exact-clustering 0.1.1

Find optimal clusterings and optimal hierarchical clusterings.

Low-effort way of calling the rust-crate exact-clustering from Python. 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 /src/lib.rs. At time of writing, they are:

def unweighted_continuous_kmeans_price_of_hierarchy(points: list[tuple[float, list[float]]],) -> float: ...
def weighted_continuous_kmeans_price_of_hierarchy(weighted_points: list[list[float]],) -> float: ...
def unweighted_discrete_kmeans_price_of_hierarchy(points: list[tuple[float, list[float]]],) -> float: ...
def weighted_discrete_kmeans_price_of_hierarchy(weighted_points: list[list[float]],) -> float: ...
def unweighted_discrete_kmedian_price_of_hierarchy(points: list[tuple[float, list[float]]],) -> float: ...
def weighted_discrete_kmedian_price_of_hierarchy(weighted_points: list[list[float]],) -> float: ...
def unweighted_continuous_kmeans_price_of_greedy(points: list[tuple[float, list[float]]],) -> float: ...
def weighted_continuous_kmeans_price_of_greedy(weighted_points: list[list[float]],) -> float: ...
def unweighted_discrete_kmeans_price_of_greedy(points: list[tuple[float, list[float]]],) -> float: ...
def weighted_discrete_kmeans_price_of_greedy(weighted_points: list[list[float]],) -> float: ...
def unweighted_discrete_kmedian_price_of_greedy(points: list[tuple[float, list[float]]],) -> float: ...
def weighted_discrete_kmedian_price_of_greedy(weighted_points: list[list[float]],) -> float: ...