Source code for Cexpchull

from ctypes import *
from depth.multivariate.Depth_approximation import depth_approximation
import sys, os, glob
import platform
from depth.multivariate.import_CDLL import libApprox

[docs]def cexpchull(x, data, solver = "neldermead", NRandom = 1000, option = 1, n_refinements = 10, sphcap_shrink = 0.5, alpha_Dirichlet = 1.25, cooling_factor = 0.95, cap_size = 1, start = "mean", space = "sphere", line_solver = "goldensection", bound_gc = True): return depth_approximation(x, data, "cexpchull", solver, NRandom, option, n_refinements, sphcap_shrink, alpha_Dirichlet, cooling_factor, cap_size, start, space, line_solver, bound_gc)
cexpchull.__doc__=""" Description Calculates approximately the continuous explected convex hull depth of points w.r.t. a multivariate data set. Arguments x Matrix of objects (numerical vector as one object) whose depth is to be calculated; each row contains a d-variate point. Should have the same dimension as data. data Matrix of data where each row contains a d-variate point, w.r.t. which the depth is to be calculated. solver The type of solver used to approximate the depth. {``'simplegrid'``, ``'refinedgrid'``, ``'simplerandom'``, ``'refinedrandom'``, ``'coordinatedescent'``, ``'randomsimplices'``, ``'neldermead'``, ``'simulatedannealing'``} NRandom The total number of iterations to compute the depth. Some solvers are converging faster so they are run several time to achieve ``NRandom`` iterations. option | If ``option=1``, only approximated depths are returned. | If ``option=2``, best directions to approximate depths are also returned. | If ``option=3``, depths calculated at every iteration are also returned. | If ``option=4``, random directions used to project depths are also returned with indices of converging for the solver selected. n_refinements Set the maximum of iteration for computing the depth of one point. For ``solver='refinedrandom'`` or ``'refinedgrid'``. sphcap_shrink It's the shrinking of the spherical cap. For ``solver='refinedrandom'`` or ``'refinedgrid'``. alpha_Dirichlet It's the parameter of the Dirichlet distribution. For ``solver='randomsimplices'``. cooling_factor It's the cooling factor. For ``solver='simulatedannealing'``. cap_size It's the size of the spherical cap. For ``solver='simulatedannealing'`` or ``'neldermead'``. start {'mean', 'random'}. For ``solver='simulatedannealing'`` or ``'neldermead'``, it's the method used to compute the first depth. space {``'sphere'``, ``'euclidean'``}. For ``solver='coordinatedescent'`` or ``'neldermead'``, it's the type of spacecin which the solver is running. line_solver {``'uniform'``, ``'goldensection'``}. For ``solver='coordinatedescent'``, it's the line searh strategy used by this solver. bound_gc For ``solver='neldermead'``, it's ``True`` if the search is limited to the closed hemisphere. References * Dyckerhoff, R. and Mosler, K. (2011). Weighted-mean trimming of multivariate data. *Journal of Multivariate Analysis*, 102, 405–421. * Dyckerhoff, R., Mozharovskyi, P., and Nagy, S. (2021). Approximate computation of projection depths. *Computational Statistics and Data Analysis*, 157, 107166. Examples >>> import numpy as np >>> from depth.multivariate import * >>> np.random.seed(0) >>> mat1=[[1, 0, 0, 0, 0],[0, 2, 0, 0, 0],[0, 0, 3, 0, 0],[0, 0, 0, 2, 0],[0, 0, 0, 0, 1]] >>> mat2=[[1, 0, 0, 0, 0],[0, 1, 0, 0, 0],[0, 0, 1, 0, 0],[0, 0, 0, 1, 0],[0, 0, 0, 0, 1]] >>> x = np.random.multivariate_normal([1,1,1,1,1], mat2, 10) >>> data = np.random.multivariate_normal([0,0,0,0,0], mat1, 100) >>> cexpchull(x, data, NRandom=1000) [0.090223 0.19577999 0.15769263 0.20123535 0.10375507 0.14635662 0.20611053 0.17846703 0.19801984 0.23230606] """