import Boundaries from '@jsmlt/jsmlt/src/classification/boundaries.js'

public class | source

Boundaries

The decision boundary module calculates decision boundaries for a trained classifier on a 2-dimensional grid of points. It works by taking a classifier, predicting the output label for many points on a 2-D grid, and using the Marching Squares algorithm to calculate the decision boundaries.

Example:

Calculating the decision boundaries for a classifier

var Boundaries = new Boundaries();

var classIndexBoundaries = boundaries.calculateClassifierDecisionBoundaries(
  classifier, // The classifier you've trained
  51, // Number of points on the x- and y-axis (so 51 x 51 = 2,601 points in total)
);

// Depending on the classifier used, the output will look something like this:
{
  "0":[               // Decision boundaries for class 0
    [                 // First (and only, as binary classification) decision boundary or class 0
      [-0.22, -0.96], // Point 1 of decision boundary
      [-0.22, -1.00], // Point 2 of decision boundary
      // <...23 more elements...>
      [-0.24, -0.92],
      [-0.26, -0.96]
    ]
  ],
  "1":[               // Decision boundaries for class 1
    [                 // First (and only, as binary classification) decision boundary or class 1
      [-0.22, -1.00], // Point 1 of decision boundary
      [-0.22, -0.96], // Point 2 of decision boundary
      // <...82 more elements...>
      [-0.20, -1.00],
      [-0.22, -1.00]
    ]
  ]
}

Constructor Summary

Public Constructor
public	constructor() Constructor.

Member Summary

Public Members
public	features: Array<Array<number>> Feature list of the grid points.
public	predictions: Array<mixed> List of classifier predictions for each grid point.
public	predictionsGrid: Array<Array<mixed>> Grid of classifier predictions for each grid point.

Method Summary

Public Methods
public	calculateClassifierDecisionBoundaries(classifier: jsmlt.Supervised.Classifier, resolution: Array<number> \| number, gridCoordinates: Array<number>): Object<string, Array<Array<Array<number>>>> Determine decision boundaries for a specific classifier.
public	generateFeaturesFromLinSpaceGrid(pointsX: number, pointsY: number, boundsX: Array<number>, boundsY: Array<number>): Array.Array<number> Generate a list of features from a grid of points with linear spacing
public	getFeatures(): Array<Array<number>> Obtain the features list corresponding with the grid coordinates of the last decision boundaries calculation
public	getGridDecisionBoundaries(grid: Array<Array<mixed>>): Object<string, Array<Array<Array<number>>>> Determine the decision boundaries for a grid of class predictions
public	getGridLevelBoundaries(grid: Array<Array<number>>, level: string): Array<Array<Array<number>>> Determine the decision boundaries for a single grid level (class label)
public	getPredictions(): Array<number> Obtain the predictions list for the last decision boundaries calculation
public	getPredictionsGrid(): Array<Array<number>> Obtain the grid of predictions for the last decision bundaries calculation

Public Constructors

public constructor() source

Constructor. Initializes boundary object properties.

Public Members

public features: Array<Array<number>> source

Feature list of the grid points. n-by-2 array, where each row consists of the x- and y-coordinates of a point on the grid.

public predictions: Array<mixed> source

List of classifier predictions for each grid point. The nth prediction is the prediction for the nth point in the features property. n-dimensional array.

public predictionsGrid: Array<Array<mixed>> source

Grid of classifier predictions for each grid point. m-by-n array, where the array element at index (j, i) contains the prediction for the grid point at x-index i and y-index j. This is simply a 2-dimensional version of the predictions property

Public Methods

public calculateClassifierDecisionBoundaries(classifier: jsmlt.Supervised.Classifier, resolution: Array<number> | number, gridCoordinates: Array<number>): Object<string, Array<Array<Array<number>>>> source

Determine decision boundaries for a specific classifier.

Params:

Name	Type	Attribute	Description
classifier	jsmlt.Supervised.Classifier		Classifier for which to generate the decision boundaries
resolution	Array<number> \| number		Number of points on the x-axis and on the y-axis. Use integer for the same resolution on the x- and y-axis, and a 2-dimensional array to specify resolutions per axis
gridCoordinates	Array<number>	optional default: [-1, -1, 1, 1]	4-dimensional array containing, in order, the x1, y1, x2, and y2-coordinates of the grid

Return:

Object<string, Array<Array<Array<number>>>>

The returned object contains the boundaries per level (class label). Each boundary then consists of some coordinates (forming a path), and each coordinate is a 2-dimensional array where the first entry is the x-coordinate and the second entry is the y-coordinate

public generateFeaturesFromLinSpaceGrid(pointsX: number, pointsY: number, boundsX: Array<number>, boundsY: Array<number>): Array.Array<number> source

Generate a list of features from a grid of points with linear spacing

Params:

Name	Type	Attribute	Description
pointsX	number		Number of points on the x-axis
pointsY	number		Number of points on the y-axis
boundsX	Array<number>		2-dimensional array of left and right bound on the points on the x-axis
boundsY	Array<number>		2-dimensional array of left and right bound on the points on the y-axis

Return:

Array.Array<number>

(pointsX * pointsY)-by-2 array, containing the coordinates of all grid points

public getFeatures(): Array<Array<number>> source

Obtain the features list corresponding with the grid coordinates of the last decision boundaries calculation

Return:

Array<Array<number>>

Features for all data points (2-dimensional)

public getGridDecisionBoundaries(grid: Array<Array<mixed>>): Object<string, Array<Array<Array<number>>>> source

Determine the decision boundaries for a grid of class predictions

Params:

Name	Type	Attribute	Description
grid	Array<Array<mixed>>		Grid of predictions, an array of row arrays, where each row array contains the predictions for the cells in that row. For an m x n prediction grid, each of the m entries of `grid` should have n entries

Return:

Object<string, Array<Array<Array<number>>>>

public getGridLevelBoundaries(grid: Array<Array<number>>, level: string): Array<Array<Array<number>>> source

Determine the decision boundaries for a single grid level (class label)

Params:

Name	Type	Attribute	Description
grid	Array<Array<number>>		See this.getGridDecisionBoundaries@param:grid
level	string		Level (class label) to calculate boundaries for

Return:

Array<Array<Array<number>>>

Boundaries for this level (class label). See this.getGridDecisionBoundaries@return

public getPredictions(): Array<number> source

Obtain the predictions list for the last decision boundaries calculation

Return:

Array<number>

List of predicted class labels

public getPredictionsGrid(): Array<Array<number>> source

Obtain the grid of predictions for the last decision bundaries calculation

Return:

Array<Array<number>>

Predicted class labels for each grid point. m-by-n array for m rows, n columns