diff --git a/exercises/Jupyter notebook CVML.ipynb b/exercises/Jupyter notebook CVML.ipynb
index b82fe71..ddbd448 100644
--- a/exercises/Jupyter notebook CVML.ipynb	
+++ b/exercises/Jupyter notebook CVML.ipynb	
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "id": "155b55bd",
    "metadata": {},
    "outputs": [],
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "id": "feeaffc8",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "id": "c1db0180",
    "metadata": {},
    "outputs": [],
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "id": "46267e93",
    "metadata": {},
    "outputs": [
@@ -90,7 +90,7 @@
        "       [-0.19186631,  0.08916672]])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "id": "34c5864c",
    "metadata": {},
    "outputs": [
@@ -113,7 +113,7 @@
        "(1000, 2)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "id": "fa762402",
    "metadata": {},
    "outputs": [
@@ -135,7 +135,7 @@
        "Text(0.5, 1.0, 'Blue circles and Red crosses')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "id": "9efb32c3",
    "metadata": {},
    "outputs": [],
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "43c0a1d3",
    "metadata": {},
    "outputs": [],
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "id": "585aa0e2",
    "metadata": {},
    "outputs": [],
@@ -207,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "id": "84e68325",
    "metadata": {},
    "outputs": [],
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "id": "fef8e12a",
    "metadata": {},
    "outputs": [],
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "id": "0e1bcf7b",
    "metadata": {},
    "outputs": [
@@ -243,54 +243,54 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "32/32 [==============================] - 0s 579us/step - loss: 0.6674 - accuracy: 0.6450\n",
+      "32/32 [==============================] - 0s 806us/step - loss: 0.6981 - accuracy: 0.5810\n",
       "Epoch 2/20\n",
-      "32/32 [==============================] - 0s 579us/step - loss: 0.5857 - accuracy: 0.7630\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.6504 - accuracy: 0.7100\n",
       "Epoch 3/20\n",
-      "32/32 [==============================] - 0s 516us/step - loss: 0.4744 - accuracy: 0.8540\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.5779 - accuracy: 0.7900\n",
       "Epoch 4/20\n",
-      "32/32 [==============================] - 0s 579us/step - loss: 0.3558 - accuracy: 0.9190\n",
+      "32/32 [==============================] - 0s 806us/step - loss: 0.4477 - accuracy: 0.9480\n",
       "Epoch 5/20\n",
-      "32/32 [==============================] - 0s 579us/step - loss: 0.2594 - accuracy: 0.9960\n",
+      "32/32 [==============================] - 0s 967us/step - loss: 0.3201 - accuracy: 0.9900\n",
       "Epoch 6/20\n",
-      "32/32 [==============================] - 0s 579us/step - loss: 0.1930 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.2324 - accuracy: 1.0000\n",
       "Epoch 7/20\n",
-      "32/32 [==============================] - 0s 579us/step - loss: 0.1507 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.1767 - accuracy: 1.0000\n",
       "Epoch 8/20\n",
-      "32/32 [==============================] - 0s 579us/step - loss: 0.1224 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.1409 - accuracy: 1.0000\n",
       "Epoch 9/20\n",
-      "32/32 [==============================] - 0s 578us/step - loss: 0.1030 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.1166 - accuracy: 1.0000\n",
       "Epoch 10/20\n",
-      "32/32 [==============================] - 0s 580us/step - loss: 0.0887 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0986 - accuracy: 1.0000\n",
       "Epoch 11/20\n",
-      "32/32 [==============================] - 0s 547us/step - loss: 0.0778 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 1000us/step - loss: 0.0856 - accuracy: 1.0000\n",
       "Epoch 12/20\n",
-      "32/32 [==============================] - 0s 579us/step - loss: 0.0691 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0754 - accuracy: 1.0000\n",
       "Epoch 13/20\n",
-      "32/32 [==============================] - 0s 483us/step - loss: 0.0622 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0675 - accuracy: 1.0000\n",
       "Epoch 14/20\n",
-      "32/32 [==============================] - 0s 579us/step - loss: 0.0566 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 999us/step - loss: 0.0610 - accuracy: 1.0000\n",
       "Epoch 15/20\n",
-      "32/32 [==============================] - 0s 579us/step - loss: 0.0519 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 903us/step - loss: 0.0556 - accuracy: 1.0000\n",
       "Epoch 16/20\n",
-      "32/32 [==============================] - 0s 515us/step - loss: 0.0480 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 905us/step - loss: 0.0513 - accuracy: 1.0000\n",
       "Epoch 17/20\n",
-      "32/32 [==============================] - 0s 547us/step - loss: 0.0446 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 935us/step - loss: 0.0474 - accuracy: 1.0000\n",
       "Epoch 18/20\n",
-      "32/32 [==============================] - 0s 515us/step - loss: 0.0416 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0442 - accuracy: 1.0000\n",
       "Epoch 19/20\n",
-      "32/32 [==============================] - 0s 483us/step - loss: 0.0390 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0416 - accuracy: 1.0000\n",
       "Epoch 20/20\n",
-      "32/32 [==============================] - 0s 515us/step - loss: 0.0368 - accuracy: 1.0000\n"
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0389 - accuracy: 1.0000\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x22d8c7c8308>"
+       "<tensorflow.python.keras.callbacks.History at 0x1abba3caa08>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -307,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "id": "20f9bb50",
    "metadata": {},
    "outputs": [],
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "id": "a2e4f082",
    "metadata": {},
    "outputs": [
@@ -345,7 +345,7 @@
        "       [ 1.5 ,  1.5 ]])"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -356,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "id": "1e5b54db",
    "metadata": {},
    "outputs": [
@@ -366,13 +366,13 @@
        "Text(0.5, 1.0, 'Blue circles and Red crosses')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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GXaUcHCRJhkKUkFau5DYDA5QWR0b4GuB7+RwgkaalUQLMzqZEODpKSbSmhueJxTRQXQ389rckzJERkubgoLYTJivm0T6m0VE+QHp7ga1b+TqaWj2fYjKVqkQw2IYzZ87M9FAiQqlsKFWZtOMZwjTwDbtKWVBA0rDbLuXzqiqq6Pn5dMiUlVEtrq7m++5uSqWBgN4/ECCp5ucDR4/ys8FB4PRpff78fGDHDr4OBvX2g4Mk7JERSr2jo/w/MkJpFaAUmqhnWswVO3YA+/ZRgr3iCo7dj/MmmrljLkGpTCg197zcicIQ5jxGskNY7CplVRXw2mskyrIykhbA4wcCdOiUl5MoV6wArrsOOH6cY1m0iPt0dZHcsrK097y1ldssXQqsXs33hw4xPEhspaOjtIHm5lKVP3eO6vwll1DlB0jMHR18nUzPdE0NzQxvf3u4ag34c95c6DGZcx1JIUyl1PcB3AKgy7KsKdU8FY0c3wBwM4BhAPdYlvVyMs5t4I5UhLDYVcriYjp8jh0jMZaW8vNQiK8/85mpjiIh7quvBvbuJWm+9Rb3mZggyR47xuMKGXV10f7Z1cX3+fm0hY6N8byjoyTrtDRtMwW0bTAVnumjRyndirOqqorzMVedNwb+kSwJ84cAvg3gRx7f3wTg4sm/ywH82+R/gxQhFUThVClXrAA++tHIx3Mj7r17gW3bKHEOD2viWbGC0uKyZdw3GCQJTkzwdUEBpdOxMX18UetF1Z+YCLcNPvRQcj3TjY3cVykeZ3SUkvaqVRy/c9sLLUh9viMphGlZ1tNKqZURNrkVwI8s+vhfVEoVKqWWWJZ1OsI+BgkgFSEs8RCAF3EfP+4e6nP//SS7UIhElJ5Oj7c4kfr6uF1mJsn2zBmSZFER1fKOjnDboJdnOhCIz665Z4+WrEMhknd3N22teXna+WOC1Ocnpqv4xlIArbb3bZOfGaQIFRXarihIJIRl927gC1/g/7Y2hvH4KZ7R3q5DjgSRiHv7dpJLQwNJLSuLxCipjkNDJM/xcdo3QyE6X8bHqY7fdReJWEhJjtffrzOC2tqAzs74CoG0t1MC3riR5zx1iuaA4mLt/ElW0Q+D2Yfpcvq4BWq5RpQqpe4FcC8ALFmyPJVjmhfwkvqSGcKyezfw1a9S0svLIwEMDFDS8lLx7QHuJ04wKB2git3TQ4JxC8URtf+LX2QQ+tgYSW18nOpvdjZtn2fOkEBzcihdVlWRTL/2NTqMKirolT9+XHvb8/OBNWu4fyDgz1zhnN9AgMcrKaHTSdTwrCxdCORrXyMhl5UxqF7sqpEeFEZ9nxuYLgmzDcAy2/tKAK5Lx7KsByzLqrcsq76oqHRaBjdXEalkWrLKijU2Ag88oMlyYoKENzFBUnAjAPu4NmwgwTz3HENxJMunrMxbqqupoVf9sss47vJykqB4zrdv5+fvfS9w880kyyNHgGefpaqcmUkJ+Otf5//Vq3nMggLuGwr5k3rd5rezkxJqfz8dUR0dPMfwMM/91ltU0SVy4LXXaH8FvCV80/dn7mC6JMzfAPiUUuph0NnTZ+yXiSOaYycZISx79lDKE7JMT+fng4N8v2VL9HHV19PRMzxMIqmqotTV3+8toYqEHAjouMrRUUqqg4Mk0cFBbevs7WVsZ3o6w5AyMrR3fcWK8Lnxm3HjNr/LlvGcoRBw9izJeelSnvfgQZJyaakOu1KKEnYg4C3hmxzzuYNkhRX9DMA1AEqUUm0A/gZAJgBYlvXvAB4DQ4qOgWFFH0nGeS90TEducns7Vd60NEqWAF8PDfHGlmrnkcZVUkI7I6DjJN3G6lRLt22jCv7ii9y/rk4Tz513koSbm6mu9/dThV+wQEu/K1bobCOAhPvkkzy25MEvW+ZtroiUz15RAVx1lU4NDQT4YOnv50OkpIR2zhMnSNpbtngHqc+3HPP5jGR5yT8Q5XsLwJ8n41wGGtORm1xRQXV8YIB2x8FBkmVmJnDvvdzG6W12G1dW1tRj28fqFX50993AHXdoIi0tBTZt0rbJlhYSVXo6SUspErt4r0MhkmR6OiXCRYuooufkkOyGh2nXdCOzSPMrzp+8PJoDmps5T/ZaDyUlHNOWLZGLf/j5HY2Nc3bAtKiYw3B6gFtaWES3oYEklgwb2PbtJJtVq4CFC+noWLKEHvOVK91tb9XVUz3TRUUkXPtn/f3h/Xi8vMo1NSScr3yF2+/dy31Xr6YEmp7O86elkTABEldHB8m9q4uSXm+v3m7FCkq7a9aEe9Ujza99zPYohLExvq+o4FgOHKBTynmNfn9H537Gxjl7YFIj5zDsgeRHj1L6kHJrzz7LghRbt1JCS0awemYmpSWRbu6/3932tm8fiUxKvtXV6bqXXnnUftVSp70vN1dn+5SXa6kSoD1RinxYFslMVHGv43tdu9uYH3yQkqXkw6enk4Q7OnjtJSU8h4QSef0G0c7jx8ZpJNDpgSHMOQrnDVJaSueDOEGysmj3a2hIPGDay3nU0EBpx54iaFkkzLe/HbjmGm0fjHQcwL95wc0+Kur2xASwfDkdS/v2cbvsycpep06RWDs79b6trZQ+v/Qlb5JxjrmxUZsgRkaAkyd53vx8YP16LYm/+CL3y8/3F7QeaW6iPUxMkPz0wRDmHITzBmlpoURZVKRrUWZlkbyknUOyPa6NjSQhZ4rgxASJOlaPr9+4USexVlVRqqyoAK68Uu/nDBsqKtLjnZig3fG110hulkX7YzSSsc97ZiZtoEqRoHNz+TsUFnK7oqLoEuGOHfToA5TC77iDr52fS+znggU6XbS7m/ZYZ5B8LHNuEDsMYc5B2G+QYFDHHg4P88aXsmvp6ZT88vO1XTNZKpszRTAQ4P+uLuD668O39ePx9Vv6zEmszspIst+OHVSLldJzkZ9PgmxsBN54gzbVkhKOW4p+RCIZ+7zv38/jpafTyZSby/M0NFDavdxRKcEpEX7zm5Rw8/L42YEDtLNaFm2t9s+Li/k+P59xnmlp9MxLLOvgIO25sc65QewwhDkHYVfRmpooTZaVMaA6J4dOiGCQ0k5tLW/MU6dIJslS2exe4qYmXZuyqGiqR9yv595P3KgbsdorIwnuuIME1NpK1Tk7m+P9q7/ivi0tJCKl9HibmnQJOreHin3e5XoDAT6ksrJ0FXqpkSnS4MAA30u20549JMX8fH1upajeK0Xyl89HRngdExMk9owMXkddnY5lPX06sgQabU6N/dM/DGHOQdjVUrlxAV3lp7WVkoqoc1JNJxkqm1vKo8RWtrSw2tDTT5Osa2o0kURrohbLjeo3ID8vT5eAy8rSUlt7O8kyFNLENDbGeauq8n6o2OddCiifO8fjDAzwWLW1lC6/9S2SVk6OrlC/bx/w6U+T4EZGdMokoKvFi0QMUGPo6uJ/IWcgvEZAfr62k/b2ukugfs0Mxv4ZHSasaA7CHoYi9r7RUWDdOgZ7b9xIiWrfPt4QBQWaTAXxqGxuKY8SQiOdIVeu1Oroiy+STNxuvlSHyuzZw2u+6irO11VX8b1k+giRjo7y4dLVRZKprfUullFdTVV8926GK50+zb/cXJpEBgf5EPntb3WxkPPnKfWNjfG9SJt9fbryEsB5ys7mn3j5e3u5f0YG983N5euREUqRAM+5Zg3nuKuLEQEFBSymLBlOkQp+mCIhscFImHMQdrVU+uWsWkWpqaWFoS6bNukslv37KT3Z6zWKmhyLlOeW8tjQABw+zHHU1elzlJZqaczteKl2VETyLN91F8l51Sp6zXt6SEySoePcHuA87d07dZ/iYpJYdjaJq6GB36Wn02OvFH8Ty9IPt7o6Sp/BIPc7d47v09NJtFIp/uxZTbTSbXNggMfs6tIPTbH1Ll3Krp1pae7XEOs8GUyFIcw5Crtaaie95mbeXMeP86aqquJN/vrrJE1RT4uKgHe/OzZ1zC2k58or9Q0ey42X6ht1eBh45BFKY3l5DPkpK9NdJ52xpcGgVnkFdturneDlobB7N+dR8umDQdqKJfd+fFyXnhsf1yFITU3A2rVUnwcGKEmWlVEq7OgAXn6Z5JmergPxu7v5u+Xm8prOnp0qvceT+TWfOllOBwxhzgMIeTY2Ap/7HG9S6cT40ktaIpHeN1lZ/GzfvtikvGg3Vyw3Xipv1N27aR44d07Pw/PP85q+8AVu4xZfGSmsyY3gi4p0fn0wqIttiL1UYj4ti2NRillSo6N8oIlEa5+H5mZqBhLX+tJLOoZ0dFSf99JLua/9GuIp6TefOllOB4wNc5ZCAqS/9CX/aY579vBmUop/4+OURDo7eQNKIHdtLVBZSVKJp7ivMxXz6FEShpQ9c0vvi3Ysv2mEfvDww3xoLFtGCVIpXvfISPRsG69yeG4FmcvLefz+ftoulSJR5ubywVRWpqvHBwK6hqYdzgLL0sN9YIDbS7iQZfH4ubla0nT+TvGU9EtWGcALBUbCnIUQaWd8nGR3+DDJ8N57gRtu0NtISqRk2kioz8mT3Ka3l2rd2BhvPnv4zObNfC3ZLtI3XIjFLfvFrso2NEyt+GNZJAdnmwg3pLLlbGcnbahpaZqgJibonIqESN53N0ksPZ2/iZg/ysponwQ4x6OjfICtXcv5b27W81xTw7lyStrOHu69vYwdFZsooHuiX3VVbNcQz3UbhMMQ5ixDYyMrdkvqXXExb7rBQRbyXbmS2wmhSouEvj4S1vHjJLHeXnpys7J4w2VM/tLS/3twkDfgoUOUaPLySChnzlDd87Jpys11//282Z21IhcsiFyZx45U3ajl5eGkA3AuysvjP6YfgrcTn1PV7u8PL23X309SdxKxWw93CYeShATLouaQDGncIDYYwpwG+PVEi2QpFc2lVFkgwJupp0eHe4yPA6+8ou2SeXnc7tw53nBXXqmLUqxaRfUZ4M0mNSWlxqRImNIid2REh5gA7jbN2exdvfNOVlsHOC9DQ/z7+McTO26sEqjdFuj1nVsnTrce7vYEgUCARVWMVDj9MISZYsQSGCye2OJi3Q1RvKuLF+ve1319lCwlg2V8nGRaUEAp5vBhkmZtre4tIymBfX3MRLnjDragXbZMe32ffFLbzwReJFhRQRK2q/NlZVNbzc4ExGzx8MO8/vJykqV8ngpEk0AjfReJiGX9BAI0owjZSt65wfTCEGaKEUu8oUhtRUW6LUNGBiW/zk5+J9XC09JIlmNj3GZ0lOrbwYOUSu66S3vO5UaVIG5nGEooROklGOSfXXX18lxXVzNAW9T5gQGS9HXXzY5UuxtuSC1BusGL+BKZj1Taeg1ihyHMFCMW1VUITOLyursZHJ2ZSemyq4s3W0MDJcW8PEqWo6OMO8zKck+Ji6RG2otAFBaS9Pr6KOFGSms8fjxcnS8oIInu28cAb5NqRyQj9dA4ZWYPDGGmGLHEG4odrKeHpJedzdcLFlBqFO9qbS3tll1dJFQJal6wgMHP0RqMCWpqKE329lLKLCpiW9iODqr1110XuQ+NXZ0HaEJ46ilmAJlSY4SXhrFjB3+n2VTwYjZoBrMdhjBTDDdnQGsricotdCc7mxJeT4/+zulxlWPW1NCu9cQTlCyFLOVcfhwwoRAdRPZ0OiHNePrQyLntmC3OIEG08CInShPo9uymYYyOMs9+27bZI4WbIhz+YAgzxXDaoKRRVyCge9w8+CBvHlFlr72WtsizZ5nSODpKQpSmY85jiidVyDIYpAe9txe47TZdnDae7B0veHmF6+p0qbFYjpcquJFjdXVsxzh+fOpnfkk0EGCWUSiks3f8FBiebpgixP5gCHMaYLdB3X8/byLnwnz4YW4j71etYhbN8DBv8PJyEurKlfp49lxyqfzT2Uk74vAwb9CxMVYU6ux0rxsZb2qclzMCmNlUOydBxkqObnAe4/jx8PN4kWdjI80mg4O0EY+M8LcYHgauvjp821RK4X5U7dkcJjabYAhzmuG1MDs7dfYNQOmwslJX0QG87ZJCXjt2kCzPn9dtFHp7Kcn29kbeNx4vrJczYrq9unbySgZBRoP9HHbydBLnnj38DcvKwossSz6/HcmQwu3EKIVEzpzRzfGWLfNWtU0RDn8whDnN8FqY5eVTq2aPjTG1LxjUHQidT3z7TXLqFOMtxWNuP35aWvi+qTTwT4dXNxWSZDxwkiegiVMejmlp2lwyMcG5l8ZwyZLCnf2GpGNndjZNQMeOUcqVccjD014Q2pnqaopwTIUpvjHN8Co6ceed/N/SArz6qt4+L49VcILBqU98ZxHe7m6SpWUxmB1gDvLICAlU9p2rfa4ldVPIsrpa/80GyFhkjG4FO+wFf5NZ8MJug2xu1pXYu7p0KwwpOiwPXvs6WL2aZHnkCPCb3wC7djFtdseO2b8uphNGwpxmRFKBV65kHvnYGCXOoSGqb5ZFCWDlyvAnvtNQv2gRSfjsWZ13LMRZVKRzj+eagX+6Ve5EIWO89FLg5z/na5Ek29qooj/0EH97STCIB3YtoaGBVfCB8LYlgK6WJBlc8uB1roO8PJpzzp2jKQGIbP++EGEIcwYgC08Wu+SHO6tmi2ouzbWcUkh7O9Wv/ft5M6Sn68reIr2MjTEV8hOf0PvOBQN/KlXuc+eSc5ycnMjfS/fMZ56h1Ldggc7lt0dIxCNdOsOATpxgZEV9ve43BOhWHKGQ7v0jqvZDD4Wvg6YmEqaUwwP42m7/vtBjNQ1hzgAixbzZbZwlJVNjMO2tcoeHaZsS9SsUIkEWFgIXXwxcc437gp7NBv5USJNuBOl8YMSKjo7w43qR5/XX8+/4ceCHP9QVnYDEJHundFhbS2mwoYHHOniQn2/erNeJbC8ajXMdSAEWe81OkUztKvyFHKtpCHMGEEkl9grz2bRp6mJtbNQqtyAri83Q/vqvvc8/G6tsJ5sonSSZKEE6YT+enTy9iLO6WtfptMNLso8myTm1C0lNbW2llFhfT9PM4cPcfuPG8FjcxkZqMC+8QHNNTY2OEZZycsPD/F0k6mLHjrllykkFjNNnBuCssg3oG8erAvbx41O7+2Vk8H9WFkkvK4sShXQd9MJsqbJtd+Ikw4Fz7pz+A0hq8pdK2M9hP78Ta9aQ5KTcHOAu2ftxygUClCJHR7l2pO1FXR3wla+wc2dLC8kuP5/nk2PYKyBdcQWPt28fz1VdzYdwb69ujSGVqF54Qav6gtlmykk1jIQ5zWhs5EI8fJh2rKoqqt32G8ctLMdpbwJ0T5lrrtGfSWHaaBLKTBZ0SKY0mYgkmZZmxX3eiQk15TM5t5fEeeONLAK9cCGJTGzMTsk+UadcYyPPoxTXSChElXzVKm0vtx/f3uFz+3ZKkk8+yf2XLqXGUlJC6bWxMVxKni2mnOmCIcxphDzZy8qYLz4wwBCiiy+mw8Z54zhjLM+fDy92IZXF7f3JvdT32WBrShVR+iVJJ0EW5Hts6AMDg/pYTvJ0EqeQ5po1TG/dtYu/Z0EBM37iyboJhahNuLW92LNHtwCWpmwATQKZmeFjdB6/pobmnFBIx5AKamooiTrX24UUq2kIcxphlxykgnZ3N2PlPv/58BvHaWA/f55xcYAOLLb3lLGHKM22sKFkEWWsJJlMgnRCjjUwqM/jRpxupLlmjd5GMoXsUpsfp5xs49b2or2dXu7WVqrXgQAdgUND4VljzuMHAtqp6PaAzsqiCr9gwYVbm9MQ5jRAJMVHH6V0edFF2gM+McGbyqv6uixqWbhdXZQS7IvVLolK1oZ0GxTMhK1pJogyKSTZ1xf+fuFCz03txxep006cbqRpR3X1VNL045SLtM2OHbyEUIhrZWyM8Z/FxToW17lvW5sOecrMpMPojTe4zi65RNdGnWktZaZhCDOJcLMbAsC3vkVb49AQVahgUPek9rIBuall0jb2K18JP6dT/T51ijenXTqYTltTMogyVtuknSh9kaSTFO1QNknRsty3dSHRgnwtcSZCmpEKm9jDyrZtm6pdCJlJK97BQWZ6pafzIerVMiMQ4BhbWqjiFxezh3pfH0vRbd1qyBIwhJk0eMWojYywJ09+Pm2Op0+TPF9/nZkgXjYgv7GSbur3qlU08hcVTa+tyZ6yGC/ilSajkqQb6Sk19TM/2zhJ1EaeWlUPlzb9kqbA6ZRzW19797qTmN2+mZ7OrJ2VK6liy7HsZFldTe1n4ULuK833ystJnFIQ+kInS8AQZtLgZTd8/nk+qbOydD73mTOUAq+5xtsG5DdW0ksSHR6ePlvTrCVKJ0n6IUg/8JJAHcTplDb9kqZbubhY7NKR7JtuxPvAA1ybSmk1Xlr8Ll8enzlnvmYEJYUwlVI3AvgGgHQA/2FZ1tcc318D4NcAJtP/8UvLsr6cjHPPFnh5NuWpLsjN5ZP7/HlWNG9sDFeznNXXpepMXZ27NGGXRCWVsrubeeWpXqTTSZRxkWSyCDIS5BxCnD5J89VXgT/+kQ/OpUsZciTOIKcTCIgtnTXSw9aNeGWNnjpFs5G9wV5RkS6AfP/9kVtE20vLdXVRsp1NURrJQMKB60qpdADfAXATgLUAPqCUWuuy6TOWZV0y+TevyBLwrkxTWclFODrKe2p0lO/r6rwDlHfv1oHF11xDlWhkxP28Uv1IqhwNDIQ3QktFpRlnsHk8kADvaMHlaWnWf5FlQb4HWfb16T+AJOaHLE+diu0vEuR8DqnWbbzBIPD973NTsRM+8ABw9KieT2cuvdf6crNLR0pMcEuayM7m+YqL+d34ONdoQQFNOz09JNHdu4EvfIH/7XCu44YGmqFCIZ1ksWCBjgGdy0hGps8WAMcsyzphWVYIwMMAbk3CcecEREI8epRpai0t4WXbPvpRqsiArhazbBnT1OxPe/vCevhh98/dFpzcHF1dXOgFBfRqrliR/EWaCqL0QkxECWiSjESUbgQoifjR/pz7uyECadol5N/+lteem8vfd+FC/u3axe/d5tarLKA4Fp2oqeF3FRU6eqKx0Z14z5/nQzYnh7bO5cs5tqEh2kDT0/l9UREv8YEHwh/EznUcCumwOcF8yQhKhkq+FECr7X0bgMtdttuqlHoNQDuAv7Qs60gSzj2jsNuDamq4yI4dIyHU1oaXbXMa2d3CjAD36uvyudeCc1Y58rNPLEhU9U6q2h2ryu0kN6d45Rf2/QYH9XGXLg3fTilX9RzQqnlbmy6eIm2UCwqmDtWumsdaGd/LCSm9o+SSxIsuuecDA4zZvOQSBqmLI0iC3/PzKXHabadOc0FBAY8pAoJM2XzICEoGYbqtWmfO2csAVliWNaiUuhnArwBc7Howpe4FcC8ALFmyPAnDSx3sT9ZgULe9HRgIt/W49d9ZsIBkOTjIAsEbN+owI3v1dUG0BedstlVURHUsmu0pEuYsUdqZJ16CjAQ5phCnD9IUeyZAM83ZsyQmkfYGBsIP4/SaA7Gls3o5iY4fn0q8W7dy/VxsuyP7+7kOe3q4lgShEFV3+4PYaUcfHqZKnpMTvb/9XEMyVPI2AMts7ytBKfK/YFlWv2VZg5OvHwOQqZQqcTuYZVkPWJZVb1lWfVFRAv1NpwFiDwoGSXqjozq/28t+aF/IF13E+0op1jN0Vl/3q37Zm21JH5/nn6fzZ8OG2CuqO6uaxwq3AhhuiFvtdoNdXXaq0qmCnMNNRXdRz0U1f9e7+PHZs9ob3ddHx48TXi2BGxuBL3+ZXUFvu42v7b9vtAIv993HeN777qN5yKsLQEYG15XY30dHSaT2h7fTjj4+zvtg4UJKqaHQ/HD4AMkhzP0ALlZKVSmlAgDuBPAb+wZKqcVKcQUppbZMnrc7CeeeUYg9qKlJhw1JDq+X/dC+kEtKKFlKKwExzq9cqT3kTz0VfcFJs636eu539iz/FxZSpfNrdHdr/xALkkaUdidOJNuklz1yuhGNNB1Yvx741Kf4+4hkdu+94SmTgPf8NzYyGeLAAT4gpYfPN7+pSTMZTqIbbuC4LItCQCDAGN/09PCHt+zf3Mz1EwxSy9m0CXj727nO5wNZAklQyS3LGlNKfQrAbjCs6PuWZR1RSv3Z5Pf/DuA2AJ9USo0BOAfgTsuy4i8VM0sg4Rvd3STLzk7abior+SR2sx86A9JLSrgQt2zRYUaisl9zjQ4JiQRns62BARrdpYQYENmemWhmTtJUb79qd6pV7nggpOlUzwEX1dzC+vUK69fz646OyNXbnWFGe/aQwKRXDzC1MnqsNU9F3ZfwIGmhsX078NWv+oupHBjg2s/K4kP+tdcYDTIfnD2CpNTDtCzrMcuyVluWVW1Z1t9Nfvbvk2QJy7K+bVnWOsuyNlqWdYVlWc8n47wzDXmy5uToXNylS/kEPnhQtzq1I5q308tzHkk6dEoTBQU6LETgJl1Mp0SZ+dKzUM1N4RJlUxPw3HOxq93AzEmT0eCUNH04phYv9q6h6fabtLfrnk2CQCD8IR1PzVOvMDdAq/Dbt3MtfulLtI2LRLtnDyVm+flE4xLP/HyByfRJEDU1rBd47hzv30CAT9fRUXbg+9KXwp/K0byd8fTbcUoTZWW8SaqrScpu0kWiNkqBH4myIB9AdQWwcyeQdzuLgDY1UYx5z3u4cSSSFCSDIN98M7btnVVMIkFEOTc4vObOfPNYUFFBm7fdey2v7eTk5STyysKJlk0UqUWF2EalolYgQAHi7Flv2/tchCHMGOBVXOPQIRJTMMiFkpdHY7nEGzozHSKpP/H023GS8IoVwHXXuRdmSET9josoBVVVwO23kzTXrgVefplkuXJl+M4vvsiDS+FGgPpnZ2d4rp9fuBGk3wKaHR16f7/E6aaai9d8EnaPuR1eKZPA1GpGb7xBT7QcdmiI8b3RyCka6UV6WDsJNRSi3fKLX9RV2Tdu5LNwYID3wtat88d+CRjC9A23hfatb4WXxFJKl/DPyeEiErUaYNmtkpLI6WNucXJ+QjL8hJzEK1UmRJR2FBeTLJ97Dnjb26aSJcBJ/NGP6HGorSUrPPYYcPPN/gdsJ8lE+lPY942VOOM4VUeH+3duhTk+/WmuJ5Ho6uvDe/Z4IZIUGe1hbSdUiQwRSbKsjGOpq2M8sKzbO+6IbR5mOwxh+oTbQuvp4evaWi6erCwuoJYWSpijo8z+qariotq3j17DxYsZ9jM4yIVmJ1W3OLlEC2ckQ/1OiCjFPtnSArzyCsnylVeYUiKkKap3ZSUv+LHHOMG/+x3wrndxW8HJk1MlTqckmYquZyJxRiNNLwdQEtVyqYweK44epfQ3OMgHuqzNJ59kmmZ7Oz3hUqTa/rC2E6pEhgB871avddOmqRpUPOt4NhXyMITpE25d+vr7uWgkPKipiQtmdJTC1KJFfP3aa1TZCwvD1RlJH7Nn+YgtKFkLIlGpMilEqRR1t1//Grj1Vp1/J5JkZWW4fTI/n6LKvn0kxVdf5Z2yfDnJ8oEHgFtuCSfJ06f5f9Mmfxf4yivUcSsr9WdtbZwwr2PEQppO+FTLU4nGRq4vpXTjtH37GDdZXMw1NzZGq8jLL3Nq7rxTr0W7rby/X9vra2v5mb1eq1Mja2lhHvrSpdzeL+nNtta+SfGSXwhw69IngccASe+yyyhALF+uQyukdWlXV/gPLB5sv+ljkrPu9E56Id7gc6fn2w0SRxk12Nzu8e7o0GR56hTvrBtu4AQ4nTknT1K/u/xy7nfJJZQ4n3+e/y+5BPj970mSixfzLhcCFLzyCgnQjrY2fg5w2yee0Nu0tfG9W201O2RS/DiPohXsiBFeQex+sWcPpUfL0mtzaIjrrraWAn1XF6dg6VKu17179Vqze97lp5UMNSB8/do1sp4epgwrpaNC/CZSxBM1kkoYwkwA+flcePYQobNneT9v3Kjb3+bnM/NBVBiAqtDQEBdttGweP21X7YgnVMhPiJAz4HwK3IhScMUVJEl7WJCIJidP6u1OngR++lOKN1deSdvlq6+yQc2OHRSF6uqAP/1TRvX/5CckuuuvD5cWoxFiZSX3eeIJ4KWX3I/hBT/qfpwe/VjCi2JFezulQPvaBKj5lJRoNVuy1xobGenxta+Fk+Z99wF/93d89nmtX3uChv24kvLrl/QiZSzNBAxh+oRUsbb3AN+6lemN9li3K67Qavpll9FbXVvLwHR7/GUgwMVbW0thpbGRwpZUlbEjlqdsvFIl4J8oPTNzomXkAFPjJ8vLKTUKaT7yCI9VV8f3IyM85htv0DB89CglSoCT2drK1Bkn0fkhxMpK7nvggPsxoiHWECUgYmuMVPdPr6jgdIkXW2oOiJlIPNvS0XR0lGTa3T31AR0tztMeGyzHlToHgH/SiyVjaTpgbJg+4VXFes0aPnEFIg0C4V7uu+/mZ3bj9Wc+w88efJDv8/PdbTR+YzNjJUs/qrcgYmZOIjGUy5eTqH76U3rEhodp13jpJRLZxATw9NN0Cy9ezLCkv/97loZavJgOpNdf55MkPV3bH0VFF0Ksr9efyzZtbdy3vp7/Kyr8k2Ykt7YXHHZMIDHHT6yormZpubw8/kmL5sxM4NlnKVUGg0zvLSvjg390lLZ4eUB79bZ3hshVV0+N9rAsrVT4Jb1YM5ZSDSNh+oTfeoSRnrzOogfOYGGn9Ch2y4YGmu+CQX0e54KLhSz92imBKBIlELtE6Ya6OtoyHnqIF1ZUxKKgo6N039bV6eY0b3sbJc2GBjb13rKF33/ve/ReCEpLSa7PPUdCfO45vheVXFT0ykpO5PXXAz/4AcnVbuu0v3ZDNCkzgh0zmW1//eD4cU6VPRNs9Wo+owCS6NgY/yTKY3SU5qNIEqGbyWjvXobILVjA81gW7afFxdGLydgRT8ZSKmEkTJ+IpR5hLF5uL+mxoYH36oIFrDh08CDvZTEL2J+ysZIl4I8opyBarnciWTkTExzcSy8xrOCmm4DDh/n5yAiJTiTCxYspHj32GL9//XXg3e+m+NTTQ/ITDAywF8ToaHiu6JEjulaZqOs33UTPfU4OJVohVfvx7IgmZUbK/JkBiA3T3k30pZf4oL7qKr4PBmka7uggwdXW0rzU368f0M4wn2DQu5ScaF/OfWIJlUtm1EiiMIQZA1Lxw3kFCw8M8Dv5vL6eJHr4MO2izsydRMkyovqdSqLcuxf4+c/pxJmYAH7zG3rIH3qIhq8FC4D3v5/b7tzJwW/apOO1QiEakxsaKIUeOaKD42tryQgDA9Q7a2t10PypU5xUibZ+4gnaUwcGdHbB66/7dwTNAbittd5ePjMEJSWUDPft4/oSM5E8oN3CfF54gbZ7O5wS6WwivURgCDOJiCfA1stG49RkS0roNO7o0E9tv2SZFKkymp0yFqJ0qrF/+qfAM8/w9datJDEpRV5czO9EqgSoind3c7vmZiZW19bSo97To8nv5z+nPvnud7P/w89/zuKRdgfQ4sV8n5EB/OpX/L60VNs9/ZBlPHGZcSBSV0k/cFtrGRmcKjuyskiAbl1H779/qjRZVMS1bx/XfKmw7oQhzCQhUoAtEJ7GVlcXnsbm1h1yz57IaWrJIMtpJ0pAk6UMaPHi8HjJ5mZKlosW8bueHto3c3IoOd5+O7d76y26cO3Vz595ht8XFtL2WVpKNfytt7hNaakW0QHtACovJ1lefTXH9+ab2pnkdAQ5A94XL+ZnfX3x5bpPI9zMSvfeS0vGs89SaM/K4jPq0592f9i7mZBqaiiRyoN+ph0zqYQhzCTBK0d3xw4GA588SaM6QHLs7KTgs3eve+3LSN7BlJJlKtTvaLndZ86Q6F57jc6eiy6iGt7dzYl94w1OwOrVJKrduxkJHQjoVKlgkHf7o4/ywiWdJSuLsTQSELtsGdVygGWm/u3fqLbfdhvHKY3D161j9PbOnRybEOT4OJ1DH/kIP2troxdfnoyzHE7VuLFxiuN+yns73NT6SBLpfIMhzCTBy3nz1FO8r4X0zpyh2a2zk/fxlVe6F0K47z53J5PYm/yQZVKlynjtlEKWp09P1SXtqYhtbczHu+IKTpAEhe7bxyRnAPjFL/QxFy6kvfLyy3WA4B/+wImprKQh7mc/o23zppu4TX4+pc/DhylVjo9TEr3oIkZz9/Ux93XZMrLGunUk6zfe0OR46BCP98QTDFl6/XWGQ9lz3aPBpUGaHZGqFiUbe/bwctet05/1908NIRJ4PcjnSwuKaDCEmSQ4G0E1NVGb7OujZJmWRuJLS6PdaGyMUmd1dfjT2m4sdzOUS5tbL6RUqoyHKGUgY2PhtkO7B1pe26W2nTu19Pg3f8NjfPOb9GJffDELctTWUoSvqaFaXFHBsV5+ORt/L15Mkh4a4nn+8AdmDX3sYyTIRx8F3vc+SpV///dkqdpa7n/DDRzHunX8/9JL4U6giQlt5xRC9wOXWEw74gnvTASx1l+NtXvlfIMhzCShupo1IaRtaUEBJcuiIhLoyIgmy4kJ/k9Lo8Bi79YXyVgeLZc4IbJMNlE6B2LPvBHJTNTcM2fCifTMGZJXYaEOBTpyhBc4PEzRfHyckmhpKaXHJUto+7jsMtoyCwooOV59NR0+3d2UNq+5huL94cM8zy238M5vaaH9VJrZ2Am9sDDcCeQMeM/M9Hb6RGpdMQsQb/3VC4UgnTCEmQQ0NtIWuWoVBR1ptbt5M+816cKQlUWyHB/XxvWurtiM5dFU8RkjSz81KO2piPX1vCu9pM5bbuHrujpKm8eP02557bUU3f/93+ktO3eOEmR/P588Bw+SbNevJ1keOkRi3L+fpDYwwO3/4R/YpvHwYaruCxcyveWHP+RYOzs5jiNHqJLbM4qeeYYOqC1b9DUANAPs38996+q0mi4S6pIlU+NvZhizLZNmtuOCzfSJtfpPJIjDZ8UKba+0LN5fAO2UUqTAsij8SLOolSv9ZTH4kS69yHJKtk6k3G9nq1on9u8PL5YB8GkhAeWREqLtktkTT/Ci7fneO3dSEqus1BLpoUO8uL4+4IMf1BVrQyFKmiJW5+bSGz4xQbG+uhp4/HFKf62tDC8aHKSU+Z//ySfZwYOsItHXx5CksjKS8IMP6uO+8Qb/nz1LEvzFL0i6oqqLdNw92QS1vJwk/NOfcp5OnmQ402uvpT5ZPA7Mtkya2Y4LUsJMdo09sQMFg7yXpAnUuXO8T1atol/g6FGdxzs0xL/PfY4aoB94SZdeFW6ELMOQqFQpxTJuvpl2htOnKVa7ZcO88gpF6HXrtKR29dWUMEMh4F/+hbbI9esZsP7aa5QelaIEd+YMj//ss3QMtbQwOP3UKf4Xg19JCSOwMzJ01ZMdO2in3LWLNsuDB0nKknkjdc0yM/kE27+fn1dUkPxaWzlmCWPauZMLZXhYFwZxywJavpzE/pOf0ANvWSTze+5xrzA/CzBdKvZsKgQcLy5Iwtyzh2qxVAiSfiRensFoEDtQUxPV7J4equU5OYxte/FF3rtr1vBeP3OGvPPxj/sjy0jSpZfdMiVkCZAQ1qzhE6amhqvfKxumtJTE9bvf0Zki6YxDQ9yvr4/xj6WlJD6l+FQ5e5be64ULWb2oqooq/+bNlGYlVy8tTXd8C4Uo4mdmUopVioT3hS9wcn71K5JqQQHJVubh/Hm+P3SINs/8fP5YixZxTtat0znsTz5JCfStt7j9bbfxOE88QUK3V4Lftk179d/5zllLlpGQTIKbbYWA48UFSZiSp52drYsBv/WWt6QWDWIH6umhpgfo9hX9/ZQoV6+mQJOdHd8iiWS7TIgs47FVLlnCjBy7I8StgjlA8vntb0k2EoheXMwn1kUX0S7Z2Ukp8557KMm9+CInsq2Nx5P+xY88QgLr6+P+o6P0vp87RyKUeJy2Nm6/ejWPLcS1ZAljaF58kaq9lI7r7uZ+Z85QgiwooEpgl4zb2hjw/vjjJNmJCT4IFiygFHrmjJa8T54ksWdlcezPPQdceumcIE0hyaNHw9tVJEpw0TpSzhVckDbMgQEKJ1lZWn1OSwuvfh4LxA6Unc0svd5eckNREQWgxYvjrxYdTbp0I8spSAZZvvkm8MtfUkUeG9O2yOeeYyC5V8Heq6+mijo2RhFcKW2PrK0laYlYvm8fny4rV/JuXbCAxLhoEYnrkkt43vZ2Sp9Ll3ISCgtZoUSuKSNDd+P61a/4/dq19GQcPUoSVoqTV1CgA2Pb2sgYtbWa+N94g+e+/nqd3C8dLc+fJ5O0tzN4XRq1/eQnPP4nP6nzWH/4Q3rhZzHsVYfEtHTsGH+aRCudz7ZCwPHigpQw8/N5z46O6sKmExOJt70uKeH9Kxk9zc26QLj93LEuEjfp0k0advWGJ4ssARLJzp18LbY9gKSybt3UsCGx7735Jsnt2DGe933v09k6S5eSrCYmtPTW20tyam/n3dvZSc9ZYyMnNBjkdQ0P84k0Pk6yk9fd3VpUuuYa7bBJT6c5oaWF5NvWRpJ7+mk+0UZGeB2/+Y1u/L12rY7qlhbBixZRas3MpHnhySd5vOXLaQvdsCHcS/7+9/NaOjpmrZTZ2MjK6qIAdHfzMkMhHQ6bCMHFE740G3FBEuaaNbTDd3VpG6az7FWskIyJ8nJd0To3l4QsPU+CQZoDQiF65hM1ers5XVNGlnIyUVP37uUECnFK4+ysrPCCvTt36r49BQX8fvduTsy5cyTMrVsZaD42RuIsLuZkDgzwGjIzeczSUn62cCEnc9EiTu7gIMlvfJy2xPZ2Zg2VlJAs8/JIuoEAHUvvfCe3veEGpmKtXMkfa2iIY7jySpoYFi7UNkwJHVq0iPbLT32KBLhzJ5+8Bw4wZMgtn7yyksHwsxQiWXZ3UysaHSW5ZWRwCkTzchJcLDbO+RK+dEESpvx4Ur5KfjwpaBqPsVs85WlpmiDfeouO2V27qK5LLObmzf5sQl7quJd06ZrumEyyBHQg95NP6ibsor6K9Ckxi2KbEP3u85/n9//4jySy227jZP/xj7QhDgxwoiYmSMqZmVTpn3tOhwctXUqyFDVeMgJKSylxPv00P8vM5P+GBj4JR0d5nupqEubixSTpnh6ee/t2So/f/z6vTXLLd+7k9bS1aVulSM6PP87PFi/mGKVBW3r6rC/EYYfYF6XLqcQI9/TwUpwl3oDYnTjzJUPogiTMSD9evN48p8oRDDLWuqyMT+kTJ3R6sj2lOprR28vZEzWkT+Is3RAvWQI6lvK660gS3/62rvIDaImzt1dnuFx+uZbUAOCf/5lxiq2tPP7hwxzr299OlXZwkHfqggU8bk4OCTAjg1LdihUU09PTKeWKw0Yp3vHV1dx2cJA/cCDAH3bhQkqPUi5ueJiOIOmLDFAKfuc7uX9ODn+4J5/k9dozkwDmlB86RGZZvZpM89vfAp/4hHvv9FkKedhXVVEABzj1585xzYr90k5wTidOKEQT1Be/yKlyEzLmQ4bQBUmYgPeP59eb55RCnT1MGhr4etMmrTVmZpJHBMkyek+RLr0abUlAejREIkt7Zk5FBfO7H36YavW7383tZJt169z7fJ85w7vq5z8nGY2McFyNjZQwR0dpy7QsEuRll5HchoYofXZ2Uup78UXaCdvaSJbiIOrt5XYDA9QxT53idmNjVMHz83VY0Cuv0I7yrW8x8+f223nOXbvIICdOkKAly+fQofAwKukz1NHBc3ziE/xcPOZzAPKwLylhUENTE9XzJUuoFEQr8xYMkmglp2Cuhgz5wQXpJY8EP968aD1MJCxw82atnsfah9wLvkOfvFTxaPAiSyA85/uVV3ihaWlUZQ8fpsr7gx/Q4XHmDLdzkuUrr/BpsnMnSWh0lCR5/rwOYE1P53HFA/7mm3z9hS/Q6ywpj+94B/fNziZZpqXppktnz/JzUdkljqy9naJ/dTWdPwsX0oFTXEx1/sc/Br7zHRLtY48x6D0U4nG+9z1em7Pz5NveRrbo7+d5hCw7O6dmRTU3k+hnEaRfVUsLnw/d3Xy433mnN+HZuzlKG12ltLAxk73DUwlDmA74aevp1bhMeph85SsUoOLtQx4Ndi6bEkYUoY1rVOkyElkCJD97XcjvfY9PifJykubDD5Pgdu0Kb0gG6GZipaU07jY08Gkikp+EF0mbwsJCqsU9PRRbqqt5JxcXk/guuYQmAZEmCwv5Y4yNcZ+8PB7TsnjOdesYEpWZyTv7d78jmf3udxx/ayt/sCefJEE+8QQb3WzYoH+sW27RQe/263r9dZKmZen89+XLdVaUhFo1NwO//nVs1Y2mATU1/BmPHdMtK1atohDglTJsbwrY389Ll4ZpwNwMGfKDC1Yl94Ifb56zJFYwyCdzV5c+hvM4gQDvIUlMSabRe4qzJ17pEohuHBXSS0+n9HXoEJ8Ev/gF1fIXXwQ+9CH25GlsJJFKjOboKMcmBShef51jS0ujpDc8zLv1zBmGJh0/zkk7f56f/eQnfCD82Z/xzn76aZ579Wr+KOI4ysjQDqSRER5D6mEGg/wRhobYinPRIv44PT28lspKikyZmSTinTt1zv2bb1JnFRw4wIfDPfdwfIWFlGyffprHWb6ckuYjj/DaX3kFuPXWxMIxEoSXQ/P4cVo+7GE/9rqYbvuJH0CmZ+NGrVHNxZAhPzASpgN+ihHYpVCx3wSDFG5276bmKJXC7Mf59KeBv/7r8Da7XohW9zJm+JUuo0HIr7SUoUOZmfQWX3klJcAbb6Qkt3o18N3vMg/8iScoVb30Ep8qa9eS8E6e1NVI+vr4/+xZEtihQ1oC7e3lnbp1K8nu5z+nF31iQts8y8tJgrm5unCIdIo8eJBk1tWlW06kp/NPipcODpI833qLJDs4SAn29Glet8RU7txJibGtjdd94438/OmnSfLveQ//P/aYVsezsnisTZuixmF2dKSueLCbKenBB/l5JFOU134A1/Hf/R0vKxna02yHsiLVo59hrFtXb+3YcWCmhzEFdk96QwPvuZ4e3rMLF+qm9V/9avwSpBdhOrN7XB0+bhKmH8L0W01HnD/l5fR2X3EFg9evvJJS25VXavvgr39NYu3ro0S6eDEdLM8/T4ly8WJerBQOXbqU+4qtIzdXVyivr9cdIV95hSQoJaAyMni8QIDHOH2ad+3QEI/d20vSu/FGikXnz1Ok2ruXRF9QQDOCUlpKlWjtujrgox8lm/3iF1TTR0fD7bkA9VG7s+fQIUrRoRAlS7uEOVlxfWAQmJjQv1c0wkykCdr9908NHre/b2kJj00uK9PCsNd+Xm10Z3thjQ0b1EHLsupj3c9ImHHALoV2dfF+KC/n/SutZM6fj9/o3djITLq//EvgX/+V2XxACsnSC6+8Et6gDNAlzaRx2I030l74sY/xoq+8ktLWihX8vL6enunCQr7u6KCDSAgwGOR3fX3cp6uLmQULF1LS6+ujgS0tjZLiY4+RNQIBHd+Zna2dPTk5fP3BDzJ/u6yMx1i0iMfet4/7vv/9nJvrruOkDg3pUKWcHF2jb3yc7NHRQQK8+moeb/16bc/dtIkStKjgYrd8/XUe4/3vZ8jUrbfyAdLSEt/vkSAiSZHV1by8gQH+bAMDfF9dHb5fMMjIr/37ae5NpCziXIQhzDhRU8On6y238P6yt2gJhWiKc3rW/dTfFOl1YID3YF8fK7kLaaYEXtKlV374+Dgzad7zHtoe6upIfnV1fP/2t9O2l5XFC7j2Wj49/u7vKKEtWECSPHeO+9h7gV9yCQktJ4fkNT5Ooly/nsU6urooEUqmUG8vJcucHP7v7eW4TpzgBIoemZdHgnvrLRLbs8+S5P/qr/j9xIQ2xsl7yZ8tKaEqnpNDz3d9PW0vBxzaj8Re1tWRmMvLSdxCrCtXkjTjLVqQICI5NI8f57ALCvjsKCjg++PH9X5ifpKU4kCAa3X3bm9Vf74hKU4fpdSNAL4BIB3Af1iW9TXH92ry+5sBDAO4x7Ksl5Nx7kSRqCqxfTv3l6aEoRAX1LJl2ugdrQWv/fzBILeTyBoh4l276OuYVri1lairo+gh/Xc2btRpg/L5c89R0nz4YUpc73oXv//lL0l6F11EiW14mLbIJUuoQpeXk9DS0yldvv3tOmPoyBGqyefOcaKVovTY3s4Jr6ggKS5bxrt8dFSXwf/sZykVf+Mb3O6FF+gBr6/nQ+D8eS2ZZ2bys9FRjmvJEtpMV6zg2C6/nJXW09IYJQDwOKdP86l2ySWch8sv539xZAls6vh0w+mIbG2lZ7yigsNftix8+1CIUuSSJZxmMRnLdxs3kjQffpj3zFyvROQHCUuYSql0AN8BcBOAtQA+oJRa69jsJgAXT/7dC+DfEj1vMhDJCB4LVqzggjt+nI6fVat4z4vR2ysMaceOqed/4QWdcSgoKIjN0Z1U2NtKrF/PC7MHbgupHj+uPy8qoihy550kx+9/n2roypW8kKEhkk5LC+9AUcs7O3VM5U030S6am6uD1U+f5vHS0zkpvb26hNrQEO2Mq1aRCZqbeY7PfpaZPY88QlU/PZ3M8MILwP/5P8DXv07CXLqUxNbXRzbIzeVxJYNIsnvWraOpYvFiSqi7dtGZ9bvfceyvvsqHxJVX8v/u3Tz+LIDdlPTmmyTLVav4+fg4p6Szk+tQ2q4MD/P7Vau0fTMrS3vE8/O5z3yoROQHyVDJtwA4ZlnWCcuyQgAeBnCrY5tbAfzIIl4EUKiUmvFgNC8i82t7FMItKyNXlJSQD44c0U9iwNt2dOjQ1PMXFU0l7IGBGeyh5Wz45VbzsrISuOMO/fnatSSnri4S6aFD9H4vWUKj2JEjVLNLS0mGJSWU7JYt4/ulS/k9QEkzO5vfX3wxSXbtWo4rL49Pl5wcbvfEE7qB2urV3OfMGZ57YoJkNjwM/Mmf0OO+Y4fOX/3kJzm+sTGKTYsW6cyidev4+fAw7a8vvcRzLV7MH/7JJ7VZ4eabtUd9+XIW+JjONpBRIKak2lr6vFas4NoLBPh9ZycvNRQiiZ4+zedNXh63XbBAZ3vu389M0N5eNuDcv5/PPsCEFUXCUgCttvdtk5/Fus20I9EafXbCVYoLb8kS3jeBAJ3BX/4yPenPP68XE6BtSc7z19Tw/h4Y0Ga0vj4dvTKtsKdCbtmi1XOnI8gO8RjffjtFlZdeItEoRbKrraV02NKiCamjgwS1bBmlsdxc7lNURCJcsoQOnI0b+bdvH0lUPOCFhVTnjx+nbTUnh9JfXR1rlpWV8bMVKzixv/wlWUDaddbX87OmJkqwIllmZPDH2LNH19z88Y+ZwnX99bRrPv44n5JKhZd0O3mSDFJZOesanwFT17405lOKZHn+vO5sKrbL8nKSY0sLnz1nzuhCVP39fP/qq/x+voYVJYMw3So8OGOV/GzDDZW6Vyl1QCl1oLc3QvXcJMBPVo8XGhspWIjH8MgRLjgJdg+FeM80NPA+GxykVnvmjI5Tq6ubev6sLN5fBQV8ui9cCNx7LwU2YKqwMuDYP1LP65hhT4UEtPodqaqxOIpkoIWFFFmkmMajj/IiBwf1U6G2lnfisWMkwuZmijTSWEwygFpbuV9uLicwEOB3H/wgyTYnh3d4czPv8GefpYf6d78jAYdCHMOxY3ps730vie/QIY7zuutImqOjHJOkZ7W20pb5oQ/pbJ/+fl7LjTfyh//JT3Tjs8ceI8PMUjjXfkEBL7+oiIK5JE1NTOgKRp2dFMy7unSX4/JyLovyci2VdnXNzzxyIDmE2QbAbi6uBOCU0fxsAwCwLOsBy7LqLcuqLyqKM+DMJ+zpXbEE3IoqLp7C0VEKXaLKFBRQWMnL062z6+t5Tx0+rIPh77jD/fx33MHkkX/6J+Av/kKTpTM+zx6/B8DdmbB06VRWdmL1ane10Z4KKXDLD3d+X1fHfOyhIbJ+drZOc8rPD8+vrqkBPvABft/XR7FmZIQTODrK7zs6eLceO8bXRUV02qSn89h/+ANV4fp6/iC9vfyBNmyg8yU/n0a73Fz+UGlplByPHCG5KqVL8zzyCNkkPV3Hd150EX+YsTE+AUtLua9SJOrOTkrgSlEalVhMkTaThERiMJ1wrv2yMl6uUrpd0sQE/wYH+bzo6uLaXLqUBZ2k+Lz0qTp7llM0Q0EA04JkEOZ+ABcrpaqUUgEAdwL4jWOb3wD4sCKuANBnWdbpJJw7IcTbYlRU8dpaEiLAJ3BXl86nlUUjRTdKSugHqK3VWT7ztsVpdzfv7OJikmsgoAlZ2jtMTJDgRkZIUgsWkKjGx7n9woWcTHswelYW91+1ig6ke+4hOaank6SUooQ4NsZzBIP0jA8N8fj79/MYWVk0AQwOAr//Pe/0khLupxTTOyUHPRCgnrlkCbeTH/yNN/jjbttGqfvoUf64Ut09yWSZbDjX3ooVbMpndzhmZOhIq+Fh/lTf/jaDBfbs4bPwxAlOozxvpN2SCSvygGVZY0qpTwHYDYYVfd+yrCNKqT+b/P7fATwGhhQdA8OKPpLoeZOFeGr02YsFSzms3Fxm+6xaRZ4IBLiQRDoE3NX9SOeXojrTho4Of9k+bg3PJKBdpM8lS/hZKMSLOH6c73t6eEdt3EhiHR4m4YmN8rvfpVPnyisZntTerjOJ3vEOraZ3djKI/G//lilV4pH44x+pZr/wAtXsU6d4/tOntdGutJTEW1amY1ArK3nXNzXxf3Y2SfvkSW57/jzDnHbv1lLu5ZfrOVizhjaXm2/WOelzgDSda+/llzm1x4+TMPPz+XMNDOhABrGrp6XpAAWJ95+Y4NoX5+mcf/g7kJTAdcuyHrMsa7VlWdWWZf3d5Gf/PkmWmPSO//nk9xssy5p9+Y4xwG7/kTbYV11FU9aKFbyfamuprcWbXxuL6hVmx1y4cKod069a7hdeAe2lpXy9fz/J7B3voOo6PMy7r7mZIswNN1C3y83V+d4vvsi/rVspof3xj7yWwkJKgTffDPz5n+tWD0VFLPABMPYxGKQDZsMGSp3Z2by7y8r43fLlnAdJw1q1it8vWaILDTc3884X773YOhctovQpvX9aW/lwWLeO17tjByXMe+/V4UT2XPI5hDVr+OyqqODUSxWiggKu9a4uTocEFGRna4dndrZWEuZrWFFSAtcvNFRXM/tGjOTl5bzPnOq0Myg+WdWJ7ILgxIRy7xSZjIN7wS2g3Z5XfeONlLKWLqW3+rvf1YRdWEhxZXxcx3RKP57Tp7l9QwPtGsEgpbsFC+i4qanh3drayrv0ve9l8Pj69TqW8+RJkpdl8e4/fpwEefYsCbKykk+xPXt41/f2UqKVrJ1LLiFDSPwloKvijo6SSHNzdS/1xx/n67vvDpcoKyt5PnvQ+hyABLcXFOj8gJYWLgmxRojtXnrVBYO6PZM4iOZrWJEhzBghAb0ioPT08P6/917/JfkTyS7KyfFZRFgs+IKlS6Pnla9e7b9qkT2gXXqTA1oll0rkknWTl8fvWlpoBGtpYcXzYJDjLCriZL7wAielrExLeIsX86L/8R/5f3QU+Nzn+Pktt9CTlp1N8f78eZKmlL0vLKSE29Wl+/xIhdy2NpJlVpZWGzo7uU1urm7EJGaA0lJ+vmgRQ4u+9z1WmbfPmXjIxekTQbJ3Ft6Yaci6HBzUzTfLyrh0xFsu3nCAryVNcmREBz1IH/O51uDMDwxhRkBjI7WtQ4f4XtrlSuylvZLL8eP+jxlPz6BImJhQGBi0FeJYuNC7kLDkcHpBSDOalOkMaJeyaQKpRP7UUyTvSy6h8+SqqzhZ0jqip4e63vAwpU3JQf/d7/hUOnGC1yOplKOjtCWePk0C6+ykGv6Tn3AceXk6oLC0VD9dhof5eU4O2aC3V9fmlPa78hTLzeVdHwrpTpJSxWjzZh7n8GGS7ZEjfOI99phOG02BhzzVkHU5Ps41KfkCixfz8k+d4s+Rm6tzCjZv1tmtOTl89uXn8ye44475Z78EDGF6orGRgecnT+o+4wcOcIFcfXX4tva6gdEkR3uwu5Ri7O5mfLVX/xQ3uGnOA4OOYsJeUma8JxC49fZ54gkSTH29ztH+z/8kMZWW8txLlpAQr7qKhCPFgt//fh73e9/jNn/4A4PKX3+dxNrQQKKUlMWuLk7g975HCfPNN0lw587x7l68mD/cxIS+85cs0eFK58+ThCsrWXHpuut0wDygy8F1dWnWOH2aY9i/nx76rCxKwJ/9LI+dmcmA+ssvn3NkCXBdjo/zkrKyOAWDg1QEvvpVvU17u+6gHArx3li5Uncy7u1lCOzllxvCvKCwZw+Fn/x83WpCKQocjY3hTpnBQaol3/wmF8zoKAWjI0eAz3wmfOGIh12yJ2Rx9vRMlTRLS9095W5q+RRbpkiZiajmXqTpFdD+zDMksTNndJS+ZOGIh3zzZp7/+uvpBf+TP9GNxZYvp6RWUEA75OLFvIs7O+k0ysqijtjby9Spd7yDd3RWFiekspK2yjNn+LT6/e95/WlpHENJCSc9PZ0E19VFEn3pJd2V8gMf4I8/OsrPcnJIwkK40v4iN5eVh06f1oWIpeBGnB7yVBYPjob2dk6zRF0BXCI9PZwOr4LX99/Pe8JJtA88QCKdb6Rpyrt5QIrgSI4twNfZ2bxfncHmZ8/SFwHo2MvWVqr0gsZGcsUTTzAJZWKCi+z8eQpbsTaOcos1n+Ix94Jfr3ksAe0f+ACdNj/+MUm6qYl2i098gqSVl0cCXLuWYUJ/8ie6PNwzz2hTwJkzJKSmJj4xJBgwPZ3kVV1Nb/rTT3Pi9+/ncc6epTr8wQ8yPkYcS0VFZKJXX+WES8xXZ6cmynPnGKB+6hR/wMxMhjoUFnLcl1zCa9ywgWNbv5755+LRtxfcmIMe8ooKrmv7encrU+iEk2iTUQ92NsMQpgcqKrgAxMAN8PXChbxXncHmorrLosnK4nuxf9oLdUjL7O5u3uMS7O4ViuFmH3WTRMSB4CvMCEiMNL1QX8+woePHGVcpxFRVpYtrHDhAYl2/nlWJnnmGpCMtL8rLdYOkiQleg0RRFxaShE+c4CQPD3O/FSsoxf7qV/TKnT3Lu7aqimT42muc4KIi/jCDgzxecTGPv2IFxxEMMq7mnnv4g4dCPHdrK+PHcnIYBdDayutIT6dJwV5wQzpGDg6GV02xrBkr7RYN27frdSmhROLkieTtjpdo5yoMYXpg+3b+6IOD1MSkzUtREQ3a0h0yWm8egdguV6ygsCIBwUNDulSWWyhGpHjMnJypXObpdU0FabpVZH/8cQZ3X3st77hLLyWJLVrEMQQCJKP6el7c449TbN+0iQ6iZcs4IXl5JDxJbC4vp7OnrY36X1GRLtzR3U2ptbCQHvijR3nu7ds5yZJdVFamVYTcXJKdFCB+29u0MykU4rgyM2nWKCyktPnTn9L++uEPk/C/9z0e39kFcvlyXdJnjqCmhpEelkU1PBCYWqbQDfES7VyFIUwP1NSwaVl9Pe/b8+f52mmTFNTVkfxGR/n/5EkdB+1sMlVSwvuuvJz3b3FxYo2jfKvmySZNZwD7/fczFfFDHyKpvPvd9HaPjfH/0BBrZHZ2ahvnTTdRDJ+YoLrb3k5pMyeHqrHYGxcs4PZXX82J7e4G7rqLRPziiyQ4SYE8d45jy84mCa5dS0Lu6aHNs7hYR2JnZ/O8HR38/NJLWVVlbIzHq6riNocP87pOnNCS5cc+5i7+798/J/qRO3HDDXTw3HgjrQ8rVkSP3oiXaOcqTBO0JEG86qdOkQ8CAQpJUnM3O1unjAmk6dTSpZHjMaU4kFeapDiA3PqVT+n5A3i34Y2nb7l4zNevB/7jPyj5ffrT/G7nThKMZPgsWMBQAIDFObZsYVhPezul0A0bWM3o3DmSmXi4u7v5Xrzq1dUktHPndMzkm2/yfSjECQ0GdSzlffcBP/sZvXC5ufy77joSY0YGt1+0iH89PbpuWSBAtf/kSQYV3ncfyfLxx3XFeSlnt22bnpMXX+R1fOhDdARJP/Jbb9WxaZjeBmipgD0qRFTyUGh+N0EzhJlENDYyPKi7m/deVRXv8/5+LqSREXKGvd+53/jLaG13nQ3SgDhI8+WX6dq0e3ilT42omG6k+dJLOoBdQoyysniHFxRQlH7tNb4vLOTxh4Y4httuo4SZkcEiHErx+5deIiGJSi2FOCSHe2SE1WulCGlLCydh0SLGVEqqY34+34sxWQLcz5xh64zTp6nGDw/zGi+7jGXcSks5xmCQr6urqbbbs5oAHT4lmT4SuH7JJQwzuuYa126RQGyEOV1k6Tepwh5PHM96nmmYrpGzADU1FFauv573nTS1l14/iVYmihYc72XP9K2el5WRtESddKvruHq1LgfX0TE1gB2gtNnXR5K5/Xbgv/03Sp1FRSQo8YbX1fH4OTl8ylx8MQmwo4Me685O2kKGh7U3e+1aiuzPP8+JDQTo4BGPeF8ft5XWuUuWkMwzM3nOD3yA15qby5jQzEz+UJLXvmsXc9a3bSMDlJRwXgIB3abDHiGQkcEf8rHHOCbJ8rniCm7rsx/5TMDZmC+WZmZikw+FGMh+4ACfT/aokPkIE4eZZFRUhPdwDgYZdx0KcZGJlNjersMu/JBmaWnkur0Sm+kMnZT4zLCgdq8YzcsvJzn+6Ee82d96yztrZfVqeqOffhr40z/VAew7d/J7kcbsEMI8c4ZEpBTvtqEhSmSLF5ME33qLhCc185TimD/4QR7nZz/j9m1tvGBpYpaVRQn24EFuf9FFOqvn7Fl+/6530R556BAJsrmZZHbFFbzue+7hOR59lPbWZ54hWb7+OoPkX3+dpJ6ertNAly/XnSIlcP3kSW4rEuby5To1bBbALePsgQdof/TTzKy9nc+aQ4d04ezRUWa2NjYmJmXO5h7nhjCTAKctp7OTzt7R0fA0spYWapF1dfy+pQX4whe4KNas0VXQvBaKVyC7ICmkuXIlb/Jduyg1RgrAzs2lUyUjg6QwPs7P163TnRV/+EN6EZ55hnfV2rV8gnz/+7w7r7yS6rpl6Z4/Bw9qm2JHB80F4+P0Uo+N0aHU1MTXkiuekUFiDIV4nuJiXldrK4991VWU9v7iL/j9tm10zgwMcJvubpLl4cP8e/e7SYrLl9Nmeeed/FHKy+kd/9jH9DycPBneKTItjTGfN9ygCdRhw5zpPHLJ7Gls5BRIe93OznBe9wp1q6hgLLE90F1KAiRS1i0VqcPJhCHMGOD25AN0Dm5TEwWo8+d5D05M6ELDJSW8P/PyKDTl5TE6RlKUDx9mvLcslPPnvRdKUkkTCLdrNjfrikNPPUX1tbbW/WT20JnTp2nL+9M/1amRhw5xoE8+SZtjQQE7O/7xj8z9bmkhmeXkcP/KSqq0GzboCufBICdAmsYUFOjXixYxAD4nh0+qsTFeS3Exj33iBMnrk5/kpEu73qVLSdrvex/nQGpnAgyytSx+D5D8xdkj6Zof+5huUyHtdUUSr6xkdaZ3vWtqP/LTp8OcPjOJhgb+RNnZWjocHiZh2uFVdWj7dgrhEuYr7aXr6hKLv7SnDgOzr2WvIUyf8HrySVfY11/nZxI2KB1g7bbMgQES5cAAyVWSTrq7uW96Okk2FNItUJ0LRVTzpJEmoKVNuzdXnD8/+hG38SJNgXiJn36aFyHOEQD4wQ9INldfzUE9/zzVa+njeuYMRXDJM3/rLdoNlSIBNzeT8Nat4/EkrEcpkp+UbpMqtlKaLT2dYUvbtjGEoa+Px5Dc1tWrSZRbt5Jch4e1U2vBAtpfhfQWLdKOLUmmBvjjOTtFfuITHLMdK1fOKpV8YIDrVKTDrCxe8sCArmTX2MipveIK2jePHyfRSuOzggI+B8+f5+vaWq5nL+eUH1VbUoftmE21NY3Txye8WvIeOqQrggUC1AwDAd6vExPaYB4MciEeO6YFpEBAP5kti+QbCmk1p7PTfaH48ZaKt9WXIwggaZ4+rckS4P8Pf5hkNTgYPV5z2zbeAU89pasXVVYyBEeybUSdvfZaqvRnz1Ktfvlliie//z0HX1UFfOpTfOJ0dVHivfJKTr70UcjJ0YWI09J4rrEx3mESy7VkCfAP/8C7fflyjutTn+IP88UvUsXu7AT++3+nZNrUxB/hbW8L94TbHVv2YP3166eaLZYvZzxnDFk+051Hnp+vS7ZJwHkgwGkPhXTI6OWXU1X/+tepBbW1kTBPneJ0jYzwwb15M/f3iiUWgSOaQymRxoTTASNh+oS9aMaRI7zHLIvSZV4e71N5Wo+P67jLs2d1CchAQIf3Sc8fkTIBLlR5HQgwJHDLFu8xRWthEUnSBICBQUfY0Q03TLVrrlypCfTUqcjl4U6e5KBvvplPmPJynXe+fj0NuLfcwsE88QRFF5Hqli3jxJaUcMJEpf2//5eiSzBI1feii3gXv/kmSfLwYUquubm64VpWFt+Pj1PiLSykGNTdTdK9+GIyQ2sr1fGtW/kDtbVxv0CA9k65S90qM11/PZ+OsVSqn0VYs0YXfhIb5rJlWgjetk1X1HrlFf7sr77Kn66ggAQ7MsJnnJQajVQk26+qLQWMgfBwpdlSW9MQpk9UVPA+tqve4+P86+ujQCNmrfFxChM5ObzXxT8hWmBvr+6ut24dyTcY1KmXlsXXmZn6ae2mzhQXx0+aQAx2TYFUOnIjTWfh3MpKvfJLSzlx73oX/x85otX1115jKmR7OwmovZ2keuAAJ2pkhGSWns67OSOD5/nQh6jSl5ZSTzx9WjcpS0+nCt3Xp4NgpclaTw+l3K1b+b6jgw4opTiR27fTMbVzJ//WrvVuNexMiZxDEGKqqQknpu3b2fnDXlHr3DmSa2+vzubJydHlQTMzmSYcCX5VbWnOlopOBcmAIUyf2L6dHu2hIZ2aDHARjI5ysZ07x4VUVETSLC6mxvjQQ1xYaTYDyMQEhaQVKyhkDQyQN86d48LMyGDKGQB8+ctUkQoLtf9DHEIpIU0gcnk4YGp2UGfnVFve3XfTZvH00/Smb9qkJbR160g6t9/OtMn0dIrq113Hi73xRqrn6em6PW5ODh1Gt9xCT/ptt5HUjh7lBYpnfHRU2zQXLuTf4KBu+7t8OT1w0tkyM5Pf5+WRICUFs6dHN3uzN3kTU0MsRUkSQCqC1iMRk4TGiZ09J0drP0rpRp0FBf7VZWe4HeC9bzyNCacLxobpE7KQLItkl5FB8hFJ8tJL6V8oLiZZ5uToNtVSTcyOwUHdcvdb32IO74YNvA8lp3flShJjQ4MW/A4d0gLTnj36RooW1J6To4t1RLJrega5R8tDv+wyd1teeTnvzCVLeGK7hCb53RKPIp7uG2/k0+Taa3lXNjdTjW5ooMp+8CCfQFLYd9Eifj84yGPn5pL4xENWUcFJE/tkXx9/sIEBiu5r1vAHXbVKZxft2qVz0O1N3uYRamrci8hIz3Lp5JGXx2krLNR9ykdG6JfzW//A2Qc9kdoJMwkjYcaANWt0exexV0rTp5IS/lVUUPhauVL3NunsJCdUVnrbZdyeqvffr7MppCEVwPt+82atzvjxnAui2TUjquhexYiFNN1sm86qPW++SXISIjpyhKR4++38/vvf51PhuuvoTV+/nhd38CAJ7OhR/hA9PZQuKyo4jpdeIuEND1PyFOn0jjsYAyq9yquq+ON84xvcvqSEUuv73kcWOHCA57/xRvcmb4IEpMspDrdphN+g8OxsXed1yRKam3t7ednj45zyFSv8B5XPdlXbL0wueQxobGRV9WPHdAUjgAuqtJQE2dCgS11J2TYxo5WU6MUSLUgdYMra4sXkCiFmsW/W15NM77tPbx+tSIcdbgU7BK456IB3Hjrgv4AHoEN3Xn+d4sbatSSj3buZLVNayifTjTdSxJYq5mlpFMufeorOn4oKesV+/GPe0S+/THFIqqJfeiknraODHguAUmxmJvezLEZfp6eTxK+7jtLsRz7CyWxvp+G6vp7b29Xyjg5vh488QDy85G5B69ORR+4n/9u+jTPxIitrbuWLR4LJJZ8G1NQwASQzk2SZlqb7d0m+eHMz7ymJkQb0d6L+bN9Of4XfEIuqKl1nUMI/3NQZv+o5EB52FJOKLpHKkVR0P+XiVq+m9FZXpyU3pShtXnSRTlE8fpz2z/p6kuKxY1TzBwZok2xooAPojTdIeD09/L6oiBKj2CLuvpuiUTDIH25ignbM2lr25bnsMtpaL71Uq+WPPkox6vXXKXnGopbHGFI0HfAKjduzR+eVf/GLXMOhEC+1vp7r9/Dh+OofzDcYwowRx48ztvoDH6C29853cvE1Nel2MeItb2vT3m+7cTvSwrVD7D6BgE4QOXuW97jXwo2VNCPFa0aM2QTcSdNvjU0gvM5mRwfVXmlfsXcvVe5Pf5qG4XXrGE6UkcGnxgc/SIm3o4OTeMstOjhw8WKSb3a2vrD+fq2CnzhByTIvjyS8eDG3fc97GEMjavnHPkZ7iqRD2sk9yZiOGEx7TVaBdCOWGEl5Fr72GtduSQkjscTefiGTJWBsmDHDLTyiuJj3YGUlF1hHh64F0dBAk5ndXhlviMVVV/mzGcVi0wSS4EUHYrdtCvr6SErSCGndOl7woUPcr7eXnxcXA7/4Be0en/gEn0of/zjrap44oRuUDQ1xkhYvZobRTTdx/+98R3evVIpjO32akqQ9znLjRr1ffb1uqXHLLfpJOF0iYZLh5akeGOB38uAW809Tk3cnADtiKQk3W4tq+IWRMGOEWyZCeTl9CpIWXVysX0tZN/vC8MpmCATCy21J1ZdY22EA5AYp1pEMFZ29zz1UdCB+abO8nPreCy9QynvjDd6pAwM85u9/r1MYr72Wuej19bQj1tezn86zz3LylixhaTbJC//IR7TIf9tt3Ob4cW538cUc39694ZKjZCalp4dn+HR2hqvj0eyXSUIyQ4q8PNX5+fqZJuYfy9LfR/Jm+83g8bvdbIchzBjhtujS06nZieBSWMjtrr6aZjUnybkdo7WVGRPJXlDJUtGBKGmVgD/bprN9w/LlJMLTp5nC+NBDFIsvu4wEl5tLQl2/Xsdu2rF4MettKsV0xvp6HbZUWUliLS0l4UmgeSBAAv0f/4OE3dMTfszKSt1+4/rr6fC5/nq+lwrrkWC3Xzowkx5y0VicNVnXrNE8X1JCIVsadUazW/o1L/ndbrbDqOQxwis8AnD3QLqldLkdo7w8vIXFggXURr/2Nd5/ibQASLaKDrikVUYLPwJ0C1t7RtDJk/RgX3st8Mtf8oTXX6/L6UjRjeee4zEk/Mh+ca+8ovO87TnsgI6hvP56euG6uzm+557jsW6/3b3QqFfv9YaG8HYU0eAShTKTZd1qaujU2b+fQQX79+vecoCuybxyJdcowHX60EPu686veWm2F9XwC0OYccArEyGWODPnMb70JarygmCQGYHj41y8ByajqzZvjq9GYDykCWhJM2m2TSHPRx6hFHnoEIsB//GP/G5wkAWCP/hBHkNyUpUKr/wOhJOhM89biE6IDyBhf/SjfH3kiN5WCgHb4fZZRgYl3Vgxi4pu7N5NQX58nNPb1MRn1oc+xIeklxDgVZvSbwZPLJk+sxmGMJOIRFK6nAuqqYmqy8KFujUNwNcSCx5rjUCnej4j0iZAb/epUwwor6khWSrFupWHDlH627OHoQgTEyS2nBzaPRoappKhW563fCbE98orU7eVuMpYPN+Rim0k2X6ZDDgdLbt20S8mGWtpaZz6XbvC20t49acCwted32IZs72ohl8YG+YsgdOu2d3N/1VVusKRvcpRIupMLHZNIHJaJRDFtikFeu31IZubGbeyYgWzeYqLaYdcvpze6E9+kvFawSAJ9PrreZyqKkp4MhDJ67ZD7JZOxLKtG/x6xpNov0zU4ePmaDl5kmYdgAIzwJjilpap+/X0MJx1dFSHGTnXnd0u2tjIv8FBHdvptl28Pa1mA4yEmSJ4VWf3Cqtw2jUXLWKubkmJLqcF6OibRNWZWFV0IE5pE6BU9tBDjHMEGLajFJPmb7iBRYqHh3UVJMlJd1Y/sr9/801ve0GyIeeJtZSbS8D6dNovvUqqjY/r1F4pCDMxMXW/4mIdYgTQipGeTsK9/369fmUNt7XxeZGf766+z+aiGn5hCDMFcKvO/s1v6nzyxYun9vNxLj67dLBypU5Rk2pFyVBn4lXRAX+2TWCSOKuqgLvu0g3SAGbySJ3ND39YH1BI06360c038/PlyzV5pZo4/ZJlHOp4qsM53Rwt2dlUycfHdcKTZTGyw7lfVRUlS0C3XSkrc7ejz/bWEsmCIcw4EC0A123xSPz12rXM7jt4kItwYICRM25PY7vEKV0RJGUtmYULUiVthjmFqqpofN27lxO2YoW2b9qLFAO0b65ZMzXgffnyqRWR3IgTSA55xipZRlDHvRApf1wQb8C3m6NlyRKtUodCTPMtKmJShJynoYG5ALW1DDFqaqL0mJfHdSi2TEAT4nzxgkeDIcwY4aerndvikQrVe/eSMKX9zMgI35eV6RAi+00xXU/nVEubqrkJ+fv3626N69bpCH+nUyiWTCGBndSSIXXGQpZu0qVDHY+nS2RpaWJdFN0cLRUVHJa0RsnKImFefrk+z4YNutf45s08T2cnt7GTpZ0Q54sXPBoSIkylVDGAHQBWAmgG8H7LsnpdtmsGMABgHMBYPFVCZgv8qB5ui0cC1KVKkFJUjaS3yhtvcCE71R059nSlk6VC2lTNTQj8ZicG33M78tdPllfbuZMxkFVV3tXdgdiJE/CWOoHIBOrcNhabZYzSpV91PFFVNztbh6TV1QGf+Yze376mnOepr6ekefgwky+uuELHAgN0ADU0aHtmdTWFAWBue8GjIVEJ8/MA9liW9TWl1Ocn3/9Pj22vtSwrmOD5Zhx+VA+3J7vYjYaGSJCSSjk8zP9jY3T0SBYEwDCPkZH4ezTHq8rZSRNIXNpM62jH6LvfD7VyJW2bpVUouP12DqyqKnrsJhC9n5Ab3AhPSsvFsk8keEmXNnh5x/3EX3ppK08+Gfl3tUum11yjCQxw11ykLYVAim50dDAlV44n57eXfevvJ1lu2xZeslDI8v7753b+uB2JEuatAK6ZfP0ggKfgTZjzAn5UD6f9MRAgSZaV8f3ICO+pjAwteebmkjsE+fks+yh1L4HYpItEVDlAq+iJSJsAzz1+xdsAANakJzYtzcJAaRUK7BccLXbTqzVGrEhm0zK3upeCBLzj9nAi53oLBklW+fmRf9cdO3SZtoICri1JRXT7/QMBRnjZtw8EeH558A4MMIM1GNRtde0q+vHj4fVZE12DsxGJEma5ZVmnAcCyrNNKqTKP7SwATyilLADftSzrgQTPO2PwG4Brf4rffz/JEqDhXEI4xsf5XwLU7YvPSwt1M6S7SZLJ8lomIm3GFYIUqQkbkDziTBacZOmjIHcs3nHnemto4Ova2nBtxP67NjYy9LWwkPtIHGVdnV479jUTCNDJI22NpPD8smWs/yqkV1PDbTo7+drLnimYj57zqIHrSqk/KKVed/m7NYbzvM2yrEsB3ATgz5VSb49wvnuVUgeUUgd6e11yfGcY8QTgtrfz+9HR8LbZ0qjw7ZOz4awiU1cXvUezVxWYhgZ/ZOsHUvkI8B/sDkSugAREKFQMRC7oAeg0Sz8Fi1OBwUFvu6UPZ48f7zgwdb2FQlSDhazElvjoo7rC1Z49dORIAQ3pc9/YqCVG+5ppaGCQ+qpVtHmeP8+1Ul7O8TiLZhQWTi0K4+bg8aq/OZc951ElTMuy3uH1nVKqUym1ZFK6XAKgy+MY7ZP/u5RSjwDYAuBpj20fAPAAwBYV0S9h+hGr91rUqo0bWYlsZIQLp6SEdp/+fi7wBQvcc3kBb2nW6yl++jS3T6bXMpXSpmdeOuBt3wSmetSB1EudXgSdJOnSmd3j1FbEFiltcJXSDckefJDDKy9ncY2JCf4GubkkWzftIxSiZNnbq9NuJyY4Vjcbak0NG3tKaTgvLWs+es4TTY38DYBJXy7uBvBr5wZKqTylVIG8BnA9gNcTPO+cQnU1I2kOHuTTOSdHd3QVafKOO2j/uesu7vPQQ1zY27ZFlma9nuKSbZHsLn2JSJvR0itda24CketuAlrajKVNRjyQ49rPJZBxJVG6dIM9hfbECZKlZbGrh5BgZyePVVysbcrBINV4iZm0rxnJHpO0W7nUigr32q1ZWWzrHk3Lmi+dIu1I1Ib5NQA/V0p9DMBJALcDgFKqAsB/WJZ1M4ByAI8oSgYZAH5qWdauBM87Z9DYSA/iqlVcyD09DBZetYqqjz0I3c1IvndvZJXf6yku2UOp6tIXT9wm4O4UssM1U0gQzb4JuIcjAYlLnZEcPB5k6UQ80qUTdodiVxcly4su0ip6fj4jMdLSONcScyl1WoGpa6aqijZLCXFzSoxuWo4fx8186RRpR0KEaVlWN4Apz4tJFfzmydcnAGxM5DxzGXb1Z8UKfiaL1e5RdG4L+DOSi1Ogt5eE3NtL7/u9905P4Hu8cZuAfzUdSIA4gfjJ076Pl73ShSwFyZYuBfbf1e1hmZ3N4PPmZkqNBQXcXopuOB1JgQAdPOXl/D2cxJYI6c2H/HE7TKZPihFLylg86WU1NVTbH3iAEmtxMRf+3r2MD5+OxZqItAlEljaBGIgTiE6egPawR0O0YHQPsoy1ja4g1spEXhEbdXUkQXtL+P5+fXw3ye8zn/Ffu9UL86FnTzQYwkwxYjF8+93WuTCDQd4c9v36+6c/fCMepxDgT00HPBxDgD/nkB1x5HxPQQSydCKaKh5vGbdo1f+ByE4Z8bA3NPD1HXfEv17mY8ylGwxhphixFE71s63bwnzhBaau2TFT4RuJBLwD/ojTNX4TcCdOIDJ5xgr7cT3I0i5dyrUkQxV3QzzV/xsbga9+lU4jy6I0+txztIl++tPxEdx8jLl0gyHMFMMuBTQ00M44NAR88YtUnexPdT9GcreFWVTEm8Aupcx0+EYi0ibgbd8EYiBOIHnkGYEoOZbwsdkRjSyT1RXSjkhq9I4dJMv0dJLl+DhTdE+dip/gTLUig6QiGGQB1lCIOeOZmfRMOp/qbgvdroI3NNCgb0dNDbBvX/S4uOlGvNImEN2+CUwlTiBG8hS4kajbdjESZSrslkB0W2G076ULcWYm/4+PM6ni5EnmqMdje3RL4bQX55gv9kzToiLFEBW6oYH3YHo6PdkTEyS3np7IrUadWRmBAOM5g7YyJllZVMlDIeafHzhAT6nbsb78ZXaYve02vp6OvtCx9ki3I1q2EKBjOAGPrCGBxHM6/ySbyP7ntp0DXmQpY3Ujy6NHgX/9V0Yx7NwZ+/zv3s3C07t2sUZlS0t4O2a//b8zM0mUoZAuAANwfcXT3tkec3nmDNfg4CAf7nO1B7kbDGGmGKJCh0IkycxMhv309nJxjo5GVluc/Zxra/l5Q0N4MPDllzODqL6e1WmcC7+xEfjWt7iQMzO1hPvNb07fQrZ70+MJegdiJ05ffXR8kKMdclz7+QSRbJZHjzKaoaWFEQyxEkljI/dXitEQoRBrqY6P64eufb1JTcvm5vAGZ3V1/P3Pn9dkKRlBtbXx9Qu3p3AePkxhoL6ev/lc7UHuBqOSpwiiFj36KIOL09P5Nz5OwgyF+JeVFdnW6LQNlZQwl/jAAUqTAG+AffsiG9337KE0m5+ve7QoReKeTsO8U00HkusYAsIlPntIEuCisvuE/RhelYeiOXh27SI5FRR4F86IhD17WItA8sTld+zs1Op1eztfHzrE76X4xosvck3W1NBufuIE5196ReXmAlu2cH1NTMRnexRzkqzZNJs4Nl/smYYwUwC7J7usjKrJuXNcQCMjJM30dH6+fHnkVDG3UKPhYUqQ9fXaZvn005Qy7bAv0vZ23hySBgfoLpRzyZsO+CdOYCq52e2dsSIaUdrH5sTx45QwV66Mn0ja20mWoRDXUG+vfn3RRdymooL1CqTgBkByLSwMJ+a8PAarnzrF7/PydCZQog7D+ZhDLjCEmQLY1eiLLmKBBMkfP3+ejp4FC0h4XrFv9v4qp04xlXLZMi68Y8f43i5NSgUZuxOhtZXn+tKXeIyJCS3VAv4k3FQj3qB3IJyc/LbzSWbXRj9ECehru+gibyLxE/QdCFCa7OjQ5p2sLBJeVxePsX078Nvfcj1Ylm5FYS/ttmcP19K6deEFPE6c4DkSdRjOlx7kbjCEmQLY1eiSElYpOnGCi/qWW6J7DO0S6urVvBmPHaNkuWYNb6hly7jYm5ooJY6OUlobHNRFF5qbtb2qo4MVjJTiIl60iDfdsmXJK4aQSKZHvGFIAjepE0htI0n7eb1gDx3yIpJNm6IHfTc2cg319lJCtSw+fMfH+eCtrKSdsqSE2ktXF9XskhLaJQMB/XCKtD63bEk833s+5pALDGGmAE6VpKSEC3bLlqn5425wxlquWKHLv913H8M0WlpIollZvIF6erhtdjZvqqYmVkPKy6N9U4rDjo4yDvT8ebYg+MQnkrOQk5HpkYh9U2AnsGSSp9PRFAtRAlOrlkvF8ve+173cWnMzY3Wvu04XUQmF2PXx1Cn+5mlp/P3Hxvi77tvH2qqXXaZbSKxcOVVqTHR9+sF8yyEXGMJMARJVSaIFAW/fztASpbSaphRvprw8esl37SJhNDXRbire+cxMrYKvWpW8RZ3MTI9kECcwldQS7QPupweP3ftvJ0tn1XIpc1ZTw1J+mZksARgM6vqo6enhNS5HR+nAHxoiSWZk8LOBAZ6jsNC7iZldwpvPKnOqYQgzBUhUJREJIBTSKncgoEOKamqYDt3fz8U+Ps59cnN1TcPiYkqaEm8n/wOB1Dh7UpHpkSziFPghvETglbkT7WESCOjyagMDNL0MDOjwoAULKJVmZfF9UZG2Y0q2Tm9veHqss4mZHfNZZU41DGGmCImoJNu3Mz6ytZUSY2YmifHECQabh0K8ocrLqUbt309JQxpYAfxOiDYtjSq4ZfFGSoWzJ5WeUTfiBBIjz2QiWoqj34fJuXP8zdLSqDFMTOhePPn5XAsnT/J/URHQ3U0pUx6k9ja4QOT5n68qc6phAtdnIWpqSHj5+SS67Gyqzz09VLMWL2a40qFDVLmGhkimJ09SLRP7ZkEBpUr5KyrizSiOoWRWvp6O6tqSMRRvAHwyIeeWSkORUhzdqpbbyUz69AwN6ZbLAE0pExNUt9esYQptfT3XREYG8M53At/5DvDXf81oi/lW3Xw2wkiYsxShEIlTisCeOqX7sqSl0RHU3w+88grJcGyM//fvJ2muW6fDkFpbKVGePMmbLVI4U7yYbjVvJqROJzn7zQOPZjMU6TwQoNlgdFSnt585w99OiK+khNsPDPA7yZ4xavb0wBBmihFvqI3drpWfz5CP4eGphcTFESCpbv39lCyluvuCBXp/CS8B6GhIdpHXmVDz7KTlJE8gcQKNlyTtiEZmQqjDw5Qc09PDHXXSfuLBB/lQbGvjQ7Ovjw9RezSCn371873IbyphCDOFSGZR1cxM2rjsaG/nDSUkGAhQMpHCHFIhSW6wwkJdqWbz5vlX5NVJZm4Emugx7fAiH6/PI1U0v/tumld6emiCWbyYv+3ICM0z4jhqbOT3WVl8WHZ16dRXP2R5IRT5TSUMYaYQiYTaiF1LVPKSEp1iKY2qJiYoYdgh+erBIOPyzpzRqZgvvUTbpwS1SwuD+VbkFUi9JOVFPtu2sT1ItCB0t7HddBPtz11d/M2zsmhWWbFCO44GBnRLIol28BuNcKEU+U0lDGGmEImE2ohdy96XRW4maVRVWUliFEIcGaFarhTwhz/oTBClKJUMDnL/vDw6GAAGNs+Hogh2TIck5UU+Dz/Mc3iRUqSxVVdzO+nNlJ2tM7wGB/l5QQElSwkxKijwH41woRT5TSUMYaYQiYTauDkK0tOBz39e3/TV1cA//iMdPufO8UbKzGQYymuv6cpI2dn8GxricTIzKZmePUtiXbgwtiKv0aS33btJHJ2dVCfvvBO44Qbf05YQGhuBr32Nqm1xMVvIig0wmZKUF/l0dlIzcH5uz+N2I9odO/jAk3bMnZ20UW7cyDG3ttKcsnIlP5dygcuW+Q86n89FMaYLJqwohUgk1MZeX7Cjg/+dEtINNwD/3/9HUpCKNFddBVx6KaXIggL+V4rkKRgfp1ou0mlurv/ajCIhtbTQNioFbXfv5ve7dwNf/zpVxdJS/v/61/X3qYSMrbubczE6ygdHMJh8ScoZKhQMAs8/T2nw+efDCzzbSam9fWqX3/x8kqG0Yt6yhWRcUUESlaiIujq+r6zkb7twoVbf9+yJ/ttVVzOKYvdummdaWkzoUawwEmYKkWiohx+v5w038O9LXwqvQVherisULVlCEhkf501ZUUEJJhDQRTj82rP27OFxJI89K4s37Ze/TJvpkSOarAH+HxkB/vmfgWeeSa1nVqS3RYu02gowWyoQSK4kZdcARkd17vamTXQ0HThASTMrK3IeN6CJ106kAwOcR8ncAihNZmYCX/lKuGqfnx/d7NDYSNtqWRl/u64uzss99xj7ZSwwhJliROvRkywCcd6I69Zp1W18nASak8PPV6xg75b8/PByb36ksPZ2km1WFo/b2UnCHR2lej8woCUggBJXfz/tb6n2zIqaXFVFyRIgwfT0+Fdb7b+NRB+EQlN/J/vDUOaytpbqf1FR7HncdXV8Lb9fQQHn0l6/1C6pxurAkQddVxcl/6VLebxHH2UKpSFNfzCEOc1IlUPCeSMGAlTBysv1DV9dTSnD3izt3Dm+fvLJ8Hx1L1RUkAiKikgqExM8Rno6Vf+MDH6em8u/3l5mr2RnUwqTdM0dO5ihkiw0NlKiPnSID4axMS1VV1REnl+32qO5uZQSAe8QrEgVxgMB2oid8NNLfHSU+7a3U1p+6ikdl3nvvdwmVgeO/UFnf0BOd8X9uQ5jw5xmOHv0JKvfiZvN8zOfISnddRe3eeYZEpcUdDh3joSSlqbz1aUQrRe2bycZNTdTcpPwJrkRlywJlzwHB3m+9HQSQX4+CVRaJiQD8hAqK+O5W1u1HbOoSDt9Iu0r9mWlqLK+8YZOGmhujvw72e2ZUpB3cJDjcbMN19SwIMZXvsL/Qrx33825evFFkv6GDXzAtLbq9Ni9e3msaOmWbmPs6QnPN5dCHsZL7h9GwpxmpDK0w0v9t0u0ogL++Z9TypNWqAUFzFcOBKJLHKWlumOtZWmnkpQkW75cdw/Mz+dnRUWRWyY4xxyLycL+EGpu1uFVw8N0gkW6Jvu+g4PaTNHeTo80oO2IXr+TXbo/cYLXZ1mssB5LrGNNDcl92zbut38/7ZYA507SYffsib1Em9TUtF/j6CiPb7zk/mEIc5qR7NCOaOQSydYVCtF+ZVcl3RpgyTmOHuXr9HTawM6d0z1hAgEtRa5Zw5v7vvu4/ec+p8m1r4+STkGBew/seEwW9ofQ+LgmmcHB6E297PtKjKPddimfy/Hcfie7mt3VRcnyoou0ZOsk2ki/mX089iB1J2nH6lCsqaE6/8ADVMOLijhP6enGSx4LDGFOM5JZvNUPuUSSaP2Qt5xjfJzkODioYzszMxnC9OablOoCgak3YU0NsHUrJdlgkDe+BGUrNXW8bgTf28vYyqVL3R8K9usQ0gOiE51zX3EWhUK6eZ1cg6jsXr+TjGf/fj4Qmpr4XjK05PzRfjPntZw9y/dSWKWsTNcJiDV3/4YbKDWbXPL4YQhzmpHMqjJ+PKWRSDFSj5kvf5kOlGCQ0kggQJITdW5oiGQwNsbCtV1d3oR2xx08z9GjJNPubpLl5s3aLuhF8MEg8NZbPI+X82X7dvZcF2/42bMc59atmug2bWJwvpMo7HNQXEw74bFjdJZJHnkoxNeRfqfduym9DQ/zb3yc0vTFF3OuyspYnk0kdPHmO4Pq7eMpLOScpaVxbgcGaJ++7rpYVkk4TB3MxGAIcwaQrEXrxx4aSaJ1I+9Nm9h1UArVjo/TFjk2RttkTg4lvv5+qtgZGXT02DOQ3K5XcqyV4jHy8xk4vXBh+HidBN/URMJYtChyL29rsntuVhbJLRQi2a5ZQ/J74AGdcnj+vCZdgA8C8YjX1QFf/Wpsv09jI4+vFM8lZofsbNpUS0pImnYJ/exZSrMbN3JMMgf23+TZZ/mgmpjgb1BezvHNVA1QA0OYcxp+VOpoEq2TvO+/nzd7fj7JJyeH5HP+PKUbpXQM5tAQ1fJt26ITzPHjWpUU58/oKMnmqqv0dk6C7+mhRFZVpbdxPhTsbWMBEmVDA9s6lJYyqyU7m8QUCuk2xZKOuGAB+yDJwyRW2PO/xaGVk0OyGxzk2KTKkFNCdwuql7n87W8pWUre+Ogow52MV3vmYAhzDsOvPTQWiba9nTem2P+kf4xSvMlzcynplZby/6pV/iQecVRIeblAQDuB7E4HJ8EXF1OdtYcGOR8Kdkk7GCRBDg+TDHt7qcquXKnJHiBRdXVRwo0n59zuuGlo4LW0tuq+SYWF+oEivXry8/V8njsXOah+xw7+Dm1tJF+JMnA+YAymF4Yw5zBSUWW7ooKhMZIBlJtLQpGGazk5/LywkCRjVyedsJPKqVMkvo0bpzZ227NnakFjeyk0iZP0eijYJe0jR6juZmRwe2nz0NGhiwmPjZHcMjLCc86d6rEXnI6bI0cYd5qeroPm29p4rM2bOWZxRsl8Dg2RzBctmhoB0NjIWExxeIVCHH9REV8br/bMwRDmHEeyjfjbtzNo++RJbRccHyexlZeT5OwmgP5+dw+0k1TOn6d0WVenSaStjSTS3+/t5ffzULBL2kJclkWC6e3V5gMJGerqIllWVHBcfnPO5QFgz4pKS+PrjIzwaunp6cDq1drhVVZG51UoREl30yZu4xYutWcPiRzg2Hp7KZH299N0YJw2MwdDmAZhqKmhN3fHDq0+Sw8gwJ8JQEqsdXdTgqqq0vbLri7dG72sLJyAvRw60R4KdlIVAiwt1cWVh4d1e2GR2C6/nITqN+fc/gCQmFKRSsfHaWtsb+f509O53ZtvUnLOzuYxJH9bWoh4hfTYzRfS3VPSJeV3MJgZJESYSqnbAfwvAGsAbLEs64DHdjcC+AaAdAD/YVnW1xI5r0FqUVPjnecdTdoTYunp0Y3XGhvp+Fi7lqTxla9w2y99ieqpHfFmPQmpHjvGeMXWVqriaWkkt4ULdS3QZctIniUl2kQg5O4VIC/FKxobua2QYlMTCfDMGS21ikqens7zBQIk4k99ShcRdjNDCMTE4DRfbN1qpMuZRqIS5usA3gfgu14bKKXSAXwHwDsBtAHYr5T6jWVZbyR4boMZQDRpT2JDs7Npt8zK4utgkMU36uv1tqnIegqFSFznzum0zexsklh1NcnyyBHm1b/6KkOiysv5P1I2UUMDSTA7m5Lx6dMkyfPn6Z1vbCT5BwLaLFBSMrUVCBA92UBMDAsWaPNFf7+RLmcDEiJMy7KOAoCSnqDu2ALgmGVZJya3fRjArQAMYc4h+M3vtnusJada7Hp5eeHbJjPrCeD4KitJZEpRwkxP5//CQt2eo6tLhxiJF/3eeyM/CAYGKK1KkRGpKTo4SPW6q4vXOjhISXTJEpoEnCmNfpINkuXMMx0ik4/psGEuBdBqe98G4HKvjZVS9wK4FwCWLFme2pEZTIHbTQYA3/wmyWV0lF70I0dYDcl5A4rUKKRx9izJMieH0pLkZwPJ9/ILWY+PM8heCPutt3Qx3qYmEp70wrnuOo43WmhUfj5DoMRxlJ5O0l26lDnz99+vpeX9+7mdFDUBtOTst/hKos480yEyNYhKmEqpPwBY7PLVFy3L+rWPc7iJn5bXxpZlPQDgAQBYt67eczuD5MPrJhsZoU0wP58EEArxvdS0dBbdlWrulkW1dHSU9rhAYGrb2mQUWLbXszxxgmQmYVGhkG76JkV5JXhcyMyP3XTNGkqM0hKioEB3dATCpeWVK3UFdmce+p4909NXx3SITA2iEqZlWe9I8BxtAJbZ3lcCMLkKsxBeN9nzz1NalPCbrCyS4aFD7uXjlOLrhgaqwnV12vERTd22Hy8zk+mBjz7KfPU77ohcvm7DBhJVKET1OTeX6nh1NW2J1dVabbYsXSzZD2EJIdbUhJsP7EVG7NKy2Grd8tCTaYbwgukQmRpMh0q+H8DFSqkqAKcA3Angv03DeQ1ihNdNdv689z5uJFtZyf+f+IQmkNJSxh5G8g7bjxcK6bCahQtJvm4qpfP89fXctr+fUmBBAYnxPe+h2n3uHCXEVat0QL5fwnLmnDvH4keNTkWygRtMh8jUINGwovcC+BaAUgC/U0q9alnWDUqpCjB86GbLssaUUp8CsBsMK/q+ZVlHEh65QdLhdZNVVlKllbqXkgtdXx9ZknHL2IlmU5PjHTyoHSwiFTorG9m3F5SUsMZnR4cOX3LCqfJ7EZa9Dmh7O0k2kZxzwXRUDEq2Q82ASNRL/giAR1w+bwdws+39YwAeS+RcBqmH10320Y+yEERPD6WzrCw6Ve64w79Nzq9NTUjbXjxX7I1uKmVFBase2W2L9pqRbnASVmPj1NJvgCb4zk7GXnZ0MFYzJ4d23a99LXKVppnEdEmyFxpMpo/BfyHSTRap8KwfScavTU1IOxCgs0gp/q+tdSfi6mqSeV6e9oTHUjPSS/LNzuZnXV3hHvRTp0iY0rp4NnueTe3L5MMQpkEYvG6ySJ97dUG0S21S6sxNEnWqyBdfTJJqbiapRnIaHT/O7+0SZmkp8PDD7n3QnecKBt0l3wMHtBMpLY1/0rI4L4/S9vLl7mYCg/kLQ5gGUREtzMdNxXVKbRLYvWzZ1Oru9m1bWnSRjrVreayGBuZ9u0ly7e3h4T3BIDN4xsenVmhvbmah37ExHu/8ebYMvtwRFSymgMZGjlnCkiyLxDk6yuNXVRnP84WGtOibGFzIsJdXs6uskVrkurUSrqxkCqK9DfDdd1NCtG9rz8YpLWXtx23b6Mxxk+Kc7WalQntxcXgb4x07dFV0KZN27JiuMWnH4CAJu7eXaZZpaVTDAcZ4jo/zepz9egzmP4yEaRAR8QRAe9krOzqYFWPHQw+FbzswoG2R9n29pDino6q7myTnrND+1FPhVdElpnRsTLfcsEu+0r7ilVdI3llZlGR7erj/2rWxhSQZzA8YwjSIiHgCoGOJAXRuK9k4koXjtq/TRLBtGyXV9nZ6sd0qtAM6f1zIMhCgqr11K8/vdHTdcQe94atX01Pe28tA/Joakm+0xmgG8w+GMA0iIp4A6FhiAJ3blpXp6ugTE1P3dbOP7t2r7ZteFdrr6kiOx47xOOKEyshwzyACwh1amZnAli2mgMWFDmVJWe1ZiHXr6q0dO1xLbBpME+wE5VRZ/eR3+8kHd25bXa0lRue+9iIXAnkv6r5XARHpr97ZSdU6M5NVim64IfF5Mphb2LBBHbQsqz76luEwEqZBRMQbAB0tBjDe0mN+TARe5zbSokGiMIRpEBXJDoBOpPRYIjnSJpDbIFGYsCKDaYdb2JEEgEfD9u3aOz0xoV+bTooG0wFDmAbTjvZ2HRwu8BsALiYCZzynkRwNpgNGJTeYdiRaesyo1gYzBSNhGkw7jFptMFdhCNNg2mHUaoO5CqOSG8wIjFptMBdhJEwDAwMDnzCEaWBgYOAThjANDAwMfMIQpoGBgYFPGMI0MDAw8AlDmAYGBgY+YQjTwMDAwCcMYRoYGBj4hCFMAwMDA58whGlgYGDgE4YwDQwMDHzCEKaBgYGBTxjCNDAwMPAJQ5gGBgYGPmEI08DAwMAnDGEaGBgY+IQhTAMDAwOfMIRpYGBg4BOGMA0MDAx8whCmgYGBgU8kRJhKqduVUkeUUhNKqfoI2zUrpQ4rpV5VSh1I5JwGBgYGM4VEu0a+DuB9AL7rY9trLcsKJng+AwMDgxlDQoRpWdZRAFBKJWc0BgYGBrMY02XDtAA8oZQ6qJS6d5rOaWBgYJBURJUwlVJ/ALDY5asvWpb1a5/neZtlWe1KqTIAv1dKNViW9bTH+e4FIKQ6umGDet3nOWYTSgDMVfPDXB37XB03MHfHPlfHDQA18ewUlTAty3pHPAd2HKN98n+XUuoRAFsAuBKmZVkPAHgAAJRSByzL8nQmzVbM1XEDc3fsc3XcwNwd+1wdN8Cxx7NfylVypVSeUqpAXgO4HnQWGRgYGMwpJBpW9F6lVBuArQB+p5TaPfl5hVLqscnNygE8q5R6DcBLAH5nWdauRM5rYGBgMBNI1Ev+CIBHXD5vB3Dz5OsTADbGeYoH4h/djGKujhuYu2Ofq+MG5u7Y5+q4gTjHrizLSvZADAwMDOYlTGqkgYGBgU/MGsKcy2mWMYz9RqVUo1LqmFLq89M5Ro/xFCulfq+Uemvyf5HHdrNmzqPNoSK+Ofn9IaXUpTMxTid8jPsapVTf5By/qpT665kYpxNKqe8rpbqUcg/vm63zDfgae+xzblnWrPgDsAaMjXoKQH2E7ZoBlMz0eGMdO4B0AMcBXAQgAOA1AGtneNz/AODzk68/D+DvZ/Oc+5lD0Hb+OAAF4AoA++bIuK8B8OhMj9Vl7G8HcCmA1z2+n3XzHcPYY57zWSNhWpZ11LKsxpkeRzzwOfYtAI5ZlnXCsqwQgIcB3Jr60UXErQAenHz9IID3zNxQfMHPHN4K4EcW8SKAQqXUkukeqAOz8bf3BYsJJj0RNpmN8w3A19hjxqwhzBgwV9MslwJotb1vm/xsJlFuWdZpAJj8X+ax3WyZcz9zOBvn2e+YtiqlXlNKPa6UWjc9Q0sYs3G+Y0FMc55otaKYMN1plslEEsbuVqEk5SEKkcYdw2FmZM5d4GcOZ2Seo8DPmF4GsMKyrEGl1M0AfgXg4lQPLAmYjfPtFzHP+bQSpjXNaZbJRBLG3gZgme19JYD2BI8ZFZHGrZTqVEotsSzr9KQa1eVxjBmZcxf4mcMZmecoiDomy7L6ba8fU0rdr5QqsWZ/ScTZON++EM+czymVfI6nWe4HcLFSqkopFQBwJ4DfzPCYfgPg7snXdwOYIinPsjn3M4e/AfDhSe/tFQD6xOwwg4g6bqXUYqVYJ1EptQW8N7unfaSxYzbOty/ENecz7cmyeazeCz6tRgF0Atg9+XkFgMcmX18EehhfA3AEVIfnxNgt7VF8E/SYzvjYASwCsAfAW5P/i2f7nLvNIYA/A/Bnk68VgO9Mfn8YESIuZtm4PzU5v68BeBHAlTM95slx/QzAaQDnJ9f4x+bCfPsce8xzbjJ9DAwMDHxiTqnkBgYGBjMJQ5gGBgYGPmEI08DAwMAnDGEaGBgY+IQhTAMDAwOfMIRpYGBg4BOGMA0MDAx8whCmgYGBgU/8/7OZqTJKc067AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -432,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 20,
    "id": "4deee696",
    "metadata": {},
    "outputs": [],
@@ -445,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 21,
    "id": "6b792675",
    "metadata": {},
    "outputs": [],
@@ -455,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 22,
    "id": "81faf37f",
    "metadata": {},
    "outputs": [
@@ -465,7 +465,7 @@
        "pandas.core.frame.DataFrame"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,7 +476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 23,
    "id": "6417b5c6",
    "metadata": {},
    "outputs": [
@@ -623,7 +623,7 @@
        "4    0      2       1  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -634,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 24,
    "id": "202237c5",
    "metadata": {},
    "outputs": [
@@ -672,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 25,
    "id": "9a94370a",
    "metadata": {},
    "outputs": [
@@ -886,7 +886,7 @@
        "max      3.000000    1.000000  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -906,7 +906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "id": "ce906842",
    "metadata": {},
    "outputs": [
@@ -930,7 +930,7 @@
        "Name: 10, dtype: float64"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -942,7 +942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "id": "5f8d9afe",
    "metadata": {},
    "outputs": [
@@ -1044,7 +1044,7 @@
        "8   52    1      0.5"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,7 +1056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 28,
    "id": "ce4a5f85",
    "metadata": {},
    "outputs": [
@@ -1071,7 +1071,7 @@
        "Name: sex, dtype: int64"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1083,7 +1083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 29,
    "id": "d129b322",
    "metadata": {},
    "outputs": [
@@ -1093,7 +1093,7 @@
        "array([1, 0], dtype=int64)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1105,7 +1105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 30,
    "id": "37d36915",
    "metadata": {},
    "outputs": [
@@ -1369,7 +1369,7 @@
        "[303 rows x 14 columns]"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1381,7 +1381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 31,
    "id": "c379d785",
    "metadata": {},
    "outputs": [
@@ -1391,7 +1391,7 @@
        "<AxesSubplot:title={'center':'age chol relation'}>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1415,7 +1415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 32,
    "id": "601da936",
    "metadata": {},
    "outputs": [
@@ -1427,7 +1427,7 @@
        "Name: sex, dtype: int64"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1440,7 +1440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 33,
    "id": "a128aabd",
    "metadata": {},
    "outputs": [
@@ -1486,7 +1486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 34,
    "id": "09ee5be2",
    "metadata": {},
    "outputs": [],
@@ -1499,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 35,
    "id": "37389405",
    "metadata": {},
    "outputs": [],
@@ -1510,7 +1510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "id": "a7ea6eb2",
    "metadata": {},
    "outputs": [
@@ -1578,7 +1578,7 @@
        "4  36  41.849864"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1590,7 +1590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 37,
    "id": "952802ba",
    "metadata": {},
    "outputs": [
@@ -1600,7 +1600,7 @@
        "<AxesSubplot:title={'center':'x and y in the dataset'}, xlabel='x', ylabel='y'>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1627,17 +1627,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 38,
    "id": "8e9d0f89",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x22d8ddab408>]"
+       "[<matplotlib.lines.Line2D at 0x1abc2971ac8>]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1668,7 +1668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 39,
    "id": "bf4cd5b6",
    "metadata": {},
    "outputs": [],
@@ -1680,7 +1680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 40,
    "id": "b4842e99",
    "metadata": {},
    "outputs": [],
@@ -1691,7 +1691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 41,
    "id": "479278e7",
    "metadata": {},
    "outputs": [
@@ -1720,7 +1720,7 @@
        "       78.98989899, 79.24242424, 79.49494949, 79.74747475, 80.        ])"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1731,7 +1731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 42,
    "id": "56cd60cd",
    "metadata": {},
    "outputs": [],
@@ -1742,7 +1742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 43,
    "id": "bd73d23f",
    "metadata": {},
    "outputs": [
@@ -1757,7 +1757,7 @@
        "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1768,17 +1768,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 44,
    "id": "7af98bd1",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x22d8dc975c8>]"
+       "[<matplotlib.lines.Line2D at 0x1abc2a45e48>]"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1814,7 +1814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 45,
    "id": "62838ac9",
    "metadata": {},
    "outputs": [],
@@ -1827,7 +1827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 46,
    "id": "4bea9b4a",
    "metadata": {},
    "outputs": [],
@@ -1839,7 +1839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 47,
    "id": "392f50b2",
    "metadata": {},
    "outputs": [
@@ -1923,7 +1923,7 @@
        "        47.33487629,  54.09063686,  63.29717058,  52.45946688])"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1934,7 +1934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 48,
    "id": "0806d238",
    "metadata": {},
    "outputs": [],
@@ -1945,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 49,
    "id": "aa0ef0f8",
    "metadata": {},
    "outputs": [
@@ -2254,7 +2254,7 @@
        "       [0]], dtype=int64)"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2265,7 +2265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 50,
    "id": "55ff0a77",
    "metadata": {},
    "outputs": [
@@ -2275,7 +2275,7 @@
        "3464.291087139079"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2295,7 +2295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 51,
    "id": "ccde02ca",
    "metadata": {},
    "outputs": [],
@@ -2307,7 +2307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 52,
    "id": "3c2b554a",
    "metadata": {},
    "outputs": [],
@@ -2317,7 +2317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 53,
    "id": "4da8d3b5",
    "metadata": {},
    "outputs": [],
@@ -2328,7 +2328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 54,
    "id": "afbf17ff",
    "metadata": {},
    "outputs": [
@@ -2355,7 +2355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 55,
    "id": "35dda627",
    "metadata": {},
    "outputs": [],
@@ -2366,7 +2366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 56,
    "id": "fe11b9d0",
    "metadata": {},
    "outputs": [
@@ -2375,94 +2375,94 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/40\n",
-      "10/10 [==============================] - 0s 508us/step - loss: 6149.2983\n",
+      "10/10 [==============================] - 0s 667us/step - loss: 2304.5127\n",
       "Epoch 2/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 1049.6282\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 665.4196\n",
       "Epoch 3/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 455.6070\n",
+      "10/10 [==============================] - 0s 778us/step - loss: 212.6413\n",
       "Epoch 4/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 202.8622\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 90.8192\n",
       "Epoch 5/40\n",
-      "10/10 [==============================] - 0s 668us/step - loss: 91.5324\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 35.4843\n",
       "Epoch 6/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 33.6845\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 24.3325\n",
       "Epoch 7/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 20.1020\n",
+      "10/10 [==============================] - 0s 2ms/step - loss: 13.1693\n",
       "Epoch 8/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 13.5234\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 12.5406\n",
       "Epoch 9/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 11.9283\n",
+      "10/10 [==============================] - 0s 2ms/step - loss: 10.9955\n",
       "Epoch 10/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 11.6073\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 9.9997\n",
       "Epoch 11/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 10.5410\n",
+      "10/10 [==============================] - 0s 2ms/step - loss: 9.8371\n",
       "Epoch 12/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 10.4354\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 10.1302\n",
       "Epoch 13/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 10.1968\n",
+      "10/10 [==============================] - ETA: 0s - loss: 15.84 - 0s 1ms/step - loss: 9.7880\n",
       "Epoch 14/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 9.7576\n",
+      "10/10 [==============================] - 0s 1000us/step - loss: 9.7385\n",
       "Epoch 15/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 9.6691\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 9.7177\n",
       "Epoch 16/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 9.5777\n",
+      "10/10 [==============================] - 0s 997us/step - loss: 10.4117\n",
       "Epoch 17/40\n",
-      "10/10 [==============================] - 0s 667us/step - loss: 9.6235\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 10.5410\n",
       "Epoch 18/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 9.4145\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 9.4638\n",
       "Epoch 19/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 10.7170\n",
+      "10/10 [==============================] - 0s 999us/step - loss: 9.4064\n",
       "Epoch 20/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 10.7544\n",
+      "10/10 [==============================] - 0s 2ms/step - loss: 9.3838\n",
       "Epoch 21/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 9.7636\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 9.5009\n",
       "Epoch 22/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 9.4378\n",
+      "10/10 [==============================] - 0s 2ms/step - loss: 9.3609\n",
       "Epoch 23/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 9.5914\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 9.5564\n",
       "Epoch 24/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 9.4083\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 10.8976\n",
       "Epoch 25/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 9.5226\n",
+      "10/10 [==============================] - 0s 889us/step - loss: 9.2925\n",
       "Epoch 26/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 9.9660\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 9.4949\n",
       "Epoch 27/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 10.0456\n",
+      "10/10 [==============================] - 0s 887us/step - loss: 10.1660\n",
       "Epoch 28/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 9.2983\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 9.7374\n",
       "Epoch 29/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 9.5830\n",
+      "10/10 [==============================] - 0s 2ms/step - loss: 9.4201\n",
       "Epoch 30/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 10.5801\n",
+      "10/10 [==============================] - 0s 2ms/step - loss: 9.5416\n",
       "Epoch 31/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 10.0829\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 9.3903\n",
       "Epoch 32/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 9.4324\n",
+      "10/10 [==============================] - 0s 889us/step - loss: 9.6326\n",
       "Epoch 33/40\n",
-      "10/10 [==============================] - 0s 665us/step - loss: 9.3277\n",
+      "10/10 [==============================] - 0s 2ms/step - loss: 9.4795\n",
       "Epoch 34/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 9.1831\n",
+      "10/10 [==============================] - ETA: 0s - loss: 7.971 - 0s 1ms/step - loss: 9.7306\n",
       "Epoch 35/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 9.8164\n",
+      "10/10 [==============================] - 0s 2ms/step - loss: 9.7753\n",
       "Epoch 36/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 10.3315\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 10.7304\n",
       "Epoch 37/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 10.1487\n",
+      "10/10 [==============================] - 0s 2ms/step - loss: 10.6714\n",
       "Epoch 38/40\n",
-      "10/10 [==============================] - 0s 443us/step - loss: 9.5550\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 10.2416\n",
       "Epoch 39/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 9.2581\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 10.7227\n",
       "Epoch 40/40\n",
-      "10/10 [==============================] - 0s 554us/step - loss: 9.5368\n"
+      "10/10 [==============================] - 0s 889us/step - loss: 10.8924\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x22d8c83fac8>"
+       "<tensorflow.python.keras.callbacks.History at 0x1abc2b0b348>"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2474,7 +2474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 57,
    "id": "a22c9fb1",
    "metadata": {},
    "outputs": [],
@@ -2484,23 +2484,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 58,
    "id": "9787ea0d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x22d8dc97c48>]"
+       "[<matplotlib.lines.Line2D at 0x1abc2c12908>]"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2521,7 +2521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 59,
    "id": "ccaf0068",
    "metadata": {},
    "outputs": [],
@@ -2531,17 +2531,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 60,
    "id": "5e966641",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1.0167854]], dtype=float32)"
+       "array([[1.0264139]], dtype=float32)"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2552,17 +2552,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 61,
    "id": "d7d8eb87",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([-0.43452635], dtype=float32)"
+       "array([-0.37661067], dtype=float32)"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2597,7 +2597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 62,
    "id": "7687f6bf",
    "metadata": {},
    "outputs": [
@@ -2642,7 +2642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dae270aa",
+   "id": "e6979b5b",
    "metadata": {},
    "source": [
     "Opdracht 5.1 c\n",
@@ -2651,7 +2651,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef725144",
+   "id": "bf474d51",
    "metadata": {},
    "source": [
     "## Opdracht 5.2: ZTDL 4: Deep Learning Intro – deep model"
@@ -2659,8 +2659,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "96009d3c",
+   "execution_count": 63,
+   "id": "a8de57da",
    "metadata": {
     "scrolled": true
    },
@@ -2711,7 +2711,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdf8c103",
+   "id": "f68cd874",
    "metadata": {},
    "source": [
     "5.2.c: De optimizer die wordt toegepast is de _binary crossentropy_ optimizer.\n",
@@ -2720,7 +2720,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a5e45fa",
+   "id": "20230f3d",
    "metadata": {},
    "source": [
     "5.2.d: Uit de resultaten van de tests is te zien dat de accuracy van de trainingsset 0.999 (99.9%) is, en van de testset 1.000 (100%). Dit geeft aan dat het model heel goed is in het classificeren van de gevraagde features. De classificatie is dus erg goed."
@@ -2728,7 +2728,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c9689cf",
+   "id": "6e60d43f",
    "metadata": {},
    "source": [
     "5.2.e: Hier is geen sprake van overfitting, aangezien de berekende lijn \n",
@@ -2737,7 +2737,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "856dd4b9",
+   "id": "331278a8",
    "metadata": {},
    "source": [
     "## Opdracht 5.3: ZTDL 4: Deep Learning Intro – Iris"
@@ -2745,8 +2745,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "0689a2bd",
+   "execution_count": 2,
+   "id": "85c1a69a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2754,13 +2754,18 @@
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.image as mpimg\n",
     "import pandas as pd\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import SGD, Adam\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "aa4b64dd",
+   "execution_count": 3,
+   "id": "7b74afa5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2769,8 +2774,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "af55003c",
+   "execution_count": 4,
+   "id": "8da80225",
    "metadata": {
     "scrolled": true
    },
@@ -2778,10 +2783,10 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x1abafb03c88>"
+       "<seaborn.axisgrid.PairGrid at 0x226e6107908>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2805,8 +2810,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "f8c01649",
+   "execution_count": 5,
+   "id": "6d64ebbd",
    "metadata": {},
    "outputs": [
     {
@@ -2891,7 +2896,7 @@
        "4           5.0          3.6           1.4          0.2  setosa"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2902,20 +2907,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "af16c550",
+   "execution_count": 6,
+   "id": "e97352af",
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'df' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-8-597ee424618a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'species'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal_length  sepal_width  petal_length  petal_width\n",
+       "0           5.1          3.5           1.4          0.2\n",
+       "1           4.9          3.0           1.4          0.2\n",
+       "2           4.7          3.2           1.3          0.2\n",
+       "3           4.6          3.1           1.5          0.2\n",
+       "4           5.0          3.6           1.4          0.2"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -2925,12 +2999,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e15fc002",
+   "execution_count": 7,
+   "id": "bececaee",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "target_names = df['species'].unique()\n",
     "target_names"
@@ -2938,10 +3023,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "2412d65e",
+   "execution_count": 8,
+   "id": "89c4d761",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'setosa': 0, 'versicolor': 1, 'virginica': 2}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "target_dict = {n:i for i, n in enumerate(target_names)}\n",
     "target_dict"
@@ -2949,10 +3045,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "247d1005",
+   "execution_count": 9,
+   "id": "19b2f7d4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    0\n",
+       "2    0\n",
+       "3    0\n",
+       "4    0\n",
+       "Name: species, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "y= df['species'].map(target_dict)\n",
     "y.head()"
@@ -2960,8 +3072,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "5823cc0c",
+   "execution_count": 10,
+   "id": "16b5e400",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2970,8 +3082,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ceffabbc",
+   "execution_count": 11,
+   "id": "b35398e4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2980,18 +3092,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "c5bc0e0c",
+   "execution_count": 12,
+   "id": "6b41f15f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.]], dtype=float32)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "y_cat[:10]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "2426931a",
+   "execution_count": 13,
+   "id": "f7232f3c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3001,8 +3133,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "7009e020",
+   "execution_count": 21,
+   "id": "22fb3e06",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3015,18 +3147,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "baac0519",
+   "execution_count": 22,
+   "id": "da7f7f71",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "4/4 [==============================] - 0s 36ms/step - loss: 3.7988 - accuracy: 0.2685 - val_loss: 1.9674 - val_accuracy: 0.3333\n",
+      "Epoch 2/20\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 1.9311 - accuracy: 0.2315 - val_loss: 2.0901 - val_accuracy: 0.0000e+00\n",
+      "Epoch 3/20\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 1.7566 - accuracy: 0.0556 - val_loss: 1.1289 - val_accuracy: 0.3333\n",
+      "Epoch 4/20\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 1.0680 - accuracy: 0.3056 - val_loss: 0.9671 - val_accuracy: 0.3333\n",
+      "Epoch 5/20\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.9063 - accuracy: 0.4815 - val_loss: 0.6677 - val_accuracy: 0.7500\n",
+      "Epoch 6/20\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.6093 - accuracy: 0.7407 - val_loss: 0.5834 - val_accuracy: 0.7500\n",
+      "Epoch 7/20\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.5498 - accuracy: 0.7407 - val_loss: 0.5333 - val_accuracy: 0.7500\n",
+      "Epoch 8/20\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4956 - accuracy: 0.8056 - val_loss: 0.4828 - val_accuracy: 0.8333\n",
+      "Epoch 9/20\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4419 - accuracy: 0.8889 - val_loss: 0.4515 - val_accuracy: 0.7500\n",
+      "Epoch 10/20\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.4412 - accuracy: 0.6944 - val_loss: 0.4246 - val_accuracy: 0.8333\n",
+      "Epoch 11/20\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.4232 - accuracy: 0.8519 - val_loss: 0.4126 - val_accuracy: 0.8333\n",
+      "Epoch 12/20\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.3882 - accuracy: 0.8241 - val_loss: 0.4019 - val_accuracy: 0.8333\n",
+      "Epoch 13/20\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.3802 - accuracy: 0.8056 - val_loss: 0.3933 - val_accuracy: 0.8333\n",
+      "Epoch 14/20\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3641 - accuracy: 0.8796 - val_loss: 0.3605 - val_accuracy: 0.9167\n",
+      "Epoch 15/20\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.3393 - accuracy: 0.9537 - val_loss: 0.3420 - val_accuracy: 1.0000\n",
+      "Epoch 16/20\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3332 - accuracy: 0.9352 - val_loss: 0.3369 - val_accuracy: 0.9167\n",
+      "Epoch 17/20\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.3345 - accuracy: 0.8981 - val_loss: 0.3221 - val_accuracy: 1.0000\n",
+      "Epoch 18/20\n",
+      "4/4 [==============================] - ETA: 0s - loss: 0.3078 - accuracy: 0.96 - 0s 6ms/step - loss: 0.3074 - accuracy: 0.9444 - val_loss: 0.3176 - val_accuracy: 0.8333\n",
+      "Epoch 19/20\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.3081 - accuracy: 0.9352 - val_loss: 0.3109 - val_accuracy: 0.9167\n",
+      "Epoch 20/20\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3107 - accuracy: 0.9259 - val_loss: 0.3034 - val_accuracy: 0.9167\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x226f006ca08>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.fit(X_train, y_train, epochs=20, validation_split=0.1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ee18629d",
+   "execution_count": 23,
+   "id": "d84d95a0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3035,18 +3224,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "5e034507",
+   "execution_count": 24,
+   "id": "7dbaab2b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[9.3513012e-01, 6.3911341e-02, 9.5858186e-04],\n",
+       "       [4.2413408e-03, 4.4961166e-01, 5.4614705e-01],\n",
+       "       [3.0909719e-02, 6.7810923e-01, 2.9098108e-01],\n",
+       "       [7.2376737e-03, 5.9461749e-01, 3.9814490e-01],\n",
+       "       [9.5231718e-01, 4.7239341e-02, 4.4344418e-04]], dtype=float32)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "y_pred[:5]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "b56fbe7e",
+   "execution_count": 25,
+   "id": "18656071",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3056,8 +3260,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "8573f46f",
+   "execution_count": 26,
+   "id": "bba99886",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3066,37 +3270,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "72765136",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(classification_report(y_test_class, y_pred_class))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "22b366b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusion_matrix(y_test_class, y_pred_class)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ba70bcd3",
+   "execution_count": 27,
+   "id": "9dcc6cc9",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00         8\n",
+      "           1       0.90      1.00      0.95         9\n",
+      "           2       1.00      0.92      0.96        13\n",
+      "\n",
+      "    accuracy                           0.97        30\n",
+      "   macro avg       0.97      0.97      0.97        30\n",
+      "weighted avg       0.97      0.97      0.97        30\n",
+      "\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test_class, y_pred_class))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "428f2066",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 8,  0,  0],\n",
+       "       [ 0,  9,  0],\n",
+       "       [ 0,  1, 12]], dtype=int64)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test_class, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "4b279da6",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "image/png": 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\n",
@@ -3116,8 +3343,8 @@
     "model er uit zal zien. De code voor het inladen van de foto hebben wij \n",
     "gehaald uit de practicumomschrijving.\n",
     "\n",
-    "Er is te zien dat dit netwerk maar 1 laag heeft, links zijn de inputs te zien\n",
-    "met rechts de activatie functies en de outputs.\n",
+    "Er is te zien dat dit netwerk maar 1 laag heeft, links zijn de inputs te \n",
+    "zien met rechts de activatie functies en de outputs.\n",
     "\"\"\"\n",
     "\n",
     "img = mpimg.imread('../data/opdr5-3-b.png')\n",
@@ -3125,6 +3352,82 @@
     "imgplot = plt.imshow(img)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4e57a19a",
+   "metadata": {},
+   "source": [
+    "5.3.c: Hier wordt voor de softmax activation function gekozen omdat bij dit netwerk sprake is van meerdere klassen ('setosa', 'versicolor', 'virginica'). De softmax functie maakt het mogelijk om een multi-class vraagstuk op te lossen omdat het een decimale waarde geeft aan elke klasse, die representeert hoe veel het netwerk 'denkt' dat het die klasse is. De gegeven waarden aan de 3 resultaten (hoe veel het 'setosa', 'versicolor' of 'virginica' is) bij elkaar opgeteld is gelijk aan 1.0. [bron](https://developers.google.com/machine-learning/crash-course/multi-class-neural-networks/softmax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b2c6988",
+   "metadata": {},
+   "source": [
+    "5.3.d: de learning rate geeft aan hoe snel het model leert. [bron](https://www.jeremyjordan.me/nn-learning-rate/).\n",
+    "we hebben de learning rate aangepast naar verschillende waarden en gekeken wat de loss en accuracy zijn. We hebben van elke learning rate de loss en accuracy van de laatste epoch opgenomen.\n",
+    "\n",
+    "\n",
+    "|learning rate |loss  | accuracy|\n",
+    "--- | --- | ---\n",
+    "|0.1|0.2843|0.9815|\n",
+    "|0.5|0.1696|0.9259|\n",
+    "|0.8|0.1036|0.9630|\n",
+    "|1.0|0.1025|0.9537|\n",
+    "\n",
+    "\n",
+    "Uit deze verkregen waarden kunnen we concluderen dat een lagere learning rate zorgt voor een hogere accuracy maar ook een hogere loss. Hoe hoger de learning rate wordt, hoe minder updates worden uitgevoerd bij elke epoch, en worden de resultaten dus minder accuraat."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccccde89",
+   "metadata": {},
+   "source": [
+    "5.3.e: Er is hier geen sprake van overfitting. We kunnen doormiddel van te kijken naar de accuracy en loss bij elke epoch zien dat deze niet minder worden. Dit geeft aan dat er geen overfitting plaatsvindt. Ook kunnen we in de classification report zien dat het netwerk een accuracy van 0.97 heeft, wat ook aangeeft dat er geen overfitting plaatsvindt omdat dit hoog is."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24bd3884",
+   "metadata": {},
+   "source": [
+    "5.3.f: de _precision_ geeft aan hoe precies de voorstellen van het netwerk zijn , de positieve voorspelde uitkomsten. De _recall_ geeft aan hoeveel voorgestelde resultaten ook daadwerkelijk goed waren. De _f1 score_ geeft een gemiddelde aan van de precision en de recall. ([bron](https://wiki.pathmind.com/accuracy-precision-recall-f1))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aaad13a5",
+   "metadata": {},
+   "source": [
+    "5.3.g: Uit de scores van het classification report kunnen we concluderen dat de betrouwbaaheid van het netwerk vrij hoog is, aangezien de waarden in het classification rapport vrij hoog zijn (niet lager dan 0.95)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85b233fb",
+   "metadata": {},
+   "source": [
+    "5.3.h: uit de confusion matrix is te zien hoe vaak het netwerk een klasse van een bepaald type heeft geïdentificeerd terwijl dit een andere klasse is, ofwel hoe vaak het netwerk \"confused\" was ([bron](https://machinelearningmastery.com/confusion-matrix-machine-learning/))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5922f60",
+   "metadata": {},
+   "source": [
+    "5.3.i: De informatie uit de confusion matrix is van belang bij het trekken van conclusies omdat hiermee duidelijk kan worden wat voor fouten het netwerk maakt."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e79ded8b",
+   "metadata": {},
+   "source": [
+    "## Opdracht 5.4: Eenvoudig Classificatie vraagstuk"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6042b086",
@@ -3138,7 +3441,7 @@
    "id": "ea5be5e2",
    "metadata": {},
    "source": [
-    "## Opdracht 6.1: ZTDL6: Conolutional Neural Networks - MNIST"
+    "## Opdracht 6.1: ZTDL6: Convolutional Neural Networks - MNIST"
    ]
   },
   {