Functions for cross validation, shuffle, cartesian product and more

Perform cross validation for machine learning, shuffle an array randomly, remove diacritical marks from text, find cartesian product and more using wink-helpers.

Use npm to install:

npm install wink-helpers --save

// Load wink helpers
var helpers = require( 'wink-helpers' );


/* Use array helpers */
console.log( helpers.array.isArray( [] ) );
// -> true

var ppl = [ { name: 'aiden', age: 42 }, { name: 'olivia', age: 37 } ];
console.log( ppl.sort( helpers.array.ascendingOn( 'age' ) ) );
// -> [ { "name": "olivia", "age": 37 }, { "name": "aiden", "age": 42 } ]

console.log( helpers.array.product( [ [ 9, 8 ], [ 1, 2 ] ] ) );
// -> [ [ 9, 1 ], [ 9, 2 ], [ 8, 1 ], [ 8, 2 ] ]

console.log( helpers.array.shuffle( [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] ) );
// -> [ 3, 7, 8, 9, 6, 4, 2, 1, 10, 5 ]
// Note: output will change on every call!

/* Use object helpers */
console.log( helpers.object.isObject( {} ) );
// -> true

console.log( helpers.object.isObject( new Set() ) );
// -> false

console.log( helpers.object.table( { mobile: 33, chargers: 45, usb: 27 } ) );
// -> [ [ "mobile", 33 ], [ "chargers", 45 ], [ "usb", 27 ] ]


/* Use string helper */
console.log( helpers.string.normalize( 'Résumé' ) );
// -> 'resume'

The helper functions are classified into array, object, string and validate.

Tests if argument value is a valid JS array; returns true if it is, otherwise returns false.

It is a set of handy compare functions for handling a variety of array sorting needs.

Plucks the values specified by the key from each element of the array of arrays or objects, and returns the resultant array. The default value of key is 0. The number of elements to be plucked is defined by the limit, whose default value is array.length.

Returns the cartesian product of the arrays present inside the array argument. For example, if the array argument is [ [ 1, 2, 3 ], [ 4 ], [ 5, 6 ] ], then the return value will be:

[
  [ 1, 4, 5 ],
  [ 1, 4, 6 ],
  [ 2, 4, 5 ],
  [ 2, 4, 6 ],
  [ 3, 4, 5 ],
  [ 3, 4, 6 ]
]

Randomly shuffles the order of the elements in the input array using algorithm described in Chapter 3 on Random Numbers of "The Art of Computer Programming Volume II" by Donald E Knuth.

Tests if argument value is a JS object; returns true if it is, otherwise returns false.

Returns keys of the obj in an array.

Returns the number of keys in the obj.

Returns all the values from each key: value pair in the obj in form of an array.

Returns the frequency or count of every unique value from each key: value pair in the obj in form of an object.

Converts each key: value pair in the obj into an array of [ key, value ] pairs. Note the returned value be an array of array. Second argument - f is optional; it is a function, which is called with each value.

Normalizes the str by converting it to lower case and stripping the diacritical marks (if any).

Alias for array.isArray().

Alias for object.isObject().

Tests if argument value is a finite integer; returns true if it is, otherwise returns false.

Tests if argument value is a finite number; returns true if it is, otherwise returns false.

Performs cross validation and generates detailed performance metrics along with the confusion matrix. It is a higher order function that returns an object containing evaluate(), metrics(), and reset() functions. The classLabels should be an array containing all the class labels that may be predicted.

The evaluate() function accepts two parameters viz. truth — the actual label and guess — the predicted label. It is typically called for every row of validation dataset. The evaluation may fail if truth or guess value is not a valid classLabels; or if guess is equal to unknown.

The metrics() returns an object containing macro-averaged avgPrecision, avgRecall, avgFMeasure values along with other details such as label-wise recall/precision/f-measure values and the confusion matrix. A value of null is returned if no evaluate() has been called before.

The reset() re-initializes the current instance for another round of evaluation; the class labels defined at instance creation time are not touched.

If you spot a bug and the same has not yet been reported, raise a new issue or consider fixing it and sending a pull request.

Wink is a family of open source packages for Statistical Analysis, Natural Language Processing and Machine Learning in NodeJS. The code is thoroughly documented for easy human comprehension and has a test coverage of ~100% for reliability to build production grade solutions.

wink-helpers is copyright 2017-21 GRAYPE Systems Private Limited.

It is licensed under the terms of the MIT License.