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Cipher
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Added a JavaScript version of Janex.py
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‎Experimental/.DS_Store

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‎Janex.js

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const readline = require("readline");
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class IntentMatcher {
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constructor(intentsFilePath) {
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this.intentsFilePath = intentsFilePath;
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this.intents = this.train();
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}
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Tokenize(inputString) {
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let processedString = inputString.toLowerCase().trim().replace(/[^\w\s]|_/g, "").replace(/\s+/g, " ");
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let words = processedString.split(" ");
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words = this.stemList(words);
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return words;
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}
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TokenizeList(inputList) {
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let tokenWords = [];
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for (let word of inputList) {
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let token = this.Tokenize(word);
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tokenWords.push(token);
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}
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return tokenWords;
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}
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train() {
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const fs = require("fs");
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const intents = JSON.parse(fs.readFileSync(this.intentsFilePath, "utf8"));
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return intents;
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}
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patternCompare(inputString) {
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let inputStringLower = inputString.toLowerCase();
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let highestSimilarity = 0;
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let mostSimilarPattern = null;
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let similarityPercentage = 0;
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for (let intentClass of this.intents.intents) {
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let overallWordList = [];
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let similarity = 0;
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for (let pattern of intentClass.patterns) {
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let wordList = [];
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let patternLower = pattern.toLowerCase();
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wordList = this.Tokenize(patternLower);
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overallWordList.push(wordList);
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let newList = [];
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let newBag = [];
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for (let word of wordList) {
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word = this.stem(word);
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newList.push(word);
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}
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let wordList2 = this.Tokenize(inputStringLower);
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for (let word of wordList2) {
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word = this.stem(word);
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newBag.push(word);
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}
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wordList = newList;
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wordList2 = newBag;
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for (let word of wordList2) {
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if (wordList.includes(word)) {
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similarity++;
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}
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}
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if (similarity > highestSimilarity) {
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similarityPercentage = similarity / (overallWordList.length + wordList2.length);
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highestSimilarity = similarity;
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mostSimilarPattern = intentClass;
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}
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}
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}
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console.log(`Similarity: ${similarityPercentage.toFixed(2)}%`);
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if (mostSimilarPattern) {
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return mostSimilarPattern;
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} else {
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throw new Error("No matching intent class found.");
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}
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}
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responseCompare(inputString, intentClass) {
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let inputStringLower = inputString.toLowerCase();
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let highestSimilarity = 0;
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let similarityPercentage = 0;
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let mostSimilarResponse = null;
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let responses = intentClass ? intentClass.responses : [];
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for (let response of responses) {
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let similarity = 0;
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let responseLower = response.toLowerCase();
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let wordList = this.Tokenize(responseLower);
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let newList = [];
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let newBag = [];
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for (let word of wordList) {
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word = this.stem(word);
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newList.push(word);
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}
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let wordList2 = this.Tokenize(inputStringLower);
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for (let word of wordList2) {
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word = this.stem(word);
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newBag.push(word);
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}
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wordList = newList;
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wordList2 = newBag;
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for (let word of wordList2) {
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if (wordList.includes(word)) {
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similarity += 1 / (wordList.length + wordList2.length);
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}
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}
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if (similarity > highestSimilarity) {
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similarityPercentage = similarity * 100;
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highestSimilarity = similarity;
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mostSimilarResponse = response;
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}
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}
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console.log(`Similarity: ${similarityPercentage.toFixed(2)}%`);
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// Convert mostSimilarResponse back into the original string
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for (let response of responses) {
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let lowResponseList = [];
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let lowResponse = response.toLowerCase();
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lowResponseList = this.stemSentence(lowResponse);
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for (let lowResponseWord of lowResponseList) {
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if (lowResponseWord === mostSimilarResponse) {
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mostSimilarResponse = response;
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}
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}
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}
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return mostSimilarResponse;
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}
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stem(inputWord) {
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let suffixes = ["ing", "ly", "ed", "es", "'s", "er", "est", "y", "ily", "able", "ful", "ness", "less", "ment", "ive", "ize", "ous"];
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for (let suffix of suffixes) {
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if (inputWord.endsWith(suffix)) {
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inputWord = inputWord.slice(0, -suffix.length);
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break;
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}
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}
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return inputWord;
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}
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stemSentence(inputString) {
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let wordList = [];
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let stemmedWords = [];
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wordList = inputString.split(" ");
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for (let inputWord of wordList) {
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let word = this.stem(inputWord);
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stemmedWords.push(word);
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}
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return stemmedWords;
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}
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stemList(inputList) {
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let stemmedWords = [];
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for (let word of inputList) {
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let stemmedWord = this.stem(word);
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stemmedWords.push(stemmedWord);
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}
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return stemmedWords;
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}
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outputCompare(output) {
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let highestSimilarity = 0;
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let mostSimilarPattern = null;
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let similarityPercentage = 0;
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for (let intentClass of this.intents.intents) {
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let overallWordList = [];
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let similarity = 0;
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for (let pattern of intentClass.patterns) {
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let wordList = [];
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let patternLower = pattern.toLowerCase();
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wordList = this.Transform(patternLower);
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overallWordList.push(wordList);
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let newList = [];
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let newBag = [];
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for (let word of wordList) {
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newList.push(word);
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}
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let wordList2 = output;
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for (let word of wordList2) {
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newBag.push(word);
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}
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wordList = newList;
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wordList2 = newBag;
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for (let word of wordList2) {
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if (wordList.includes(word)) {
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similarity++;
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}
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}
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if (similarity > highestSimilarity) {
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similarityPercentage = similarity / (overallWordList.length + wordList2.length);
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highestSimilarity = similarity;
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mostSimilarPattern = intentClass;
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}
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}
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}
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console.log(`Similarity: ${similarityPercentage.toFixed(2)}%`);
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if (mostSimilarPattern) {
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return mostSimilarPattern;
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} else {
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throw new Error("No matching intent class found.");
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}
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}
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}
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// Example usage:
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const intentsFilePath = "./intents.json";
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const matcher = new IntentMatcher(intentsFilePath);
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const rl = readline.createInterface({
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input: process.stdin,
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output: process.stdout,
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});
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rl.question("You: ", (input) => {
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// Use the input here
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console.log("User input:", input);
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// Close the readline interface
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rl.close();
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const intentClass = matcher.patternCompare(input);
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const response = matcher.responseCompare(input, intentClass);
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console.log(response);
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});

‎Janex.py

Lines changed: 35 additions & 35 deletions
Original file line numberDiff line numberDiff line change
@@ -87,41 +87,6 @@ def patterncompare(self, input_string):
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else:
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raise self.NoMatchingIntentError("No matching intent class found.")
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def Transform(self, input_string):
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# Example usage
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max_seq_len = 10
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d_model = 64
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num_heads = 4
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d_ff = 128
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num_layers = 2
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pos_enc = PositionalEncoding(d_model, max_seq_len)
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# Text preprocessing
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tokens = self.Tokenize(input_string)
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max_seq_len = max_seq_len # Specify the desired maximum sequence length
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print(tokens)
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# Convert tokens to numerical representation (e.g., using word embeddings)
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word_embeddings = {} # Replace with your word embeddings dictionary
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X = np.array([word_embeddings.get(token, np.zeros(d_model)) for token in tokens])
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# Pad or truncate X to match the desired sequence length
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X = X[:max_seq_len]
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padding = np.zeros((max_seq_len - X.shape[0], d_model))
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X = np.vstack((X, padding))
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positions = np.arange(max_seq_len)
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pos_encoding = pos_enc.get_positional_encoding(positions)
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X_with_pos_enc = X + pos_encoding
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transformer = Transformer(d_model, num_heads, d_ff, num_layers)
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output = transformer.forward(X_with_pos_enc)
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return output
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def responsecompare(self, input_string, intent_class):
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input_string = input_string.lower()
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HighestSimilarity = 0
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else:
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raise self.NoMatchingIntentError("No matching intent class found.")
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def Transform(self, input_string):
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# Example usage
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max_seq_len = 10
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d_model = 64
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num_heads = 4
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d_ff = 128
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num_layers = 2
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pos_enc = PositionalEncoding(d_model, max_seq_len)
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# Text preprocessing
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tokens = self.Tokenize(input_string)
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max_seq_len = max_seq_len # Specify the desired maximum sequence length
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print(tokens)
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# Convert tokens to numerical representation (e.g., using word embeddings)
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word_embeddings = {} # Replace with your word embeddings dictionary
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X = np.array([word_embeddings.get(token, np.zeros(d_model)) for token in tokens])
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# Pad or truncate X to match the desired sequence length
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X = X[:max_seq_len]
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padding = np.zeros((max_seq_len - X.shape[0], d_model))
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X = np.vstack((X, padding))
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positions = np.arange(max_seq_len)
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pos_encoding = pos_enc.get_positional_encoding(positions)
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X_with_pos_enc = X + pos_encoding
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transformer = Transformer(d_model, num_heads, d_ff, num_layers)
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output = transformer.forward(X_with_pos_enc)
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return output
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def softmax(x, axis=-1):
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# Apply softmax operation to the input array along the specified axis
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e_x = np.exp(x - np.max(x, axis=axis, keepdims=True))

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