correlation matrix completed

1 files changed, 12 insertions, 10 deletions

diff --git a/src/client/cognitive_services/CognitiveServices.ts b/src/client/cognitive_services/CognitiveServices.ts
index 863236b60..cc366abc2 100644
--- a/src/client/cognitive_services/CognitiveServices.ts
+++ b/src/client/cognitive_services/CognitiveServices.ts
@@ -8,6 +8,7 @@ import { InkData } from "../../new_fields/InkField";
 import { UndoManager } from "../util/UndoManager";
 import requestPromise = require("request-promise");
 import { List } from "../../new_fields/List";
+import { ClientRecommender } from "../ClientRecommender";
 
 type APIManager<D> = { converter: BodyConverter<D>, requester: RequestExecutor, analyzer: AnalysisApplier };
 type RequestExecutor = (apiKey: string, body: string, service: Service) => Promise<string>;
@@ -257,20 +258,21 @@ export namespace CognitiveServices {
                 //target[keys[0]] = Docs.Get.DocumentHierarchyFromJson(results, "Key Word Analysis");
                 target[keys[0]] = keyterms;
                 console.log("analyzed!");
-                vectorize(keyterms);
-                return null;
-            },
-
-
+                await vectorize(keyterms);
+            }
         };
-        function vectorize(keyterms: any) {
+        async function vectorize(keyterms: any) {
             console.log("vectorizing...");
-            keyterms = ["father", "king"];
+            //keyterms = ["father", "king"];
             let args = { method: 'POST', uri: Utils.prepend("/recommender"), body: { keyphrases: keyterms }, json: true };
-            requestPromise.post(args).then((value) => {
-                value.forEach((wordvec: any) => {
-                    console.log(wordvec.word);
+            await requestPromise.post(args).then(async (wordvecs) => {
+                var vectorValues = new Set<number[]>();
+                wordvecs.forEach((wordvec: any) => {
+                    //console.log(wordvec.word);
+                    vectorValues.add(wordvec.values as number[]);
                 });
+                ClientRecommender.Instance.mean(vectorValues);
+                //console.log(vectorValues.size);
             });
         }