def estOE(d):
gt = d['gt']['graph']
gt = bfu.undersample(gt, 1)
e = gk.OCE(d['estimate'], gt)
N = np.double(len(gk.edgelist(gt))) +\
np.double(len(gk.bedgelist(gt)))
return (e['directed'][0] + e['bidirected'][0]) / N
def estOE(d):
gt= d['gt']['graph']
gt=bfu.undersample(gt,1)
e = gk.OCE(d['estimate'],gt)
N = np.double(len(gk.edgelist(gt))) +\
np.double(len(gk.bedgelist(gt)))
return (e['directed'][0]+e['bidirected'][0])/N
def estCOE(d):
gt = d['gt']['graph']
gt = bfu.undersample(gt, 1)
e = gk.OCE(d['estimate'], gt)
n = len(gt)
N = np.double(n**2+(n-1)**2/2.0\
-len(gk.edgelist(gt))
-len(gk.bedgelist(gt)))
return (e['directed'][1] + e['bidirected'][1]) / N
def estCOE(d):
gt= d['gt']['graph']
gt=bfu.undersample(gt,1)
e = gk.OCE(d['estimate'],gt)
n = len(gt)
N = np.double(n**2+(n-1)**2/2.0\
-len(gk.edgelist(gt))
-len(gk.bedgelist(gt)))
return (e['directed'][1]+e['bidirected'][1])/N
def checkvedge(v, g2):
""" Nodes to check to merge the virtual nodes of v ( b<-a->c )
"""
l = gk.bedgelist(g2)
if (v[1],v[2]) in l:
l = single_nodes(v,g2) + checkbedges(v,l,g2)
for n in v:
if n in g2[n]: l.append((n,n))
else:
l = checkbedges(v,l,g2)
return list(set(l))
def vedgelist(g, pathtoo=False):
""" Return a list of tuples for edges of g and forks
a superugly organically grown function that badly needs refactoring
"""
l = []
el = gk.edgelist(g)
bl = gk.bedgelist(g)
if pathtoo: l.extend(make_longpaths(g,el))
l2,r = make_allforks_and_rest(g,el,bl,dofullforks=True)
l.extend(l2)
A, singles = makechains(r)
if singles:
B, singles = makesinks(singles)
else:
B, singles = [], []
l = longpaths_pick(l)+threedges_pick(l) + A + B + singles
return l
def udensity(g): return (len(gk.edgelist(g))+len(gk.bedgelist(g))/2.)/np.double(len(g)**2 + len(g)*(len(g)-1)/2.) def esig(l,n):
