Distribution Modeler

Project Overview:

Distribution Modeler Module plots the density distribution histogram graph with a distribution model curve plotted on top. The module provides users with the ability to select different distributions and visualize how they fit to the data. Also, the module allows users to modify/interact with the parameters that make up the distribution. For instance, a normal distribution is calculated based off of the mean, variance, and standard deviation. By changing these values in real time, the model curve is recalculated and visualized. This provides users with a better understanding of how parameters influence the distribution.

Project Structure:

The project has many moving pieces and might seem a little overwhelming at first. Thankfully, the artists of Jake and Tem have created this lovely diagram to help visualize the project structure and understand how the Angular project works.

How to implement a new distribution:

For this example, we will use the Normal Distribution.

1. Create new file for service following the name style "ModelerDistribution" + "distribution Name"

ModelerDistributionNormal.service.coffe

2. Implement Normal Distribution Class

• Create class outline from base class

'use strict'

BaseService = require 'scripts/BaseClasses/BaseService.coffee'

###
  @name:
  @type: service
  @desc: Normal Distribution Class
###

module.exports = class NormalDist extends BaseService
  @inject 'socrat_analysis_modeler_getParams'
  initialize: () ->
    @calc = @socrat_analysis_modeler_getParams

• Add required member variables and member functions

Member Variables: Name and parameters involved in creating model distribution

    @NormalMean = 5
    @NormalStandardDev = 1
    @NormalVariance = 1
    @name = 'Normal'

• Member functions: Include: getName: returns name of the distribution getParams: returns models current distribution parameters getChartData: returns the model data by calling get"PDF"points setParams: updates the models current distribution parameters from the Main Controller (UI). PDF: Distributions Probability Density Function CDF: Cumulative Distribution Function (these can be found on wikipedia) get"PDF"Points: creates and array of [{x:, y:} , {x:, y:}] data on interval based off of the PDF function of the dataset

  getName: () ->
    return @name
  getGaussianFunctionPoints: (leftBound, rightBound) ->
    data = []
    for i in [leftBound...rightBound] by .1
      data.push
        x: i
        y: @PDF(i)
    console.log(data)
    data

  getChartData: (params) ->
    
    curveData = getGaussianFunctionPoints( params.xMin , params.xMax)
    return curveData


  stdNormalCDF: (x) ->
    return 0.5 * 0.5 * @calc.erf( x/ Math.sqrt(2))
  

  PDF: (x) ->
    return (1 / (@NormalStandardDev * Math.sqrt(Math.PI * 2))) * Math.exp(-(Math.pow(i - @NormalMean, 2) / (2 * @NormalVariance)))

  CDF: (x)->
    return @stdNormalCDF((x-@NormalMean)/ @NormalStandardDev)

  getParams: () ->
    params =
      mean: @NormalMean
      standardDev: @NormalStandardDev
      variance: @NormalVariance

  setParams: (newParams) ->
    @NormalMean = parseFloat(newParams.stats.mean.toPrecision(4))
    @NormalStandardDev =parseFloat(newParams.stats.standardDev.toPrecision(4))
    @NormalVariance = parseFloat(newParams.stats.variance.toPrecision(4))

3. Import the service into the my_module.module file, under the component/service section with the rest of the distributions.

 'socrat_modeler_distribution_Normal': require 'scripts/analysis/tools/Modeler/ModelerDistributionNormal.service.coffee'

4. Import the new distribution into the router page ModelerRouter.service.coffe, add it to the available models list.

• Inject the file name

 'socrat_modeler_distribution_normal',

• Initialize the distribution

@Normal = @socrat_modeler_distribution_normal

*Add to available models

@models = [@Normal, @Laplace, @ChiSquared, @MaxwellBoltzman, @LogNormal, @Cauchy, @Exponential]

5. Lastly, add the distribution to the DistList.service.coffee so the model appears in the SideBar.

   name: 'Normal'
      value: 0
      x: ['integer', 'number', 'string']
      y: false
      z: false
      message: "This class models a normal distribution fitting curve to the dataset"
      xLabel: "Add x"