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)
if reg_c is not 'dense':
dm.regime_C(reg_c)
else:
# -*- 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()
regimes_C = ['sparse', 'extreme_dilution', 'dense']
figFile = r'E:\KCL\FinalProject\figures\withExt\trials_1\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)
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()
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 15 18:01:49 2019
@author: Jerry
"""
from GRN import DynamicModel, Recoder
N = 2000
alpha = 0.03
avgDegree = 1000
temperature = 0.00
totalSteps = 90
binDist = 20
dm = DynamicModel(temperature, N, avgDegree, alpha)
re = Recoder(dm, totalSteps, binDist)
multi_overlap_observation = Recoder.multi_observation(6)(
Recoder.hamming_overlap)
multi_overlap_observation(re)
re.plot()
mean_overlap_observation = Recoder.observation_mean(Recoder.hamming_overlap)
mean_overlap_observation(re)
re.plot()
dist = Recoder.distribution(Recoder.hamming_overlap)
dist(re)
re.hist()
regimes_C = ['sparse', 'extreme_dilution', 'dense']
figFile = r'E:\KCL\FinalProject\figures\withExt\trials_1\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=False)
re = Recoder(dm, totalSteps, binDist, False)
simulator = MonteCarloSimulator(dm, totalSteps, numSim)
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()
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)
for n in range(totalSteps):
simulator.simulation(runByStep=True)
compute_observable_matrix()
resMat = recorder.disMat
"""
import tensorflow as tf
from GRN import DynamicModel,Recoder
N = 2000
alpha = 0.09
avgDegree = 3
temperature = 0.0
totalSteps = 50
binDist = 20
runTimes = 1
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=True)
re = Recoder(dm,totalSteps,binDist,False)
multi_overlap_observation = Recoder.multi_observation(55)(Recoder.hamming_overlap)
for n in range(runTimes):
multi_overlap_observation(re)
re.plot()
print("activity level: ",re.activity)