####Getting Started
Install package with:
```
  npm install document-tfidf
```

####Features:
  * countTermFrequencies
  * storeTermFrequencies
  * normalizeTermFrequencies
  * identifyUniqueTerms
  * fullTFIDFAnalysis


#### Documentation
  * Term Frequency - Inverse Document Frequency (TFIDF) Module:
    * countTermFrequencies: function(text [, options])
      * Counts the number of times each token appears in the input text.
      * Current options include tokenLength, which dictates the number of words that comprise each token.  tokenLength defaults to 1.
      * Depends on nGrams module, which can get all tokens with arbitrary length.
    * storeTermFrequencies: function(tokenSet, TFStorage)
      * Adds the tokenSet to the collectionStorage for improved analysis over time.
      * It’s recommended to save this collection in a persistent data store, although this is unnecessary.
      * If collectionStorage is not provided, it will create it as an object and return that object.
    * normalizeTermFrequencies: function(tokenSet, TFStorage)
      * For each token in tokenSet, normalizeTermFrequencies will divide its count by the total number found in TFStorage and return the token set with normalized counts.
    * identifyUniqueTerms: function(normalizedTokenSet [, options])
      * From the input normalizedTokenSet, identifyUniqueTerms will return the most unique tokens, as defined by the highest TFIDF
      * Current options include uniqueThreshold.  If specified, identifyUniqueTerms will return all terms with a TFIDF equal to or greater than the uniqueThreshold
    * fullTFIDAnalysis: function(text [, options])
      * Completes all of the above TFIDF calculations
      * options correspond with the options for each piece of the analysis


View the full specs and check out more text analysis in my [Text Analysis Suite](https://github.com/Syeoryn/textAnalysisSuite).