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bibauthorid_meld_edges.pyx
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Wed, Mar 19, 17:10
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R3600 invenio-infoscience
bibauthorid_meld_edges.pyx
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from bibauthorid_prob_matrix import Bib_matrix
def meld_edges(p1, p2):
'''
Creates one out_edges set from two.
The operation is associative and commutative.
The objects are: (out_edges for in a cluster, number of vertices in the same cluster)
'''
cdef int verts1, verts2
out_edges1, verts1 = p1
out_edges2, verts2 = p2
assert verts1 > 0 and verts2 > 0, 'MELD_EDGES: verts problem %s %s ' % (str(verts1), str(verts2))
cdef float vsum, invsum
vsum = verts1 + verts2
invsum = 1. / vsum
assert len(out_edges1) == len(out_edges2), "MELD_EDGES: Invalid arguments for meld edges"
size = len(out_edges1)
result = list()
for i in xrange(size):
result.append(median(out_edges1[i][0], out_edges1[i][1], out_edges2[i][0], out_edges1[i][1],
verts1, verts2, invsum))
return (result, vsum)
cdef tuple median(float e10,float e11, float e20, float e21, int verts1, int verts2, float invsum):
cdef float i1, i2, inter_cert, inter_prob
if e10 < 0:
return (e10,e11)
if e20 < 0:
return (e20,e21)
i1 = e11 * verts1
i2 = e21 * verts2
inter_cert = i1 + i2
inter_prob = e10 * i1 + e20 * i2
try:
return (inter_prob / inter_cert, inter_cert * invsum)
except ZeroDivisionError:
return (0.,0.)
#old code before cythonization
#def meld_edges(p1, p2):
# '''
# Creates one out_edges set from two.
# The operation is associative and commutative.
# The objects are: (out_edges for in a cluster, number of vertices in the same cluster)
# '''
# out_edges1, verts1 = p1
# out_edges2, verts2 = p2
# assert verts1 > 0 and verts2 > 0, PID()+'MELD_EDGES: verts problem %s %s ' % (str(verts1), str(verts2))
# vsum = verts1 + verts2
# invsum = 1. / vsum
#
# special_numbers = Bib_matrix.special_numbers #local reference optimization
#
# def median(e1, e2):
#
# #dirty optimization, should check if value is in dictionary instead
# # if e1[0] in special_numbers: return e1
# # if e2[0] in special_numbers: return e2
# if e1[0] < 0:
# assert e1[0] in special_numbers, "MELD_EDGES: wrong value for median? %s" % str(e1)
# return e1
# if e2[0] < 0:
# assert e2[0] in special_numbers, "MELD_EDGES: wrong value for median? %s" % str(e2)
# return e2
#
# i1 = e1[1] * verts1
# i2 = e2[1] * verts2
# inter_cert = i1 + i2
# inter_prob = e1[0] * i1 + e2[0] * i2
# try:
# return (inter_prob / inter_cert, inter_cert * invsum)
# except ZeroDivisionError:
# return (0.,0.)
#
# assert len(out_edges1) == len(out_edges2), "Invalid arguments for meld edges"
# size = len(out_edges1)
#
# result = numpy.ndarray(shape=(size, 2), dtype=float, order='C')
# gc.disable()
## for i in xrange(size):
## result[i] = median(out_edges1[i], out_edges2[i])
## assert (result[i][0] >= 0 and result[i][0] <= 1) or result[i][0] in Bib_matrix.special_numbers, PID()+'MELD_EDGES: value %s' % result[i]
## assert (result[i][1] >= 0 and result[i][1] <= 1) or result[i][1] in Bib_matrix.special_numbers, PID()+'MELD_EDGES: compat %s' % result[i]
# result = [median(x,y) for x,y in izip(out_edges1, out_edges2)]
# gc.enable()
# return (result, vsum)
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