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Commented out weave functions for Py3 support

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This commit is contained in:
Mike Croucher 2015-03-01 10:17:21 +00:00
parent 560950466d
commit a0dc90596c
1 changed files with 52 additions and 56 deletions
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108
GPy/util/choleskies.py
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@ -2,10 +2,9 @@
# Licensed under the GNU GPL version 3.0
import numpy as np
from scipy import weave
#from scipy import weave
from . import linalg
def safe_root(N):
i = np.sqrt(N)
j = int(i)
@ -13,58 +12,58 @@ def safe_root(N):
raise ValueError("N is not square!")
return j
def flat_to_triang(flat):
"""take a matrix N x D and return a M X M x D array where
#def flat_to_triang(flat):
# """take a matrix N x D and return a M X M x D array where
#
# N = M(M+1)/2
#
# the lower triangluar portion of the d'th slice of the result is filled by the d'th column of flat.
# """
# N, D = flat.shape
# M = (-1 + safe_root(8*N+1))/2
# ret = np.zeros((M, M, D))
# flat = np.ascontiguousarray(flat)
#
# code = """
# int count = 0;
# for(int m=0; m<M; m++)
# {
# for(int mm=0; mm<=m; mm++)
# {
# for(int d=0; d<D; d++)
# {
# ret[d + m*D*M + mm*D] = flat[count];
# count++;
# }
# }
# }
# """
# weave.inline(code, ['flat', 'ret', 'D', 'M'])
# return ret
N = M(M+1)/2
the lower triangluar portion of the d'th slice of the result is filled by the d'th column of flat.
"""
N, D = flat.shape
M = (-1 + safe_root(8*N+1))/2
ret = np.zeros((M, M, D))
flat = np.ascontiguousarray(flat)
code = """
int count = 0;
for(int m=0; m<M; m++)
{
for(int mm=0; mm<=m; mm++)
{
for(int d=0; d<D; d++)
{
ret[d + m*D*M + mm*D] = flat[count];
count++;
}
}
}
"""
weave.inline(code, ['flat', 'ret', 'D', 'M'])
return ret
def triang_to_flat(L):
M, _, D = L.shape
L = np.ascontiguousarray(L) # should do nothing if L was created by flat_to_triang
N = M*(M+1)/2
flat = np.empty((N, D))
code = """
int count = 0;
for(int m=0; m<M; m++)
{
for(int mm=0; mm<=m; mm++)
{
for(int d=0; d<D; d++)
{
flat[count] = L[d + m*D*M + mm*D];
count++;
}
}
}
"""
weave.inline(code, ['flat', 'L', 'D', 'M'])
return flat
#def triang_to_flat(L):
# M, _, D = L.shape
#
# L = np.ascontiguousarray(L) # should do nothing if L was created by flat_to_triang
#
# N = M*(M+1)/2
# flat = np.empty((N, D))
# code = """
# int count = 0;
# for(int m=0; m<M; m++)
# {
# for(int mm=0; mm<=m; mm++)
# {
# for(int d=0; d<D; d++)
# {
# flat[count] = L[d + m*D*M + mm*D];
# count++;
# }
# }
# }
# """
# weave.inline(code, ['flat', 'L', 'D', 'M'])
# return flat
def triang_to_cov(L):
return np.dstack([np.dot(L[:,:,i], L[:,:,i].T) for i in xrange(L.shape[-1])])
@ -93,9 +92,6 @@ def multiple_dpotri_old(Ls):
def multiple_dpotri(Ls):
return np.dstack([linalg.dpotri(np.asfortranarray(Ls[:,:,i]), lower=1)[0] for i in range(Ls.shape[-1])])
def indexes_to_fix_for_low_rank(rank, size):
"""
work out which indexes of the flatteneed array should be fixed if we want the cholesky to represent a low rank matrix