#after generations, do 25 more at a time and test for convergence
test_size = 25
################## neuron /data specific specifications #############
ntype = 'arky'
morph_file = 'GP_arky_41comp.p'
dataname = 'arky120'
exp_to_fit = gpe.data[dataname + '-2s'][[0, 2, 4]]
dirname = 'cond_' + dataname + str(seed)
if not dirname in os.listdir(rootdir):
os.mkdir(rootdir + dirname)
os.chdir(rootdir + dirname)
######## set up parameters and fitness to be used for all opts ############
params1, fitness = pfc.params_fitness(morph_file, ntype, modeltype)
# set-up and do the optimization
fit1 = fit_commands.fit_commands(dirname, exp_to_fit, modeltype, ntype,
fitness, params1, generations, popsiz, seed,
test_size)
if test_size > 0:
mean_dict, std_dict, CV = converge.iterate_fit(fit1,
test_size,
popsiz,
std_crit=0.02,
max_evals=12000)
###########look at results
drawing.plot_history(fit1, fit1.measurement)
#after generations, do 25 more at a time and test for convergence
test_size = 25
################## neuron /data specific specifications #############
ntype = 'D1'
morph_file = 'MScell-primDend.p'
dataname = 'non05Jan2015_SLH004'
exp_to_fit = a2a.alldata[dataname][[0, 20]] #0,6 are hyperpol
dirname = 'tmp_' + dataname + str(seed)
if not dirname in os.listdir(rootdir):
os.mkdir(rootdir + dirname)
os.chdir(rootdir + dirname)
######## set up parameters and fitness
params, fitness = params_fitness.params_fitness(morph_file, ntype, modeltype,
ghkkluge)
########### set-up and do optimization
fit, mean_dict, std_dict, CV = fit_commands.fit_commands(
dirname, exp_to_fit, modeltype, ntype, fitness, params, generations,
popsiz, seed, test_size)
#look at results
drawing.plot_history(fit, fit.measurement)
#Save parameters of good results from end of optimization, and all fitness values
startgood = 0 #set to 0 to print all
threshold = 5 #set to large number to print all
save_params.save_params(fit, startgood, threshold)