withExt=True)
re = Recoder(dm, totalSteps, binDist, False)
simulator = MonteCarloSimulator(dm, totalSteps, numSim)
matShape = (simulator.numSim, simulator.numSim)
compute_q_matrix = Recoder.observable_matrix(re, simulator,
matShape)(Recoder.EA_overlap)
for reg_p in regimes_P:
for reg_c in regimes_C:
if reg_p is not 'limited':
dm.regime_P(reg_p)
else:
dm.regime_P(reg_p, limitedRegimePatNum)
if reg_c is not 'dense':
dm.regime_C(reg_c)
else:
dm.regime_C(reg_c, denseRegimeExpRate)
dm._init_memMat()
print("now is in " + reg_p + " and " + reg_c + " regime...")
simulator.simulation()
compute_q_matrix()
re.draw_distance_matrix()
figName = 'distanceMatrix_' + reg_p + '_' + reg_c + '.png'
re.fig.savefig(figFile + figName)
re.distribution_q_matrix(numSim)
figName = 'distribution_' + reg_p + '_' + reg_c + '.png'
itNum = 20 #total time steps to run
#deciding which regime we're now in
reg_p = 'limited'
reg_c = 'sparse'
#recording configuration we got
cfg = {
'delta': delta,
'gamma': gamma,
'alpha': alpha,
'T': T,
'dims': dims,
'avgDegree': avgDegree,
'numSim': numSim,
'itNum': itNum,
'reg_p': reg_p,
'reg_c': reg_c
}
dm = DynamicModel(T,
dims,
avgDegree,
alpha,
gamma=gamma,
delta=delta,
tfConfig=config)
dm.regime_P(reg_p, patNumFiniteReg)
dm.regime_C(reg_c)
dm.generate()
dm.save(fname)