Stress Majorization

Stress Majorization implementation in Typescript, for 2 dimensional data. Was concieved primary for graph drawing.

A future implementation may include n dimensional data.

Features

• Distance helpers
• Weight helpers
• Pure js, no dependencies
• allows for custom datasets.
• allows for partial iteration of pairs
• 🚧 tests coming soon

Installing

Using npm:

$ npm install stress-majorization

Using yarn:

$ yarn add stress-majorization

Examples

Simple case :

const StressMajorization = require('stress-majorization');
const result = StressMajorization(
  /*data:*/ [[0, 0], [0, 1]],
  /*options:*/ {
    weight: (xi, yi, xj, yj, i, j) => {
      /*your custom code 
      returns number*/
    },
    stress: (xi, yi, xj, yj, i, j) => {
      /*your custom code 
      returns number*/
    }
    /* Default options
    fromPoint: (i) => i,
    toPoint: (i, n) => i,
    ignore: (iIdx, jIdx) => iIdx === jIdx,
    epsilon: Math.pow(10, -6),
    maxIterations: 10000
    */
  }
);
// returns data with updated positions and an array containing epsilon for each iteration
//    => [data, epsilons]

⚠️ Note : in this example, points are named i and j, their indexes are iIdx and jIdx

Custom datasets

Note: dataset can be an object with attributes {a: {x: 10, y: 10}, b: {x: 11, y: 12}, /*...*/} or an array : [a, b, c, ...]. If dataset is an object, keys will be retrieved using Object.keys.

To allow custom data, define fromPoint and toPoint callbacks

const weight = () => 1; // example fill
const stress = () => 1; // not realcase
 
const customData = [
  { x: 0, y: 0, z: 5 },
  { x: 1, y: 10, z: 'Hello' } /*, ...*/
];
 
const [result, epsilons] = StressMajorization(customData, {
  weight: weight,
  stress: stress,
  fromPoint: ([x, y], n) => ({ ...n, x, y }),
  toPoint: ({ x, y }) => [x, y]
});

Ignore pairs

By default StressMajorization is solved over all possible pairs of items in data (O(n²)), a pair of the same item will be ignored. It is possible to skip pairs by passing an ignore function in the options.

// ignoring same position nodes
const result = StressMajorization(data, {
  weight: weight,
  stress: stress,
  fromPoint: ([x, y]) => ({ x, y }),
  toPoint: ({ x, y }) => [x, y],
  ignore: (i, j, iIdx, jIdx) => i.x === j.x && i.y === j.y
});

⚠️ Note: Be careful when defining custom a ignore function, iterating over a pair (i, i) (same item in the pair) can result in funny behaviour

Helpers

Several helpers come alongside the algorithm.

Distance which allows for custom distance calculations.

DistanceFactory was created to allow for distance calculations over custom data.

import { Distance, DistanceFactory } from 'stress-majorization';
 
// Retrieve euclidean distance computation
const euclidean = Distance.default;
// euclidean =
//     (i: [number, number], j: [number, number]) => number
 
// custom data
const distance = DistanceFactory(euclidean, i => i.x, i => i.y);
// distance = (i, j) => number
// coordinates of (i, j) will be retrieved using the accessors

Weight Different weight were defined for stress majorization, they are put together in this object. Weight Calculation depend on distance computation.

import { Distance, DistanceFactory, WeightFactory } from 'stress-majorization';
 
// Retrieve euclidean distance computation
const distance = DistanceFactory(Distance.default, i => i.x, i => i.y);
// distance = (i, j) => number
 
const weight = WeightFactory(distance);
/* weight = {
   one: () => 1,
   distance: (i, j) => distance(i, j),
   distanceInversePow: (i, j) => Math.pow(distance(i, j), -1),
   distanceInverse2Pow: (i, j) => Math.pow(distance(i, j), -2),
   exponentialInverseDistance: (i, j) => Math.exp(-distance(i, j))
 }*/

This weight can be then injected

const result = StressMajorization(data, { weight: weight /*...*/ });

Note: custom distances can be passed to the factory, not only Distance functions:

const hamilton = (i, j) => Math.abs(i.x - j.x) + Math.abs(i.y - j.y);
const weight = WeightFactory(hamilton);

API

StressMajorization(data, options)

data

can be anything as long as it is an array of things with number coordinates

const data = [[0, 0], [0, 1] /*...*/]; // works
const data = [{ x: 1, y: 1 }, { x: 0, y: 5 } /*...*/]; // also works (with custom accessors)
const data = [{ a: 1, z: 1 }, { a: 0, z: 5 } /*...*/]; // also works (with custom accessors)
const data = { a: [0, 0], b: [0, 1] }; // works, not continous natural numbers
const data = [];
data[0] = { x: 1, y: 1 };
data[5] = { x: 1, y: 10 }; // will work

options: Configuration

i and j are items of the data array. They have the same type, information other that those accessible via coordinate accessors are also available.

const options = {
  // weight between two points. Xs and Ys are the position of the last iteration, i, j the items from the dataset
  weight: (xi, yi, xj, yj, i, j) => number,
 
  // Stress function, i, j represents items from the dataset
  stress: (xi, yi, xj, yj, i, j) => number,
 
  // converts `Point` (internal object structure) to coordinates of the output object. Passes new coordinates (Point ) and the current item (n)
  fromPoint: ([x, y], n) => ({ x, y }), // default to (i) => i
 
  // converts data item to `Point` (internal)
  toPoint: ({ x, y }) => [x, y], // default to (i) => i
 
  // will ignore stress on this pair if yields true, iIdx and jIdx are the position of i, j in the data array
  ignore: (xi, yi, xj, yj, iIdx, jIdx, i, j) => boolean, // default to (iIdx, jIdx) => iIdx === jIdx
 
  // Threshold of change between each iteration, if the change is less that epsilon, the aglorithm will stop and return the solution
  epsilon: number, // default to Math.pow(10, -6)
 
  // Maximum iterations allowed, if the algorithm either reaches this or epsilon, it will return whatever the result was computed up to this point. If set to 0, it will ignore the maximum iteration check
  maxIterations: number // default to 10000
};

Distance

Distances measurement algorithm

const Distance = {
    // euclidean distance
    euclidean: (i: [number, number], j: [number, number]) : number,
 
    // squared distance
    squared: (i: [number, number], j: [number, number]) : number
}

DistanceFactory(distance, x, y)

Note Point will be used instead of [number, number] because it's easier to read

• distance: (i: Point, j:Point) => number
• x: (i: any) => number
• y: (i: any) => number

returns

(i, j) => number;

Note Defined in Typescript as :

type Point = [number, number];
const DistanceFactory = <T>(
  distance: (i: Point, j: Point) => number,
  x: (i: T) => number,
  y: (i: T) => number
): (i: T, j: T) => number

WeightFactory(distance)

Creates weight functions based on the distance measurement

• distance : (i: any, j: any) => number. Need to be adapter for the data (by using DistanceFactory for example)

returns

{
   one: () => 1,
   distance: (i, j) => distance(i, j),
   distanceInversePow: (i, j) => Math.pow(distance(i, j), -1),
   distanceInverse2Pow: (i, j) => Math.pow(distance(i, j), -2),
   exponentialInverseDistance: (i, j) => Math.exp(-distance(i, j))
 }

Point

contains basic operations for vector manipulation

Point.add(i: Point, j: Point) : i + j

Point.sub(i: Point, j: Point) : i - j

Point.mult(a: number, i: Point) : a * i

Point.div(i: Point, a: number) : i/a

Point.lenght(i: Point) : lenght of the vector using euclidean distance

License

see LICENSE <3

Authors

Me and a lot of reading.