def export_colors(datafile, outfile):
try:
A = mmread(datafile)
A = A.toarray()
d = np.zeros_like(A)
G = s.DirectedGraph(A, d)
colors = G.coloring()
with open(outfile, 'wb') as out:
pickle.dump(colors, out)
except Exception as e:
with open('error_log.txt', 'w') as error_log:
error_log.write(f'\n {e}')
finally:
return
f = np.array([[sig, sig2, sig, sig, sig, sig, sig2, sig],
[sig2, sig2, sig2, sig2, sig2, sig2, sig2, sig2],
[sig, sig2, sig, sig, sig, sig, sig2, sig],
[sig, sig2, sig, sig, sig, sig, sig2, sig],
[sig, sig2, sig, sig, sig, sig, sig2, sig],
[sig, sig2, sig, sig, sig, sig, sig2, sig],
[sig2, sig2, sig2, sig2, sig2, sig2, sig2, sig2],
[sig, sig2, sig, sig, sig, sig, sig2, sig]])
a = np.array([zero, f1, f1, f1, f1, f1, f1, f1])
labels = ['1', '2', '3', '4', '5', '6', '7', '8']
counts = []
for i in range(20):
G = s.DirectedGraph(A, (a, f), labels=labels)
for i in range(5):
base = np.random.choice(G.indices, int(G.n * 0.9), replace=False)
G.specialize(base)
data = G.coloring()
for key in data.keys():
counts.append(len(data[key]))
print(G.n)
plt.hist(counts)
plt.show()
# print(G.coloring())
# G.network_vis()#use_eqp=True)
import sys
import os
path = os.getcwd()
sys.path.insert(1, path[:-4])
import core.specializer as s
import numpy as np
from importlib import reload
import networkx as nx
import matplotlib.pyplot as plt
import time
import autograd.numpy as anp
import pickle
from scipy.io import mmread
if __name__ == "__main__":
# A = mmread('/home/ethan/Research/NetworkSpecialization/data/inf-power/inf-power.mtx')
A = mmread('../data/inf-power/inf-power.mtx')
A = A.toarray()
d = np.zeros_like(A)
G = s.DirectedGraph(A, d)
colors = G.coloring()
with open('colors', 'wb') as out:
pickle.dump(colors, out)