CognxSafeTrack
Refactor: Modularize WhatsAppLogic into specialized Handlers (Command Pattern)
fb4b8be | export function levenshteinDistance(a: string, b: string): number { | |
| const matrix: number[][] = []; | |
| for (let i = 0; i <= b.length; i++) matrix[i] = [i]; | |
| for (let j = 0; j <= a.length; j++) matrix[0][j] = j; | |
| for (let i = 1; i <= b.length; i++) { | |
| for (let j = 1; j <= a.length; j++) { | |
| if (b.charAt(i - 1) === a.charAt(j - 1)) { | |
| matrix[i][j] = matrix[i - 1][j - 1]; | |
| } else { | |
| matrix[i][j] = Math.min( | |
| matrix[i - 1][j - 1] + 1, | |
| matrix[i][j - 1] + 1, | |
| matrix[i - 1][j] + 1 | |
| ); | |
| } | |
| } | |
| } | |
| return matrix[b.length][a.length]; | |
| } | |
| export function isFuzzyMatch(text: string, target: string, threshold = 0.8): boolean { | |
| const normalized = text.trim().toUpperCase(); | |
| const tar = target.toUpperCase(); | |
| if (normalized === tar) return true; | |
| if (normalized.includes(tar) || tar.includes(normalized)) return true; | |
| const distance = levenshteinDistance(normalized, tar); | |
| const maxLength = Math.max(normalized.length, tar.length); | |
| const similarity = 1 - distance / maxLength; | |
| return similarity >= threshold; | |
| } | |