tsbitmapper.py 文件源码

def fit_predict(self, ts):
        """
        Unsupervised training of TSBitMaps.

        :param ts: 1-D numpy array or pandas.Series 
        :return labels: `+1` for normal observations and `-1` for abnormal observations
        """
        assert self._lag_window_size > self._feature_window_size, 'lag_window_size must be >= feature_window_size'

        self._ref_ts = ts
        scores = self._slide_chunks(ts)
        self._ref_bitmap_scores = scores

        thres = np.percentile(scores[self._lag_window_size: -self._lead_window_size + 1], self._q)

        labels = np.full(len(ts), 1)
        for idx, score in enumerate(scores):
            if score > thres:
                labels[idx] = -1

        return labels