def _generate_min_degree(gamma, average_degree, max_degree, tolerance, max_iters):
"""Returns a minimum degree from the given average degree."""
# Defines zeta function whether or not Scipy is available
try:
from scipy.special import zeta
except ImportError:
def zeta(x, q):
return _hurwitz_zeta(x, q, tolerance)
min_deg_top = max_degree
min_deg_bot = 1
min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
itrs = 0
mid_avg_deg = 0
while abs(mid_avg_deg - average_degree) > tolerance:
if itrs > max_iters:
raise nx.ExceededMaxIterations("Could not match average_degree")
mid_avg_deg = 0
for x in range(int(min_deg_mid), max_degree + 1):
mid_avg_deg += (x ** (-gamma + 1)) / zeta(gamma, min_deg_mid)
if mid_avg_deg > average_degree:
min_deg_top = min_deg_mid
min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
else:
min_deg_bot = min_deg_mid
min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
itrs += 1
# return int(min_deg_mid + 0.5)
return round(min_deg_mid)
def _powerlaw_sequence(gamma, low, high, condition, length, max_iters, seed):
"""Returns a list of numbers obeying a constrained power law distribution.
``gamma`` and ``low`` are the parameters for the Zipf distribution.
``high`` is the maximum allowed value for values draw from the Zipf
distribution. For more information, see :func:`_zipf_rv_below`.
``condition`` and ``length`` are Boolean-valued functions on
lists. While generating the list, random values are drawn and
appended to the list until ``length`` is satisfied by the created
list. Once ``condition`` is satisfied, the sequence generated in
this way is returned.
``max_iters`` indicates the number of times to generate a list
satisfying ``length``. If the number of iterations exceeds this
value, :exc:`~networkx.exception.ExceededMaxIterations` is raised.
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
"""
for i in range(max_iters):
seq = []
while not length(seq):
seq.append(_zipf_rv_below(gamma, low, high, seed))
if condition(seq):
return seq
raise nx.ExceededMaxIterations("Could not create power law sequence")
def generateConnexe(longueur, largeur, probarete, limite, fonctionpoids,
parampoids):
i = 0
g = generate(longueur, largeur, probarete, fonctionpoids, parampoids)
while not (nx.is_connected(g)) and i < limite:
i += 1
g = generate(longueur, largeur, probarete, fonctionpoids, parampoids)
if nx.is_connected(g):
return g
else:
raise nx.ExceededMaxIterations('Pas de graphe connexe généré avant ' +
str(limite) + ' itérations')
return
def _generate_communities(degree_seq, community_sizes, mu, max_iters, seed):
"""Returns a list of sets, each of which represents a community in
the graph.
``degree_seq`` is the degree sequence that must be met by the
graph.
``community_sizes`` is the community size distribution that must be
met by the generated list of sets.
``mu`` is a float in the interval [0, 1] indicating the fraction of
intra-community edges incident to each node.
``max_iters`` is the number of times to try to add a node to a
community. This must be greater than the length of
``degree_seq``, otherwise this function will always fail. If
the number of iterations exceeds this value,
:exc:`~networkx.exception.ExceededMaxIterations` is raised.
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
The communities returned by this are sets of integers in the set {0,
..., *n* - 1}, where *n* is the length of ``degree_seq``.
"""
# This assumes the nodes in the graph will be natural numbers.
result = [set() for _ in community_sizes]
n = len(degree_seq)
free = list(range(n))
for i in range(max_iters):
v = free.pop()
c = seed.choice(range(len(community_sizes)))
# s = int(degree_seq[v] * (1 - mu) + 0.5)
s = round(degree_seq[v] * (1 - mu))
# If the community is large enough, add the node to the chosen
# community. Otherwise, return it to the list of unaffiliated
# nodes.
if s < community_sizes[c]:
result[c].add(v)
else:
free.append(v)
# If the community is too big, remove a node from it.
if len(result[c]) > community_sizes[c]:
free.append(result[c].pop())
if not free:
return result
msg = 'Could not assign communities; try increasing min_community'
raise nx.ExceededMaxIterations(msg)
def _generate_min_degree(gamma, average_degree, max_degree, tolerance, max_iters):
"""Returns a minimum degree from the given average degree."""
min_deg_top = max_degree
min_deg_bot = 1
min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
itrs = 0
mid_avg_deg = 0
while abs(mid_avg_deg - average_degree) > tolerance:
if itrs > max_iters:
raise nx.ExceededMaxIterations("Could not match average_degree")
mid_avg_deg = 0
for x in range(int(min_deg_mid), max_degree + 1):
mid_avg_deg += (x ** (-gamma + 1)) / zeta(gamma, min_deg_mid, tolerance)
if mid_avg_deg > average_degree:
min_deg_top = min_deg_mid
min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
else:
min_deg_bot = min_deg_mid
min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
itrs += 1
return round(min_deg_mid)
def _generate_communities(degree_seq, community_sizes, lab_coms, mu, labels,
noise, std, max_iters, seed, type_attr):
"""Returns a list of sets, each of which represents a community.
``degree_seq`` is the degree sequence that must be met by the
graph.
``community_sizes`` is the community size distribution that must be
met by the generated list of sets.
``mu`` is a float in the interval [0, 1] indicating the fraction of
intra-community edges incident to each node.
``max_iters`` is the number of times to try to add a node to a
community. This must be greater than the length of
``degree_seq``, otherwise this function will always fail. If
the number of iterations exceeds this value,
:exc:`~networkx.exception.ExceededMaxIterations` is raised.
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
The communities returned by this are sets of integers in the set {0,
..., *n* - 1}, where *n* is the length of ``degree_seq``.
"""
card_lab = []
for lb in labels:
if lb == "auto": # number of labels equal to the number of communities
card_lab.append([i for i in range(1, len(community_sizes) + 1)])
else:
card_lab.append([i for i in range(1, lb + 1)])
# This assumes the nodes in the graph will be natural numbers.
result = [set() for _ in community_sizes]
n = len(degree_seq)
lab_nodes = [list(range(n)) for i in range(len(card_lab))]
free = list(range(n))
for i in range(max_iters):
v = free.pop()
c = random.choice(range(len(community_sizes)))
# s = int(degree_seq[v] * (1 - mu) + 0.5)
s = round(degree_seq[v] * (1 - mu))
# If the community is large enough, add the node to the chosen
# community. Otherwise, return it to the list of unaffiliated nodes.
if s < community_sizes[c]:
result[c].add(v)
if type_attr == "continuous":
for j, attr in enumerate(lab_coms):
lab_nodes[j][v] = float(np.random.normal(attr[c], std, 1))
#lab_nodes[j][v] = round(float(np.random.normal(attr[c], std, 1)))
#if categorical
else:
for j, attr in enumerate(lab_coms):
if random.uniform(0, 1) < 1 - noise:
lab_nodes[j][v] = attr[c]
else:
l = random.choice(card_lab[j])
lab_nodes[j][v] = l
# copy_card_lab = copy.copy(card_lab)
# copy_card_lab.remove(lab_coms[c])
# l = random.choice(copy_card_lab)
else:
free.append(v)
# If the community is too big, remove a node from it.
if len(result[c]) > community_sizes[c]:
free.append(result[c].pop())
if not free:
return lab_nodes, result
msg = 'Could not assign communities'
raise nx.ExceededMaxIterations(msg)
