def run_experiment(pm, trials):
''' The shell function to cycle through repeated trials.'''
pm.validate()
s = time.time()
# Data structure that will contain the data and will be pickled
# to a file.
maps = {'Sparsity':[],
'Coverage':[],
'Representation':[]}
units = {'Number of fields':[],
'Coverage':[]}
fields = {'Area':[]}
for rnd in range(trials):
logging.info('Trial number %i:',rnd+1)
maps, units, fields = run_simulation(pm)
# Store data in file
tot = {'maps':maps,
'units':units,
'fields':fields}
fn = 'exp results size%s,modes%s,plccells%d,grdcells%d,runs%d'%(pm.L,
str(pm.modules),
pm.plc_cells,
pm.grd_cells,
rnd+10)
cPickle.dump(tot,open(fn,'w'))
logging.info('Total experiment time so far: %.3f', time.time()-s)
def fitness(a):
''' Fitness function for optimization
a = (thresh,f_I) '''
[f_I, thresh] = a
try:
assert _validate_params(thresh,f_I)
except:
return 10**8
pm.f_I = f_I
pm.thresh=thresh
maps, _, _ = run_simulation(pm)
assert len(maps['Sparsity']) == 1
sparsity = maps['Sparsity'][0]
assert len(maps['Coverage']) == 1
coverage = maps['Coverage'][0]
return sparsity, coverage
