Multidimensional KS test module in python
Project description
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 representingd
-dimensional observations,cdf
: 2-dimensional numpy number array with rows representing second sampled
-dimensional observations,counts_rvs
: in case ofrvs
having multiple duplicates, an array without duplicates and a separate array of counts can be provided,counts_cdf
: in case ofcdf
having multiple duplicates, an array without duplicates and a separate array of counts can be provided, additionally ifcdf
is not givencounts_cdf
are taken as counts of elements ofrvs
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 toFalse
,use_avx
: integer value indicating ifAVX
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 useAVX2
. Defaults to3
,max_alpha_beta
: boolean value indicating how λ and β values should be combined. ValueTrue
(default) results inmax(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 toTrue
, - pvalue calculated using permutation method if
permutation_samples
is larger than0
.
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