issue/6258

src/class/learning/Cluster.js

@@ -2,6 +2,7 @@ import { kmpp } from 'skmeans/kinit';
 // import { kmpp } from 'skmeans/dist/node/kinit';
 import floor from 'lodash/floor';
 import _toNumber from 'lodash/toNumber';
+import _isNaN from 'lodash/isNaN';
 import LearningView from './LearningView';
 import Chart from './Chart';
 import DataTable from '../DataTable';
@@ -155,7 +156,7 @@ class Cluster {
         this.#isTrained = false;
         const { data, select } = this.#table;
         const filtered = data.filter(
-            (row) => !select.flat().some((selected) => !_toNumber(row[selected]))
+            (row) => !select.flat().some((selected) => _isNaN(_toNumber(row[selected])))
         );
         const [attr] = select;
 

src/class/learning/DecisionTree.js

@@ -4,6 +4,7 @@ import _max from 'lodash/max';
 import _sum from 'lodash/sum';
 import _mean from 'lodash/mean';
 import _toNumber from 'lodash/toNumber';
+import _isNaN from 'lodash/isNaN';
 import LearningBase from './LearningBase';
 import { DecisionTreeClassifier as DTClassifier } from 'ml-cart';
 import Utils from './Utils';
@@ -159,7 +160,7 @@ function getData(testRate = 0.2, data) {
     const { select = [[0], [1]], data: table, fields } = data;
     const [attr, predict] = select;
     const filtered = table.filter(
-        (row) => !select[0].some((selected) => !_toNumber(row[selected]))
+        (row) => !select[0].some((selected) => _isNaN(_toNumber(row[selected])))
     );
     const dataArray = filtered
         .map((row) => ({

src/class/learning/LearningView.js

@@ -15,7 +15,7 @@ const STATUS = {
 
 export default class LearningView {
     constructor({ name = 'model name', status = STATUS.NO_MODEL, value = 0} = {}) {
-        this.id = Entry.generateHash(); 
+        this.id = Entry.generateHash();
         this.visible = true;
         this.value = value;
         const fontFamily = EntryStatic.fontFamily || 'NanumGothic';

src/class/learning/LogisticRegression.js

@@ -4,6 +4,7 @@ import _max from 'lodash/max';
 import _sum from 'lodash/sum';
 import _mean from 'lodash/mean';
 import _toNumber from 'lodash/toNumber';
+import _isNaN from 'lodash/isNaN';
 import LearningBase from './LearningBase';
 import Utils from './Utils';
 
@@ -184,7 +185,7 @@ function getData(validationRate, testRate, data, trainParam) {
     const { select = [[0], [1]], data: table, fields } = data;
     const [attr, predict] = select;
     const filtered = table.filter(
-        (row) => !select[0].some((selected) => !_toNumber(row[selected]))
+        (row) => !select[0].some((selected) => _isNaN(_toNumber(row[selected])))
     );
     const dataArray = filtered
         .map((row) => ({

src/class/learning/NumberClassification.js

@@ -6,6 +6,7 @@ import _floor from 'lodash/floor';
 import _sum from 'lodash/sum';
 import _mean from 'lodash/mean';
 import _toNumber from 'lodash/toNumber';
+import _isNaN from 'lodash/isNaN';
 import DataTable from '../DataTable';
 
 export const classes = [
@@ -334,7 +335,7 @@ function convertTableToKnnData(tableData = {}) {
     const { select = [[0], [1]], data: table = [] } = tableData;
     const [attr, predict] = select;
     const filtered = table.filter(
-        (row) => !select[0].some((selected) => !_toNumber(row[selected]))
+        (row) => !select[0].some((selected) => _isNaN(_toNumber(row[selected])))
     );
     return filtered.reduce(
         (accumulator, row) => {

src/class/learning/Regression.js

@@ -5,6 +5,7 @@ import Chart from './Chart';
 import _sum from 'lodash/sum';
 import _mean from 'lodash/mean';
 import _toNumber from 'lodash/toNumber';
+import _isNaN from 'lodash/isNaN';
 import LearningBase from './LearningBase';
 import Utils from './Utils';
 
@@ -224,7 +225,7 @@ function convertToTfData(data, trainParam) {
     const [attr, predict] = select;
     const { epochs = 1, batchSize = 1 } = trainParam;
     const filtered = table.filter(
-        (row) => !select.flat().some((selected) => !_toNumber(row[selected]))
+        (row) => !select.flat().some((selected) => _isNaN(_toNumber(row[selected])))
     );
     const totalDataSize = Math.ceil(filtered.length / batchSize) * epochs;
     return filtered.reduce(

src/class/learning/Svm.js

@@ -4,6 +4,7 @@ import _max from 'lodash/max';
 import _sum from 'lodash/sum';
 import _mean from 'lodash/mean';
 import _toNumber from 'lodash/toNumber';
+import _isNaN from 'lodash/isNaN';
 import Utils from './Utils';
 const { callApi } = require('../../util/common');
 const SVM = require('libsvm-js/asm');
@@ -168,7 +169,7 @@ class Svm extends LearningBase {
         const { select = [[0], [1]], data: table, fields } = data;
         const [attr, predict] = select;
         const filtered = table.filter(
-            (row) => !select[0].some((selected) => !_toNumber(row[selected]))
+            (row) => !select[0].some((selected) => _isNaN(_toNumber(row[selected])))
         );
         const dataArray = filtered
             .map((row) => ({