Write more tests

src/Daemon.ts

@@ -47,7 +47,7 @@ class Daemon {
         return proposals.indexOf(maxWeight);
     }
 
-    adjust(journal: Journal, humanValue: number) {
+    adjust(journal: Journal, humanValue: number): void {
         const steps = this._getStepSlice(journal);
         const adjustmentWeight = this._getAdjustmentWeight(journal.length);
         this._adjustWeight([...steps, humanValue], adjustmentWeight);

src/Predictor.ts

@@ -2,7 +2,19 @@ import Daemon from './Daemon';
 import Journal from './Journal';
 import Supervisor from './Supervisor';
 
-const DEFAULT_CONFIG = {
+interface DaemonConfig {
+    human: number;
+    robot: number;
+    epsilon: number;
+}
+
+interface PredictorConfig {
+    base: number;
+    supervisor_epsilon: number;
+    daemons: DaemonConfig[];
+}
+
+const DEFAULT_CONFIG: PredictorConfig = {
     base: 2,
     supervisor_epsilon: 0.01,
     daemons: [
@@ -35,10 +47,7 @@ export default class Predictor {
      */
     supervisor: Supervisor;
 
-    /**
-     * @param {Object} config
-     */
-    constructor(config = DEFAULT_CONFIG) {
+    constructor(config: PredictorConfig = DEFAULT_CONFIG) {
         this.base = config.base;
         this.score = 0;
         this.journal = new Journal();
@@ -47,25 +56,17 @@ export default class Predictor {
     }
 
     pass(humanValue: number): number {
-        const value = humanValue;
-        if (value < 0 || value >= this.base) {
+        if (humanValue < 0 || humanValue >= this.base) {
             throw new Error(`Passed value must be in [0, ${this.base})`);
         }
         const prediction = this.supervisor.predict(this.journal);
-        this.score += prediction === value ? -1 : 1;
-        this.supervisor.adjust(this.journal, value);
-        this.journal.makeMove(value, prediction);
+        this.score += prediction === humanValue ? -1 : 1;
+        this.supervisor.adjust(this.journal, humanValue);
+        this.journal.makeMove(humanValue, prediction);
         return prediction;
     }
 
-    /**
-     * @param {Object} daemonConfigs
-     *
-     * @returns {Daemon[]}
-     *
-     * @private
-     */
-    private _createDaemons(daemonConfigs) {
+    private _createDaemons(daemonConfigs: DaemonConfig[]): Daemon[] {
         return daemonConfigs.map(config => {
             return new Daemon(
                 this.base,

src/Supervisor.ts

@@ -3,8 +3,20 @@ import Daemon from './Daemon';
 
 const DEFAULT_EPSILON = 0.01;
 
+interface DaemonRate {
+    daemon: Daemon;
+    rate: number;
+}
+
+interface Prediction {
+    daemonRate: DaemonRate;
+    rate: number;
+    power: number;
+    value: number;
+}
+
 class Supervisor {
-    daemons: { daemon: Daemon; rate: number }[] = [];
+    daemonRates: DaemonRate[] = [];
 
     readonly epsilon: number;
 
@@ -13,7 +25,7 @@ class Supervisor {
             throw Error('Empty daemon list');
         }
 
-        this.daemons = daemons.map(daemon => ({
+        this.daemonRates = daemons.map(daemon => ({
             daemon: daemon,
             rate: 0,
         }));
@@ -28,43 +40,29 @@ class Supervisor {
         return ordered[0].value;
     }
 
-    adjust(journal: Journal, humanValue) {
+    adjust(journal: Journal, humanValue: number) {
         const predictions = this._createPredictions(journal);
         for (const prediction of predictions) {
             if (prediction.value === humanValue) {
-                prediction.daemon.rate += this._getAdjustmentWeight(
+                prediction.daemonRate.rate += this._getAdjustmentWeight(
                     journal.length
                 );
             }
-            prediction.daemon.daemon.adjust(journal, humanValue);
+            prediction.daemonRate.daemon.adjust(journal, humanValue);
         }
     }
 
-    /**
-     * @param {Journal} journal
-     *
-     * @returns {Array}
-     *
-     * @private
-     */
-    private _createPredictions(journal: Journal) {
-        return this.daemons.map(daemon => ({
-            daemon: daemon,
-            power: daemon.daemon.power,
-            rate: daemon.rate,
-            value: daemon.daemon.predict(journal),
+    private _createPredictions(journal: Journal): Prediction[] {
+        return this.daemonRates.map(daemonRate => ({
+            daemonRate: daemonRate,
+            power: daemonRate.daemon.power,
+            rate: daemonRate.rate,
+            value: daemonRate.daemon.predict(journal),
         }));
     }
 
-    /**
-     * @param {Array} predictions
-     *
-     * @returns {Array}
-     *
-     * @private
-     */
-    private _sortPredictions(predictions) {
-        return predictions.sort((result1, result2) => {
+    private _sortPredictions(predictions: Prediction[]) {
+        return predictions.sort((result1: Prediction, result2: Prediction) => {
             const rateDiff = result2.rate - result1.rate;
             if (Math.abs(rateDiff) > 0.000001) {
                 return rateDiff;

tests/PredictorTest.ts

@@ -0,0 +1,17 @@
+import { it, describe } from 'mocha';
+import { expect } from 'chai';
+
+import Predictor from '../src/Predictor';
+
+describe('Predictor', function() {
+    it('Get prediction for one daemon state', function() {
+        const predictor = new Predictor({
+            base: 2,
+            supervisor_epsilon: 0.01,
+            daemons: [{ robot: 1, human: 1, epsilon: 0.01 }],
+        });
+        const predicted = predictor.pass(1);
+        expect(predicted).to.equals(0);
+        expect(predictor.stepCount()).to.equals(1);
+    });
+});

tests/SupervisorTest.ts

@@ -0,0 +1,30 @@
+import { it, describe } from 'mocha';
+import { expect } from 'chai';
+
+import Supervisor from '../src/Supervisor';
+import Daemon from '../src/Daemon';
+import Journal from '../src/Journal';
+
+describe('Supervisor', function() {
+    it('Get prediction for one daemon state', function() {
+        const supervisor = new Supervisor([new Daemon(2, 1, 1, 0.01)], 0.01);
+        const journal = new Journal();
+
+        const human1 = 1;
+        const predicted1 = supervisor.predict(journal);
+        expect(0).to.equals(predicted1, 'First prediction for empty journal');
+
+        journal.makeMove(human1, predicted1);
+        supervisor.adjust(journal, human1);
+
+        const human2 = 1;
+        const predicted2 = supervisor.predict(journal);
+        expect(1).to.equals(
+            predicted2,
+            `Second prediction for (${human1}, ${predicted1})`
+        );
+
+        journal.makeMove(human2, predicted1);
+        supervisor.adjust(journal, human2);
+    });
+});