Adaptive training mode#67
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…key is sometimes released before the first one. This allows to time inputs
…stency independant of each other in the score calculation. mistakes are tracked trough the amount of chars since last mistake on that metric. That allows us to have continuous mistake tracking rather than discrete, as the EMA works way better with continuous variables.
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Speed, consistency and accuracy are now completely independent. you can tune the parameters to focus more on a certain aspect. If you have questions you can either open an issue on my fork or comment on this draft's thread. |
made sure combined weight is never infinite as it would cause weightedPickIndex to always pick the first infinite weight
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This is only an early proposal of an idea I had from Monkeytype's weakspot funbox and kopachef's fork of Monkeytype.
I'm using an exponential moving average (EMA) to adapt generated phrases to my current weaknesses. The score of each n-gram depends on typing speed, number of mistakes, and consistency*.
All of the parameters are user facing, with explanations on what they do in the tooltips, which is important in my eyes as I believe every user has different needs.
You can test it here. I recommend using only 1 repetition and many combinations for the EMA mode, and lower the required accuracy as the algorithm will drill your mistakes automatically.
I'm making this pull request to see if people are interested in that, and if so I will work on making a proper feature, not just a personal prototype.
*currently has no effect on bigrams, as I use the normalized variance of inter-key intervals between each character of the n-gram, but while typing this message I just thought of using some sort of ratio between the intervals and the typing speed