def aggregateScores(node):
relmap = common.relationMap(node)
scores = {}
totallen = sum(1 for n in relmap if n.ispublic)
ibdsamples = {}
for relative, relation in relmap.iteritems():
if not relative.ispublic:
continue
h1, h2 = relation.keys()[0]
if min(map(sum, relation.keys())) < 2:
raise SiblingError
ibdsample = ibdsamples[relative] = \
sampleFromPdfVector(cached.convolvedDensity(relation))
if ibdsample == 0:
continue
for k, s in individualScores(node, relative, relation, ibdsample).iteritems():
# total score is L-p norm
scores.setdefault(k, []).append(s ** 0.5)
return scores, ibdsamples
def individualScores(victim, relative, relation, sample):
"""sample is specified as an array index"""
global _node, _rel
pair = relation.keys()[0]
nodeheight = pair[0]-pair[1]
relmap = common.relationMap(relative, min(8, 9-nodeheight))
if victim not in relmap:
_node = victim
_rel = relative
assert False
scores = {}
for possiblevictim, relation in relmap.iteritems():
h1, h2 = relation.keys()[0]
if abs(h1 - h2) > 3 or min(map(sum, relation.keys())) < 2:
continue
pdfvector = cached.convolvedDensity(relation)
scores[possiblevictim] = pdfvector[sample] if sample < len(pdfvector) else 0
meanscore = utils.mean(scores.itervalues())
maxscore = max(scores.itervalues())
return dict(((k, val / maxscore) for k, val in scores.iteritems()))
def aggregateScores(node):
relmap = common.relationMap(node)
scores = {}
totallen = sum(1 for n in relmap if n.ispublic)
ibdsamples = {}
for relative, relation in relmap.iteritems():
if not relative.ispublic:
continue
h1, h2 = relation.keys()[0]
if min(map(sum, relation.keys())) < 2:
raise SiblingError
ibdsample = ibdsamples[relative] = \
sampleFromPdfVector(cached.convolvedDensity(relation))
if ibdsample == 0:
continue
for k, s in individualScores(node, relative, relation,
ibdsample).iteritems():
# total score is L-p norm
scores.setdefault(k, []).append(s**0.5)
return scores, ibdsamples
def individualScores(victim, relative, relation, sample):
"""sample is specified as an array index"""
global _node, _rel
pair = relation.keys()[0]
nodeheight = pair[0] - pair[1]
relmap = common.relationMap(relative, min(8, 9 - nodeheight))
if victim not in relmap:
_node = victim
_rel = relative
assert False
scores = {}
for possiblevictim, relation in relmap.iteritems():
h1, h2 = relation.keys()[0]
if abs(h1 - h2) > 3 or min(map(sum, relation.keys())) < 2:
continue
pdfvector = cached.convolvedDensity(relation)
scores[possiblevictim] = pdfvector[sample] if sample < len(
pdfvector) else 0
meanscore = utils.mean(scores.itervalues())
maxscore = max(scores.itervalues())
return dict(((k, val / maxscore) for k, val in scores.iteritems()))
def zeroProb(node):
"""prob that i.b.d. of node and cand is zero"""
relation = common.relationMap(node).get(cand)
return cached.convolvedDensity(relation)[0] if relation else 1.0
def zeroProb(node):
"""prob that i.b.d. of node and cand is zero"""
relation = common.relationMap(node).get(cand)
return cached.convolvedDensity(relation)[0] if relation else 1.0