import {ThompsonSampling} from 'modelscript/src/ReinforcedLearning.mjs'

public class | source

ThompsonSampling

Extends:

ReinforcedLearningBase → ThompsonSampling

Implementation of the Thompson Sampling algorithm

Constructor Summary

Public Constructor
public	constructor(options: Object): this creates a new instance of the Thompson Sampling(TS) algorithm.

Member Summary

Public Members
public	numbers_of_rewards_0
public	numbers_of_rewards_1
public	total_reward

Method Summary

Public Methods
public	learn(tsRow: Object, getBound: Function): this single step trainning method
public	predict(): number returns next action based off of the thompson sampling
public	train(tsRow: Object \| Object[], getBound: Function): this training method for thompson sampling calculations

Inherited Summary

From class ReinforcedLearningBase
public	bounds
public	getBound
public	iteration
public	last_selected
public	total_reward
public	learn() interface instance method for reinforced learning step
public	predict() interface instance method for reinforced prediction step
public	train() interface instance method for reinforced training step

Public Constructors

public constructor(options: Object): this source

creates a new instance of the Thompson Sampling(TS) algorithm. TS a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists in choosing the action that maximizes the expected reward with respect to a randomly drawn belief

Override:

ReinforcedLearningBase#constructor

Params:

Name	Type	Attribute	Description
options	Object	optional default: {}

Return:

this

Example:

const dataset = new ms.ml.ThompsonSampling({bounds:10});

See:

https://en.wikipedia.org/wiki/Thompson_sampling

Public Members

public numbers_of_rewards_0 source

public numbers_of_rewards_1 source

public total_reward source

Override:

ReinforcedLearningBase#total_reward

Public Methods

public learn(tsRow: Object, getBound: Function): this source

single step trainning method

Override:

ReinforcedLearningBase#learn

Params:

Name	Type	Attribute	Description
tsRow	Object		row of bound selections
getBound	Function	optional default: this.getBound	select value of tsRow by selection value

Return:

this

public predict(): number source

returns next action based off of the thompson sampling

Override:

ReinforcedLearningBase#predict

Return:

number

returns thompson sample

public train(tsRow: Object | Object[], getBound: Function): this source

training method for thompson sampling calculations

Override:

ReinforcedLearningBase#train

Params:

Name	Type	Attribute	Description
tsRow	Object \| Object[]		row of bound selections
getBound	Function	optional default: this.getBound	select value of tsRow by selection value

Return:

this