Provide an implementation of common metrics to help with classification and regression

[PerlinWarp]

Both feature engineering and preprocessing of EMG data will likely be added for any significantly difficult project using pyomyo. If so it would be nice to contribute these implementations back so that efficiency improvements/bug fixes can be shared.
It would be nice to include a metrics.py file containing implementations and a plot_metrics.py file that can give some intuition on what each feature captures.

Some examples of metrics to include are Mean Absolute Value (MAV), Root Mean Square (RMS), Willison Amplitude (WAMP), Waveform Length (WL) and Zero Crossings (ZL). More info can be found in this paper for different metrics.
My hope was to implement them and create a notebook showing their feature importance's for different tasks and models but have not had the time so far.