Integrate K-Means clustering as an extra algorithm.

Algorithm

K-Means takes the original data and calculates the positions of K points of each classe the data gas breb dividere into.

Integration

Input data just need to be transformed from the dataset to these new clustering points.
Could be inserted as a drawer option. If the user wants to use K-Means clustering he will select the option and specify also how many clusters are to be found.

Documentation

Modules are working and ready to use.
It would be sad if the only one able to use them was me.
Documentation is key: just describe the usage process and briefly how the methods work.

A good doc

The final user should know, in descending order of importance :

• README module:
  • module information
  • usage tutorial
  • developer instruction
• Inside file with jsdoc:
  • classes/functions description
  • methods description

To sum up things:

• a clean and simple README of all the modules

would be perfect: give an introduction without becoming module-specific.

Styling 🔥

Why

"It's not good if it just work. It's good if it works and looks good."
a FrontEnd WebDeveloper

We know that.
The UI has to be nice and clean for a 360° experience.
Just kidding, standard colors and shape sucks.

How

Create separeted .css files with hierarchy order.

./css
  - root.css
  - container.css
  - input.css
  - text.css
  ...

What

Here's a list of elements that need to be styled:

• canvas
  • colors of canvas
• body
  • font size & font family
• options container (both draw and algorithm options)
  • slider
  • checkbox
  • radio

Input Boosting

Why

In the context of Support Vector Machines it's interesting to see how adding some features to the dataset can drastically improve the results of the optimization. Sometimes it's a kernel alternative.

How

Boosting is a simple operation:

• extract one data point with all his features t0 | t0=[x0,x1,...,xn]
• choose a function f | f=(x0,x1,...,xn) dependent on one feature or more whose output will be a set of features t=[y0,...,yt] with 1<=|t|<=|t0|
• replace the data point with the previous features, t0, and the ones you just boosted, t, with function f
  Example

// initial dataset
let dataset = [ [1,2], [2,3], [-2,-4], [-1,0] ];
// boosting function
let f = (v) => v.push( Math.pow(v[0],2) );
// boosting the dataset
dataset.foreach( data => f(data));
// output
// dataset =[ [1,2,1], [2,3,4], [-2,-4,4], [-1,0,1] ];

Where

The boosting operation must no reside inside one machine learning module. They have to be included in one ES6 module on their own, in order to achieve separation, modulation and cleaner code.

Example:

export const booster = function(){};
booster.prototype = {
  boost: function(data,f){
    ...
  },
  getBoostingFunctions: function(){
    ...
  },
  // list of boosting functions
};

When

There are 2 distinct moments when boosting functions need to be known:

1. Training phase: manager module would feed the training phase with the boosted data whenever we tell him
2. Prediction phase: it's a bit tricky this time. Prediction is not manager's role. It's handled by the drawer prototype function.
   As we can see, most of the work of managing data is done by the manager class, except from prediction.

A quick fix would be to move the responsibility of the prediction from drawer to manager. The only thing drawer class has to do to receive the predicted value is to sent a "request" to the manager. The latter will calculate the prediction, boosting the data if needed (he knows the boosting functions), and will return the value.

Who

The class that controls the datasets should also control when to boost them.
Manager prototype allow us to easly set new data into algorithm classes.
In this context should be reasonable to specify another option: the boosting.
If boosting is selected then manager applies the boosting functions selected to the data. The algorithm will be fed with the boosted data and they will be agnostic on the boosting operation.
But not only the training phase should be checked: prediction phase needs to know which boosting functions to apply, if there's anyone.