IVF SQ

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cdef class Index

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def trained(self)

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def n_lists(self)

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def dim(self)

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def centers(self)

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cdef class IndexParams

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def __init__(self, *, n_lists=1024, metric="sqeuclidean", metric_arg=2.0, kmeans_n_iters=20, max_train_points_per_cluster=256, add_data_on_build=True, conservative_memory_allocation=False)

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def get_handle(self)

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def metric(self)

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def metric_arg(self)

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def add_data_on_build(self)

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def n_lists(self)

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def kmeans_n_iters(self)

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def max_train_points_per_cluster(self)

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def conservative_memory_allocation(self)

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cdef class SearchParams

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def __init__(self, *, n_probes=20)

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def get_handle(self)

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def n_probes(self)

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def build(IndexParams index_params, dataset, resources=None)

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

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def extend(Index index, new_vectors, new_indices, resources=None)

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>>> import cupy as cp
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>>> from cuvs.neighbors import ivf_sq
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>>> n_samples = 50000
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>>> n_features = 50
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>>> n_queries = 1000
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>>> dataset = cp.random.random_sample((n_samples, n_features),
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...                                   dtype=cp.float32)
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>>> index = ivf_sq.build(ivf_sq.IndexParams(), dataset)
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>>> n_rows = 100
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>>> more_data = cp.random.random_sample((n_rows, n_features),
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...                                     dtype=cp.float32)
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>>> indices = n_samples + cp.arange(n_rows, dtype=cp.int64)
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>>> index = ivf_sq.extend(index, more_data, indices)
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>>> # Search using the built index
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>>> queries = cp.random.random_sample((n_queries, n_features),
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...                                   dtype=cp.float32)
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>>> distances, neighbors = ivf_sq.search(ivf_sq.SearchParams(),
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...                                      index, queries,
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...                                      k=10)

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def load(filename, resources=None)

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def save(filename, Index index, bool include_dataset=True, resources=None)

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>>> import cupy as cp
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>>> from cuvs.neighbors import ivf_sq
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>>> n_samples = 50000
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>>> n_features = 50
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>>> dataset = cp.random.random_sample((n_samples, n_features),
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...                                   dtype=cp.float32)
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>>> # Build index
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>>> index = ivf_sq.build(ivf_sq.IndexParams(), dataset)
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>>> # Serialize and deserialize the ivf_sq index built
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>>> ivf_sq.save("my_index.bin", index)
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>>> index_loaded = ivf_sq.load("my_index.bin")

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def search(SearchParams search_params, Index index, queries, k, neighbors=None, distances=None, resources=None, filter=None)

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>>> import cupy as cp
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>>> from cuvs.neighbors import ivf_sq
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>>> n_samples = 50000
4
>>> n_features = 50
5
>>> n_queries = 1000
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>>> dataset = cp.random.random_sample((n_samples, n_features),
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...                                   dtype=cp.float32)
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>>> # Build the index
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>>> index = ivf_sq.build(ivf_sq.IndexParams(), dataset)
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>>>
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>>> # Search using the built index
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>>> queries = cp.random.random_sample((n_queries, n_features),
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...                                   dtype=cp.float32)
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>>> k = 10
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>>> search_params = ivf_sq.SearchParams(n_probes=20)
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>>>
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>>> distances, neighbors = ivf_sq.search(search_params, index, queries,
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...                                     k)