def validate(self,tr,add_feat_tr):
feats = build_feat(tr, add_feat_tr, window_length=self.window_length)
feat_target = get_target(tr,window_length=self.window_length)
net0,feats,feat_target = self.fit(tr,add_feat_tr)
pred = clf.predict_feat(feats[0],net0,add_feat_tr,new_append_value=0,\
window_length = self.window_length,lags = len(add_feat_tr)-1)
mse_error = clf.MSE(pred,feat_target.reshape(len(pred),).tolist())
return mse_error
def interpolate(self):
if len(self.add_feat_pred)/10<=2:
tr = np.asarray(self.tr)
add_feat_tr = np.asarray(self.add_feat_tr)
add_feat_pred = np.asarray(self.add_feat_pred)
else:
tr = np.asarray(downsampling(self.tr,len(self.tr)/10)).T[1]
add_feat_tr = np.asarray(downsampling(self.add_feat_tr,len(self.add_feat_tr)/10)).T[1]
add_feat_pred = np.asarray(downsampling(self.add_feat_pred,len(self.add_feat_pred)/10)).T[1]
print len(tr),len(add_feat_tr),len(add_feat_pred)
net0,feats,feat_target=self.fit(tr,add_feat_tr)
pred_res = clf.predict_feat(feats[-1],net0,add_feat_pred,new_append_value=0,\
window_length = self.window_length,lags = len(add_feat_pred))
if len(self.add_feat_pred)/10>2:
pred_res = upsampling(pred_res,upscale = len(self.add_feat_pred))
if self.trend==1:
pred_res += self.trend_clf(tr,len(add_feat_pred))
return pred_res