quaterion.distances.manhattan module

class Manhattan

Bases: BaseDistance

Compute Manhattan distances (and its interpretation as similarities).

Note

Interpretation of Manhattan distances as similarities is based on the trick in the book “Collective Intelligence” by Toby Segaran, and it’s in the range of 0 -> 1.

static distance(x: Tensor, y: Tensor) → Tensor

Calculate distances, i.e., the lower the value, the more similar the samples.

Parameters:

• x – shape: (batch_size, embedding_dim)

• y – shape: (batch_size, embedding_dim)

Returns:

Distances - shape – (batch_size,)

static distance_matrix(x: Tensor, y: Optional[Tensor] = None) → Tensor

Calculate a distance matrix, i.e., distances between all possible pairs in x and y.

Parameters:

• x – shape: (batch_size, embedding_dim)

• y – shape: (batch_size, embedding_dim). If y is None, it assigns x to y.

Returns:

Distance matrix - shape – (batch_size, batch_size)

static similarity(x: Tensor, y: Tensor) → Tensor

Calculate similarities, i.e., the higher the value, the more similar the samples.

Parameters:

• x – shape: (batch_size, embedding_dim)

• y – shape: (batch_size, embedding_dim)

Returns:

Similarities - shape – (batch_size,)

static similarity_matrix(x: Tensor, y: Optional[Tensor] = None) → Tensor

Calculate a similarity matrix, i.e., similarities between all possible pairs in x and y.

Parameters:

• x – shape: (batch_size, embedding_dim)

• y – shape: (batch_size, embedding_dim). If y is None, it assigns x to y.

Returns:

Similarity matrix - shape – (batch_size, batch_size)