models.py 文件源码

def _predict_as_table(self, prediction, confidence):
        from Orange.data import Domain, ContinuousVariable
        means, lows, highs = [], [], []
        n_vars = prediction.shape[2] if len(prediction.shape) > 2 else 1
        for i, name in zip(range(n_vars),
                           self._table_var_names or range(n_vars)):
            mean = ContinuousVariable('{} (forecast)'.format(name))
            low = ContinuousVariable('{} ({:d}%CI low)'.format(name, confidence))
            high = ContinuousVariable('{} ({:d}%CI high)'.format(name, confidence))
            low.ci_percent = high.ci_percent = confidence
            mean.ci_attrs = (low, high)
            means.append(mean)
            lows.append(low)
            highs.append(high)
        domain = Domain(means + lows + highs)
        X = np.column_stack(prediction)
        table = Timeseries.from_numpy(domain, X)
        table.name = (self._table_name or '') + '({} forecast)'.format(self)
        return table