def runSORN(c, src):
# Initialize experiment (source and statictics)
(source, stats_single, _) = experiment.start(src)
# Initialize SORN network, has simulation() and step() function
sorn = Sorn(c, source)
# Create a StatsCollection and fill it with methods for all statistics
# that should be tracked (and later plotted)
stats = StatsCollection(sorn)
stats.methods = stats_single
sorn.stats = stats
# Datalog is used to store all results and parameters
#stats.dlog.set_handler('*',utils.StoreToH5,
# utils.logfilename("result.h5"))
#stats.dlog.append('c', utils.unbunchify(c))
#stats.dlog.set_handler('*',utils.TextPrinter)
# Final experimental preparations
experiment.reset(sorn)
# Start stats
sorn.stats.start()
sorn.stats.clear()
# Run experiment once
pickle_objects = experiment.run(sorn)
# Save sources etc
for key in pickle_objects:
filename = os.path.join(c.logfilepath, "%s.pickle" % key)
topickle = pickle_objects[key]
pickle.dump(topickle, gzip.open(filename, "wb"),
pickle.HIGHEST_PROTOCOL)
# Control: Firing-rate model: Substitute spikes by drawing random spikes
# according to firing rate for each inputindex
if sorn.c.stats.control_rates:
experiment.control_rates(sorn)
# Report stats and close
stats.single_report()
stats.disable = True
#stats.dlog.close()
sorn.quicksave(filename=os.path.join(c.logfilepath, 'net.pickle'))
# Parameters are read from the second command line argument
param = import_module(utils.param_file())
experiment_module = import_module(param.c.experiment.module)
experiment_name = param.c.experiment.name
experiment = getattr(experiment_module, experiment_name)(param)
c = param.c
c.logfilepath = utils.logfilename("") + "/"
(source, stats_single, _) = experiment.start()
sorn = Sorn(c, source)
# Create a StatsCollection and fill it with methods for all statistics
# that should be tracked (and later plotted)
stats = StatsCollection(sorn)
stats.methods = stats_single
sorn.stats = stats
# Datalog is used to store all results and parameters
stats.dlog.set_handler("*", utils.StoreToH5, utils.logfilename("result.h5"))
stats.dlog.append("c", utils.unbunchify(c))
stats.dlog.set_handler("*", utils.TextPrinter)
# Final experimental preparations
experiment.reset(sorn)
# Start stats
sorn.stats.start()
sorn.stats.clear()
# Prepare experiment
experiment = getattr(experiment_module,
experiment_name)(param,
cluster_param=experiment_param)
# Save current parameter for logging
c.cluster.current_param = current_param
# Start experiment
(source, _, stats_cluster) = experiment.start()
sorn = Sorn(c, source)
# Create a StatsCollection and fill it with methods for all
# statistics that should be tracked (and later plotted)
stats = StatsCollection(sorn, dlog)
stats.methods = stats_cluster
sorn.stats = stats
# Run experiments
experiment.reset(sorn)
sorn.stats.start()
dlog.progress("Start inner loop")
for n in range(c.N_iterations):
experiment.reset(sorn)
stats.clear()
dlog.progress("Inner Iteration %d of %d at param %.3f" %
(n + 1, c.N_iterations, tmp))
pickle_objects = experiment.run(sorn)
experiment_param = c.cluster.vary_param
# Prepare experiment
experiment = getattr(experiment_module,experiment_name)(param,
cluster_param=experiment_param)
# Save current parameter for logging
c.cluster.current_param = current_param
# Start experiment
(source,_,stats_cluster) = experiment.start()
sorn = Sorn(c,source)
# Create a StatsCollection and fill it with methods for all
# statistics that should be tracked (and later plotted)
stats = StatsCollection(sorn,dlog)
stats.methods = stats_cluster
sorn.stats = stats
# Run experiments
experiment.reset(sorn)
sorn.stats.start()
dlog.progress("Start inner loop")
for n in range(c.N_iterations):
experiment.reset(sorn)
stats.clear()
dlog.progress("Inner Iteration %d of %d at param %.3f"%(n+1,
c.N_iterations,tmp))
pickle_objects = experiment.run(sorn)
# Parameters are read from the second command line argument
param = import_module(utils.param_file())
experiment_module = import_module(param.c.experiment.module)
experiment_name = param.c.experiment.name
experiment = getattr(experiment_module, experiment_name)(param)
c = param.c
c.logfilepath = utils.logfilename('') + '/'
(source, stats_single, _) = experiment.start()
sorn = Sorn(c, source)
# Create a StatsCollection and fill it with methods for all statistics
# that should be tracked (and later plotted)
stats = StatsCollection(sorn)
stats.methods = stats_single
sorn.stats = stats
# Datalog is used to store all results and parameters
stats.dlog.set_handler('*', utils.StoreToH5, utils.logfilename("result.h5"))
stats.dlog.append('c', utils.unbunchify(c))
stats.dlog.set_handler('*', utils.TextPrinter)
# Final experimental preparations
experiment.reset(sorn)
# Start stats
sorn.stats.start()
sorn.stats.clear()
