Function sample_from_strata has a Cognitive Complexity of 141 (exceeds 5 allowed). Consider refactoring.

Open

def sample_from_strata(
    strata, bates_param=1, latin="none", matching_init="approx", full_output=False
):
    """Stratified sampling with given strata.

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File cube.py has 835 lines of code (exceeds 250 allowed). Consider refactoring.

Open

"""
Functions for (super-uniform) sampling from the unit hypercube.
"""
import random
import itertools

File indicator.py has 585 lines of code (exceeds 250 allowed). Consider refactoring.

Open

"""
This module contains several functions to measure diversity and a few
related concepts. The diversity indicators all have different advantages and
disadvantages. An overview is given in [Wessing2015]_.

File subset.py has 461 lines of code (exceeds 250 allowed). Consider refactoring.

Open

"""
This module contains algorithms for the task of subset selection: suppose
you have a set of points in :math:`\\mathbb{R}^n` and want to select a sample
of them distributed as uniform as possible. This problem is related to
clustering, with the difference that when using clustering, you usually want

Function sample_maximin has a Cognitive Complexity of 38 (exceeds 5 allowed). Consider refactoring.

Open

def sample_maximin(
    num_points,
    dimension,
    num_steps=None,
    initial_points=None,

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Function wmh_index has a Cognitive Complexity of 26 (exceeds 5 allowed). Consider refactoring.

Open

def wmh_index(sep_dist, dist_p, num_points, dim, approx=None, full_output=False):
    """Quality index of Wahl, Mercadier, and Helbert.

    In [Wahl2017]_, the idea to use the probability to obtain a sample
    with a separation distance less or equal to `sep_dist` was presented.

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Function sample_k_means has a Cognitive Complexity of 23 (exceeds 5 allowed). Consider refactoring.

Open

def sample_k_means(
    num_points,
    dimension,
    num_steps=None,
    initial_points=None,

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Function weitzman_diversity has a Cognitive Complexity of 15 (exceeds 5 allowed). Consider refactoring.

Open

def weitzman_diversity(points, dist_args={}):
    """Calculate the Weitzman diversity for a set of points.

    This diversity indicator was introduced in [Weitzman1992]_. It is to be
    maximized.

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Function stratify_generalized has a Cognitive Complexity of 13 (exceeds 5 allowed). Consider refactoring.

Open

def stratify_generalized(
    num_strata, dimension, cuboid=None, detect_special_case=True, avoid_odd_numbers=True
):
    """Generalized stratification of the unit hypercube.

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Function select_greedy_energy has a Cognitive Complexity of 13 (exceeds 5 allowed). Consider refactoring.

Open

def select_greedy_energy(
    points,
    num_selected_points,
    existing_points=None,
    exponent=None,

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Function improved_latin_design has a Cognitive Complexity of 12 (exceeds 5 allowed). Consider refactoring.

Open

def improved_latin_design(
    num_points,
    dimension,
    num_candidates=100,
    target_value=None,

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Function average_inverse_dist has a Cognitive Complexity of 11 (exceeds 5 allowed). Consider refactoring.

Open

def average_inverse_dist(points, exponent=None, max_dist=1.0, dist_args={}):
    """Calculate the average inverse distance.

    For each pair of points, the value ``(max_dist / dist) ** exponent`` is
    computed. The average of all these values is the indicator value, which

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Function obtain_representative_index has a Cognitive Complexity of 10 (exceeds 5 allowed). Consider refactoring.

Open

    def obtain_representative_index(
        self, selection_target="centroid_of_hypercube", tournament_size=0, dist_args={},
    ):
        """Return the index to a point representing this cluster.

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Function unanchored_L2_discrepancy has a Cognitive Complexity of 10 (exceeds 5 allowed). Consider refactoring.

Open

def unanchored_L2_discrepancy(points):
    """Calculate unanchored L2 discrepancy.

    Discrepancy is to be minimized. Note that the square root is already
    taken. Coordinates of points must be >=0 and <=1. Run time is quadratic.

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Function _init has a Cognitive Complexity of 10 (exceeds 5 allowed). Consider refactoring.

Open

def _init(n_dim):
    """Initialize the Sobol matrix"""
    poly = POLY[:, 0]
    V = np.zeros((DIM_MAX, LOG_MAX), dtype=int)
    V[:, :8] = POLY[:, 1:]

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Function covering_radius_upper_bound has a Cognitive Complexity of 10 (exceeds 5 allowed). Consider refactoring.

Open

def covering_radius_upper_bound(points, strata, dist_args):
    """Upper bound for the covering radius.

    The idea for this measure was presented in [Wessing2018]_.

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Function sample has 8 arguments (exceeds 4 allowed). Consider refactoring.

Open

def sample(n_points, lower, upper, A1=None, b1=None, A2=None, b2=None, thin=1):

Function sample_maximin has 8 arguments (exceeds 4 allowed). Consider refactoring.

Open

def sample_maximin(

Function sample has a Cognitive Complexity of 9 (exceeds 5 allowed). Consider refactoring.

Open

def sample(n_points, lower, upper, A1=None, b1=None, A2=None, b2=None, thin=1):
    """Sample a number of points from a convex polytope A1 x <= b1 using the Hit & Run
    algorithm.

    Lower and upper bounds need to be provided to ensure that the polytope is bounded.

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Function strata_from_points has a Cognitive Complexity of 9 (exceeds 5 allowed). Consider refactoring.

Open

def strata_from_points(points, cuboid=None):
    """Partitions the cuboid so that each point has its own hyperbox.

    This partitioning is stochastic (ties are broken randomly). The obtained
    strata will have different volumes. This function can be used to

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