Distance | NVIDIA cuVS

Python module: cuvs.distance

DISTANCE_NAMES

DISTANCE_NAMES = {v: k for k, v in DISTANCE_TYPES.items()}

DISTANCE_TYPES

DISTANCE_TYPES = {
"l2": cuvsDistanceType.L2SqrtExpanded,
"sqeuclidean": cuvsDistanceType.L2Expanded,
"euclidean": cuvsDistanceType.L2SqrtExpanded,
"l1": cuvsDistanceType.L1,
"cityblock": cuvsDistanceType.L1,
"inner_product": cuvsDistanceType.InnerProduct,
"chebyshev": cuvsDistanceType.Linf,
"canberra": cuvsDistanceType.Canberra,
"cosine": cuvsDistanceType.CosineExpanded,
"lp": cuvsDistanceType.LpUnexpanded,
"correlation": cuvsDistanceType.CorrelationExpanded,
"jaccard": cuvsDistanceType.JaccardExpanded,
"hellinger": cuvsDistanceType.HellingerExpanded,
"braycurtis": cuvsDistanceType.BrayCurtis,
"jensenshannon": cuvsDistanceType.JensenShannon,
"hamming": cuvsDistanceType.HammingUnexpanded,
"kl_divergence": cuvsDistanceType.KLDivergence,
"minkowski": cuvsDistanceType.LpUnexpanded,
"russellrao": cuvsDistanceType.RusselRaoExpanded,
"dice": cuvsDistanceType.DiceExpanded,
"bitwise_hamming": cuvsDistanceType.BitwiseHamming
}

pairwise_distance

@auto_sync_resources @auto_convert_output

def pairwise_distance(X, Y, out=None, metric="euclidean", p=2.0, resources=None)

Compute pairwise distances between X and Y

Valid values for metric: [“euclidean”, “l2”, “l1”, “cityblock”, “inner_product”, “chebyshev”, “canberra”, “lp”, “hellinger”, “jensenshannon”, “kl_divergence”, “russellrao”, “minkowski”, “correlation”, “cosine”]

Parameters

Name	Type	Description
`X`	`CUDA array interface compliant matrix shape (m, k)`
`Y`	`CUDA array interface compliant matrix shape (n, k)`
`out`	`Optional writable CUDA array interface matrix shape (m, n)`
`metric`	`string denoting the metric type (default="euclidean")`
`p`	`metric parameter (currently used only for "minkowski")`
`resources`	`cuvs.common.Resources, optional`

Examples

>>> import cupy as cp
>>> from cuvs.distance import pairwise_distance
>>> n_samples = 5000
>>> n_features = 50
>>> in1 = cp.random.random_sample((n_samples, n_features),
...                               dtype=cp.float32)
>>> in2 = cp.random.random_sample((n_samples, n_features),
...                               dtype=cp.float32)
>>> output = pairwise_distance(in1, in2, metric="euclidean")