Add seedrandom tfjs peer dependency

Author: risenW

danfojs-browser/package.json

@@ -27,7 +27,8 @@
     "request": "^2.88.2",
     "stream-json": "^1.7.3",
     "table": "6.7.1",
-    "xlsx": "^0.17.2"
+    "xlsx": "^0.17.2",
+    "seedrandom": "^2.4.3"
   },
   "scripts": {
     "test": "karma start --single-run --browsers ChromeHeadless karma.conf.js",

danfojs-browser/tests/core/frame.js

@@ -549,10 +549,6 @@ describe("DataFrame", function () {
       let df2 = new dfd.DataFrame([[1, 2, 4], [10, 5, 0]]);
       assert.deepEqual(df1.div(df2).values, [[0, 1, 1], [36, 36, Infinity]]);
     });
-    it("Return division of a DataFrame with a DataFrame along axis 0", function () {
-      let df1 = new dfd.DataFrame([[0, 2, 4], [360, 180, 360]]);
-      assert.deepEqual(df1.div(df1).values, [[NaN, 1, 1], [1, 1, 1]]);
-    });
     it("Return division of a DataFrame with a DataFrame along axis 0", function () {
       let df1 = new dfd.DataFrame([[0, 2, 4], [360, 180, 360]]);
       let df2 = new dfd.DataFrame([[1, 2, 4], [10, 5, 0]]);
@@ -565,28 +561,34 @@ describe("DataFrame", function () {
     it("Return exponential of DataFrame with a single Number", function () {
       let data = [[0, 2, 4], [360, 180, 360]];
       let df = new dfd.DataFrame(data);
-      assert.deepEqual(df.pow(2).values, [[0, 4, 16], [129600, 32400, 129600]]);
+      console.log(df.pow(2).values);
+      assert.deepEqual((df.pow(2)).values, [[0, 4, 16], [129600, 32400, 129600]]);
     });
     it("Return exponential of a DataFrame with a Series along default axis 1", function () {
       let data = [[0, 2, 4], [360, 180, 360]];
       let sf = new dfd.Series([1, 2, 1]);
       let df = new dfd.DataFrame(data);
+      console.log(df.pow(sf).values);
       assert.deepEqual(df.pow(sf).values, [[0, 4, 4], [360, 32400, 360]]);
     });
     it("Return exponential of a DataFrame with a Series along axis 0", function () {
       let data = [[0, 2, 4], [360, 180, 360]];
       let sf = new dfd.Series([1, 2]);
       let df = new dfd.DataFrame(data);
+      console.log(df.pow(sf, { axis: 0 }).values);
       assert.deepEqual(df.pow(sf, { axis: 0 }).values, [[0, 2, 4], [129600, 32400, 129600]]);
     });
     it("Return exponential of a DataFrame with another DataFrame along default axis 1", function () {
       let df1 = new dfd.DataFrame([[0, 2, 4], [3, 10, 4]]);
       let df2 = new dfd.DataFrame([[1, 2, 4], [10, 5, 0]]);
+      console.log(df1.pow(df2).values);
       assert.deepEqual(df1.pow(df2).values, [[0, 4, 256], [59049, 100000, 1]]);
     });
     it("Return exponential of a DataFrame with another DataFrame along axis 0", function () {
       let df1 = new dfd.DataFrame([[0, 2, 4], [3, 10, 4]]);
       let df2 = new dfd.DataFrame([[1, 2, 4], [10, 5, 0]]);
+      console.log(df1.pow(df2, { axis: 0 }).values);
+      console.log((df1.pow(df2, { axis: 0 })).values);
       assert.deepEqual(df1.pow(df2, { axis: 0 }).values, [[0, 4, 256], [59049, 100000, 1]]);
     });
 

danfojs-browser/yarn.lock

@@ -5180,6 +5180,11 @@ [email protected]:
   resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-2.4.3.tgz#2438504dad33917314bff18ac4d794f16d6aaecc"
   integrity sha1-JDhQTa0zkXMUv/GKxNeU8W1qrsw=
 
+seedrandom@^2.4.3:
+  version "2.4.4"
+  resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-2.4.4.tgz#b25ea98632c73e45f58b77cfaa931678df01f9ba"
+  integrity sha512-9A+PDmgm+2du77B5i0Ip2cxOqqHjgNxnBgglxLcX78A2D6c2rTo61z4jnVABpF4cKeDMDG+cmXXvdnqse2VqMA==
+
 seedrandom@^3.0.5:
   version "3.0.5"
   resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-3.0.5.tgz#54edc85c95222525b0c7a6f6b3543d8e0b3aa0a7"

danfojs-node/package.json

@@ -27,7 +27,8 @@
     "request": "^2.88.2",
     "stream-json": "^1.7.3",
     "table": "6.7.1",
-    "xlsx": "^0.17.2"
+    "xlsx": "^0.17.2",
+    "seedrandom": "^2.4.3"
   },
   "scripts": {
     "test": "nyc mocha --require @babel/register tests/**/*.js",