def _utilities2_pairwise_breaking(qfx2_utilities):
print('[vote] building pairwise matrix')
hstack = np.hstack
cartesian = utool.cartesian
tnxs = [util[1] for utils in qfx2_utilities for util in utils]
altx2_tnx = utool.unique_ordered2(tnxs)
tnx2_altx = {nid: altx for altx, nid in enumerate(altx2_tnx)}
nUtilities = len(qfx2_utilities)
nAlts = len(altx2_tnx)
altxs = np.arange(nAlts)
pairwise_mat = np.zeros((nAlts, nAlts))
qfx2_porder = [
np.array([tnx2_altx[util[1]] for util in utils])
for utils in qfx2_utilities
]
def sum_win(ij):
""" pairiwse wins on off-diagonal """
pairwise_mat[ij[0], ij[1]] += 1
def sum_loss(ij):
""" pairiwse wins on off-diagonal """
pairwise_mat[ij[1], ij[1]] -= 1
nVoters = 0
for qfx in range(nUtilities):
# partial and compliment order over alternatives
porder = utool.unique_ordered2(qfx2_porder[qfx])
nReport = len(porder)
if nReport == 0:
continue
#sys.stdout.write('.')
corder = np.setdiff1d(altxs, porder)
# pairwise winners and losers
pw_winners = [porder[r:r + 1] for r in range(nReport)]
pw_losers = [hstack((corder, porder[r + 1:])) for r in range(nReport)]
pw_iter = zip(pw_winners, pw_losers)
pw_votes_ = [cartesian((winner, losers)) for winner, losers in pw_iter]
pw_votes = np.vstack(pw_votes_)
#pw_votes = [(w,l) for votes in pw_votes_ for w,l in votes if w != l]
list(map(sum_win, iter(pw_votes)))
list(map(sum_loss, iter(pw_votes)))
nVoters += 1
#print('')
PLmatrix = pairwise_mat / nVoters
# sum(0) gives you the sum over rows, which is summing each column
# Basically a column stochastic matrix should have
# M.sum(0) = 0
#print('CheckMat = %r ' % all(np.abs(PLmatrix.sum(0)) < 1E-9))
return PLmatrix, altx2_tnx
def _utilities2_pairwise_breaking(qfx2_utilities):
print('[vote] building pairwise matrix')
hstack = np.hstack
cartesian = utool.cartesian
tnxs = [util[1] for utils in qfx2_utilities for util in utils]
altx2_tnx = utool.unique_ordered2(tnxs)
tnx2_altx = {nid: altx for altx, nid in enumerate(altx2_tnx)}
nUtilities = len(qfx2_utilities)
nAlts = len(altx2_tnx)
altxs = np.arange(nAlts)
pairwise_mat = np.zeros((nAlts, nAlts))
qfx2_porder = [np.array([tnx2_altx[util[1]] for util in utils])
for utils in qfx2_utilities]
def sum_win(ij):
""" pairiwse wins on off-diagonal """
pairwise_mat[ij[0], ij[1]] += 1
def sum_loss(ij):
""" pairiwse wins on off-diagonal """
pairwise_mat[ij[1], ij[1]] -= 1
nVoters = 0
for qfx in range(nUtilities):
# partial and compliment order over alternatives
porder = utool.unique_ordered2(qfx2_porder[qfx])
nReport = len(porder)
if nReport == 0:
continue
#sys.stdout.write('.')
corder = np.setdiff1d(altxs, porder)
# pairwise winners and losers
pw_winners = [porder[r:r + 1] for r in range(nReport)]
pw_losers = [hstack((corder, porder[r + 1:])) for r in range(nReport)]
pw_iter = zip(pw_winners, pw_losers)
pw_votes_ = [cartesian((winner, losers)) for winner, losers in pw_iter]
pw_votes = np.vstack(pw_votes_)
#pw_votes = [(w,l) for votes in pw_votes_ for w,l in votes if w != l]
list(map(sum_win, iter(pw_votes)))
list(map(sum_loss, iter(pw_votes)))
nVoters += 1
#print('')
PLmatrix = pairwise_mat / nVoters
# sum(0) gives you the sum over rows, which is summing each column
# Basically a column stochastic matrix should have
# M.sum(0) = 0
#print('CheckMat = %r ' % all(np.abs(PLmatrix.sum(0)) < 1E-9))
return PLmatrix, altx2_tnx
def _get_alts_from_utilities(qfx2_utilities):
""" get temp name indexes """
tnxs = [utool[1] for utils in qfx2_utilities for utool in utils]
altx2_tnx = utool.unique_ordered2(tnxs)
tnx2_altx = {nid: altx for altx, nid in enumerate(altx2_tnx)}
nUtilities = len(qfx2_utilities)
nAlts = len(altx2_tnx)
altxs = np.arange(nAlts)
return tnxs, altx2_tnx, tnx2_altx, nUtilities, nAlts, altxs
def _get_alts_from_utilities(qfx2_utilities):
""" get temp name indexes """
tnxs = [utool[1] for utils in qfx2_utilities for utool in utils]
altx2_tnx = utool.unique_ordered2(tnxs)
tnx2_altx = {nid: altx for altx, nid in enumerate(altx2_tnx)}
nUtilities = len(qfx2_utilities)
nAlts = len(altx2_tnx)
altxs = np.arange(nAlts)
return tnxs, altx2_tnx, tnx2_altx, nUtilities, nAlts, altxs
