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Diffstat (limited to 'src/client/ClientRecommender.tsx')
| -rw-r--r-- | src/client/ClientRecommender.tsx | 175 |
1 files changed, 175 insertions, 0 deletions
diff --git a/src/client/ClientRecommender.tsx b/src/client/ClientRecommender.tsx new file mode 100644 index 000000000..63f85c737 --- /dev/null +++ b/src/client/ClientRecommender.tsx @@ -0,0 +1,175 @@ +import { Doc } from "../new_fields/Doc"; +import { StrCast } from "../new_fields/Types"; +import { List } from "../new_fields/List"; +import { CognitiveServices } from "./cognitive_services/CognitiveServices"; +import React = require("react"); +import { observer } from "mobx-react"; +import { observable, action, computed, reaction } from "mobx"; +var assert = require('assert'); +import "./ClientRecommender.scss"; +import { JSXElement } from "babel-types"; + +export interface RecommenderProps { + title: string; +} + +@observer +export class ClientRecommender extends React.Component<RecommenderProps> { + + static Instance: ClientRecommender; + private docVectors: Set<number[]>; + @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]]; + } + + @action + public reset_docs() { + this.docVectors = new Set(); + this.corr_matrix = [[0, 0], [0, 0]]; + } + + /*** + * Computes the cosine similarity between two vectors in Euclidean space. + */ + + private distance(vector1: number[], vector2: number[], metric: string = "cosine") { + assert(vector1.length === vector2.length, "Vectors are not the same length"); + let similarity: number; + switch (metric) { + case "cosine": + var dotproduct = 0; + var mA = 0; + var mB = 0; + for (let i = 0; i < vector1.length; i++) { // here you missed the i++ + dotproduct += (vector1[i] * vector2[i]); + mA += (vector1[i] * vector1[i]); + mB += (vector2[i] * vector2[i]); + } + mA = Math.sqrt(mA); + mB = Math.sqrt(mB); + similarity = (dotproduct) / ((mA) * (mB)); // here you needed extra brackets + return similarity; + case "euclidian": + var sum = 0; + for (let i = 0; i < vector1.length; i++) { + sum += Math.pow(vector1[i] - vector2[i], 2); + } + similarity = Math.sqrt(sum); + return similarity; + default: + return 0; + } + } + + /*** + * Computes the mean of a set of vectors + */ + + public mean(paragraph: Set<number[]>) { + const n = 200; + const num_words = paragraph.size; + let meanVector = new Array<number>(n).fill(0); // mean vector + if (num_words > 0) { // check to see if paragraph actually was vectorized + paragraph.forEach((wordvec: number[]) => { + for (let i = 0; i < n; i++) { + meanVector[i] += wordvec[i]; + } + }); + meanVector = meanVector.map(x => x / num_words); + this.addToDocSet(meanVector); + } + return meanVector; + } + + private addToDocSet(vector: number[]) { + if (this.docVectors) { + this.docVectors.add(vector); + } + } + + /*** + * Uses Cognitive Services to extract keywords from a document + */ + + public async extractText(dataDoc: Doc, extDoc: Doc) { + let data = StrCast(dataDoc.title); + //console.log(data); + let converter = (results: any) => { + let keyterms = new List<string>(); + results.documents.forEach((doc: any) => { + let keyPhrases = doc.keyPhrases; + keyPhrases.map((kp: string) => keyterms.push(kp)); + }); + return keyterms; + }; + await CognitiveServices.Text.Appliers.analyzer(extDoc, ["key words"], data, converter); + } + + /*** + * Creates distance matrix for all Documents analyzed + */ + + @action + public createDistanceMatrix(documents: Set<number[]> = this.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)); + for (let i = 0; i < n; i++) { + 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"); + } + } + this.corr_matrix = matrix; + return matrix; + } + + @computed + private get generateRows() { + const n = this.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>; + children.push(cell); + } + //let row = React.createElement("tr", { children: children, key: i }); + let row = <tr>{children}</tr>; + rows.push(row); + } + return rows; + } + + render() { + return (<div className="wrapper"> + <h3 >{this.props.title ? this.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> + </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> + </tr> + </tbody> + </table> */} + <table className="space"> + <tbody> + {this.generateRows} + </tbody> + </table> + </div>); + } + +}
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