# Copyright (c) 2021. Lucas G. S. Jeub
#
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# of this software and associated documentation files (the "Software"), to deal
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# furnished to do so, subject to the following conditions:
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#
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# SOFTWARE.
import torch
[docs]
class DistanceDecoder(torch.nn.Module):
"""
implements the distance decoder which predicts the probability of an edge as the exponential of the
negative euclidean distance between nodes
"""
[docs]
def __init__(self):
super(DistanceDecoder, self).__init__()
self.dist = torch.nn.PairwiseDistance()
[docs]
def forward(self, z, edge_index, sigmoid=True):
"""
compute decoder values
Args:
z: input coordinates
edge_index: edges
sigmoid: if ``True``, return exponential of negative distance, else return negative distance
"""
value = -self.dist(z[edge_index[0]], z[edge_index[1]])
return torch.exp(value) if sigmoid else value
[docs]
def forward_all(self, z, sigmoid=True):
"""
compute value for all node pairs
Args:
z: input coordinates
sigmoid: if ``True``, return exponential of negative distance, else return negative distance
"""
adj = -torch.cdist(z, z)
return torch.exp(adj) if sigmoid else adj