def mserror(ts_id, win, **kwargs): ts = dat.get_series(ts_id)[:, 0] tsdf = pd.Series(ts) bn = analysis.get_best_net(ts_id) mse = lambda win: np.mean(win - bn.predict( np.array(win, dtype='float32')[:, None, None]))**2 if win == 0: #no window. just return all errors at once pr = (bn.predict(ts[:, None, None])[:, 0, 0] - ts)**2 return pr return \ rolling_apply(tsdf, win ,mse ,center=True )
def env(ts_id, **kwargs): """use dbts_id='test' kwargs to test things""" global gts_id gts_id = kwargs.setdefault('dbts_id', ts_id) global trn global vld global dim_out global dim_in global noise ts = dat.get(ts_id) tl = int(.75 * len(ts)) #potential <-param here trn = dat.list_call(ts[:tl]) vld = dat.list_call(ts[tl:]) dim_out = dim_in = dat.dim(ts_id) noise = np.std(dat.get_series(ts_id)) * .75 #<- critical param
def data(self): er=[analysis.errs(self.name,awin) for awin in self.wins ] tsd=dat.get_series(self.name) return tsd,er