torch_geometric.nn.pool.knn

knn(x: Tensor, y: Tensor, k: int, batch_x: Optional[Tensor] = None, batch_y: Optional[Tensor] = None, cosine: bool = False, num_workers: int = 1, batch_size: Optional[int] = None) → Tensor[source]

Finds for each element in y the k nearest points in x.

import torch
from torch_geometric.nn import knn

x = torch.tensor([[-1.0, -1.0], [-1.0, 1.0], [1.0, -1.0], [1.0, 1.0]])
batch_x = torch.tensor([0, 0, 0, 0])
y = torch.tensor([[-1.0, 0.0], [1.0, 0.0]])
batch_y = torch.tensor([0, 0])
assign_index = knn(x, y, 2, batch_x, batch_y)
Parameters:

  • x (torch.Tensor) – Node feature matrix \(\mathbf{X} \in \mathbb{R}^{N \times F}\).

  • y (torch.Tensor) – Node feature matrix \(\mathbf{X} \in \mathbb{R}^{M \times F}\).

  • k (int) – The number of neighbors.

  • batch_x (torch.Tensor, optional) – Batch vector \(\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N\), which assigns each node to a specific example. (default: None)

  • batch_y (torch.Tensor, optional) – Batch vector \(\mathbf{b} \in {\{ 0, \ldots, B-1\}}^M\), which assigns each node to a specific example. (default: None)

  • cosine (bool, optional) – If True, will use the cosine distance instead of euclidean distance to find nearest neighbors. (default: False)

  • num_workers (int, optional) – Number of workers to use for computation. Has no effect in case batch_x or batch_y is not None, or the input lies on the GPU. (default: 1)

  • batch_size (int, optional) – The number of examples \(B\). Automatically calculated if not given. (default: None)

Return type:

torch.Tensor