Shortcuts

# quaterion.distances.dot_product module¶

class DotProduct[source]

Bases: BaseDistance

Compute dot product similarities (and its interpretation as distances).

Warning

Interpretation of dot product as distances may have unexpected effects. Make sure that you entirely understand how it exactly works, and when combined and with the chosen loss function in particular, because those values are negative.

static distance(x: Tensor, y: Tensor) [source]

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: Tensor | None = None) [source]

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) [source]

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: Tensor | None = None) [source]

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)

Discover Qdrant

## Similarity Learning

Explore practical problem solving with Similarity Learning

Learn Similarity Learning

## Community

Find people dealing with similar problems and get answers to your questions

Join Community