1 files changed, 60 insertions, 28 deletions

diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
index 329a46f6..543b0ab7 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,7 +70,7 @@
     {
      "data": {
       "text/plain": [
-       "304"
+       "231"
       ]
      },
      "execution_count": 3,
@@ -90,8 +90,7 @@
     "    orientation='horizontal'\n",
     ")\n",
     "\n",
-    "\n",
-    "humanFiles = reader.readmultiplefiles('../input/human_output_100/', 1300, False)\n",
+    "humanFiles = reader.readmultiplefiles('../input/human_models_75/', 1300, False)\n",
     "modelToFileName = {}\n",
     "for name in humanFiles:\n",
     "    modelToFileName[GraphStat(name)] = name\n",
@@ -138,20 +137,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Find representative for out degree"
+    "## Find representative for out degree"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### For Yakindumm\n",
     "#### For all human models\n",
     "* the rep found is ../input/humanOutput\\R_20158_run_1.csv\n",
     "* the average distance between it and others is 0.05515988287586802\n",
     "\n",
     "#### For human models with $100 \\pm 10$ nodes\n",
     "* the rep found is ../input/human_output_100\\R_2015225_run_1.csv\n",
-    "* the average distance between it and others is $0.046150929558524685$"
+    "* the average distance between it and others is $0.046150929558524685$\n",
+    "\n",
+    "#### for human model with $100 \\pm 10$ nodes and new metric\n",
+    "* the rep found is ../input/human_output_100\\R_2015248_run_1.csv\n",
+    "* average distance:  0.052753778714861366\n",
+    "* median:  0.0468131868131868\n",
+    "* std:  0.0246917800149673\n",
+    "* max: 0.15993907083015996\n",
+    "* min: 0.0"
    ]
   },
   {
@@ -163,8 +171,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "../input/human_output_100\\R_2015225_run_1.csv\n",
-      "../input/human_output_100\\R_2015225_run_1.csv\n"
+      "../input/human_models_75\\146.csv\n",
+      "../input/human_models_75\\146.csv\n"
      ]
     }
    ],
@@ -177,16 +185,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "average distance:  0.04615092955852465\n",
-      "std:  0.017305709419913242\n",
-      "max: 0.1411706837186424\n",
+      "average distance:  0.14846721554626746\n",
+      "median:  0.11344781014856264\n",
+      "std:  0.0839789139821567\n",
+      "max: 0.40999194198227235\n",
       "min: 0.0\n"
      ]
     }
@@ -196,6 +205,7 @@
     "for model in models:\n",
     "    distances.append(ks_value(od_rep_model.out_d, model.out_d))\n",
     "print('average distance: ', np.mean(distances))\n",
+    "print('median: ', np.median(distances))\n",
     "print('std: ', np.std(distances))\n",
     "print('max:', max(distances))\n",
     "print('min:', min(distances))"
@@ -205,20 +215,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Find Representative for node activities"
+    "## Find Representative for node activities"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### For Yakindumm\n",
     "#### For all human models\n",
     "* the rep found is ../input/humanOutput\\R_2016176_run_1.csv\n",
     "* the average distance between it and others is 0.05275267434589047\n",
     "\n",
     "#### For human models with $100 \\pm 10$ nodes\n",
     "* the rep found is ../input/human_output_100\\R_2017419_run_1.csv\n",
-    "* the average distance between it and others is $0.04679429311806747$"
+    "* the average distance between it and others is $0.04679429311806747$\n",
+    "\n",
+    "#### for human model with $100 \\pm 10$ nodes and new metric\n",
+    "* the rep found is ../input/human_output_100\\R_2017131_run_1.csv\n",
+    "* average distance:  0.024629205820449567\n",
+    "* median:  0.023787888564682946\n",
+    "* std:  0.013845547883198073\n",
+    "* max: 0.09044674910251294\n",
+    "* min: 0.0"
    ]
   },
   {
@@ -230,8 +249,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "../input/human_output_100\\R_2017419_run_1.csv\n",
-      "../input/human_output_100\\R_2017419_run_1.csv\n"
+      "../input/human_models_75\\97.csv\n",
+      "../input/human_models_75\\97.csv\n"
      ]
     }
    ],
@@ -244,16 +263,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "average distance:  0.046794293118067494\n",
-      "std:  0.02880119213919405\n",
-      "max: 0.18702970297029703\n",
+      "average distance:  0.16296252607812808\n",
+      "median:  0.11606886657101867\n",
+      "std:  0.1196590575339798\n",
+      "max: 0.49411764705882355\n",
       "min: 0.0\n"
      ]
     }
@@ -263,6 +283,7 @@
     "for model in models:\n",
     "    distances.append(ks_value(na_rep_model.na, model.na))\n",
     "print('average distance: ', np.mean(distances))\n",
+    "print('median: ', np.median(distances))\n",
     "print('std: ', np.std(distances))\n",
     "print('max:', max(distances))\n",
     "print('min:', min(distances))"
@@ -272,20 +293,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Find Representative for MPC"
+    "## Find Representative for MPC"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### For Yakindumm\n",
+    "\n",
     "#### For all human models\n",
     "* the rep found is ../input/humanOutput\\R_2015246_run_1.csv\n",
     "* the average distance between it and others is 0.08556632702185384\n",
     "\n",
     "#### For human models with $100 \\pm 10$ nodes\n",
     "* the rep found is ../input/human_output_100\\R_2016324_run_1.csv\n",
-    "* the average distance between it and others is $0.07028909225833631$"
+    "* the average distance between it and others is $0.07028909225833631$\n",
+    "\n",
+    "#### for human model with $100 \\pm 10$ nodes and new metric\n",
+    "* average distance:  0.054782550772603904\n",
+    "* median:  0.048330503678551184\n",
+    "* std:  0.028208257424907526\n",
+    "* max: 0.21181525241675614\n",
+    "* min: 0.0"
    ]
   },
   {
@@ -297,8 +327,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "../input/human_output_100\\R_2016324_run_1.csv\n",
-      "../input/human_output_100\\R_2016324_run_1.csv\n"
+      "../input/human_models_75\\151.csv\n",
+      "../input/human_models_75\\151.csv\n"
      ]
     }
    ],
@@ -311,16 +341,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "average distance:  0.07028909225833632\n",
-      "std:  0.03728189051222417\n",
-      "max: 0.21961550993809065\n",
+      "average distance:  0.18751521685526348\n",
+      "median:  0.13400833829660508\n",
+      "std:  0.13829078925748192\n",
+      "max: 0.5745366639806608\n",
       "min: 0.0\n"
      ]
     }
@@ -330,6 +361,7 @@
     "for model in models:\n",
     "    distances.append(ks_value(mpc_rep_model.mpc, model.mpc))\n",
     "print('average distance: ', np.mean(distances))\n",
+    "print('median: ', np.median(distances))\n",
     "print('std: ', np.std(distances))\n",
     "print('max:', max(distances))\n",
     "print('min:', min(distances))"