A long press on the mode button for the t generator (labelled E in the manual) activates a variant of the currently selected mode, represented by a blinking LED.
- Green blinking: Independent Bernoulli. The choice between t1 and t3 is no longer exclusive. The coin toss is independent, and both channels, or none of them can be on.
- Orange blinking: Deterministic divider/multiplier. A clock division or multiplication ratio selected by BIAS is applied to t3, and its reciprocal is applied to t1. There is no randomness.
- Red blinking: Three states. The module randomly selects between a trigger on t1, on t3, or on no output at all.
A short press on the button reverts to the normal mode.
Commented out "markov" mode
The code contains an unaccessible, additional mode for the t generator.
Just a bunch of heuristics to make “balanced” rhythmic patterns.
To implement a true Markov model, you’d need a large table storing the probability of playing a hit at clock tick t, for all the possible decisions taken in the past n ticks (aka “contexts”). You need long contexts if you want to learn well large-scale structures (like, a whole bar), with the drawback that you’d need a lot of training data to estimate the probabilities, a lot of memory to store everything, and then you might overfit and learn some patterns by heart. Not so great.
A different idea to drastically reduce the number of parameters of the model is to state that the probability of playing a hit at clock tick t is only influenced by several “features” extracted from the context – the code and comments are clear about what these features are.
The weight of each of these features is empirically chosen, and is influenced by the BIAS knob. I didn’t spend a lot of time tweaking these weights. One could actually train this properly, it’s just a little logistic regression. But you’d still have to introduce some very arbitrary choices in how the weights are modulated by the BIAS knob.