1 files changed, 101 insertions, 0 deletions
diff --git a/src/client/ClientRecommender.ts b/src/client/ClientRecommender.ts
new file mode 100644
index 000000000..7ff79ab50
--- /dev/null
+++ b/src/client/ClientRecommender.ts
@@ -0,0 +1,101 @@
+import { Doc } from "../new_fields/Doc";
+import { StrCast } from "../new_fields/Types";
+import { List } from "../new_fields/List";
+import { CognitiveServices } from "./cognitive_services/CognitiveServices";
+
+
+var assert = require('assert');
+
+export class ClientRecommender {
+
+    static Instance: ClientRecommender;
+    private docVectors: Set<number[]>;
+
+    constructor() {
+        //console.log("creating client recommender...");
+        ClientRecommender.Instance = this;
+        this.docVectors = new Set<number[]>();
+    }
+
+
+    /***
+     * Computes the cosine similarity between two vectors in Euclidean space. 
+     */
+
+    private distance(vector1: number[], vector2: number[]) {
+        assert(vector1.length === vector2.length, "Vectors are not the same length");
+        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);
+        var similarity = (dotproduct) / ((mA) * (mB)); // here you needed extra brackets
+        return similarity;
+    }
+
+    /***
+     * 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
+        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.Manager.analyzer(extDoc, ["key words"], data, converter);
+    }
+
+    /***
+     * Creates distance matrix for all Documents analyzed
+     */
+
+    public createDistanceMatrix(documents: Set<number[]> = this.docVectors) {
+        const documents_list = Array.from(documents);
+        const n = documents_list.length;
+        var matrix = new Array(n).fill(0).map(() => new Array(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);
+            }
+        }
+        return matrix;
+    }
+
+}
+\ No newline at end of file