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