Updated notebooks.

notebooks/JBrainy model investigation.ipynb

@@ -16,16 +16,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +43,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the JBrainy directory to the path\n",
+    "import sys\n",
+    "sys.path.append('../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,20 +91,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = load_training_data(\"data/jmh-results-9307f70f.csv\",\n",
-    "                                      \"software-perf.csv\",\n",
-    "                                      \"data/hardware-perf-data.csv\")\n",
+    "data = load_training_data(\"../data/jmh-results-9307f70f.csv\",\n",
+    "                          \"../data/software-counters-21-01-20.csv\",\n",
+    "                          \"../data/hardware-perf-data.csv\")\n",
     "\n",
     "benchmark_data = data[\"data\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -383,7 +403,7 @@
        "[8 rows x 101 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -401,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -419,7 +439,7 @@
        "       'addAll(int, Collection)', 'clear()', 'contains(Object)',\n",
        "       'containsAll(Collection)', 'containsKey(Object)',\n",
        "       'containsValue(Object)', 'entrySet()', 'equals(Object)',\n",
-       "       'get(Object)', 'hashCode()', 'indexOf(Object)', 'isEmpty()',\n",
+       "       'get(int)', 'hashCode()', 'indexOf(Object)', 'isEmpty()',\n",
        "       'iterator()', 'keySet()', 'lastIndexOf(Object)', 'listIterator()',\n",
        "       'listIterator(int)', 'put(Object, Object)', 'putAll(Map)',\n",
        "       'remove(Object)', 'remove(int)', 'removeAll(Collection)',\n",
@@ -441,7 +461,7 @@
        "      dtype=object)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -459,7 +479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -509,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -518,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -828,7 +848,7 @@
        "[8 rows x 101 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -843,7 +863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -857,7 +877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -867,7 +887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4507,14 +4527,14 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.15+"
   }
  },
  "nbformat": 4,

notebooks/Plotting Julia.ipynb

@@ -8407,7 +8407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -8417,7 +8417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -8493,7 +8493,7 @@
        "│ 638529 │ Set       │ 499   │ 1000  │ 10000               │ LinkedHashSet  │"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }