exact-clustering 0.5.0

Find optimal clusterings and optimal hierarchical clusterings.

Low-effort way of calling the rust-crate exact-clustering from Python.

pip install exact-clustering

Consult docs.rs for more extensive documentation.

from exact_clustering import *

weighted_points = [
    (1.0, [0, 0]),
    (1.0, [1, 0]),
    (3.0, [0, 2]),
]

print(
    weighted_continuous_kmeans_price_of_hierarchy(
        weighted_points
    )
)
# 1.0

View provided methods via help(exact_clustering) in the python-repl or take a look at exact_clustering.pyi. At time of writing, they are:

class KMeans:
    def __new__(cls, data: Sequence[Sequence[float]]) -> KMeans: ...
    def price_of_hierarchy(self) -> float: ...
    def price_of_greedy(self) -> float: ...

class KMedianL1:
    def __new__(cls, data: Sequence[Sequence[float]]) -> KMedianL1: ...
    def price_of_hierarchy(self) -> float: ...
    def price_of_greedy(self) -> float: ...

class KMedianL2:
    def __new__(cls, data: Sequence[Sequence[float]]) -> KMedianL2: ...
    def price_of_hierarchy(self) -> float: ...
    def price_of_greedy(self) -> float: ...

class KMedianL2Squared:
    def __new__(cls, data: Sequence[Sequence[float]]) -> KMedianL2Squared: ...
    def price_of_hierarchy(self) -> float: ...
    def price_of_greedy(self) -> float: ...

class UnweightedKMedianL1:
    def __new__(cls, data: Sequence[tuple[float, Sequence[float]]]) -> UnweightedKMedianL1: ...
    def price_of_hierarchy(self) -> float: ...
    def price_of_greedy(self) -> float: ...

class UnweightedKMedianL2:
    def __new__(cls, data: Sequence[tuple[float, Sequence[float]]]) -> UnweightedKMedianL2: ...
    def price_of_hierarchy(self) -> float: ...
    def price_of_greedy(self) -> float: ...

class UnweightedKMedianL2Squared:
    def __new__(cls, data: Sequence[tuple[float, Sequence[float]]]) -> UnweightedKMedianL2Squared: ...
    def price_of_hierarchy(self) -> float: ...
    def price_of_greedy(self) -> float: ...

class WeightedKMeans:
    def __new__(cls, data: Sequence[tuple[float, Sequence[float]]]) -> WeightedKMeans: ...
    def price_of_hierarchy(self) -> float: ...
    def price_of_greedy(self) -> float: ...

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.