/usr/share/pyshared/openopt/solvers/UkrOpt/interalgMOP.py is in python-openopt 0.38+svn1589-1.
This file is owned by root:root, with mode 0o644.
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from numpy import tile, isnan, array, atleast_1d, asarray, logical_and, all, searchsorted, logical_or, any, \
nan, isinf, arange, vstack, inf, where, logical_not, take, argmax, argmin, min, abs, hstack, empty, insert, \
isfinite, append, atleast_2d, prod, logical_xor, argsort, asfarray, ones, log2, zeros, log1p, array_split
from interalgLLR import *
try:
from bottleneck import nanargmin, nanmin, nanargmax, nanmax
except ImportError:
from numpy import nanmin, nanargmin, nanargmax, nanmax
def r43_seq(Arg):
targets_vals, targets_tols, solutionsF, lf, uf = Arg
lf, uf = asarray(lf), asarray(uf)
if lf.size == 0 or len(solutionsF) == 0: return None
m = len(lf)
n = lf.shape[2]/2
r = zeros((m, 2*n))
for _s in solutionsF:
s = atleast_1d(_s)
tmp = ones((m, 2*n))
for i in range(len(targets_vals)):
val, tol = targets_vals[i], targets_tols[i]#, t.val, t.tol
#TODO: mb optimize it
o, a = lf[:, i], uf[:, i]
if val == inf:
ff = s[i] + tol
ind = a > ff
if any(ind):
t1 = a[ind]
t2 = o[ind]
# TODO: check discrete cases
Tmp = (ff-t2) / (t1-t2)
Tmp[t1==t2] = 0.0 # for discrete cases
tmp[ind] *= Tmp
tmp[ff<o] = 0.0
elif val == -inf:
ff = s[i] - tol
ind = o < ff
if any(ind):
t1 = a[ind]
t2 = o[ind]
# TODO: check discrete cases
Tmp = (t1-ff) / (t1-t2)
Tmp[t1==t2] = 0.0 # for discrete cases
tmp[ind] *= Tmp
tmp[a<ff] = 0.0
else: # finite val
ff = abs(s[i]-val) - tol
if ff <= 0:
continue
_lf, _uf = o - val, a - val
ind = logical_or(_lf < ff, _uf > - ff)
_lf = _lf[ind]
_uf = _uf[ind]
_lf[_lf>ff] = ff
_lf[_lf<-ff] = -ff
_uf[_uf<-ff] = -ff
_uf[_uf>ff] = ff
r20 = a[ind] - o[ind]
Tmp = 1.0 - (_uf - _lf) / r20
Tmp[r20==0] = 0.0 # for discrete cases
tmp[ind] *= Tmp
#raise('unimplemented yet')
# if any(tmp<0) or any(tmp>1):
# raise 0
# debug
# s1, s2 = tmp[tmp==0].size, tmp[tmp==1].size
# s1, s2 = s1 / float(tmp.size), s2 / float(tmp.size)
# print 'size 0: %0.2f size 1: %0.2f rest: %0.2f' % (s1, s2, 1-s1-s2)
# debug end
new = 0
if new:
ind_0 = tmp == 0.0
ind_1 = tmp == 1.0
r[ind_1] = inf
ind_m = logical_not(logical_and(ind_0, ind_1))
#r[ind_m] -= log1p(-tmp[ind_m]) * 1.4426950408889634
r[ind_m] -= log1p(-tmp[ind_m]) * 1.4426950408889634
#r20 = log2(1-tmp[ind_m]) #* 1.4426950408889634
else:
r -= log1p(-tmp) * 1.4426950408889634 # log2(e)
#r -= r20
return r
from multiprocessing import Pool
def r43(targets, SolutionsF, lf, uf, pool, nProc):
lf, uf = asarray(lf), asarray(uf)
target_vals = [t.val for t in targets]
target_tols = [t.tol for t in targets]
if nProc == 1 or len(SolutionsF) <= 1:
return r43_seq((target_vals, target_tols, SolutionsF, lf, uf))
splitBySolutions = True #if len(SolutionsF) > max((4*nProc, ))
if splitBySolutions:
ss = array_split(SolutionsF, nProc)
Args = [(target_vals, target_tols, s, lf, uf) for s in ss]
result = pool.imap_unordered(r43_seq, Args)#, callback = cb)
r = [elem for elem in result if elem is not None]
return PythonSum(r)
else:
lf2 = array_split(lf, nProc)
uf2 = array_split(uf, nProc)
Args = [(target_vals, target_tols, SolutionsF, lf2[i], uf2[i]) for i in range(nProc)]
result = pool.map(r43_seq, Args)
r = [elem for elem in result if elem is not None]
return vstack(r)
def r14MOP(p, nlhc, residual, definiteRange, y, e, vv, asdf1, C, r40, itn, g, nNodes, \
r41, fTol, Solutions, varTols, _in, dataType, \
maxNodes, _s, indTC, xRecord):
assert p.probType == 'MOP'
if len(p._discreteVarsNumList):
adjustDiscreteVarBounds(y, e, p)
if itn == 0:
# TODO: change for constrained probs
_s = atleast_1d(inf)
if p.nProc != 1:
p.pool = Pool(processes = p.nProc)
else:
p.pool = None
ol, al = [], []
targets = p.targets # TODO: check it
m, n = y.shape
ol, al = [[] for k in range(m)], [[] for k in range(m)]
for i, t in enumerate(targets):
o, a, definiteRange = func82(y, e, vv, t.func, dataType, p)
o, a = o.reshape(2*n, m).T, a.reshape(2*n, m).T
for j in range(m):
ol[j].append(o[j])
al[j].append(a[j])
#ol.append(o.reshape(2*n, m).T.tolist())
#al.append(a.reshape(2*n, m).T.tolist())
nlhf = r43(targets, Solutions.F, ol, al, p.pool, p.nProc)
fo_prev = 0
# TODO: remove NaN nodes here
if y.size == 0:
return _in, g, fo_prev, _s, Solutions, xRecord, r41, r40
nodes = func11(y, e, nlhc, indTC, residual, ol, al, _s, p)
#y, e = func4(y, e, o, a, fo)
newNLH = False
assert p.solver.dataHandling == 'raw', '"sorted" mode is unimplemented for MOP yet'
if nlhf is None:
tnlh_curr = nlhc
elif nlhc is None:
tnlh_curr = nlhf
else:
tnlh_curr = nlhf + nlhc
asdf1 = [t.func for t in p.targets]
r5F, r5Coords = getr4Values(vv, y, e, tnlh_curr, asdf1, C, p.contol, dataType, p)
nIncome, nOutcome = r44(Solutions, r5Coords, r5F, targets, p.solver.sigma)
fo = 0 # unused for MOP
# TODO: better of nlhc for unconstrained probs
if len(_in) != 0:
an = hstack((nodes, _in))
else:
an = atleast_1d(nodes)
# if nIncome != 0 or not hasattr(an[0], 'tnlh_all'):
hasNewParetoNodes = False if nIncome == 0 else True
if hasNewParetoNodes:
ol2 = [node.o for node in an]
al2 = [node.a for node in an]
nlhc2 = [node.nlhc for node in an]
nlhf2 = r43(targets, Solutions.F, ol2, al2, p.pool, p.nProc)
tnlh_all = asarray(nlhc2) if nlhf2 is None else nlhf2 if nlhc2[0] is None else asarray(nlhc2) + nlhf2
else:
tnlh_all = vstack([tnlh_curr] + [node.tnlh_all for node in _in]) if len(_in) != 0 else tnlh_curr
for i, node in enumerate(nodes):
node.tnlh_all = tnlh_all[i]
r10 = logical_not(any(isfinite(tnlh_all), 1))
if any(r10):
ind = where(logical_not(r10))[0]
#an = take(an, ind, axis=0, out=an[:ind.size])
an = asarray(an[ind])
tnlh_all = take(tnlh_all, ind, axis=0, out=tnlh_all[:ind.size])
# else:
# tnlh_all = hstack([node.tnlh_all for node in an])
T1, T2 = tnlh_all[:, :tnlh_all.shape[1]/2], tnlh_all[:, tnlh_all.shape[1]/2:]
T = where(logical_or(T1 < T2, isnan(T2)), T1, T2)
t = nanargmin(T, 1)
w = arange(t.size)
NN = T[w, t].flatten()
for i, node in enumerate(an):
node.tnlh_all = tnlh_all[i]
node.tnlh_curr_best = NN[i]
astnlh = argsort(NN)
an = an[astnlh]
p._t = t
# TODO: form _s in other level (for active nodes only), to reduce calculations
if len(an) != 0:
nlhf_fixed = asarray([node.nlhf for node in an])
nlhc_fixed = asarray([node.nlhc for node in an])
T = nlhf_fixed + nlhc_fixed if nlhc_fixed[0] is not None else nlhf_fixed
p.__s = \
nanmin(vstack(([T[w, t], T[w, n+t]])), 0)
else:
p.__s = array([])
# p._nObtainedSolutions = len(solutions)
# if p._nObtainedSolutions > maxSolutions:
# solutions = solutions[:maxSolutions]
# p.istop = 0
# p.msg = 'user-defined maximal number of solutions (p.maxSolutions = %d) has been exeeded' % p.maxSolutions
# return an, g, fo, None, solutions, coords, xRecord, r41, r40
# TODO: fix it
p._frontLength = len(Solutions.F)
p._nIncome = nIncome
p._nOutcome = nOutcome
p.iterfcn(p.x0)
#print('iter: %d (%d) frontLenght: %d' %(p.iter, itn, len(Solutions.coords)))
if p.istop != 0:
return an, g, fo, None, Solutions, xRecord, r41, r40
#an, g = func9(an, fo, g, p)
nn = maxNodes#1 if asdf1.isUncycled and all(isfinite(o)) and p._isOnlyBoxBounded and not p.probType.startswith('MI') else maxNodes
an, g = func5(an, nn, g, p)
nNodes.append(len(an))
return an, g, fo, _s, Solutions, xRecord, r41, r40
def r44(Solutions, r5Coords, r5F, targets, sigma):
# print Solutions.F
# if len(Solutions.F) != Solutions.coords.shape[0]:
# raise 0
# TODO: rework it
#sf = asarray(Solutions.F)
nIncome, nOutcome = 0, 0
m= len(r5Coords)
n = len(r5Coords[0])
# TODO: mb use inplace r5Coords / r5F modification instead?
for j in range(m):
if isnan(r5F[0][0]):
continue
if Solutions.coords.size == 0:
Solutions.coords = array(r5Coords[j]).reshape(1, -1)
Solutions.F.append(r5F[0])
nIncome += 1
continue
M = Solutions.coords.shape[0]
r47 = empty(M, bool)
r47.fill(False)
# r48 = empty(M, bool)
# r48.fill(False)
for i, target in enumerate(targets):
f = r5F[j][i]
# TODO: rewrite it
F = asarray([Solutions.F[k][i] for k in range(M)])
#d = f - F # vector-matrix
val, tol = target.val, target.tol
Tol = sigma * tol
if val == inf:
r52 = f > F + Tol
# r36olution_better = f <= F#-tol
elif val == -inf:
r52 = f < F - Tol
# r36olution_better = f >= F#tol
else:
r52 = abs(f - val) < abs(F - val) - Tol
# r36olution_better = abs(f - val) >= abs(Solutions.F[i] - val)#-tol # abs(Solutions.F[i] - target) < abs(f[i] - target) + tol
r47 = logical_or(r47, r52)
# r48 = logical_or(r48, r36olution_better)
accept_c = all(r47)
#print sum(asarray(Solutions.F))/asarray(Solutions.F).size
if accept_c:
nIncome += 1
#new
r48 = empty(M, bool)
r48.fill(False)
for i, target in enumerate(targets):
f = r5F[j][i]
F = asarray([Solutions.F[k][i] for k in range(M)])
val, tol = target.val, target.tol
if val == inf:
r36olution_better = f < F
elif val == -inf:
r36olution_better = f > F
else:
r36olution_better = abs(f - val) > abs(F - val)
#assert 0, 'unimplemented yet'
r48 = logical_or(r48, r36olution_better)
r49 = logical_not(r48)
remove_s = any(r49)
if remove_s:# and False :
r50 = where(r49)[0]
nOutcome += r50.size
Solutions.coords[r50[0]] = r5Coords[j]
Solutions.F[r50[0]] = r5F[j]
if r50.size > 1:
r49[r50[0]] = False
indLeft = logical_not(r49)
indLeftPositions = where(indLeft)[0]
newSolNumber = Solutions.coords.shape[0] - r50.size + 1
Solutions.coords = take(Solutions.coords, indLeftPositions, axis=0, out = Solutions.coords[:newSolNumber])
solutionsF2 = asarray(Solutions.F, object)
solutionsF2 = take(solutionsF2, indLeftPositions, axis=0, out = solutionsF2[:newSolNumber])
Solutions.F = solutionsF2.tolist()
else:
Solutions.coords = vstack((Solutions.coords, r5Coords[j]))
Solutions.F.append(r5F[j])
return nIncome, nOutcome
#r43 = lambda targets, SolutionsF, lf, uf: r43_seq([t.val for t in targets], [t.tol for t in targets], SolutionsF, lf, uf)
#parallel_r43(targets, SolutionsF, lf, uf, 2)
#def r43(targets, Solutions, lf, uf):
# lf, uf = asarray(lf), asarray(uf)
# solutionsF = Solutions.F
# S = len(solutionsF)
# if S == 0: return None
#
# #print '!', asarray(lf).shape, asarray(uf).shape
# #lf, uf = asarray(lf), asarray(uf)
#
# m = len(lf)
# n = lf[0][0].size/2
# r = zeros((m, 2*n))
#
# ss = asarray(solutionsF)
# tmp = ones((S, m, 2*n))
# for i, t in enumerate(targets):
# val, tol = t.val, t.tol
#
# #TODO: mb optimize it
# o, a = tile(lf[:, i], (S, 1, 1)), tile(uf[:, i], (S, 1, 1))
#
# if val == inf:
# ff = s[i] + tol
# ind = a > ff
# if any(ind):
# t1 = a[ind]
# t2 = o[ind]
#
# # TODO: check discrete cases
# Tmp = (ff-t2) / (t1-t2)
# Tmp[t1==t2] = 0.0 # for discrete cases
# tmp[ind] *= Tmp
# tmp[ff<o] = 0.0
#
# elif val == -inf:
# ff = (ss[:, i] - tol).reshape(S, 1, 1)
# try:
# ind = o < ff
# except:
# pass
# if any(ind):
# try:
# t1 = a[ind]
# except:
# pass
# t2 = o[ind]
# # TODO: check discrete cases
# Tmp = (t1-ff) / (t1-t2)
# Tmp[t1==t2] = 0.0 # for discrete cases
# try:
# tmp[ind] *= Tmp
# except:
# pass
# tmp[a<ff] = 0.0
# else: # finite val
# ff = abs(s[i]-val) - tol
# if ff <= 0:
# continue
# _lf, _uf = o - val, a - val
# ind = logical_or(_lf < ff, _uf > - ff)
# _lf = _lf[ind]
# _uf = _uf[ind]
# _lf[_lf>ff] = ff
# _lf[_lf<-ff] = -ff
# _uf[_uf<-ff] = -ff
# _uf[_uf>ff] = ff
#
# r20 = a[ind] - o[ind]
# Tmp = 1.0 - (_uf - _lf) / r20
# Tmp[r20==0] = 0.0 # for discrete cases
# tmp[ind] *= Tmp
# #raise('unimplemented yet')
## if any(tmp<0) or any(tmp>1):
## raise 0
# r -= log1p(-sum(tmp, axis=0)) * 1.4426950408889634 # log2(e)
# return r
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