def to_epoch(anom): r = list() for a in anom: cur = list() for t in a: cur.append(utils.to_epoch(t)) r.append(cur) return r
def to_epoch(anom): r = list() for a in anom: cur = list() for t in a: cur.append(utils.to_epoch(t)) r.append(cur) return r
def fit(self, X): """Fit detector. Parameters ---------- X : dataframe of shape (n_samples, n_features) The input samples. """ # a=str(ts[:,0]) X = X.to_numpy() timestamp = np.asarray(X[:, 0].astype(np.datetime64)) pca = IncrementalPCA(n_components=1) value = np.reshape(pca.fit_transform(X[:, 1:]), -1) X = pd.Series(value, index=timestamp) X.index = X.index.map(lambda d: to_epoch(str(d))) lts = TimeSeries(X.to_dict()) self.ts = timestamp self.ts_value = value self.detector = anomaly_detector.AnomalyDetector(lts) return self