Fix all lint errors

src/autoencoder.test.ts

@@ -1,79 +1,68 @@
-import AutoencoderGPU from "./autoencoder";
+import AutoencoderGPU from './autoencoder';
 
 const trainingData = [
   [0, 0, 0],
   [0, 1, 1],
   [1, 0, 1],
-  [1, 1, 0]
+  [1, 1, 0],
 ];
 
-const xornet = new AutoencoderGPU<number[], number[]>(
-  {
-    inputSize: 3,
-    hiddenLayers: [ 5, 2, 5 ]
-  }
-);
+const xornet = new AutoencoderGPU<number[], number[]>({
+  inputSize: 3,
+  hiddenLayers: [5, 2, 5],
+});
 
 const errorThresh = 0.011;
 
-const result = xornet.train(
-  trainingData, {
-    iterations: 100000,
-    errorThresh
-  }
-);
-
-test(
-  "denoise a data sample",
-  async () => {
-    expect(result.error).toBeLessThanOrEqual(errorThresh);
-
-    function xor(...args: number[]) {
-      return Math.round(xornet.denoise(args)[2]);
-    }
+const result = xornet.train(trainingData, {
+  iterations: 100000,
+  errorThresh,
+});
 
-    const run1 = xor(0, 0, 0);
-    const run2 = xor(0, 1, 1);
-    const run3 = xor(1, 0, 1);
-    const run4 = xor(1, 1, 0);
+test('denoise a data sample', async () => {
+  expect(result.error).toBeLessThanOrEqual(errorThresh);
 
-    expect(run1).toBe(0);
-    expect(run2).toBe(1);
-    expect(run3).toBe(1);
-    expect(run4).toBe(0);
+  function xor(...args: number[]) {
+    return Math.round(xornet.denoise(args)[2]);
   }
-);
 
-test(
-  "encode and decode a data sample",
-  async () => {
-    expect(result.error).toBeLessThanOrEqual(errorThresh);
+  const run1 = xor(0, 0, 0);
+  const run2 = xor(0, 1, 1);
+  const run3 = xor(1, 0, 1);
+  const run4 = xor(1, 1, 0);
 
-    const run1$input = [0, 0, 0];
-    const run1$encoded = xornet.encode(run1$input);
-    const run1$decoded = xornet.decode(run1$encoded);
+  expect(run1).toBe(0);
+  expect(run2).toBe(1);
+  expect(run3).toBe(1);
+  expect(run4).toBe(0);
+});
 
-    const run2$input = [0, 1, 1];
-    const run2$encoded = xornet.encode(run2$input);
-    const run2$decoded = xornet.decode(run2$encoded);
+test('encode and decode a data sample', async () => {
+  expect(result.error).toBeLessThanOrEqual(errorThresh);
 
-    for (let i = 0; i < 3; i++) expect(Math.round(run1$decoded[i])).toBe(run1$input[i]);
-    for (let i = 0; i < 3; i++) expect(Math.round(run2$decoded[i])).toBe(run2$input[i]);
-  }
-);
+  const run1$input = [0, 0, 0];
+  const run1$encoded = xornet.encode(run1$input);
+  const run1$decoded = xornet.decode(run1$encoded);
+
+  const run2$input = [0, 1, 1];
+  const run2$encoded = xornet.encode(run2$input);
+  const run2$decoded = xornet.decode(run2$encoded);
 
-test(
-  "test a data sample for anomalies",
-  async () => {
-    expect(result.error).toBeLessThanOrEqual(errorThresh);
+  for (let i = 0; i < 3; i++)
+    expect(Math.round(run1$decoded[i])).toBe(run1$input[i]);
+  for (let i = 0; i < 3; i++)
+    expect(Math.round(run2$decoded[i])).toBe(run2$input[i]);
+});
 
-    function includesAnomalies(...args: number[]) {
-      expect(xornet.likelyIncludesAnomalies(args)).toBe(false);
-    }
+test('test a data sample for anomalies', async () => {
+  expect(result.error).toBeLessThanOrEqual(errorThresh);
 
-    includesAnomalies(0, 0, 0);
-    includesAnomalies(0, 1, 1);
-    includesAnomalies(1, 0, 1);
-    includesAnomalies(1, 1, 0);
+  function includesAnomalies(...args: number[]) {
+    expect(xornet.likelyIncludesAnomalies(args)).toBe(false);
   }
-);
+
+  includesAnomalies(0, 0, 0);
+  includesAnomalies(0, 1, 1);
+  includesAnomalies(1, 0, 1);
+  includesAnomalies(1, 1, 0);
+});

src/autoencoder.ts

@@ -1,8 +1,23 @@
-import { IKernelFunctionThis, KernelOutput, Texture, TextureArrayOutput } from "gpu.js";
-import { IJSONLayer, INeuralNetworkData, INeuralNetworkDatum, INeuralNetworkTrainOptions, NeuralNetworkIO, NeuralNetworkRAM } from "./neural-network";
-import { INeuralNetworkGPUOptions, NeuralNetworkGPU } from "./neural-network-gpu";
-import { INeuralNetworkState } from "./neural-network-types";
-import { UntrainedNeuralNetworkError } from "./errors/untrained-neural-network-error";
+import {
+  IKernelFunctionThis,
+  KernelOutput,
+  Texture,
+  TextureArrayOutput,
+} from 'gpu.js';
+import {
+  IJSONLayer,
+  INeuralNetworkData,
+  INeuralNetworkDatum,
+  INeuralNetworkTrainOptions,
+  NeuralNetworkIO,
+  NeuralNetworkRAM,
+} from './neural-network';
+import {
+  INeuralNetworkGPUOptions,
+  NeuralNetworkGPU,
+} from './neural-network-gpu';
+import { INeuralNetworkState } from './neural-network-types';
+import { UntrainedNeuralNetworkError } from './errors/untrained-neural-network-error';
 
 function loss(
   this: IKernelFunctionThis,
@@ -16,7 +31,7 @@ function loss(
   // if ( o ≈ i0 ) then return 10% of the loss value.
   // Otherwise, return 1000% of the full loss value.
   // eslint-disable-next-line @typescript-eslint/ban-ts-comment
-  // @ts-ignore
+  // @ts-expect-error
   if (Math.round(actual) !== Math.round(inputs[this.thread.x])) error *= 32;
   else error *= 0.03125;
 
@@ -26,12 +41,13 @@ function loss(
 /**
  * An autoencoder learns to compress input data down to relevant features and reconstruct input data from its compressed representation.
  */
-export class AutoencoderGPU<DecodedData extends INeuralNetworkData, EncodedData extends INeuralNetworkData> extends NeuralNetworkGPU<DecodedData, DecodedData> {
+export class AutoencoderGPU<
+  DecodedData extends INeuralNetworkData,
+  EncodedData extends INeuralNetworkData
+> extends NeuralNetworkGPU<DecodedData, DecodedData> {
   private decoder?: NeuralNetworkGPU<EncodedData, DecodedData>;
 
-  constructor (
-    options?: Partial<INeuralNetworkGPUOptions>
-  ) {
+  constructor(options?: Partial<INeuralNetworkGPUOptions>) {
     // Create default options for the autoencoder.
     options ??= {};
 
@@ -40,7 +56,7 @@ export class AutoencoderGPU<DecodedData extends INeuralNetworkData, EncodedData
     // Define the denoiser subnet's input and output sizes.
     options.inputSize = options.outputSize = decodedSize;
 
-    options.hiddenLayers ??= [ Math.round(decodedSize * 0.66) ];
+    options.hiddenLayers ??= [Math.round(decodedSize * 0.66)];
 
     options.loss ??= loss;
 
@@ -92,7 +108,8 @@ export class AutoencoderGPU<DecodedData extends INeuralNetworkData, EncodedData
     this.run(input);
 
     // Get the auto-encoded input.
-    let encodedInput: TextureArrayOutput = this.encodedLayer as TextureArrayOutput;
+    let encodedInput: TextureArrayOutput = this
+      .encodedLayer as TextureArrayOutput;
 
     // If the encoded input is a `Texture`, convert it into an `Array`.
     if (encodedInput instanceof Texture) encodedInput = encodedInput.toArray();
@@ -110,7 +127,7 @@ export class AutoencoderGPU<DecodedData extends INeuralNetworkData, EncodedData
    * @param {DecodedData} input
    * @returns {boolean}
    */
-  likelyIncludesAnomalies(input: DecodedData, anomalyThreshold: number = 0.2): boolean {
+  likelyIncludesAnomalies(input: DecodedData, anomalyThreshold = 0.2): boolean {
     // Create the anomaly vector.
     const anomalies: number[] = [];
 
@@ -119,7 +136,9 @@ export class AutoencoderGPU<DecodedData extends INeuralNetworkData, EncodedData
 
     // Calculate the anomaly vector.
     for (let i = 0; i < (input.length ?? 0); i++) {
-      anomalies[i] = Math.abs((input as number[])[i] - (denoised as number[])[i]);
+      anomalies[i] = Math.abs(
+        (input as number[])[i] - (denoised as number[])[i]
+      );
     }
 
     // Calculate the sum of all anomalies within the vector.
@@ -141,13 +160,24 @@ export class AutoencoderGPU<DecodedData extends INeuralNetworkData, EncodedData
    * @param {Partial<INeuralNetworkTrainOptions>} options
    * @returns {INeuralNetworkState}
    */
-  train(data: Partial<DecodedData>[] | INeuralNetworkDatum<Partial<DecodedData>, Partial<DecodedData>>[], options?: Partial<INeuralNetworkTrainOptions>): INeuralNetworkState {
-    const preprocessedData: INeuralNetworkDatum<Partial<DecodedData>, Partial<DecodedData>>[] = [];
+  train(
+    data:
+      | Array<Partial<DecodedData>>
+      | Array<INeuralNetworkDatum<Partial<DecodedData>, Partial<DecodedData>>>,
+    options?: Partial<INeuralNetworkTrainOptions>
+  ): INeuralNetworkState {
+    const preprocessedData: Array<INeuralNetworkDatum<
+      Partial<DecodedData>,
+      Partial<DecodedData>
+    >> = [];
 
     if (data.length && data.length > 0)
-    for (let datum of data) {
-      preprocessedData.push( { input: datum as Partial<DecodedData>, output: datum as Partial<DecodedData> } );
-    }
+      for (const datum of data) {
+        preprocessedData.push({
+          input: datum as Partial<DecodedData>,
+          output: datum as Partial<DecodedData>,
+        });
+      }
 
     const results = super.train(preprocessedData, options);
 
@@ -179,7 +209,7 @@ export class AutoencoderGPU<DecodedData extends INeuralNetworkData, EncodedData
 
     const decoder = new NeuralNetworkGPU().fromJSON(json);
 
-    return decoder as unknown as NeuralNetworkGPU<EncodedData, DecodedData>;
+    return (decoder as unknown) as NeuralNetworkGPU<EncodedData, DecodedData>;
   }
 
   /**

src/errors/untrained-neural-network-error.ts

@@ -1,13 +1,15 @@
-import { NeuralNetwork } from "../neural-network";
+interface IErrorableNeuralNetworkConstructor {
+  name: string;
+}
 
 interface IErrorableNeuralNetwork {
-  constructor: Function;
+  constructor: IErrorableNeuralNetworkConstructor;
 }
 
 export class UntrainedNeuralNetworkError extends Error {
-  constructor (
-    neuralNetwork: IErrorableNeuralNetwork
-  ) {
-    super(`Cannot run a ${neuralNetwork.constructor.name} before it is trained.`);
+  constructor(neuralNetwork: IErrorableNeuralNetwork) {
+    super(
+      `Cannot run a ${neuralNetwork.constructor.name} before it is trained.`
+    );
   }
 }