Merge branch 'master' into script_documents

1 files changed, 137 insertions, 0 deletions

diff --git a/src/server/Recommender.ts b/src/server/Recommender.ts
new file mode 100644
index 000000000..aacdb4053
--- /dev/null
+++ b/src/server/Recommender.ts
@@ -0,0 +1,137 @@
+// //import { Doc } from "../new_fields/Doc";
+// //import { StrCast } from "../new_fields/Types";
+// //import { List } from "../new_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() {
+//         console.log("creating recommender...");
+//         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');
+//                 console.log(similarity);
+//             }
+//             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() {
+//     //     console.log("phrasing...");
+//     //     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
+//     //     });
+//     //     console.log("phrased!!!");
+//     // }
+
+// }