Train Your Swift: Computational Statistics in Swift Examples
summary
Library written in Swift that ranks, classifies, summarizes and generates anime using real data: titles, descriptions and up/down votes. Implements a Wilson score lower bound to sort by confidence (uses Z for ~95% CI), a naive Bayes Bernoulli model over tokenized bag‑of‑words to classify genre, a sentence‑intersection summarizer that picks the top sentence per paragraph, and a Markov chain text generator that learns word transitions. Works in playgrounds for prototyping, notes Japanese tokenization via kanji for cleaner tokens.
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chapters (9)
- 0:00Start
- 1:15Calculating a Ranking System for Data Points
- 2:04Data Set
- 4:55Implementation
- 5:37Wilson Confidence Interval
- 8:38Bernoulli Document Model
- 8:55Tokenize
- 13:27For Loop
- 21:04Markov Chain Model
