+ class="h-full w-full relative bg-white z-1"
+ class:rounded-md={showBorder}
+ class:border-1={showBorder}
+ class:border-neutral-300={showBorder}>
{#if enableInspector && !isNaN(hoverIndex)}
+
+
+
+
+
+
+ Process data
+
+ {#if !hasSomeData() && !$state.isInputConnected}
+
+ {:else}
+
+
+
+ g.name.trim() || g.recordings.length > 0)}
+ isLightTheme={false}
+ underlineIconText={false}
+ iconText={$t('content.data.data')}
+ titleText={$t('content.data.data')}
+ bodyText={$t('content.data.dataDescription')} />
+
+
+ {#each $gestures as gesture (gesture.ID)}
+
+
+
+
+
+
+
+ {#if gesture.recordings.length}
+ {#each gesture.recordings as recording (String(gesture.ID) + String(recording.ID))}
+
+
+
+ {/each}
+ {/if}
+
+
+ {/each}
+
+
+ Process data
+
+
+
+
+
+
diff --git a/src/router/Router.svelte b/src/router/Router.svelte
index 1bfe9d5f5..ff62c7218 100644
--- a/src/router/Router.svelte
+++ b/src/router/Router.svelte
@@ -16,6 +16,7 @@
import TrainingPage from '../pages/training/TrainingPage.svelte';
import { currentPageComponent } from '../views/currentComponentStore';
import { currentPath, isValidPath, navigate, Paths, PathType } from './paths';
+ import FingerprintPage from '../pages/ProcessDataPage.svelte';
const stripLeadingSlash = (s: string): string => (s.startsWith('/') ? s.slice(1) : s);
@@ -33,6 +34,8 @@
return DataPage;
case Paths.TRAINING:
return TrainingPage;
+ case Paths.PROCESS:
+ return FingerprintPage;
case Paths.MODEL:
return ModelPage;
case Paths.FILTERS:
diff --git a/src/router/paths.ts b/src/router/paths.ts
index 76b7e75da..9a8f3035d 100644
--- a/src/router/paths.ts
+++ b/src/router/paths.ts
@@ -14,6 +14,7 @@ export const Paths = {
GET_STARTED: 'resources/get-started',
DATA: 'add-data',
TRAINING: 'train-model',
+ PROCESS: 'process-data',
MODEL: 'test-model',
FILTERS: 'training/filters',
} as const;
@@ -45,6 +46,9 @@ export const getTitle = (path: PathType, t: MessageFormatter) => {
case Paths.TRAINING: {
return `${t('content.index.toolProcessCards.train.title')} | ${appName}`;
}
+ case Paths.PROCESS: {
+ return `Process data | ${appName}`;
+ }
case Paths.MODEL: {
return `${t('content.index.toolProcessCards.model.title')} | ${appName}`;
}
diff --git a/src/script/datafunctions.ts b/src/script/datafunctions.ts
index ed3b3be5a..fc216934c 100644
--- a/src/script/datafunctions.ts
+++ b/src/script/datafunctions.ts
@@ -34,13 +34,19 @@ export function clamp(value: number, min: number, max: number): number {
interface FilterStrategy {
computeOutput(data: number[]): number;
+ computeNormalizedOutput(data: number[]): number;
getText(): { name: string; description: string };
}
class MeanFilter implements FilterStrategy {
+ constructor(private filterType: FilterType) {}
computeOutput(data: number[]): number {
return data.reduce((a, b) => a + b) / data.length;
}
+ computeNormalizedOutput(data: number[]): number {
+ const result = this.computeOutput(data);
+ return normalizeFilterResult(result, this.filterType);
+ }
getText() {
return {
name: get(t)('content.filters.mean.title'),
@@ -50,10 +56,15 @@ class MeanFilter implements FilterStrategy {
}
class SDFilter implements FilterStrategy {
+ constructor(private filterType: FilterType) {}
computeOutput(data: number[]): number {
const mean = data.reduce((a, b) => a + b) / data.length;
return Math.sqrt(data.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / data.length);
}
+ computeNormalizedOutput(data: number[]): number {
+ const result = this.computeOutput(data);
+ return normalizeFilterResult(result, this.filterType);
+ }
getText() {
return {
name: get(t)('content.filters.std.title'),
@@ -63,10 +74,15 @@ class SDFilter implements FilterStrategy {
}
class RootMeanSquareFilter implements FilterStrategy {
+ constructor(private filterType: FilterType) {}
computeOutput(data: number[]): number {
const res = Math.sqrt(data.reduce((a, b) => a + Math.pow(b, 2), 0) / data.length);
return res;
}
+ computeNormalizedOutput(data: number[]): number {
+ const result = this.computeOutput(data);
+ return normalizeFilterResult(result, this.filterType);
+ }
getText() {
return {
name: get(t)('content.filters.rms.title'),
@@ -76,6 +92,7 @@ class RootMeanSquareFilter implements FilterStrategy {
}
class ZeroCrossingRateFilter implements FilterStrategy {
+ constructor(private filterType: FilterType) {}
computeOutput(data: number[]): number {
let count = 0;
for (let i = 1; i < data.length; i++) {
@@ -85,6 +102,10 @@ class ZeroCrossingRateFilter implements FilterStrategy {
}
return count / (data.length - 1);
}
+ computeNormalizedOutput(data: number[]): number {
+ const result = this.computeOutput(data);
+ return normalizeFilterResult(result, this.filterType);
+ }
getText() {
return {
name: get(t)('content.filters.zcr.title'),
@@ -94,9 +115,14 @@ class ZeroCrossingRateFilter implements FilterStrategy {
}
class TotalAccFilter implements FilterStrategy {
+ constructor(private filterType: FilterType) {}
computeOutput(data: number[]): number {
return data.reduce((a, b) => a + Math.abs(b));
}
+ computeNormalizedOutput(data: number[]): number {
+ const result = this.computeOutput(data);
+ return normalizeFilterResult(result, this.filterType);
+ }
getText() {
return {
name: get(t)('content.filters.acc.title'),
@@ -106,9 +132,14 @@ class TotalAccFilter implements FilterStrategy {
}
class MaxFilter implements FilterStrategy {
+ constructor(private filterType: FilterType) {}
computeOutput(data: number[]): number {
return Math.max(...data);
}
+ computeNormalizedOutput(data: number[]): number {
+ const result = this.computeOutput(data);
+ return normalizeFilterResult(result, this.filterType);
+ }
getText() {
return {
name: get(t)('content.filters.max.title'),
@@ -118,9 +149,14 @@ class MaxFilter implements FilterStrategy {
}
class MinFilter implements FilterStrategy {
+ constructor(private filterType: FilterType) {}
computeOutput(data: number[]): number {
return Math.min(...data);
}
+ computeNormalizedOutput(data: number[]): number {
+ const result = this.computeOutput(data);
+ return normalizeFilterResult(result, this.filterType);
+ }
getText() {
return {
name: get(t)('content.filters.min.title'),
@@ -130,6 +166,7 @@ class MinFilter implements FilterStrategy {
}
class PeaksFilter implements FilterStrategy {
+ constructor(private filterType: FilterType) {}
computeOutput(data: number[]): number {
const lag = 5;
const threshold = 3.5;
@@ -178,6 +215,10 @@ class PeaksFilter implements FilterStrategy {
}
return peaksCounter;
}
+ computeNormalizedOutput(data: number[]): number {
+ const result = this.computeOutput(data);
+ return normalizeFilterResult(result, this.filterType);
+ }
getText() {
return {
name: get(t)('content.filters.peaks.title'),
@@ -201,26 +242,37 @@ function stddev(arr: number[]): number {
export function determineFilter(filter: FilterType): FilterStrategy {
switch (filter) {
case Filters.MAX:
- return new MaxFilter();
+ return new MaxFilter(filter);
case Filters.MIN:
- return new MinFilter();
+ return new MinFilter(filter);
case Filters.STD:
- return new SDFilter();
+ return new SDFilter(filter);
case Filters.PEAKS:
- return new PeaksFilter();
+ return new PeaksFilter(filter);
case Filters.ACC:
- return new TotalAccFilter();
+ return new TotalAccFilter(filter);
case Filters.MEAN:
- return new MeanFilter();
+ return new MeanFilter(filter);
case Filters.ZCR:
- return new ZeroCrossingRateFilter();
+ return new ZeroCrossingRateFilter(filter);
case Filters.RMS:
- return new RootMeanSquareFilter();
+ return new RootMeanSquareFilter(filter);
default:
throw new Error('Filter not found');
}
}
+const normalizeFilterResult = (value: number, filter: FilterType) => {
+ const { min, max } = getFilterLimits(filter);
+ const newMin = 0;
+ const newMax = 1;
+ const existingMin = min;
+ const existingMax = max;
+ return (
+ ((newMax - newMin) * (value - existingMin)) / (existingMax - existingMin) + newMin
+ );
+};
+
export function getFilterLimits(filter: FilterType): { min: number; max: number } {
switch (filter) {
case Filters.MAX:
diff --git a/src/script/ml.ts b/src/script/ml.ts
index 04e0672ea..cdafbbc5d 100644
--- a/src/script/ml.ts
+++ b/src/script/ml.ts
@@ -27,9 +27,14 @@ import { logEvent } from './utils/logging';
let text: (key: string, vars?: object) => string;
t.subscribe(t => (text = t));
+export interface ModelSettings {
+ axes: AxesType[];
+ filters: Set
;
+}
+
// Whenever model is trained, the settings at the time is saved in this variable
// Such that prediction continues on with the same settings as during training
-let modelSettings: { axes: AxesType[]; filters: Set };
+let modelSettings: ModelSettings;
// Add parameter to allow unsubscribing from store, when predicting ends.
// Prevents memory leak.
@@ -56,13 +61,15 @@ export function createModel(): LayersModel {
model.compile({
loss: 'categoricalCrossentropy',
- optimizer: tf.train.sgd(0.5),
+ optimizer: tf.train.sgd(0.1),
metrics: ['accuracy'],
});
return model;
}
+let prevLoss: number;
+
export async function trainModel(): Promise {
state.update(obj => {
obj.isTraining = true;
@@ -91,7 +98,7 @@ export async function trainModel(): Promise {
gestureData.forEach((MLClass, index) => {
MLClass.recordings.forEach(recording => {
// prepare features
- const inputs: number[] = makeInputs(recording.data);
+ const inputs: number[] = makeInputs(modelSettings, recording.data);
features.push(inputs);
// Prepare labels
@@ -105,20 +112,31 @@ export async function trainModel(): Promise {
const tensorFeatures = tf.tensor(features);
const tensorLabels = tf.tensor(labels);
const nn: LayersModel = createModel();
- const totalNumEpochs = get(settings).numEpochs;
+ const totalNumEpochs = 160;
try {
await nn.fit(tensorFeatures, tensorLabels, {
epochs: totalNumEpochs,
batchSize: 16,
validationSplit: 0.1,
- callbacks: {
- onTrainEnd,
- onEpochEnd: (epoch: number) => {
- // Epochs indexed at 0
- updateTrainingProgress(epoch / (totalNumEpochs - 1));
- },
- },
+ callbacks: [
+ new tf.CustomCallback({ onTrainEnd }),
+ new tf.CustomCallback({
+ onEpochEnd(epoch, logs) {
+ // Epochs indexed at 0
+ updateTrainingProgress(epoch / (totalNumEpochs - 1));
+ if (logs) {
+ if (prevLoss && logs.loss && logs.acc) {
+ if (logs.loss >= prevLoss && logs.acc === 1) {
+ // Prevent overfitting.
+ nn.stopTraining;
+ }
+ }
+ prevLoss = logs.loss;
+ }
+ },
+ }),
+ ],
});
model.set(nn);
} catch (err) {
@@ -209,9 +227,12 @@ function onTrainEnd() {
// makeInput reduces array of x, y and z inputs to a single number array with values.
// Depending on user settings. There will be anywhere between 1-24 parameters in
-
// Exported for testing.
-export function makeInputs(sample: { x: number[]; y: number[]; z: number[] }): number[] {
+export function makeInputs(
+ modelSettings: ModelSettings,
+ sample: { x: number[]; y: number[]; z: number[] },
+ filterFunction: 'computeOutput' | 'computeNormalizedOutput' = 'computeOutput',
+): number[] {
const dataRep: number[] = [];
if (!modelSettings) {
@@ -223,13 +244,12 @@ export function makeInputs(sample: { x: number[]; y: number[]; z: number[] }): n
modelSettings.filters.forEach(filter => {
const filterOutput = determineFilter(filter);
if (modelSettings.axes.includes(Axes.X))
- dataRep.push(filterOutput.computeOutput(sample.x));
+ dataRep.push(filterOutput[filterFunction](sample.x));
if (modelSettings.axes.includes(Axes.Y))
- dataRep.push(filterOutput.computeOutput(sample.y));
+ dataRep.push(filterOutput[filterFunction](sample.y));
if (modelSettings.axes.includes(Axes.Z))
- dataRep.push(filterOutput.computeOutput(sample.z));
+ dataRep.push(filterOutput[filterFunction](sample.z));
});
-
return dataRep;
}
@@ -305,7 +325,7 @@ export function classify() {
// insufficient data is expected.
return;
}
- const input: number[] = makeInputs(data);
+ const input: number[] = makeInputs(modelSettings, data);
const inputTensor = tf.tensor([input]);
const prediction: Tensor = get(model).predict(inputTensor) as Tensor;
prediction
diff --git a/src/script/navigation/Menus.ts b/src/script/navigation/Menus.ts
index e4116d41e..33e4df5ee 100644
--- a/src/script/navigation/Menus.ts
+++ b/src/script/navigation/Menus.ts
@@ -18,6 +18,10 @@ class Menus {
title: 'menu.data.helpHeading',
navigationPath: Paths.DATA,
},
+ {
+ title: '1.5. Process data',
+ navigationPath: Paths.PROCESS,
+ },
{
title: 'menu.trainer.helpHeading',
navigationPath: Paths.TRAINING,
diff --git a/src/script/stores/mlStore.ts b/src/script/stores/mlStore.ts
index 2f3283746..0249b623e 100644
--- a/src/script/stores/mlStore.ts
+++ b/src/script/stores/mlStore.ts
@@ -117,8 +117,8 @@ const initialMLSettings: MlSettings = {
minSamples: 80,
automaticClassification: true,
updatesPrSecond: 4,
- numEpochs: 80,
- learningRate: 0.5,
+ numEpochs: 160,
+ learningRate: 0.1,
includedAxes: [Axes.X, Axes.Y, Axes.Z],
includedFilters: new Set([
Filters.MAX,