multidimensionalks

multidimensionalks

Python c extension with method for calculating multidimensional Kolmogorov-Smirnov test

multidimensionalks.test(rvs, cdf=None, counts_rvs=None, counts_cdf=None, n_jobs=1, permutation_samples=0, binomial_significance=False, use_avx=3, max_alpha_beta=True, scale_result=False, deduplicate_data=True, debug=False)

Example usage

from multidimensionalks import test
import numpy as np

test(np.array([[1, 2, 3], [1, 3, 2]]), cdf=np.array([[1,2,2]]))

Parameters

• rvs: 2-dimensional numpy number array with rows representing d-dimensional observations,
• cdf: 2-dimensional numpy number array with rows representing second sampled-dimensional observations,
• counts_rvs: in case of rvs having multiple duplicates, an array without duplicates and a separate array of counts can be provided,
• counts_cdf: in case of cdf having multiple duplicates, an array without duplicates and a separate array of counts can be provided, additionally if cdf is not given counts_cdf are taken as counts of elements of rvs array,
• n_jobs: number of threads used during calculation,
• permutation_samples: number of times data is shuffled and the statistic value is calculated to estimate pvalue,
• binomial_significance: boolean value indicating if statistical significance should be calculated. Defaults to False,
• use_avx: integer value indicating if AVX instructions should be used during the calculations. 0 disables avx, 3 means to try the best supported set, 1 will try to use AVX512 instruction set and use no otherwise, 2 will try to use AVX2. Defaults to 3,
• max_alpha_beta: boolean value indicating how λ and β values should be combined. Value True (default) results in max(λ, β). (λ+β)/2 is used otherwise.
• scale_result: Whether to scale the statistic by $\sqrt{\frac{|rvs|+|cdf|}{|rvs|\times|cdf|}}$ (default False),
• deduplicate_data: Whether to deduplicate data points before running the algorithms,
• debug: Whether to print debug data to stdout.

Return value

If no pvalue calculation method is selected returns ks statistic value, otherwise returns a tuple:

• ks statistic,
• pvalue calculated using statistical method if binomial_significance is set to True,
• pvalue calculated using permutation method if permutation_samples is larger than 0.