About

This website performs Machine Learning of Non-trivial Optimal Regular Expressions.

These learned Regular Expressions are:

1. Descriptive
2. Non-trivial
3. Optimal or near-optimal
4. Executable
5. Matching or Exact Matching
6. Learned from positive example input strings

This has not been done before—it’s a breakthrough in Computer Science and Machine Learning.

This solves the Regular Expression Induction (REI) problem in a significant and practical way.

Up to 17 regexes are learned for each input set—providing options between Optimality, Readability, and Abstractions.

Definitions

• Descriptive: The input strings can be reconstructed from the learned regex.
• Optimal: The shortest regex based on Significant Length.
• Executable: Compatible with standard regex engines.
• Matching: Matches all input strings.
• Exact Matching: Matches all and only the input strings.
• Abstractions: Use of character classes (e.g., \d, \w) and ranges (e.g., [3-5bg-j]).
• Plain Length: Total characters in the regex.
• Significant Length: Count of input string characters in the regex.
• Expansion Factor: (matched strings count) ÷ (original input strings count). For exact matches: 1.0X.

Notes

• Purpose:
  1. As close to optimal as possible
  2. Executable and readable by humans
• Supports human analysis of string/sequences
• Introduces a new form of explainable machine learning
• Shortest regex determined by Significant Length
• Significant vs. Plain Length example for input string aab:

  Id	Regex	Significant Length	Plain Length
  1	aab	3: aab	3: aab
  2	a{2}b	2: a{2}b	5: a{2}b

  1. Using Plain Length, shortest is aab (3 < 5)
  2. Using Significant Length, shortest is a{2}b (2 < 3)
• Why Significant Length? It emphasizes structure in the regex.