def optimize_hypers(self, comp, vals):
mygp = gp.GP(self.cov_func.__name__)
mygp.real_init(comp.shape[1], vals)
mygp.optimize_hypers(comp, vals)
self.mean = mygp.mean
self.ls = mygp.ls
self.amp2 = mygp.amp2
self.noise = mygp.noise
# Save hyperparameter samples
#self.hyper_samples.append((self.mean, self.noise, self.amp2, self.ls))
#self.dump_hypers()
return
def optimize_hypers(self, comp, vals, durs):
# First the GP to observations
mygp = gp.GP(self.cov_func.__name__)
mygp.real_init(comp.shape[1], vals)
mygp.optimize_hypers(comp,vals)
self.mean = mygp.mean
self.ls = mygp.ls
self.amp2 = mygp.amp2
self.noise = mygp.noise
# Now the GP to times
timegp = gp.GP(self.cov_func.__name__)
timegp.real_init(comp.shape[1], durs)
timegp.optimize_hypers(comp, durs)
self.time_mean = timegp.mean
self.time_amp2 = timegp.amp2
self.time_noise = timegp.noise
self.time_ls = timegp.ls
# Save hyperparameter samples
self.hyper_samples.append((self.mean, self.noise, self.amp2, self.ls))
self.time_hyper_samples.append((self.time_mean, self.time_noise, self.time_amp2,
self.time_ls))
self.dump_hypers()
