Upload DBbun_Davis_ML_demo.ipynb

DBbun_Davis_ML_demo.ipynb

@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "0fd4f78b-44e7-424d-8f1c-3bbc29468cba",
    "metadata": {},
    "outputs": [],
@@ -85,7 +85,7 @@
   },
   {
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-   "execution_count": 4,
+   "execution_count": 3,
    "id": "2a2efd9d-75b0-43f4-894b-f7347e3febdb",
    "metadata": {},
    "outputs": [],
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "0ac6980b",
    "metadata": {},
    "outputs": [
@@ -153,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "2a457810",
    "metadata": {},
    "outputs": [],
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "0a9bb0f2",
    "metadata": {},
    "outputs": [
@@ -308,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "bb03cb92",
    "metadata": {},
    "outputs": [
@@ -395,30 +395,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "8b77b4d6",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n",
-      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n",
-      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n",
-      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n",
-      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n",
-      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n",
-      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -427,14 +407,6 @@
       "Saved table: ./ml_tables\\cluster_sizes.csv\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -559,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "73f43632",
    "metadata": {},
    "outputs": [