1 files changed, 0 insertions, 133 deletions
diff --git a/src/server/Recommender.ts b/src/server/Recommender.ts
deleted file mode 100644
index 935ec3871..000000000
--- a/src/server/Recommender.ts
+++ /dev/null
@@ -1,133 +0,0 @@
-// //import { Doc } from "../fields/Doc";
-// //import { StrCast } from "../fields/Types";
-// //import { List } from "../fields/List";
-// //import { CognitiveServices } from "../client/cognitive_services/CognitiveServices";
-
-// // var w2v = require('word2vec');
-// var assert = require('assert');
-// var arxivapi = require('arxiv-api-node');
-// import requestPromise = require("request-promise");
-// import * as use from '@tensorflow-models/universal-sentence-encoder';
-// import { Tensor } from "@tensorflow/tfjs-core/dist/tensor";
-// require('@tensorflow/tfjs-node');
-
-// //http://gnuwin32.sourceforge.net/packages/make.htm
-
-// export class Recommender {
-
-//     private _model: any;
-//     static Instance: Recommender;
-//     private dimension: number = 0;
-//     private choice: string = ""; // Tensorflow or Word2Vec
-
-//     constructor() {
-//         Recommender.Instance = this;
-//     }
-
-//     /***
-//      * Loads pre-trained model from TF
-//      */
-
-//     public async loadTFModel() {
-//         let self = this;
-//         return new Promise(res => {
-//             use.load().then(model => {
-//                 self.choice = "TF";
-//                 self._model = model;
-//                 self.dimension = 512;
-//                 res(model);
-//             });
-//         }
-
-//         );
-//     }
-
-//     /***
-//      * Loads pre-trained model from word2vec
-//      */
-
-//     // private loadModel(): Promise<any> {
-//     //     let self = this;
-//     //     return new Promise(res => {
-//     //         w2v.loadModel("./node_modules/word2vec/examples/fixtures/vectors.txt", function (err: any, model: any) {
-//     //             self.choice = "WV";
-//     //             self._model = model;
-//     //             self.dimension = model.size;
-//     //             res(model);
-//     //         });
-//     //     });
-//     // }
-
-//     /***
-//      * Testing
-//      */
-
-//     public async testModel() {
-//         if (!this._model) {
-//             await this.loadTFModel();
-//         }
-//         if (this._model) {
-//             if (this.choice === "WV") {
-//                 let similarity = this._model.similarity('father', 'mother');
-//             }
-//             else if (this.choice === "TF") {
-//                 const model = this._model as use.UniversalSentenceEncoder;
-//                 // Embed an array of sentences.
-//                 const sentences = [
-//                     'Hello.',
-//                     'How are you?'
-//                 ];
-//                 const embeddings = await this.vectorize(sentences);
-//                 if (embeddings) embeddings.print(true /*verbose*/);
-//                 // model.embed(sentences).then(embeddings => {
-//                 //     // `embeddings` is a 2D tensor consisting of the 512-dimensional embeddings for each sentence.
-//                 //     // So in this example `embeddings` has the shape [2, 512].
-//                 //     embeddings.print(true /* verbose */);
-//                 // });
-//             }
-//         }
-//         else {
-//             console.log("model not found :(");
-//         }
-//     }
-
-//     /***
-//      * Uses model to convert words to vectors
-//      */
-
-//     public async vectorize(text: string[]): Promise<Tensor | undefined> {
-//         if (!this._model) {
-//             await this.loadTFModel();
-//         }
-//         if (this._model) {
-//             if (this.choice === "WV") {
-//                 let word_vecs = this._model.getVectors(text);
-//                 return word_vecs;
-//             }
-//             else if (this.choice === "TF") {
-//                 const model = this._model as use.UniversalSentenceEncoder;
-//                 return new Promise<Tensor>(res => {
-//                     model.embed(text).then(embeddings => {
-//                         res(embeddings);
-//                     });
-//                 });
-
-//             }
-//         }
-//     }
-
-//     // public async trainModel() {
-//     //     w2v.word2vec("./node_modules/word2vec/examples/eng_news-typical_2016_1M-sentences.txt", './node_modules/word2vec/examples/my_phrases.txt', {
-//     //         cbow: 1,
-//     //         size: 200,
-//     //         window: 8,
-//     //         negative: 25,
-//     //         hs: 0,
-//     //         sample: 1e-4,
-//     //         threads: 20,
-//     //         iter: 200,
-//     //         minCount: 2
-//     //     });
-//     // }
-
-// }