Brute Force KNN

Index

class cuvs.neighbors.brute_force.Index

Brute Force index object. This object stores the trained Brute Force which can be used to perform nearest neighbors searches.

Attributes:

trained

Index build

cuvs.neighbors.brute_force.build(dataset, metric='sqeuclidean', metric_arg=2.0, resources=None)

Build the Brute Force index from the dataset for efficient search.

Parameters:

datasetCUDA array interface compliant matrix shape (n_samples, dim)

Supported dtype [float, int8, uint8]

metricDistance metric to use. Default is sqeuclidean

metric_argvalue of ‘p’ for Minkowski distances

resourcesOptional cuVS Resource handle for reusing CUDA resources.

If Resources aren’t supplied, CUDA resources will be allocated inside this function and synchronized before the function exits. If resources are supplied, you will need to explicitly synchronize yourself by calling resources.sync() before accessing the output.

Returns:

index: cuvs.neighbors.brute_force.Index

Examples

>>> import cupy as cp
>>> from cuvs.neighbors import brute_force
>>> n_samples = 50000
>>> n_features = 50
>>> n_queries = 1000
>>> k = 10
>>> dataset = cp.random.random_sample((n_samples, n_features),
...                                   dtype=cp.float32)
>>> index = brute_force.build(dataset, metric="cosine")
>>> distances, neighbors = brute_force.search(index, dataset, k)
>>> distances = cp.asarray(distances)
>>> neighbors = cp.asarray(neighbors)

Index search