ tensor([[0, 0, 1, 1, 2, 2, 3, 3],
         [1, 2, 0, 2, 0, 3, 1, 2]])
 ```
 
+## Nearest
+
+Clusters points which are nearest to a given query point in metric space.
+
+```python
+import torch
+from torch_cluster import nearest
+
+x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
+batch_x = torch.Tensor([0, 0, 0, 0])
+query_x = torch.Tensor([[-1, 0], [1, 0]])
+query_batch = torch.Tensor([0, 0])
+cluster = nearest(x, query_x, batch_x, query_batch)
+```
+
+```
+print(cluster)
+tensor([0, 0, 1, 1])
+```
+
 ## Running tests
 
 ```

torch_cluster/nearest.py

@@ -23,7 +23,7 @@ def nearest(x, y, batch_x=None, batch_y=None):
         >>> batch_x = torch.Tensor([0, 0, 0, 0])
         >>> y = torch.Tensor([[-1, 0], [1, 0]])
         >>> batch_x = torch.Tensor([0, 0])
-        >>> out = fps(x, y, batch_x, batch_y)
+        >>> cluster = nearest(x, y, batch_x, batch_y)
     """
 
     if batch_x is None: