#!/usr/bin/python# Wrapper for the MPI-Kmeans library by Peter Gehler from ctypes import c_int, c_double, c_uintfrom numpy.ctypeslib import ndpointerimport numpy as Nfrom numpy import empty,array,reshape,arangedef kmeans(X, nclst, maxiter=0, numruns=1):    """Wrapper for Peter Gehlers accelerated MPI-Kmeans routine."""        mpikmeanslib = N.ctypeslib.load_library("libmpikmeans.so", ".")    mpikmeanslib.kmeans.restype = c_double    mpikmeanslib.kmeans.argtypes = [ndpointer(dtype=c_double, ndim=1, flags='C_CONTIGUOUS'), \                                    ndpointer(dtype=c_double, ndim=1, flags='C_CONTIGUOUS'), \                                    ndpointer(dtype=c_uint, ndim=1, flags='C_CONTIGUOUS'), \                                    c_uint, c_uint, c_uint, c_uint, c_uint ]        npts,dim = X.shape    assignments=empty( (npts), c_uint )        bestSSE=N.Inf    bestassignments=empty( (npts), c_uint)    Xvec = array( reshape( X, (-1,) ), c_double )    permutation = N.random.permutation( range(npts) ) # randomize order of points    CX = array(X[permutation[:nclst],:], c_double).flatten()    SSE = mpikmeanslib.kmeans( CX, Xvec, assignments, dim, npts, min(nclst, npts), maxiter, numruns)    return reshape(CX, (nclst,dim)), SSE, (assignments+1)if __name__ == "__main__":    from numpy import array    from numpy.random import rand        X = array( rand(12), c_double )    X.shape = (4,3)    clst,dist,labels = kmeans(X, 2)    print "cluster centers=\n",clst    print "dist=",dist    print "cluster labels",labels