Fixed the Projector infinite loop issue

When closure compilation is turned on, it uses prototype definitions to
mangle and optimize the calls. This made some method not defined
statically problematic and it seems to have turned `numeric.div` into
something else.

According to numericjs, unlike other methods we use like dot and
transpose, is genearted dynamically from numeric.ops2 and Closure seems
to not handle it well.
https://github.com/sloisel/numeric/blob/master/src/numeric.js

Concretely, it turns numeric.div(a, b) into mangledName(numeric, a) and
this led division operation to infinitely loop.
```
c = d.slice(0, a.PCA_SAMPLE_SIZE);
c = Oa(numeric, numeric.dot(numeric.transpose(c), c));
var e = numeric.svd(c)
```
In above code snippet, `Oa` is the `div` method.

tensorboard/plugins/projector/vz_projector/data.ts

@@ -261,14 +261,18 @@ export class DataSet {
       if (dim > PCA_SAMPLE_DIM) {
         vectors = vector.projectRandom(vectors, PCA_SAMPLE_DIM);
       }
-      let sampledVectors = vectors.slice(0, PCA_SAMPLE_SIZE);
-
-      let sigma = numeric.div(
-          numeric.dot(numeric.transpose(sampledVectors), sampledVectors),
-          sampledVectors.length);
-      let svd = numeric.svd(sigma);
-
-      let variances: number[] = svd.S;
+      const sampledVectors = vectors.slice(0, PCA_SAMPLE_SIZE);
+      const {dot, transpose, svd: numericSvd} = numeric;
+      // numeric dynamically generates `numeric.div` and Closure compiler has
+      // incorrectly compiles `numeric.div` property accessor. We use below
+      // signature to prevent Closure from mangling and guessing.
+      const div = numeric['div'];
+
+      const scalar = dot(transpose(sampledVectors), sampledVectors);
+      const sigma = div(scalar, sampledVectors.length);
+      const svd = numericSvd(sigma);
+
+      const variances: number[] = svd.S;
       let totalVariance = 0;
       for (let i = 0; i < variances.length; ++i) {
         totalVariance += variances[i];
@@ -277,7 +281,6 @@ export class DataSet {
         variances[i] /= totalVariance;
       }
       this.fracVariancesExplained = variances;
-
       let U: number[][] = svd.U;
       let pcaVectors = vectors.map(vector => {
         let newV = new Float32Array(NUM_PCA_COMPONENTS);
@@ -392,7 +395,7 @@ export class DataSet {
       let sampledIndices =
           this.shuffledDataIndices.slice(0, TSNE_SAMPLE_SIZE);
       let labels = new Array(sampledIndices.length);
-      sampledIndices.forEach((index, i) => 
+      sampledIndices.forEach((index, i) =>
           labels[i] = this.points[index].metadata[superviseColumn].toString());
       this.superviseLabels = labels;
     }