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Python module: cuvs.distance

DISTANCE_NAMES

1	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

NameTypeDescription

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")

Distance

DISTANCE_NAMES

DISTANCE_TYPES

pairwise_distance

Common

Neighbors Multi GPU Cagra

1	DISTANCE_TYPES = {
2	"l2": cuvsDistanceType.L2SqrtExpanded,
3	"sqeuclidean": cuvsDistanceType.L2Expanded,
4	"euclidean": cuvsDistanceType.L2SqrtExpanded,
5	"l1": cuvsDistanceType.L1,
6	"cityblock": cuvsDistanceType.L1,
7	"inner_product": cuvsDistanceType.InnerProduct,
8	"chebyshev": cuvsDistanceType.Linf,
9	"canberra": cuvsDistanceType.Canberra,
10	"cosine": cuvsDistanceType.CosineExpanded,
11	"lp": cuvsDistanceType.LpUnexpanded,
12	"correlation": cuvsDistanceType.CorrelationExpanded,
13	"jaccard": cuvsDistanceType.JaccardExpanded,
14	"hellinger": cuvsDistanceType.HellingerExpanded,
15	"braycurtis": cuvsDistanceType.BrayCurtis,
16	"jensenshannon": cuvsDistanceType.JensenShannon,
17	"hamming": cuvsDistanceType.HammingUnexpanded,
18	"kl_divergence": cuvsDistanceType.KLDivergence,
19	"minkowski": cuvsDistanceType.LpUnexpanded,
20	"russellrao": cuvsDistanceType.RusselRaoExpanded,
21	"dice": cuvsDistanceType.DiceExpanded,
22	"bitwise_hamming": cuvsDistanceType.BitwiseHamming
23	}

1	>>> import cupy as cp
2	>>> from cuvs.distance import pairwise_distance
3	>>> n_samples = 5000
4	>>> n_features = 50
5	>>> in1 = cp.random.random_sample((n_samples, n_features),
6	... dtype=cp.float32)
7	>>> in2 = cp.random.random_sample((n_samples, n_features),
8	... dtype=cp.float32)
9	>>> output = pairwise_distance(in1, in2, metric="euclidean")