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Add classifier and model test
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10 files changed

+560
-14
lines changed

10 files changed

+560
-14
lines changed

‎src/__tests__/ml/classifier.test.ts‎

Lines changed: 191 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -7,14 +7,22 @@
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* SPDX-License-Identifier: MIT
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*/
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import { writable } from 'svelte/store';
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import { get, writable } from 'svelte/store';
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import BaseVector from '../../script/domain/BaseVector';
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import { ClassifierInput } from '../../script/domain/ClassifierInput';
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import Filters from '../../script/domain/Filters';
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import { stores } from '../../script/stores/Stores';
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import TestMLModelTrainer from '../mocks/mlmodel/TestMLModelTrainer';
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import type { Filter } from '../../script/domain/Filter';
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import FilterTypes, { FilterType } from '../../script/domain/FilterTypes';
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import ClassifierFactory from '../../script/domain/ClassifierFactory';
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import LayersModelTrainer from '../../script/mlmodels/LayersModelTrainer';
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import StaticConfiguration from '../../StaticConfiguration';
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import TestTrainingDataRepository from '../mocks/TestTrainingDataRepository';
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import TestGestureRepository from '../mocks/TestGestureRepository';
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import Confidences from '../../script/domain/stores/Confidences';
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import Snackbar from '../../components/snackbar/Snackbar';
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import { repeat } from '../testUtils';
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describe('Classifier tests', () => {
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test('Changing matrix does not mark model as untrained', async () => {
@@ -104,4 +112,186 @@ describe('Classifier tests', () => {
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20, 2,
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]);
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});
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test('Classifying Should Not Throw', async () => {
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const vectors = [
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new BaseVector([1, 2, 4], ['x', 'y', 'z']),
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new BaseVector([4, 8, 16], ['x', 'y', 'z']),
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new BaseVector([10, 20, 40], ['x', 'y', 'z']),
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];
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const classifierInput = new ClassifierInput(vectors);
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const filterMax: Filter = FilterTypes.createFilter(FilterType.MAX);
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const filterMean: Filter = FilterTypes.createFilter(FilterType.MEAN);
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const filterMin: Filter = FilterTypes.createFilter(FilterType.MIN);
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const filters: Filters = new Filters(writable([filterMax, filterMean, filterMin]));
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let iterations = 0;
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const trainingData = new TestTrainingDataRepository().getTrainingData();
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const trainedModel = await new LayersModelTrainer(
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StaticConfiguration.defaultNeuralNetworkSettings,
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() => (iterations += 1),
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).trainModel(trainingData);
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const model = writable(trainedModel);
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const gestureRepository = new TestGestureRepository();
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gestureRepository.addGesture({
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color: 'blue',
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ID: 1,
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name: 'test',
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output: {},
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recordings: [],
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});
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gestureRepository.addGesture({
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color: 'blue',
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ID: 2,
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name: 'test',
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output: {},
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recordings: [],
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});
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gestureRepository.addGesture({
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color: 'blue',
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ID: 3,
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name: 'test',
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output: {},
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recordings: [],
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});
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const confidences = new Confidences();
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const classifier = new ClassifierFactory().buildClassifier(
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model,
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async () => void 0,
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filters,
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gestureRepository,
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(gestureId, confidence) => confidences.setConfidence(gestureId, confidence),
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new Snackbar(),
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);
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expect(async () => await classifier.classify(classifierInput)).not.throws();
171+
});
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test('Classifier should set confidence', async () => {
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const vectors = [
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new BaseVector([1, 2, 4], ['x', 'y', 'z']),
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new BaseVector([4, 8, 16], ['x', 'y', 'z']),
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new BaseVector([10, 20, 40], ['x', 'y', 'z']),
178+
];
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const classifierInput = new ClassifierInput(vectors);
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const filterMax: Filter = FilterTypes.createFilter(FilterType.MAX);
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const filterMean: Filter = FilterTypes.createFilter(FilterType.MEAN);
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const filterMin: Filter = FilterTypes.createFilter(FilterType.MIN);
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const filters: Filters = new Filters(writable([filterMax, filterMean, filterMin]));
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let iterations = 0;
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const trainingData = new TestTrainingDataRepository().getTrainingData();
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const trainedModel = await new LayersModelTrainer(
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StaticConfiguration.defaultNeuralNetworkSettings,
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() => (iterations += 1),
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).trainModel(trainingData);
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const model = writable(trainedModel);
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const gestureRepository = new TestGestureRepository();
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gestureRepository.addGesture({
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color: 'blue',
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ID: 1,
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name: 'test',
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output: {},
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recordings: [],
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});
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gestureRepository.addGesture({
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color: 'blue',
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ID: 2,
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name: 'test',
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output: {},
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recordings: [],
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});
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gestureRepository.addGesture({
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color: 'blue',
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ID: 3,
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name: 'test',
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output: {},
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recordings: [],
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});
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const confidences = new Confidences();
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const classifier = new ClassifierFactory().buildClassifier(
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model,
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async () => void 0,
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filters,
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gestureRepository,
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(gestureId, confidence) => confidences.setConfidence(gestureId, confidence),
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new Snackbar(),
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);
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await classifier.classify(classifierInput);
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230+
expect(get(confidences).size).toBe(3);
231+
});
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test('Classifier should correctly classify', async () => {
234+
const vectors = [
235+
new BaseVector([1, 2, 4], ['x', 'y', 'z']),
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new BaseVector([4, 8, 16], ['x', 'y', 'z']),
237+
new BaseVector([10, 20, 40], ['x', 'y', 'z']),
238+
];
239+
const classifierInput = new ClassifierInput(vectors);
240+
const filterMax: Filter = FilterTypes.createFilter(FilterType.MAX);
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const filterMean: Filter = FilterTypes.createFilter(FilterType.MEAN);
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const filterMin: Filter = FilterTypes.createFilter(FilterType.MIN);
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const filters: Filters = new Filters(writable([filterMax, filterMean, filterMin]));
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let iterations = 0;
246+
247+
const trainingData = new TestTrainingDataRepository().getTrainingData();
248+
const trainedModel = await new LayersModelTrainer(
249+
StaticConfiguration.defaultNeuralNetworkSettings,
250+
() => (iterations += 1),
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).trainModel(trainingData);
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const model = writable(trainedModel);
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254+
const gestureRepository = new TestGestureRepository();
255+
gestureRepository.addGesture({
256+
color: 'blue',
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ID: 1,
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name: 'test',
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output: {},
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recordings: [],
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});
262+
gestureRepository.addGesture({
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color: 'blue',
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ID: 2,
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name: 'test',
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output: {},
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recordings: [],
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});
269+
gestureRepository.addGesture({
270+
color: 'blue',
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ID: 3,
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name: 'test',
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output: {},
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recordings: [],
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});
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277+
const confidences = new Confidences();
278+
279+
const classifier = new ClassifierFactory().buildClassifier(
280+
model,
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async () => void 0,
282+
filters,
283+
gestureRepository,
284+
(gestureId, confidence) => confidences.setConfidence(gestureId, confidence),
285+
new Snackbar(),
286+
);
287+
288+
// This is based on known correct results
289+
await classifier.classify(classifierInput)
290+
291+
expect(get(confidences).get(1)).toBeCloseTo(0);
292+
expect(get(confidences).get(2)).toBeCloseTo(0);
293+
expect(get(confidences).get(3)).toBeCloseTo(1);
294+
}, {
295+
repeats: 20, retry: 2
296+
});
107297
});

‎src/__tests__/ml/model.test.ts‎

Lines changed: 41 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,41 @@
1+
/**
2+
* @vitest-environment jsdom
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*/
4+
/**
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* (c) 2023, Center for Computational Thinking and Design at Aarhus University and contributors
6+
*
7+
* SPDX-License-Identifier: MIT
8+
*/
9+
import TestTrainingDataRepository from '../mocks/TestTrainingDataRepository';
10+
import LayersModelTrainer from '../../script/mlmodels/LayersModelTrainer';
11+
import StaticConfiguration from '../../StaticConfiguration';
12+
import KNNModelTrainer from '../../script/mlmodels/KNNModelTrainer';
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import KNNNonNormalizedModelTrainer from '../../script/mlmodels/KNNNonNormalizedModelTrainer';
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15+
describe('ML Model tests', async () => {
16+
describe("Layers Model", async () => {
17+
test('Model should train the expected number of times', async () => {
18+
let iterations = 0;
19+
20+
const trainingData = new TestTrainingDataRepository().getTrainingData();
21+
await new LayersModelTrainer(
22+
StaticConfiguration.defaultNeuralNetworkSettings,
23+
() => (iterations += 1),
24+
).trainModel(trainingData);
25+
26+
expect(iterations).toBe(StaticConfiguration.defaultNeuralNetworkSettings.noOfEpochs);
27+
});
28+
29+
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})
31+
describe("KNN-non normalized Model", async () => {
32+
test('Model should train the expected number of times', async () => {
33+
let iterations = 0;
34+
35+
const trainingData = new TestTrainingDataRepository().getTrainingData();
36+
const knnModel = await new KNNNonNormalizedModelTrainer(2).trainModel(trainingData);
37+
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expect(async () => await knnModel.predict([0, 0, 0, 0, 0, 0, 0, 0, 0])).not.throws()
39+
});
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});
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});

‎src/__tests__/mocks/TestGestureRepository.ts‎

Lines changed: 60 additions & 0 deletions
Original file line numberDiff line numberDiff line change
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1+
/**
2+
* (c) 2023, Center for Computational Thinking and Design at Aarhus University and contributors
3+
*
4+
* SPDX-License-Identifier: MIT
5+
*/
6+
7+
import {
8+
type Subscriber,
9+
type Invalidator,
10+
type Unsubscriber,
11+
writable,
12+
get,
13+
} from 'svelte/store';
14+
import type { GestureRepository } from '../../script/domain/GestureRepository';
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import Gesture from '../../script/domain/stores/gesture/Gesture';
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import type { PersistantGestureData } from '../../script/domain/stores/gesture/Gestures';
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import GestureConfidence from '../../script/domain/stores/gesture/GestureConfidence';
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19+
class TestGestureRepository implements GestureRepository {
20+
private gestures = writable<Gesture[]>([]);
21+
22+
getGesture(gestureId: number): Gesture {
23+
const foundGesture = get(this.gestures).find(g => g.getId() === gestureId);
24+
if (!foundGesture) {
25+
throw new Error('Could not find gesture with id ' + gestureId);
26+
}
27+
return foundGesture;
28+
}
29+
30+
clearGestures(): void {
31+
this.gestures.set([]);
32+
}
33+
34+
addGesture(gestureData: PersistantGestureData): Gesture {
35+
const gesture = new Gesture(
36+
writable(gestureData),
37+
new GestureConfidence(0.5, writable(0)),
38+
() => void 0,
39+
);
40+
this.gestures.update(s => {
41+
return [...s, gesture];
42+
});
43+
return gesture;
44+
}
45+
46+
removeGesture(gestureId: number): void {
47+
this.gestures.update(s => {
48+
return s.filter(g => g.getId() !== gestureId);
49+
});
50+
}
51+
52+
subscribe(
53+
run: Subscriber<Gesture[]>,
54+
invalidate?: Invalidator<Gesture[]> | undefined,
55+
): Unsubscriber {
56+
return this.gestures.subscribe(run, invalidate);
57+
}
58+
}
59+
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export default TestGestureRepository;

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