The drunk Gopher’s walk: A talk about fuzzy, I Ching and ale.
summary
Describes a small Go fuzzy package (three source files and currently three functions) that implements a find-best-match algorithm comparing strings by sound, letters and length and returning probabilistic scores such as 80% similarity for near matches. Shows concrete use: parsing messy Excel client data, linking directly into Oracle Cloud workflows instead of exporting to Python, and reducing manual triage. Package sits in the golang LSP not in GOROOT, is usable locally, has been deployed successfully but needs more matching functions and community contributions.
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chapters (12)
- 0:00Introduction
- 1:57Fuzzy package in Go
- 4:04Fuzzy machine algorithms
- 8:43Why fuzzy machine matters
- 10:10Poor data challenges
- 14:12Solving with fuzzy logic
- 16:05Fuzzy functions in Go
- 20:05False positives in matching
- 21:52Training data for fuzzy
- 24:04Limitations and future
- 25:28Go and fuzzy journey
- 28:20Fuzzy logic, I Ching, ale
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