Classifier.md

Classifier

• Classifier
  • new Classifier([model])
  • .model : Model
  • .train(input, label) ⇒ this
  • .predict(input, [maxMatches], [minimumConfidence]) ⇒ Array
  • .splitWords(input) ⇒ Array
  • .tokenize(input) ⇒ Object
  • .vectorize(tokens) ⇒ Object
  • .cosineSimilarity(v1, v2) ⇒ float

new Classifier([model])

Param	Type	Default	Description
[model]	Model | Object
[model.nGramMin]	int	1	Minimum n-gram size
[model.nGramMax]	int	1	Maximum n-gram size
[model.vocabulary]	Array | Set | false	[]	Terms mapped to indexes in the model data, set to false to store terms directly in the data entries
[model.data]	Object	{}	Key-value store of labels and training data vectors

classifier.model : Model

Model instance

classifier.train(input, label) ⇒ this

Train the current model using an input string (or array of strings) and a corresponding label

Param	Type	Description
input	string | Array	String, or an array of strings
label	string	Corresponding label

classifier.predict(input, [maxMatches], [minimumConfidence]) ⇒ Array

Return an array of one or more Prediction instances

Param	Type	Default	Description
input	string		Input string to make a prediction from
[maxMatches]	int	1	Maximum number of predictions to return
[minimumConfidence]	float	0.2	Minimum confidence required to include a prediction

classifier.splitWords(input) ⇒ Array

Split a string into an array of lowercase words, with all non-letter characters removed

Param	Type
input	string

classifier.tokenize(input) ⇒ Object

Create an object literal of unique tokens (n-grams) as keys, and their respective occurrences as values based on an input string, or array of words

Param	Type
input	string | Array

classifier.vectorize(tokens) ⇒ Object

Convert a tokenized object into a new object with all keys (terms) translated to their index in the returned vocabulary (which is also returned along with the object, with any new terms added to the end)

Param	Type
tokens	Object

classifier.cosineSimilarity(v1, v2) ⇒ float

Return the cosine similarity between two vectors

Param	Type
v1	Object
v2	Object