Module Contents

flambe.nn.distance.hyperbolic.EPSILON = 1e-05[source]

Compute the arcosh, numerically stable.

flambe.nn.distance.hyperbolic.mdot(x, y)[source]

Compute the inner product.

flambe.nn.distance.hyperbolic.dist(x, y)[source]

Get the hyperbolic distance between x and y.


Project onto the hyeprboloid embedded in in n+1 dimensions.

flambe.nn.distance.hyperbolic.log_map(x, y)[source]

Perform the log step.


Compute the norm

flambe.nn.distance.hyperbolic.exp_map(x, y)[source]

Perform the exp step.

flambe.nn.distance.hyperbolic.loss(x, y)[source]

Get the loss for the optimizer.

class flambe.nn.distance.hyperbolic.HyperbolicDistance[source]

Bases: flambe.nn.distance.distance.DistanceModule

Implement a HyperbolicDistance object.

forward(self, mat_1: Tensor, mat_2: Tensor)[source]

Returns the squared euclidean distance between each element in mat_1 and each element in mat_2.

  • mat_1 (torch.Tensor) – matrix of shape (n_1, n_features)
  • mat_2 (torch.Tensor) – matrix of shape (n_2, n_features)

dist – distance matrix of shape (n_1, n_2)

Return type:


class flambe.nn.distance.hyperbolic.HyperbolicMean[source]

Bases: flambe.nn.distance.distance.MeanModule

Compute the mean point in the hyperboloid model.

forward(self, data: Tensor)[source]

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor
Returns:The encoded output, as a float tensor
Return type:torch.Tensor