Add new templates, examples and tests for the GaussianNB classifier

nok · Dec 2, 2017 · b68c5df · b68c5df
1 parent 3ed7609
commit b68c5df
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Showing 12 changed files with 616 additions and 16 deletions.
diff --git a/examples/estimator/classifier/GaussianNB/java/basics.ipynb b/examples/estimator/classifier/GaussianNB/java/basics.ipynb
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "scrolled": false
    },
@@ -137,9 +137,9 @@
       "            }\n",
       "\n",
       "            // Parameters:\n",
-      "            double[] priors = {0.33333333333333331, 0.33333333333333331, 0.33333333333333331};\n",
-      "            double[][] sigmas = {{0.12176400309242481, 0.14227600309242491, 0.029504003092424898, 0.011264003092424885}, {0.26110400309242499, 0.096500003092424902, 0.21640000309242502, 0.038324003092424869}, {0.39625600309242481, 0.10192400309242496, 0.29849600309242508, 0.073924003092424875}};\n",
-      "            double[][] thetas = {{5.0059999999999993, 3.4180000000000006, 1.464, 0.24399999999999991}, {5.9359999999999999, 2.7700000000000005, 4.2599999999999998, 1.3259999999999998}, {6.5879999999999983, 2.9739999999999998, 5.5519999999999996, 2.0259999999999998}};\n",
+      "            double[] priors = {0.333333333333, 0.333333333333, 0.333333333333};\n",
+      "            double[][] sigmas = {{0.121764003092, 0.142276003092, 0.0295040030924, 0.0112640030924}, {0.261104003092, 0.0965000030924, 0.216400003092, 0.0383240030924}, {0.396256003092, 0.101924003092, 0.298496003092, 0.0739240030924}};\n",
+      "            double[][] thetas = {{5.006, 3.418, 1.464, 0.244}, {5.936, 2.77, 4.26, 1.326}, {6.588, 2.974, 5.552, 2.026}};\n",
       "\n",
       "            // Prediction:\n",
       "            GaussianNB clf = new GaussianNB(priors, sigmas, thetas);\n",
@@ -148,18 +148,94 @@
       "\n",
       "        }\n",
       "    }\n",
-      "}\n"
+      "}\n",
+      "CPU times: user 1.06 ms, sys: 777 µs, total: 1.83 ms\n",
+      "Wall time: 1.15 ms\n"
      ]
     }
    ],
    "source": [
+    "%%time\n",
+    "\n",
     "from sklearn_porter import Porter\n",
     "\n",
     "porter = Porter(clf)\n",
     "output = porter.export()\n",
     "\n",
     "print(output)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run classification in Java:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the transpiled estimator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with open('GaussianNB.java', 'w') as f:\n",
+    "    f.write(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compiling:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "javac -cp . GaussianNB.java"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prediction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "java -cp . GaussianNB 1 2 3 4"
+   ]
   }
  ],
  "metadata": {