diff options
Diffstat (limited to 'src/client/ClientRecommender.tsx')
| -rw-r--r-- | src/client/ClientRecommender.tsx | 83 |
1 files changed, 56 insertions, 27 deletions
diff --git a/src/client/ClientRecommender.tsx b/src/client/ClientRecommender.tsx index 63f85c737..a6d1a32b3 100644 --- a/src/client/ClientRecommender.tsx +++ b/src/client/ClientRecommender.tsx @@ -1,5 +1,5 @@ import { Doc } from "../new_fields/Doc"; -import { StrCast } from "../new_fields/Types"; +import { StrCast, Cast } from "../new_fields/Types"; import { List } from "../new_fields/List"; import { CognitiveServices } from "./cognitive_services/CognitiveServices"; import React = require("react"); @@ -8,30 +8,42 @@ import { observable, action, computed, reaction } from "mobx"; var assert = require('assert'); import "./ClientRecommender.scss"; import { JSXElement } from "babel-types"; +import { ToPlainText, RichTextField } from "../new_fields/RichTextField"; export interface RecommenderProps { title: string; } +export interface RecommenderDocument { + actualDoc: Doc; + vectorDoc: number[]; + score: number; +} + @observer export class ClientRecommender extends React.Component<RecommenderProps> { static Instance: ClientRecommender; - private docVectors: Set<number[]>; + private mainDoc?: RecommenderDocument; + private docVectors: Set<RecommenderDocument> = new Set(); @observable private corr_matrix = [[0, 0], [0, 0]]; constructor(props: RecommenderProps) { //console.log("creating client recommender..."); super(props); if (!ClientRecommender.Instance) ClientRecommender.Instance = this; - this.docVectors = new Set<number[]>(); - //this.corr_matrix = [[0, 0], [0, 0]]; + ClientRecommender.Instance.docVectors = new Set(); + //ClientRecommender.Instance.corr_matrix = [[0, 0], [0, 0]]; } @action public reset_docs() { - this.docVectors = new Set(); - this.corr_matrix = [[0, 0], [0, 0]]; + ClientRecommender.Instance.docVectors = new Set(); + ClientRecommender.Instance.corr_matrix = [[0, 0], [0, 0]]; + } + + public deleteDocs() { + console.log("deleting previews..."); } /*** @@ -67,11 +79,24 @@ export class ClientRecommender extends React.Component<RecommenderProps> { } } + public computeSimilarities() { + ClientRecommender.Instance.docVectors.forEach((doc: RecommenderDocument) => { + if (ClientRecommender.Instance.mainDoc) { + const distance = ClientRecommender.Instance.distance(ClientRecommender.Instance.mainDoc.vectorDoc, doc.vectorDoc, "euclidian"); + doc.score = distance; + } + } + ); + let doclist = Array.from(ClientRecommender.Instance.docVectors); + doclist.sort((a: RecommenderDocument, b: RecommenderDocument) => a.score - b.score); + return doclist; + } + /*** * Computes the mean of a set of vectors */ - public mean(paragraph: Set<number[]>) { + public mean(paragraph: Set<number[]>, dataDoc: Doc, mainDoc: boolean) { const n = 200; const num_words = paragraph.size; let meanVector = new Array<number>(n).fill(0); // mean vector @@ -82,14 +107,16 @@ export class ClientRecommender extends React.Component<RecommenderProps> { } }); meanVector = meanVector.map(x => x / num_words); - this.addToDocSet(meanVector); + const internalDoc: RecommenderDocument = { actualDoc: dataDoc, vectorDoc: meanVector, score: 0 }; + if (mainDoc) ClientRecommender.Instance.mainDoc = internalDoc; + ClientRecommender.Instance.addToDocSet(internalDoc); } return meanVector; } - private addToDocSet(vector: number[]) { - if (this.docVectors) { - this.docVectors.add(vector); + private addToDocSet(internalDoc: RecommenderDocument) { + if (ClientRecommender.Instance.docVectors) { + ClientRecommender.Instance.docVectors.add(internalDoc); } } @@ -97,9 +124,11 @@ export class ClientRecommender extends React.Component<RecommenderProps> { * Uses Cognitive Services to extract keywords from a document */ - public async extractText(dataDoc: Doc, extDoc: Doc) { - let data = StrCast(dataDoc.title); - //console.log(data); + public async extractText(dataDoc: Doc, extDoc: Doc, mainDoc: boolean = false) { + let fielddata = Cast(dataDoc.data, RichTextField); + let data: string; + fielddata ? data = fielddata[ToPlainText]() : data = ""; + console.log(data); let converter = (results: any) => { let keyterms = new List<string>(); results.documents.forEach((doc: any) => { @@ -108,7 +137,7 @@ export class ClientRecommender extends React.Component<RecommenderProps> { }); return keyterms; }; - await CognitiveServices.Text.Appliers.analyzer(extDoc, ["key words"], data, converter); + await CognitiveServices.Text.Appliers.analyzer(dataDoc, extDoc, ["key words"], data, converter, mainDoc); } /*** @@ -116,7 +145,7 @@ export class ClientRecommender extends React.Component<RecommenderProps> { */ @action - public createDistanceMatrix(documents: Set<number[]> = this.docVectors) { + public createDistanceMatrix(documents: Set<RecommenderDocument> = ClientRecommender.Instance.docVectors) { const documents_list = Array.from(documents); const n = documents_list.length; var matrix = new Array<number>(n).fill(0).map(() => new Array<number>(n).fill(0)); @@ -124,22 +153,22 @@ export class ClientRecommender extends React.Component<RecommenderProps> { var doc1 = documents_list[i]; for (let j = 0; j < n; j++) { var doc2 = documents_list[j]; - matrix[i][j] = this.distance(doc1, doc2, "euclidian"); + matrix[i][j] = ClientRecommender.Instance.distance(doc1.vectorDoc, doc2.vectorDoc, "euclidian"); } } - this.corr_matrix = matrix; + ClientRecommender.Instance.corr_matrix = matrix; return matrix; } @computed private get generateRows() { - const n = this.corr_matrix.length; + const n = ClientRecommender.Instance.corr_matrix.length; let rows: JSX.Element[] = []; for (let i = 0; i < n; i++) { let children: JSX.Element[] = []; for (let j = 0; j < n; j++) { - //let cell = React.createElement("td", this.corr_matrix[i][j]); - let cell = <td>{this.corr_matrix[i][j].toFixed(4)}</td>; + //let cell = React.createElement("td", ClientRecommender.Instance.corr_matrix[i][j]); + let cell = <td>{ClientRecommender.Instance.corr_matrix[i][j].toFixed(4)}</td>; children.push(cell); } //let row = React.createElement("tr", { children: children, key: i }); @@ -151,22 +180,22 @@ export class ClientRecommender extends React.Component<RecommenderProps> { render() { return (<div className="wrapper"> - <h3 >{this.props.title ? this.props.title : "hello"}</h3> + <h3 >{ClientRecommender.Instance.props.title ? ClientRecommender.Instance.props.title : "hello"}</h3> {/* <table className="space" > <tbody> <tr key="1"> - <td key="1">{this.corr_matrix[0][0].toFixed(4)}</td> - <td key="2">{this.corr_matrix[0][1].toFixed(4)}</td> + <td key="1">{ClientRecommender.Instance.corr_matrix[0][0].toFixed(4)}</td> + <td key="2">{ClientRecommender.Instance.corr_matrix[0][1].toFixed(4)}</td> </tr> <tr key="2"> - <td key="1">{this.corr_matrix[1][0].toFixed(4)}</td> - <td key="2">{this.corr_matrix[1][1].toFixed(4)}</td> + <td key="1">{ClientRecommender.Instance.corr_matrix[1][0].toFixed(4)}</td> + <td key="2">{ClientRecommender.Instance.corr_matrix[1][1].toFixed(4)}</td> </tr> </tbody> </table> */} <table className="space"> <tbody> - {this.generateRows} + {ClientRecommender.Instance.generateRows} </tbody> </table> </div>); |