Example #1
0
 def get_data(self):
     more=['name','length','nsamples']
     d=dict.fromkeys(data.get_kwargs(ts[0]).keys())#,[]) <- python gotcha!
     for ak in d: d[ak]=[] #<-soln
     for am in more: d[am]=[]
     for s in ts:
         kw=data.get_kwargs(s)
         kw[more[0]]=s
         kw[more[1]]=data.get_series(s).shape[0]
         kw[more[2]]=len(data.get(s))
         for ak in kw: d[ak].append(kw[ak])
     return pd.DataFrame.from_dict(d)
Example #2
0
def errs(ts_id, win, **kwargs):
    ts = data.get_series(ts_id)[:, 0]
    tsdf = pd.Series(ts)
    bn = get_best_net(ts_id)
    mse = lambda win: np.mean(
        bn.predict(np.array(win, dtype='float32')[:, None, None]) - win)**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
        )
Example #3
0
 def get_data(self):
     more = ['name', 'length', 'nsamples']
     d = dict.fromkeys(data.get_kwargs(
         ts[0]).keys())  #,[]) <- python gotcha!
     for ak in d:
         d[ak] = []  #<-soln
     for am in more:
         d[am] = []
     for s in ts:
         kw = data.get_kwargs(s)
         kw[more[0]] = s
         kw[more[1]] = data.get_series(s).shape[0]
         kw[more[2]] = len(data.get(s))
         for ak in kw:
             d[ak].append(kw[ak])
     return pd.DataFrame.from_dict(d)
Example #4
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def errs(ts_id,win,**kwargs):
    ts=data.get_series(ts_id)[:,0]
    tsdf=pd.Series(ts)
    bn=get_best_net(ts_id)
    mse=lambda win:np.mean(
        bn.predict(np.array(win,dtype='float32')[:,None,None])
        -win
    )**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
        )
Example #5
0
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=data.get(ts_id) 
    tl=int(.75*len(ts)) #potential <-param here
    trn=data.list_call(ts[:tl])
    vld=data.list_call(ts[tl:])
    dim_out=dim_in=data.dim(ts_id)

    noise=np.std(data.get_series(ts_id))*.75 #<- critical param
Example #6
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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 = data.get(ts_id)
    tl = int(.75 * len(ts))  #potential <-param here
    trn = data.list_call(ts[:tl])
    vld = data.list_call(ts[tl:])
    dim_out = dim_in = data.dim(ts_id)

    noise = np.std(data.get_series(ts_id)) * .75  #<- critical param
Example #7
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 def data(self):
     er=[analysis.errs(self.name,awin) for awin in self.wins ]
     tsd=data.get_series(self.name)
     return tsd,er
Example #8
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 def data(self):
     er = [analysis.errs(self.name, awin) for awin in self.wins]
     tsd = data.get_series(self.name)
     return tsd, er