Spam filter

This spam filter lets you choose between using naive Bayes classifier or Fisher's method.

Data set was downloaded from http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/.
It is also available on http://dcomp.sor.ufscar.br/talmeida/smsspamcollection/.

To set up the filter, all you have to do is install the module, by typing:
npm install spam-filter

Usage:

$3

Filter is initialized in a following way:
``
const filter = require('spam-filter')(methodName)
`
methodName can be 'naiveBayes', 'fisher' or empty in which case naive Bayes classifier will be used.

$3

Naive Bayes classifier provides option to set and get thresholds for categories.
`
filter.setThreshold(category, 2)
filter.getThreshold(category)
`

$3

Fisher's method provides option to set and get minimum values for categories.
`
filter.setMinimum(category, 0.7)
filter.getMinimum(category)
`

category is a string, default categories are 'good' and 'bad'.
Custom categories are possible, but not recommended.

$3

Filter provides a set of methods that are available regardless of which filtering method is being used.
Those are:
filter.isSpam(spamMsg) - returns a boolean. Only works with default categories.

filter.classify(spamMsg) - returns the category, or 'none' if string can't be categorized.

filter.generate() - generates a classifier object with 5500 categorized text messages.
Generated object exists by default when module is installed.

filter.empty() - empties the classifier object.

filter.train(spamMsg, category) - trains the classifier, use category 'good' for non-spam and 'bad' for spam.

filter.save() - saves the state of the classifier object to the dataSet.js file.
Unsaved changes to the classifier object will disappear once the program that uses the filter ends.

Examples:

Overriding the data set with your own:
`
const filter = require('spam-filter')('fisher')
const newMessages = [
['Lorem ipsum dolor sit amet, consectetur adipiscing elit.', 'good'],
['Donec faucibus vulputate feugiat.', 'bad'],
['Duis eu sapien nec elit consectetur convallis.', 'good']
]

filter.empty()
newMessages.forEach(function (newMessage) {
filter.train(newMessage[0], newMessage[1])
})
filter.setMinimum('bad', 0.65).save()
`

Writing a function that will train the classifier if the message can't be categorized, and then determine if it is spam:

`
const filter = require('spam-filter')()

function filterAndTrain(message) {
if (filter.classify(message) === 'none') {
filter.train(message, 'bad').save()
}
return filter.isSpam(message)
}
``