import numpy as np
from ctypes import *
from depth.multivariate.Depth_approximation import depth_approximation
import sys, os, glob
import platform
if sys.platform=='linux':
for i in sys.path :
if i.split('/')[-1]=='site-packages':
ddalpha_exact=glob.glob(i+'/*ddalpha*.so')
ddalpha_approx=glob.glob(i+'/*depth_wrapper*.so')
libr=CDLL(ddalpha_exact[0])
libRom=CDLL(ddalpha_approx[0])
if sys.platform=='darwin':
for i in sys.path :
if i.split('/')[-1]=='site-packages':
ddalpha_exact=glob.glob(i+'/*ddalpha*.so')
ddalpha_approx=glob.glob(i+'/*depth_wrapper*.so')
libr=CDLL(ddalpha_exact[0])
libRom=CDLL(ddalpha_approx[0])
if sys.platform=='win32' and platform.architecture()[0] == "64bit":
site_packages = next(p for p in sys.path if 'site-packages' in p)
os.add_dll_directory(site_packages)
ddalpha_exact=glob.glob(site_packages+'/depth/src/*ddalpha*.dll')
ddalpha_approx=glob.glob(site_packages+'/depth/src/*depth_wrapper*.dll')
libr=CDLL(r""+ddalpha_exact[0])
libRom=CDLL(r""+ddalpha_approx[0])
if sys.platform=='win32' and platform.architecture()[0] == "32bit":
site_packages = next(p for p in sys.path if 'site-packages' in p)
os.add_dll_directory(site_packages)
ddalpha_exact=glob.glob(site_packages+'/depth/src/*ddalpha*.dll')
ddalpha_approx=glob.glob(site_packages+'/depth/src/*depth_wrapper*.dll')
libr=CDLL(r""+ddalpha_exact[0])
libRom=CDLL(r""+ddalpha_approx[0])
[docs]def halfspace(x, data,numDirections=1000,exact=True,method="recursive",
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):
if exact:
if (method =="recursive" or method==1):
method=1
elif (method =="plane" or method==2):
method=2
elif (method =="line" or method==3):
method=3
else:
print("Wrong argument, method=str(recursive) or str(plane) or str(line)")
print("recursive by default")
method=3
points_list=data.flatten()
objects_list=x.flatten()
points=(c_double*len(points_list))(*points_list)
objects=(c_double*len(objects_list))(*objects_list)
k=numDirections
points=pointer(points)
objects=pointer(objects)
numPoints=pointer(c_int(len(data)))
numObjects=pointer(c_int(len(x)))
dimension=pointer(c_int(len(data[0])))
algNo=pointer((c_int(method)))
depths=pointer((c_double*len(x))(*np.zeros(len(x))))
libr.HDepthEx(points,objects, numPoints,numObjects,dimension,algNo,depths)
res=np.zeros(len(x))
for i in range(len(x)):
res[i]=depths[0][i]
return res
else:
return depth_approximation(x, data, "halfspace", solver, NRandom ,option, n_refinements,
sphcap_shrink, alpha_Dirichlet, cooling_factor, cap_size, start, space, line_solver, bound_gc)
halfspace.__doc__="""
Description
Calculates the exact and approximated Tukey (=halfspace, location) depth (Tukey, 1975) 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.
exact
The type of the used method. The default is ``exact=False``, which leads to approx-
imate computation of the Tukey depth.
If ``exact=True``, the Tukey depth is computed exactly, with ``method='recursive'`` by default.
method
For ``exact=True``, the Tukey depth is calculated as the minimum over all combinations of k points from data (see Details below).
In this case parameter method specifies k, with possible values 1 for ``method='recursive'`` (by default), d−2
for ``method='plane'``, d−1 for ``'method=line'``.
The name of the method may be given as well as just parameter exact, in which
case the default method will be used.
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
* Tukey, J. W. (1975). Mathematics and the picturing of data. In R. James (Ed.), *Proceedings of the International Congress of Mathematicians*, Volume 2, Canadian Mathematical Congress, 523–531.
* Donoho, D. L. and M. Gasko (1992). Breakdown properties of location estimates based on halfspace depth and projected outlyingness. *The Annals of Statistics*, 20(4), 1803–1827.
* Dyckerhoff, R. and Mozharovskyi, P. (2016): Exact computation of the halfspace depth. *Computational Statistics and Data Analysis*, 98, 19–30.
* 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 *
>>> mat1=[[1, 0, 0],[0, 2, 0],[0, 0, 1]]
>>> mat2=[[1, 0, 0],[0, 1, 0],[0, 0, 1]]
>>> x = np.random.multivariate_normal([1,1,1], mat2, 10)
>>> data = np.random.multivariate_normal([0,0,0], mat1, 200)
>>> halfspace(x, data)
[0. 0.005 0.005 0. 0.04 0.01 0. 0. 0.04 0.01 ]
>>> halfspace(x, data, exact=True)
[0. 0.005 0.005 0. 0.04 0.01 0. 0. 0.04 0.01 ]
"""