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()