import numpy as np
from ctypes import *
from multiprocessing import *
import scipy.spatial as scsp
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])
def count_convexes(objects,points,cardinalities, seed = 0):
tmp_x=points.flatten()
tmp_x=pointer((c_double*len(tmp_x))(*tmp_x))
dimension=pointer(c_int(len(points[0])))
numClasses=pointer(c_int(1))
tmp_objects=objects.flatten()
tmp_objects=pointer((c_double*len(tmp_objects))(*tmp_objects))
PY_numObjects=len(objects)
numObjects=pointer(c_int(PY_numObjects))
tmp_cardinalities=pointer(c_int(cardinalities))
seed=pointer(c_int(seed))
length=PY_numObjects*1
init_zeros=np.zeros(length,dtype=int)
isInConv=pointer((c_int*length)(*init_zeros))
libr.IsInConvexes(tmp_x,dimension,tmp_cardinalities,numClasses,tmp_objects,numObjects,seed,isInConv)
res=np.zeros(length)
for i in range(length):
res[i]=isInConv[0][i]
res.reshape(PY_numObjects,1)
return res
def is_in_convex(x, data, cardinalities, seed = 0):
res=count_convexes(x, data, cardinalities, seed)
return res
[docs]def qhpeeling(x, data):
points_list=data.flatten()
objects_list=x.flatten()
nrow_data=len(data)
depths=np.zeros(len(x))
tmpData=data
for i in range(nrow_data):
if (len(tmpData)<(len(data[0])*(len(data[0])+1)+0.5)):
break
tmp=is_in_convex(x,tmpData,len(tmpData))
depths+=tmp
tmp_conv=scsp.ConvexHull(tmpData)
tmpData=np.delete(tmpData,np.unique(np.array(tmp_conv.simplices)),0)
depths=depths/nrow_data
return depths
qhpeeling.__doc__= """
Description
Calculates the convex hull peeling depth of points w.r.t. a multivariate data set.
Usage
depth.qhpeeling(x, data)
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.
References
* Barnett, V. (1976). The ordering of multivariate data. *Journal of the Royal Statistical Society*, *Series A*, 139, 318–355.
* Eddy, W. F. (1981). Graphics for the multivariate two-sample problem: Comment. *Journal of the American Statistical Association*, 76, 287–289.
Examples
>>> from depth.multivariate import *
>>> 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)
>>> qhpeeling(x, data)
[0. 0. 0. 0. 0. 0. 0.01 0. 0. 0.01]
"""