def pre_train_person(self, dataset_in):
if len(OptPars.parsPerPers.keys()) <= 1:
parameterDict = open(str(Path(__file__).absolute()).split('models')[0] + 'models/pars_other.txt', 'r').read()
OptPars.parsPerPers = eval(parameterDict)
if self.name in OptPars.parsPerPers.keys() and dataset_in[0]['item'].identifier in OptPars.parsPerPers[self.name].keys():
commandlist = OptPars.parsPerPers[self.name][dataset_in[0]['item'].identifier]
else:
#print('pre_train_person for model', self.name, 'person', str(dataset_in[0]['item'].identifier))
dataset = []
for a in dataset_in:
a['aux']['id'] = a['item'].identifier
for k in a['aux'].keys():
a[k] = a['aux'][k]
dataset.append(a)
#Optimpizing paramaters per person
trialList = dataset
if len(self.parameter.keys()) > 0:
with np.errstate(divide='ignore'):
bounds = [(-5,5)*len(self.parameter.keys())]
bounded_step = RandomDisplacementBounds(np.array([b[0] for b in bounds]), np.array([b[1] for b in bounds]))
personOptimum = basinhopping(self.itemsOnePersonThisModelPeformance, [1/len(self.parameter.keys())] * len(self.parameter.keys()), T=OptPars.T, take_step= bounded_step, niter= OptPars.iterations, minimizer_kwargs ={'args':tuple([trialList]), "method":"L-BFGS-B"})
optpars = personOptimum.x
else:
optpars = []
commandlist = self.toCommandList(optpars)
if self.name not in OptPars.parsPerPers.keys():
OptPars.parsPerPers[self.name] = {}
OptPars.parsPerPers[self.name][dataset[0]['item'].identifier] = commandlist
self.executeCommands(commandlist)
def pre_train_person(self, dataset):
#Optimpizing paramaters per person
trialList = []
for pers in dataset:
trialList.extend([pers])
if len(self.parameter.keys()) > 0:
with np.errstate(divide='ignore'):
personOptimum = basinhopping(self.itemsOnePersonThisModelPeformance, [1]*len(self.parameter.keys()), niter=OptPars.iterations, stepsize=3, T=4, minimizer_kwargs={"args" : (trialList)})
optpars = personOptimum.x
else:
optpars = []
self.executeCommands(self.toCommandList(optpars))
pylab.close('all')
# Rough fit:
method = 'Powell'
fit_func = functools.partial( tmpl.image_distance, piece_idx=piece_idx, upsample = 1)
p0 = minimize(fit_func, p0, method='Nelder-Mead', tol=0.1).x
#fit_func = functools.partial( tmpl.image_distance, piece_idx=piece_idx, upsample = 1)
#p0 = minimize(fit_func, p0, method=method, tol=0.1).x
#fit_func = functools.partial( tmpl.image_distance, piece_idx=piece_idx, upsample = 1)
#p0 = minimize(fit_func, p0, method='Anneal', tol=0.1).x
fit_func = functools.partial( tmpl.image_distance, piece_idx=piece_idx, upsample = 2)
minimizer_kwargs = {"method": "Nelder-Mead"}
p0 = basinhopping(fit_func, p0, minimizer_kwargs=minimizer_kwargs, niter=200).x
#print ret
#assert(0)
#fit_func = functools.partial( tmpl.image_distance, piece_idx=piece_idx, upsample = 3)
#p0 = minimize(fit_func, p0, method='Nelder-Mead', tol=0.5).x
## Refine the fit:
#fit_func = functools.partial( tmpl.image_distance, piece_idx=piece_idx, upsample = 5)
#p = minimize(fit_func, p0, method='Nelder-Mead', tol=0.1).x
p = p0
print p
p_dash = tmpl.optvector_to_ctrl_points(p)
