def reciprocal_complementary_w(adjmatrix, x=1):
"""
Parametri
---------
adjmatrix : numpy.matrix
Matrica susedstva grafa G.
x : float
Varijabilni parametar.
Rezultati
---------
rcw : float
Reciprocal-Complementary-Wienner indeks.
"""
dist = distance_matrix(adjmatrix)
dia = diameter(adjmatrix)
rcw = 0
for i in range(len(dist) - 1):
for j in range(i + 1, len(dist)):
rcw += 1 / (dia + 1 - dist[i, j])**x
return rcw
def n1n2(adjmatrix):
"""Pravi listu sa vrednostima za n1 i n2.
Parametri
---------
adjmatrix : numpy.matrix
Matrica susedstva grafa G.
Rezultati
---------
n1n2 : int
Proizvod broja cvorova sa jedne i druge strane
zadate grane.
"""
dmatrix = distance_matrix(adjmatrix)
n1n2 = []
for u in range(len(dmatrix) - 1):
for v in range(u + 1, len(dmatrix)):
if dmatrix[u, v] == 1:
n1 = 0
n2 = 0
for p in range(len(dmatrix)):
if dmatrix[u, p] < dmatrix[v, p]:
n1 += 1
elif dmatrix[u, p] > dmatrix[v, p]:
n2 += 1
n1n2.append((n1, n2))
return n1n2
def eccentricity(adjmatrix):
"""
:param adjmatrix:
:return:
"""
dmatrix = gp.distance_matrix(adjmatrix).tolist()
ecc = list()
for row in dmatrix:
ecc.append(max(row))
return ecc
def vertex_distance(adjmatrix):
"""
:param adjmatrix:
:return:
"""
dmatrix = gp.distance_matrix(adjmatrix).tolist()
vdist = list()
for row in dmatrix:
vdist.append(sum(row))
return vdist
def gutman(adjmatrix):
"""
:param adjmatrix:
:return:
"""
dmatrix = gp.distance_matrix(adjmatrix)
degs = gp.degree(adjmatrix)
gut = 0
for i in range(len(degs) - 1):
for j in range(i + 1, len(degs)):
gut += degs[i] * degs[j] * dmatrix[i, j]
return gut
def balaban(adjmatrix, sum_balaban=False):
'''
Parametri
---------
adjmatrix : numpy matrix ili list of lists
Matrica susedstva grafa G.
sum_balaban : bool
Uslov koji ukoliko je ispunjen, funkcija racuna
sum-Balabanov indeks. Ukoliko uslov nije ispunjen
racuna se originalni Balabanov indeks.
Rezultati
---------
jj : float
Balabanov ili sum-Balabanov indeks u zavisnosti od
ispunjenosti uslova sum_balaban.
'''
n = len(adjmatrix)
m = 0
for row in adjmatrix.tolist():
m += sum(row) / 2
dm = distance_matrix(adjmatrix)
c = m / (m - n + 2)
jj = 0
rowsum = []
for row in dm:
rowsum.append(sum(row))
for i in range(len(dm) - 1):
for j in range(i + 1, len(dm)):
if dm[i, j] == 1:
if not sum_balaban:
jj += 1 / np.sqrt(rowsum[i] * rowsum[j])
else:
jj += 1 / np.sqrt(rowsum[i] + rowsum[j])
return c * jj
def distance_partition(adjmatrix):
"""Distance partition.
Parametri
---------
adjmatrix : numpy.matrix
Matrica susedstva grafa G.
Rezultati
---------
k : int
Broj javljanja rastojanja k u matrici rastojanja.
"""
dm = distance_matrix(adjmatrix)
k = dict()
for i in range(len(dm) - 1):
for j in range(i + 1, len(dm)):
k[int(dm[i, j])] = k.get(int(dm[i, j]), 0) + 1
return k
def estrada(M, mat='adjacency', c=0):
"""Some kind of the Estrada index.
Parametri
---------
M : numpy.matrix
Matrica iz koje se racuna spektar grafa i potom
energija.
mat : str
Nacin na koji biramo matricu.
Podrazumevana vrednost je 'adjacency'.
c : float
Konstanta od koje se oduzima sopstvena vrednost grafa.
Podrazumavana vrednost je 0.
Npr. kod laplasove energije c = 2*m/n .
Rezultati
---------
ee : float
Neki Estradin indeks.
"""
if mat == 'laplacian':
M = gp.laplacian_matrix(M)
elif mat == "distance":
M = gp.distance_matrix(M)
elif mat == 'randic':
M = gp.randic_matrix(M)
elif mat == 'xu':
M = gp.degree_extended_matrix(M)
elif mat == 'harary':
M = gp.harary_matrix(M)
else:
pass
ee = 0
spec = gp.matrix_spectrum(M)
for l in spec:
ee += np.exp(l - c)
return ee
def diameter(adjmatrix):
"""Izracunava diametar grafa (maksimalno rastojanje).
Parametri
---------
adjmatrix : numpy.matrix
Matrica susedstva grafa G.
Rezultati
---------
dia : int
Maksimalno rastojanje u matrici rastojanja.
"""
dist = distance_matrix(adjmatrix)
dia = None
for row in dist:
if dia is None or dia < max(row):
dia = max(row)
return dia
def ti(self, adjmatrix):
dist = distance_matrix(adjmatrix)
deg = degree(adjmatrix)
y = 0
if self.choose == 't':
for i in range(len(dist) - 1):
for j in range(i + 1, len(dist)):
if deg[i] == 1 and deg[j] == 1:
y += self.func(dist[i, j], self.x)
return y
elif self.choose == 's':
for i in range(len(dist) - 1):
for j in range(i + 1, len(dist)):
if deg[i] == 1 or deg[j] == 1:
y += self.func(dist[i, j], self.x)
return y
else:
for i in range(len(dist) - 1):
for j in range(i + 1, len(dist)):
y += self.func(dist[i, j], self.x)
return y
