Median Absolute Deviation (MAD).
The median absolute deviation is the median of the absolute differences between each data point and the data's overall median. In an online setting, the median of the data is unknown beforehand. Therefore, both the median of the data and the median of the differences of the data with respect to the latter are updated online. To be precise, the median of the data is updated before the median of the differences. As a consequence, this online version of the MAD does not coincide exactly with its batch counterpart.
The median of the data.
>>> from river import stats >>> X = [4, 2, 5, 3, 0, 4] >>> mad = stats.MAD() >>> for x in X: ... print(mad.update(x).get()) 0 2 1 1 1 1
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via
copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
Return the current value of the statistic.
Revert and return the called instance.
Update and return the called instance.