diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index cadedb30..e9555440 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -48,7 +48,7 @@ jobs:
       run: |
         pip install notebook>=5.2.2
         python Otherfiles/notebook_check.py
-      if: matrix.python-version == 3.8 && matrix.os == 'ubuntu-20.04'
+      if: matrix.python-version == 3.9 && matrix.os == 'ubuntu-20.04'
     - name: Other tests
       run: |
           python -m vulture pycm/ Otherfiles/ setup.py --min-confidence 65 --exclude=__init__.py --sort-by-size
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 35224ceb..4e881145 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
 and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
 
 ## [Unreleased]
+## [3.9] - 2023-05-01
 ### Added
 - `OVERALL_PARAMS` dictionary
 - `__imbalancement_handler__` function
@@ -691,7 +692,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - TPR
 - documents and `README.md`
 
-[Unreleased]: https://github.com/sepandhaghighi/pycm/compare/v3.8...dev
+[Unreleased]: https://github.com/sepandhaghighi/pycm/compare/v3.9...dev
+[3.9]: https://github.com/sepandhaghighi/pycm/compare/v3.8...v3.9
 [3.8]: https://github.com/sepandhaghighi/pycm/compare/v3.7...v3.8
 [3.7]: https://github.com/sepandhaghighi/pycm/compare/v3.6...v3.7
 [3.6]: https://github.com/sepandhaghighi/pycm/compare/v3.5...v3.6
diff --git a/Document/Distance.ipynb b/Document/Distance.ipynb
index 2d9e7622..5995a019 100644
--- a/Document/Distance.ipynb
+++ b/Document/Distance.ipynb
@@ -1608,9 +1608,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 1.5652475842498528, 1: 0.7071067811865475, 2: 0.31622776601683794}"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.Dennis)"
    ]
@@ -1648,9 +1659,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 1.0, 1: 0.47759225007251715, 2: 0.2542302383508219}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.Digby)"
    ]
@@ -1689,9 +1711,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.14583333333333334, 1: 0.041666666666666664, 2: 0.041666666666666664}"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.Dispersion)"
    ]
@@ -1729,9 +1762,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.4666666666666667, 1: 0.06666666666666667, 2: 0.02857142857142857}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.Doolittle)"
    ]
@@ -1769,9 +1813,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: -0.012698412698412698, 1: -0.009259259259259259, 2: -0.02142857142857143}"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.Eyraud)"
    ]
@@ -1809,9 +1864,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.5509898714915045, 1: 0.11957315586905015, 2: 0.3435984122732345}"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.FagerMcGowan)"
    ]
@@ -1849,9 +1915,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.5416666666666666, 1: 0.4166666666666667, 2: 0.4166666666666667}"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.Faith)"
    ]
@@ -1889,9 +1966,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.875, 1: 0.8421052631578947, 2: 0.6153846153846154}"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.FleissLevinPaik)"
    ]
@@ -1929,9 +2017,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 2.4, 1: 2.0, 2: 1.2}"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.ForbesI)"
    ]
@@ -1969,9 +2068,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 1.0, 1: 0.3333333333333333, 2: 0.2}"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.ForbesII)"
    ]
@@ -2009,9 +2119,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 5.0, 1: 0.5, 2: 2.5}"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.Fossum)"
    ]
@@ -2050,9 +2171,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 4.947742862177545, 1: 1.1129094954405283, 2: 0.4195337173255813}"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.GilbertWells)"
    ]
@@ -2091,9 +2223,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.7322795271987701, 1: 0.6666666666666666, 2: 0.5533003790381138}"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.Goodall)"
    ]
@@ -2133,9 +2276,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.5, 1: 0.0, 2: 0.09090909090909091}"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.GoodmanKruskalLambda)"
    ]
@@ -2175,9 +2329,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.5, 1: -0.2, 2: 0.09090909090909091}"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.GoodmanKruskalLambdaR)"
    ]
@@ -2216,9 +2381,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.6, 1: 0.0, 2: 0.0}"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.GuttmanLambdaA)"
    ]
@@ -2257,9 +2433,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.3333333333333333, 1: 0.0, 2: 0.16666666666666666}"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.GuttmanLambdaB)"
    ]
@@ -2298,9 +2485,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.6666666666666666, 1: 0.5, 2: 0.16666666666666666}"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.Hamann)"
    ]
@@ -2340,9 +2538,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.6592592592592592, 1: 0.3494318181818182, 2: 0.4068287037037037}"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.HarrisLahey)"
    ]
@@ -2381,9 +2590,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.6888888888888889, 1: 0.48863636363636365, 2: 0.4097222222222222}"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.HawkinsDotson)"
    ]
@@ -2422,9 +2642,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.12121212121212122, 1: 0.09090909090909091, 2: 0.030303030303030304}"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.KendallTau)"
    ]
@@ -2463,9 +2694,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.0, 1: -0.2, 2: -0.17647058823529413}"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.KentFosterI)"
    ]
@@ -2504,9 +2746,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.0, 1: -0.06451612903225801, 2: -0.15384615384615394}"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.distance(metric=DistanceType.KentFosterII)"
    ]
diff --git a/Document/Document.ipynb b/Document/Document.ipynb
index e5975dee..b5adf791 100644
--- a/Document/Document.ipynb
+++ b/Document/Document.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Version : 3.8 <a class=\"tocSkip\"></a>"
+    "### Version : 3.9 <a class=\"tocSkip\"></a>"
    ]
   },
   {
@@ -311,7 +311,7 @@
    "metadata": {},
    "source": [
     "### Source code\n",
-    "- Download [Version 3.8](https://github.com/sepandhaghighi/pycm/archive/v3.8.zip) or [Latest Source](https://github.com/sepandhaghighi/pycm/archive/dev.zip)\n",
+    "- Download [Version 3.9](https://github.com/sepandhaghighi/pycm/archive/v3.9.zip) or [Latest Source](https://github.com/sepandhaghighi/pycm/archive/dev.zip)\n",
     "- Run `pip install -r requirements.txt` or `pip3 install -r requirements.txt` (Need root access)\n",
     "- Run `python3 setup.py install` or `python setup.py install` (Need root access)"
    ]
@@ -324,7 +324,7 @@
     "\n",
     "\n",
     "- Check [Python Packaging User Guide](https://packaging.python.org/installing/)     \n",
-    "- Run `pip install pycm==3.8` or `pip3 install pycm==3.8` (Need root access)"
+    "- Run `pip install pycm==3.9` or `pip3 install pycm==3.9` (Need root access)"
    ]
   },
   {
@@ -674,6 +674,8 @@
        " 'Lambda B': 0.42857142857142855,\n",
        " 'Mutual Information': 0.5242078379544426,\n",
        " 'NIR': 0.5,\n",
+       " 'NPV Macro': 0.7904761904761904,\n",
+       " 'NPV Micro': 0.7916666666666666,\n",
        " 'Overall ACC': 0.5833333333333334,\n",
        " 'Overall CEN': 0.4638112995385119,\n",
        " 'Overall J': (1.225, 0.4083333333333334),\n",
@@ -961,7 +963,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.8-py3.5.egg\\pycm\\pycm_util.py:399: RuntimeWarning: Used classes is not a subset of classes in actual and predict vectors.\n"
+      "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.9-py3.5.egg\\pycm\\pycm_util.py:400: RuntimeWarning: Used classes is not a subset of classes in actual and predict vectors.\n"
      ]
     }
    ],
@@ -1282,6 +1284,8 @@
        " 'Lambda B': 0.42857142857142855,\n",
        " 'Mutual Information': 0.5242078379544426,\n",
        " 'NIR': 0.5,\n",
+       " 'NPV Macro': 0.7904761904761904,\n",
+       " 'NPV Micro': 0.7916666666666666,\n",
        " 'Overall ACC': 0.5833333333333334,\n",
        " 'Overall CEN': 0.4638112995385119,\n",
        " 'Overall J': (1.225, 0.4083333333333334),\n",
@@ -1849,7 +1853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1858,18 +1862,170 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ACC': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'AGF': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'AGM': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'AM': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'AUC': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'AUCI': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'AUPR': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'BB': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'BCD': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'BM': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'CEN': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'DOR': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'DP': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'DPI': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'ERR': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'F0.5': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'F1': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'F2': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'FDR': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'FN': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'FNR': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'FOR': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'FP': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'FPR': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'G': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'GI': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'GM': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'HD': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'IBA': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'ICSI': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'IS': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'J': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'LS': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'MCC': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'MCCI': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'MCEN': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'MK': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'N': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'NLR': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'NLRI': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'NPV': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'OC': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'OOC': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'OP': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'P': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'PLR': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'PLRI': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'POP': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'PPV': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'PRE': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'Q': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'QI': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'RACC': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'RACCU': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'TN': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'TNR': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'TON': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'TOP': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'TP': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'TPR': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'Y': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'dInd': {0: 'None', 1: 'None', 2: 'None'},\n",
+       " 'sInd': {0: 'None', 1: 'None', 2: 'None'}}"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm3.class_stat"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'95% CI': 'None',\n",
+       " 'ACC Macro': 'None',\n",
+       " 'ARI': 'None',\n",
+       " 'AUNP': 'None',\n",
+       " 'AUNU': 'None',\n",
+       " 'Bangdiwala B': 'None',\n",
+       " 'Bennett S': 'None',\n",
+       " 'CBA': 'None',\n",
+       " 'CSI': 'None',\n",
+       " 'Chi-Squared': 'None',\n",
+       " 'Chi-Squared DF': 'None',\n",
+       " 'Conditional Entropy': 'None',\n",
+       " 'Cramer V': 'None',\n",
+       " 'Cross Entropy': 'None',\n",
+       " 'F1 Macro': 'None',\n",
+       " 'F1 Micro': 'None',\n",
+       " 'FNR Macro': 'None',\n",
+       " 'FNR Micro': 'None',\n",
+       " 'FPR Macro': 'None',\n",
+       " 'FPR Micro': 'None',\n",
+       " 'Gwet AC1': 'None',\n",
+       " 'Hamming Loss': 'None',\n",
+       " 'Joint Entropy': 'None',\n",
+       " 'KL Divergence': 'None',\n",
+       " 'Kappa': 'None',\n",
+       " 'Kappa 95% CI': 'None',\n",
+       " 'Kappa No Prevalence': 'None',\n",
+       " 'Kappa Standard Error': 'None',\n",
+       " 'Kappa Unbiased': 'None',\n",
+       " 'Krippendorff Alpha': 'None',\n",
+       " 'Lambda A': 'None',\n",
+       " 'Lambda B': 'None',\n",
+       " 'Mutual Information': 'None',\n",
+       " 'NIR': 'None',\n",
+       " 'NPV Macro': 'None',\n",
+       " 'NPV Micro': 'None',\n",
+       " 'Overall ACC': 'None',\n",
+       " 'Overall CEN': 'None',\n",
+       " 'Overall J': 'None',\n",
+       " 'Overall MCC': 'None',\n",
+       " 'Overall MCEN': 'None',\n",
+       " 'Overall RACC': 'None',\n",
+       " 'Overall RACCU': 'None',\n",
+       " 'P-Value': 'None',\n",
+       " 'PPV Macro': 'None',\n",
+       " 'PPV Micro': 'None',\n",
+       " 'Pearson C': 'None',\n",
+       " 'Phi-Squared': 'None',\n",
+       " 'RCI': 'None',\n",
+       " 'RR': 'None',\n",
+       " 'Reference Entropy': 'None',\n",
+       " 'Response Entropy': 'None',\n",
+       " 'SOA1(Landis & Koch)': 'None',\n",
+       " 'SOA10(Pearson C)': 'None',\n",
+       " 'SOA2(Fleiss)': 'None',\n",
+       " 'SOA3(Altman)': 'None',\n",
+       " 'SOA4(Cicchetti)': 'None',\n",
+       " 'SOA5(Cramer)': 'None',\n",
+       " 'SOA6(Matthews)': 'None',\n",
+       " 'SOA7(Lambda A)': 'None',\n",
+       " 'SOA8(Lambda B)': 'None',\n",
+       " 'SOA9(Krippendorff Alpha)': 'None',\n",
+       " 'Scott PI': 'None',\n",
+       " 'Standard Error': 'None',\n",
+       " 'TNR Macro': 'None',\n",
+       " 'TNR Micro': 'None',\n",
+       " 'TPR Macro': 'None',\n",
+       " 'TPR Micro': 'None',\n",
+       " 'Zero-one Loss': 'None'}"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm3.overall_stat"
    ]
@@ -1899,7 +2055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1908,7 +2064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -1917,7 +2073,7 @@
        "pycm.ConfusionMatrix(classes: ['L1', 'L2', 'L3'])"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1975,7 +2131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1986,7 +2142,7 @@
        " 2: {'FN': [0, 3, 7], 'FP': [5, 10], 'TN': [1, 4, 6, 9], 'TP': [2, 8, 11]}}"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2030,7 +2186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -2041,7 +2197,7 @@
        "       [0, 2, 3]])"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2052,7 +2208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -2063,7 +2219,7 @@
        "       [0. , 0.4, 0.6]])"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2074,7 +2230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -2084,7 +2240,7 @@
        "       [0. , 1. ]])"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2148,7 +2304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -2225,7 +2381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2237,22 +2393,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x51b8410>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x4c5def0>"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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lwJeAWWXdETEkJmvqiaStgd/bdnmrbQrwYKufa+fZ2InaSNLSmv05wIHAJsC88ov5re2DuxhDREySTiU7SZcB+1Dc21tK0eBaH8D2ucBhwLskrQEeA46w3fJ2V9eSne1GXeQ53bpeRPRWp5Kd7SNbnD8LOGu89XazZRcRFTHoCwF8h+YLAaT7GRFrDfKzsf86aVFExMAb2Jad7f+czEAiYrANbLIbJWkn4FPArjz97WJ/1cW4ImKADMI9u3Y62V8BzgHWUKxUfBFwcTeDiojBMwxvF9vQ9nWAbN9r+2NA3hkbEU8zDG8Xe1zSFOBuSScA/w08u7thRcSgGYZu7EnARsCJwO7A0cAx3QwqIgZLJxfv7JZ23i52c/lxFfD27oYTEYOq31t27YzGXk+DycW2c98uItYa+GQHnFLzeQPgUIqR2YiItQY+2dleUHfop5Iy4Tgi1hrn4p090U439pk1u1MoBim27lpEETGQBr5lR7EkuylWKV4D/Jri/REREWsNQ7L7a9uP1x6QNK1L8UTEgOr3ZNdOJ/u/Ghz7WacDiYjBNrDz7Mp13mcAG5bvjhiNcjOKScYREcBgLATQrBv7dxRvFNsG+Df+kuweAT7U3bAiYtAM7Gis7QuBCyUdavubkxhTRAygfm/ZtZOKd5e0xeiOpC0lfbKLMUXEAOr3e3btJLsDbP9hdMf2wxSvRIyIAIZkIQBgqqRptp8AkLQhkKknEfE0/d6NbSfZXQJcJ+krFJOL30GxWnFExFoDO0AxyvaZkm4F9qMYkf2E7Wu7HllEDIxed1Hb0dZLsm1/F/gugKS9JJ1t+/iuRhYRA2Uokp2k3YAjgbdQPBv7rW4GFRGDZ2CTnaSdgSMoktyDwOUUL93Zd5Jii4gB0qlkJ+nLwEHAA7Zf3OC8gM9RzAp5FJhte2GrepvdUbwLeB3wRtuvsv0F4KmJBB8Rw6+DU08uAGY1OX8AsFO5HUfxqteWmiW7Q4HfAddLOk/S6/jLI2MREWuNLt7ZiVcp2v4x8FCTIocAF7lwI7CFpOe2qrfZ42JXAFdI2hh4E/AvwHMknQNcYft7LaOOrps3b16vQ+h7+Y4mxzi6sdMljdTsz7U9dxyXmgHcV7O/tDy2rNkPtTP15E/ApcCl5arFhwMfBJLsImKtcSS7FbZnrsulGhz7Hy8FqzeuWYC2H7L9pbxZLCLqTeLjYkuBbWv2twHub/VD/T3lOSIGwiQ/GzsfeJsKewIrbTftwkKb8+wiIlrp1ONiki4D9qG4t7cU+CiwPoDtc4GrKaadLKGYevL2dupNsouIjujUPDvbR7Y4b2DcT3Al2UVERwzsExQREe0amoUAIiJaSbKLiEpIsouIShj4xTsjIlrJPbuIqIwku4iohCS7iKiEJLuIqIQku4gYeqOLd/azJLuI6Ii07CKiEpLsIqISkuwiYuhlUnFEVEYGKCKiEtKyi4hKSLKLiKGXe3YRURlJdhFRCUl2EVEJGY2NiKGXe3YRURlJdhFRCUl2EVEJSXYRUQn9nuy6NnwiaVWDY++UdJukRZJukLRrt64fEZNndPHOdrY265sl6ZeSlkj6YIPzsyUtL3PJIkn/1KrOyW7ZfdX2uQCSDgbmALMmOYaI6IJOtewkTQXOBl4PLAVuljTf9h11RS+3fUK79U7qxBjbj9Tsbgx4Mq8fEd0zOv2k1daGPYAltu+x/STwNeCQdY1v0mcBSjpe0v8DzgROnOzrR0R3dDDZzQDuq9lfWh6rd6ikWyV9Q9K2rSqd9GRn+2zbOwAfAD7SqIyk4ySNSBpZvnz55AYYEePWbqIrk9300X/f5XZcfXUNLlHfC/wOsL3tlwA/AC5sFWMvR2O/BpzT6ITtucBcgJkzZ6arGzEAxvG42ArbM5ucXwrUttS2Ae6vLWD7wZrd84AzWsbXbnSdIGmnmt03AHdP5vUjons62I29GdhJ0gskPQM4Aphfd63n1uweDNzZqtJutuw2krS0Zn8OsJ2k/YDVwMPAMV28fkRMok6NxtpeI+kE4FpgKvBl24slnQ6M2J4PnFjO6FgDPATMblVv15Kd7f5eAiEiOqbTCwHYvhq4uu7YaTWfTwVOHU+deYIiIjqi35+gSLKLiI5IsouISsjinREx9LJ4Z0RURpJdRFRCkl1EVEKSXURUQpJdRAy90cU7+1mSXUR0RFp2EVEJSXYRUQlJdhEx9DKpOCIqIwMUEVEJadlFRCUk2UXE0Ms9u4iojCS7iKiEJLuIGHp5XCwiKiMtu4iohCS7iKiEJLuIqIQku4gYeplnFxGVkdHYiKiEtOwiohL6Pdn1d7szIgbC6D27drY265sl6ZeSlkj6YIPz0yRdXp6/SdL2repMsouIjuhUspM0FTgbOADYFThS0q51xY4FHra9I/BZ4IxW9SbZRURHTJkypa2tDXsAS2zfY/tJ4GvAIXVlDgEuLD9/A3idWmTSvr9nt2DBghWS7u11HDWmAyt6HUSfy3fUXD9+P9utyw8vWLDgWknT2yy+gaSRmv25tufW7M8A7qvZXwq8vK6OtWVsr5G0EtiKJt9r3yc728/qdQy1JI3YntnrOPpZvqPmhvH7sT2rg9U1aqF5AmWeJt3YiOg3S4Fta/a3Ae4fq4yk9YDNgYeaVZpkFxH95mZgJ0kvkPQM4Ahgfl2Z+cAx5efDgB/abtqy6/tubB+a27pI5eU7ai7fTxPlPbgTgGuBqcCXbS+WdDowYns+cD5wsaQlFC26I1rVqxbJMCJiKKQbGxGVkGQXEZWQZNeEpFUNju0taaGkNZIO60Vc/WSM7+i9ku6QdKuk6ySt0xyuQTbG9/NOSbdJWiTphgZPB0QXJNmN32+B2cBXexxHP/sFMNP2Syhmt5/Z43j6zVdt/43t3Si+mzm9DqgKkuzGyfZvbN8K/LnXsfQr29fbfrTcvZFinlSUbD9Ss7sxLSbDRmdk6kl027HANb0Oot9IOh54L/AM4LU9DqcS0rKLrpF0FDAT+EyvY+k3ts+2vQPwAeAjvY6nCpLsoisk7Qd8GDjY9hO9jqePfQ14U6+DqIIku+g4SS8DvkSR6B7odTz9RtJONbtvAO7uVSxVkicompD0Z57+APIc4CfAFcCWwOPA72y/qAfh9YUxvqMDgb8BlpXHfmv74MmOrR+M8f1sB+wHrAYeBk6wvbgH4VVKkl1EVEK6sRFRCUl2EVEJSXYRUQlJdhFRCUl2EVEJSXYDQtJT5SoZt0uaJ2mjdahrH0lXlp8PbvQS4pqyW0h69wSu8TFJp4xx7m3l77G4XB3llPL4BVlJJrolyW5wPGZ7N9svBp4E3ll7UoVx//e0Pd/2p5sU2QIYd7Ibi6QDgJOA/cv5iX8LrOxU/RFjSbIbTD8BdpS0vaQ7JX0RWAhsK2l/ST8r19ybJ2kTAEmzJN0l6QbgH0YrkjRb0lnl5+dIukLSLeX2SuDTwA5lq/IzZbn3Sbq5XK/u4zV1fVjSLyX9AHjhGLGfCpxi+34A24/bPq++kKTTymvcLmnu6AuQJZ1Ys1be18pjrynjWyTpF5I2HStOSRtLuqr8/W6X9JZ1+O8Qg8R2tgHYgFXln+sB/wG8C9ieYqmpPctz04EfAxuX+x8ATgM2oHih8E4U79v8OnBlWWY2cFb5+XLgpPLzVIrX020P3F4Tx/4UL4wRxf8srwT2BnYHbgM2AjYDllAktfrf4yFg8zF+xwuAw8rPz6w5fjHwxvLz/cC08vMW5Z/fAfYqP29SfkdjxXkocF5N3Q1jyTZ8W1p2g2NDSYuAEYoFRM8vj99r+8by857ArsBPy7LHUDyatAvwa9t3u/gXfskY13gtcA6A7adsN+pe7l9uv6BoTe5CkURfDVxh+1EX67XVv/puvPaVdJOk28q4Rh/JuxW4tFxRZU157KfAHEknUiTANU3ivA3YT9IZkl49xu8YQyjr2Q2Ox1ysbLtW2bP7U+0h4Pu2j6wrtxudWyBSwKdsf6nuGie1eY3FFK3AH455AWkD4IsUqx3fJ+ljFK1TKB6c3xs4GPjfkl5k+9OSrqJ4JvfGcsWVhnGW9e9elv2UpO/ZPr2NuGPApWU3XG4E9pK0I4CkjSTtDNwFvEDSDmW5I8f4+esousdImippM+CPwKY1Za4F3lFzL3CGpGdTdJ//XtKG5T2zN45xjU8BZ0rauvz5aWWLrNZoYltRXuewsuwUYFvb1wPvpxg82UTSDrZvs30GRct3l7HilPQ84FHblwD/SjFAEhWQlt0Qsb1c0mzgMknTysMfsf0rSccBV0laAdwAvLhBFe8B5ko6FngKeJftn0n6qaTbgWtsv0/SXwM/K1uWq4CjbC+UdDmwCLiXYhClUYxXS3oO8INy0MHAl+vK/EHSeRRdzt9QvCEeivuIl0janKLl9tmy7Cck7VvGfEcZ5xON4gR2BD6jYjWS1ZTJPYZfVj2JiEpINzYiKiHJLiIqIckuIiohyS4iKiHJLiIqIckuIiohyS4iKuH/AzGYSAq9I88AAAAAAElFTkSuQmCC",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -2269,22 +2425,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x522e4d0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0xf95d430>"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -2301,22 +2457,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0xfc48270>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0xf996fb0>"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -2333,22 +2489,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1cbf5d90>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce50750>"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -2365,22 +2521,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x525e270>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce7da90>"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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HWLb4OwCWzFuQqijXQL4snUS7bbvRtntXmm28MfscN5DJL7xUI9+ooX+m72/Pp9kmzRuhlhsmZbnUWo5UDAwD+gM7AydL2jlF1qfMrGe03B9LXx5LHxhLvxm43cx6AIuAX2aqR4MGRzP7Pra6GWANefyG1qZjBxaVzalaXzx7Lm06dKiWp9323dm6x7Zc9vJIfvfaKHbuc3AD19LFLSqfS5vOHavWW3fqwKI5c6vl+XrSxyyaXc7u/Q9r6Opt0HI4b3VvYIaZzYxadk8CR9elbtFc1YcCz0ZJDxHmrk6rwe85SrpQ0ueEluPgNHnOlVQqqXRFPsfPFP8PzKqfT1GzZrTbrjt/6Xc8/xh0IacPu5UWW7RqoAq6Gqzm/7f4rHqVlZU8c8UfOfbGqxqyVhu8bFuN0TdZkvj9jpZzk4rrBMyKrZeRYqpV4FhJkyU9K6lLLH2TqNx3JCUC4FbAYjNbU0uZVRq8t9rMhgHDJJ0CXAmcmSLPcGA4QFsV5210XDR7Dm06r20ptu7UnsVzq7dCFs+ewxcT3qdyzRoWfDWLb6Z/TrvtuvtN/kbSplMHFpWVV60vnj2H1u23rlpfuWQp5VM/5bZ+JwDw/TfzuPv4s7ngmRF03WuPBq/vBkNal6lZ55tZr0ylpUhLjgPPA0+Y2cqoL+MhQssQYBszK5e0LfCqpI+A76kpY2xpzN7qJ6mlWZvvvpr4Ie22685WXbtQvNFG7HPc0UwePa5ankkvjGWHg/YHYLOt2tBu+22Z/+VXjVFdB3Tdew++/TzcJ16zahUTnh3F7gP6VG1vsUUr/vL1ZG6Y9jY3THub7r339MAYKVJ2SxbKgHhLsDNQHs9gZgvMbGW0eh+wd2xbefTvTGA8sCcwH2gtKdEgrFFmsgZtOUrqYWaJrtoBwPRM+fNdZUUFT11yFYOfe4yi4iL+9/BTzJn2GUddeSlfvf8hk8eMY+q48ez8s4O4pvRVKisr+dcfrueHhYsbu+pNVnGzZpz4l+u46+jTqKyoYP8zTqTjzjsy6ro/03Wv3dljQN/aC2milLsXOk4AekS9y7OBk4BTqh1L6mBmiRv6A4FpUXobYFnUoiwBDgBuMTOT9BpwHKFhdibwXMbzSb4HliuSKqkemW8DugKHAasJvUUXmdmUTOW0VbEdy6b1UkdXP/7+Q1ljV8Gtg14/OZjS9z+oU2TbpXlze6J9h9ozAnt8/dXEWi6rkXQEcAdhKM8IM/uTpKFAqZmNknQjISiuARYC55vZJ5L2B+4FKglXxneY2T+iMrdl7VCeD4DTYq3PGuqt5WhmPsDcuSZkHe451srMxgBjktKujn0eAgxJsd//gN3SlDmT0BOeFX980DmXE/n09Es2PDg653Iily3HDYEHR+dcnQlvOTrnXE0i26df8oYHR+dcDoiiApub1YOjc67OBKjAxqd4cHTO1Z28Q8Y551IqsNjowdE5lxvecnTOuRQKLDZ6cHTO1Z0Exd5b7ZxzNflltXPOpVBgsdGDo3Ou7grx8cFah21KukVSK0kbSXpF0nxJpzVE5ZxzeUJCRdkt+SKbMe19o1kDjyS8vnwH4LJ6rZVzLu8UFymrJV9kc1m9UfTvEYQJbRYW2o1X51zdFOJldTbB8XlJnwDLgQsktQVW1G+1nHP5ptAaTbVeVpvZFcB+QC8zWw0so44TbDvnCoxCyzGbJavipH6SPpU0Q9IVKbYPkjRP0qRoOSdK7ynpbUlTojmtT4zt86CkL2L79MxUh1pbjpI2BS4EtgHOBToCOwIvZHeazrmmIFctR0nFwDCgD6GfY4KkUWY2NSnrU2Z2UVLaMuAMM5suqSMwUdJYM0tM6XmZmT2bTT2y6ZB5AFgF7B+tlwHXZ1O4c67pyGHLsTcww8xmmtkqwoyBWV2tmtlniemfo/mrvwXars/5ZBMctzOzWwjTqWJmywn3X51zDghBr6hYWS1AiaTS2HJuUnGdgFmx9bIoLdmx0aXzs5K61KyTegMbA5/Hkv8U7XO7pOaZzimb4LhKUgvAogNuB6Sd69U51xQJKbsFmG9mvWLL8BqF1WRJ688D3cxsd+Bl4KFqBUgdgEeAs8ysMkoeAuwE7EOYu/ryTGeUTXC8BvgP0EXSY8ArwO+y2M8515QUKbuldmVAvCXYGSiPZzCzBWaWaKTdB+yd2CapFTAauNLM3ontM8eClYTbhRnnsK61Q8bMxkl6H9iXENF/bWbza9vPOdfE5G4ozwSgh6TuwGzgJOCU6odSBzObE60OBKZF6RsDI4GHzeyZVPsoNF+PAT7OVIlsHh88AFhhZqOB1sDvJXXN4gSdc01FNE1ClpfVGZnZGuAiYCwh6D1tZlMkDZU0MMo2OBqu8yEwGBgUpZ8AHAQMSjFk5zFJHwEfASXU0rGczSDwe4A9JO1BeGxwBPAw8NMs9nXONQmC4tzNsGVmY4AxSWlXxz4PIdxDTN7vUeDRNGUeui51yOZs1piZEbrS7zKzO4GW63IQ51xhkyi4F09k03JcImkIcBpwUDRAc6Na9nHONTVN7fFB4ETC0J1fmtlcwnijW+u1Vs65vNMkW47AnWZWIWkHwjihJ+q3Ws65vNMEW47/BZpL6kQY43gW8GB9Vso5l2eU5RjHPGo5ZhMcZWbLgF8AfzWznwO71G+1nHP5RsVFWS35IqvgKGk/4FTCqHOA4vqrknMu7yTedpurd5ZtALK55/hrwniikdFAzG2B1+q3Ws65fKP8aRRmJZvHB/9LuO+YWJ9JGJHunHNr5VGrMBvZvOy2LeFFE7sAmyTS13W0uXOugCm/hulkI5uG8GPAJ0B34I/Al4QHw51zbq0Cu+eYTXDcysz+Aaw2s9fN7GzCG3qccw6I4l6B9VZn0yGzOvp3jqQBhPeqda6/Kjnn8lKBXVZnExyvl7QFcAnwV6AV8Nt6rZVzLs/k1yVzNrLprU7MMvgdcEj9Vsc5l68Kbd7qtMFR0l+pOW9DFTPz4TzOuUA0qcvq0garhXMu7+VTZ0s2MgXHp4CWZjYvniipHfB9vdbKOZdf8myYTjYyhfq7gANTpPcBbq+f6jjn8lUu3+coqZ+kTyXNkHRFiu2DJM2LzRNzTmzbmZKmR8uZsfS9JX0UlXmXarlJmik4/sTM/pWcaGaPESawcc65tXI0CDyabWAY0B/YGThZ0s4psj5lZj2j5f5o3y0J00n/mDD16jWS2kT57wHOBXpES79M9cgUHDOdRWHdXHDO1U2iQyY373PsDcwws5lmtgp4kjCHVTYOB8aZ2UIzWwSMA/pJ6gC0MrO3ozmxHiZMz5pWpnuO30rqbWbvxRMl7QPMS7NPznXddUfuHvlwQx3O5cDTnXZo7Cq4dbBoycKclLMOQ3lKJMU7fIeb2fDYeidgVmy9jNASTHaspIOAz4DfmtmsNPt2ipayFOlpZQqOlwFPS3oQmBil9QLOIEyy7ZxzkXWamnW+mfXKXFgNycMKnweeMLOVks4DHgIOzbBvNmVWk/ZsohZj76jQQdEi4Mdm9m6mQp1zTUxuX3ZbBnSJrXcmPLZcxcwWmNnKaPU+YO9a9i2j+mPPNcpMlvEJGTP7lnBz0znnMsvdUJ4JQA9J3YHZhCvVU6ofSh3MbE60OhCYFn0eC9wQ64TpCwwxs4WSlkjaF3iXcAX810yVyObZauecq4WgKDf9tGa2RtJFhEBXDIyIZiEYCpSa2ShgsKSBwBpgIeHKligIXsfa1yoONbPETdXzCZMDtgBejJa0PDg653Ijh4PAzWwMMCYp7erY5yGE6VtS7TsCGJEivRTYNds6eHB0ztVd4p5jAcn04onnyfziiYH1UiPnXB4SFBfWpKSZWo5/brBaOOfyX1NpOZrZ6w1ZEedcHmtKl9UJknoANxKecYzPPrhtPdbLOZdvCiw4ZtP3/gDhge01hDeBPww8Up+Vcs7lm2goTzZLnsimpi3M7BVAZvaVmV1LeEzHOecCUXDBMZuhPCskFQHTo4GZs4F29Vst51zeaYKX1b8BNgUGE55fPB04M+MezrkmRQgVFWW15ItsZh9MPIazFDirfqvjnMtbBdZyzKa3+jVSDAY3M7/v6JwLmuJQHuDS2OdNgGMJPdfOObdWUwuOZjYxKektST5A3DkX07QeHwSqJqxJKCJ0yrSvtxo55/JPE72snsja14yvAb4AflmflXLO5aEmGBx/ZGYr4gmSmtdTfZxzeSl3L7vdUGRzNv9LkfZ2rivinMtzuZtDZoOQ6X2O7QlTF7aQtCdrZ+9qRRgU7pxzQRO753g4YV6GzsBfWBscvwd+X7/Vcs7ll9z2VkvqB9xJmEPmfjO7KU2+44BngH3MrFTSqYRppRN2B/Yys0mSxgMdgOXRtr7RJIIpZXqf40PAQ5KONbN/rsN5Oeeaohy1HCUVA8OAPoQpVSdIGmVmU5PytSQ81lw1VbSZPQY8Fm3fDXjOzCbFdjs1mkumVtncc9xbUutYhdpIuj6bwp1zTURu563uDcwws5lmtgp4Ejg6Rb7rgFuAFSm2AZwMPLEeZwNkFxz7m9nixIqZLQKOWN8DOucK0Tq9z7FEUmlsOTepsE7ArNh6WZS29mihH6SLmb2QoVInUjM4PiBpkqSrpMyROpuhPMWSmpvZyqhSLQAfyuOcqy77y+r5ZtYrU0kp0qre7xC9QvF2ormqU1dFPwaWmdnHseRTzWx2dDn+T8Ibxh5OV0Y2wfFR4BVJD0QVPDtTgc65JkhAUc46ZMqALrH1zkB5bL0lYf7p8VHjrz0wStLA2P3Ek0hqNZrZ7OjfJZIeJ1y+r39wNLNbJE0GDiN8BdeZ2dja9nPONSWCopwN5ZkA9JDUnfBy7ZOAUxIbzew7oKTqyKEX+tJEYIxalscDB8XyNANam9l8SRsBRwIvZ6pENi1HzOw/wH+igxwgaZiZXZjNvs65JkK5eULGzNZEsw6MJQzlGWFmUyQNBUrNbFQtRRwElJnZzFhac2BsFBiLCYHxvkyFZBUcJfUk9PycSHi2+l/Z7Oeca0JyOAjczMYAY5LSrk6T9+Ck9fHAvklpPxBempO1TE/I7EBozp4MLACeIkyydci6HMA51wSo8J6tztRy/AR4AzjKzGYASPptg9TKOZd/CuzxwUyh/lhgLvCapPsk/YzUXezOORd6q7NZ8kTa4GhmI83sRGAnYDzwW2BrSfdI6ttA9XPO5QOt0yDwvFBrTc3sBzN7zMyOJIw3mgRcUe81c87llwJ7Zdk6hXEzW2hm9/rMg865GlSU3ZInshrK45xzGSmng8A3CB4cnXO5kUedLdnw4OicywHl1SVzNjw4OufqTvhltXPOpZRHPdHZ8ODonMsNv6x2zrkk3lvtnHNpeG+1c84l895q55yryXurnXMuDW85Nl1T/vsez1z/N6yikv1POILD/++UlPnef/F17h/8Ry7/1z103W1Hpr1Zyr//fB8Vq9dQvFEzfnH5/7HjfnsBMOH5Vxj798dBonW7rRj059+z+ZZb8MJdD/LW06Np2SZMGT7wkl+y68H78t5zL/Py/U9VHWv2pzO54t/30mXn7ev/C8hD7Q8+kJ5D/4CKivniiWf4ZNjwatu7nfBzdr/ycpbP/QaAGQ88yhdPPEPb/X9Mz2t/X5Wv1Xbb8vYFv6V87MtsP+g0epxzJi27d+Xfu/6YVYsWVSuzzR678bPnn+ad839D2eixbNqpI/vf/zdUXExRs2bMeOARPn/kyfo/+YbmQ3mapsqKCp669k4GP3grrdu35eZjz2f3Q/enQ49u1fKtWLqM8Q//i257/KgqbfM2W3D+vX+i9dYllH/2BX89+3fc+OYzVKyp4Jnrh3H1iw+w+ZZb8K+b72X8oyM5cvAgAA4ddBx9zjmxWvm9jz6M3kcfBoTA+PfzrvLAmIaKitjrT9fw+slnsXzOXA4b80/KX3qF76d/Xi3frFFj+ODKodXS5v3vXcb1DfPIb9x6C/q/OY5vXn8TgPkTJlL+8msc8uwjKY+5+x8u5Zvxb1alrfh2Hq8efSKVq1bTbNNNOfzVF5j90qus+ObbXJ9yI8rtm8Al9QPuJMz3cr+Z3ZQm33HAM8A+ZlYqqRswDfg0yvKOmZ0X5d0beBBoQZiC4ddmZsllJhRWO7gefTn5E9p27UTnSfXDAAAPrUlEQVTJNh1ptvFG7D3gUD585X818j1/xwj6/L+T2Kj5xlVpXXbpQeutw2RpHXp0Y83K1axeuQrMwIyVy5djZqxYuozW7UpqlJlO6Quv0usof0FSOlvuuTtLv/yKH76eReXq1Xz93Gg6Hn7YOpfTeUA/5r72XypWrABg8ZRpLCubnTLv9mefzuzRL7FiwYKqtMrVq6lctRqAouYb59U7DbOWmJo1By+7lVQMDAP6AzsDJ0vaOUW+lsBg4N2kTZ+bWc9oOS+Wfg9wLtAjWvplqkcB/pTqx+K582nToV3Vepv2JXz3zbxqeWZNmc6iOfPY7dD90pbzwX/+S+edt2ej5htTvFEzTvrjb/jTgHMYcsDxzJ3xJfsf378q7+uP/pvrjzyHR664hWXfLalR1sTRr7HPkR4c02nRfmuWlc+tWl8+Zy4t2m9dI1/nI/rSd9wo9ht+Fy06tq+xvcvRR/D1cy9kdbxO/frw+SNP1NzWsT19x43iyAmv8+mw+wqs1Qihtzpn73PsDcwws5lmtgp4Ejg6Rb7rgFuAFbXWTuoAtDKzt6PW4sPAMZn2abDgKOlCSe9KekPS2ZJ6SBoiaf8Uec+VVCqpdN7CRamKawQpWt+xH3RlZSXP3nA3xw45P20J5dO/4N+3DueUoWEqnorVa3jjiVEMee5ebnzrGTrttG24/wgcdMpAhr7yKL8fNZxW7bbinzfeU62sLyZNY+MWm9Bxh+45OLcCleoXMekqqnzca4ze9xBe6jOQb9/4H73vuLna9k3atWWLnXZkbuwyOZ2ef/w9k2+4FausrLFteflcXuozkDEH9KHr8T+neclW63Yu+SD7N4GXJH6/o+XcpJI6AbNi62VRWhVJewJdzCzVX63ukj6Q9LqkA2NllmUqs8bp1H7GOdMFOAA4BzgEeJ5w7f9OckYzG25mvcysV9st2zRgFdNr3b4ti+as/Wu/aO58tohdAq/8YRnl07/g9tN+y5UHn8wXk6by9/Ou5KuPwq2PRXPmMfyCazjz1iG07Rp+JrOmzQCgbddOSGKv/gcz84MpALQq2ZKi4mKKior4yQkD+HLyJ9XqM3H0q/TyVmNGy+fMZdNYS7BFh/YsT2qxrVq0uOqSd+ZjT9Nmt12rbe9yVH9mvzgOW7Om1uO12X1X9rv7dga88yqdBxzOXjdcW+MyfsU33/L9Z9Np++Ne63taGyaxLi3H+Ynf72gZnqK0ZFV/1SQVAbcDl6TINwfYxsz2BC4GHpfUqrYyU2mwDhkzS0yt8ClwekMdN1e67rYT3345m/mz5tB66xImjn6Vs277Q9X2Fi0359b3/l21fvupv+UXV5xH1912ZNn3S7n73CEcfck5bLf32l++1luXMGfGVyxZsJiWW7Xmk7cm0n67rgB89+0CtmgXWheTxr1RrYVYWVnJ+y++zsWP31Hfp53XFk76iM27d2OzLp1ZPvcbtjl6AO9ceHG1PJu0a8uKb8PtkY59f8aSGdU7a7Y55kgm3/iXrI43Zr+fVX3e5/abmPPya5SPfZkWHbZm1aLFVKxYyUZbtKJkn734bPgDdTu5DU5OB4GXERpTCZ2B8th6S2BXYLxCsG0PjJI00MxKgZUAZjZR0ufADlGZnTOUWYP3VmepuFkxJ17zK/529uVUVlSw33H96dijO8/f8QBdd9uB3X92QNp9X39kJPO+KufFYY/w4rDQw/mrB2+h9dYlHHHRGdx2ym8o3qgZW3Zsxxk3Xw7AyFvupWza5yCxVaetOeW6tb/UMyZMpnX7tpRs07F+TzrPWUUF7185lIMe/0cYyvPUs3z/2Qx2uXQwiz78mPJxr9Lj7DPo2PdQrKKCVYsX895v1k6PtGnnTrTo0IF5b79XrdweZ5/Ojhf8PzZpW8LhL49izqv/pfSyPyQfvkqr7bdjj6vXlvvp30fw3Sef5f6EG1vuHh+cAPSQ1B2YDZwEVI2bM7PvgKrLNknjgUuj3uq2wEIzq5C0LaHjZaaZLZS0RNK+hA6cM4C/ZqqEMvRkbxB67bazvTfy4cauhlsHz+4zoLGr4NbBkCUL+XzN6joNUuy103b27vAbssrb7KcnTTSzjPcVJB0B3EEYyjPCzP4kaShQamajkvKOZ21wPBYYCqwBKoBrzOz5KF8v1g7leRH4VaahPN5ydM7lRg6fkDGzMYSxiPG0q9PkPTj2+Z/AP9PkKyVcjmfFg6NzLjf8CRnnnEvmb+VxzrmU5C1H55xLIkFRYYWTwjob51zj8fc5OudcCn7P0TnnkiQeHywgHhydczngvdXOOZeatxydcy6JBMU+NatzztXkl9XOOZeCX1Y751wy75BxzrnUvOXonHNJJCgurHBSWGfjnGs0/uIJ55xLxe85OudckgJ8fLCwQr1zrpFEvdXZLNmUJvWT9KmkGZKuyJDvOEkWzQ+DpD6SJkr6KPr30Fje8VGZk6KlXaY6eMvROZcbOWo5SioGhgF9CFOqTpA0ysymJuVrCQwmzCaYMB84yszKJe0KjAU6xbafGs0lUytvOTrn6i7x+GA2S+16AzPMbKaZrQKeBI5Oke864BZgRSLBzD4ws8R81FOATSQ1X59T8uDonMuN3F1WdwJmxdbLqN76Q9KeQBczeyFDOccCH5jZyljaA9El9VWqpXvdL6udc7mR/WV1iaT4pe1wMxseLynFPlXzS0sqAm4HBqWvinYBbgb6xpJPNbPZ0eX4P4HTgYfTleHB0TmXI1kHx/lm1ivD9jKgS2y9M1AeW29JmH96fNT4aw+MkjTQzEoldQZGAmeY2eeJncxsdvTvEkmPEy7f0wZHv6x2zuWAQssxm6V2E4AekrpL2hg4CRiV2Ghm35lZiZl1M7NuwDtAIjC2BkYDQ8zsraraSc0klUSfNwKOBD7OVAkPjs653MhRcDSzNcBFhJ7macDTZjZF0lBJA2vZ/SJge+CqpCE7zYGxkiYDk4DZwH2ZCvLLaudc3YmcPiFjZmOAMUlpV6fJe3Ds8/XA9WmK3Xtd6uDB0TmXG4X1gIwHR+dcrhRWdPTg6JzLgaw7W/KGB0fnXG54cHTOuRT8lWXOOZeKtxydc6667Ad45w0Pjs653PDg6JxzqXhwdM65GnyCLeecq0HeW+2ccyl5y9E555IU4OyDHhydczniwdE552rylqNzzqVQWLHRg6NzLhe8t9o552ryDhnnnEunsIJjYbWDnXONJ3ezDyKpn6RPJc2QdEWGfMdJMkm9YmlDov0+lXT4upaZ4C1H51wO5O6tPJKKgWFAH8Ic1hMkjTKzqUn5WgKDgXdjaTsTpnLdBegIvCxph2hzrWXGecvROZcbKspuqV1vYIaZzTSzVcCTwNEp8l0H3AKsiKUdDTxpZivN7AtgRlRetmVW2eBbjhM/nja/uMc+XzV2PepBCTC/sSvh1kmh/sy61rWAiR9MGqvNWpdkmX0TSaWx9eFmNjy23gmYFVsvA34cL0DSnkAXM3tB0qVJ+76TtG+n6HPGMpNt8MHRzNo2dh3qg6RSM+tVe063ofCfWXpm1i+HxaW6PreqjVIRcDswaB32TdVktRRpVTb44Oica3LKgC6x9c5AeWy9JbArMD56TVp7YJSkgbXsm6nMGvyeo3NuQzMB6CGpu6SNCR0soxIbzew7Mysxs25m1o1wGT3QzEqjfCdJai6pO9ADeK+2MlPxlmPjGV57FreB8Z9ZAzCzNZIuAsYCxcAIM5siaShQamZpg1qU72lgKrAGuNDMKgBSlZmpHjLLeNntnHNNkl9WO+dcCh4cnXMuBQ+ODUDS0hRp50n6SNIkSW9GI/udcxsIv+fYACQtNbPNk9Jamdn30eeBwAU5HivmnKsDbzk2kkRgjGxGLQNSnXMNy4fyNCJJFwIXAxsDhzZydVwK0c/oDGAV8ADwBnAc8LqZ/a8x6+bql7ccG5GZDTOz7YDLgSsbuz4upS7AAcA5wCHA80ALqj+/6wqQ33NsAKnuOSZtLwIWmdkWDVgt51wG3nJsJJJ6xFYHANMbqy7OuZr8nmPD2FRSWWz9NqCrpMOA1cAi4MxGqZlzLiW/rHbOuRT8sto551Lw4Oiccyl4cHTOuRQ8ODrnXAoeHJ1zLgUPjnlCUkX0Bp+PJT0jadM6lHWwpBeizwNrmTS9taQL1uMY1ybNChffdkZ0HlMkTU3kk/SgpOPW9VjO1QcPjvljuZn1NLNdCc/5nhffqGCdf55mNsrMbsqQpTWwzsExHUn9gd8Afc1sF2Av4Ltcle9crnhwzE9vANtL6iZpmqS7gfeBLpL6Snpb0vtRC3NzAEn9JH0i6U3gF4mCJA2S9Lfo89aSRkr6MFr2B24CtotarbdG+S6TNEHSZEl/jJX1B0mfSnoZ2DFN3YcAl5pZOYCZrTCz+5IzSbo6OsbHkoYrmmZO0uCotTlZ0pNR2k+j+k2S9IGklunqKWkzSaOj8/tY0ol1+Dm4AuZPyOQZSc2A/sB/oqQdgbPM7AJJJYQXWBxmZj9Iuhy4WNItwH2EN//MAJ5KU/xdhLfN/FxSMbA5cAWwq5n1jI7flzCjW2/CHMGjJB0E/ECY0W1Pwv+r94GJKY6xa5r0ZH8zs6HRMR8BjiS89OEKoLuZrZTUOsp7KWEipbeiPwYrMtSzLVBuZgOisv15dpeStxzzRwtJk4BS4GvgH1H6V2aWeEPMvsDOwFtR3jOBrsBOwBdmNt3CI1GPpjnGocA9AGZWYWapLnf7RssHhAC4EyEIHQiMNLNl0bsqM057mYVDJL0r6aOoXrtE6ZOBxySdRphdDuAt4DZJg4HWZrYmQz0/Ag6TdLOkA9Oco3PecswjyxOtt4ToSvOHeBIwzsxOTsrXk9y9TFfAjWZ2b9IxfpPlMaYAewOvpj2AtAlwN9DLzGZJuhbYJNo8ADgIGAhcJWkXM7tJ0mjgCOCd6Jn1lPWMyt87ynujpJcSLVTn4rzlWFjeAQ6QtD2ApE0l7QB8AnSXtF2U7+Q0+78CnB/tWyypFbAEaBnLMxY4O3Yvs5OkdsB/gZ9LahHd8zsqzTFuBG6R1D7av3nU4otLBML50XGOi/IWAV3M7DXgd4TOos0lbWdmH5nZzYSW9U7p6impI7DMzB4F/kzoEHKuBm85FhAzmydpEPCEpOZR8pVm9pmkc4HRkuYDbxLu/SX7NTBc0i+BCuB8M3tb0luSPgZeNLPLJP0IeDtquS4FTjOz9yU9BUwCviJ0GqWq4xhJWwMvR50sBoxIyrNY0n2ES+AvgQnRpmLg0eg+oYDbo7zXSTokqvPUqJ4rU9UT2B64VVIl4Y1I52f37bqmxt/K45xzKfhltXPOpeDB0TnnUvDg6JxzKXhwdM65FDw4OudcCh4cnXMuBQ+OzjmXwv8HLnvIe1MLOQEAAAAASUVORK5CYII=",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -2509,7 +2665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -2518,7 +2674,7 @@
        "False"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2529,7 +2685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -2538,7 +2694,7 @@
        "False"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2549,7 +2705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -2560,6 +2716,7 @@
        " 'TPR Macro',\n",
        " 'F1 Macro',\n",
        " 'PPV Macro',\n",
+       " 'NPV Macro',\n",
        " 'ACC',\n",
        " 'Overall ACC',\n",
        " 'MCC',\n",
@@ -2571,7 +2728,7 @@
        " 'Zero-one Loss']"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2589,7 +2746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -2598,7 +2755,7 @@
        "True"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2610,7 +2767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -2619,7 +2776,7 @@
        "False"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2728,7 +2885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2738,7 +2895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2747,7 +2904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -2769,7 +2926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -2779,7 +2936,7 @@
        " 'cm3': {'class': 0.33611, 'overall': 0.52857}}"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2790,7 +2947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -2799,7 +2956,7 @@
        "['cm2', 'cm3']"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2810,7 +2967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -2819,7 +2976,7 @@
        "pycm.ConfusionMatrix(classes: [0, 1, 2])"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2830,7 +2987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -2839,7 +2996,7 @@
        "'cm2'"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2850,7 +3007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2859,7 +3016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -2881,7 +3038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2890,7 +3047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -2912,7 +3069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2923,7 +3080,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
@@ -2952,7 +3109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -3005,7 +3162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
@@ -3014,7 +3171,7 @@
        "0.75"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3033,22 +3190,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1dc40bd0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1de8f530>"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3089,14 +3246,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.8-py3.5.egg\\pycm\\pycm_curve.py:379: RuntimeWarning: The curve axes contain non-numerical value(s).\n"
+      "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.9-py3.5.egg\\pycm\\pycm_curve.py:382: RuntimeWarning: The curve axes contain non-numerical value(s).\n"
      ]
     },
     {
@@ -3105,7 +3262,7 @@
        "0.29166666666666663"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3124,22 +3281,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1dc768f0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1dec9b30>"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3208,7 +3365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
@@ -3294,61 +3451,63 @@
       "73-NLR\n",
       "74-NLRI\n",
       "75-NPV\n",
-      "76-OC\n",
-      "77-OOC\n",
-      "78-OP\n",
-      "79-Overall ACC\n",
-      "80-Overall CEN\n",
-      "81-Overall J\n",
-      "82-Overall MCC\n",
-      "83-Overall MCEN\n",
-      "84-Overall RACC\n",
-      "85-Overall RACCU\n",
-      "86-P\n",
-      "87-P-Value\n",
-      "88-PLR\n",
-      "89-PLRI\n",
-      "90-POP\n",
-      "91-PPV\n",
-      "92-PPV Macro\n",
-      "93-PPV Micro\n",
-      "94-PRE\n",
-      "95-Pearson C\n",
-      "96-Phi-Squared\n",
-      "97-Q\n",
-      "98-QI\n",
-      "99-RACC\n",
-      "100-RACCU\n",
-      "101-RCI\n",
-      "102-RR\n",
-      "103-Reference Entropy\n",
-      "104-Response Entropy\n",
-      "105-SOA1(Landis & Koch)\n",
-      "106-SOA2(Fleiss)\n",
-      "107-SOA3(Altman)\n",
-      "108-SOA4(Cicchetti)\n",
-      "109-SOA5(Cramer)\n",
-      "110-SOA6(Matthews)\n",
-      "111-SOA7(Lambda A)\n",
-      "112-SOA8(Lambda B)\n",
-      "113-SOA9(Krippendorff Alpha)\n",
-      "114-SOA10(Pearson C)\n",
-      "115-Scott PI\n",
-      "116-Standard Error\n",
-      "117-TN\n",
-      "118-TNR\n",
-      "119-TNR Macro\n",
-      "120-TNR Micro\n",
-      "121-TON\n",
-      "122-TOP\n",
-      "123-TP\n",
-      "124-TPR\n",
-      "125-TPR Macro\n",
-      "126-TPR Micro\n",
-      "127-Y\n",
-      "128-Zero-one Loss\n",
-      "129-dInd\n",
-      "130-sInd\n"
+      "76-NPV Macro\n",
+      "77-NPV Micro\n",
+      "78-OC\n",
+      "79-OOC\n",
+      "80-OP\n",
+      "81-Overall ACC\n",
+      "82-Overall CEN\n",
+      "83-Overall J\n",
+      "84-Overall MCC\n",
+      "85-Overall MCEN\n",
+      "86-Overall RACC\n",
+      "87-Overall RACCU\n",
+      "88-P\n",
+      "89-P-Value\n",
+      "90-PLR\n",
+      "91-PLRI\n",
+      "92-POP\n",
+      "93-PPV\n",
+      "94-PPV Macro\n",
+      "95-PPV Micro\n",
+      "96-PRE\n",
+      "97-Pearson C\n",
+      "98-Phi-Squared\n",
+      "99-Q\n",
+      "100-QI\n",
+      "101-RACC\n",
+      "102-RACCU\n",
+      "103-RCI\n",
+      "104-RR\n",
+      "105-Reference Entropy\n",
+      "106-Response Entropy\n",
+      "107-SOA1(Landis & Koch)\n",
+      "108-SOA2(Fleiss)\n",
+      "109-SOA3(Altman)\n",
+      "110-SOA4(Cicchetti)\n",
+      "111-SOA5(Cramer)\n",
+      "112-SOA6(Matthews)\n",
+      "113-SOA7(Lambda A)\n",
+      "114-SOA8(Lambda B)\n",
+      "115-SOA9(Krippendorff Alpha)\n",
+      "116-SOA10(Pearson C)\n",
+      "117-Scott PI\n",
+      "118-Standard Error\n",
+      "119-TN\n",
+      "120-TNR\n",
+      "121-TNR Macro\n",
+      "122-TNR Micro\n",
+      "123-TON\n",
+      "124-TOP\n",
+      "125-TP\n",
+      "126-TPR\n",
+      "127-TPR Macro\n",
+      "128-TPR Micro\n",
+      "129-Y\n",
+      "130-Zero-one Loss\n",
+      "131-dInd\n",
+      "132-sInd\n"
      ]
     }
    ],
@@ -3545,7 +3704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
@@ -3554,7 +3713,7 @@
        "{'L1': 3, 'L2': 1, 'L3': 3}"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3580,7 +3739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
@@ -3589,7 +3748,7 @@
        "{'L1': 7, 'L2': 8, 'L3': 4}"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3615,7 +3774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
@@ -3624,7 +3783,7 @@
        "{'L1': 0, 'L2': 2, 'L3': 3}"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3650,7 +3809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
@@ -3659,7 +3818,7 @@
        "{'L1': 2, 'L2': 1, 'L3': 2}"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3692,7 +3851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
@@ -3701,7 +3860,7 @@
        "{'L1': 5, 'L2': 2, 'L3': 5}"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3733,7 +3892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
@@ -3742,7 +3901,7 @@
        "{'L1': 7, 'L2': 10, 'L3': 7}"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3774,7 +3933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
@@ -3783,7 +3942,7 @@
        "{'L1': 3, 'L2': 3, 'L3': 6}"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3815,7 +3974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [
     {
@@ -3824,7 +3983,7 @@
        "{'L1': 9, 'L2': 9, 'L3': 6}"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3856,7 +4015,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
@@ -3865,7 +4024,7 @@
        "{'L1': 12, 'L2': 12, 'L3': 12}"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3913,7 +4072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
@@ -3922,7 +4081,7 @@
        "{'L1': 0.6, 'L2': 0.5, 'L3': 0.6}"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3956,7 +4115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
@@ -3965,7 +4124,7 @@
        "{'L1': 1.0, 'L2': 0.8, 'L3': 0.5714285714285714}"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4000,7 +4159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
@@ -4009,7 +4168,7 @@
        "{'L1': 1.0, 'L2': 0.3333333333333333, 'L3': 0.5}"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4044,7 +4203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -4053,7 +4212,7 @@
        "{'L1': 0.7777777777777778, 'L2': 0.8888888888888888, 'L3': 0.6666666666666666}"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4087,7 +4246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
@@ -4096,7 +4255,7 @@
        "{'L1': 0.4, 'L2': 0.5, 'L3': 0.4}"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4132,7 +4291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
@@ -4141,7 +4300,7 @@
        "{'L1': 0.0, 'L2': 0.19999999999999996, 'L3': 0.4285714285714286}"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4175,7 +4334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
@@ -4184,7 +4343,7 @@
        "{'L1': 0.0, 'L2': 0.6666666666666667, 'L3': 0.5}"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4218,7 +4377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -4229,7 +4388,7 @@
        " 'L3': 0.33333333333333337}"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4263,7 +4422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -4272,7 +4431,7 @@
        "{'L1': 0.8333333333333334, 'L2': 0.75, 'L3': 0.5833333333333334}"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4304,7 +4463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -4313,7 +4472,7 @@
        "{'L1': 0.16666666666666663, 'L2': 0.25, 'L3': 0.41666666666666663}"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4357,7 +4516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -4366,7 +4525,7 @@
        "{'L1': 0.75, 'L2': 0.4, 'L3': 0.5454545454545454}"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4377,7 +4536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -4386,7 +4545,7 @@
        "{'L1': 0.8823529411764706, 'L2': 0.35714285714285715, 'L3': 0.5172413793103449}"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4397,7 +4556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
@@ -4406,7 +4565,7 @@
        "{'L1': 0.6521739130434783, 'L2': 0.45454545454545453, 'L3': 0.5769230769230769}"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4417,7 +4576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
@@ -4426,7 +4585,7 @@
        "{'L1': 0.6144578313253012, 'L2': 0.4857142857142857, 'L3': 0.5930232558139535}"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4499,7 +4658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
@@ -4508,7 +4667,7 @@
        "{'L1': 0.6831300510639732, 'L2': 0.25819888974716115, 'L3': 0.1690308509457033}"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4542,7 +4701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
@@ -4553,7 +4712,7 @@
        " 'L3': 0.17142857142857126}"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4585,7 +4744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -4594,7 +4753,7 @@
        "{'L1': 0.7777777777777777, 'L2': 0.2222222222222221, 'L3': 0.16666666666666652}"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4630,7 +4789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
@@ -4639,7 +4798,7 @@
        "{'L1': 'None', 'L2': 2.5000000000000004, 'L3': 1.4}"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4684,7 +4843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
@@ -4693,7 +4852,7 @@
        "{'L1': 0.4, 'L2': 0.625, 'L3': 0.7000000000000001}"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4736,7 +4895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
@@ -4745,7 +4904,7 @@
        "{'L1': 'None', 'L2': 4.000000000000001, 'L3': 1.9999999999999998}"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4779,7 +4938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -4788,7 +4947,7 @@
        "{'L1': 0.4166666666666667, 'L2': 0.16666666666666666, 'L3': 0.4166666666666667}"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4822,7 +4981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
@@ -4831,7 +4990,7 @@
        "{'L1': 0.7745966692414834, 'L2': 0.408248290463863, 'L3': 0.5477225575051661}"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4863,7 +5022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
@@ -4874,7 +5033,7 @@
        " 'L3': 0.20833333333333334}"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4915,7 +5074,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
@@ -4926,7 +5085,7 @@
        " 'L3': 0.21006944444444442}"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4971,7 +5130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -4980,7 +5139,7 @@
        "{'L1': 0.6, 'L2': 0.25, 'L3': 0.375}"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5022,7 +5181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
@@ -5031,7 +5190,7 @@
        "{'L1': 1.2630344058337937, 'L2': 0.9999999999999998, 'L3': 0.26303440583379367}"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5088,7 +5247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
@@ -5097,7 +5256,7 @@
        "{'L1': 0.25, 'L2': 0.49657842846620864, 'L3': 0.6044162769630221}"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5161,7 +5320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -5170,7 +5329,7 @@
        "{'L1': 0.2643856189774724, 'L2': 0.5, 'L3': 0.6875}"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5214,7 +5373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
@@ -5223,7 +5382,7 @@
        "{'L1': 0.8, 'L2': 0.65, 'L3': 0.5857142857142856}"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5265,7 +5424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
@@ -5274,7 +5433,7 @@
        "{'L1': 0.4, 'L2': 0.5385164807134504, 'L3': 0.5862367008195198}"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5315,7 +5474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
@@ -5324,7 +5483,7 @@
        "{'L1': 0.717157287525381, 'L2': 0.6192113447068046, 'L3': 0.5854680534700882}"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5382,7 +5541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
@@ -5391,7 +5550,7 @@
        "{'L1': 'None', 'L2': 0.33193306999649924, 'L3': 0.1659665349982495}"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5441,7 +5600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -5452,7 +5611,7 @@
        " 'L3': 0.17142857142857126}"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5516,7 +5675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -5525,7 +5684,7 @@
        "{'L1': 'None', 'L2': 'Poor', 'L3': 'Poor'}"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5589,7 +5748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
@@ -5598,7 +5757,7 @@
        "{'L1': 'Poor', 'L2': 'Negligible', 'L3': 'Negligible'}"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5662,7 +5821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
@@ -5671,7 +5830,7 @@
        "{'L1': 'None', 'L2': 'Poor', 'L3': 'Poor'}"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5738,7 +5897,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
@@ -5747,7 +5906,7 @@
        "{'L1': 'Very Good', 'L2': 'Fair', 'L3': 'Poor'}"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5816,7 +5975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
@@ -5825,7 +5984,7 @@
        "{'L1': 'Moderate', 'L2': 'Negligible', 'L3': 'Negligible'}"
       ]
      },
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@@ -5898,7 +6057,7 @@
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@@ -5907,7 +6066,7 @@
        "{'L1': 'None', 'L2': 'Moderate', 'L3': 'Weak'}"
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-     "execution_count": 140,
+     "execution_count": 143,
      "metadata": {},
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@@ -5952,7 +6111,7 @@
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@@ -5963,7 +6122,7 @@
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@@ -6004,7 +6163,7 @@
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@@ -6013,7 +6172,7 @@
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@@ -6054,7 +6213,7 @@
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@@ -6063,7 +6222,7 @@
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@@ -6106,7 +6265,7 @@
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     {
@@ -6117,7 +6276,7 @@
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@@ -6161,7 +6320,7 @@
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     {
@@ -6170,7 +6329,7 @@
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@@ -6212,7 +6371,7 @@
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     {
@@ -6221,7 +6380,7 @@
        "{'L1': 0.36, 'L2': 0.27999999999999997, 'L3': 0.35265306122448975}"
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@@ -6232,7 +6391,7 @@
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     {
@@ -6241,7 +6400,7 @@
        "{'L1': 0.48, 'L2': 0.34, 'L3': 0.3477551020408163}"
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@@ -6252,7 +6411,7 @@
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     {
@@ -6261,7 +6420,7 @@
        "{'L1': 0.576, 'L2': 0.388, 'L3': 0.34383673469387754}"
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@@ -6330,7 +6489,7 @@
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     {
@@ -6339,7 +6498,7 @@
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@@ -6391,7 +6550,7 @@
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@@ -6400,7 +6559,7 @@
        "{'L1': 'None', 'L2': 0.6, 'L3': 0.3333333333333333}"
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      "metadata": {},
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@@ -6455,7 +6614,7 @@
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@@ -6464,7 +6623,7 @@
        "{'L1': 0.8576400016262, 'L2': 0.708612108382005, 'L3': 0.5803410802752335}"
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@@ -6519,7 +6678,7 @@
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@@ -6528,7 +6687,7 @@
        "{'L1': 0.7285871475307653, 'L2': 0.6286946134619315, 'L3': 0.610088876086563}"
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@@ -6571,7 +6730,7 @@
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@@ -6580,7 +6739,7 @@
        "{'L1': 1.0, 'L2': 0.5, 'L3': 0.6}"
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      "metadata": {},
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@@ -6621,7 +6780,7 @@
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     {
@@ -6630,7 +6789,7 @@
        "{'L1': 0.6, 'L2': 0.3333333333333333, 'L3': 0.5}"
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@@ -6673,7 +6832,7 @@
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@@ -6682,7 +6841,7 @@
        "{'L1': 0.7745966692414834, 'L2': 0.4082482904638631, 'L3': 0.5477225575051661}"
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      "metadata": {},
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@@ -6725,7 +6884,7 @@
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@@ -6734,7 +6893,7 @@
        "{'L1': 0.42857142857142855, 'L2': 0.1111111111111111, 'L3': 0.1875}"
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@@ -6805,7 +6964,7 @@
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@@ -6814,7 +6973,7 @@
        "{'L1': 0.8, 'L2': 0.41666666666666663, 'L3': 0.55}"
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@@ -6862,7 +7021,7 @@
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@@ -6873,7 +7032,7 @@
        " 'L3': 0.10000000000000009}"
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@@ -6993,7 +7152,7 @@
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@@ -7004,7 +7163,7 @@
        " 'L3': [0.21908902300206645, (0.17058551491594975, 1.0294144850840503)]}"
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@@ -7015,7 +7174,7 @@
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     {
@@ -7026,7 +7185,7 @@
        " 'L3': [0.21908902300206645, (-0.2769850810763853, 1.0769850810763852)]}"
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@@ -7037,7 +7196,7 @@
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@@ -7048,7 +7207,7 @@
        " 'L3': [0.14231876063832774, (0.19325746190524654, 0.6804926643446272)]}"
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@@ -7059,7 +7218,7 @@
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@@ -7068,7 +7227,7 @@
        "[0.14231876063832777, (0.2805568916340536, 0.8343177950165198)]"
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@@ -7079,7 +7238,7 @@
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@@ -7088,7 +7247,7 @@
        "[0.14231876063832777, (0.30438856248221097, 0.8622781041844558)]"
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@@ -7189,7 +7348,7 @@
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@@ -7198,7 +7357,7 @@
        "{'L1': 0.25, 'L2': 0.0735, 'L3': 0.23525}"
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@@ -7262,7 +7421,7 @@
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@@ -7271,7 +7430,7 @@
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@@ -7282,7 +7441,7 @@
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@@ -7291,7 +7450,7 @@
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@@ -7302,7 +7461,7 @@
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@@ -7311,7 +7470,7 @@
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@@ -7378,7 +7537,7 @@
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@@ -7387,7 +7546,7 @@
        "0.6805555555555555"
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@@ -7398,7 +7557,7 @@
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@@ -7407,7 +7566,7 @@
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@@ -7418,7 +7577,7 @@
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@@ -7427,7 +7586,7 @@
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@@ -7438,7 +7597,7 @@
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@@ -7447,7 +7606,7 @@
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@@ -7528,7 +7687,7 @@
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@@ -7537,7 +7696,7 @@
        "{'L1': 'None', 'L2': 0.8416212335729143, 'L3': 0.4333594729285047}"
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@@ -7601,7 +7760,7 @@
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    "metadata": {},
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     {
@@ -7610,7 +7769,7 @@
        "{'L1': 2, 'L2': 3, 'L3': 5}"
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@@ -7665,7 +7824,7 @@
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@@ -7674,7 +7833,7 @@
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@@ -7717,7 +7876,7 @@
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@@ -7726,7 +7885,7 @@
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@@ -7767,7 +7926,7 @@
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@@ -7776,7 +7935,7 @@
        "0.16666666666666674"
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@@ -7838,7 +7997,7 @@
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@@ -7847,7 +8006,7 @@
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@@ -7862,14 +8021,14 @@
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-   "execution_count": 178,
+   "execution_count": 181,
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-      "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.8-py3.5.egg\\pycm\\pycm_obj.py:839: RuntimeWarning: The weight format is wrong, the result is for unweighted kappa.\n"
+      "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.9-py3.5.egg\\pycm\\pycm_obj.py:850: RuntimeWarning: The weight format is wrong, the result is for unweighted kappa.\n"
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@@ -7878,7 +8037,7 @@
        "0.35483870967741943"
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@@ -7947,7 +8106,7 @@
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@@ -7997,7 +8156,7 @@
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@@ -8006,7 +8165,7 @@
        "(-0.07707577422109269, 0.7867531935759315)"
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@@ -8056,7 +8215,7 @@
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@@ -8065,7 +8224,7 @@
        "6.6000000000000005"
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@@ -8106,7 +8265,7 @@
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@@ -8115,7 +8274,7 @@
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@@ -8158,7 +8317,7 @@
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@@ -8167,7 +8326,7 @@
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@@ -8212,7 +8371,7 @@
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@@ -8264,7 +8423,7 @@
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@@ -8316,7 +8475,7 @@
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@@ -8325,7 +8484,7 @@
        "(0.30438856248221097, 0.8622781041844558)"
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@@ -8385,7 +8544,7 @@
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@@ -8394,7 +8553,7 @@
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-     "execution_count": 187,
+     "execution_count": 190,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8447,7 +8606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 191,
    "metadata": {},
    "outputs": [
     {
@@ -8456,7 +8615,7 @@
        "0.34426229508196726"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 191,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8511,7 +8670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 192,
    "metadata": {},
    "outputs": [
     {
@@ -8520,7 +8679,7 @@
        "0.3893129770992367"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8575,7 +8734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 193,
    "metadata": {},
    "outputs": [
     {
@@ -8584,7 +8743,7 @@
        "1.4833557549816874"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 193,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8639,7 +8798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [
     {
@@ -8648,7 +8807,7 @@
        "1.5"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8712,7 +8871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 195,
    "metadata": {},
    "outputs": [
     {
@@ -8721,7 +8880,7 @@
        "1.5833333333333335"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 195,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8776,7 +8935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 196,
    "metadata": {},
    "outputs": [
     {
@@ -8785,7 +8944,7 @@
        "2.4591479170272446"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 196,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8842,7 +9001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 197,
    "metadata": {},
    "outputs": [
     {
@@ -8851,7 +9010,7 @@
        "0.9757921620455572"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 197,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8908,7 +9067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 198,
    "metadata": {},
    "outputs": [
     {
@@ -8917,7 +9076,7 @@
        "0.09997757835164581"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 198,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8989,7 +9148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 199,
    "metadata": {},
    "outputs": [
     {
@@ -8998,7 +9157,7 @@
        "0.5242078379544428"
       ]
      },
-     "execution_count": 196,
+     "execution_count": 199,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9041,7 +9200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 200,
    "metadata": {},
    "outputs": [
     {
@@ -9050,7 +9209,7 @@
        "0.42857142857142855"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 200,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9093,7 +9252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 201,
    "metadata": {},
    "outputs": [
     {
@@ -9102,7 +9261,7 @@
        "0.16666666666666666"
       ]
      },
-     "execution_count": 198,
+     "execution_count": 201,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9174,7 +9333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 202,
    "metadata": {},
    "outputs": [
     {
@@ -9183,7 +9342,7 @@
        "'Fair'"
       ]
      },
-     "execution_count": 199,
+     "execution_count": 202,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9243,7 +9402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 203,
    "metadata": {},
    "outputs": [
     {
@@ -9252,7 +9411,7 @@
        "'Poor'"
       ]
      },
-     "execution_count": 200,
+     "execution_count": 203,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9320,7 +9479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 204,
    "metadata": {},
    "outputs": [
     {
@@ -9329,7 +9488,7 @@
        "'Fair'"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 204,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9393,7 +9552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 205,
    "metadata": {},
    "outputs": [
     {
@@ -9402,7 +9561,7 @@
        "'Poor'"
       ]
      },
-     "execution_count": 202,
+     "execution_count": 205,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9474,7 +9633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 206,
    "metadata": {},
    "outputs": [
     {
@@ -9483,7 +9642,7 @@
        "'Relatively Strong'"
       ]
      },
-     "execution_count": 203,
+     "execution_count": 206,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9552,7 +9711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 207,
    "metadata": {},
    "outputs": [
     {
@@ -9561,7 +9720,7 @@
        "'Weak'"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 207,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9641,7 +9800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 208,
    "metadata": {},
    "outputs": [
     {
@@ -9650,7 +9809,7 @@
        "'Moderate'"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 208,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9721,7 +9880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 209,
    "metadata": {},
    "outputs": [
     {
@@ -9730,7 +9889,7 @@
        "'Very Weak'"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 209,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9788,7 +9947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 210,
    "metadata": {},
    "outputs": [
     {
@@ -9797,7 +9956,7 @@
        "'Low'"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 210,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9859,7 +10018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 211,
    "metadata": {},
    "outputs": [
     {
@@ -9868,7 +10027,7 @@
        "'Strong'"
       ]
      },
-     "execution_count": 208,
+     "execution_count": 211,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9916,7 +10075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 212,
    "metadata": {},
    "outputs": [
     {
@@ -9925,7 +10084,7 @@
        "0.5833333333333334"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 212,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9966,7 +10125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 213,
    "metadata": {},
    "outputs": [
     {
@@ -9975,7 +10134,7 @@
        "0.3541666666666667"
       ]
      },
-     "execution_count": 210,
+     "execution_count": 213,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10016,7 +10175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 214,
    "metadata": {},
    "outputs": [
     {
@@ -10025,7 +10184,7 @@
        "0.3645833333333333"
       ]
      },
-     "execution_count": 211,
+     "execution_count": 214,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10073,7 +10232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 215,
    "metadata": {},
    "outputs": [
     {
@@ -10082,7 +10241,7 @@
        "0.5833333333333334"
       ]
      },
-     "execution_count": 212,
+     "execution_count": 215,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10123,9 +10282,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 216,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7916666666666666"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.NPV_Micro"
    ]
@@ -10169,7 +10339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 217,
    "metadata": {},
    "outputs": [
     {
@@ -10178,7 +10348,7 @@
        "0.5833333333333334"
       ]
      },
-     "execution_count": 213,
+     "execution_count": 217,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10219,7 +10389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 218,
    "metadata": {},
    "outputs": [
     {
@@ -10228,7 +10398,7 @@
        "0.7916666666666666"
       ]
      },
-     "execution_count": 214,
+     "execution_count": 218,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10269,7 +10439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 219,
    "metadata": {},
    "outputs": [
     {
@@ -10278,7 +10448,7 @@
        "0.20833333333333337"
       ]
      },
-     "execution_count": 215,
+     "execution_count": 219,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10319,7 +10489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 220,
    "metadata": {},
    "outputs": [
     {
@@ -10328,7 +10498,7 @@
        "0.41666666666666663"
       ]
      },
-     "execution_count": 216,
+     "execution_count": 220,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10376,7 +10546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 221,
    "metadata": {},
    "outputs": [
     {
@@ -10385,7 +10555,7 @@
        "0.5833333333333334"
       ]
      },
-     "execution_count": 217,
+     "execution_count": 221,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10426,7 +10596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 222,
    "metadata": {},
    "outputs": [
     {
@@ -10435,7 +10605,7 @@
        "0.611111111111111"
       ]
      },
-     "execution_count": 218,
+     "execution_count": 222,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10476,9 +10646,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 223,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7777777777777777"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cm.NPV_Macro"
    ]
@@ -10515,7 +10696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 224,
    "metadata": {},
    "outputs": [
     {
@@ -10524,7 +10705,7 @@
        "0.5666666666666668"
       ]
      },
-     "execution_count": 219,
+     "execution_count": 224,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10565,7 +10746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 225,
    "metadata": {},
    "outputs": [
     {
@@ -10574,7 +10755,7 @@
        "0.7904761904761904"
       ]
      },
-     "execution_count": 220,
+     "execution_count": 225,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10615,7 +10796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 226,
    "metadata": {},
    "outputs": [
     {
@@ -10624,7 +10805,7 @@
        "0.20952380952380956"
       ]
      },
-     "execution_count": 221,
+     "execution_count": 226,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10665,7 +10846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 227,
    "metadata": {},
    "outputs": [
     {
@@ -10674,7 +10855,7 @@
        "0.43333333333333324"
       ]
      },
-     "execution_count": 222,
+     "execution_count": 227,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10715,7 +10896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 228,
    "metadata": {},
    "outputs": [
     {
@@ -10724,7 +10905,7 @@
        "0.5651515151515151"
       ]
      },
-     "execution_count": 223,
+     "execution_count": 228,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10765,7 +10946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 229,
    "metadata": {},
    "outputs": [
     {
@@ -10774,7 +10955,7 @@
        "0.7222222222222223"
       ]
      },
-     "execution_count": 224,
+     "execution_count": 229,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10829,7 +11010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 230,
    "metadata": {},
    "outputs": [
     {
@@ -10838,7 +11019,7 @@
        "(1.225, 0.4083333333333334)"
       ]
      },
-     "execution_count": 225,
+     "execution_count": 230,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10879,7 +11060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 231,
    "metadata": {},
    "outputs": [
     {
@@ -10888,7 +11069,7 @@
        "0.41666666666666663"
       ]
      },
-     "execution_count": 226,
+     "execution_count": 231,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10929,7 +11110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 232,
    "metadata": {},
    "outputs": [
     {
@@ -10938,7 +11119,7 @@
        "5"
       ]
      },
-     "execution_count": 227,
+     "execution_count": 232,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10979,7 +11160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 233,
    "metadata": {},
    "outputs": [
     {
@@ -10988,7 +11169,7 @@
        "0.4166666666666667"
       ]
      },
-     "execution_count": 228,
+     "execution_count": 233,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11056,7 +11237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 234,
    "metadata": {},
    "outputs": [
     {
@@ -11065,7 +11246,7 @@
        "0.18926430237560654"
       ]
      },
-     "execution_count": 229,
+     "execution_count": 234,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11113,7 +11294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 235,
    "metadata": {},
    "outputs": [
     {
@@ -11122,7 +11303,7 @@
        "0.4638112995385119"
       ]
      },
-     "execution_count": 230,
+     "execution_count": 235,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11177,7 +11358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 236,
    "metadata": {},
    "outputs": [
     {
@@ -11186,7 +11367,7 @@
        "0.5189369467580801"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 236,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11250,7 +11431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 237,
    "metadata": {},
    "outputs": [
     {
@@ -11259,7 +11440,7 @@
        "0.36666666666666664"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 237,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11300,7 +11481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 238,
    "metadata": {},
    "outputs": [
     {
@@ -11309,7 +11490,7 @@
        "4.0"
       ]
      },
-     "execution_count": 233,
+     "execution_count": 238,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11352,7 +11533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 239,
    "metadata": {},
    "outputs": [
     {
@@ -11361,7 +11542,7 @@
        "0.4777777777777778"
       ]
      },
-     "execution_count": 234,
+     "execution_count": 239,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11402,7 +11583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 240,
    "metadata": {},
    "outputs": [
     {
@@ -11411,7 +11592,7 @@
        "0.6785714285714285"
       ]
      },
-     "execution_count": 235,
+     "execution_count": 240,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11452,7 +11633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 241,
    "metadata": {},
    "outputs": [
     {
@@ -11461,7 +11642,7 @@
        "0.6857142857142857"
       ]
      },
-     "execution_count": 236,
+     "execution_count": 241,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11524,7 +11705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 242,
    "metadata": {},
    "outputs": [
     {
@@ -11533,7 +11714,7 @@
        "0.3533932006492363"
       ]
      },
-     "execution_count": 237,
+     "execution_count": 242,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11574,7 +11755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 243,
    "metadata": {},
    "outputs": [
     {
@@ -11583,7 +11764,7 @@
        "0.5956833971812706"
       ]
      },
-     "execution_count": 238,
+     "execution_count": 243,
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        "0.1777777777777778"
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@@ -11686,7 +11867,7 @@
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@@ -11747,7 +11928,7 @@
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        "0.37254901960784315"
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@@ -11821,7 +12002,7 @@
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@@ -11830,7 +12011,7 @@
        "0.3715846994535519"
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@@ -11906,7 +12087,7 @@
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@@ -11915,7 +12096,7 @@
        "0.374757281553398"
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@@ -11930,14 +12111,14 @@
   },
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    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": 249,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
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      "text": [
-      "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.8-py3.5.egg\\pycm\\pycm_obj.py:861: RuntimeWarning: The weight format is wrong, the result is for unweighted alpha.\n"
+      "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.9-py3.5.egg\\pycm\\pycm_obj.py:873: RuntimeWarning: The weight format is wrong, the result is for unweighted alpha.\n"
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     {
@@ -11946,7 +12127,7 @@
        "0.3715846994535519"
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@@ -12050,7 +12231,7 @@
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@@ -12070,7 +12251,7 @@
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        "0.03749999999999999"
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@@ -12188,7 +12369,7 @@
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   },
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    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 254,
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    "metadata": {},
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     {
@@ -12422,6 +12625,8 @@
       "Lambda B                                                          0.16667\n",
       "Mutual Information                                                0.52421\n",
       "NIR                                                               0.41667\n",
+      "NPV Macro                                                         0.77778\n",
+      "NPV Micro                                                         0.79167\n",
       "Overall ACC                                                       0.58333\n",
       "Overall CEN                                                       0.46381\n",
       "Overall J                                                         (1.225,0.40833)\n",
@@ -12539,7 +12744,7 @@
   },
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-   "execution_count": 250,
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@@ -12564,7 +12769,7 @@
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@@ -12575,7 +12780,7 @@
        " 'L3': {'L1': 0, 'L2': 2, 'L3': 3}}"
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@@ -12618,7 +12823,7 @@
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@@ -12691,7 +12896,7 @@
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@@ -12716,7 +12921,7 @@
   },
   {
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     {
@@ -12727,7 +12932,7 @@
        " 'L3': {'L1': 0.0, 'L2': 0.4, 'L3': 0.6}}"
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@@ -12738,7 +12943,7 @@
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@@ -12761,7 +12966,7 @@
   },
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     {
@@ -12877,6 +13082,8 @@
       "Lambda B                                                          0.16667\n",
       "Mutual Information                                                0.52421\n",
       "NIR                                                               0.41667\n",
+      "NPV Macro                                                         0.77778\n",
+      "NPV Micro                                                         0.79167\n",
       "Overall ACC                                                       0.58333\n",
       "Overall CEN                                                       0.46381\n",
       "Overall J                                                         (1.225,0.40833)\n",
@@ -12987,7 +13194,7 @@
   },
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@@ -13014,7 +13221,7 @@
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     {
@@ -13054,6 +13261,7 @@
       "F1 Macro                                                          0.56515\n",
       "FPR Macro                                                         0.20952\n",
       "Kappa                                                             0.35484\n",
+      "NPV Macro                                                         0.77778\n",
       "Overall ACC                                                       0.58333\n",
       "PPV Macro                                                         0.61111\n",
       "SOA1(Landis & Koch)                                               Fair\n",
@@ -13149,7 +13357,7 @@
   },
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@@ -13200,7 +13408,7 @@
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@@ -13218,7 +13426,7 @@
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     {
@@ -13228,7 +13436,7 @@
        " 'Status': True}"
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@@ -13246,7 +13454,7 @@
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@@ -13256,7 +13464,7 @@
        " 'Status': True}"
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@@ -13277,7 +13485,7 @@
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        " 'Status': True}"
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@@ -13319,7 +13527,7 @@
        " 'Status': True}"
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@@ -13339,7 +13547,7 @@
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@@ -13349,7 +13557,7 @@
        " 'Status': True}"
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@@ -13473,7 +13681,7 @@
        " 'Status': True}"
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@@ -13491,7 +13699,7 @@
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@@ -13501,7 +13709,7 @@
        " 'Status': True}"
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@@ -13522,7 +13730,7 @@
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@@ -13532,7 +13740,7 @@
        " 'Status': True}"
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@@ -13554,7 +13762,7 @@
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@@ -13564,7 +13772,7 @@
        " 'Status': True}"
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@@ -13584,7 +13792,7 @@
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@@ -13594,7 +13802,7 @@
        " 'Status': True}"
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@@ -13614,7 +13822,7 @@
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@@ -13624,7 +13832,7 @@
        " 'Status': True}"
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@@ -13645,7 +13853,7 @@
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@@ -13655,7 +13863,7 @@
        " 'Status': True}"
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@@ -13676,7 +13884,7 @@
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@@ -13686,7 +13894,7 @@
        " 'Status': False}"
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@@ -13799,7 +14007,7 @@
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     {
@@ -13809,7 +14017,7 @@
        " 'Status': True}"
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@@ -13829,7 +14037,7 @@
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-   "execution_count": 281,
+   "execution_count": 288,
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     {
@@ -13839,7 +14047,7 @@
        " 'Status': True}"
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@@ -13861,7 +14069,7 @@
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+   "execution_count": 289,
    "metadata": {},
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     {
@@ -13871,7 +14079,7 @@
        " 'Status': True}"
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@@ -13894,7 +14102,7 @@
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+   "execution_count": 290,
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@@ -13904,7 +14112,7 @@
        " 'Status': True}"
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@@ -13927,7 +14135,7 @@
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@@ -13937,7 +14145,7 @@
        " 'Status': True}"
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@@ -13959,7 +14167,7 @@
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@@ -13969,7 +14177,7 @@
        " 'Status': True}"
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@@ -13990,7 +14198,7 @@
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@@ -14000,7 +14208,7 @@
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@@ -14093,7 +14301,7 @@
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@@ -14103,7 +14311,7 @@
        " 'Status': True}"
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@@ -14121,7 +14329,7 @@
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+   "execution_count": 295,
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     {
@@ -14131,7 +14339,7 @@
        " 'Status': True}"
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@@ -14151,7 +14359,7 @@
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+   "execution_count": 296,
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@@ -14161,7 +14369,7 @@
        " 'Status': True}"
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@@ -14181,7 +14389,7 @@
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@@ -14191,7 +14399,7 @@
        " 'Status': False}"
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@@ -14253,7 +14461,7 @@
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+   "execution_count": 298,
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@@ -14263,7 +14471,7 @@
        " 'Status': True}"
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+     "execution_count": 298,
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@@ -14281,7 +14489,7 @@
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+   "execution_count": 299,
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@@ -14291,7 +14499,7 @@
        " 'Status': False}"
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-     "execution_count": 292,
+     "execution_count": 299,
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@@ -14333,7 +14541,7 @@
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+   "execution_count": 300,
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     {
@@ -14353,7 +14561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 294,
+   "execution_count": 301,
    "metadata": {
     "scrolled": true
    },
@@ -14375,7 +14583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 295,
+   "execution_count": 302,
    "metadata": {},
    "outputs": [
     {
@@ -14395,7 +14603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 296,
+   "execution_count": 303,
    "metadata": {},
    "outputs": [
     {
@@ -14415,7 +14623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 297,
+   "execution_count": 304,
    "metadata": {},
    "outputs": [
     {
@@ -14435,7 +14643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 298,
+   "execution_count": 305,
    "metadata": {},
    "outputs": [
     {
@@ -14455,7 +14663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 299,
+   "execution_count": 306,
    "metadata": {},
    "outputs": [
     {
@@ -14475,7 +14683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 300,
+   "execution_count": 307,
    "metadata": {},
    "outputs": [
     {
@@ -14495,7 +14703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 301,
+   "execution_count": 308,
    "metadata": {},
    "outputs": [
     {
@@ -14515,7 +14723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 302,
+   "execution_count": 309,
    "metadata": {},
    "outputs": [
     {
@@ -14535,7 +14743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 303,
+   "execution_count": 310,
    "metadata": {},
    "outputs": [
     {
@@ -14555,7 +14763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 304,
+   "execution_count": 311,
    "metadata": {},
    "outputs": [
     {
@@ -14578,7 +14786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 305,
+   "execution_count": 312,
    "metadata": {},
    "outputs": [
     {
@@ -14600,7 +14808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 306,
+   "execution_count": 313,
    "metadata": {},
    "outputs": [
     {
@@ -14622,7 +14830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 307,
+   "execution_count": 314,
    "metadata": {},
    "outputs": [
     {
@@ -14643,7 +14851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 308,
+   "execution_count": 315,
    "metadata": {},
    "outputs": [
     {
@@ -14663,7 +14871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 309,
+   "execution_count": 316,
    "metadata": {},
    "outputs": [
     {
@@ -14683,7 +14891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 310,
+   "execution_count": 317,
    "metadata": {},
    "outputs": [
     {
@@ -14703,7 +14911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 311,
+   "execution_count": 318,
    "metadata": {},
    "outputs": [
     {
@@ -14723,7 +14931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 312,
+   "execution_count": 319,
    "metadata": {},
    "outputs": [
     {
@@ -14743,7 +14951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 313,
+   "execution_count": 320,
    "metadata": {},
    "outputs": [
     {
@@ -14763,7 +14971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 314,
+   "execution_count": 321,
    "metadata": {},
    "outputs": [
     {
@@ -14783,7 +14991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 315,
+   "execution_count": 322,
    "metadata": {},
    "outputs": [
     {
@@ -14803,7 +15011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 316,
+   "execution_count": 323,
    "metadata": {},
    "outputs": [
     {
@@ -14823,7 +15031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 317,
+   "execution_count": 324,
    "metadata": {},
    "outputs": [
     {
@@ -14843,7 +15051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 318,
+   "execution_count": 325,
    "metadata": {},
    "outputs": [
     {
@@ -14866,7 +15074,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 319,
+   "execution_count": 326,
    "metadata": {},
    "outputs": [
     {
@@ -14889,7 +15097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 320,
+   "execution_count": 327,
    "metadata": {},
    "outputs": [
     {
@@ -14912,7 +15120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 321,
+   "execution_count": 328,
    "metadata": {},
    "outputs": [
     {
@@ -14935,7 +15143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 322,
+   "execution_count": 329,
    "metadata": {},
    "outputs": [
     {
@@ -14958,7 +15166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 323,
+   "execution_count": 330,
    "metadata": {},
    "outputs": [
     {
@@ -14982,7 +15190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 324,
+   "execution_count": 331,
    "metadata": {},
    "outputs": [
     {
@@ -15006,7 +15214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 325,
+   "execution_count": 332,
    "metadata": {},
    "outputs": [
     {
@@ -15030,7 +15238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 326,
+   "execution_count": 333,
    "metadata": {},
    "outputs": [
     {
@@ -15053,7 +15261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 327,
+   "execution_count": 334,
    "metadata": {},
    "outputs": [
     {
@@ -15076,7 +15284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 328,
+   "execution_count": 335,
    "metadata": {},
    "outputs": [
     {
diff --git a/Document/Document_files/cm1.html b/Document/Document_files/cm1.html
index cb79547e..f8e480c0 100644
--- a/Document/Document_files/cm1.html
+++ b/Document/Document_files/cm1.html
@@ -196,6 +196,14 @@ <h2>Overall Statistics : </h2>
 <td style="border:1px solid black;padding:4px;">0.41667</td>
 </tr>
 <tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#NPV_Macro" style="text-decoration:None;">NPV Macro</a></td>
+<td style="border:1px solid black;padding:4px;">0.77778</td>
+</tr>
+<tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:transparent;"><a href="http://www.pycm.io/doc/index.html#NPV_Micro" style="text-decoration:None;">NPV Micro</a></td>
+<td style="border:1px solid black;padding:4px;">0.79167</td>
+</tr>
+<tr style="text-align:center;">
 <td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#Overall_ACC" style="text-decoration:None;">Overall ACC</a></td>
 <td style="border:1px solid black;padding:4px;">0.58333</td>
 </tr>
@@ -779,6 +787,6 @@ <h2>Class Statistics : </h2>
 <td style="border:1px solid black;padding:4px;border-collapse: collapse;text-align:left;">Similarity index</td>
 </tr>
 </table>
-<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.8</p>
+<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.9</p>
 </body>
 </html>
diff --git a/Document/Document_files/cm1.obj b/Document/Document_files/cm1.obj
index 34f91b7c..e9160239 100644
--- a/Document/Document_files/cm1.obj
+++ b/Document/Document_files/cm1.obj
@@ -1 +1 @@
-{"Prob-Vector": null, "Transpose": true, "Imbalanced": false, "Predict-Vector": null, "Matrix": [["L1", [["L1", 3], ["L3", 2], ["L2", 0]]], ["L2", [["L1", 0], ["L3", 1], ["L2", 1]]], ["L3", [["L1", 0], ["L3", 3], ["L2", 2]]]], "Actual-Vector": null, "Sample-Weight": null, "Digit": 5}
\ No newline at end of file
+{"Predict-Vector": null, "Sample-Weight": null, "Digit": 5, "Actual-Vector": null, "Imbalanced": false, "Prob-Vector": null, "Matrix": [["L1", [["L1", 3], ["L2", 0], ["L3", 2]]], ["L2", [["L1", 0], ["L2", 1], ["L3", 1]]], ["L3", [["L1", 0], ["L2", 2], ["L3", 3]]]], "Transpose": true}
\ No newline at end of file
diff --git a/Document/Document_files/cm1.pycm b/Document/Document_files/cm1.pycm
index 4e805d68..c87eb12b 100644
--- a/Document/Document_files/cm1.pycm
+++ b/Document/Document_files/cm1.pycm
@@ -58,6 +58,8 @@ Lambda A                                                          0.42857
 Lambda B                                                          0.16667
 Mutual Information                                                0.52421
 NIR                                                               0.41667
+NPV Macro                                                         0.77778
+NPV Micro                                                         0.79167
 Overall ACC                                                       0.58333
 Overall CEN                                                       0.46381
 Overall J                                                         (1.225,0.40833)
diff --git a/Document/Document_files/cm1_colored.html b/Document/Document_files/cm1_colored.html
index 322a4d58..537d5c5b 100644
--- a/Document/Document_files/cm1_colored.html
+++ b/Document/Document_files/cm1_colored.html
@@ -196,6 +196,14 @@ <h2>Overall Statistics : </h2>
 <td style="border:1px solid black;padding:4px;">0.41667</td>
 </tr>
 <tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#NPV_Macro" style="text-decoration:None;">NPV Macro</a></td>
+<td style="border:1px solid black;padding:4px;">0.77778</td>
+</tr>
+<tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:transparent;"><a href="http://www.pycm.io/doc/index.html#NPV_Micro" style="text-decoration:None;">NPV Micro</a></td>
+<td style="border:1px solid black;padding:4px;">0.79167</td>
+</tr>
+<tr style="text-align:center;">
 <td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#Overall_ACC" style="text-decoration:None;">Overall ACC</a></td>
 <td style="border:1px solid black;padding:4px;">0.58333</td>
 </tr>
@@ -779,6 +787,6 @@ <h2>Class Statistics : </h2>
 <td style="border:1px solid black;padding:4px;border-collapse: collapse;text-align:left;">Similarity index</td>
 </tr>
 </table>
-<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.8</p>
+<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.9</p>
 </body>
 </html>
diff --git a/Document/Document_files/cm1_colored2.html b/Document/Document_files/cm1_colored2.html
index 1e123316..a91a7155 100644
--- a/Document/Document_files/cm1_colored2.html
+++ b/Document/Document_files/cm1_colored2.html
@@ -196,6 +196,14 @@ <h2>Overall Statistics : </h2>
 <td style="border:1px solid black;padding:4px;">0.41667</td>
 </tr>
 <tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#NPV_Macro" style="text-decoration:None;">NPV Macro</a></td>
+<td style="border:1px solid black;padding:4px;">0.77778</td>
+</tr>
+<tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:transparent;"><a href="http://www.pycm.io/doc/index.html#NPV_Micro" style="text-decoration:None;">NPV Micro</a></td>
+<td style="border:1px solid black;padding:4px;">0.79167</td>
+</tr>
+<tr style="text-align:center;">
 <td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#Overall_ACC" style="text-decoration:None;">Overall ACC</a></td>
 <td style="border:1px solid black;padding:4px;">0.58333</td>
 </tr>
@@ -779,6 +787,6 @@ <h2>Class Statistics : </h2>
 <td style="border:1px solid black;padding:4px;border-collapse: collapse;text-align:left;">Similarity index</td>
 </tr>
 </table>
-<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.8</p>
+<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.9</p>
 </body>
 </html>
diff --git a/Document/Document_files/cm1_filtered.html b/Document/Document_files/cm1_filtered.html
index 25bc01e3..bf27d2d9 100644
--- a/Document/Document_files/cm1_filtered.html
+++ b/Document/Document_files/cm1_filtered.html
@@ -95,6 +95,6 @@ <h2>Class Statistics : </h2>
 <td style="border:1px solid black;padding:4px;border-collapse: collapse;text-align:left;">Sensitivity, recall, hit rate, or true positive rate</td>
 </tr>
 </table>
-<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.8</p>
+<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.9</p>
 </body>
 </html>
diff --git a/Document/Document_files/cm1_filtered2.html b/Document/Document_files/cm1_filtered2.html
index f4e02cf7..8f3e9708 100644
--- a/Document/Document_files/cm1_filtered2.html
+++ b/Document/Document_files/cm1_filtered2.html
@@ -87,6 +87,6 @@ <h2>Class Statistics : </h2>
 <td style="border:1px solid black;padding:4px;border-collapse: collapse;text-align:left;">Sensitivity, recall, hit rate, or true positive rate</td>
 </tr>
 </table>
-<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.8</p>
+<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.9</p>
 </body>
 </html>
diff --git a/Document/Document_files/cm1_no_vectors.obj b/Document/Document_files/cm1_no_vectors.obj
index 34f91b7c..e9160239 100644
--- a/Document/Document_files/cm1_no_vectors.obj
+++ b/Document/Document_files/cm1_no_vectors.obj
@@ -1 +1 @@
-{"Prob-Vector": null, "Transpose": true, "Imbalanced": false, "Predict-Vector": null, "Matrix": [["L1", [["L1", 3], ["L3", 2], ["L2", 0]]], ["L2", [["L1", 0], ["L3", 1], ["L2", 1]]], ["L3", [["L1", 0], ["L3", 3], ["L2", 2]]]], "Actual-Vector": null, "Sample-Weight": null, "Digit": 5}
\ No newline at end of file
+{"Predict-Vector": null, "Sample-Weight": null, "Digit": 5, "Actual-Vector": null, "Imbalanced": false, "Prob-Vector": null, "Matrix": [["L1", [["L1", 3], ["L2", 0], ["L3", 2]]], ["L2", [["L1", 0], ["L2", 1], ["L3", 1]]], ["L3", [["L1", 0], ["L2", 2], ["L3", 3]]]], "Transpose": true}
\ No newline at end of file
diff --git a/Document/Document_files/cm1_normalized.html b/Document/Document_files/cm1_normalized.html
index ae62104b..a1b6ed7f 100644
--- a/Document/Document_files/cm1_normalized.html
+++ b/Document/Document_files/cm1_normalized.html
@@ -196,6 +196,14 @@ <h2>Overall Statistics : </h2>
 <td style="border:1px solid black;padding:4px;">0.41667</td>
 </tr>
 <tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#NPV_Macro" style="text-decoration:None;">NPV Macro</a></td>
+<td style="border:1px solid black;padding:4px;">0.77778</td>
+</tr>
+<tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:transparent;"><a href="http://www.pycm.io/doc/index.html#NPV_Micro" style="text-decoration:None;">NPV Micro</a></td>
+<td style="border:1px solid black;padding:4px;">0.79167</td>
+</tr>
+<tr style="text-align:center;">
 <td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#Overall_ACC" style="text-decoration:None;">Overall ACC</a></td>
 <td style="border:1px solid black;padding:4px;">0.58333</td>
 </tr>
@@ -779,6 +787,6 @@ <h2>Class Statistics : </h2>
 <td style="border:1px solid black;padding:4px;border-collapse: collapse;text-align:left;">Similarity index</td>
 </tr>
 </table>
-<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.8</p>
+<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.9</p>
 </body>
 </html>
diff --git a/Document/Document_files/cm1_stat.obj b/Document/Document_files/cm1_stat.obj
index fbcf00f5..795c8aca 100644
--- a/Document/Document_files/cm1_stat.obj
+++ b/Document/Document_files/cm1_stat.obj
@@ -1 +1 @@
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\ No newline at end of file
+{"Predict-Vector": null, "Sample-Weight": null, "Digit": 5, "Class-Stat": {"LS": {"L1": 2.4, "L3": 1.2, "L2": 2.0}, "GI": {"L1": 0.6000000000000001, "L3": 0.17142857142857126, "L2": 0.30000000000000004}, "TON": {"L1": 9, "L3": 6, "L2": 9}, "TN": {"L1": 7, "L3": 4, "L2": 8}, "OP": {"L1": 0.5833333333333334, "L3": 0.5589430894308943, "L2": 0.5192307692307692}, "F2": {"L1": 0.6521739130434783, "L3": 0.5769230769230769, "L2": 0.45454545454545453}, "PLR": {"L1": "None", "L3": 1.4, "L2": 2.5000000000000004}, "PPV": {"L1": 1.0, "L3": 0.5, "L2": 0.3333333333333333}, "IBA": {"L1": 0.36, "L3": 0.35265306122448975, "L2": 0.27999999999999997}, "RACCU": {"L1": 0.1111111111111111, "L3": 0.21006944444444442, "L2": 0.04340277777777778}, "F1": {"L1": 0.75, "L3": 0.5454545454545454, "L2": 0.4}, "NPV": {"L1": 0.7777777777777778, "L3": 0.6666666666666666, "L2": 0.8888888888888888}, "PLRI": {"L1": "None", "L3": "Poor", "L2": "Poor"}, "HD": {"L1": 2, "L3": 5, "L2": 3}, "ERR": {"L1": 0.16666666666666663, 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0.4166666666666667, "SOA10(Pearson C)": "Strong", "PPV Macro": 0.611111111111111, "P-Value": 0.18926430237560654, "FPR Micro": 0.20833333333333337, "Hamming Loss": 0.41666666666666663, "SOA7(Lambda A)": "Moderate", "Overall MCEN": 0.5189369467580801, "SOA8(Lambda B)": "Very Weak", "CBA": 0.4777777777777778, "SOA1(Landis & Koch)": "Fair", "Kappa 95% CI": [-0.07707577422109269, 0.7867531935759315], "NPV Macro": 0.7777777777777777, "Overall J": [1.225, 0.4083333333333334], "Overall ACC": 0.5833333333333334, "ARI": 0.09206349206349207, "Zero-one Loss": 5, "Conditional Entropy": 0.9757921620455572, "Gwet AC1": 0.3893129770992367, "Standard Error": 0.14231876063832777, "TPR Micro": 0.5833333333333334, "Response Entropy": 1.5, "RCI": 0.3533932006492363, "TPR Macro": 0.5666666666666668, "NPV Micro": 0.7916666666666666, "SOA4(Cicchetti)": "Poor", "TNR Micro": 0.7916666666666666, "Mutual Information": 0.5242078379544428, "Lambda B": 0.16666666666666666, "FNR Macro": 0.43333333333333324, "F1 Micro": 0.5833333333333334, "Kappa": 0.35483870967741943, "Chi-Squared": 6.6000000000000005, "SOA5(Cramer)": "Relatively Strong", "SOA3(Altman)": "Fair", "SOA9(Krippendorff Alpha)": "Low", "Cross Entropy": 1.5833333333333335, "CSI": 0.1777777777777778, "F1 Macro": 0.5651515151515151, "Overall MCC": 0.36666666666666664, "AUNU": 0.6785714285714285, "Scott PI": 0.34426229508196726, "Bennett S": 0.37500000000000006, "Bangdiwala B": 0.37254901960784315, "PPV Micro": 0.5833333333333334, "AUNP": 0.6857142857142857, "Overall CEN": 0.4638112995385119, "Overall RACC": 0.3541666666666667}, "Matrix": [["L1", [["L1", 3], ["L2", 0], ["L3", 2]]], ["L2", [["L1", 0], ["L2", 1], ["L3", 1]]], ["L3", [["L1", 0], ["L2", 2], ["L3", 3]]]], "Transpose": true}
\ No newline at end of file
diff --git a/Document/Document_files/cm1_summary.html b/Document/Document_files/cm1_summary.html
index 7aeda609..074d7041 100644
--- a/Document/Document_files/cm1_summary.html
+++ b/Document/Document_files/cm1_summary.html
@@ -76,6 +76,10 @@ <h2>Overall Statistics : </h2>
 <td style="border:1px solid black;padding:4px;">0.35484</td>
 </tr>
 <tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#NPV_Macro" style="text-decoration:None;">NPV Macro</a></td>
+<td style="border:1px solid black;padding:4px;">0.77778</td>
+</tr>
+<tr style="text-align:center;">
 <td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#Overall_ACC" style="text-decoration:None;">Overall ACC</a></td>
 <td style="border:1px solid black;padding:4px;">0.58333</td>
 </tr>
@@ -218,6 +222,6 @@ <h2>Class Statistics : </h2>
 <td style="border:1px solid black;padding:4px;border-collapse: collapse;text-align:left;">Test outcome negative</td>
 </tr>
 </table>
-<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.8</p>
+<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.9</p>
 </body>
 </html>
diff --git a/Document/Document_files/cm1_summary.pycm b/Document/Document_files/cm1_summary.pycm
index 89d8d3f1..14e29324 100644
--- a/Document/Document_files/cm1_summary.pycm
+++ b/Document/Document_files/cm1_summary.pycm
@@ -28,6 +28,7 @@ ACC Macro                                                         0.72222
 F1 Macro                                                          0.56515
 FPR Macro                                                         0.20952
 Kappa                                                             0.35484
+NPV Macro                                                         0.77778
 Overall ACC                                                       0.58333
 PPV Macro                                                         0.61111
 SOA1(Landis & Koch)                                               Fair
diff --git a/Document/Document_files/sparse_cm.pycm b/Document/Document_files/sparse_cm.pycm
index 7dfe5301..c135ddab 100644
--- a/Document/Document_files/sparse_cm.pycm
+++ b/Document/Document_files/sparse_cm.pycm
@@ -24,6 +24,7 @@ ACC Macro                                                         0.9
 F1 Macro                                                          0.47368
 FPR Macro                                                         0.5
 Kappa                                                             0.0
+NPV Macro                                                         None
 Overall ACC                                                       0.9
 PPV Macro                                                         None
 SOA1(Landis & Koch)                                               Slight
diff --git a/Document/Example1_files/cm1.html b/Document/Example1_files/cm1.html
index 4dce3d7d..3fcf65f5 100644
--- a/Document/Example1_files/cm1.html
+++ b/Document/Example1_files/cm1.html
@@ -196,6 +196,14 @@ <h2>Overall Statistics : </h2>
 <td style="border:1px solid black;padding:4px;">0.42105</td>
 </tr>
 <tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#NPV_Macro" style="text-decoration:None;">NPV Macro</a></td>
+<td style="border:1px solid black;padding:4px;">0.92857</td>
+</tr>
+<tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:transparent;"><a href="http://www.pycm.io/doc/index.html#NPV_Micro" style="text-decoration:None;">NPV Micro</a></td>
+<td style="border:1px solid black;padding:4px;">0.92105</td>
+</tr>
+<tr style="text-align:center;">
 <td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#Overall_ACC" style="text-decoration:None;">Overall ACC</a></td>
 <td style="border:1px solid black;padding:4px;">0.84211</td>
 </tr>
@@ -779,6 +787,6 @@ <h2>Class Statistics : </h2>
 <td style="border:1px solid black;padding:4px;border-collapse: collapse;text-align:left;">Similarity index</td>
 </tr>
 </table>
-<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.8</p>
+<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.9</p>
 </body>
 </html>
diff --git a/Document/Example1_files/cm2.html b/Document/Example1_files/cm2.html
index 6ccf6d9d..4ec720a1 100644
--- a/Document/Example1_files/cm2.html
+++ b/Document/Example1_files/cm2.html
@@ -196,6 +196,14 @@ <h2>Overall Statistics : </h2>
 <td style="border:1px solid black;padding:4px;">0.42105</td>
 </tr>
 <tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#NPV_Macro" style="text-decoration:None;">NPV Macro</a></td>
+<td style="border:1px solid black;padding:4px;">0.98551</td>
+</tr>
+<tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:transparent;"><a href="http://www.pycm.io/doc/index.html#NPV_Micro" style="text-decoration:None;">NPV Micro</a></td>
+<td style="border:1px solid black;padding:4px;">0.98684</td>
+</tr>
+<tr style="text-align:center;">
 <td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#Overall_ACC" style="text-decoration:None;">Overall ACC</a></td>
 <td style="border:1px solid black;padding:4px;">0.97368</td>
 </tr>
@@ -779,6 +787,6 @@ <h2>Class Statistics : </h2>
 <td style="border:1px solid black;padding:4px;border-collapse: collapse;text-align:left;">Similarity index</td>
 </tr>
 </table>
-<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.8</p>
+<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.9</p>
 </body>
 </html>
diff --git a/Document/Example1_files/cm3.html b/Document/Example1_files/cm3.html
index aa5cc848..bc61c45d 100644
--- a/Document/Example1_files/cm3.html
+++ b/Document/Example1_files/cm3.html
@@ -196,6 +196,14 @@ <h2>Overall Statistics : </h2>
 <td style="border:1px solid black;padding:4px;">0.42105</td>
 </tr>
 <tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#NPV_Macro" style="text-decoration:None;">NPV Macro</a></td>
+<td style="border:1px solid black;padding:4px;">0.95108</td>
+</tr>
+<tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:transparent;"><a href="http://www.pycm.io/doc/index.html#NPV_Micro" style="text-decoration:None;">NPV Micro</a></td>
+<td style="border:1px solid black;padding:4px;">0.94737</td>
+</tr>
+<tr style="text-align:center;">
 <td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#Overall_ACC" style="text-decoration:None;">Overall ACC</a></td>
 <td style="border:1px solid black;padding:4px;">0.89474</td>
 </tr>
@@ -779,6 +787,6 @@ <h2>Class Statistics : </h2>
 <td style="border:1px solid black;padding:4px;border-collapse: collapse;text-align:left;">Similarity index</td>
 </tr>
 </table>
-<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.8</p>
+<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.9</p>
 </body>
 </html>
diff --git a/Document/Example2.ipynb b/Document/Example2.ipynb
index 6fab7682..79f61032 100644
--- a/Document/Example2.ipynb
+++ b/Document/Example2.ipynb
@@ -142,7 +142,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -154,7 +154,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
diff --git a/Document/Example3.ipynb b/Document/Example3.ipynb
index 5e8db09a..fb8887d4 100644
--- a/Document/Example3.ipynb
+++ b/Document/Example3.ipynb
@@ -152,6 +152,8 @@
       "Lambda B                                                          0.0\n",
       "Mutual Information                                                0.31669\n",
       "NIR                                                               0.66667\n",
+      "NPV Macro                                                         0.9\n",
+      "NPV Micro                                                         0.83333\n",
       "Overall ACC                                                       0.83333\n",
       "Overall CEN                                                       0.39624\n",
       "Overall J                                                         (1.3,0.65)\n",
@@ -374,6 +376,8 @@
       "Lambda B                                                          0.66667\n",
       "Mutual Information                                                1.12581\n",
       "NIR                                                               0.33333\n",
+      "NPV Macro                                                         0.93333\n",
+      "NPV Micro                                                         0.91667\n",
       "Overall ACC                                                       0.83333\n",
       "Overall CEN                                                       0.16279\n",
       "Overall J                                                         (2.16667,0.72222)\n",
diff --git a/Document/Example4.ipynb b/Document/Example4.ipynb
index 81598416..a9c3c234 100644
--- a/Document/Example4.ipynb
+++ b/Document/Example4.ipynb
@@ -144,6 +144,8 @@
       "Lambda B                                                          0.0\n",
       "Mutual Information                                                0.10088\n",
       "NIR                                                               0.8\n",
+      "NPV Macro                                                         0.76741\n",
+      "NPV Micro                                                         0.78333\n",
       "Overall ACC                                                       0.35\n",
       "Overall CEN                                                       0.3648\n",
       "Overall J                                                         (0.60294,0.15074)\n",
@@ -430,6 +432,8 @@
       "Lambda B                                                          0.0\n",
       "Mutual Information                                                0.10088\n",
       "NIR                                                               0.8\n",
+      "NPV Macro                                                         0.76741\n",
+      "NPV Micro                                                         0.78333\n",
       "Overall ACC                                                       0.35\n",
       "Overall CEN                                                       0.3648\n",
       "Overall J                                                         (0.60294,0.15074)\n",
@@ -554,7 +558,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{\"Digit\": 5, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Imbalanced\": true, \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"Sample-Weight\": null, \"Prob-Vector\": null, \"Transpose\": false, \"Matrix\": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]]}\n"
+      "{\"Prob-Vector\": null, \"Digit\": 5, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Imbalanced\": true, \"Transpose\": false, \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"Matrix\": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]], \"Sample-Weight\": null}\n"
      ]
     }
    ],
@@ -571,7 +575,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{\"Digit\": 5, \"Class-Stat\": {\"MK\": {\"200\": 0.08791208791208782, \"500\": 0.38888888888888884, \"100\": 0.0, \"600\": \"None\"}, \"TPR\": {\"200\": 0.375, \"500\": 0.3333333333333333, \"100\": \"None\", \"600\": 0.0}, \"TN\": {\"200\": 3, \"100\": 9, \"500\": 16, \"600\": 19}, \"TON\": {\"200\": 13, \"500\": 18, \"100\": 9, \"600\": 20}, \"NLR\": {\"200\": 0.8333333333333334, \"500\": 0.7083333333333334, \"100\": \"None\", \"600\": 1.0}, \"POP\": {\"200\": 20, \"500\": 20, \"100\": 20, \"600\": 20}, \"FOR\": {\"200\": 0.7692307692307692, \"500\": 0.11111111111111116, \"100\": 0.0, \"600\": 0.050000000000000044}, \"FPR\": {\"200\": 0.25, \"500\": 0.05882352941176472, \"100\": 0.55, \"600\": 0.0}, \"OOC\": {\"200\": 0.5669467095138409, \"500\": 0.4082482904638631, \"100\": \"None\", \"600\": \"None\"}, \"N\": {\"200\": 4, \"500\": 17, \"100\": 20, \"600\": 19}, \"TNR\": {\"200\": 0.75, \"500\": 0.9411764705882353, \"100\": 0.45, \"600\": 1.0}, \"PLRI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"None\"}, \"DOR\": {\"200\": 1.7999999999999998, \"500\": 7.999999999999997, \"100\": \"None\", \"600\": \"None\"}, \"F1\": {\"200\": 0.5217391304347826, \"500\": 0.4, \"100\": 0.0, \"600\": 0.0}, \"ACC\": {\"200\": 0.45, \"500\": 0.85, \"100\": 0.45, \"600\": 0.95}, \"LS\": {\"200\": 1.0714285714285714, \"500\": 3.3333333333333335, \"100\": \"None\", \"600\": \"None\"}, \"BCD\": {\"200\": 0.225, \"500\": 0.025, \"100\": 0.275, \"600\": 0.025}, \"TP\": {\"200\": 6, \"100\": 0, \"500\": 1, \"600\": 0}, \"PRE\": {\"200\": 0.8, \"500\": 0.15, \"100\": 0.0, \"600\": 0.05}, \"MCEN\": {\"200\": 0.3739448088748241, \"500\": 0.5802792108518123, \"100\": 0.3349590631259315, \"600\": 0.0}, \"AUCI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"Poor\"}, \"ERR\": {\"200\": 0.55, \"500\": 0.15000000000000002, \"100\": 0.55, \"600\": 0.050000000000000044}, \"AGM\": {\"200\": 0.5669417382415922, \"500\": 0.7351956938438939, \"100\": \"None\", \"600\": 0}, \"MCCI\": {\"200\": \"Negligible\", \"500\": \"Weak\", \"100\": \"None\", \"600\": \"None\"}, \"DPI\": {\"200\": \"Poor\", \"500\": \"Poor\", \"100\": \"None\", \"600\": \"None\"}, \"Q\": {\"200\": 0.28571428571428575, \"500\": 0.7777777777777778, \"100\": \"None\", \"600\": \"None\"}, \"RACCU\": {\"200\": 0.33062499999999995, \"500\": 0.015625, \"100\": 0.07562500000000001, \"600\": 0.0006250000000000001}, \"FN\": {\"200\": 10, \"100\": 0, \"500\": 2, \"600\": 1}, \"GI\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"AUPR\": {\"200\": 0.6160714285714286, \"500\": 0.41666666666666663, \"100\": \"None\", \"600\": \"None\"}, \"F2\": {\"200\": 0.4225352112676056, \"500\": 0.35714285714285715, \"100\": 0.0, \"600\": 0.0}, \"QI\": {\"200\": \"Weak\", \"500\": \"Strong\", \"100\": \"None\", \"600\": \"None\"}, \"AM\": {\"200\": -9, \"500\": -1, \"100\": 11, \"600\": -1}, \"AUC\": {\"200\": 0.5625, \"500\": 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{\"200\": 1.5, \"500\": 5.666666666666665, \"100\": \"None\", \"600\": \"None\"}, \"MCC\": {\"200\": 0.10482848367219183, \"500\": 0.32673201960653564, \"100\": \"None\", \"600\": \"None\"}, \"AGF\": {\"200\": 0.33642097801219245, \"500\": 0.5665926996700735, \"100\": 0.0, \"600\": 0.0}, \"HD\": {\"200\": 11, \"500\": 3, \"100\": 11, \"600\": 1}, \"FP\": {\"200\": 1, \"100\": 11, \"500\": 1, \"600\": 0}, \"GM\": {\"200\": 0.5303300858899106, \"500\": 0.5601120336112039, \"100\": \"None\", \"600\": 0.0}, \"CEN\": {\"200\": 0.3570795472009597, \"500\": 0.5389466410223563, \"100\": 0.3349590631259315, \"600\": 0.0}, \"P\": {\"200\": 16, \"500\": 3, \"100\": 0, \"600\": 1}, \"NPV\": {\"200\": 0.23076923076923078, \"500\": 0.8888888888888888, \"100\": 1.0, \"600\": 0.95}, \"NLRI\": {\"200\": \"Negligible\", \"500\": \"Negligible\", \"100\": \"None\", \"600\": \"Negligible\"}, \"BM\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"OC\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": \"None\", \"600\": \"None\"}, \"PPV\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": 0.0, \"600\": \"None\"}, \"TOP\": {\"200\": 7, \"500\": 2, \"100\": 11, \"600\": 0}, \"FNR\": {\"200\": 0.625, \"500\": 0.6666666666666667, \"100\": \"None\", \"600\": 1.0}, \"DP\": {\"200\": 0.1407391082701595, \"500\": 0.49789960499474867, \"100\": \"None\", \"600\": \"None\"}, \"sInd\": {\"200\": 0.5240141808835057, \"500\": 0.5267639848569737, \"100\": \"None\", \"600\": 0.29289321881345254}, \"Y\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"OP\": {\"200\": 0.1166666666666667, \"500\": 0.373076923076923, \"100\": \"None\", \"600\": -0.050000000000000044}}, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Imbalanced\": true, \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"Sample-Weight\": null, \"Prob-Vector\": null, \"Transpose\": false, \"Matrix\": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]], \"Overall-Stat\": {\"Phi-Squared\": \"None\", \"Overall ACC\": 0.35, \"Conditional Entropy\": 1.235789374242786, \"ARI\": 0.02298247455136956, \"Kappa\": 0.07801418439716304, \"F1 Micro\": 0.35, \"Kappa No Prevalence\": -0.30000000000000004, \"SOA5(Cramer)\": \"None\", \"FPR Micro\": 0.21666666666666667, \"Gwet AC1\": 0.19504643962848295, \"Chi-Squared\": \"None\", \"Bangdiwala B\": 0.3135593220338983, \"FNR Macro\": \"None\", \"FPR Macro\": 0.2147058823529412, \"Joint Entropy\": 2.119973094021975, \"NIR\": 0.8, \"SOA3(Altman)\": \"Poor\", \"RR\": 5.0, \"ACC Macro\": 0.675, \"CBA\": 0.17708333333333331, \"Krippendorff Alpha\": -0.09740259740259723, \"SOA9(Krippendorff Alpha)\": \"Low\", \"Kappa Unbiased\": -0.12554112554112543, \"F1 Macro\": 0.23043478260869565, \"Zero-one Loss\": 13, \"KL Divergence\": \"None\", \"Kappa 95% CI\": [-0.21849807698648957, 0.3745264457808156], \"Bennett S\": 0.1333333333333333, \"Response Entropy\": 1.3366664819166876, \"CSI\": \"None\", \"Overall MCEN\": 0.3746281299595305, \"SOA10(Pearson C)\": \"None\", \"Cramer V\": \"None\", \"SOA6(Matthews)\": \"Negligible\", \"Standard Error\": 0.1066536450385077, \"SOA7(Lambda A)\": \"None\", \"95% CI\": [0.14095885572452488, 0.559041144275475], \"AUNP\": \"None\", \"PPV Micro\": 0.35, \"SOA4(Cicchetti)\": \"Poor\", \"SOA1(Landis & Koch)\": \"Slight\", \"Scott PI\": -0.12554112554112543, \"Chi-Squared DF\": 9, \"Hamming Loss\": 0.65, \"Kappa Standard Error\": 0.15128176601206766, \"TPR Macro\": \"None\", \"Lambda A\": 0.0, \"SOA8(Lambda B)\": \"None\", \"Overall CEN\": 0.3648028121279775, \"Overall RACC\": 0.29500000000000004, \"SOA2(Fleiss)\": \"Poor\", \"Cross Entropy\": 1.709947752496911, \"PPV Macro\": \"None\", \"Overall MCC\": 0.1264200803632855, \"Mutual Information\": 0.10087710767390168, \"TNR Micro\": 0.7833333333333333, \"TNR Macro\": 0.7852941176470588, \"Overall RACCU\": 0.42249999999999993, \"Lambda B\": 0.0, \"FNR Micro\": 0.65, \"P-Value\": 0.9999981549942787, \"RCI\": 0.11409066398451011, \"Pearson C\": \"None\", \"Reference Entropy\": 0.8841837197791889, \"AUNU\": \"None\", \"TPR Micro\": 0.35, \"Overall J\": [0.6029411764705883, 0.15073529411764708]}}\n"
+      "{\"Prob-Vector\": null, \"Digit\": 5, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Imbalanced\": true, \"Transpose\": false, \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"Matrix\": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]], \"Sample-Weight\": null, \"Overall-Stat\": {\"SOA7(Lambda A)\": \"None\", \"Pearson C\": \"None\", \"Scott PI\": -0.12554112554112543, \"ACC Macro\": 0.675, \"Overall ACC\": 0.35, \"RR\": 5.0, \"TNR Micro\": 0.7833333333333333, \"FPR Micro\": 0.21666666666666667, \"SOA2(Fleiss)\": \"Poor\", \"SOA1(Landis & Koch)\": \"Slight\", \"NIR\": 0.8, \"Zero-one Loss\": 13, \"Bennett S\": 0.1333333333333333, \"Hamming Loss\": 0.65, \"Bangdiwala B\": 0.3135593220338983, \"Overall MCC\": 0.1264200803632855, \"FNR Micro\": 0.65, \"Response Entropy\": 1.3366664819166876, \"PPV Macro\": \"None\", \"Cramer V\": \"None\", \"CSI\": \"None\", \"Conditional Entropy\": 1.235789374242786, \"Kappa No Prevalence\": -0.30000000000000004, \"CBA\": 0.17708333333333331, \"Kappa 95% CI\": [-0.21849807698648957, 0.3745264457808156], \"Overall RACC\": 0.29500000000000004, \"ARI\": 0.02298247455136956, \"AUNU\": \"None\", \"Overall MCEN\": 0.3746281299595305, \"Gwet AC1\": 0.19504643962848295, \"FNR Macro\": \"None\", \"SOA5(Cramer)\": \"None\", \"Kappa\": 0.07801418439716304, \"Overall J\": [0.6029411764705883, 0.15073529411764708], \"Chi-Squared DF\": 9, \"Reference Entropy\": 0.8841837197791889, \"FPR Macro\": 0.2147058823529412, \"Krippendorff Alpha\": -0.09740259740259723, \"Standard Error\": 0.1066536450385077, \"Lambda B\": 0.0, \"Phi-Squared\": \"None\", \"KL Divergence\": \"None\", \"Chi-Squared\": \"None\", \"TPR Micro\": 0.35, \"SOA6(Matthews)\": \"Negligible\", \"F1 Micro\": 0.35, \"Overall CEN\": 0.3648028121279775, \"Kappa Standard Error\": 0.15128176601206766, \"PPV Micro\": 0.35, \"Joint Entropy\": 2.119973094021975, \"AUNP\": \"None\", \"SOA8(Lambda B)\": \"None\", \"NPV Macro\": 0.7674145299145299, \"Lambda A\": 0.0, \"TNR Macro\": 0.7852941176470588, \"Overall RACCU\": 0.42249999999999993, \"Kappa Unbiased\": -0.12554112554112543, \"SOA10(Pearson C)\": \"None\", \"NPV Micro\": 0.7833333333333333, \"SOA4(Cicchetti)\": \"Poor\", \"SOA9(Krippendorff Alpha)\": \"Low\", \"P-Value\": 0.9999981549942787, \"Cross Entropy\": 1.709947752496911, \"SOA3(Altman)\": \"Poor\", \"Mutual Information\": 0.10087710767390168, \"RCI\": 0.11409066398451011, \"95% CI\": [0.14095885572452488, 0.559041144275475], \"TPR Macro\": \"None\", \"F1 Macro\": 0.23043478260869565}, \"Class-Stat\": {\"F0.5\": {\"200\": 0.6818181818181818, \"500\": 0.45454545454545453, \"100\": 0.0, \"600\": 0.0}, \"FN\": {\"200\": 10, \"100\": 0, \"500\": 2, \"600\": 1}, \"AUC\": {\"200\": 0.5625, \"500\": 0.6372549019607843, \"100\": \"None\", \"600\": 0.5}, \"PRE\": {\"200\": 0.8, \"500\": 0.15, \"100\": 0.0, \"600\": 0.05}, \"NLR\": {\"200\": 0.8333333333333334, \"500\": 0.7083333333333334, \"100\": \"None\", \"600\": 1.0}, \"GM\": {\"200\": 0.5303300858899106, \"500\": 0.5601120336112039, \"100\": \"None\", \"600\": 0.0}, \"MCEN\": {\"200\": 0.3739448088748241, \"500\": 0.5802792108518123, \"100\": 0.3349590631259315, \"600\": 0.0}, \"POP\": {\"200\": 20, \"500\": 20, \"100\": 20, \"600\": 20}, \"F2\": {\"200\": 0.4225352112676056, \"500\": 0.35714285714285715, \"100\": 0.0, \"600\": 0.0}, \"LS\": {\"200\": 1.0714285714285714, \"500\": 3.3333333333333335, \"100\": \"None\", \"600\": \"None\"}, \"dInd\": {\"200\": 0.673145600891813, \"500\": 0.6692567908186672, \"100\": \"None\", \"600\": 1.0}, \"HD\": {\"200\": 11, \"500\": 3, \"100\": 11, \"600\": 1}, \"PLRI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"None\"}, \"AGM\": {\"200\": 0.5669417382415922, \"500\": 0.7351956938438939, \"100\": \"None\", \"600\": 0}, \"FPR\": {\"200\": 0.25, \"500\": 0.05882352941176472, \"100\": 0.55, \"600\": 0.0}, \"J\": {\"200\": 0.35294117647058826, \"500\": 0.25, \"100\": 0.0, \"600\": 0.0}, \"OC\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": \"None\", \"600\": \"None\"}, \"DOR\": {\"200\": 1.7999999999999998, \"500\": 7.999999999999997, \"100\": \"None\", \"600\": \"None\"}, \"Y\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"MK\": {\"200\": 0.08791208791208782, \"500\": 0.38888888888888884, \"100\": 0.0, \"600\": \"None\"}, \"IS\": {\"200\": 0.09953567355091428, \"500\": 1.736965594166206, \"100\": \"None\", \"600\": \"None\"}, \"TON\": {\"200\": 13, \"500\": 18, \"100\": 9, \"600\": 20}, \"RACCU\": {\"200\": 0.33062499999999995, \"500\": 0.015625, \"100\": 0.07562500000000001, \"600\": 0.0006250000000000001}, \"MCC\": {\"200\": 0.10482848367219183, \"500\": 0.32673201960653564, \"100\": \"None\", \"600\": \"None\"}, \"TPR\": {\"200\": 0.375, \"500\": 0.3333333333333333, \"100\": \"None\", \"600\": 0.0}, \"PLR\": {\"200\": 1.5, \"500\": 5.666666666666665, \"100\": \"None\", \"600\": \"None\"}, \"TNR\": {\"200\": 0.75, \"500\": 0.9411764705882353, \"100\": 0.45, \"600\": 1.0}, \"IBA\": {\"200\": 0.17578125, \"500\": 0.1230296039984621, \"100\": \"None\", \"600\": 0.0}, \"AM\": {\"200\": -9, \"500\": -1, \"100\": 11, \"600\": -1}, \"FOR\": {\"200\": 0.7692307692307692, \"500\": 0.11111111111111116, \"100\": 0.0, \"600\": 0.050000000000000044}, \"PPV\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": 0.0, \"600\": \"None\"}, \"AUCI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"Poor\"}, \"DP\": {\"200\": 0.1407391082701595, \"500\": 0.49789960499474867, \"100\": \"None\", \"600\": \"None\"}, \"ERR\": {\"200\": 0.55, \"500\": 0.15000000000000002, \"100\": 0.55, \"600\": 0.050000000000000044}, \"QI\": {\"200\": \"Weak\", \"500\": \"Strong\", \"100\": \"None\", \"600\": \"None\"}, \"FNR\": {\"200\": 0.625, \"500\": 0.6666666666666667, \"100\": \"None\", \"600\": 1.0}, \"N\": {\"200\": 4, \"500\": 17, \"100\": 20, \"600\": 19}, \"NLRI\": {\"200\": \"Negligible\", \"500\": \"Negligible\", \"100\": \"None\", \"600\": \"Negligible\"}, \"Q\": {\"200\": 0.28571428571428575, \"500\": 0.7777777777777778, \"100\": \"None\", \"600\": \"None\"}, \"DPI\": {\"200\": \"Poor\", \"500\": \"Poor\", \"100\": \"None\", \"600\": \"None\"}, \"OP\": {\"200\": 0.1166666666666667, \"500\": 0.373076923076923, \"100\": \"None\", \"600\": -0.050000000000000044}, \"BB\": {\"200\": 0.375, \"500\": 0.3333333333333333, \"100\": 0.0, \"600\": 0.0}, \"F1\": {\"200\": 0.5217391304347826, \"500\": 0.4, \"100\": 0.0, \"600\": 0.0}, \"BM\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"GI\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"BCD\": {\"200\": 0.225, \"500\": 0.025, \"100\": 0.275, \"600\": 0.025}, \"AUPR\": {\"200\": 0.6160714285714286, \"500\": 0.41666666666666663, \"100\": \"None\", \"600\": \"None\"}, \"AGF\": {\"200\": 0.33642097801219245, \"500\": 0.5665926996700735, \"100\": 0.0, \"600\": 0.0}, \"MCCI\": {\"200\": \"Negligible\", \"500\": \"Weak\", \"100\": \"None\", \"600\": \"None\"}, \"ICSI\": {\"200\": 0.2321428571428572, \"500\": -0.16666666666666674, \"100\": \"None\", \"600\": \"None\"}, \"G\": {\"200\": 0.5669467095138409, \"500\": 0.408248290463863, \"100\": \"None\", \"600\": \"None\"}, \"P\": {\"200\": 16, \"500\": 3, \"100\": 0, \"600\": 1}, \"FDR\": {\"200\": 0.1428571428571429, \"500\": 0.5, \"100\": 1.0, \"600\": \"None\"}, \"CEN\": {\"200\": 0.3570795472009597, \"500\": 0.5389466410223563, \"100\": 0.3349590631259315, \"600\": 0.0}, \"RACC\": {\"200\": 0.28, \"500\": 0.015, \"100\": 0.0, \"600\": 0.0}, \"NPV\": {\"200\": 0.23076923076923078, \"500\": 0.8888888888888888, \"100\": 1.0, \"600\": 0.95}, \"sInd\": {\"200\": 0.5240141808835057, \"500\": 0.5267639848569737, \"100\": \"None\", \"600\": 0.29289321881345254}, \"OOC\": {\"200\": 0.5669467095138409, \"500\": 0.4082482904638631, \"100\": \"None\", \"600\": \"None\"}, \"TP\": {\"200\": 6, \"100\": 0, \"500\": 1, \"600\": 0}, \"ACC\": {\"200\": 0.45, \"500\": 0.85, \"100\": 0.45, \"600\": 0.95}, \"TOP\": {\"200\": 7, \"500\": 2, \"100\": 11, \"600\": 0}, \"FP\": {\"200\": 1, \"100\": 11, \"500\": 1, \"600\": 0}, \"TN\": {\"200\": 3, \"100\": 9, \"500\": 16, \"600\": 19}}}\n"
      ]
     }
    ],
@@ -588,7 +592,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{\"Digit\": 5, \"Predict-Vector\": null, \"Imbalanced\": true, \"Actual-Vector\": null, \"Sample-Weight\": null, \"Prob-Vector\": null, \"Transpose\": false, \"Matrix\": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]]}\n"
+      "{\"Prob-Vector\": null, \"Digit\": 5, \"Predict-Vector\": null, \"Imbalanced\": true, \"Transpose\": false, \"Actual-Vector\": null, \"Matrix\": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]], \"Sample-Weight\": null}\n"
      ]
     }
    ],
diff --git a/Document/Example4_files/cm.obj b/Document/Example4_files/cm.obj
index 442c32d6..719026eb 100644
--- a/Document/Example4_files/cm.obj
+++ b/Document/Example4_files/cm.obj
@@ -1 +1 @@
-{"Digit": 5, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Imbalanced": true, "Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "Sample-Weight": null, "Prob-Vector": null, "Transpose": false, "Matrix": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]]}
\ No newline at end of file
+{"Prob-Vector": null, "Digit": 5, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Imbalanced": true, "Transpose": false, "Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "Matrix": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]], "Sample-Weight": null}
\ No newline at end of file
diff --git a/Document/Example4_files/cm_no_vectors.obj b/Document/Example4_files/cm_no_vectors.obj
index df37bfa8..5c59b1fc 100644
--- a/Document/Example4_files/cm_no_vectors.obj
+++ b/Document/Example4_files/cm_no_vectors.obj
@@ -1 +1 @@
-{"Digit": 5, "Predict-Vector": null, "Imbalanced": true, "Actual-Vector": null, "Sample-Weight": null, "Prob-Vector": null, "Transpose": false, "Matrix": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]]}
\ No newline at end of file
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\ No newline at end of file
diff --git a/Document/Example4_files/cm_stat.obj b/Document/Example4_files/cm_stat.obj
index 2e58fb1d..be4df80b 100644
--- a/Document/Example4_files/cm_stat.obj
+++ b/Document/Example4_files/cm_stat.obj
@@ -1 +1 @@
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200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Imbalanced": true, "Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "Sample-Weight": null, "Prob-Vector": null, "Transpose": false, "Matrix": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]], "Overall-Stat": {"Phi-Squared": "None", "Overall ACC": 0.35, "Conditional Entropy": 1.235789374242786, "ARI": 0.02298247455136956, "Kappa": 0.07801418439716304, "F1 Micro": 0.35, "Kappa No Prevalence": -0.30000000000000004, "SOA5(Cramer)": "None", "FPR Micro": 0.21666666666666667, "Gwet AC1": 0.19504643962848295, "Chi-Squared": "None", "Bangdiwala B": 0.3135593220338983, "FNR Macro": "None", "FPR Macro": 0.2147058823529412, "Joint Entropy": 2.119973094021975, "NIR": 0.8, "SOA3(Altman)": "Poor", "RR": 5.0, "ACC Macro": 0.675, "CBA": 0.17708333333333331, "Krippendorff Alpha": -0.09740259740259723, "SOA9(Krippendorff Alpha)": "Low", "Kappa Unbiased": -0.12554112554112543, "F1 Macro": 0.23043478260869565, "Zero-one Loss": 13, "KL Divergence": "None", "Kappa 95% CI": [-0.21849807698648957, 0.3745264457808156], "Bennett S": 0.1333333333333333, "Response Entropy": 1.3366664819166876, "CSI": "None", "Overall MCEN": 0.3746281299595305, "SOA10(Pearson C)": "None", "Cramer V": "None", "SOA6(Matthews)": "Negligible", "Standard Error": 0.1066536450385077, "SOA7(Lambda A)": "None", "95% CI": [0.14095885572452488, 0.559041144275475], "AUNP": "None", "PPV Micro": 0.35, "SOA4(Cicchetti)": "Poor", "SOA1(Landis & Koch)": "Slight", "Scott PI": -0.12554112554112543, "Chi-Squared DF": 9, "Hamming Loss": 0.65, "Kappa Standard Error": 0.15128176601206766, "TPR Macro": "None", "Lambda A": 0.0, "SOA8(Lambda B)": "None", "Overall CEN": 0.3648028121279775, "Overall RACC": 0.29500000000000004, "SOA2(Fleiss)": "Poor", "Cross Entropy": 1.709947752496911, "PPV Macro": "None", "Overall MCC": 0.1264200803632855, "Mutual Information": 0.10087710767390168, "TNR Micro": 0.7833333333333333, "TNR Macro": 0.7852941176470588, "Overall RACCU": 0.42249999999999993, "Lambda B": 0.0, "FNR Micro": 0.65, "P-Value": 0.9999981549942787, "RCI": 0.11409066398451011, "Pearson C": "None", "Reference Entropy": 0.8841837197791889, "AUNU": "None", "TPR Micro": 0.35, "Overall J": [0.6029411764705883, 0.15073529411764708]}}
\ No newline at end of file
+{"Prob-Vector": null, "Digit": 5, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Imbalanced": true, "Transpose": false, "Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "Matrix": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]], "Sample-Weight": null, "Overall-Stat": {"SOA7(Lambda A)": "None", "Pearson C": "None", "Scott PI": -0.12554112554112543, "ACC Macro": 0.675, "Overall ACC": 0.35, "RR": 5.0, "TNR Micro": 0.7833333333333333, "FPR Micro": 0.21666666666666667, "SOA2(Fleiss)": "Poor", "SOA1(Landis & Koch)": "Slight", "NIR": 0.8, "Zero-one Loss": 13, "Bennett S": 0.1333333333333333, "Hamming Loss": 0.65, "Bangdiwala B": 0.3135593220338983, "Overall MCC": 0.1264200803632855, "FNR Micro": 0.65, "Response Entropy": 1.3366664819166876, "PPV Macro": "None", "Cramer V": "None", "CSI": "None", "Conditional Entropy": 1.235789374242786, "Kappa No Prevalence": -0.30000000000000004, "CBA": 0.17708333333333331, "Kappa 95% CI": [-0.21849807698648957, 0.3745264457808156], "Overall RACC": 0.29500000000000004, "ARI": 0.02298247455136956, "AUNU": "None", "Overall MCEN": 0.3746281299595305, "Gwet AC1": 0.19504643962848295, "FNR Macro": "None", "SOA5(Cramer)": "None", "Kappa": 0.07801418439716304, "Overall J": [0.6029411764705883, 0.15073529411764708], "Chi-Squared DF": 9, "Reference Entropy": 0.8841837197791889, "FPR Macro": 0.2147058823529412, "Krippendorff Alpha": -0.09740259740259723, "Standard Error": 0.1066536450385077, "Lambda B": 0.0, "Phi-Squared": "None", "KL Divergence": "None", "Chi-Squared": "None", "TPR Micro": 0.35, "SOA6(Matthews)": "Negligible", "F1 Micro": 0.35, "Overall CEN": 0.3648028121279775, "Kappa Standard Error": 0.15128176601206766, "PPV Micro": 0.35, "Joint 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1.0}, "GM": {"200": 0.5303300858899106, "500": 0.5601120336112039, "100": "None", "600": 0.0}, "MCEN": {"200": 0.3739448088748241, "500": 0.5802792108518123, "100": 0.3349590631259315, "600": 0.0}, "POP": {"200": 20, "500": 20, "100": 20, "600": 20}, "F2": {"200": 0.4225352112676056, "500": 0.35714285714285715, "100": 0.0, "600": 0.0}, "LS": {"200": 1.0714285714285714, "500": 3.3333333333333335, "100": "None", "600": "None"}, "dInd": {"200": 0.673145600891813, "500": 0.6692567908186672, "100": "None", "600": 1.0}, "HD": {"200": 11, "500": 3, "100": 11, "600": 1}, "PLRI": {"200": "Poor", "500": "Fair", "100": "None", "600": "None"}, "AGM": {"200": 0.5669417382415922, "500": 0.7351956938438939, "100": "None", "600": 0}, "FPR": {"200": 0.25, "500": 0.05882352941176472, "100": 0.55, "600": 0.0}, "J": {"200": 0.35294117647058826, "500": 0.25, "100": 0.0, "600": 0.0}, "OC": {"200": 0.8571428571428571, "500": 0.5, "100": "None", "600": "None"}, "DOR": {"200": 1.7999999999999998, "500": 7.999999999999997, "100": "None", "600": "None"}, "Y": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "MK": {"200": 0.08791208791208782, "500": 0.38888888888888884, "100": 0.0, "600": "None"}, "IS": {"200": 0.09953567355091428, "500": 1.736965594166206, "100": "None", "600": "None"}, "TON": {"200": 13, "500": 18, "100": 9, "600": 20}, "RACCU": {"200": 0.33062499999999995, "500": 0.015625, "100": 0.07562500000000001, "600": 0.0006250000000000001}, "MCC": {"200": 0.10482848367219183, "500": 0.32673201960653564, "100": "None", "600": "None"}, "TPR": {"200": 0.375, "500": 0.3333333333333333, "100": "None", "600": 0.0}, "PLR": {"200": 1.5, "500": 5.666666666666665, "100": "None", "600": "None"}, "TNR": {"200": 0.75, "500": 0.9411764705882353, "100": 0.45, "600": 1.0}, "IBA": {"200": 0.17578125, "500": 0.1230296039984621, "100": "None", "600": 0.0}, "AM": {"200": -9, "500": -1, "100": 11, "600": -1}, "FOR": {"200": 0.7692307692307692, "500": 0.11111111111111116, 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"F1": {"200": 0.5217391304347826, "500": 0.4, "100": 0.0, "600": 0.0}, "BM": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "GI": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "BCD": {"200": 0.225, "500": 0.025, "100": 0.275, "600": 0.025}, "AUPR": {"200": 0.6160714285714286, "500": 0.41666666666666663, "100": "None", "600": "None"}, "AGF": {"200": 0.33642097801219245, "500": 0.5665926996700735, "100": 0.0, "600": 0.0}, "MCCI": {"200": "Negligible", "500": "Weak", "100": "None", "600": "None"}, "ICSI": {"200": 0.2321428571428572, "500": -0.16666666666666674, "100": "None", "600": "None"}, "G": {"200": 0.5669467095138409, "500": 0.408248290463863, "100": "None", "600": "None"}, "P": {"200": 16, "500": 3, "100": 0, "600": 1}, "FDR": {"200": 0.1428571428571429, "500": 0.5, "100": 1.0, "600": "None"}, "CEN": {"200": 0.3570795472009597, "500": 0.5389466410223563, "100": 0.3349590631259315, "600": 0.0}, "RACC": {"200": 0.28, "500": 0.015, "100": 0.0, "600": 0.0}, "NPV": {"200": 0.23076923076923078, "500": 0.8888888888888888, "100": 1.0, "600": 0.95}, "sInd": {"200": 0.5240141808835057, "500": 0.5267639848569737, "100": "None", "600": 0.29289321881345254}, "OOC": {"200": 0.5669467095138409, "500": 0.4082482904638631, "100": "None", "600": "None"}, "TP": {"200": 6, "100": 0, "500": 1, "600": 0}, "ACC": {"200": 0.45, "500": 0.85, "100": 0.45, "600": 0.95}, "TOP": {"200": 7, "500": 2, "100": 11, "600": 0}, "FP": {"200": 1, "100": 11, "500": 1, "600": 0}, "TN": {"200": 3, "100": 9, "500": 16, "600": 19}}}
\ No newline at end of file
diff --git a/Document/Example5.ipynb b/Document/Example5.ipynb
index abc7f5c3..fb79e830 100644
--- a/Document/Example5.ipynb
+++ b/Document/Example5.ipynb
@@ -138,6 +138,8 @@
       "Lambda B                                                          0.42857\n",
       "Mutual Information                                                0.52421\n",
       "NIR                                                               0.5\n",
+      "NPV Macro                                                         0.79048\n",
+      "NPV Micro                                                         0.79167\n",
       "Overall ACC                                                       0.58333\n",
       "Overall CEN                                                       0.46381\n",
       "Overall J                                                         (1.225,0.40833)\n",
@@ -343,6 +345,8 @@
       "Lambda B                                                          0.52174\n",
       "Mutual Information                                                0.5544\n",
       "NIR                                                               0.7\n",
+      "NPV Macro                                                         0.86908\n",
+      "NPV Micro                                                         0.91154\n",
       "Overall ACC                                                       0.82308\n",
       "Overall CEN                                                       0.28807\n",
       "Overall J                                                         (1.82323,0.60774)\n",
diff --git a/Document/Example6.ipynb b/Document/Example6.ipynb
index 1e3362f8..1c860078 100644
--- a/Document/Example6.ipynb
+++ b/Document/Example6.ipynb
@@ -88,11 +88,11 @@
       "Class2        0.04762       0.95238       \n",
       "\n",
       "\n",
-      "ACC: {'Class2': 0.9976333515383216, 'Class1': 0.9976333515383216}\n",
-      "MCC: {'Class2': 0.9378574017402594, 'Class1': 0.9378574017402594}\n",
-      "CEN: {'Class2': 0.30489006849060607, 'Class1': 0.012858728415908176}\n",
-      "MCEN: {'Class2': 0.46949279678726225, 'Class1': 0.023280122318969122}\n",
-      "DP: {'Class2': 2.276283896527635, 'Class1': 2.276283896527635}\n",
+      "ACC: {'Class1': 0.9976333515383216, 'Class2': 0.9976333515383216}\n",
+      "MCC: {'Class1': 0.9378574017402594, 'Class2': 0.9378574017402594}\n",
+      "CEN: {'Class1': 0.012858728415908176, 'Class2': 0.30489006849060607}\n",
+      "MCEN: {'Class1': 0.023280122318969122, 'Class2': 0.46949279678726225}\n",
+      "DP: {'Class1': 2.276283896527635, 'Class2': 2.276283896527635}\n",
       "Kappa: 0.9377606597584491\n",
       "RCI: 0.8682877002417864\n",
       "SOA1: Almost Perfect\n"
@@ -142,11 +142,11 @@
       "Class2        0.95238       0.04762       \n",
       "\n",
       "\n",
-      "ACC: {'Class2': 0.982098458478369, 'Class1': 0.982098458478369}\n",
-      "MCC: {'Class2': 0.13048897476798949, 'Class1': 0.13048897476798949}\n",
-      "CEN: {'Class2': 0.4655917826576813, 'Class1': 0.06481573363174531}\n",
-      "MCEN: {'Class2': 0.4264929996758212, 'Class1': 0.11078640690031397}\n",
-      "DP: {'Class2': 0.864594924328404, 'Class1': 0.864594924328404}\n",
+      "ACC: {'Class1': 0.982098458478369, 'Class2': 0.982098458478369}\n",
+      "MCC: {'Class1': 0.13048897476798949, 'Class2': 0.13048897476798949}\n",
+      "CEN: {'Class1': 0.06481573363174531, 'Class2': 0.4655917826576813}\n",
+      "MCEN: {'Class1': 0.11078640690031397, 'Class2': 0.4264929996758212}\n",
+      "DP: {'Class1': 0.864594924328404, 'Class2': 0.864594924328404}\n",
       "Kappa: 0.08122239707598865\n",
       "RCI: 0.022375346499017443\n",
       "SOA1: Slight\n"
@@ -196,11 +196,11 @@
       "Class2        0.04762       0.95238       \n",
       "\n",
       "\n",
-      "ACC: {'Class2': 0.019661387220098307, 'Class1': 0.019661387220098307}\n",
-      "MCC: {'Class2': -0.13000800945464058, 'Class1': -0.13000800945464058}\n",
-      "CEN: {'Class2': 0.06103563616795208, 'Class1': 0.014927427128936136}\n",
-      "MCEN: {'Class2': 0.03655796690365652, 'Class1': 0.01281422838054554}\n",
-      "DP: {'Class2': -0.8416930356875597, 'Class1': -0.8416930356875597}\n",
+      "ACC: {'Class1': 0.019661387220098307, 'Class2': 0.019661387220098307}\n",
+      "MCC: {'Class1': -0.13000800945464058, 'Class2': -0.13000800945464058}\n",
+      "CEN: {'Class1': 0.014927427128936136, 'Class2': 0.06103563616795208}\n",
+      "MCEN: {'Class1': 0.01281422838054554, 'Class2': 0.03655796690365652}\n",
+      "DP: {'Class1': -0.8416930356875597, 'Class2': -0.8416930356875597}\n",
       "Kappa: -0.0017678372492452412\n",
       "RCI: 0.02192606003351106\n",
       "SOA1: Poor\n"
@@ -261,11 +261,11 @@
       "Class4        0.0           0.0           2e-05         0.99998       \n",
       "\n",
       "\n",
-      "ACC: {'Class2': 0.9999500199920032, 'Class3': 0.9999250299880048, 'Class1': 0.9999750099960016, 'Class4': 0.9999500199920032}\n",
-      "MCC: {'Class2': 0.7999750068731099, 'Class3': 0.7302602381427055, 'Class1': 0.8944160139432883, 'Class4': 0.9333083339583177}\n",
-      "CEN: {'Class2': 0.25701944178769376, 'Class3': 0.3649884090288471, 'Class1': 0.13625493172565745, 'Class4': 0.0001575200922489127}\n",
-      "MCEN: {'Class2': 0.3333333333333333, 'Class3': 0.4654427710721536, 'Class1': 0.17964888034078544, 'Class4': 0.00029569133318617423}\n",
-      "DP: {'Class2': 2.869241573973406, 'Class3': 2.7032690544190636, 'Class1': 'None', 'Class4': 3.1691421556058055}\n",
+      "ACC: {'Class3': 0.9999250299880048, 'Class4': 0.9999500199920032, 'Class1': 0.9999750099960016, 'Class2': 0.9999500199920032}\n",
+      "MCC: {'Class3': 0.7302602381427055, 'Class4': 0.9333083339583177, 'Class1': 0.8944160139432883, 'Class2': 0.7999750068731099}\n",
+      "CEN: {'Class3': 0.3649884090288471, 'Class4': 0.0001575200922489127, 'Class1': 0.13625493172565745, 'Class2': 0.25701944178769376}\n",
+      "MCEN: {'Class3': 0.4654427710721536, 'Class4': 0.00029569133318617423, 'Class1': 0.17964888034078544, 'Class2': 0.3333333333333333}\n",
+      "DP: {'Class3': 2.7032690544190636, 'Class4': 3.1691421556058055, 'Class1': 'None', 'Class2': 2.869241573973406}\n",
       "Kappa: 0.8666333383326446\n",
       "RCI: 0.8711441699127425\n",
       "SOA1: Almost Perfect\n"
@@ -324,11 +324,11 @@
       "Class4       0.25         0.25         0.25         0.25         \n",
       "\n",
       "\n",
-      "ACC: {'Class2': 0.625, 'Class3': 0.625, 'Class1': 0.625, 'Class4': 0.625}\n",
-      "MCC: {'Class2': 0.0, 'Class3': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n",
-      "CEN: {'Class2': 0.8704188162777186, 'Class3': 0.8704188162777186, 'Class1': 0.8704188162777186, 'Class4': 0.8704188162777186}\n",
-      "MCEN: {'Class2': 0.9308855421443073, 'Class3': 0.9308855421443073, 'Class1': 0.9308855421443073, 'Class4': 0.9308855421443073}\n",
-      "DP: {'Class2': 0.0, 'Class3': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n",
+      "ACC: {'Class3': 0.625, 'Class4': 0.625, 'Class1': 0.625, 'Class2': 0.625}\n",
+      "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n",
+      "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.8704188162777186, 'Class1': 0.8704188162777186, 'Class2': 0.8704188162777186}\n",
+      "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.9308855421443073, 'Class1': 0.9308855421443073, 'Class2': 0.9308855421443073}\n",
+      "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n",
       "Kappa: 0.0\n",
       "RCI: 0.0\n",
       "SOA1: Slight\n"
@@ -387,13 +387,13 @@
       "Class4        0.76923       0.07692       0.07692       0.07692       \n",
       "\n",
       "\n",
-      "ACC: {'Class2': 0.76, 'Class3': 0.76, 'Class1': 0.4, 'Class4': 0.4}\n",
-      "MCC: {'Class2': 0.10714285714285714, 'Class3': 0.10714285714285714, 'Class1': -0.2358640882624316, 'Class4': -0.2358640882624316}\n",
-      "CEN: {'Class2': 0.8704188162777186, 'Class3': 0.8704188162777186, 'Class1': 0.6392779429225794, 'Class4': 0.6392779429225796}\n",
-      "MCEN: {'Class2': 0.9308855421443073, 'Class3': 0.9308855421443073, 'Class1': 0.647512271542988, 'Class4': 0.647512271542988}\n",
-      "DP: {'Class2': 0.16596653499824943, 'Class3': 0.16596653499824943, 'Class1': -0.33193306999649924, 'Class4': -0.3319330699964992}\n",
-      "Kappa: -0.07361963190184047\n",
-      "RCI: 0.11603030564493641\n",
+      "ACC: {'Class3': 0.76, 'Class4': 0.4, 'Class1': 0.4, 'Class2': 0.76}\n",
+      "MCC: {'Class3': 0.10714285714285714, 'Class4': -0.2358640882624316, 'Class1': -0.2358640882624316, 'Class2': 0.10714285714285714}\n",
+      "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.6392779429225796, 'Class1': 0.6392779429225794, 'Class2': 0.8704188162777186}\n",
+      "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.647512271542988, 'Class1': 0.647512271542988, 'Class2': 0.9308855421443073}\n",
+      "DP: {'Class3': 0.16596653499824943, 'Class4': -0.3319330699964992, 'Class1': -0.33193306999649924, 'Class2': 0.16596653499824943}\n",
+      "Kappa: -0.07361963190184051\n",
+      "RCI: 0.1160303056449364\n",
       "SOA1: Poor\n"
      ]
     }
@@ -450,11 +450,11 @@
       "Class4        0.76923       0.07692       0.07692       0.07692       \n",
       "\n",
       "\n",
-      "ACC: {'Class2': 0.999400898652022, 'Class3': 0.999400898652022, 'Class1': 0.000998502246630055, 'Class4': 0.000998502246630055}\n",
-      "MCC: {'Class2': 0.24970032963739885, 'Class3': 0.24970032963739885, 'Class1': -0.43266656861311537, 'Class4': -0.43266656861311537}\n",
-      "CEN: {'Class2': 0.8704188162777186, 'Class3': 0.8704188162777186, 'Class1': 0.0029588592520426657, 'Class4': 0.0029588592520426657}\n",
-      "MCEN: {'Class2': 0.9308855421443073, 'Class3': 0.9308855421443073, 'Class1': 0.002903385725603509, 'Class4': 0.002903385725603509}\n",
-      "DP: {'Class2': 1.6794055876913858, 'Class3': 1.6794055876913858, 'Class1': -1.9423127303715728, 'Class4': -1.9423127303715728}\n",
+      "ACC: {'Class3': 0.999400898652022, 'Class4': 0.000998502246630055, 'Class1': 0.000998502246630055, 'Class2': 0.999400898652022}\n",
+      "MCC: {'Class3': 0.24970032963739885, 'Class4': -0.43266656861311537, 'Class1': -0.43266656861311537, 'Class2': 0.24970032963739885}\n",
+      "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.0029588592520426657, 'Class1': 0.0029588592520426657, 'Class2': 0.8704188162777186}\n",
+      "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.002903385725603509, 'Class1': 0.002903385725603509, 'Class2': 0.9308855421443073}\n",
+      "DP: {'Class3': 1.6794055876913858, 'Class4': -1.9423127303715728, 'Class1': -1.9423127303715728, 'Class2': 1.6794055876913858}\n",
       "Kappa: -0.0003990813465900262\n",
       "RCI: 0.5536610475678805\n",
       "SOA1: Poor\n"
@@ -513,11 +513,11 @@
       "Class4       0.25         0.25         0.25         0.25         \n",
       "\n",
       "\n",
-      "ACC: {'Class2': 0.7115384615384616, 'Class3': 0.7115384615384616, 'Class1': 0.7115384615384616, 'Class4': 0.36538461538461536}\n",
-      "MCC: {'Class2': 0.0, 'Class3': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n",
-      "CEN: {'Class2': 0.6392779429225794, 'Class3': 0.6392779429225794, 'Class1': 0.6392779429225794, 'Class4': 0.6522742127953861}\n",
-      "MCEN: {'Class2': 0.647512271542988, 'Class3': 0.647512271542988, 'Class1': 0.647512271542988, 'Class4': 0.7144082229288313}\n",
-      "DP: {'Class2': 0.0, 'Class3': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n",
+      "ACC: {'Class3': 0.7115384615384616, 'Class4': 0.36538461538461536, 'Class1': 0.7115384615384616, 'Class2': 0.7115384615384616}\n",
+      "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n",
+      "CEN: {'Class3': 0.6392779429225794, 'Class4': 0.6522742127953861, 'Class1': 0.6392779429225794, 'Class2': 0.6392779429225794}\n",
+      "MCEN: {'Class3': 0.647512271542988, 'Class4': 0.7144082229288313, 'Class1': 0.647512271542988, 'Class2': 0.647512271542988}\n",
+      "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n",
       "Kappa: 0.0\n",
       "RCI: 0.0\n",
       "SOA1: Slight\n"
@@ -576,11 +576,11 @@
       "Class4       0.25         0.25         0.25         0.25         \n",
       "\n",
       "\n",
-      "ACC: {'Class2': 0.7499500149955014, 'Class3': 0.7499500149955014, 'Class1': 0.7499500149955014, 'Class4': 0.25014995501349596}\n",
-      "MCC: {'Class2': 0.0, 'Class3': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n",
-      "CEN: {'Class2': 0.0029588592520426657, 'Class3': 0.0029588592520426657, 'Class1': 0.0029588592520426657, 'Class4': 0.539296694603886}\n",
-      "MCEN: {'Class2': 0.002903385725603509, 'Class3': 0.002903385725603509, 'Class1': 0.002903385725603509, 'Class4': 0.580710610324597}\n",
-      "DP: {'Class2': 0.0, 'Class3': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n",
+      "ACC: {'Class3': 0.7499500149955014, 'Class4': 0.25014995501349596, 'Class1': 0.7499500149955014, 'Class2': 0.7499500149955014}\n",
+      "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n",
+      "CEN: {'Class3': 0.0029588592520426657, 'Class4': 0.539296694603886, 'Class1': 0.0029588592520426657, 'Class2': 0.0029588592520426657}\n",
+      "MCEN: {'Class3': 0.002903385725603509, 'Class4': 0.580710610324597, 'Class1': 0.002903385725603509, 'Class2': 0.002903385725603509}\n",
+      "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n",
       "Kappa: 0.0\n",
       "RCI: 0.0\n",
       "SOA1: Slight\n"
diff --git a/Document/Example7.ipynb b/Document/Example7.ipynb
index 06ce9426..f35d23b3 100644
--- a/Document/Example7.ipynb
+++ b/Document/Example7.ipynb
@@ -108,7 +108,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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1oKBYUYX7Zq5m976CcrZWu61ZuoEfZyygeduk0hTJJX89n4ztTh72rCEn89EbX5KzJ483/vUBAKGhITw88TaSWydy8fUD+OeoV1BVQkNDufq2i2h4yAVAf04beCIvP/wOt/35UeLqxnDzA1eV7s/USbMIDQtFQoSrbx9Knfi4ajr6I+fkEzryl6H9WJayqTRF8o/H36N5ckMAXp30FRee35vLh/ajoKCQ/fvzufKm5wCY9cMyOrVP5tuPHwIgN3c/14x8gZ0ZZV9fKvHki58y6aVbGf7n09m8NYO/3PAMQJlt1XpVdOFcnJnBJgIpqvqUT3l/4G7gNFXN81llE3CmiEzCSb/0AZ7ByZ23F5HWwBbgUpwvgMDtV9eQRnEmUuiNc6FUcL51flZV/4nrQ1T3kEZT/UMazZEZ0miqaEhj/9eCH9L4xbWBhjSeAvwALOO3zMRo4DkgEshwy+aq6g1umuZ1nBEuAryuqk+42zofJ8CHAq+p6qPl7Vu1jX5R1WKcK7zGGFP7VVFPXVV/LGNrfnNjqpqDM6zR33vTy1qvLH/YO0qNMcaXhnrjth0L6sYYA565Gc2CujHGgE29a4wxnlJFd5TWNAvqxhgDln4xxhhPsfSLMcZ4SAWmCajNLKgbYwxYT90YYzzFGzHdgroxxgCojX4xxhgPsfSLMcZ4iDdiugV1Y4wBwOZ+McYYD7GeujHGeIhdKDXGGA+xoG6MMd6h3ojp1fbgaWOMObqEhgS/BCAizUXkGxFJEZEVInKrW/6EiKwSkaUi8pGIxPus00NE5rj1l4lIlFt+vPt3qog85z7/NCAL6sYYA076JdglsELgdlXtjPMQ6ZtEpAswE+imqj2ANcC9ACISBkwCblDVrsDpQMlT518CRgDt3aV/uYdRwcM2xhhvCqnAEoCqpqvqIvf1XiAFSFbVL1W10K02F2jmvj4XWKqqS9x1MlS1SESSgLqqOkdVFXgLGBLMYRhjjBEJehGRESKywGcZ4X+T0go4Dph3yFvXAp+7rzsAKiIzRGSRiNzllicDaT7rpLllAdmFUmOMgQqNflHVCcCEQHVEJA74ABipqtk+5WNwUjST3aIw4BTgBCAPmCUiC4FsDqfl7Zv11I0xBlCRoJfyiEg4TkCfrKof+pQPBwYCf3FTKuD0wL9T1V2qmgdMB3q65c18NtsM2Fpe2xbUjTEGIEyCXwJwR6hMBFJU9Smf8v7A3cAFbvAuMQPoISIx7kXT04CVqpoO7BWRPu42rwI+KfcwKnrcxhjjSVU3S2Nf4EpgmYgsdstGA88BkcBMd2TiXFW9QVV3i8hTwM846ZXpqjrNXe9vwBtANE4O/nPKYUHdGGOgyu4oVdUf8T+TzPQA60zCGdZ4aPkCoFtF2regbowxYBN6GWOMl9iTj4wxxkssqBtjjIeEWlCvVjJ5fk3vguedcOdFNb0Lnrdz3fU1vQsmWPaMUmOM8RBLvxhjjIdYUDfGGO8I5vb/o4EFdWOMAbtQaowxnmLpF2OM8RAL6sYY4yHeiOkW1I0xBmyaAGOM8RYb/WKMMR5io1+MMcY7QjzyHDiPHIYxxlSOSPBL4O1IcxH5RkRSRGSFiNzqlj8hIqtEZKmIfCQi8Yes10JEckTkDp+y/iKyWkRSReSeYI7DgroxxlB1QR0oBG5X1c5AH+AmEekCzAS6qWoPYA1w7yHrPY3P4+pEJBR4ARgAdAEuc7cTkKVfjDEGkCq6UOo+MDrdfb1XRFKAZFX90qfaXGCYT9tDgPVArk+d3kCqqq5367wLDAZWBmrfeurGGIOTUw92EZERIrLAZxnhb5si0go4Dph3yFvX4vbKRSQWuBt48JA6ycBmn7/T3LKArKdujDGAVKCLq6oTgAkBtycSB3wAjFTVbJ/yMTgpmslu0YPA06qac8ivBX8/HbS8fbOgbowxVO0wdREJxwnok1X1Q5/y4cBA4CxVLQnQJwLDRORxIB4oFpH9wEKguc9mmwFby2vbgroxxlB1U7+I092eCKSo6lM+5f1x0iynqWpeSbmqnupT5wEgR1WfF5EwoL2ItAa2AJcCl5fXvgV1Y4yhSnvqfYErgWUistgtGw08B0QCM900y1xVvaGsjahqoYjcDMwAQoHXVHVFeY1bUDfGGKouqKvqj/jPh08PYt0HDvl7ejDr+bKgbowxQIhNE2CMMd7hkfm8LKgbYwxYUDfGGE/xfFAXkakEGOiuqhdUyx4ZY0wN8MgzMgL21J88YnthjDE1zPM9dVX97kjuiDHG1KQ/zOgXEWkPjMOZ+jGqpFxV21TjfhljzBHllZ56MFPYvA68hDMBzRnAW8Db1blTxhhzpFXhfOo1KpigHq2qswBR1V/dO57OrN7dMsaYI8srQT2YIY37RSQEWOvOQ7AFaFy9u2WMMUfWH2H0S4mRQAxwC/AwTi99eHXulDHGHGkhoTW9B1Wj3KCuqj+7L3OAa6p3d46cpCZ1eOqRgTRqEEuxKlM+WMLr7yw4qM45p7fnthtPRVUpLCzmoSdmsWBxWqXarVc3iucfH0yzpvVI27qHm+78mOy9B6qlrZqWnr6Tu+56ml27dhMSIlxySX+GDz/49oZ16zYzevSzrFixjlGjruS66y6qdLv5+QXcdddTrFixjvj4Ojz99F00a9aEpUvXcP/9zwOgqvz975dzzjknVbq9mrYtPZOxo98kY1c2ISHChcNO4fIrD86QTv9sPm9OdJ6mFhMTyb33X0aHTs0q1W5+fgFj732TlJWbqBcfy/gn/4+myQ1Yvmwjjz7gPP9BVRlx40DOPPvYSrV1JNT2tEqw5Ld52suoIPINfm5CUtVqzau3OnZ8uU/4qIxGDWNp3DCOFau2ExsTwdQpVzNi1Aekrs8orRMTHU7evgIAOrVvxAuPD+GsC/8T1Pb79GrBsAu6c8fYaQeV3zPydPbs2c9Lr8/lb9f0oV7dKMY/+22l2vq9Ni6ufAANZMeOTHbuzKRr13bk5OQxdOgoXnhhDO3atSitk5GRxZYtO5g1ay5168ZVKKinpW3n3nuf4e23xx1UPnnyNFav3shDD93EtGnfM3PmHJ555m727dtPeHg4YWGh7NiRyeDBt/DDD28SFlZ9XbScgur/Yt65cw+7du6hc5cW5Obu54pLxvGv526gTduk0jpLfllH6zaJ1K0Xy+wflvPKi9N4a8rdQW1/65YMHhjzJhPeuO2g8v+++x2pq7cw+h+XM2P6z3wzawnj//V/7NuXT3h4KGFhoezcuYfLhj7CF1+Pr9bPOS78zEqH5H5TZwcdc74f1LfWfgUEc6H0DuBOd7kfWAwsCLjGUWDnrlxWrNoOQG5ePuvWZ5DYuM5BdUqCLDgB3vcLcMTw3nwyeTif//daRv3tlKDbPef09rw/dRkA709dxjlntC+3raNV48b16dq1HQBxcTG0adOc7dszDqrToEE8PXp0ICzs8B+Nn3zyDcOG3cbgwbcwduzzFBUVBdXu11/P48ILzwLgvPP6MmfOElSV6Oio0sBy4EB+lT1ouKY1alSPzl2cL8rY2Chat0lkx/asg+occ1xb6taLBaB7j9bs2L679L3pU+dx1aXjuWzoozz64GSKioqDave7r5cwcHAfAM46tyfz561yP+eI0s85/0AB4ncW2trnD3OhVFUXHlI0W0R+941JInKNqr7+e9evDs2a1qNLp8YsXnb4k6LOO6MDd91yGg3qx3Dt3/8HwKkntaJVi/oM/subiMCrzw6jd8/mzF+0+bD1D9WoQSw7dzkPDN+5K5eG9WMDtuUVaWnbSUlZxzHHdAyq/rp1m/n88x+YMuVxwsPDeOCBF5k69TuGDCn/B+L27RkkJTUEICwslDp1Ytm9O5v69euxZMlqRo9+lq1bd/L447dVa++xJmzdksGqlM1069GqzDoff/gTJ5/SFYAN69L58ouFTHz7TsLDQxn38BQ+/2x+abAOZOeOLJokJgDO5xwXF01WVi4JCXEsW7qBh+5/m/StmTw07uqj4nOu7cE6WMHcfFTf588Q4HggsRJtPogz9t1fWyOAEQD1m11InQa9K9FMcGKiw3npyQt56IlZ5OTmH/b+jG/WMOObNfTu2ZzbbuzHFTe8y6l9WtPvpNZMf+8adxsRtGqRwPxFm/n47auIiAglJjqC+HpRpXXGP/Mt38/ZEHBf/LXlBbm5+7jllnGMHn09cXExQa0zZ84Sli9fx7Bhzk/+/fvzadAgHoCbbnqUtLTtFBQUkp6+k8GDbwHgqqsuYOjQs/3+yinplR9zTEemTXuRdes2c/fdT9Ov3/FERkZUxWHWuLy8/dw56hXuuPti4uKi/db5ef5qPvnwJya+fTsA8+etJmXlJq66dDzg/IKpX9/5xXr7LS+zdUsGBQWFbEvfzWVDHwXgsivO4IILT8bfj8mSwNi9R2v+98lYNqxL5x9j3qTvqV2JjAyv4iOuWn+YoI7z8FPFeZJHIbABuC7QCiKytKy3gCZlref7hO7qzqkDhIWF8PK/LuTj6SuY8fWagHXnL9pMy+bxJMRHIyK8OHEO73yw+LB6Q658Cyg7p74zI5dGDZ3eeqOGsezKzA3Y1u6sfZU4wppXUFDILbeMY9Cg0zn33JODXk9VufDCM7n99sMHWr3wwhig7Jx6YmJD0tN3kZjYkMLCIvbuzSU+/uDUWtu2zYmOjmLNml/p3r397ziy2qWgoIg7R05gwJ96c+Y5x/mts3Z1Gg+PncS/X76Z+Pg4wPmcB17Qh7+PGnJY/X895zxprayceuMm8WzftpsmiQkUFhaRk7OPevViD6rTum0SUdGRrFu7lS7dWlbFoVabsGCS0UEQkeY4N2kmAsXABFV9VkSeAAYB+cA64BpVzRKRc4DxQIT73p2q+rW7reOBN4BonCcg3arl5GaDOYzOqtpGVVurantVPRf4uZx1mgBXuQdw6JIRYL0j6p//OJ/UDRlMnOT/cFo2jy993bVTE8LDQ9mdtY/v56znkiE9iIl2eh5NGsfRICG4HuhX36UybFB3AIYN6s7Mb9cGbOtopqqMGfMcbdo055prDg8agZx00jHMmDGbjAwnN5yVtZctW3YEte6ZZ57IRx/NAmDGjNn06dMDEWHz5m0UFjp5+S1bdrBhwxaSk4/+Wy5UlYfHvk3rNolcMfxsv3XS0zO5Y+QEHh53NS1b/dav6t2nE7NmLiIzIxuAPXtySd8a3D/R087owWefzAVg1peLOOHEjogIW9J2lX7O6Vsz+HXjdpKSG1TmEI+IENGgl3IUAreramegD3CTiHQBZgLdVLUHsAa4162/Cxikqt1xhov73rH/Ek72or279C+v8WB66j8BPQ8pm+OnzNdnQJyqHtaVFZFvg2iz2vU6thlDB3UjZc2O0hTJ4//+juTEugBMfn8xA87qyEWDulFYWMz+/YXcfNcnAPwwZyPtWjfkw7euBCAvr4CRY6aSsTvPb1u+XnptDi88PoRLLuzB1vRsbrzzY4Ay2zqaLVy4kk8++YYOHVqVpkhuu+0qtm7dCcBllw1g587dDB06ipycPEJCQnjzzU+ZPv1F2rVrwciRV3LttWMpLlbCw0MZO/aGoILwsGHncOedT3HOOSOoVy+Op5++q3R//vOf9wkLCyMkRHjggRuoX79e9X0AR8jiX9Yxbeo82rVPLk2R3HTrYLalZwIw7M/9+M9L09izJ4fxjzgpvdDQECb9917atE3ixr9fwE0j/k1xsRIWHsI9Yy4lqWn5QXjwRX25/943GDxgLPXqxfDYE84P+MWL1vHGxBmEhYUiIcI9911KQkJcNR191amqm49UNR1Id1/vFZEUIFlVv/SpNhcY5tb5xad8BRAlIpFAfaCuqs4BEJG3gCHA54HaL3NIo4gkAsnAJOByfnuQal3gZVXtVIHjrLAjkX75o6vuIY3myAxpNFUzpPFPX/4YdMyZft6pf8W9/uea4KaPDyIirYDvcXro2T7lU4H3VHXSIfWHATeo6tki0gsYr6pnu++dCtytqgMD7Vugnvp5wNVAM+Bf/BbUs4HRgTZqjDFHmyDSKqV8r/+VRUTigA+AkYcE9DE4KZrJh9TvCvwTOLekyF/T5e1boPnU3wTeFJGhqvpBeRsyxpijWVXO/SIi4TgBfbIa22z9AAAXHElEQVSqfuhTPhwYCJzle8FTRJoBHwFXqeo6tzgNp1Ndohlw+LjrQwRzofR4ESm9iiciCSLySBDrGWPMUSNMgl8CEWf87EQgRVWf8invD9wNXKCqeT7l8cA04F5VnV1S7ubm94pIH3ebVwHlXmwLJqgPUNXS29NUdTdwfhDrGWPMUUNEg17K0Re4EjhTRBa7y/nA80AdYKZb9rJb/2agHXC/T/2SEQF/A14FUnGGQQa8SArBjX4JFZFIVT3gHLhEA5FBrGeMMUeNKhz98iP+8+HTy6j/COA3+6GqC4BuFWk/mKA+CZglIiV3gV4DvFmRRowxpraronuPalwwc7887t4hejbOt88XQO2+NcwYYyqoIqNfarNgeuoA23Bud70EZ5oAGw1jjPGU8i6AHi3KDOoi0gG4FLgM59b+93BuVjrjCO2bMcYcMX+Ex9mtAn7AmZMgFUBERh2RvTLGmCPMK+mXQNcGhuKkXb4Rkf+IyFn4v6JrjDFHvRAJfqnNygzqqvqRqv4Z6AR8C4wCmojISyJyblnrGWPM0SikAkttVu7+qWquqk52J5FphvM4u3uqfc+MMeYIqsKpd2tUsKNfAFDVTOAVdzHGGM+oqodk1LQKBXVjjPEqj8R0C+rGGAPeGf1iQd0YY6j9o1qCZUHdGGOw9IsxxniK9dSNMcZDQkMsp26MMZ7hlfSLV47DGGMqpapuPhKR5iLyjYikiMgKEbnVLX9CRFaJyFIR+eiQx4TeKyKpIrJaRM7zKe/vlqWKSFA3fVpQN8YYqnTul0LgdlXtDPQBbhKRLsBMoJuq9gDWAPcCuO9dCnQF+gMvikioiIQCLwADgC7AZW7dgCz9YowxVOnj7NKBdPf1XhFJAZJV9UufanOBYe7rwcC77iNDN4hIKtDbfS9VVdcDiMi7bt2VAY+jag7DGGOObuGiQS8iMkJEFvgsI/xtU0RaAccB8w5561p+e4h0MrDZ5700t6ys8oCsp26MMVSsp66qE4AJgeqISBzOU+JGqmq2T/kYnBTN5JIif03gv9Nd7hAdC+rGGEPVjlMXkXCcgD5ZVT/0KR8ODATOUtWSAJ0GNPdZvRmw1X1dVnmZLP1ijDFAqAS/BCIiAkwEUlT1KZ/y/sDdwAWqmuezyqfApSISKSKtgfbAfOBnoL2ItBaRCJyLqZ+WdxzWUzfGGKq0p94XuBJYJiKL3bLRwHNAJDDTifvMVdUbVHWFiPwX5wJoIXCTqhYBiMjNwAwgFHhNVVeU17gFdWOMoepmaVTVH/GfJ58eYJ1HgUf9lE8PtJ4/FtSNMQYIt7lfqleDu/vW9C543uXfptf0Lnje4vW19p+Yp6y8tvLbsAm9jDHGQ+whGcYY4yHljWo5WlhQN8YYLP1ijDGeEuaRu3YsqBtjDBBqOXVjjPEOj3TULagbYwxYTt0YYzzFgroxxniI5dSNMcZDbPSLMcZ4iKVfjDHGQ+yOUmOM8RCb+8UYYzzEIyl1C+rGGAPeyal75cvJGGMqJTxEg14CEZHmIvKNiKSIyAoRudUtv9j9u1hEevnUDxeRN0VkmbvOvT7v9ReR1SKSKiL3BHMc1lM3xhiqtKdeCNyuqotEpA6wUERmAsuBi4BXDql/MRCpqt1FJAZYKSJTgM3AC8A5QBrws4h8qqorAzVuQd0YY6i6oK6q6UC6+3qviKQAyao6E8B96PRBqwCxIhIGRAP5QDbQG0hV1fXueu8Cg3EeUF32cVTNYRhjzNEtpAKLiIwQkQU+ywh/2xSRVsBxwLwATb8P5OJ8EWwCnlTVTCAZp7deIs0tC8h66sYYAxzegS6bqk4AJgTensQBHwAjVTU7QNXeQBHQFEgAfhCRrwB/e1TuuEsL6sYYQ9WOfhGRcJyAPllVPyyn+uXAF6paAOwQkdlAL5xeenOfes2AreW1bekXY4yhYumXQMRJmk8EUlT1qSCa3gScKY5YoA+wCvgZaC8irUUkArgU+LS8jVlP3RhjAKm6O0r7AlcCy0RksVs2GogE/g00AqaJyGJVPQ9nhMvrOKNjBHhdVZc6+yQ3AzOAUOA1VV1RXuMW1I0xBv8J7N9DVX8MsLmP/NTPwRnW6G9b04HpFWnfgroxxlCxC6W1mQV1Y4yh6nrqNc2CujHGYFPvGmOMp1j6xRhjPMQjMd2CujHGgAV1Y4zxFK/Mp/6HDepNYiJ4qE9HGkRFUIzyUeo2pqw5+A7cAS0bMbyLc5duXkER4xaksjYrt1LthocID/XpSOf6cew5UMA9P60iPfcAXevHMaZ3e8DpMUxYvolv0jIq1VZNy8/MZMPrr1GYnQ0iNDy1H03OOuugOhnz5rF9xhcAhERG0uLyvxDTvLm/zQWtuKCAja+/Tt6mXwmNjaXN9SOIbNiQ3A0b+HXS26X1kgYOIuG44yrVVm2QGBvJuH4daRgdgary39XpTFp58Lk8sE1jruvRDHDO5YfmpLI6s/Ln8vh+HenasA5ZBwq47ZsUtuYcoHvDOjzY1zmXEXjhl1+Z9WvtP5c9EtMR1dr5XL7jp/xQrTvWMCqchtERrNqdS0xYKJPOO5bbf0hhQ3ZeaZ0eDeuwYc8+9hYUcnJSAn/t1oLhM5cEtf2k2EgeOLEDf/162UHlF7dLol18LOMWpHJui0ac0awB9/60iqjQEAqKiylSZ9+mDOhJ/4/nUVSNn0LHpOLq2zhQsCeLgj17iGnRkqL9+0l59BHa/u1Gops2La2Ts24dUYmJhMXGsmf5MrZOnUrne0cHtf0Du3ax8c036Hj7HQeV7/j2W/ZtSaPlX64g8+f5ZP2ymDYjRlCcfwAJDUNCQynYk8XKhx+mxz8fR0JDq/S4fS1eX/2homF0BI1iIkjJyCEmLJT3Bx/H32etZF3Wb+fysY3rsj4rj+z8Qk5tlsBNx7Xk0qmLA2z1N03jInns1I5c/fnSg8ov7ZREx/qxPPhTKgNaN+Lslg24/dtDzuXoCD4a0pPT351brefyymv7VfqDXpc9Neg9bFt3UK39DvjD9tR37S9g1/4CAPIKi9iQvY/GMREHBfWlu/aWvl62ay+NYyJL/x7QqhGXdkgmPERYnrGX8QtSKQ7ilDitWQNeWfYrALM27+TuXm0B2F/0W4CNCA0pfyq2o0B4vXjC68UDEBoVRVRSEgVZWQcF9bi2bUtfx7ZuQ0FWVunfGXPnsuObr9HCQmJbt6bF5X9BQsqfrmjPksUkDRwEQELP49k0ZQqqSkjEb///igsKK318tcWuffns2pcPOOfy+qw8GsdEHBTUF+/4bZLAJTv20sTnXB7UtjF/6dKUiJAQlu7cy0Nz1gZ1Lp/ZogEv/OKcy19u3Ml9J7UDDj6XI4+ic9lGv5RDRDrhzP07z70NtqS8v6p+UV3t/h5JsZF0SohluU8QP9SQtk34KX03AK3qRnNui0ZcN3MJharc06stA1o2ZtrGHeW21Sg6gu15BwAoUsjJLyQ+Ioys/EK6NajD2BPbkxQTxdi5q6u1Z3OkHdi1i7xNm4ht3brMOrtmz6Zu124A7EtPZ/eCBXS66y4kNIxN70wmc948Gpx0Urlt5WdlEVG/PgASGkpodDRFuTmExdUhd8N6Nr75JvmZmbS65tpq7aXXhKZxkXRuEMfSnWWfy0M7JPJDWiYAbepF0791I674zDmX7z+pHQPbNubT1PLP5SaxkWzL/e1c3ptfSHxkGFkHCunRqA6PnNKBpnFR3P39qqPiXPbK7IbVEtRF5BbgJiAFmCgit6rqJ+7bjwG1JqhHh4XwxCmdeXLRenILi/zW6dW4HoPbJHLdV07qpXeTeDonxPHWeccCTm8k0+31P3lKZ5rGRREeEkJiTCTv9HdytlNWb2Xqhu1+ewMl5/vyjL1cMn0RrepG82Cfjszemkl+MF2mWq5o/37Wv/IyzS/5M6HR0X7r7F29iozZP9Lxzrucv1elkLfpV1Ieewxw8uRhdeoAsO6lFzmwaxdaVER+ZiYrH34IgMZnnkXDvn3Bb0rR+eBjW7eh6wMPsi89nY1vvE69bt0ICQ+v4iOuGTFhITx7ZhfGzVtHboH/c7l3Yj0u6pDIFdOc1Eufpgl0bRjHfy9wztPIsN/O5efO6kKzuCjCQ4SkuCg+HNwTgLdXbuGjtdsDTva9dOdeLvhoIW3qRfNYv478kJZJfi2P7NZTD+x64HhVzXGf/PG+iLRS1WcJcD3CfXrICIAW191Bw7MuqKbdc4SJ8MQpXfh8484yL0q2i4/h/t7t+ft3K9iT7/xkF4TPNu7g+SUbD6t/x48pQNk59R15+TSJiWTHvnxCBeIiwkq3W2Jj9j72FxbRNj6WlMwcjmZaVMj6V16mfu8TSejZ02+dvLQ0Nr71Fu1vuZWwuDh3RWhw0kkkX3jRYfXb/u1GoOycekRCAvmZmUQkJKBFRRTt20dobOxBdaKTkgiJiGDfli3EtmpV+QOtYWEiPHNmFz5bt4Ovyrgo2SEhlodO6cBfv1zOngMl5zJ8snY7Ty/ceFj9W2Y5T00rK6e+LfcAibGRbM9zzuU6EWGl2y2xfs8+9hUW0z4+lhUZtftc9khMr7ZfHKElKRdV3QicDgwQkacI8Nmp6gRV7aWqvao7oAPcf2J7NmTnMXn1Fr/vJ8ZE8uQpXbh/7mo27d1XWj5/exZnNW9IQqTTw6sbEUaiT44ykO+2ZDCwdRMAzmreiJ+3OznkprGRpbcpJ8ZE0rJONOk5+3/vodUKqsrGt94iKjGJJuec47dOfmYG619+idbXXkdUkyal5XU6dWL3okUUZDu54MLcXA5kBDeCol6PY8iYOweA3YsWUrdTJ0SktHcPcCAjgwPbtxPZsEFlDrHWePjUDqzfk8ebK/yfy0mxkTx3Vhfu+X41v2b/di7PTc/i3FaNqB/lnMv1IsJoGhvcufzN5gyGtHf+n53bqhHz0p1zOTkuqvRcbhobSet60Ww5Cs7lEAl+qc2qq6e+TUSOVdXF4EwtKSIDgdeA7tXUZoUc27AuA1s3YW1WbmmK5IUlG0l0T+gPUrdxfbcW1IsM455ezgWgomLlyi8XsyE7jxeXbuSFM7oRIkJhcTHjF6xjm5srD+STddt4+KSOfDywF3vyCxk9e5WzP43qcXWXZhQWK6owfsE6svKP7ot5uetSyZw7l+jk5NIUSfKQC8nPdPK5jU47ja2fTaMwN5dN70wGQEJC6TxmDNFNm9L0gsGsffYZUEVCQ2l+2eVENig/CDc85RQ2vDaR5feNcYY0/t/1AOSkrmXbF184eXQRWlx+OWFxdarp6I+cnk3qMrhdE1Zn5pSmSJ5ZuIGk2CgA3ludzt+Odc7lse7FzEJVLvn0F9Zl5fHsoo28el53RKCwWHl4Tipbc8s/lz9Ys41/9uvEF8NOIOtAAXd8u6p0f67v0ZXCYqVYlYd/SiXrQO0/l2t7sA5WtQxpFJFmQKGqbvPzXl9VnV3eNqp7SKOp/iGN5sgMaTRVM6QxPS/4IY1JMX+wIY2qmhbgvXIDujHGHGlV+OSjGuWVUTzGGFMpUoEl4HZEmovINyKSIiIrRORWt/xi9+9iEel1yDo9RGSO+/4yEYlyy493/04Vkefc558GZEHdGGNwhjQGu5SjELhdVTvjPET6JhHpgvMM0ouA7w9uV8KAScANqtoVZ2BJgfv2SzgjAtu7S//yGregbowxOE92DnYJRFXTVXWR+3ovzv06yaqaoqqr/axyLrBUVZe462SoapGIJAF1VXWOOhc/3wKGlHccFtSNMYaK9dRFZISILPBZRvjfprQCjgPmBWi6A6AiMkNEFonIXW55MuB7fTLNLQvoDzv3izHGHCz4AS2qOgGYEHBrInHAB8BIVc0OUDUMOAU4AcgDZonIQsDfOuVezbWeujHG4NwpHux/5W5LJBwnoE9W1Q/LqZ4GfKequ1Q1D5gO9HTLm/nUawZs9bP+QSyoG2MMIBIS9BJ4OyLARCBFVZ8KoukZQA8RiXEvmp4GrFTVdGCviPRxt3kV8EmgDYGlX4wxxlVl9xP1Ba4ElolIyaT1o4FI4N9AI2CaiCxW1fNUdbc7hcrPOOmV6ao6zV3vb8AbQDTwubsEZEHdGGMAqaLEhar+SNnfEB+Vsc4knGGNh5YvALpVpH0L6sYYA+WmVY4WFtSNMQbwyuS7FtSNMQaCGtVyNLCgbowxWFA3xhhPEfHG82otqBtjDGA5dWOM8RBLvxhjjKfYkEZjjPEM66kbY4yHBPFQoaOCBXVjjAGk3MdfHB0sqBtjDGCjX4wxxkMs/WKMMZ5iQd0YYzyjqqberWkW1I0xBvBKT90bX03GGFNJIRIS9BKIiDQXkW9EJEVEVojIrW75xe7fxSLSy896LUQkR0Tu8CnrLyKrRSRVRO4J5jisp26MMUAV9nELgdtVdZGI1AEWishMYDlwEfBKGes9jc/j6sSZYewF4Bych1D/LCKfqurKQI1bUDfGGKrujlL3gdHp7uu9IpICJKvqTPA/ykZEhgDrgVyf4t5Aqqqud+u8CwwGAgZ1S78YYwzg5NSDW0RkhIgs8FlG+N2iSCvgOGBema2KxAJ3Aw8e8lYysNnn7zS3LCDrqRtjDBUbp66qE4AJ5WwvDvgAGKmq2QGqPgg8rao5h+yDvx3S8vbNgroxxlC10wSISDhOQJ+sqh+WU/1EYJiIPA7EA8Uish9YCDT3qdcM2Fpu26rlBn4TJBEZ4X6Dm2pin3H1s8+4csTpbr8JZKrqSD/vfwvcoaoL/Lz3AJCjqk+KSBiwBjgL2AL8DFyuqisCtW859arlN69mqpR9xtXPPuPK6QtcCZwpIovd5XwRuVBE0oCTgGkiMiPQRlS1ELgZmAGkAP8tL6CD9dSrlIgsUNXDxp+aqmOfcfWzz/joZj11Y4zxEAvqVcvykNXPPuPqZ5/xUczSL8YY4yHWUzfGGA+xoG6MMR5iQb0K/J6Z1EzFiMhrIrJDRJbX9L54VVmzC5qji+XUK8mdSW0NPjOpAZeVN5OaqRgR6QfkAG+parea3h8vEpEkIMl3dkFgiJ3LRxfrqVde6UxqqpoPlMykZqqQqn4PZNb0fniZqqar6iL39V6cG17KnUDK1C4W1Cvvd82kZkxtFszsgqZ2sqBeeb9rJjVjaqsKzC5oaiEL6pWXxu+YSc2Y2qiCswuaWsiCeuX9DLQXkdYiEgFcCnxaw/tkTIW5swtOBFJU9ama3h/z+1hQr6TfO5OaqRgRmQLMATqKSJqIXFfT++RBfmcXrOmdMhVjQxqNMcZDrKdujDEeYkHdGGM8xIK6McZ4iAV1Y4zxEAvqxhjjIRbUzRElIkXuULnlIvI/EYmpxLZOF5HP3NcXBJohU0TiReTG39uWMUcLC+rmSNunqse6My3mAzf4vimOCp+Xqvqpqo4PUCUesKBuPM+CuqlJPwDtRKSVO4f3i8AioLmInCsic0Rkkdujj4PSuetXiciPwEUlGxKRq0Xkefd1ExH5SESWuMvJwHigrfsr4Ykjf6jGHBkW1E2NEJEwYACwzC3qiDNX+nFALnAfcLaq9gQWALeJSBTwH2AQcCqQWMbmnwO+U9VjgJ7ACuAeYJ37K+HOajosY2qcBXVzpEWLyGKcQL0JZ64RgF9Vda77ug/QBZjt1h0OtAQ6ARtUda06t0JPKqONM4GXAFS1SFX3VM+hGFP7hNX0Dpg/nH2qeqxvgTOPFLm+RcBMVb3skHrHYtMaGxOQ9dRNbTQX6Csi7QBEJEZEOgCrgNYi0tatd1kZ688C/uauGyoidYG9QJ3q3W1jap4FdVPrqOpO4GpgiogsxQnynVR1PzACmOZeKP21jE3cCpwhIstwnrPZVVUzcNI5y+1CqfEym6XRGGM8xHrqxhjjIRbUjTHGQyyoG2OMh1hQN8YYD7GgbowxHmJB3RhjPMSCujHGeMj/A3eF4BQxVX6eAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -134,7 +134,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -156,7 +156,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -181,7 +181,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
diff --git a/Document/Example8.ipynb b/Document/Example8.ipynb
index 233a9bac..3ef0dca7 100644
--- a/Document/Example8.ipynb
+++ b/Document/Example8.ipynb
@@ -170,7 +170,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -202,7 +202,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -234,7 +234,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -273,7 +273,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -305,7 +305,7 @@
     },
     {
      "data": {
-      "image/png": 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F16cP/OlPFXEkKq0JSzfxwPiVHDiUB8CmzAM8MH4lAJd2beplaKa0Qvk8H9lZwOOI5AJ5uH6YnX7LB+Oa0PzdDYxA5C7cYIEbUFVEzgCGInII13fzZ1S3l+UliQZrMwwTEZkIDFfVWcGWd+/eXRcVjHgyMS916BR6tKzndRhRb+nPmeTkHd6nmxgfR9fmdTyIyJTFgg07SX/mwjKtKyKLVbV7OYdULjzr0xGRVKArsCBg/h9FZJGILNq2bZsXoRkT1YIlnOLmGxNOnlwGR0RqAB8Bd6rqHv9lqvoG8Aa4mo4H4RkPjbnlNK9D8Mb06XDHHa4J5aabYOjQostfeAHefNM1tzRoAG+/DS1auGX33w9TpqCAPPwwPes0YFPmAca+fx81cg4AUH//btY2b8dZq76C99+HZ59169aoAa+95oa4A7z0EowY4UZN3XyzG3kIcMUVsHatm87MdOddLVtWscekkksdOsXrECpE2JOOiFTBJZz3VXV8uPdvTMTJy4PbbnMDLlJSXOfxgAFu4EWBrl3dybXJyS5J3HcfjBkDU6bAkiUsnjiHoaMWMvnph3ng5Q+4d2Y6g39feCLvGxOfpv6Vg9yTli3h88+hbl2YNg3++EdYsMCNghoxwp2QmJgI553nBpG0bev2VeDuu10HtjFlEO7RawK8BaxR1RfCuW9jIlYoZ52ffXbh1SFOPdUNnQV01Sro1YtmDWpxTMP6HGjfkYu2rODpyzvRtE41BGhbTem96Tu63fEHt/7pp7uEE7At1qxxz5OTXY2qVy/4+OOicai6IfX+oxuNKYVw9+n0BK4F+ojIMt8jcPSEMZVLaa8i8dZbcP75vPbZel7LrAHTptEwIZ9Rl7ehzjdfwsaNXNq1KV8N7cOGZy5kVsudJPY7p/DyQ0G2BUDHjjBvHuzY4Ya8T51a9BwRgC++gEaNDj/nypgQhbV5TVW/BOxCWyaoO/pW0i+y0lxF4r33XDPb55+TP38jazudRm61nSScfrrr6znttKJXfgB33tVNNx2+rblzXdL58kv3/IQTXP/Quee6vp4uXYJvy2o5YRGrn4eIvp+OqVzuOve4kgvFohCvIrF38jSy7n+YTR9Po1tSEn/u3RrXYt0VHn7IFbr66qK1kB07XPNdYDPZihUuEU2bBvXrF84fMsQ9AB580MVWIDcXxo9319EzFS5WPw92GRxjvBbKVSSWLqX6X2/nvmufYB2ub0dE3CCEHe5KJ6xY4R79+hWuN26cuxCk/9WHf/4ZLr/cnbB7XMAX29athWXGjy9aq/n0U3f1CP9EZEwpWU3HGK8Vc9Z5emo7XqnZgedev5e4rH2MnP48Mv15d/mhSZPg0CE403fx4Fq1XPObf5PY6NGHD79+/HGXqP7858L9F5yE/bvfuWVVqsArrxQOOCjYljWtmaPk6RUJSmJXJDCV3aTlm3ly8mrG3nIaqcdU9zocEyUi+YoEVtMxJoKoKhOWbSIpIZ4LOjXm4s6N6duuIdWT7KNqYoP16RgTQVTh3a9/YvwSd+6MiFjCMTHF3s3GeOxATh5vf7WBG05PpXpSAm9e1526yYleh2VMhbCajjEeW/PLHp6fuZbZ37uRY/VrJBEXZ6ezmdhkSccYD2zOPMDUle5mdic1r8vsv/ViQJdS3uHVmChkSccYD/xr5joeGL+SrOxcAFo1qOFxRMaEhyUdY8Jk8U872bLb3Wrg/vOPZ/JfzrBBAqbSsaRjTBhk7s/hmje/5eU5aQA0rFmVZvWSPY7KmPCzn1nGVJD8fGVh+k56tKpPneRE3ry+Oyc2s9tFm8rNajrGVJD3F/zEFW98w8qM3QD0bHOMNaeZSs8+AcaUo6zsXHZm5dCsXjK/65ZCzapV6NAkyH1sjKmkLOkYU05UlavfXADAhD+fTnJiApd2bepxVMZEFks6xhylX3YfpFGtJESEO89pS82kBN99bowxgaxPx5ijsGrzbno/P5cJy9ztpc8+viHdU+t5HJUxkcuSjjFlsOfgIQBOOLYWf+jZkh4t65ewhjEGLOkYU2ovffoD5//7C7Kyc4mLE+4/rx1N6lTzOixjooL16RgTgvx8JU+VKvFx9GxTn5y8POLtopzGlJolHWNKsD8nl6tHLKBPu4b8tW9buqfWs34bY8rImteMOYL8fHcr9+TEBDqn1LbbRRtTDizpGBPEgh93cM6Ln/PL7oMAPH5JR7v1gDHlwJKOMUE0rl2NusmJ7PWNUjPGlA/r0zHG55W5afyy+yBPXNqR5vWT+ejW070OyZiYYzUdY3z2HDxE5oFD5Pn6cowx5c9qOqbS2pmVw2OfrOLmM1vRsWlt7u/fjjgbBm1MhbKajqm04uOERem7WLNlD4AlHGPCwJKOqVRWZGQybNIqVJXa1aow555eDOrezOuwjKk0LOmYSmVFxm6mrtzCZt9Q6KSEeI8jMqZysT4dE9NUlYnLNlMnuQq9j2/I1ac055ITm1CzahWvQzOmUrKajolpufnKa5+tZ8zCjYDrt7GEY4x3rKZjYs7BQ3n87+ufuO70FiQlxPPukFM4pkaS12EZY7CajolBi9J38dTUNXy2dhsAjWpVtStCGxMhLOmYmLBl9wHmfr8VgDPaHsOMO8+if4djPY7KGBPIko6JCY9NWs29Hy7n4KE8AI4/tqbHERljgrE+HRO1Fv+0i5bHVKde9UQeuugEVKFqFRsCbUwks5qOiUpb9xzkyje+5tW5aQCk1E2mWb1kj6MyxpTEajomaqgqyzN2c2KzOjSsVZXXr+1Gj5b1vQ7LGFMKVtMxUWPEFz9y+atf8cOvewHo064R1ZPsd5Mx0cQ+sSai7c/JZe/BXBrVqsoV3ZtTNzmR1g1qeB2WMaaMrKZjIlZ+vnL5q/O5e+xyAGonV2FQ92Z2NWhjopjVdEzE2bY3mwY1k4iLE/7Spy2NatnVBIyJFVbTMRFl6c+7OPO5OXy6+lcALuzcmO6p9TyOyhhTXizpmIiwPycXgI5Na3PVKc05oUktjyMyxlQESzrGc/+auZYBw78iOzePKvFxPHpxB5rWqeZ1WMaYCmB9OsYT+fmK4m4Z3a1FXXLzFVWvozLGVDSr6Ziw233gEL/7z3z+93U6AL2Pb8j957WzS9gYUwlY0jFho76qTK2qCaTWr059u8eNMZWOJR0TFvPTtnPB/33JrqwcRIQXrziRi7s08TosY0yYWdIxYVGvRiKJ8cLO/Tleh2KM8ZANJDAVQlV5ZW4a+3PyuO+8drQ7thYTbuuJiF1NwJjKzJKOqRAiwubdB9l3MBdVRUQs4RhjLOmY8rNjXzZPTV3D7We3oVWDGjw+oAMJ8daCa4wpZN8IptzkqTJv3TaWZ2QCWMIxxhzGvhXMUVmRkclz078HoGHNqnxxXx8u65ricVTGmEhlSccclS/TtjNucQbb9mYDUC3RTvA0xhyZ9emYUlFVJi3fTOPa1TilZT1uOqMV15zaglpVq3gdmjEmClhNx5RKdm4+z89cywcLfgIgMSHOEo4xJmRW0zElOngojzELN3LNqS2oWiWeD246lSZ2FWhjTBlYTceU6LO123h00iq+TNsOQLN6ycTbLaONMWVgSccE9cvug8xf75JM/w6N+OT2M+h1XAOPozLGRDtLOiaoez9czj1jl3MoLx8RoVNKba9DMsbEAOvTMb9Z8vMujmtUkxpJCQwb0IEqcXFUsRM8jTHlyL5RDAAbd+5n4GvzeWPejwC0blCD5vWTPY7KGBNrLOlUYqrKmi17ADc4YPjVJ/HHs1p5HJUxJpZZ0qnEhs9J45LhX7Fx534ALujUmBpJ1uJqjKk49g1TyezPyeVATh71ayRxxSnNaFgriaZ2zo0xJkysplOJ5OblM2D4Vzz48UrAXaDzipObE2fn3BhjwsRqOpVA5v4c6iQnkhAfxy1ntaJF/epeh2SMqaSsphPjFvy4g9OfmcM3P+4AYFD3ZpzSsp7HURljKitLOjHq4KE8ADqn1OHSrk1pVs+GPxtjvGdJJwY9PW0NV7z+NXn5SrXEeP5xWScbLGCMiQjWpxMj8vMVEdwla5rWRhBy8/OJj7ObqhljIofVdGLAzqwcBv5nPuOXbALgos5NGHp+O5ISLOEYYyKLJZ0opqoA1KlWhfo1kkiqYv9OY0xks2+pKPXFD9v43WvzycrOJS5OGHFddy7q3MTrsIwxpliWdKJUcmI82bn5bNub7XUoxhgTMhtIECVUleFz0kiIj+PW3q3p1qIek/9yBiJ2NQFjTPSwpBMlRIR1W/dRJU5QVUTEEo4xJupY0olg2/dl89z077nr3ONoXLsa/xrUhcQEaxE1xkQv+waLYAdy8pj+3S8s/mkXgCUcY0zUK9O3mIjcISK1xHlLRJaISL/yDq4yWpmxm1fmpgHuxmpfDe1jo9KMMTEjtKQjch4iaxFJQ2QocKOq7gH6AQ1awh8nwxjf8gWIpPrWq4/IXET2ITLcb3s1EVnm99iOyL99y5r71lk6avVqmDrVrfP++3DiiYWPuDhYtqxonAMGQMeOR3VAvDb1uy2MnJ/O7v2HAKhZtYrHEcWQ6dPh+OOhTRt45pnDl7/wArRvD507Q9++8NNPhctGjoS2bd1j5Eg3b+/eou/JY46BO+90y+bNg5NOgoQE+PDDovu5/373Pu3YEcaMKZy/YQP06OH2ccUVkJNTvq/fmEigqsU/IF5hvUIrhUSF5V1hre/ExJeAyxT+/B5s95W/UmGMb7q6whkKf1IYXsw+Fiuc5Zt+Q+FWVWVg+/aqLVroYVasUG3Zsui8jz5Sveoq1Q4dDi8fwfLz83Xisk26MiNTVVX3Z+fq7gM5HkcVg3JzVVu1Ul2/XjU7W7VzZ9VVq4qWmTNHNSvLTb/6qurgwW56xw73ftuxQ3XnTje9c+fh+zjpJNXPP3fTGzaoLl+ueu21quPGFZaZPFn1nHNUDx1S3bdPtVs31d273bJBg1RHjXLTt9ziYjCmDIBFWtJ3u0ePUGo6pwBpqP6Iag4w+mbYKyIzgQuAGYfg8ndgu6/8h0BfRATVLFS/BA4ecesibYGGwBcFeRCoBVAjLw+aBGlaGjUKrrqq8Pm+fe5X6kMPhfByIktWTh6Pf7Ka/33tflVXS4ynltVuyt+337oaTqtWkJgIV14JEycWLXP22ZDsuxr3qadCRoabnjEDzj0X6tWDunXd9PTpRdf94QfYuhXOPNM9T011Naa4gI/Y6tXQq5erAVWvDl26uG2pwpw5MHCgK3f99TBhQrkeAmMiQShJpymw0e95xi3wDTAUOFlV9ws0bQO3A6CaC+wG6ocYw1XAmN+u6QLDgGsQyXgpLQ1efvnwNcaMKZp0Hn4Y7r678Asjwh08lMeYhT+jqtRISmDsLafyj8s7eR1WbNu0CZo1K3yekuLmHclbb8H554e+7qhRrkmspGHsXbrAtGmwfz9s3w5z58LGjbBjB9Sp45JRKPEZE6VCSTqHfYrS4FhcE1umiFyzFRo0hr0BxTRwvSO4Ehjl9/wq4L+optzRpg1cey3k5xcuXbDAJZeCvptlyyAtDS677LANvzhrXYghhNeUFVu4/6OVLEx3o9JaNahBvN0yukJNXh7kC/xICeK992DRIrj3Xvdcg7yVA9cdPbroD6Ej6dcPLrgATj/dlT/tNJdoQtmHMTEglKSTAfj9zCPlQ+gJ7BeRLsB9WbBtPvwHAJEEoDaws8Qtu/UTUF3sN3cIMBZgZY0acPCg+0VYIPDD/fXXsHixa8444wxYtw569wbgpdk/hPDywuOX3QdZ8rNLMpd1bcpHt55md/AMo7fTD7kaRYGMjOBNt59+Ck89BZMmQVKSm5eSUvy6y5dDbi506xZaMH//u/uxNGuWSzZt27pBCJmZbjvFxWdMlAsl6SwE2iLSEpFE4MrxsMvXWXUJ8FJbGH4dpPjKDwTm+DWX/UZE3haRrSLynW/WVRSt5QD8DPQFSD1wwCWdBg3ckvx8GDfOtccXuPVW2LwZ0tPhyy/huOPgs89CevEVZcLSTfR8Zg4th06h5zNzmLB0E7d/sIR7xi4nP1+JixO6tbCEE07LGx/n+l02bHCjwkaPdqMd/S1dCrfc4hJOw4aF8/v3h5kzYdcu95g5080rENjHWJy8PNeZr42bAAAXR0lEQVSUBrBihXv06+dqNWefXTjSbeRIuOSSsr9gYyJUyVckUM1F5HZgBhAPvL0Y+n0sMnswtBsL3T6FPXXgH4ik4Wo4hVlBJB03MCDxAOx/Bm59DB7zLR2MG4zg725gBCJ3PVWtmutMLWhmmDfP/eps1epoXnOFmrB0Ew+MX8kB3+2iN2Ue4IHxK7m9Txsu6tyYOGtG80ReXDwMH+6SRV4e3HgjdOgAjzwC3bu7BHTvvW5QyqBBbqXmzV0CqlfP9RuefLKb/8gjbl6BsWMLh/YXWLjQNfnu2gWffAKPPgqrVsGhQ4WDDWrVck15Bf04zz7rflA99BB07QpDhlTsQTHGAxKkQlLySiLHAlcDC1X1CxFpDvRW1XdDWDcVmKyqJZ5Q0717d120aFGp4yuQOnQKPcLchLX050xy8vIPm58YH0fX5nXCGosptGDDTtKfudDrMIwJCxFZrKrdvY4jmDJde01VfwFe8Hv+M1BiwgmFiPwR+CNA8+bNy2OTYRUs4RQ33xhjKpMyJR0RORV4GTgBSMQ1u+1T1dpHG5CqvgG8Aa6mc7TbG3PLaSUXmj4d7rjDNbvcdBMMHVp0eXY2XHedG7BQv74bsp2a6patWOH6Afbsgbg4zh78TzZk5f226oiPHqd55i/cePc7LpZhw2DEiMJ+qn/8w41m2rHDnaOxcCHccINrCiqQkwO33+76quLiXEf37353FEel8kkdOsXrEIwxlP0q08Nx/TbjgO7AdUDb8goqrPLy4Lbb3EiilBTXbj9ggLscSoG33nInBaaluQ7o++93iSc3F665Bv73P3f+xY4d3LEhiwcmrubAoTz6r53P/irViBPh3v7HF27vrrvgnnuKxlG1KjzxBHz3nXv4e+op17G9bp0bTLGz5IGBxhgTicp82WJVTQPiVTVPVd8BepdbVOEUypnqEye6M8TB1UZmz3ZDXWfOdGedd+niltWvz6Xdm/P05Z1oXU25aeEE3u3zexrVqsqlXZsWH0f16m7Id9Wqhy97+2144AE3HRfnhtcaY0wUKmvS2S9u+PQyEXlORO4Cqpe0koiMAr4GjheRDBHxfnhOKGeb+5dJSIDatV1z2Lp1bmRd//7u4o7PPQfApV2bMjtzNie//CQf3XMutaoGVCiHD3fJ6sYb3eim4mRmur8PP+z2MWgQ/PrrUbxgY4zxTlmTzrW4fpzbgSzcyaMldjKo6lWq2lhVq6hqiqq+Vcb9h+SOviG0+IVyJviRyuTmunOD3n/f/f34Y1cL8rtKQn6+ku+/+q23wvr1rkzjxu7yPcXJzXUnCvbsCUuWuDPYA5vmTIlCei8YYypcWUevFVzz/QCF59xEnLvOPa7kQiWdbe5fJiXFJYHdu915Gikp7uKNBc1dF1zgEkONGrB4MZqayvZdWdTfn+mukvDZZ9CoUeF2b74ZLrqo+Pjq13eX/Sm4zM+gQa6PyZRKSO8FY0yFK1VNR0RWisiKIz0qKsgKdfLJJZ+pPmBA4T1UPvwQ+vQpbFZbscJdvDE3Fz7/3A1A8F0lQdLTmfLqWA6kti68SsKWLYXb/fjjku//IwIXX1y4/uzZRQc5GGNMFCltTedyoBFFrzoN0ALYXC4RhVtCQslnqg8Z4i482qaNq+GMHu3WrVsX/vY3l7hEXE3nwqInIP6hZ0tI8jvM993nmtZE3LDr118vXJaa6oZe5+S4KzHMnOkSzLPPuv3feacbav3OOxV+WIwxpiKU6ooEIjIZeFBVVwTM7w48qqoXl2dwR3tFgkiRuT+HHVk5tG5Qw+tQjDGVQCRfkaC0AwlSAxMOgKouAlLLJaIYdMXr3/DoxFVeh2GMMZ4rbfNakJNIflPtaAKJZQ9eeAJ1qtndQI0xprQ1nYUicnPgTN/5NouDlDdAr+Ma0KWZXezTGGNKW9O5E/hYRH5PYZLpjrv+2uG37jS/WbV5Nxt3HuC8jsd6HYoxxnimVElHVX8FTheRs4GCsb5TVHVOuUcWY0bM+5Ev03bQr30ju6eOMabSKtP9dMIlVkavAWzcuZ/qSQnUq57odSjGmBgXyaPXynqVaVNKzeolex2CMcZ4rsxXmTal91Xadv4xdY3XYRhjjGcs6YTR6s17GL8kg90HDnkdijHGeMKa18Lo2tNaMOSMljaQwBhTaVnSCaOqVeK9DsEYYzxlzWth9vm6bVz6yldkZed6HYoxxoSdJZ0wS0qIIyFO2Lo32+tQjDEm7Kx5LcxObVWfD2893eswjDHGE1bT8cihvHzy8yP3xFxjjKkIlnQ88O2GnZz0xCxWbtrtdSjGGBNWlnQ80LZhDc7veCzJiTaazRhTuVifjgfqVk/kuYFdvA7DGGPCzmo6Hvpl90H2HLSrExhjKg9LOh5J357FqU/P5pPlm70OxRhjwsaSjkda1E/msQEdOLNNA69DMcaYsLE+HY+ICNefnup1GMYYE1ZW0/FQXr4yf/121v6y1+tQjDEmLCzpeCg3P5+bRy5i5NfpXodijDFhYc1rHkpKiOe9m3pwXKOaXodijDFhYUnHY12b1/U6BGOMCRtrXosAo7792YZOG2MqBUs6EWDsoo2WdIwxlYI1r0WA/95wCrWq2b/CGBP77JsuAtROruJ1CMYYExbWvBYhXv0sjWGTVnkdhjHGVChLOhFix74cft1zEFW7sZsxJnZZ81qEePii9l6HYIwxFc5qOhHGbmFtjIlllnQiyPA5P3Dui59bE5sxJmZZ0okgrRvU4My2DcjOzfc6FGOMqRDWpxNBzu/UmPM7NfY6DGOMqTBW04kwqkrGrv1eh2GMMRXCkk6EeevLDZz53Fx27Mv2OhRjjCl31rwWYXof35CkhDiqJNjvAWNM7LGkE2HaNKxBm4Y1vA7DGGMqhP2cjkAHcvL4dPWv5NgoNmNMjLGkE4G+StvOTe8u4tsNO70OxRhjypU1r0Wgnm2O4f2benByaj2vQzHGmHJlSScCVUuMp2ebY7wOwxhjyp01r0WonVk5vDFvPT/tyPI6FGOMKTdW04lQBw/l8Y+p31O7WhVa1K/udTjGGFMuLOlEqCZ1qvHtg31pWKuq16EYY0y5sea1CGYJxxgTayzpRLC9Bw/xwPgVzFr9q9ehGGNMubCkE8GqJybwzY872bjTLgBqjIkN1qcTweLihDl390JEvA7FGGPKhdV0IlxBwrG7iRpjYoElnQiXn69c+9YCnpux1utQjDHmqFnSiXBxcUKrY6pzrI1kM8bEAOvTiQKPXdLR6xCMMaZcWE0nSuTnK7uycrwOwxhjjoolnShxzVsLuO2DJV6HYYwxR8Wa16LEFSc38zoEY4w5apZ0osQlJzb1OgRjjDlq1rwWRTL357D4J7ubqDEmelnSiSKPf7Kam0YuIi/fThQ1xkQna16LIjef1YprT2uBXRTHGBOtLOlEkRMa1/I6BGOMOSrWvBZl0rbu5Z2vNngdhjHGlIklnSjz2dptPDF5NVv3HPQ6FGOMKTVrXosyg7o14/KTUqhXPdHrUIwxptQs6USZ2slVvA7BGGPKzJrXotDKjN38ddRS9mXneh2KMcaUiiWdKLQvO5f567ezYVuW16EYY0ypWPNaFOrRsh7fPngOcXF2xo4xJrpY0olClmyMMdHKmtei1Pe/7OHC//uCpT/v8joUY4wJmSWdKHVsrapUT0wgJzff61CMMSZk1rwWpeokJzL2T6d5HYYxxpSK1XSi3KG8fA4eyvM6DGOMCYklnSi2dc9BTnpiFuMWZ3gdijHGhMSSThRrUDOJq05pTvvGNb0OxRhjQmJ9OlFMRHjwghO8DsMYY0JmNZ0YsGX3ATbu3O91GMYYU6KKSzoi5yGyFpE0RIYGWZ6EyBjf8gWIpPrmn4LIMkSWfbB6NXz8sSu/di2ceGLho1Yt+Pe/3bJx46BDB4iLg0WLCvexYwecfTbUqAG33150/6NGQadO0LkznHcebN9e/scgDHLz8un3wjxe/SzN61BMRZg+HY4/Htq0gWeeOXz5Cy9A+/bufdy3L/z0k5s/d27Rz0vVqjBhgls2fLjbnkjR9/2uXXDZZW5bp5wC331XuCwzEwYOhHbt4IQT4Ouv3fx773XzOnd262ZmVsxxMLFDVcv/AfEK6xVaKSQqLFdoH1Dmzwr/8U1fqTDGN52skKCq9OvcWbVBA9VDh7SI3FzVRo1U09Pd89WrVb//XrVXL9WFCwvL7dun+sUXqq+9pnrbbYXzDx1y2922zT2/917VRx/VaDX9uy2atnWv12GY8pabq9qqler69arZ2aqdO6uuWlW0zJw5qllZbvrVV1UHDz58Ozt2qNatW1huyRLVDRtUW7Qo/Ayoqt5zj+qwYW56zRrVPn0Kl113neqIEW46O1t11y43PWNG4efzvvvcw3gOWKQV8d1eDo+KqumcAqSh+iOqOcBo4JKAMpcAI33THwJ9ERFU96OaC5CUn+9+jQWaPRtat4YWLdzzE05wvwYDVa8OZ5zhfuX5U3WPrCz3d88eaNKkzC/Wa/07HEvrBjW8DsOUt2+/dTWSVq0gMRGuvBImTixa5uyzITnZTZ96KmQEGcn44Ydw/vmF5bp2hdTUw8utXu1qS+BqL+np8Ouv7vMxbx4MGeKWJSZCnTpuul8/SEgofv/G+KmopNMU2Oj3PMM3L3gZl2R2A/UBEOmByKrRq1fDf/5T+KYuMHo0XHVV2aOrUgVee801rzVp4j5sBR+oKPXNjzuYt26b12GY8rRpEzRrVvg8JcXNO5K33nLJJVCon5cuXWD8eDf97beuqS4jA378ERo0gD/8wSWsm25yP9gCvf128P0b46eikk6wK1JqyGVUF6Da4bp27eDpp+Gg362Zc3Jg0iQYNKjs0R065JLO0qWwebNrj376aQBenLWu7Nv10HPTv+fFT6MzdnO4F2etc7XwQMFq/gDvvef6M++9t+j8LVtg5Uro37/knQ4d6vp1TjwRXn7ZJZiEBMjNhSVL4NZb3WemevXD+5eeesqV/f3vQ3uBptKqqKSTAfj9RCMF2HzEMiIJQG1gp3+B9GrV3Bvcv0Nz2jQ46SRo1Kjs0S1b5v62bu0+xIMHw/z5ALw0+4eyb9dD/xp8Iu8N6eF1GKacvDT7B1ez2ejXYJCREbwZ+NNP3Zf+pEmQlFR02dixroO/Sgh3nK1VC955x30+3n0Xtm2Dli1dHCkp0MP3/ho40CWhAiNHwuTJ8P77R06KxvhUVNJZCLRFpCUiicCVwCQAETlPRNbeCyfNgxd95QcCc1BV3zoJAMdmZ7tRa/7tz6NGHV3TGkDTpq5JbZuvOWrWLNcvFMWWb8yk34vzaDl0Cj2fmcOEpcU0w5jocPLJ8MMPsGGDq+GPHg0DBhQts3Qp3HKLSzgNGx6+jdJ8XjIz3X4A3nwTzjrLJaJjj3XNfGvXumWzZ7sRc+BG1z37rNt/QZ+RMcWpsFEKcIHCOt8otr+rKrnwxCDYArR6GWpOgcyD8JPCtwqtfOtdq7BKYdmaatVUP/64cEhGVpZqvXqqmZlFh2qMH6/atKlqYqJqw4aq/foVLmvRwo3cqV7dlSkY/fPaa6rt2ql26qR60UWq27e74vdPLmFcSOT5eEmGtntomra4f/Jvj3YPTdOPl2R4HZopo9/eh1OmqLZt60axPfmkm/fww6oTJ7rpvn3de75LF/e4+OLCjWzYoNqkiWpeXtGNv/SS+yzEx6s2bqw6ZIibP3++aps2qscfr3rZZao7dxaus3Spardu7vNyySWFy1q3Vk1JKdz/LbeU+7EwpUcEj14TDdZuXEFE5DRgmKr29z1/wJf4ng5Wvnv37rrI/7ybMEgdOoUeLeuFdZ9Ha+nPmeTkHX6Lg8T4OLo2r+NBROZoLdiwk/RnLvQ6DBOlRGSxqnb3Oo5gwn1FghJHtYnIH0VkkYgs2rbNRmOFIljCKW6+McZ4JdzXXitxVJuqvgG8Aa6mE46gAo25JQz3qZk+He64A/Ly3BDUoQEXbZg3D+68E1ascG35AwcWLhs5Ep580k0/9BA96zQj89cdjPvg/t+KHLt3B592PYfBz3xU/LbAnYdxwgmuw3n4cDfvvPPcyKfcXDjzTHjlFYiPr4ADYYJJHTrF6xCMqRDhTjqhjGqLfXl5cNttbgBDSorrMB4woLBzFqB5c/jvf+H554uuu3MnPPaYGx4rAt268eA7n3DPpzlc8IeXfys2ZeSdNP7D1cVvq8DDD0OvXkXnjR3rOpFVXZIaN86dnGiMMUch3M1rC4G2ItJSAka1VSqhnGmemurOH4oL+BfNmAHnngv16kHdunDuuVy4eQVPX96JpnWqIUCP3B201CzOHHJ58dsCWLzYnXXer1/R+bVqub+5uW5Ekw2FNcaUg7AmHXVXHrgdmAGsAcaq6qpwxhARSnumeQjrXtq1KV8N7cOGZy5kTK10kq+5uuREkZ8Pd98N//xn8OX9+7thuDVrHt4kZ4wxZRD2Wxuo6lRVPU5VW6vqU+Hef0nu6Nu24ndSmjPNy7JuqJc9efVVuOCCoknM34wZrl8nOxvmzAktPlMuwvI+NMYDdhO3AHede1zF7yTUM82PtO5nnxVdt3fvwufLl7smsW7dSt7W11/DF1+45LNvn2tGq1Gj6CVOqlZ1/U0TJ7pmPRMWYXkfGuMBu4mbF0I50/xI+veHmTPdNbJ27XLT/tfVKs0Z6O+/Dz//7K4m/PzzcN11LuHs2+dqOOAS2NSp7qrDxhhzlCzpeCEhwQ1N7t/fDVUePNjdhO6RR9zlRAAWLnS1mnHj3GVOOnRw8+vVc6PNTj7ZPR55xM0rMHbs4UnnSNs6kqwslwQ7d3ZXHm7YEP70p/J7/caYSiusVyQoLS+uSGCMMdEukq9IENFJR0S2Ab7773IMEJ33lK44dkyCs+NyODsmh4vlY9JCVRt4HUQwEZ10/InIokjN3F6xYxKcHZfD2TE5nB0Tb1ifjjHGmLCxpGOMMSZsoinpvOF1ABHIjklwdlwOZ8fkcHZMPBA1fTrGGGOiXzTVdIwxxkS5iEs6InKeiKwVkTQRGRpkeZKIjPEtXyAiqeGPMrxCOCZnicgSEckVkUpxZc4QjsnfRGS1iKwQkdki0sKLOMMphGPyJxFZKSLLRORLEWkfbDuxpqTj4lduoIioiNiItork9f2y/R9APLAeaAUkAsuB9gFl/gz8xzd9JTDG67gj4JikAp2Bd4GBXsccIcfkbCDZN32rvU8UoJbf9ABgutdxR8Jx8ZWrCcwDvgG6ex13LD8iraZzCpCmqj+qag4wGrgkoMwlwEjf9IdAX5GYvtlLicdEVdNVdQVQWe5PHcoxmauq+31Pv8HdMDCWhXJM9vg9rU7AXXtjVCjfKQBPAM8BB8MZXGUUaUmnKeB3+WUyfPOCllF3f57dQP2wROeNUI5JZVPaYzIEmFahEXkvpGMiIreJyHrcF+xfwxSbl0o8LiLSFWimqpPDGVhlFWlJJ1iNJfDXWChlYklle72hCPmYiMg1QHfgCHeqixkhHRNVfUVVWwP3Aw9VeFTeK/a4iEgc8CJwd9giquQiLelkAP53FEsBNh+pjIgkALWBnWGJzhuhHJPKJqRjIiLnAH8HBqhqdphi80pp3yejgUsrNKLIUNJxqQl0BD4TkXTgVGCSDSaoOJGWdBYCbUWkpYgk4gYKTAooMwm43jc9EJijvp7AGBXKMalsSjwmviaT13EJZ6sHMYZbKMfE/3akFwI/hDE+rxR7XFR1t6oeo6qpqpqK6/8boKp2efsKElFJx9dHczswA1gDjFXVVSLyuIgU3OXsLaC+iKQBfwOOOAQyFoRyTETkZBHJAAYBr4vIKu8irnghvk/+CdQAxvmGCMd0og7xmNwuIqtEZBnus3P9ETYXM0I8LiaM7IoExhhjwiaiajrGGGNimyUdY4wxYWNJxxhjTNhY0jHGGBM2lnSMMcaEjSUdY4wxYWNJxxhjTNhY0jHGGBM2/w8w1AEcI2wmfwAAAABJRU5ErkJggg==",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -337,7 +337,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -376,7 +376,7 @@
     },
     {
      "data": {
-      "image/png": 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TUbmIlBTLbXDMbB/gZeA6d99Uer27Pwk8CaGlU5X3euFnfatSXWqDyq7ovv56mDAhLG/dGqbcb9wYXi9ZEuosXQpmnHP6bUy2JrTd+BWPvf4ATbZvZsb+XfjDhbeW/Lf20kvwox/Bp59Cn4QvnEuWQI8ecNddcOONYb+XXAJffQV16sCVV4ZYZY/XcegbcYdQI9Le0rEw2PQy8A93fyXd7y97mIICuOqqMM1+5kwYMSI8J3r4YZg6NTyuuQbOPrt43SWXwE03waxZ8MknXHrm0TSom8PQicN4us+ZnHjl3/imYSMe2fpZcZ3Nm+HPf4bvfGfXeK6/Hr7//eLXubnw0ENh/x99FC4FKB2fSC2S1qRjYaba08Asd/9jOt9b9lDJXNGdaMQIuOCCsDxzJuTnw8knh9f77MPpx3bh3rMOpd+S6bx50HG0adqAA675GUdMnli8j9tvh1/9CurXL7nv0aNDHIccUlx2wAFw5JFhuVGjMFhc+uI/kVok3S2dfoQLh04ys6nR4wdpjkH2JMlc0V1k8eIwNfWkk8LrOXOgadPQ8unVK7R4CgoY1L4+zVrvx/wHzuCDoSdxwoBexfucMiV0mf3whyX3vWUL3H8/3Hln+bEuWhTql9VCEqkl0jqm4+7vU/ZVsjXi2gEVX58qe4BkruguMnIknHtuuIs5hFbOe++FRNC+fbiT+bBhu14nUbTPwsLQfTZs2K7r77wzrNtnn7Lf+5tv4Jxz4E9/gsaNkzkyqeVq6/mrVv+I2/Und4s7BIlbMld0Fxk5srhrrahur16hSyw3FwYNCrdhatGCravXceET75fc5+bN8MUX0L9/+ImNjz4KCWrSJPj449Dl1rFjSCy//324/gJg586QcC68sOR4kuzRauv5K+N/xE2kShKv6G7TJiSWf/5z1+1mz4YNG6Bv35J1N2wIv1Oy334wfnyYiWbGmj7H8qNFHwPHFd8Fo0mTcHV4kf79w32x+vQJLaYid90VWjxXXx1aYpdfHsZybrihhv4IIpmjVrd0RJK6ohvCBILBg0t2veXkhKQxYAAcdlhIEFdcAUCHpx5l0IQXwiSFdetC4kjFBx/Ac8+FhHbEEeHx5ptVOGCRzBbrHQmSUZU7EoiI7Il0RwKRWuaBMV9y6p8q/mkNEdmVxnREUtB1/33YvlO3vhHZXUo6Iik4q1dbzuoVdxQi2UfdayIikjZKOiIpeHTcXI6+5z9xhyGSdZR0RFJw0AGN+cFhB8QdhkjW0ZiOSApO7rE/J/fYP+4wRLKOWjoiIpI2SjoiKXjqvQUceufbbN9ZEHcoIllFSUckBQcf0Jjzj2pHnfLuWC0iZdKYjkgK+nVpQb8uLeIOQyTrqKUjIiJpo6QjkoIRnyyh261vsXrz9rhDEckqSjoiKTioVSMuP74TDermxB2KSFbRmI5ICnq1b0av9s3iDkMk66ilI5IidyfTf49KJNMo6Yik4LWpy+l0y5ssWLsl7lBEsoqSjkgKurdqxP8d0JWmDerGHYpIVtGYjkgKDmrVmINaNY47DJGso5aOSArcnbz8QgoLNaYjsjuUdERSMHH2Grrd9hbTl38ddygiWUVJRyQFnffbm5sGdqdV4/pxhyKSVTSmI5KCDvvuzVUndok7DJGso5aOSAoKCp2tefnkFxTGHYpIVlHSEUnBlCUb6HHH23y4YF3coYhkFSUdkRS0a96QX//gIDruu3fcoYhkFY3piKRg/8b1ufK7B8YdhkjWUUtHJAUFhc7GrXnsyNfPVYvsDiUdkRTMW/0NR/x2LP+ZuTruUESyipKOSApaNtqLO0/vQY/WuhWOyO7QmI5ICprtXY+f9OsUdxgiWUctHZEUFBQ6qzdtZ2teftyhiGQVJR2RFKzevJ2jfz+O16euiDsUkayipCOSgiYN6nL3oEPp01E/WS2yOzSmI5KChvVyueiYDnGHIZJ11NIRSUFhobNsw1Y2bd8ZdygiWUVJRyQFW3cWcNz9E3jhk6VxhyKSVdS9JpKC+rl1eODcnhzetmncoYhkFSUdkRTk5tThvD7t4g5DJOuoe00kRQvXbmH9lry4wxDJKko6Iik66aGJDPtgYdxhiGQVda+JpOhP5x9Bt/0bxR2GSFZR0hFJ0ZlHtIk7BJGso+41kRTNW72ZVZu2xx2GSFZR0hFJ0TlPfMgTE+fHHYZIVlH3mkiK/nBuT9o0axB3GCJZRUlHJEWnHNIq7hBEso6610RSNPurzSxdvzXuMESyipKOSIouH/4pD/9nTtxhiGQVda+JpOi+s3vStGHduMMQySpKOiIpOq5ri7hDEMk66l4TSdGslZuYt3pz3GGIZBUlHZEU3TBqGvePmR13GCJZRd1rIim6e9Ah7JWbE3cYIllFSUckRb07NI87BJGso+41kRTNWPE1Xyz/Ou4wRLKKko5Iin77r5n87t8z4w5DJKuoe00kRbf/sAdmcUchkl2UdERSdGibJnGHIJJ11L0mkqIZK75m8uL1cYchklWUdERS9Kf/zOW20TPiDkMkq6h7TSRFN5/anbx8jzsMkayipCOSoi4tG8UdgkjWUfeaSIq+WP41789dG3cYIllFSUckRc98sJChr0yPOwyRrKLuNZEUXf+9bmzJy487DJGsoqQjkqJ2zRvGHYJI1lH3mkiKZq7YxNiZq+IOQySrKOmIpGjUpKXc+OK0uMMQySrqXhNJ0c9POJCLjmkfdxgiWUVJRyRFrZrUB+rHHYZIVlH3mkiKZq3cxGtTl8cdhkhWUdIRSdFbn6/kuhemxh2GSFZR95pIii7t14lBvdrg7ph+WEckKUo6Iilqvnc9mu9dL+4wRLKKutdEUjT7q82MmrSUnQWFcYcikjWUdERS9N85a/jVS9PZka+kI5Isda+JpOj8o9vx/cNa0bBuTtyhiGQNJR2RFDWuX5fG9evGHYZIVkm5e83MrjWzxhY8bWafmdkp1RmcSCabt3ozz3+0mK2607RI0qoypnOZu28CTgH2A34C3PftWrNTMZuN2TzMhpa5B7PzMJuJ2QzM/hmVHYHZh1HZ9JPXry/e/vjj4YgjwqN1axg0KJS/9hr07BnK+/SB998vrjN8OHTtGh7DhxeXjxgBhx0W6p16KqzVj3HJ7pm0aAO3jf6Cr7ftjDsUyUZjxkD37tClC9x3367rlyyBE0+EXr3CeerNN0N5Xh785Cfh/HX44TBxYnGdvDy48koWwqGYfYnZOSX2aXYuZo5Zn+j1vphNwOwbzB4rte09mC3F7JvqPGzcPaUHMD16fgQ4K1qe4u445DjMd+jsUM9hmkOPEvuArg5THJpFr1tGz90cukbLrdfk5rpv2OC7OPts9+HDw/Lmze6FhWF52jT37t3D8rp17p06hef168Py+vXuO3e677ef+5o1YbubbnK/885d30OkAlt27PRVm7Z5fkFh3KFItsnPd+/c2X3+fPcdO9x79nSfMaPkNldc4f6Xv4TlGTPcO3QIy4895n7ppWF51Sr3I490LygIr++4w/3WWx2Y5FDHoYUXn3MbOfzX4SOHPlHZ3g7HOfzc4TEveY4+xuEAh288xTxR1qMqLZ3JZvYO8APgbTNrBBRN4zkamIf7AtzzgJHAmaXqXwE8jvuGKPutjp7n4D43Wl6xvm5dWLOmZM3Nm2H8+OKWzj77QNHFeVu2FC+//TacfDI0bw7NmoXlMWPAPTy2bAnPmzaFlpPIbmhYL5eWjeqTU0cXhspu+uST0MLp3Bnq1YPBg0OPTSKzcG4C+Prr4nPUzJkwYEBYbtkSmjaFSZPC62eegVtuCcvuhbgnduH8DngA2P5tifsW3N8vUVa87iPcV1b1UEurStK5HBgKHOXuW4G6hC42gDbA0oRtl0VliboB3TD7ALOPMDt1l3cwO7quOxx4YMnyV18Nf/TGjUuWHXQQnHZa+MMDLF8O7doVb9O2bSirWxeeeCI0T1u3Dh/i5Zfv9h9A9mwL127h7x8sZMOWvLhDkWxT3rkp0V13wfPPh3U/+AE8+mgoP/zwkKDy82HhQpg8GZYuhY0bw/rbb2cGHIzZi5jtD4BZL6Ad7v+u8WOrRFWSTl9gtrtvNLOLgNuAr6N1ZX3181Kvc4GuQH/gAuApzJp+u9bsAOC533ToAHVKhTliBFxwQcmys86CL7+E0aPh9tujdyz9loRvDzt3hqQzZQqsWBH6S++9F4CHx86p7LhFgPAjbr/510xWb94RdyiSRR4eO6f8c1OiESPg0kth2bIwnnPxxVBYCJddFhJRnz5w3XVw7LGQmxuS0LJl0K8fh8As4EPgQczqAA8Dv6zxg0tCVZLOE8BWMzsc+BWwGHg2WrcMSEjjtAVWlKq/DHgN9524LwRmE5IQmDUG3gBu+2KffUrWWrcuNE1PO63sqL77XZg/P0wMaNs2fAP49h2XhZbN1OgmjQceGD7o886D//0PgEfGzU3+LyB7tO/1aMnUO06mS8t9Kt9YJPLIuLnln5sSPf10ODcB9O0L27eH81puLjz8cDiPvfZaaOF07Qr77gsNG4Yv4MGLwJFAI+BQYCJmi4BjgNe/nUyQZlVJOvnu7oSxmkfc/RHCwQF8CnTFrBNm9YDBwOtm9oyZrTazL4DRwIkAmLUgdLctiLZ/FXgW9xd3edcXX4Qf/hDqJ/yOybx5xd8cPvsszODYd18YOBDeeQc2bAiPd94JZW3ahC61orGisWPh4IOr8KeQPc3oKcs56cF36fXbsXz3gQmMnqKfOJDdcNRRMHdu6B7Ly4ORI+GMM0pu0749jBsXlmfNCklnv/1g69YwHg3h3JWbCz16hC/Qp5+eOJttADAT969xb4F7R9w7Ah8BZ+A+KQ1HuouqXBy62cxuAS4CvmtmOYRxHXDPx+xq4G0gB3gG9xmfmdVZAr8fBD+N1p2C2UygALgJ93WErrrvAvtiduk/GjQIGf2II8K7jhwJQ0vNwH75ZXj22TBW06ABvPBC+ACaNw9dbUcdFba7445QBnDnnaFVVLcudOgAw4ZV4U8he5LRU5Zzyyufs21nAQDLN27jllc+B2BQr9JDlyJlyM2Fxx4LX4ILCkKX2SGHhHNUnz4hAT30EFxxRWjVmIVzlBmsXh3q1akTvkA/91zxfu+/Hy6+mNnQA7iY4nH28oXWT2OgHmaDgFNwn4nZA8CPgYaYLQOewv2uqh66eVl9i8lUNGsVBfSpu79nZu2B/u7+bCX1OgL/dvdDk3mfPn36+KRJ6UvIHYe+wXc6NU/b+0n2mbJkI3ll3OSzXk4derVvWkYNkWIfL1zPovvKGR6oJmY22d1j6T6rTMotHXf/CvhjwuslFI/pVImZXQlcCdC+vX6DXjJLWQmnonIRKZZy0jGzY4BHgYOBeoRutG/cvUlVg3L3J4EnIbR0qrq/3fXCz/qm+y2lpo0ZA9deG7oyfvrTXbtor78eJkwIy1u3hi6MoimoS5aEOkuXghnnnH4bk60Jl0z+F5dNep2OG1fS65p/0LB1q/Bv5+uv4aKLQr38fLjxxnAFeRn74s03oWPH0Lc/eDCsXw9HHhm6TOrpt3pqo45D34g7hFhVZSLBY4SpznOBBoRxmserIyiRalVQAFddBW+9FSaQjBgRnhMVzQaaOhWuuQbOPrt43SWXwE03hcHcTz7h0jOPpkHdHCa37cFFg+9mWeOW1K+bw00Du4ftH388DOxOmxYGdX/5yzBYXMa+aNkylN98c0h8c+eGC5mffrrG/ywicajS7+m4+zwgx90L3P3vhGtuRDJLMld/J0q8DmzmzNBaOfnk8HqffTj92C7ce/ZhbOx+KMua7I8Z3H5aj+JJBGbhrhnu8M03YfJKbm6Z+6Jhw7Dd+PFw7rmhfMiQcL2ZSC1UldlrWy1Mb55qYZbDSmDviiqY2QhCYmphYTbEne6ur3RSs8q6+vvjj8vedvHi0NV10knh9Zw54TYjZ58dyr/3PbjvPgb1asOgXm3YvrOA+iMb0KbnAcX7uPrqMPuodeuQfF54Icw0KmdfbNgQynNzi+MrfXW6SC1RlZbOxYRxnKuBLYSLQc+pqIK7X+DuB7h7XXdvm4kJ59oBXeMOQapbMld/Fxk5MrQ4cqIfZsvPh/fegwcfhE8/hQULSkyvr1/WD7i9/XaY4r8Z018lAAAUR0lEQVRiReiuu/rqcA+t8va1O/FJ1tvTzzEpJx13X+zu29x9k7v/xt1viLrbstr1J3eLOwSpbslc/V1k5MiSt1hq2zbcWr5z59ASGTQoXIAcmfDl6l1/2uDvfw+tGbPQrdepU7hFU3n7atEiTFrIz688Psl6e/o5ZreTjpl9bmbTy3vURJAiVZLM1d8As2eHrq6+fUvW3bCh+O4V48eHSQKRz5ZsYEteAYWFCa2VxCvJV60K++3cufx9mYXfTXnppVA+fDicWfqm7CK1QypjOmcD+1PyLtIAHdj1/moi8Uvm6m8IEwgGDy7ZtZWTE7rDBgwI3WC9e4erxAH+/GdueOABbPNaOOLwcCfgp54Kd8G49NJwF3P3cJV4ixahTnn7uv/+8N633RZaQ7rrudRSu31HAjP7N/Brd59eqrwPYWLA6dUYX9rvSCAiku0y+Y4EqYzpdCydcAA83DyuY5UjEskiqzdt547XvmDa0o1xhyKSFVJJOvUrWNcg1UBEslGhw2tTV7Bk/da4QxHJCqmM6XxqZle4+98SC83scmBy9YQlkh1aNanPtDtPiTsMkayRStK5DnjVzC6kOMn0Idx/7axya4mIyB5vt5OOu68CjjWzEwm/RgfwhruPr9bIRLLEExPnk19QyDV7+EV/Ismoyk8bTAAmVGMsIllp9leb9LMGIkmqyr3XRAT40+BecYcgkjWqdJdpERGR3aGkI1JFkxat54pnJ7F60/a4QxHJeEo6IlW0fWchS9dvZdP2/LhDEcl4GtMRqaLjurZgzHXfjTsMkayglo6IiKSNko5IFbk7P3tuEs99tDjuUEQynpKOSBWZGVvzCtixsyDuUEQynsZ0RKrBc5d/J+4QRLKCWjoiIpI2Sjoi1eC5jxZzwZMfxR2GSMZT0hGpBnXrGHvVrUNB4e79Eq/InkZjOiLVYPDR7Rl8dPu4wxDJeGrpiIhI2ijpiFSDpeu3ctqf32P8l6viDkUkoynpiFSDRvVz2b9xffbKzYk7FJGMpjEdkWrQtGE9nrn0qLjDEMl4aumIiEjaKOmIVJNfjprGVf/4LO4wRDKautdEqsmBLfcmL78w7jBEMpqSjkg1+T/9u8QdgkjGU/eaiIikjZKOSDUZO3MVvX83lkVrt8QdikjGUtIRqSatm9Zn4KGtqJur/1Yi5dGYjkg1OaR1E35/1mFxhyGS0fSVTKSauetO0yLlUdIRqUbH3juOe9/6Mu4wRDKWutdEqtHZR7bl0DZN4g5DJGMp6YhUoxsHdo87BJGMpu41kWq2s0B3JRApj5KOSDV6YMyXHPGbd+IOQyRjqXtNpBr1PXBfGtTNoaDQyaljcYcjknGUdESq0fFd9+P4rvvFHYZIxlL3mkg1276zgO07C+IOQyQjKemIVKOFa7dw0O1jGPPFV3GHIpKRlHREqlGrxvW5aWB3DjqgUdyhiGQkjemIVKMG9XK46kT9ro5IedTSEalm2/IKWPvNjrjDEMlISjoi1ewnwz7hF89PjjsMkYyk7jWRanZZv04U6k7TImVS0hGpZqcc0iruEEQylrrXRKrZjvwCFq3dQl6+7sEmUpqSjkg1e2fGKvo/OJFF67bEHYpIxlHSEalmR3ZoxkM/Opz99tkr7lBEMo7GdESqWZumDTind9u4wxDJSGrpiNSAhWu3sGLjtrjDEMk4SjoiNeDMx97n/707P+4wRDKOutdEasCDPzqcts0axh2GSMZR0hGpAbpWR6Rs6l4TqQGrNm1n8uL1cYchknGUdERqwN8/WMTgJz/CdTsckRLUvSZSA87t3Zbvdm1BoUOOxR2NSOZQ0hGpAV1a7kOXlvvEHYZIxlH3mkgN2JZXwP/mreWrr7fHHYpIRlHSEakBa7/ZwY+f+pj/zlkTdygiGUXdayI1oFWT+oy44hi67a8uNpFESjoiNaBuTh36Hrhv3GGIZBx1r4nUkClLNvDh/HVxhyGSUZR0RGrIn/4zl3vfmhV3GCIZRd1rIjXkjtN7kFtHF+mIJFLSEakhB+6nSQQipal7TaSGrNi4jZcnL2Pz9p1xhyKSMZR0RGrI58u/5pcvTmPxuq1xhyKSMdS9JlJDjj1wXybe2J/WTRvEHYpIxlDSEakhjerXpVH9unGHIZJR1L0mUoNe+WwZnyzU7+qIFFHSEalBv3/zS16dsizuMEQyhrrXRGrQ61f3o1nDenGHIZIxlHREapAmEYiUpO41kRo0efF6nnpvQdxhiGQMJR2RGvTfOWu5581Z7CwojDsUkYyg7jWRGnTldzvzi/4HUjdH3+9EQElHpEbtvZf+i4kk0tcvkRq0cWsej42fy4wVX8cdikhGyKykY3YqZrMxm4fZ0DK3GTUKevSAQw6BH/84lC1eDL17wxFHhPK//rV4+xdegJ49Q/mvflVcvngxDBgQ1vXvD8uiaymmToW+fcP2PXuG+kUeewy6dAEzWLu2eo9daqW8/EIefGcO05cp6chuGDMGuncP55v77tt1fXnnL4Dhw1kEh2I2F7MhAJg1xOwNzL7EbAZmxTs164DZOMymYzYRs7YJ5ZMxmxrV+XlCnXqYPYnZnGif5yR9bO6eGQ/IcZjv0NmhnsM0hx69e/f2b82Z437EEe7r14fXq1aF5x073LdvD8ubN7t36OC+fLn72rXu7dq5r14d1l1yift//hOWzz3XfdiwsDxunPtFF4Xl2bPD+7iHfbRq5b5hQ3j92WfuCxeG/a9Z4yKVKSws9G15+XGHIdkkP9+9c2f3+fPDua1nT/cZM0puU975a906906dvCVMcWjmsCB6buhwoodzbT2H9xy+H71+0WFItHySw3MJ2+0VLe/jsMihdfT6Nw53R8t1HFp4kuf6TGrpHA3Mw30B7nnASODMElv87W9w1VXQrFl43bJleK5XD/baKyzv2AGF0UyhBQugWzfYb7/w+nvfg5dfDsszZ4ZvCgAnngivvRaWu3WDrl3DcuvW4T3WrAmve/WCjh2r8ZCltjMz6tfNiTsMySaffBJaOJ07h3Pb4MHF56ci5Z2/3n4bTj6Z1VCA+wZgLHAq7ltxnwAQnV8/A9pGe+sBjIuWJ1B03nXPw31HVL4XJXvGLgPujbYrxD3prp9MSjptgKUJr5dFZcXmzAmPfv3gmGNCE7TI0qWhqdmuHdx8c0gYXbrAl1/CokWQnw+jR4ftAA4/vDgBvfoqbN4M60r9nv0nn0BeHhx4YDUfquxJRk1ayvD/LYo7DMkWy5eH81iRtm1DWaLyzl+l65Z1HjVrCpxOcaKZBhR1j50FNMJs32jbdphNJ5yb78d9RVQf4HeYfYbZi5jtn+zhZVLSKet3fb3Eq/x8mDsXJk6EESPgpz+FjRvDunbtYPp0mDcPhg+HVatCi+iJJ+D88+H440MrJTeaTfTgg/Duu6H18u670KZN8TqAlSvh4ovh73+HOhX/mR4eOyflg5ba750Zq3hj+sq4w5As8PDYOeC+6wordXos7/xVVt3E86hZLjAC+DPuRVct3wicgNkU4ARgOZAfavpS3HsCXYAhUXLJJbSSPsD9SOBD4MFkjzGTks4yIDFFtwVWlNiibVs480yoWxc6dQoDbXPnltxL69ZhEsB774XXp58OH38MH34Ytk/sOnvlFZgyBe65J5Q1aRKeN22C006Du+8OLapKPDJubqXbyJ7ryYt7M+rnfeMOQ7LAI+PmhvPc0oROn2XLwvkqUXnnr9J1dz2PPgnMxf1P35a4r8D9bNx7AbdGZSVnvrivAGYAxwPrgK3Aq9HaF4Ejkz3GtCcdMzvVzGab2TwrOUPtU6ArZp0wqwcMBl4vUXnQIJgQuiVZuzZ0tXXuHD6UbdtC+YYN8MEHIcEArF5dXP6Xv4TWUVH9orGfe++Fyy4Ly3l5cNZZcMkl8KMfVfPRy57o9Wkr6HffeDoNfYN+941n9JTllVeSPddRR4Uv0wsXhvPRyJFwxhkltynv/DVwILzzDi0gB7NmwCnA2wCY3Q00Aa4rsS+zFpgV5YJbgGei8raYNYiWmwH9gNm4O/AvoH9UZwAwM9nDS2vSMbMc4HHg+4TBqwvMrAcA7vnA1YQ/0CxgFO4zfrZiBbwe5Z6BA2HffcOU6RNPhD/8IbyeNQu+853Qz3nCCXDjjXDYYaHOtdeG7fv1g6FDw0QBCF103buH16tWwa0hwTNqFPz3vzBsWJiCfcQRYRo1wJ//HL5JLFsWxo+KEphIOUZPWc7NL09n+cZtOLB84zZueeVzJR4pX25uuDxj4EA4+GA477zQe3PHHcXnwvLOX82bw+23MxkOJnyR/y3u66Np0LcSzrufRdOgi05g/YHZmM0B9geiphMHAx9jNg14F3gQ98+jdTcDd0XjPRcDv0z28MzL7gOsEWbWF7jL3QdGr28BcPd7y6vTp08fnzRpUpoiTE3HoW/wnU7N4w5DMtCUJRvJK+O+a/Vy6tCrfdMyasie7OOF61l032lV3o+ZTXb3PtUQUrVLd/da5TPUADO70swmmdmkNUXTlUWyUFkJp6Jykdou3TeGqnyGGuDuTxIGvOjTp0/6mmJV8MLPNFBcK4wZE7pkCwpC9+nQUjfGuP764nHFrVvDmOHGjaHs+uuLt/vySxg5kn4bGjNg3Cgum/Q6HTeupNc1/2BDwya0adqg+N/Mp5+GCSsvvADnnhu6c3/xizChJScndJ2cf37Ydvz40H2clxfuwvH00yVnXUpW6zj0jbhDqHHp/tda+Qw1kbgUFISLj8eODWN3Rx0VBnB79Cje5uGHi5cffTTMHoIwxlg09rd+fbhG7JRTuKnDBp6dfygXdTmakf+8BYAGdXO4aWD34ve8+ebQf1+kYUN49tkw03LFipBcBg6Exo1hyBAYNy705d9xR7g84PLLa/CPIlK90t299inQ1cw6WXkz1ETiksyV4IlGjIALLti1/KWX4Pvfh4YNGdSrDZf8n7PwDh0BOKBJA+49+zAG9Yp6lR99FM45p/juGlD+XTHWrQt33iiaDHPyycUXCIpkibQmHS9jhpq7z0hnDCLlSuZK8CKLF4cprSedtOu6kSNLJKNBvdrwwdCTaNusAW9ee3xxwlm+PFxN/vOf77qPIol3xWjRAnbuhKKJNS+9VPqaDJGMl/bOYHd/E3gz3e9bk64d0DXuEKQ6JHMleJGRI8P4S06p+6qtXAmff16yu6w8110H99+/6z4S93XxxaELreiuGCNHhrGjHTvglFM0nlPL7AnnEv2LrQbXn9wt7hCkOiRzJXiRkSPh8cd3LR81KlxcXLdu5e83aVLowoNwsd+bb4YkMmhQ+XfF6Nu3+G4b77wTLpCWWmNPOJdk0m1wROKVzJXgALNnhztc9C1jxmJ54zxlWbgw3Ix20aLQavrLX0LCqeiuGEV32NixI7SSKuqaE8lASjoiRZK5EhxCYhk8eNeut0WLQkvphBNKlu/unSwquivGH/4QYuvZM9xXsKwxJZEMltY7EqQiG+5IICKSSTL5jgQZn3TMbA2wOO44qkkLoLb9znVtOyYdT+arbcdUE8fTwd33q+Z9VouMTzq1iZlNytRvH6mqbcek48l8te2YatvxVEZjOiIikjZKOiIikjZKOun1ZNwB1IDadkw6nsxX246pth1PhTSmIyIiaaOWjoiIpI2STjUxs1PNbLaZzTOzoeVsc56ZzTSzGWb2z4TyAjObGj0y4q7blR2PmT2cEPMcM9uYsG6Imc2NHkPSG3n5qnhM2fgZtTezCWY2xcymm9kPEtbdEtWbbWZJ3Ciu5qV6PGbW0cy2JXw+f01/9GVL4pg6mNm46HgmWvhZ6aJ1Gfn/qMrcXY8qPoAcYD7QGagHTAN6lNqmKzAFaBa9bpmw7pu4j2F3j6fU9tcAz0TLzYEF0XOzaLlZNh9Ttn5GhLGCX0TLPYBFCcvTgL2ATtF+crL4eDoCX8T9maR4TC8CQ6Llk4DnouWM/H9UHQ+1dKrH0cA8d1/g7nnASODMUttcATzu7hsA3H11mmPcHckcT6ILgBHR8kBgrLuvj451LHBqjUabnKocUyZK5ngcaBwtN6H4BxPPBEa6+w53XwjMi/YXp6ocT6ZK5ph6AOOi5QkJ6zP1/1GVKelUjzZA4g+bLIvKEnUDupnZB2b2kZkl/gOqb2aTovJBNR1sEpI5HiB0DxC+LY/f3bppVpVjguz8jO4CLjKzZYSfE7lmN+qmW1WOB6BT1O32rpkdX6ORJi+ZY5oGnBMtnwU0MrN9k6yblZR0qkdZP7pSelpgLqGLrT/hW/RTZtY0WtfewxXJPwb+ZGYH1lSgSUrmeIoMBl5y94IU6qZTVY4JsvMzugAY5u5tgR8Az5lZnSTrpltVjmcl4fPpBdwA/NPMGhO/ZI7pRuAEM5sCnAAsB/KTrJuVlHSqxzIg4ScnacuuTf9lwGvuvjPq0phNSEK4+4roeQEwEehV0wFXIpnjKTKYkt1Qu1M3napyTNn6GV0OjAJw9w+B+oT7fGXiZ5Ty8UTdhOui8smEcZRM+GGaSo/J3Ve4+9lRwrw1Kvs6mbpZK+5BpdrwILRiFhC6ZIoGDA8ptc2pwPBouQWh6bwvYZBwr4TyuVQwwJ0pxxNt1x1YRHS9V1TWHFgYHVezaLl5NnxGFRxTVn5GwFvApdHywYSTlgGHUHIiwQLin0hQlePZryh+wqD98mz5Nxf9e6oTLd8D/DZazsj/R9Xyd4k7gNryIDT35xC+Zd0alf0WOCNaNuCPwEzgc2BwVH5s9Hpa9Hx53MeSzPFEr+8C7iuj7mWEwel5wE/iPpaqHlO2fkaEQeoPorinAqck1L01qjcb+H7cx1KV4yGMicyIyj8DTo/7WHbjmM4lfImZAzxF9OUmWpeR/4+q+tAdCUREJG00piMiImmjpCMiImmjpCMiImmjpCMiImmjpCMiImmjpCMiImmjpCMiImmjpCMiImnz/wEGaybyOTr67QAAAABJRU5ErkJggg==",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -415,7 +415,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -452,7 +452,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -489,7 +489,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/Otherfiles/meta.yaml b/Otherfiles/meta.yaml
index e0e3e70e..24b50215 100644
--- a/Otherfiles/meta.yaml
+++ b/Otherfiles/meta.yaml
@@ -1,5 +1,5 @@
 {% set name = "pycm" %}
-{% set version = "3.8" %}
+{% set version = "3.9" %}
 
 package:
     name: {{ name|lower }}
diff --git a/Otherfiles/test.html b/Otherfiles/test.html
index cb79547e..f8e480c0 100644
--- a/Otherfiles/test.html
+++ b/Otherfiles/test.html
@@ -196,6 +196,14 @@ <h2>Overall Statistics : </h2>
 <td style="border:1px solid black;padding:4px;">0.41667</td>
 </tr>
 <tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#NPV_Macro" style="text-decoration:None;">NPV Macro</a></td>
+<td style="border:1px solid black;padding:4px;">0.77778</td>
+</tr>
+<tr style="text-align:center;">
+<td style="border:1px solid black;padding:4px;text-align:left;background-color:transparent;"><a href="http://www.pycm.io/doc/index.html#NPV_Micro" style="text-decoration:None;">NPV Micro</a></td>
+<td style="border:1px solid black;padding:4px;">0.79167</td>
+</tr>
+<tr style="text-align:center;">
 <td style="border:1px solid black;padding:4px;text-align:left;background-color:aqua;"><a href="http://www.pycm.io/doc/index.html#Overall_ACC" style="text-decoration:None;">Overall ACC</a></td>
 <td style="border:1px solid black;padding:4px;">0.58333</td>
 </tr>
@@ -779,6 +787,6 @@ <h2>Class Statistics : </h2>
 <td style="border:1px solid black;padding:4px;border-collapse: collapse;text-align:left;">Similarity index</td>
 </tr>
 </table>
-<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.8</p>
+<p style="text-align:center;border-top:1px solid black;">Generated By <a href="http://www.pycm.io" style="text-decoration:none;color:red;">PyCM</a> Version 3.9</p>
 </body>
 </html>
diff --git a/Otherfiles/test.obj b/Otherfiles/test.obj
index 34f91b7c..e9160239 100644
--- a/Otherfiles/test.obj
+++ b/Otherfiles/test.obj
@@ -1 +1 @@
-{"Prob-Vector": null, "Transpose": true, "Imbalanced": false, "Predict-Vector": null, "Matrix": [["L1", [["L1", 3], ["L3", 2], ["L2", 0]]], ["L2", [["L1", 0], ["L3", 1], ["L2", 1]]], ["L3", [["L1", 0], ["L3", 3], ["L2", 2]]]], "Actual-Vector": null, "Sample-Weight": null, "Digit": 5}
\ No newline at end of file
+{"Predict-Vector": null, "Sample-Weight": null, "Digit": 5, "Actual-Vector": null, "Imbalanced": false, "Prob-Vector": null, "Matrix": [["L1", [["L1", 3], ["L2", 0], ["L3", 2]]], ["L2", [["L1", 0], ["L2", 1], ["L3", 1]]], ["L3", [["L1", 0], ["L2", 2], ["L3", 3]]]], "Transpose": true}
\ No newline at end of file
diff --git a/Otherfiles/test.pycm b/Otherfiles/test.pycm
index 4e805d68..c87eb12b 100644
--- a/Otherfiles/test.pycm
+++ b/Otherfiles/test.pycm
@@ -58,6 +58,8 @@ Lambda A                                                          0.42857
 Lambda B                                                          0.16667
 Mutual Information                                                0.52421
 NIR                                                               0.41667
+NPV Macro                                                         0.77778
+NPV Micro                                                         0.79167
 Overall ACC                                                       0.58333
 Overall CEN                                                       0.46381
 Overall J                                                         (1.225,0.40833)
diff --git a/Otherfiles/version_check.py b/Otherfiles/version_check.py
index 151f9ac7..2e53e140 100644
--- a/Otherfiles/version_check.py
+++ b/Otherfiles/version_check.py
@@ -4,7 +4,7 @@
 import sys
 import codecs
 Failed = 0
-PYCM_VERSION = "3.8"
+PYCM_VERSION = "3.9"
 
 
 SETUP_ITEMS = [
diff --git a/README.md b/README.md
index 684a238d..981e8f84 100644
--- a/README.md
+++ b/README.md
@@ -94,14 +94,14 @@ PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scie
 ⚠️  Plotting capability requires **Matplotlib (>= 3.0.0)** or **Seaborn (>= 0.9.1)**
 
 ### Source code
-- Download [Version 3.8](https://github.com/sepandhaghighi/pycm/archive/v3.8.zip) or [Latest Source ](https://github.com/sepandhaghighi/pycm/archive/dev.zip)
+- Download [Version 3.9](https://github.com/sepandhaghighi/pycm/archive/v3.9.zip) or [Latest Source ](https://github.com/sepandhaghighi/pycm/archive/dev.zip)
 - Run `pip install -r requirements.txt` or `pip3 install -r requirements.txt` (Need root access)
 - Run `python3 setup.py install` or `python setup.py install` (Need root access)
 
 ### PyPI
 
 - Check [Python Packaging User Guide](https://packaging.python.org/installing/)
-- Run `pip install pycm==3.8` or `pip3 install pycm==3.8` (Need root access)
+- Run `pip install pycm==3.9` or `pip3 install pycm==3.9` (Need root access)
 
 ### Conda
 
diff --git a/pycm/pycm_param.py b/pycm/pycm_param.py
index 386f6a1e..b714dc45 100644
--- a/pycm/pycm_param.py
+++ b/pycm/pycm_param.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 """Parameters and constants."""
-PYCM_VERSION = "3.8"
+PYCM_VERSION = "3.9"
 
 
 OVERVIEW = '''
diff --git a/setup.py b/setup.py
index f69c4ae9..dd347069 100644
--- a/setup.py
+++ b/setup.py
@@ -36,14 +36,14 @@ def read_description():
 setup(
     name='pycm',
     packages=['pycm'],
-    version='3.8',
+    version='3.9',
     description='Multi-class confusion matrix library in Python',
     long_description=read_description(),
     long_description_content_type='text/markdown',
     author='PyCM Development Team',
     author_email='info@pycm.io',
     url='https://github.com/sepandhaghighi/pycm',
-    download_url='https://github.com/sepandhaghighi/pycm/tarball/v3.8',
+    download_url='https://github.com/sepandhaghighi/pycm/tarball/v3.9',
     keywords="confusion-matrix python3 python machine_learning ML",
     project_urls={
         'Webpage': 'https://www.pycm.io',