# -*- coding: utf-8 -*-
"""
Created on Wed Jun 19 10:26:13 2019
@author: Jerry
"""
from GRN import DynamicModel, Recoder, MonteCarloSimulator
N = 2000
alpha = 0.003
avgDegree = 3
temperature = 0.00
totalSteps = 50
binDist = 20
numSim = 20
dm = DynamicModel(temperature, N, avgDegree, alpha)
re = Recoder(dm, totalSteps, binDist, False)
simulator = MonteCarloSimulator(dm, totalSteps, numSim)
matShape = (simulator.numSim, re.dynModel.P)
simulator.simulation()
compute_observable_matrix = Recoder.observable_matrix(
re, simulator, matShape, True)(Recoder.hamming_overlap)
print("computing observable matrix...")
compute_observable_matrix()
print("Done. Now drawing...")
re.draw_distance_matrix()
totalSteps = 50
binDist = 100
numSim = 100
regimes_P = ['saturated', 'sub_extensive', 'limited']
regimes_C = ['sparse', 'extreme_dilution', 'dense']
figFile = r'E:\KCL\FinalProject\figures\withoutExt\trials_0\alpha_1_C_3/'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.allocator_type = 'BFC'
config.gpu_options.per_process_gpu_memory_fraction = 0.95
dm = DynamicModel(temperature,
N,
avgDegree,
alpha,
tfConfig=config,
gamma=gamma,
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)
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)
alpha = 0.25
avgDegree = 3
temperature = 0.00
totalSteps = 120
binDist = 20
numSim = 70
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
config.gpu_options.allocator_type = 'BFC'
config.gpu_options.per_process_gpu_memory_fraction = 0.95
dm = DynamicModel(temperature,
N,
avgDegree,
alpha,
ifDeco=False,
tfConfig=config,
withExt=False)
recorder = Recoder(dm,
totalSteps,
binDist)
simulator = MonteCarloSimulator(dm,
totalSteps,
numSim)
matShape = (totalSteps, dm.P)
compute_observable_matrix = Recoder.observable_matrix(recorder,
simulator,
matShape,
True)(Recoder.hamming_overlap)
'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.load(fname)
initState = dm.pat['ita'][0, :].T.reshape((dm.N, 1))
# initState = np.zeros((dm.N,1),dtype=np.float32)
# for i in range(8):
# initState[i,0] = 1.0
obs = Observable(dm)
obs.Quant.Traj_m = np.zeros((dm.P, itNum), dtype=np.float32)
obs.Quant.Traj_a = np.zeros((1, itNum), dtype=np.float32)