ebisu | domain Python library for flashcard quiz | Machine Learning library

Install pip install ebisu (both Python3 and Python2 ok 🤠). Data model For each fact in your quiz app, you store a model representing a prior distribution. This is a 3-tuple: (alpha, beta, t) and you can create a default model for all newly learned facts with ebisu.defaultModel. (As detailed in the Choice of initial model parameters section, alpha and beta define a Beta distribution on this fact’s recall probability t time units after it’s most recent review.). Predict a fact’s current recall probability ebisu.predictRecall(prior: tuple, tnow: float) -> float where prior is this fact’s model and tnow is the current time elapsed since this fact’s most recent review. tnow may be any unit of time, as long as it is consistent with the half life’s unit of time. The value returned by predictRecall is a probability between 0 and 1. Update a fact’s model with quiz results ebisu.updateRecall(prior: tuple, success: int, total: int, tnow: float) -> tuple where prior and tnow are as above, and where success is the number of times the student successfully exercised this memory during the current review session out of total times—this way your quiz app can review the same fact multiple times in one sitting. Bonus: you can also pass in a floating point success between 0 and 1 for soft-binary quizzes! The returned value is this fact’s new prior model—the old one can be discarded. IPython Notebook crash course For a conversational introduction to the API in the context of a mocked quiz app, see this IPython Notebook crash course. Further information Module docstrings in a pinch but full details plus literate source below, under Source code. Alternative implementations Ebisu.js is a JavaScript port for browser and Node.js. ebisu-java is for Java and JVM languages. ebisu_dart is a Dart port for browser and native targets. obliviate is available for .NET.