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Type definitions for Natural 0.2.1
This commit is contained in:
Dylan R. E. Moonfire
@@ -0,0 +1,262 @@
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// Type definitions for Natural 0.2.1
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// Project: https://github.com/NaturalNode/natural
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// Definitions by: Dylan R. E. Moonfire <https://github.com/dmoonfire/>
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// Definitions: https://github.com/borisyankov/DefinitelyTyped
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/// <reference path="natural.d.ts" />
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import natural = require('natural');
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// Tokenizers
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var tokenizer = new natural.WordTokenizer();
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console.log(tokenizer.tokenize("your dog has fleas."));
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tokenizer = new natural.TreebankWordTokenizer();
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console.log(tokenizer.tokenize("my dog hasn't any fleas."));
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// [ 'my', 'dog', 'has', 'n\'t', 'any', 'fleas', '.' ]
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tokenizer = new natural.RegexpTokenizer({pattern: /\-/});
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console.log(tokenizer.tokenize("flea-dog"));
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// [ 'flea', 'dog' ]
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tokenizer = new natural.WordPunctTokenizer();
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console.log(tokenizer.tokenize("my dog hasn't any fleas."));
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// [ 'my', 'dog', 'hasn', '\'', 't', 'any', 'fleas', '.' ]
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// String Distance
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console.log(natural.JaroWinklerDistance("dixon","dicksonx"))
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console.log(natural.JaroWinklerDistance('not', 'same'));
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console.log(natural.LevenshteinDistance("ones","onez"));
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console.log(natural.LevenshteinDistance('one', 'one'));
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console.log(natural.LevenshteinDistance("ones","onez", {
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insertion_cost: 1,
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deletion_cost: 1,
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substitution_cost: 1
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}));
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console.log(natural.DiceCoefficient('thing', 'thing'));
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console.log(natural.DiceCoefficient('not', 'same'));
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// Stemmers
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console.log(natural.PorterStemmer.stem("words")); // stem a single word
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console.log(natural.PorterStemmerRu.stem("падший"));
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console.log(natural.PorterStemmerEs.stem("jugaría"));
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console.log(natural.PorterStemmerFa.stem("jugaría"));
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console.log(natural.PorterStemmerFr.stem("jugaría"));
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console.log(natural.PorterStemmerIt.stem("jugaría"));
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console.log(natural.PorterStemmerNo.stem("jugaría"));
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console.log(natural.PorterStemmerPt.stem("jugaría"));
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// Classifiers
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var classifier = new natural.BayesClassifier();
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classifier.addDocument('i am long qqqq', 'buy');
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classifier.addDocument('buy the q\'s', 'buy');
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classifier.addDocument('short gold', 'sell');
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classifier.addDocument('sell gold', 'sell');
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classifier.train();
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console.log(classifier.classify('i am short silver'));
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console.log(classifier.classify('i am long copper'));
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console.log(classifier.getClassifications('i am long copper'));
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classifier.addDocument(['sell', 'gold'], 'sell');
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classifier.events.on('trainedWithDocument', function (obj: any) {
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console.log(obj);
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});
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classifier.save('classifier.json', function(err, classifier) {
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// the classifier is saved to the classifier.json file!
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});
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natural.BayesClassifier.load('classifier.json', null, function(err, classifier) {
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console.log(classifier.classify('long SUNW'));
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console.log(classifier.classify('short SUNW'));
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});
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var classifier = new natural.BayesClassifier();
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classifier.addDocument(['sell', 'gold'], 'sell');
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classifier.addDocument(['buy', 'silver'], 'buy');
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var raw = JSON.stringify(classifier);
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var restoredClassifier = natural.BayesClassifier.restore(JSON.parse(raw));
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console.log(restoredClassifier.classify('i should sell that'));
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// Phonetics
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var metaphone = natural.Metaphone, soundEx = natural.SoundEx;
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var wordA = 'phonetics';
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var wordB = 'fonetix';
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if(metaphone.compare(wordA, wordB))
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console.log('they sound alike!');
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console.log(metaphone.process('phonetics'));
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console.log(metaphone.process('phonetics', 3));
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var dm = natural.DoubleMetaphone;
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var encodings = dm.process('Matrix');
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console.log(encodings[0]);
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console.log(encodings[1]);
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if(soundEx.compare(wordA, wordB))
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console.log('they sound alike!');
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// Inflectors
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var nounInflector = new natural.NounInflector();
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console.log(nounInflector.pluralize('radius'));
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console.log(nounInflector.singularize('beers'));
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var countInflector = natural.CountInflector;
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console.log(countInflector.nth(1));
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console.log(countInflector.nth(111));
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var verbInflector = new natural.PresentVerbInflector();
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console.log(verbInflector.singularize('become'));
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console.log(verbInflector.pluralize('becomes'));
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var NGrams = natural.NGrams;
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console.log(NGrams.bigrams('some words here'));
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console.log(NGrams.bigrams(['some', 'words', 'here']));
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console.log(NGrams.trigrams('some other words here'));
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console.log(NGrams.trigrams(['some', 'other', 'words', 'here']));
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console.log(NGrams.ngrams('some other words here for you', 4));
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console.log(NGrams.ngrams(['some', 'other', 'words', 'here', 'for',
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'you'], 4));
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console.log(NGrams.ngrams('some other words here for you', 4, '[start]', '[end]'));
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console.log(NGrams.ngrams('some other words here for you', 4, null, '[end]'));
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var NGramsZH = natural.NGramsZH;
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console.log(NGramsZH.bigrams('中文测试'));
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console.log(NGramsZH.bigrams(['中', '文', '测', '试']));
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console.log(NGramsZH.trigrams('中文测试'));
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console.log(NGramsZH.trigrams(['中', '文', '测', '试']));
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console.log(NGramsZH.ngrams('一个中文测试', 4));
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console.log(NGramsZH.ngrams(['一', '个', '中', '文', '测',
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'试'], 4));
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var TfIdf = natural.TfIdf,
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tfidf = new TfIdf();
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tfidf.addDocument('this document is about node.');
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tfidf.addDocument('this document is about ruby.');
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tfidf.addDocument('this document is about ruby and node.');
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tfidf.addDocument('this document is about node. it has node examples');
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console.log('node --------------------------------');
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tfidf.tfidfs('node', function(i, measure) {
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console.log('document #' + i + ' is ' + measure);
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});
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console.log('ruby --------------------------------');
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tfidf.tfidfs('ruby', function(i, measure) {
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console.log('document #' + i + ' is ' + measure);
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});
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console.log(tfidf.tfidf('node', 0));
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console.log(tfidf.tfidf('node', 1));
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var tfidf = new TfIdf();
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tfidf.addFileSync('data_files/one.txt');
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tfidf.addFileSync('data_files/two.txt');
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tfidf.addDocument('this document is about node.');
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tfidf.addDocument('this document is about ruby.');
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tfidf.addDocument('this document is about ruby and node.');
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tfidf.tfidfs('node ruby', function(i, measure) {
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console.log('document #' + i + ' is ' + measure);
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});
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tfidf.addDocument(['document', 'about', 'node']);
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tfidf.addDocument(['document', 'about', 'ruby']);
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tfidf.addDocument(['document', 'about', 'ruby', 'node']);
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tfidf.addDocument(['document', 'about', 'node', 'node', 'examples']);
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tfidf.tfidfs(['node', 'ruby'], function(i, measure) {
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console.log('document #' + i + ' is ' + measure);
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});
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tfidf.listTerms(0 /*document index*/).forEach(function(item) {
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console.log(item.term + ': ' + item.tfidf);
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});
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var tfidf = new TfIdf();
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tfidf.addDocument('document one', 'un');
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tfidf.addDocument('document Two', 'deux');
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var s = JSON.stringify(tfidf);
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// save "s" to disk, database or otherwise
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// assuming you pulled "s" back out of storage.
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var tfidf = new TfIdf(JSON.parse(s));
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// Tries
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var Trie = natural.Trie;
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var trie = new Trie();
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// Add one string at a time
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trie.addString("test");
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// Or add many strings
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trie.addStrings(["string1", "string2", "string3"]);
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console.log(trie.contains("test")); // true
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console.log(trie.contains("asdf")); // false
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console.log(trie.findPrefix("tester")); // ['test', 'er']
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console.log(trie.findPrefix("string4")); // [null, '4']
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console.log(trie.findPrefix("string3")); // ['string3', '']
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trie.addString("tes");
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trie.addString("est");
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console.log(trie.findMatchesOnPath("tester")); // ['tes', 'test'];
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console.log(trie.keysWithPrefix("string")); // ["string1", "string2", "string3"]
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trie.contains("TEST"); // false
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var ciTrie = new Trie(false);
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ciTrie.addString("test");
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ciTrie.contains("TEsT"); // true
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// Digraph
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var EdgeWeightedDigraph = natural.EdgeWeightedDigraph;
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var digraph = new EdgeWeightedDigraph();
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digraph.add(5,4,0.35);
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digraph.add(5,1,0.32);
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digraph.add(1,3,0.29);
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digraph.add(6,2,0.40);
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digraph.add(3,6,0.52);
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digraph.add(6,4,0.93);
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console.log(digraph.v());
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console.log(digraph.e());
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// ShortestPathTree
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var ShortestPathTree = natural.ShortestPathTree;
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var spt = new ShortestPathTree(digraph, 5);
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console.log(spt.getDistTo(4));
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console.log(spt.hasDistTo(4));
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console.log(spt.hasDistTo(5));
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console.log(spt.pathTo(4));
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var ShortestPathTree = natural.ShortestPathTree;
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var spt = new ShortestPathTree(digraph, 5);
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console.log(spt.getDistTo(4));
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console.log(spt.hasDistTo(4));
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console.log(spt.hasDistTo(5));
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console.log(spt.pathTo(4));
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// WordNet
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var wordnet = new natural.WordNet();
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wordnet.lookup('node', function(results) {
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results.forEach(function(result) {
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console.log('------------------------------------');
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console.log(result.synsetOffset);
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console.log(result.pos);
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console.log(result.lemma);
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console.log(result.synonyms);
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console.log(result.pos);
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console.log(result.gloss);
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});
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});
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var wordnet = new natural.WordNet();
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wordnet.get(4424418, 'n', function(result) {
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console.log('------------------------------------');
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console.log(result.lemma);
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console.log(result.pos);
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console.log(result.gloss);
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console.log(result.synonyms);
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});
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var wordnet = new natural.WordNet('/my/wordnet/dict');
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// Spellcheck
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var corpus = ['something', 'soothing'];
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var spellcheck = new natural.Spellcheck(corpus);
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spellcheck.isCorrect('cat'); // false
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spellcheck.getCorrections('soemthing', 1); // ['something']
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spellcheck.getCorrections('soemthing', 2); // ['something', 'soothing']
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Vendored
+182
@@ -0,0 +1,182 @@
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// Type definitions for Natural 0.2.1
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// Project: https://github.com/NaturalNode/natural
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// Definitions by: Dylan R. E. Moonfire <https://github.com/dmoonfire/>
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// Definitions: https://github.com/borisyankov/DefinitelyTyped
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/// <reference path="../node/node.d.ts"/>
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declare module "natural" {
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import events = require("events");
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class WordTokenizer {
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tokenize(text: string): string[];
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}
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class AggressiveTokenizer {
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tokenize(text: string): string[];
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}
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class TreebankWordTokenizer {
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tokenize(text: string): string[];
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}
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interface RegexTokenizerOptions {
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pattern: RegExp;
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discardEmpty?: boolean;
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}
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class RegexpTokenizer {
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constructor(options: RegexTokenizerOptions);
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tokenize(text: string): string[];
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}
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class WordPunctTokenizer {
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tokenize(text: string): string[];
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}
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function JaroWinklerDistance(s1: string, s2: string, dt?: number): number;
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function LevenshteinDistance(source: string, target: string, options?: any): number;
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function DiceCoefficient(str1: string, str2: string): number;
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interface Stemmer {
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stem(token: string): string;
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}
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var PorterStemmer: {
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stem(token: string): string;
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}
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var PorterStemmerRu: {
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stem(token: string): string;
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}
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var PorterStemmerEs: {
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stem(token: string): string;
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}
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var PorterStemmerFa: {
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stem(token: string): string;
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}
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var PorterStemmerFr: {
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stem(token: string): string;
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}
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var PorterStemmerIt: {
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stem(token: string): string;
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}
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var PorterStemmerNo: {
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stem(token: string): string;
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}
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var PorterStemmerPt: {
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stem(token: string): string;
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}
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interface BayesClassifierCallback { (err: any, classifier: any): void }
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class BayesClassifier {
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events: events.EventEmitter;
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addDocument(text: string, stem: string): void;
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addDocument(text: string[], stem: string): void;
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train(): void;
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classify(observation: string): string;
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getClassifications(observation: string): string[];
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save(filename: string, callback: BayesClassifierCallback): void;
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static load(filename: string, stemmer: Stemmer, callback: BayesClassifierCallback): void;
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static restore(classifier: any, stemmer?: Stemmer): BayesClassifier;
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}
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var Metaphone: {
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compare(stringA: string, stringB: string): boolean;
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process(token: string, maxLength?: number): string;
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};
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var SoundEx: {
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compare(stringA: string, stringB: string): boolean;
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process(token: string, maxLength?: number): string;
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};
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var DoubleMetaphone: {
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compare(stringA: string, stringB: string): boolean;
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process(token: string, maxLength?: number): string[];
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};
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class NounInflector {
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pluralize(token: string): string;
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singularize(token: string): string;
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}
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var CountInflector: {
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nth(i: number): string;
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}
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class PresentVerbInflector {
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pluralize(token: string): string;
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singularize(token: string): string;
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}
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var NGrams: {
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bigrams(sequence: string, startSymbol?: string, endSymbol?: string): string[][];
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bigrams(sequence: string[], startSymbol?: string, endSymbol?: string): string[][];
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trigrams(sequence: string, startSymbol?: string, endSymbol?: string): string[][];
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trigrams(sequence: string[], startSymbol?: string, endSymbol?: string): string[][];
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ngrams(sequence: string, n: number, startSymbol?: string, endSymbol?: string): string[][];
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ngrams(sequence: string[], n: number, startSymbol?: string, endSymbol?: string): string[][];
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}
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var NGramsZH: {
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bigrams(sequence: string, startSymbol?: string, endSymbol?: string): string[][];
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bigrams(sequence: string[], startSymbol?: string, endSymbol?: string): string[][];
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trigrams(sequence: string, startSymbol?: string, endSymbol?: string): string[][];
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trigrams(sequence: string[], startSymbol?: string, endSymbol?: string): string[][];
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ngrams(sequence: string, n: number, startSymbol?: string, endSymbol?: string): string[][];
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ngrams(sequence: string[], n: number, startSymbol?: string, endSymbol?: string): string[][];
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}
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interface TfIdfCallback { (i: number, measure: number): void }
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interface TfIdfTerm {
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term: string;
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tfidf: number;
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}
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class TfIdf {
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constructor(deserialized?: any);
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addDocument(document: string, key?: string, restoreCache?: boolean): void;
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addDocument(document: string[], key?: string, restoreCache?: boolean): void;
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addFileSync(path: string, encoding?: string, key?: string, restoreCache?: boolean): void;
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tfidf(terms: string, d: number): void;
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tfidfs(terms: string, callback: TfIdfCallback): void;
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tfidfs(terms: string[], callback: TfIdfCallback): void;
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listTerms(d: number): TfIdfTerm[];
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}
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class Trie {
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constructor(caseSensitive?: boolean);
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addString(text: string): boolean;
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addStrings(strings: string[]): void;
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contains(token: string): boolean;
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findPrefix(text: string): string[];
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findMatchesOnPath(text: string): string[];
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keysWithPrefix(text: string): string[];
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}
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class EdgeWeightedDigraph {
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add(start: number, end: number, weight: number): void;
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v(): number;
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e(): number;
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}
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class ShortestPathTree {
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constructor(diagraph: EdgeWeightedDigraph, startVertex: number);
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getDistTo(vertex: number): number;
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hasDistTo(vertex: number): boolean;
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pathTo(vertex: number): number[];
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}
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class LongestPathTree {
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constructor(diagraph: EdgeWeightedDigraph, startVertex: number);
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getDistTo(vertex: number): number;
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hasDistTo(vertex: number): boolean;
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pathTo(vertex: number): number[];
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}
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interface WordNetLookupResults {
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synsetOffset: number;
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pos: string;
|
||||
lemma: string;
|
||||
synonyms: string[];
|
||||
gloss: string;
|
||||
}
|
||||
interface WordNetLookupCallback { (results: WordNetLookupResults[]): void }
|
||||
interface WordNetGetCallback { (results: WordNetLookupResults): void }
|
||||
class WordNet {
|
||||
constructor(filename?: string);
|
||||
lookup(word: string, callback: WordNetLookupCallback): void;
|
||||
get(synsetOffset: number, pos: string, callback: WordNetGetCallback): void;
|
||||
}
|
||||
|
||||
class Spellcheck {
|
||||
constructor(wordlist: string[]);
|
||||
isCorrect(word: string): boolean;
|
||||
getCorrections(word: string, maxDistance?: number): string[];
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user