# TrieMatch
A multiple pattern matching library. Trying to implement some algorithms for matching many string patterns in the pattern.
This project is a WIP and I would like to receive your idea on improving it.
## Trie (Prefix Tree)
This class implements a prefix tree for matching the patterns.
It uses a normal Trir data structure, and Aho-Corasick is used for pattern search (`Trie.search`). Just make sure to use `Trie.link_nodes()`.
```python
import this
from triematch import Trie
zen_of_klingon = this.s
print(zen_of_klingon)
zen_of_klingon = zen_of_klingon.lower()
words = {
"gur": "the",
"mra": "zen",
"gur mra": "the zen",
"guna": "than",
"chevgl": "purity",
"Pbzcyrk": "complex",
}
wordset = Trie(words) ## or Trie(**words) like a dict initalization
## Similar behavior with dict object
"error" in wordset # Output: True
wordset.get('error') # Output: False
wordset.setdefault("error", "reebef") # Outpit: reebef
wordset
# Output: {'error': 'reebef', 'complex': 'Pbzcyrk', 'purity': 'chevgl', 'mra': 'zen', 'gur': 'the', 'gur mra': 'the zen'}
## Get list of all patterns which zen_of_klingon.strtswith(pattern)
list(wordset.match(zen_of_klingon))
# Output: [(3, 'the'), (7, 'the zen')]
## where wordset[zen_of_klingon[:3]] == 'the'
wordset.link_nodes() ## do this to speed up the search process
list(wordset.search(zen_of_klingon))
# Output: [(0, 3, 'the'), (0, 7, 'the zen'), (4, 7, 'zen'), (54, 58, 'than'), ...]
## where wordset[zen_of_klingon[4:7]] == 'zen'
## Compressed regex of Trie
wordset.to_regex()
'Pbzcyrk|chevgl|gu(?:na|r)|mra'
```
## Tuples as Trie keys
`TupleTrie` treats keys as tuples (instead of strings), so you can pass keys like tuple of numbers as keys.
```python
from triematch import TupleTrie
trie = TupleTrie()
trie[127,0,0,1] = "home"
trie[(8,8,8,8)] = "Google Public DNS"
trie["hello", "python"] = object()
list(trie.match((127,0,0,1,2,3)))
## Output; [(4, 'home')]
```
## Radix
A compressed prefix tree. This is a memory efficient data structure compared to `Trie` with same features (but current version is slower that Trie).
Raw data
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"description": "# TrieMatch\nA multiple pattern matching library. Trying to implement some algorithms for matching many string patterns in the pattern.\n\nThis project is a WIP and I would like to receive your idea on improving it.\n\n## Trie (Prefix Tree)\nThis class implements a prefix tree for matching the patterns.\n\nIt uses a normal Trir data structure, and Aho-Corasick is used for pattern search (`Trie.search`). Just make sure to use `Trie.link_nodes()`.\n\n```python\nimport this\nfrom triematch import Trie\n\nzen_of_klingon = this.s\nprint(zen_of_klingon)\n\nzen_of_klingon = zen_of_klingon.lower()\n\nwords = {\n \"gur\": \"the\",\n \"mra\": \"zen\",\n \"gur mra\": \"the zen\",\n \"guna\": \"than\",\n \"chevgl\": \"purity\",\n \"Pbzcyrk\": \"complex\",\n}\n\n\nwordset = Trie(words) ## or Trie(**words) like a dict initalization\n\n## Similar behavior with dict object\n\"error\" in wordset # Output: True\nwordset.get('error') # Output: False\nwordset.setdefault(\"error\", \"reebef\") # Outpit: reebef\n\nwordset\n# Output: {'error': 'reebef', 'complex': 'Pbzcyrk', 'purity': 'chevgl', 'mra': 'zen', 'gur': 'the', 'gur mra': 'the zen'}\n\n## Get list of all patterns which zen_of_klingon.strtswith(pattern)\nlist(wordset.match(zen_of_klingon))\n# Output: [(3, 'the'), (7, 'the zen')]\n## where wordset[zen_of_klingon[:3]] == 'the'\n\nwordset.link_nodes() ## do this to speed up the search process\nlist(wordset.search(zen_of_klingon))\n# Output: [(0, 3, 'the'), (0, 7, 'the zen'), (4, 7, 'zen'), (54, 58, 'than'), ...]\n## where wordset[zen_of_klingon[4:7]] == 'zen'\n\n## Compressed regex of Trie\nwordset.to_regex()\n'Pbzcyrk|chevgl|gu(?:na|r)|mra'\n```\n\n## Tuples as Trie keys\n`TupleTrie` treats keys as tuples (instead of strings), so you can pass keys like tuple of numbers as keys.\n\n```python\nfrom triematch import TupleTrie\n\ntrie = TupleTrie()\ntrie[127,0,0,1] = \"home\"\ntrie[(8,8,8,8)] = \"Google Public DNS\"\ntrie[\"hello\", \"python\"] = object()\n\nlist(trie.match((127,0,0,1,2,3)))\n## Output; [(4, 'home')]\n```\n\n## Radix\nA compressed prefix tree. This is a memory efficient data structure compared to `Trie` with same features (but current version is slower that Trie).\n",
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