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) 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: Optional[Tensor] = None) Tensor [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) 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: Optional[Tensor] = None) Tensor [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)