chore: Lint

Author: robertleeplummerjr

src/layer/fully-connected.ts

@@ -278,9 +278,12 @@ export class FullyConnected extends Filter {
 
   compare(): void {
     const inputLayerDeltas = this.inputLayer.deltas;
-    this.inputLayer.deltas = (
-      this.compareInputDeltasKernel as IKernelRunShortcut
-    )(inputLayerDeltas, this.deltas, this.filters);
+    this.inputLayer.deltas = (this
+      .compareInputDeltasKernel as IKernelRunShortcut)(
+      inputLayerDeltas,
+      this.deltas,
+      this.filters
+    );
     release(inputLayerDeltas);
 
     const { biasDeltas, filterDeltas } = this;

src/layer/pool.ts

@@ -313,11 +313,8 @@ export class Pool extends Filter {
   }
 
   predict(): void {
-    const {
-      result: weights,
-      switchX,
-      switchY,
-    } = (this.predictKernelMap as IKernelMapRunShortcut<ISubKernelObject>)(
+    const { result: weights, switchX, switchY } = (this
+      .predictKernelMap as IKernelMapRunShortcut<ISubKernelObject>)(
       this.inputLayer.weights
     );
     this.switchX = switchX;

src/neural-network-gpu.ts

@@ -307,11 +307,9 @@ export class NeuralNetworkGPU<
     }
     const result = this._divideMSESum(data.length, sum);
     release(sum);
-    return (
-      result instanceof Texture
-        ? (result.toArray() as number[])
-        : (result as number[])
-    )[0];
+    return (result instanceof Texture
+      ? (result.toArray() as number[])
+      : (result as number[]))[0];
   }
 
   adjustWeights(): void {
@@ -621,11 +619,11 @@ export class NeuralNetworkGPU<
     if (this.inputLookup) {
       formattedInput = lookup.toArray(
         this.inputLookup,
-        input as unknown as INumberHash,
+        (input as unknown) as INumberHash,
         this.inputLookupLength
       );
     } else {
-      formattedInput = input as unknown as Float32Array;
+      formattedInput = (input as unknown) as Float32Array;
     }
     this.validateInput(formattedInput);
     const outputTextures = this.runInput(formattedInput);
@@ -635,13 +633,13 @@ export class NeuralNetworkGPU<
         : outputTextures;
 
     if (this.outputLookup) {
-      return lookup.toObject(
+      return (lookup.toObject(
         this.outputLookup,
         output as Float32Array
-      ) as unknown as OutputType;
+      ) as unknown) as OutputType;
     }
 
-    return output as unknown as OutputType;
+    return (output as unknown) as OutputType;
   }
 
   // @ts-expect-error the underlying network works as normal, but we are working on the GPU
@@ -694,10 +692,9 @@ export class NeuralNetworkGPU<
     }
     // use Array.from, keeping json small
     const jsonLayerWeights = this.weights.map((layerWeights) => {
-      return (
-        layerWeights instanceof Texture
-          ? (layerWeights.toArray() as Float32Array[])
-          : (layerWeights as Float32Array[])
+      return (layerWeights instanceof Texture
+        ? (layerWeights.toArray() as Float32Array[])
+        : (layerWeights as Float32Array[])
       ).map((layerWeights) => Array.from(layerWeights));
     });
     const jsonLayerBiases = this.biases.map((layerBiases) =>

src/utilities/to-svg.ts

@@ -2,7 +2,11 @@ import { FeedForward, IFeedForwardJSON } from '../feed-forward';
 import { recurrentZeros } from '../layer/recurrent-zeros';
 import { Recurrent } from '../recurrent';
 import { IRNNJSON, RNN } from '../recurrent/rnn';
-import { INeuralNetworkData, INeuralNetworkJSON, NeuralNetwork } from '../neural-network'
+import {
+  INeuralNetworkData,
+  INeuralNetworkJSON,
+  NeuralNetwork,
+} from '../neural-network';
 import { GRU } from '../recurrent/gru';
 import { LSTM } from '../recurrent/lstm';
 import { NeuralNetworkGPU } from '../neural-network-gpu';