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-rw-r--r--src/server/Recommender.ts274
1 files changed, 137 insertions, 137 deletions
diff --git a/src/server/Recommender.ts b/src/server/Recommender.ts
index 8684a29f1..aacdb4053 100644
--- a/src/server/Recommender.ts
+++ b/src/server/Recommender.ts
@@ -1,137 +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!!!");
- // }
-
-}
+// //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!!!");
+// // }
+
+// }
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