def generate(BIGN, M, a, b, path):
L = [x for x in range(0, BIGN, 1)]
G = nx.Graph()
G.add_nodes_from(L)
names = G.nodes()
rands = stats.beta.rvs(a=a, b=b, size=BIGN)
compute(G, M, rands, names, path)
def generate(BIGN, M, scale, path):
L = [x for x in range(0, BIGN, 1)]
G = nx.Graph()
G.add_nodes_from(L)
names = G.nodes()
rands = stats.binom.rvs(n=N, p=P, size=BIGN)
compute(G, M, rands, names, path)
def generate(BIGN, M, MIU, DELTA, path):
L = [x for x in range(0, BIGN, 1)]
G = nx.Graph()
G.add_nodes_from(L)
names = G.nodes()
rands = stats.norm.rvs(loc=MIU, scale=DELTA, size=BIGN)
compute(G, M, rands, names, path)
import networkx as nx import numpy as np import math as math import itertools as itertools from computeByAssumtion import compute L = [x for x in range(0, 6, 1)] G = nx.Graph() G.add_nodes_from(L) names = G.nodes() rands = [15, 13, 8, 7, 5, 2] print(rands) compute(G, 2, rands, names, 'test.graphml')