eslint automatic fixes

src/CharRNN/index.js

@@ -11,7 +11,7 @@ A LSTM Generator: Run inference mode for a pre-trained LSTM.
 
 import * as tf from "@tensorflow/tfjs";
 import axios from "axios";
-import sampleFromDistribution from "./../utils/sample";
+import sampleFromDistribution from "../utils/sample";
 import CheckpointLoader from "../utils/checkpointLoader";
 import callCallback from "../utils/callcallback";
 
@@ -309,6 +309,6 @@ class CharRNN {
   }
 }
 
-const charRNN = (modelPath = "./", callback) => new CharRNN(modelPath, callback);
+const charRNN = (modelPath, callback) => new CharRNN(modelPath, callback);
 
 export default charRNN;

src/DBSCAN/index.js

@@ -33,9 +33,7 @@ async function loadDataset(inputData) {
   } else {
     data = inputData;
   }
-  const dataFlat = data.map(d => {
-    return Object.values(d);
-  });
+  const dataFlat = data.map(d => Object.values(d));
   return dataFlat;
 }
 
@@ -138,6 +136,7 @@ class DBSCAN {
   getClusterId() {
     return this.lastClusterId;
   }
+
   /**
    * increment cluster id
    */
@@ -159,9 +158,7 @@ class DBSCAN {
         return tf
           .stack([values.asType("float32"), indices.asType("float32")], 1)
           .arraySync()
-          .filter(v => {
-            return v[0] <= this.config.eps;
-          })
+          .filter(v => v[0] <= this.config.eps)
           .reduce((prev, cur) => {
             prev.push(cur[1]);
             return prev;

src/FaceApi/index.js

@@ -268,7 +268,7 @@ class FaceApiBase {
    * @param {Object} faceApiOptions
    */
   setReturnOptions(faceApiOptions) {
-    const output = Object.assign({}, this.config);
+    const output = { ...this.config};
     const options = ["withLandmarks", "withDescriptors"];
 
     options.forEach(prop => {
@@ -306,7 +306,7 @@ class FaceApiBase {
     if (Array.isArray(result) === true) {
       output = result.map(item => {
         // if landmarks exist return parts
-        const newItem = Object.assign({}, item);
+        const newItem = { ...item};
         if (newItem.landmarks) {
           const { landmarks } = newItem;
           newItem.parts = {
@@ -333,7 +333,7 @@ class FaceApiBase {
       });
       // single detection is an object
     } else {
-      output = Object.assign({}, result);
+      output = { ...result};
       if (output.landmarks) {
         const { landmarks } = result;
         output.parts = {

src/FeatureExtractor/Mobilenet.js

@@ -10,7 +10,7 @@ A class that extract features from Mobilenet
 import * as tf from "@tensorflow/tfjs";
 import axios from "axios";
 import handleArguments from "../utils/handleArguments";
-import Video from "./../utils/Video";
+import Video from "../utils/Video";
 import { imgToTensor } from "../utils/imageUtilities";
 import { saveBlob } from "../utils/io";
 import callCallback from "../utils/callcallback";
@@ -127,7 +127,7 @@ class Mobilenet {
    *    the video is ready. If no callback is provided, it will return a
    *    promise that will be resolved once the video element has loaded.
    */
-  classification(video, objOrCallback = null, callback) {
+  classification(video, objOrCallback, callback) {
     const { options, callback: cb } = handleArguments(objOrCallback, callback);
 
     this.usageType = "classifier";
@@ -367,7 +367,7 @@ class Mobilenet {
     return { value: prediction[0] };
   }
 
-  async load(filesOrPath = null, callback) {
+  async load(filesOrPath, callback) {
     if (typeof filesOrPath !== "string") {
       let model = null;
       let weights = null;

src/KMeans/index.js

@@ -42,9 +42,7 @@ async function loadDataset(inputData) {
   } else {
     data = inputData;
   }
-  const dataFlat = data.map((d) => {
-    return Object.values(d)
-  });
+  const dataFlat = data.map(d => Object.values(d));
   return dataFlat;
 }
 
@@ -158,7 +156,7 @@ class KMeans {
         const centroidKTensor = centroidK.map(d => d.tensor);
         if (centroidKTensor.length === 0) {
           return centroid;
-        } else if (centroidKTensor.length === 1) {
+        } if (centroidKTensor.length === 1) {
           return centroidKTensor[0];
         }
         // grab mean for for cluster
@@ -186,4 +184,4 @@ class KMeans {
 
 const kmeans = (dataset, options, callback) => new KMeans(dataset, options, callback);
 
-export default kmeans;
+export default kmeans;

src/NeuralNetwork/NeuralNetwork.js

@@ -129,9 +129,7 @@ class NeuralNetwork {
    * @param {*} _inputs
    */
   predictSync(_inputs) {
-    const output = tf.tidy(() => {
-      return this.model.predict(_inputs);
-    });
+    const output = tf.tidy(() => this.model.predict(_inputs));
     const result = output.arraySync();
 
     output.dispose();
@@ -145,9 +143,7 @@ class NeuralNetwork {
    * @param {*} _inputs
    */
   async predict(_inputs) {
-    const output = tf.tidy(() => {
-      return this.model.predict(_inputs);
-    });
+    const output = tf.tidy(() => this.model.predict(_inputs));
     const result = await output.array();
 
     output.dispose();
@@ -211,16 +207,16 @@ class NeuralNetwork {
    * @param {*} filesOrPath
    * @param {*} callback
    */
-  async load(filesOrPath = null, callback) {
+  async load(filesOrPath, callback) {
     if (filesOrPath instanceof FileList) {
       const files = await Promise.all(
         Array.from(filesOrPath).map(async file => {
           if (file.name.includes('.json') && !file.name.includes('_meta')) {
             return { name: 'model', file };
-          } else if (file.name.includes('.json') && file.name.includes('_meta.json')) {
+          } if (file.name.includes('.json') && file.name.includes('_meta.json')) {
             const modelMetadata = await file.text();
             return { name: 'metadata', file: modelMetadata };
-          } else if (file.name.includes('.bin')) {
+          } if (file.name.includes('.bin')) {
             return { name: 'weights', file };
           }
           return { name: null, file: null };

src/NeuralNetwork/NeuralNetworkData.js

@@ -100,7 +100,7 @@ class NeuralNetworkData {
    * @param {*} dataRaw
    */
   getDataStats(dataRaw) {
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
 
     const inputMeta = this.getInputMetaStats(dataRaw, meta.inputs, 'xs');
     const outputMeta = this.getInputMetaStats(dataRaw, meta.outputs, 'ys');
@@ -125,7 +125,7 @@ class NeuralNetworkData {
    */
   // eslint-disable-next-line no-unused-vars, class-methods-use-this
   getInputMetaStats(dataRaw, inputOrOutputMeta, xsOrYs) {
-    const inputMeta = Object.assign({}, inputOrOutputMeta);
+    const inputMeta = { ...inputOrOutputMeta};
 
     Object.keys(inputMeta).forEach(k => {
       if (inputMeta[k].dtype === 'string') {
@@ -151,7 +151,7 @@ class NeuralNetworkData {
    */
   getDataUnits(dataRaw, _arrayShape = null) {
     const arrayShape = _arrayShape !== null ? _arrayShape : undefined;
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
 
     // if the data has a shape pass it in
     let inputShape;
@@ -182,7 +182,7 @@ class NeuralNetworkData {
   // eslint-disable-next-line class-methods-use-this, no-unused-vars
   getInputMetaUnits(_dataRaw, _inputsMeta) {
     let units = 0;
-    const inputsMeta = Object.assign({}, _inputsMeta);
+    const inputsMeta = { ..._inputsMeta};
 
     Object.entries(inputsMeta).forEach(arr => {
       const { dtype } = arr[1];
@@ -271,7 +271,7 @@ class NeuralNetworkData {
    */
   // eslint-disable-next-line class-methods-use-this, no-unused-vars
   convertRawToTensors(dataRaw) {
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
     const dataLength = dataRaw.length;
 
     return tf.tidy(() => {
@@ -281,18 +281,14 @@ class NeuralNetworkData {
       dataRaw.forEach(row => {
         // get xs
         const xs = Object.keys(meta.inputs)
-          .map(k => {
-            return row.xs[k];
-          })
+          .map(k => row.xs[k])
           .flat();
 
         inputArr.push(xs);
 
         // get ys
         const ys = Object.keys(meta.outputs)
-          .map(k => {
-            return row.ys[k];
-          })
+          .map(k => row.ys[k])
           .flat();
 
         outputArr.push(ys);
@@ -319,7 +315,7 @@ class NeuralNetworkData {
    * @param {*} dataRaw
    */
   normalizeDataRaw(dataRaw) {
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
 
     const normXs = this.normalizeInputData(dataRaw, meta.inputs, 'xs');
     const normYs = this.normalizeInputData(dataRaw, meta.outputs, 'ys');
@@ -340,7 +336,7 @@ class NeuralNetworkData {
     // the data length
     const dataLength = dataRaw.length;
     // the copy of the inputs.meta[inputOrOutput]
-    const inputMeta = Object.assign({}, inputOrOutputMeta);
+    const inputMeta = { ...inputOrOutputMeta};
 
     // normalized output object
     const normalized = {};
@@ -392,9 +388,7 @@ class NeuralNetworkData {
     // value with the onehot array
     // if none exists, return the given value
     if (options.legend) {
-      const normalized = inputArray.map(v => {
-        return options.legend[v] ? options.legend[v] : v;
-      });
+      const normalized = inputArray.map(v => options.legend[v] ? options.legend[v] : v);
       return normalized;
     }
 
@@ -460,7 +454,7 @@ class NeuralNetworkData {
    * @param {*} _meta
    */
   applyOneHotEncodingsToDataRaw(dataRaw) {
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
 
     const output = dataRaw.map(row => {
       const xs = {
@@ -498,7 +492,7 @@ class NeuralNetworkData {
    * @param {*} dataRaw
    */
   getDataOneHot(dataRaw) {
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
 
     const inputMeta = this.getInputMetaOneHot(dataRaw, meta.inputs, 'xs');
     const outputMeta = this.getInputMetaOneHot(dataRaw, meta.outputs, 'ys');
@@ -521,7 +515,7 @@ class NeuralNetworkData {
    * @param {*} xsOrYs
    */
   getInputMetaOneHot(_dataRaw, _inputsMeta, xsOrYs) {
-    const inputsMeta = Object.assign({}, _inputsMeta);
+    const inputsMeta = { ..._inputsMeta};
 
     Object.entries(inputsMeta).forEach(arr => {
       // the key
@@ -615,7 +609,7 @@ class NeuralNetworkData {
       let json;
       // handle loading parsedJson
       if (dataUrlOrJson instanceof Object) {
-        json = Object.assign({}, dataUrlOrJson);
+        json = { ...dataUrlOrJson};
       } else {
         const {data} = await axios.get(dataUrlOrJson);
         json = data;
@@ -684,7 +678,7 @@ class NeuralNetworkData {
    * @param {*} filesOrPath
    * @param {*} callback
    */
-  async loadData(filesOrPath = null, callback) {
+  async loadData(filesOrPath, callback) {
     try {
       let loadedData;
 
@@ -769,7 +763,7 @@ class NeuralNetworkData {
    * @param {*} filesOrPath
    * @param {*} callback
    */
-  async loadMeta(filesOrPath = null, callback) {
+  async loadMeta(filesOrPath, callback) {
     if (filesOrPath instanceof FileList) {
       const files = await Promise.all(
         Array.from(filesOrPath).map(async file => {
@@ -778,13 +772,13 @@ class NeuralNetworkData {
               name: 'model',
               file,
             };
-          } else if (file.name.includes('.json') && file.name.includes('_meta.json')) {
+          } if (file.name.includes('.json') && file.name.includes('_meta.json')) {
             const modelMetadata = await file.text();
             return {
               name: 'metadata',
               file: modelMetadata,
             };
-          } else if (file.name.includes('.bin')) {
+          } if (file.name.includes('.bin')) {
             return {
               name: 'weights',
               file,
@@ -925,11 +919,11 @@ class NeuralNetworkData {
    * @param {*} _data
    */
   findEntries(_data) {
-    const parentCopy = Object.assign({}, _data);
+    const parentCopy = { ..._data};
 
     if (parentCopy.entries && parentCopy.entries instanceof Array) {
       return parentCopy.entries;
-    } else if (parentCopy.data && parentCopy.data instanceof Array) {
+    } if (parentCopy.data && parentCopy.data instanceof Array) {
       return parentCopy.data;
     }