def test():
import numpy as np
from numpy import diag
import numpy.random as rand
from numpy.linalg import norm, inv
import models
from viz import plotNetwork
from util import gnp, barabasi_albert, from_edgelist, rowStochastic
rand.seed(1233)
#A, N = from_edgelist('./networks/facebook_combined.txt')
N = 2000
A = gnp(N,0.02)
A = rowStochastic(A)
s = rand.rand(N)
max_rounds = 1000
#A = gnp(N, 0.12, rand_weights=True)
B = diag(rand.rand(N)) * 0.5
#models.deGroot(A, s, max_rounds)
models.friedkinJohnsen(A, s, max_rounds, conv_stop=False)
def ga(A,
B,
s,
max_rounds,
eps=1e-6,
plot=False,
conv_stop=True,
save=False,
**kwargs):
'''Simulates the Generalized Asymmetric Coevolutionary Game.
This model does nto require an adjacency matrix. Connections between
nodes are calculated depending on the proximity of their opinions.
Args:
A (NxN numpy array): Adjacency matrix (its diagonal is the stubborness)
B (NxN numpy array): The stubborness of each node
s (1xN numpy array): Initial opinions (intrinsic beliefs) vector
op_eps: ε parameter of the model
max_rounds (int): Maximum number of rounds to simulate
eps (double): Maximum difference between rounds before we assume that
the model has converged (default: 1e-6)
plot (bool): Plot preference (default: False)
conv_stop (bool): Stop the simulation if the model has converged
(default: True)
save (bool): Save the simulation data into text files
**kargs: Arguments c, eps, and p for dynamic_weights function (eps and
p need to be specified only if c='pow') (default: c='linear')
Returns:
A txN vector of the opinions of the nodes over time
'''
# Check if c function was specified
if kwargs:
c = kwargs['c']
# Extra parameters for pow function
eps_c = kwargs.get('eps', 0.1)
p_c = kwargs.get('eps', 2)
else:
# Otherwise use linear as default
c = 'linear'
eps_c = None
p_c = None
N, z, max_rounds = preprocessArgs(s, max_rounds)
opinions = np.zeros((max_rounds, N))
opinions[0, :] = s
for t in trange(1, max_rounds):
Q = dynamic_weights(A, s, z, c, eps_c, p_c) + B
Q = rowStochastic(Q)
B_temp = np.diag(np.diag(Q))
Q = Q - B_temp
z = np.dot(Q, z) + np.dot(B_temp, s)
opinions[t, :] = z
if conv_stop and \
norm(opinions[t - 1, :] - opinions[t, :], np.inf) < eps:
print('G-A converged after {t} rounds'.format(t=t))
break
if plot:
plotOpinions(opinions[0:t + 1, :], 'Hegselmann-Krause', dcolor=True)
if save:
timeStr = datetime.now().strftime("%m%d%H%M")
simid = 'ga' + timeStr
saveModelData(simid,
N=N,
max_rounds=max_rounds,
eps=eps,
rounds_run=t + 1,
A=A,
s=s,
B=B,
c=c,
eps_c=eps_c,
p_c=p_c,
opinions=opinions[0:t + 1, :])
return opinions[0:t + 1, :]
def ga(A, B, s, max_rounds, eps=1e-6, plot=False, conv_stop=True, save=False,
**kwargs):
'''Simulates the Generalized Asymmetric Coevolutionary Game.
This model does nto require an adjacency matrix. Connections between
nodes are calculated depending on the proximity of their opinions.
Args:
A (NxN numpy array): Adjacency matrix (its diagonal is the stubborness)
B (NxN numpy array): The stubborness of each node
s (1xN numpy array): Initial opinions (intrinsic beliefs) vector
op_eps: ε parameter of the model
max_rounds (int): Maximum number of rounds to simulate
eps (double): Maximum difference between rounds before we assume that
the model has converged (default: 1e-6)
plot (bool): Plot preference (default: False)
conv_stop (bool): Stop the simulation if the model has converged
(default: True)
save (bool): Save the simulation data into text files
**kargs: Arguments c, eps, and p for dynamic_weights function (eps and
p need to be specified only if c='pow') (default: c='linear')
Returns:
A txN vector of the opinions of the nodes over time
'''
# Check if c function was specified
if kwargs:
c = kwargs['c']
# Extra parameters for pow function
eps_c = kwargs.get('eps', 0.1)
p_c = kwargs.get('eps', 2)
else:
# Otherwise use linear as default
c = 'linear'
eps_c = None
p_c = None
N, z, max_rounds = preprocessArgs(s, max_rounds)
opinions = np.zeros((max_rounds, N))
opinions[0, :] = s
for t in trange(1, max_rounds):
Q = dynamic_weights(A, s, z, c, eps_c, p_c) + B
Q = rowStochastic(Q)
B_temp = np.diag(np.diag(Q))
Q = Q - B_temp
z = np.dot(Q, z) + np.dot(B_temp, s)
opinions[t, :] = z
if conv_stop and \
norm(opinions[t - 1, :] - opinions[t, :], np.inf) < eps:
print('G-A converged after {t} rounds'.format(t=t))
break
if plot:
plotOpinions(opinions[0:t+1, :], 'Hegselmann-Krause', dcolor=True)
if save:
timeStr = datetime.now().strftime("%m%d%H%M")
simid = 'ga' + timeStr
saveModelData(simid, N=N, max_rounds=max_rounds, eps=eps,
rounds_run=t+1, A=A, s=s, B=B, c=c, eps_c=eps_c,
p_c=p_c, opinions=opinions[0:t+1, :])
return opinions[0:t+1, :]
