machine-learning/SVM_Separate_XOR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-05T16:18:27.321539Z",
     "start_time": "2020-08-05T16:18:26.681553Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "mP6nNNTVNGhz",
    "cell_id": "00000-67ae40e1-7c7f-4360-ad7d-9631bd26f5bf",
    "deepnote_cell_type": "code"
   },
   "source": "# Import packages to visualize the classifer\nfrom matplotlib.colors import ListedColormap\nimport matplotlib.pyplot as plt\nimport warnings\n\n# Import packages to do the classifying\nimport numpy as np\nfrom sklearn.svm import SVC",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xHZroysZNLd_",
    "cell_id": "00001-2747f704-e56d-4d52-b52e-dfb4780a80e9",
    "deepnote_cell_type": "code"
   },
   "source": "def versiontuple(v):\n    return tuple(map(int, (v.split(\".\"))))\n\n\ndef plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):\n\n    # setup marker generator and color map\n    markers = ('s', 'x', 'o', '^', 'v')\n    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n    cmap = ListedColormap(colors[:len(np.unique(y))])\n\n    # plot the decision surface\n    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n                           np.arange(x2_min, x2_max, resolution))\n    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n    Z = Z.reshape(xx1.shape)\n    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n    plt.xlim(xx1.min(), xx1.max())\n    plt.ylim(xx2.min(), xx2.max())\n\n    for idx, cl in enumerate(np.unique(y)):\n        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n                    alpha=0.8, c=cmap(idx),\n                    marker=markers[idx], label=cl)\n\n    # highlight test samples\n    if test_idx:\n        # plot all samples\n        if not versiontuple(np.__version__) >= versiontuple('1.9.0'):\n            X_test, y_test = X[list(test_idx), :], y[list(test_idx)]\n            warnings.warn('Please update to NumPy 1.9.0 or newer')\n        else:\n            X_test, y_test = X[test_idx, :], y[test_idx]\n\n        plt.scatter(X_test[:, 0],\n                    X_test[:, 1],\n                    c='',\n                    alpha=1.0,\n                    linewidths=1,\n                    marker='o',\n                    s=55, label='test set')",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 729
    },
    "colab_type": "code",
    "id": "GsS2REyENLmE",
    "outputId": "f970f226-3ebb-4c88-d686-c48d68d780b8",
    "cell_id": "00002-cb89da2f-6db7-4d92-842f-31da842ce30d",
    "deepnote_cell_type": "code"
   },
   "source": "# Create Dataset\nnp.random.seed(0)\nX_xor = np.random.randn(200, 2)\ny_xor = np.logical_xor(X_xor[:, 0] > 0,\n                       X_xor[:, 1] > 0)\ny_xor = np.where(y_xor, 1, -1)\n\nfig = plt.figure(figsize=(10,10))\nplt.scatter(X_xor[y_xor == 1, 0],\n            X_xor[y_xor == 1, 1],\n            c='b', marker='x',\n            label='1')\nplt.scatter(X_xor[y_xor == -1, 0],\n            X_xor[y_xor == -1, 1],\n            c='r',\n            marker='s',\n            label='-1')\n\nplt.xlim([-3, 3])\nplt.ylim([-3, 3])\nplt.legend(loc='best')\nplt.tight_layout()\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": "<Figure size 720x720 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light",
      "tags": [],
      "image/png": {
       "width": 712,
       "height": 712
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 783
    },
    "colab_type": "code",
    "id": "01tws6QLNLo4",
    "outputId": "d4a81201-7391-4d94-dff8-e7e85b92e658",
    "cell_id": "00003-15f6d450-2b2a-4fb4-9410-be713299c72f",
    "deepnote_cell_type": "code"
   },
   "source": "# Create a SVC classifier using a linear kernel\nsvm = SVC(kernel='linear', C=1000, random_state=0)\n# Train the classifier\nsvm.fit(X_xor, y_xor)\n\n# Visualize the decision boundaries\nfig = plt.figure(figsize=(10,10))\nplot_decision_regions(X_xor, y_xor, classifier=svm)\nplt.legend(loc='upper left')\nplt.tight_layout()\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 720x720 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light",
      "tags": [],
      "image/png": {
       "width": 712,
       "height": 712
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 783
    },
    "colab_type": "code",
    "id": "qFXDy5bBNLrh",
    "outputId": "ea07f403-05db-46ca-d852-73d6d143a071",
    "cell_id": "00004-9db194c8-0b02-4342-ba83-5a1ad0dd7d0e",
    "deepnote_cell_type": "code"
   },
   "source": "# Create a SVC classifier using an RBF kernel\nsvm = SVC(kernel='rbf', random_state=0, gamma=1/10000, C=1)\n# Train the classifier\nsvm.fit(X_xor, y_xor)\n\n# Visualize the decision boundaries\nfig = plt.figure(figsize=(10,10))\nplot_decision_regions(X_xor, y_xor, classifier=svm)\nplt.legend(loc='upper left')\nplt.tight_layout()\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 720x720 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light",
      "tags": [],
      "image/png": {
       "width": 712,
       "height": 712
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 783
    },
    "colab_type": "code",
    "id": "Qm5lWDEFNLud",
    "outputId": "c72522b5-8706-4174-c98c-84b0e0231134",
    "cell_id": "00005-9410b834-2707-47dd-9284-47192dfe455d",
    "deepnote_cell_type": "code"
   },
   "source": "# Create a SVC classifier using an RBF kernel\nsvm = SVC(kernel='rbf', random_state=0, gamma=1, C=1)\n# Train the classifier\nsvm.fit(X_xor, y_xor)\n\n# Visualize the decision boundaries\nfig = plt.figure(figsize=(10,10))\nplot_decision_regions(X_xor, y_xor, classifier=svm)\nplt.legend(loc='upper left')\nplt.tight_layout()\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 720x720 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light",
      "tags": [],
      "image/png": {
       "width": 712,
       "height": 712
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 783
    },
    "colab_type": "code",
    "id": "7o9SQTF5NLyQ",
    "outputId": "7b57051a-f762-4277-c05c-d7500ccfe44f",
    "cell_id": "00006-050d16f8-6213-4b16-a3c4-3dc0b75e4c96",
    "deepnote_cell_type": "code"
   },
   "source": "# Create a SVC classifier using an RBF kernel\nsvm = SVC(kernel='rbf', random_state=0, gamma=10, C=1)\n# Train the classifier\nsvm.fit(X_xor, y_xor)\n\n# Visualize the decision boundaries\nfig = plt.figure(figsize=(10,10))\nplot_decision_regions(X_xor, y_xor, classifier=svm)\nplt.legend(loc='upper left')\nplt.tight_layout()\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
    },
    {
     "data": {
      "image/png": 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+SUCy+Y+6PnGidezc4qJ7P0mq1ebzkJrPp1qNd11Ra/++Je37uBovHJc2HSIcI9MmJne4/x3Qm5x5QwdkSeOS/gdJD1uWNb10+/UAHhdIJG+s3NiYG4onJ1tnMiep8pq2sD+oNL9I8IfjickdcS8HiB0hGVIALRaO43xXUoL+yQfCl5ajrv0zpE+daj6fqMK+Ca0d/hcJUuvBM2NjyW43IRwDnU1M7pB87Rbj4+71JLbIYTCcpAeEIE1HXccV9k3p4477RUJYCMfA6ryQXLn4pGaOb1R9YVQzM1vYt5oRQbRYAPBpP+q6XG62W/h/TZ8U3cJ+mM/DtNYOf0j2EI6B9JuY3KGpPQc0tWu/SiN/oPr0+6pU3BfwaWixQndUkIGApemo6/aw728vkMILiaZVbdP0G4FKRYRj9G5yUmo0Vl7P5dxX/1mwVDKekKTyC6qc+W3NnJHq2kxFOcUIyEAICoXW3lQv8CUtTMUZ9k3p447rRUIYqlWpfuYy4Ri9azSkjRs7X8+gickdmqg+J0k6fHy77OlnVKm4hwfv3k2PcpoQkIGQpOWo67jCvilV2zT9RkCS8iNzmth1UhJlL2AgSyl4oiBVD56Szo5IkmrT90jaSkhOCQIygDVFHfZNq9qu9iLBhEkbvarVpHxjPu5lAKlx5Pkry83I+48tqHZ0TDMzVJTTgIAMwDgmVm07vUgwZdJGL7yNecVNRzk+GgjS0n/sR/Sy9h9Ts6J85r+VtMW4/xegNwRkAEYyvY87SfORmVoBRKBQ0BG9vHx3/7EF1Y49IEJyMhGQARjL5D5u0yZtdEM4xlByue5TLLCSLwl7FWVCcjIRkAFgQKZM2lhVY45wjMFlZZRbGJYqyl5InpnZIone5KTgoBAAGFAch6gMYiL3UtxLALKpUNCRx1/WuN7Q7jPflqb/s2rHLnPISAJQQQaAAZg2aQOAoQoFHSlccd+uPKe904dUOyYVClviXRdWRUAGgAGYOGljVZyIBsSvWNRU5YD2Th+SfVAqPU9INhUBGQAGZPqkDduWNDsr5cSJaIAp2kKyNrshuVSKd1loRQ8yAAzB1Ekb3vSKc9se4l9ewDTFoqb2HFBp5A9UapSVv/ifZZfn4l4VfAjIAJBGjTnCMWCyYlETkzs0MblDU3sOKN84S0g2CAEZAAAgTksVZUKyOQjIAAAAcSMkG4WADAAp0T5/ueV+LifNz6+8cSIaYA5CsjGYYgEAKVCtSouL7hSN556TnJ/O6vmP/7Fy1bs1UXiLUW5AUvinXJR3qTQ5GveKMokKMgAknOO44fjUKXdPnnP+vP6nW/5fvZSbUGPxBuNO9gOwBirJsSMgA0DCefOXx8akn/1Mcqx1eik3oa+N/VgHH/mBMaPnAPSBkBwrAjIApID/JD8P4RhIuKWQrNlZQnLECMgAkAKOI5040Xrt+RNfoL0CSLpiUefufZKQHDECMpAxq046SKEsPF8vHJ86JX3yk1L5lv9LXxv7sV469QuEZCANCMmRIyADGVKtukHKC0xesKpW411XWLLyfC1L2rDB7UEeHXXvH3zkB/ra2I+V23CVNgsgDQjJkSIgAxnhn3TghUav6ri4mL7Kataeb6Hg9iB7YdgLyROFt+JdGIDgFIvuEfKE5NAxBxnICP8mrlOn3JvkVh39wSotsvZ8pZXPKY3PEci8Uknn7Ie088JrsstiTnJIqCADGdJp0kFaw6KUvecrSWrMaSL3UtyrABCmUqlZSbbjXkw6EZCBDOk06cDfo5s2WXu+dnlO+cZZKZ+PeykAwlYqqbTpUNyrSC1aLICM8Pfgem0G3n0pfZXVrD1f25byjbPuzNRiMe7lAIhKY04SbRZBo4IMZIR/0oEXDr3T1zZsSFdYlLL3fCWpmDtKOAYyZGLXSTbshYQKMpAhhYJbWfVPOkhbJdUva88XQMYUizqnJ7XzzaNs2AsYFWQgY7I26SBrzxdAxjAfORQEZAAAgCRjPnLgCMgAUqN9OsXHH6/+fgBIDf/oN0Ly0AjIAFKh/VjpalV68UXptdfc+2k9ZhoAlnkhudFQpRL3YpKNgAwg8dqPlf74Y+kHP5Deekv64Q/d+2k+ZhoAlo2Pazz3/bhXkXhMsQCQeJ2OlXYc6e67pdlZLZ80leZjpgFg2cKC6meZjzwMKsgAUqH9WGnLkp5+uvVjCMcAUq9Q0JHPPUcv8pAIyABSof1Yacdxe5D90nzMNAAsY/Tb0AjIABKv/VjpUkm69Va3B3nTJvf+2FizR5mQDCD1CMlDoQcZQOJ1Olb6C19w3/f5z0vr1jXbL9J6zDSQWZOTUqOx8nouJ5XL0a/HJMWiztUf0s4rf6FqdVSFQtwLSg4CMoBUaD9WulCQvvxlNxxLHDMNBMLEMNpoSBs3dr4OKZ9XvjEnaUvcK0kUAjKA1GgPv+vWrf5+rMLEIIT4EUaTqTGv2vE5FQpMtegVPcgAgJW8INR+IwgByVIsamrPAeUbZ+lF7gMBGQAAIM2WQrIWPuI00R4RkAEAADIgP0IFuVcEZAAAkFy5nDQ/v/KWy8W9MvMs9SJjbWzSAwAAvcnlum/ejAubRntTLGqqckB7pw/JLu9SaZINe6shIANAStizBzRhP+SejDIsE4MQ4kcYTTZCcs8sJ4Yjpe666z7n2We/F/nnBYA0s21JF87r3LaAQjKAdKpUtHf6kOo5QvJTT1lvOo5zX/t1epABICVKJUnbtutw42txLwWAyYpFTd3x1bhXYTQCMgAAAOBDQAaAlLFnDyz1WwBAF/k8h4esgoAMACnitVnsvPAaIRlAd5ywtyqmWABAypRKkl3eFPcygHSYnOw+0SXpUz2YatEVARkAUupw42uaiOMTpzlQIHsaDWnjxs7X04CQ3BEtFgCQRrnR+HqRvUDRfktLoADShqkWK1BBBoAUKpUk23Z7kc8FdXhI0lHZBtAjKsgAkFKlkqRN9CIvo7INdMdUixYEZABIOQ4OAbAmplq0ICADQJot9SIfLr8b90qAZMrlpPn5lbdcLu6VBY+QvIweZABIMa8X2b5wQBNR9SLnct17fYGkyVp/un+qhf2lzG5fICADQMpFPhc5a4ECSJtiUcXyUdn6UtwriQ0tFgCQEZnvRc7Sr8qBIDSy22ZBBRkAAuI4kmV1vx+n/K5R2W8ekMqHNDG5I+7lxIPKNtCziV0nVZl+MrOHh1BBBhA4x1n9fhpVq9KJE83n6jju/Wo13nV5ikUpf+92VRpPSpVK3MsBYDr/hr0YzhuKGwEZQKBMD4phcBxpcVE6dar53E+ccO8vLprzAmH3bkm5DkfmAkAnxaKKuaNxryIWtFgACIw/KErSI480g+LYmFktB0GyLPe5Su5z9Z7/2Jh73aTnXF8Y1eGz+zQR90IwPE4GRFSuXJa0Je5VRIoKMoDAeEFxbMwNiZOTzXBsWlAMmj8ke0x7zoWClP/cFtmNZ5iLnAacDIgITOw6qfzVi5mbi0xABhCoJATFMHhtFX7+VhNTFItSfs9WVRZ+m15kAGvz9SJn6X8ZBGQAgUpKUAySv+d4bMz97bZXRTfxue/eLWlkQ9zLAJAUxaJ23/Bu3KuIFD3IAALTHhT9PchSeivJliVt2NDaSuJV0TdsSOdzBpA99TOXVa1uUaEQ90rCR0AGEJgsB8VCoXUTovfcw3jOJs9bBpBORz73nPZOH1LtmFQopH/DHgEZQKCiDIqmaX+OYTznatWdFOJ9Tb2q/YYN6quqwzSLAZg2NSKX674eIGjFoqYqB7T3TEWVyhYVi3EvKFwEZACBGyQoUhVdW1Bj9AoFaWZmi+zpZ6TyC9k9Wa9f3tSITtfjwCg3RG33bu0+e0Ez+nzcKwkdm/QAxC6Lh4sMIsgxekyzADCQhYWlXuS4FxIuAjKAWCXlFDpTBDlGj2kWAPpSKOjI4y8rf/WiasfSHZIJyABileXDRQaRxTF6AAxSKGjqya8rryuamYl7MeEhIAOIXVYPF+mXF46nplrnLU9NEZIBRGv3yIW4lxAqNukBiF23qighuZVlSefPS7feKu3b597ft096+233Ol+rkEU9NcK0qRlAhhCQAQRmkEkUWT1cZBCOI23f7n5tTp50vzYnT0qzs/1NscCAog6lpk3NADKEgAwgEIPO583y4SL98n9tTp1qvoigXxsAgkVABjC0YefzZvlwkX55Xxvvay3xtQKAoBGQAQwtiMpmFKfQpQH92gCMsDwPeUtfp3gmBVMsAASCSRTha+/XLpWa4/FOnJA+/jjuFQLIhKV5yON6I7XzkAnIAALBfN7w+fu1b7zR3aC3b597f8MG934a/6HKrFxOmp9feQtragbQD+/QEF2JeyWhCKTFwrKsP5T0G5LqjuOk/4BuAC2YRBGdQsGtFJ882fz67tvXvM80ixRhlBsQm6B6kP8fSf9S0jcDejwACcIkimitW8c0CwAIUyAB2XGcqmVZO4J4LADJxCSKaDHNAgDCwxQLIOMGOdyjGyZRRCfWaRac8AYg5SILyJZlTUiakKTbbtse1acFsIpBD/dAvILq+Z6ZkbSw2P8COOENQMpFNsXCcZzDjuPc5zjOfRs3bo7q0wLown+4hzdtwgtZi4vJnz7Rvv6kPx+/bj3f3jSLXsJxpSLVp99XceRfS8Vi+IsGgAShxQLIqDQfW5yFyvgwPd/VqlQ/c1ml3AuamNwR6joBdECbkvECqSBblvWSpD+X9N9YlvWeZVmUI4AESOPhHmmvjPsN0/OdH5nTxK6TwS4IQG+8NqX2G21KxghqisXXgngcANFK47HFaa6MowdU5gAEgBYLIKP63egV5LSLsDECLWS5XPcQGjc2EAIIAAEZyKh+DvdIWk9vGivjQRp4eoWHSiziwm8IEBECMpBhvWz08vf0Sq2VZhOPNU7LsddhVey96RWlHNMrkED8hgARISADGbfWRq+k9fSm4djrsCr2TK8ADGFymxIkEZAB9CBpPb1JPvY67Ip9c3oF1WMgNrSDGI+ADGBNSezpTeqx10mr2BuHyhyAABCQAbRor1B+/LF08mTye3qTJGkVe6NQmQsGm+GQcQRkAMs69b6ePCmdP5/snt6kCatiX6tJ+cb8cItDNpi6GY7fECAiBGQAktbufd23L5k9vUkT1hQO25Z04byKm44yvQLJRfUaESEgA5DUf+8r4TgcoU3haMyptOkQ0ytWQ1sB0JfDx7erfvVW7Y57ISEgIANYRu9r9DrNOw5rCsdE7iVJpeEeJM1MbSsADHS4/K7s2QPStq1GHhg1rHVxLwCAObr1vjpOPOtJu2q19evrff2r1WAr9rYtaXZWyucHfxAA8FSrqi7cL23brlJKX3NTQQawHNC8Xtf775cefXRl76sUzuluWRTVCYVe7/G5bQ9JxZT+S4bgsRkOaxkZSfVrbgIykHH+yRUbNrjhWJJef7219/X118M53S2rIpt33Jhzw3FayzwIBz3XyDhaLIAM81cxT5yQHnzQvX76tHtdcsPagw+2fpx/0sLiIi0Yg/KHZA893wAQPyrIQIb1U8XkdLfgJfGEwlSjrQDoyeHj21Vr3KMUd1gQkIGs63VyBRMughXWvGMMgbYCYE2Hy+/Kbjyj/J6tqR6pTosFkHG9Tq5gwkWwus07HhsL7oTC5ekVABCEalVVPZj6cCxRQQYyrdcqJtXOcIQ171hqm17BBj0A6AsBGciwXk9tC+10N4R3QiHTKwBgYARkION6rWKGWe1ERnCUM4CEICAD6LmK2U+1s9MRyoTpjIv7KGcCOjCULEyv8BCQAQTOf/iIiYeKEN4zKu6ADiRYVqZXeAjIAAIV1RHKgzI9vGcW1V3AaNWF+zMTjiUCMjqguoZhRHaE8gBMD++ZRnUXMFe1KumJuFcRKQIyWlBdQxBMPVTE5PCOjKOCDoPtP/aEalfHMtF77OGgECzzV9e8AyC86triIgdCoHcmHyriD8kewnFEcjlpfn7ljaOcmxX09hsVdMRs/8HNbjjOUHuFRAUZPlTXEATTDxXpFt57XVcmW5CCqm6u9rFRzGvO5bo/DwArZDUcSwRktDH1V+NZkJbgZfKhIsOG98y2IKWlP5hWBaBnWQ7HEgE5U3oJYMNW1zCYtAUvUw8VGSa8s8EvZPA7xFkAACAASURBVFR3AWMcLr+r2tX/LrPhWCIgZ0YvAcz0X42nVVqDV2hHKA9p0PBOC1KPBm3HoLoLGCFr8467ISBnQK8BzORfjacZwSt6g4Z3WpB6kJZ2jKhRQYdJ7sh2OJYIyJnQTwAz9VfjaUfwSoaktSAdbnxNE71+8GqVX4SPCjpMUK2quvCExH/2jHnLin5GW5n6q/E0M3ksGlztLUjlsvunfyyiSfK7RmU3ntHh8ru9/YXVxowxng3IhOV5x1kaeNwFFeSMSFrlK0vo/U6GpLUgFYtSpbJV9vQzmrC/PNwYNaqbQOplfWpFOwJyBhDAzJa04GWaKMfjJa0FqViU7DJVXgCrIxyvREDOAAKY+ZIWvEwRx3g8WpDWwGYzIBmqVUm+tgrCcQsCckYQwMxH8OpPWsfjJR7tGIDxDpffVWXh9yVJdW1Wfs8WwnEbAnKGEMCQJozHCxiVXyAT/HOOd++WdiuZB1KFjYAMILEYjxcgKr9A6nEISO8Y8wYgsRiP15v9V55f7jcEkDGVilSpEI77RAUZQCLFOZ0lyskZwxp/bFS1o2Paf0w6opfd36UOehw0gEQ5XH5XlcYhKbdR9YVReo37QEAGkEhxTWeJY3LGMNw1bVXt6JhUO+he4DhoIL0qFUnS4bP7ZM8ekLZt1/g4vcb9IiADMUhSBdJkUU9nSerkjEJBqh1nsx2QeratvRe/tVwx1rYtQ50RlGUEZCAA/QTepFUgTRfldBYmZwAwxlKleFm9rp0XXqNiHBACMjCkfgLvMBVIqs5mYHIGgNj5KsUeKsbBIiADQ+g38A5agaTqbI5ukzNMDMntP3+/U39ef1R9Ob4FAehfpSLV6y2Xdl54Tdq0SaXJ0ZgWlX4EZGAIgwTefiuQSe17TbJu1fo4J2f0q/1F1QO/Oqrv/NtHtPfoDk1t/I40P7/yL3EoCGCWSkV7pw+pntvVen2TCMchIyADQxok8PZTgaTvNVprVevjmJyxmk5hXlr5ournP5esjTn97cKdcnb9gqx/zKwnwEjVqlSrSZL2XvyW6rldhOEYEJCBIfUTeAetQNL3Go1eqvVRT85YzWphvtOLqh07pPXvzmbv54a5z0iKalV7j/6e6jd8XRq5ScpRKY4LARkYwmqB13GkRx9tBicvVA1SgUxS3+uw4tyM2Gu1PsrJGd2sFea9a/4XVXffLf34v/Tw4GkLlMx9Nl/afub60V4xvuEOjT++hf0lMSMgA0PoFnjfeUc6d675ce2/pu+nApmkvtdhmbAZMSnV+rXCvNQ8dttb+1tvSes+7uHBCZSIWpA/c0kK29Wq9h97QjU1K8bjj40Sjg1AQAaG1B54JWnnTun06WaVd61NdauFr7hOjIuaKZsRh6nWR1397hbmJXfNr74q3Xmn9PTT0smT7u3j+c/oG5cf11O9fpJLl6Tr1923HUfLM6RMDBuAlJwXeMvh+AEqxgYiIAMBaB/l5rVWBLWpzqS+17CYsBlxmGp9HNXv1cL8jTe64Xh21g3G+/ZJf/7n0pUPNmj+45t7D+/Xr0vr1zff9oKHaWEDSBLCsfEIyEAIwvg1vQl9r2GLu71hmB7xqKvfvYT5Bx90w7H3gmP9eumTI9f1by4V9Ht/eEAqMskCiEy1qsPHt7tvLhCOTUdABkKQpU11QTLh6zZItT6O6ncvYb7TCw77+RE99+wu7Z0+pKlKCkJyL/2muVz3jwGi4FWMr45Jd2x1e43HOejJZARkIGBZ2lQXJJO+boNU6+Oofq8V5ru94HjgV0f14//UoUfT4w+UjtPsQfZaLcIyyOaqXvpNo+iVTtLGMBOl/EWMF47ze7Ym/jVpVhCQgYBlZVNd0JL+dYur+t0tzK/2guPTn5bWO6s8qD/QlUqdA2gYkrK5qpMkr90EQb6IMCxs7z+4mXCcQARkIARZ2FQXhqR+3UyqfntWe8Fx/rykXtdjWNgwzuSk9N57bpX9v/7X1vfdeGOwLy6oUvfGgK/F4fK7kqTqwv2E44QiIAMhycKmujAk8etmavW72wuO11+XZs5KOnOmObbNzx+4DAgbRvMH1vYZf15rSpCfiyq18Q6X35XdeGa51zifT36rfxYRkAEgAKZWv1d9wXH1qvSpT638SwQuYCBeOKZinHwEZAAISBKr34lHCwgMQThOFwIykEFRn/gG9GSQsGtKC4h3Ooyf4xDUM4JwnD4EZCBj4jjxDeapL4wG3yM7LFPC7iBuuKH1/vXr0i23JPs5oSeE43RaF/cCAETHf+LbiROt0xcWF1cWwJBOhYKkkZv03vXbpUuX4l5OsnkV4mvXWm/+9wX5uebnV96oUseGcJxeVJCBDInjxDeYqTQ5qr/7p3mt//l1febSpdbpCASu3kVZIaYabRTCcboRkIGMiePEN5jpX/zWX2rm+Iymdu3vPoeK2bvACoTj9CMgAxkT14lvSChm7wKualWSdPj4dtmzB5S/l3CcZgRkIENMPPENAEx3uPyuqgtPSCMjqjXuIRxnAAEZmZelkWemnvgGAMaybdmzr0nbtiufl/LiZLwsICAj07I48szUE98AwDi2rZ0X3HDc6VR2pBdj3pBZWR55xolvALAGwnGmUUFGZjHyDGHzXmR5P0vt96Naw1AtRBzljCwiHGceARmZxsgzhKValX70I2nnTunRR91rr7winTsn/eIvRtPC00sLUX1hVIfP7tNEtwdhlBsGldQRgYRjiBYLZFy3kWdpbq9A+BxHWlhww/DJk24wfuUV6dVXpXfecd8X9s9YLy1EhYKkzVtkzx7Q4fK74S4I2eONCGy/mTwikHCMJVSQkVmMPENYLKtZNX71VelP/sR9++abpX373PeF/bPVawtRqSTZ9nZVr9yvierL6d2dCqyFcAwfAjIyi5FnCJMXkk+fln72M/daLhdNOPavoZcWonxeUmMkmkWZJKktAAge4RhtCMjINEaeISyO47ZV+PNXo+Feiyokc2riGvynBF66JF2/7r49O6vllERYTj/CMTogICPzGHmGoH38sRtEX33VDam/8Rvuz9WrrzYDqxeSwzqYhhaiPl2/Lq1f33zbC84m98tieIRjdEFABoAAeZMjRkbcCRY7djTbeXbulC5fbrbwhHkwTb8tRLXGPTp8/HVN0IKMoJg+IpBwjFUQkAEgIP7JEWNj0j/5J24APn1auv9+Nyz/5CfSz3++ssIbRiW51xaiYlGqVLbKnn5GKr+gickdwS4kKEnoGU7CGqNi8vMlHGMNBGTAUEMf8IDIrTU5QpLWrYv2YJpeW4gSEZL9PcPt102RhDVmHeEYPWAOMmCgarV1HrNXbaxW410X1uYPyR4vAFuWO+bNb98+c174FItSfs/W7MxFzuWk+Xn35jhu77G/FxnpQzhGjwjIgGF6OeDBJO3rGXR9QT1O3FY7fOa116QXX2x934svutdNUSxK+Xu3ZyMkl8uSbbu3bdukW25xbxs3NoOzKf2yGB7hGH2gxQIwTK8HPJigl6OMo3ycuK02OcJxpLfflv7mb6S775aeftoNx2+95f7dBx902y9MUCxKFW2X/eYBqXzIzHaLoJncL4vhEY7RJwIyYKBeD3iIk7/SLbWGwX42nQX1OGt9jij6udeaHHHPPe61n/7ULVo6jhuWv/AFc8KxZ0VIzr3U+gGkDCQN4Rh9ICADBkrCAQ9BVbrDrpiHVZ3uFrrXmhzx5S+74dh739NPmxeOPcWiZNe3y278vuzG7zffMTurc/ZD0Ydk08eGSclYI4A1EZABwyTpgIegKt1hVczDqk6vFbq7TY5wHOnkydb3nTxp1ve0nZuBR1uu2faodl54TaXyoYEec+Kx84O9OklCG0TUa7RtHW58LdCHTF1LzXJ7RdwLQZIEEpAty3pM0h9IWi/p/3Yc57kgHhfIon4PeIhTUJXusCrmYVSnBw3dSXrhs5ZSSbLt7bI12f9fvvi+qsdO6YhejqfBPE1zin19tYG5+L65Y/4GQe8xBjR0QLYsa72kFyQ9Kuk9Sacty/r3juP89bCPDWRVrwc8xCmowBd2cAy6Oj1o6E7SC59eDBo2qtWtqh0d0/5jUuH46VU/NpSQZvqc4kpFh8/uW/vjJNmzwQc//yzsTiZyLyWn/5xwjCEEUUEek/Rjx3HekSTLsv5Y0m9KIiADQ+j1gIe4BBX4wg6OYVSnBw3dSXjhEza3aLxVtdpXVNNXun/ghfPZmaDhqVS0d/qQ6jfcIW3esvbH54LPqssHxtQ7/3bAvnAgnv7zfhGOMaQgAvJWSRd899+TtDeAxwVguKACX1jBMazq9DCh2/QXPlEoFNburrDt7bIvuBM0VjOx66Sb6kwyYF9wpeGG4/HHt8Q63nC1L6dtb+/Yf27U94FwjABEtknPsqwJSROSdNttAfZLAYhVUIEvjOAYRnU6Tb3EJuupz7kxp8r0k5qqHDAynPUtJ42Pmz37u+P3xaTvQ4zhOKpxkoiG5Qx5XJVlWb8k6Z87jvOrS/dLkuQ4jt3t79x1133Os89+b6jPCwC9CvofrrQcbJIGdnlO+cZZFXNHe/r4r/30BS1YN6+4PuJ8qJdufWb49cweyGTlsv37EEtFOcZwzP8Tkuupp6w3Hce5r/16EAH5E5LelrRP0vuSTkv6bcdxznT7OwRkACZ4fPJ+3dSor7j+US6vY+XVN5BRLTKH3bUcs9Kxi/frU9dXfs//bn1ej9+x+ve8V1kLx57l70PDDctTeyKsKMdcOe72WyUTT0BFq24BeegWC8dxrlmW9T9K+k9yx7z94WrhGABMcVOjroWNmzteXwu9xOboJxBNqXsIzmiuDUzz+zAqu7xLe6cPaffBd5ffXxg5PfgM7E4qFe0/4x5gU1uIr+c47MOOEI9AepAdx/mPkv5jEI8FAMOoVnv/2K8sSB2Hey10f5ygf13az3p7wa9zYYLS5KgqlS9pRl9avlY784Cqx95wZ2B30ssPr/cfzMyMdr55VNq0Sfldo8or3vbnsA47Qnw4SQ9AalQqUv3MZeVH5nr6+GsL17S4uLji+k3ONc0cn1lxvb4wqlptS2BVqmpVqh3rfb29qB0bVen5HkaEhYj2E0grA2u1ukW1Yw9o//GRjh9fOP766mP9vIrxyIhmFrZJmzapNDna/eMjFNZhR4gPARnoU9AVv15RGWzV/n2YmZHq0++rlHvB3SDUi+mf6DOfmF15/do1Te3av/J6va6dF16TbW/X+Hj/a25XO3ZZ43pDR3YFd/jo3ulDsg9K44+7ITnqnxs2K6Eb9/u/RTMzv9bx/bXpe9xT/B47v/KdMzPujOjcLuV3uaG4ZMjgEibbpBMBGehDGBW/XgRduUy6jt+HhUWVcv96qQLV47+cpVLnU9Xm57v+vvac/ZD2XvyWZo53+Ht9GtcPdeTxl6VCQP/ST05qSn9ff/uz26SX1kmSrr+0Xjdu27LmpsMgDHoMdy+PS0U6HVabge2d4lc9/v0V75tZ2KZ6brsxFWO/tJ2SCRcBGeigW5U4jIpfTwKuXHpMr+j1/X3otwkxl+t8xHAu1/3vlEruvNcg7N4d7Deh0ZBuv12f+clPli/96KO7lHtv7U2HQQhjsxIV6ezwTvGb0daO7zelYtwJp2SmDwEZaGOX55TX5Y7vC7zi14cgK5ee2vEtRlZkpIi+D+XyYH8v7sMQ1vLpTzfffP+nWtSNkX3qIDcrhVWRhrlM/09rNUy2SRcCMjKpa2XyuG9+ZydBV/z6EWTlcsne6UOyy7s0/liwIbnbl6jX/m2jvw9JNOS8+34/VVCblZI6PouWECD5CMjIHNuWdOWy8rqy4n35q1ejHW7fr4DXNVU5oL3ThzRz7IZgHnBkg+oLo5K2rMivq33d2wXyfZic7N4+MWjlOIE2b5jT3344Krs8F/pvC8LYrBT0+Kyww2u1Ki0sSI8+2mwJeeUVaWSE13RAkhCQkWqVSuv9el3ShfM6t+0hdW3mjaF9IjbFoqaqXw/0Ifce/T3VjkkzM81RY97XvbTpUOcd6p0M+31oNDpvwOsUmtPs05/WZy5dUr5xVrb9pVA3eoaxWWmtinQ/ATfsfmbHkX70I+mdd9z7jz7qhuOTJ6XPflZ68EEqyUBSEJCRWrYt6eL7Gs81d0TvXljQkW0Hs3sWbCcBl7Wm9HXtP/aEdLY563T3woIKm04vTZjYEejng0+3TYef+YyK80dl+w5tCEuQm5XaK9I33ij98IfNavK+fW747CXgRtXPvHOndO6cu65Tp9xvh2W514dF6wYQHQIyUsWrGK9asSwQjkNVKHQ+KYvfL4dvtdaR8rstd8MMW0FtVvJXpL0w/NOfSrfe6l73QmgvATeKfmbLcqvGkvQnfyJ9+KH79j/4B82Wi0ExzQOIFgEZqVGpuAdFjOe+T8UybvyLbbQkhS1/RdoLuFNT0uuvu2/3E3CTehww0zyA6BGQkUjtvcWS7xQ1r2Js2r/0QNyuXNZrr23Rz3+erLDV3q7RHnDbdXsOYR8H7G3IO3lS+uQnmx0vJ5cOdhy0ihz3NA9aO5BFBGQkjr9S7FfMvZ6sijFTFsI1yCEgKTbx2HlVjl7UG/9OeuA3t2hsLFmj06TOAfcb33A3wHnh8+OPO/clR3Uc8Llz7uM8/HBzk96rr7rXhxFX9TtJv20AgkRARiJUq9LMjPt2ffp9jd9wSkcea+tzTdr/rZmyEC5eZLQqFDQ1c0B7z1T04x9v0e/+brJaDToF3G98Q3rrrebUiEcekV58UXrvPTegtm8WDPs4YMuSfvEX3Q15XmD3epJHRob7HGFXv7t9Tlo7kFUEZBivWl06WnjkLyVJu29YWDpFLWGBGIjb7t3affaC3nY+H3nYGlZ7wJXcyvG5c247w6lTbuX4ww+lu+92N/W1P5cojgPu9DmG3aAXVfW7XdytHUCcCMgw2nI41hvNinGhIIlwjACE1eZicPuM40iXLkmXL0cbtoLQHj696uzp0+5zkqSbb5aeflpat67zYwwzYaPXXtygjxyOovq92udO4sZGYFgEZBirJRxTMUYYwmpzMbh9xrKk9eul++6LPmwFwb8+b+2vvtq8lsu5leSgQ1zcvbhRVL87iaO1AzABARlGqValWm3pzhXCMRCGfL414EQVtoL28cduz/HPftacGrFpkzsGTgp2OoUJvbhBV6bXEldrB2ACAjKM4VWM81cvqpg7Ko24O+9TG46ZsoAYRR22guY4bqX4vffcnuOnn24eHHLrre6pe0EefJLFXtw4WzuAuBGQYQR/OJ568uu+ULwjzmWFiykLybZan3Hcuq1NkqzfiXYtIfHC28MPuxvy1q1rDW9Bv67Oai9uXK0dQNwIyIhdSzjec0AqFONeErA2g/uMu67tJz+Rbop+OWGJMrxluRc36b9tAAZBQEasVoTjIuEYEQq6zeXSJenaNTdBzM42r1uWdMstwVSXDZ6QEYcowhu9uED2EJARK8JxQqQ1lAW99uvX3aS0dWvr9fl5ybaD+RwRVq45YthFLy6QPQRkxKZalfIjc5r6HOHYeCa3EyAUcY81Mw29uEC2dBmlDgDIKv9YsxMnWlsMFhfd+1lELy6QHVSQEYtm7/G8tHt33MtB1NLQstGpf9lxpE8Y8L/Vbr3VmzerMP+6atP3qFLZ2vUXN1kdawYAHgP+T46s6T7SDZmRhpaNTkG+VOr8vKK2youMCUkqvyB7+pmeQnLWxpoBgERARsQIx4hce7X6gw+azaS33NK8HkT1OorDXwL4HBOPnVf1+Pc1o61dPybLY80AgICMSNWOzzHvOImSfOpfe7X6gw/cNojr11uvB1G9jqI9JILPwVgzAFlHQEbkpu74qlQsxb0M9CMpfcEIBGPNAGQdARmRqVYlLXwkJaDoiBS6dEn6+c/dt69fd/88f97988Ybzegd9ot5I2OnsWbekc6erM5FBpB+BGREwus9Htcb0vh43MtB3HI56b33Vs4Lsyw3GIYRAL1Q3P75HKfz++JmwEZGf/hlLjKALGEOMkLn35h35PGX+dcUbgC+5Rbpzjtbb1u3JmuSRUYwFxlA1lBBRqiYWoHYeRsMO6U475rjJGPDYUyYiwwgawjICNXy1ArCcfIl9XAPb22lkjvBYv361vdfv+5Ws7s9h6Q+77UsLKhe7/3DmYsMIEtosUDopu74KuE4Dbye2PZb2lsi0vi8CwUd2XxQunBett3bX+k2Fzkp7RXt6zRl3aauC8g6KsgIDVMrDJPWSmivcjm3gnztWut1yzKzvSLs2dOlks7ZD2nnxe+ueqKelPy5yP1uMGyfzhHWtA42PgLmIiAjFEytMJABUxFaRH34SNJeBESx3vFxja9xop6U7LnI/g2GUmu4HxtbGX6jCq39rgtAtAjICJw/HKdiakXWK69hietrx/dzIJ3mIpteOZb622AYZWhl4yNgNgIyApW6cCyZV3ltR+Drj+nfT4O1h7akhLheNxhGHVrZ+AiYi016CAzzjmMS1SayXE6an195M7F/N0hpf959TrNIon42GPpDsies0Jr0jY9AmlFBRiCYd5xCVKZdaX6uhYKO1A5q54V7ZdvbVSrFvaDg9bvBsFtoDTokJ33jI5B2BGQMjXCcEP1uiktbK4IX+Gdn3WkWnvXrpdtvj29dcfOmWVz5C1WrW1L3n28/GwyjDK1J3vgIZAEBGUMhHCdImiuhvfACf/thIdevx7cmU+TzyjfmJG2JeyWh6HWDYdShNakbH4EsICBjYC3heM8BqbDKINUki3ocGcK1fn1rKHacdPUUo6NeNxhGHVqTuvERSDsCMgayIhyvdspA0pleeSXA96e9nWJ+Xj0fJ4dMILQCICBjIJkJx0lgeoBHMjTmVTs+p0JhNO6VAEDsGPOGvtkHCceZkPbxZmgqFjV1x1el2VnZ5bm4V7Oq9hFojEQDEAYqyOjJ8m+grxCOMyPOynQYI+ZoRVldyzSLUSP320Z1DDQAEJCxJrs8p3zjrIq5o9KINPG5k4RjhCuMEXO0oqzN4GkWUR4DDQAEZKzKC8etFWPCMYBoRX0MNIBsowcZXXUOxwAQjyiPgQaQbQRkdEQ4BjJqaZqFibodA81GPQBBIyBjBcIxkFHFoqb2HFC+cda4aRbtx0CXy+6fp04RkgEEj4CMFoRjGIERc/HxQrIuq1qNezFN3Y6BHhsL5xjobhgzB2QDm/SwjHAMYzBxAh1EfQx0O8bMAdlBQIYkwjGQSGHMizZcXMdAM2YOyBYCMtxew9lZTd1LOAYSJYx50RFrD5amBk3GzAHZQkDOOC8cn7v3ScIxkBRe5Xh2Vvrgg+b19eul22+Pb119SlrLgheSvXAsJTMcJ+VFCRAnAnKGEY6ROBlsKejIqxx/8IEbij3Xr8e3pj4lsWWh25i5JIXkpL0oAeJCQM4owjESKQUtBXAlrWWhfcycP9BLZq65XRJflABxISBn0HI43vaQVCzFvRwAGZWkloVuY+akaMfMDSNpL0qAOBGQM6YlHJcIx0CirV/f2lbhOImaF520loW4x8wFIUkvSoA4EZAzhHCMQNAHbI72DXnz85Jtu28b/n1KasuC17frD8lJak1I2osSIC4E5IwgHCMw9AGvFHUYzeW6fz6P4d+npLYsJHmTW1JflABxICBngG2LcIx06CUYxiHqMGpABTgISWtZSPomt6S+KAHiQEDOiNKmQ4RjJF9KgqHp7Q9RiutkvEGkYZNb0l6UAHFZF/cCEIHGXNwrAODnVZzbb4a0P6A7f0j2JC1gJulFCRAXAnLKeb3HE7tOxr0UAEi8bpvcHCee9QAIBy0WKVapiHnHCJ6pfcBoxfcpcGxyA7KDgJxy4yNv0nuMYGWsR7YnJoZRvk+BY5MbkB0E5BSrn53T7rgXAUQprs1vhNHMYJMbkA0E5JSyy3PKN87qyJ7nJBXjXg4QLi8Yz862JpX1693DNEzb/GZixRk9Y5MbkH4E5BSqVCQ1Gprac0AqEo6RAd5UiA8+cEOxx38Ms0moOAOA0ZhikVLjue9Lu2mwABCv9ukOTHsAkAQE5BSqn52TFhbiXgaAjKtWW0egeVMgqtV41wUAayEgp8xy7/HnnnN3kwBADPzHMnsh2RuJtrhIJRmA2ehBTpFKRcovnKf3GNm1fn1r37HjSPPzbH6LkDfhwZvu4DjJPZYZQHYRkFNm98gFeo+RPd5UiI0bV15nQ1xkqlW3OtwegOfnm98awjGAJCAgA0g+QnDs/C0VkhuEX3lFevVV931eZfnECUIyAPMRkAEAQ/OfKue1VDQabjDet0969FGOZQaQHARkAEAgvJDshWDLkh5+2A3HHMsMIEkIyACAQHiTKjztLeEcywwgKQjIKVGtSvUzl7VbzD8GED3/GDdvUoV33x+MCccAkoCAnALVqlQ7dln5qxd1ZM9zUoERbwCiZVlu64R/jBstFQCSioCcEnldYf4xgFgVCs1pFRItFQiH/2es030gCJyklybMPwYQs/agQnBBkDi+HFEhIAMAAtV+jDTHSiMIHF+OKA3VYmFZ1pOS/rmkX5Q05jjO94JYFAAgmdpP0/NCzIYNbgsGMKhOs7Ylji9HOIatIP9Q0lcl8csNAMg4KnwImz8kewjHCMNQFWTHcX4kSRY/mQCQeVT4ELb2WdsSx5cjHEyxQPwmJ90zadvlclK5HP16AAys/TQ9ifCCYKw2a1vi5wzBWjMgW5Z1QtLtHd71rOM4/67XT2RZ1oSkCUm67bbtPS8QGdBorDxyy7sOIFGo8CEszNpGlNYMyI7jPLLWx/TCcZzDkg5L0l133UcnGgCkDBU+hI1Z24gKLRYAgEBQ4UMUkjJrmwNNkm3YMW9fkfR/Stos6T9YljXtOM6vBrIyAEDiUOELBuEq2Rh3mHxDjXlzHOfbjuPc6TjOBsdxthCOAQBJqfCZitPiko1xh+lAiwXil8t1n2IBIJW8imi3P7PKH66k1j7usTG+JscBbQAAGrpJREFUPknAuMN0ICAjfoxyA8yzsBjaQ3u/ft6wwf1z3z7p5Enpxhuln/8827+GJlylA+MOk4+ADABotXu3dp+ZUe3YZklbAg2rXoV0akq69VZpdlZ6+23ppz9t3s96pZRwlXyMO0y+YY+aBgCkTaGgI4+/rHG9odrxuUAf2gt/e/e6oXhuTnrrLWl+vhmOsx4iuoUreleToX3cYbns/unvSYb5qCADQFxMPkWyUNCR2kHtbPxV4A/tr5DmctKHHza3HBCOmSWddIw7TAcCckrUr94q1WrZbdwDkiijp0h6IdBxmk+10XBDctZ/DU24SgfGHSYfLRYpUChIyuW088Jrkm3HvRwA6MoLx14P8uiodPfd7uuETZv4NbTk/j/dH6a8cEX9I1kYd5hsBOSUKE2OSps2af+V5xmWCcBYXoV0717pC19wK6VPP+3e//zn3ftUSglXQNxosUiR/K5R6exI3MsAEDeTe5vV+utn70//iWOEQQBxIyADwGoMD5sdJaC32d8+0OnPKPlDudfa4b9PYAeyh4AMAKsJM2xyimTsvENLHnlEev11aWHBvT4yIj34oNsPneWDS4CsIiADQFxMrUD7NRqqVEZVLMa9kOD5j3X2Ksevvuq+vW+f9Mor0unTHFwCZBEBGUB6JbE9wiSlkkrlF2RPP6NKZWvqQnL7sc6O0wzCp0+71zm4BMgmplikTK1xjzsPGUCzPaL9ZlAvrukmJneolHtB9bPBnqhnCn9Itiz3tZO/wyXOcNw+6i7Lo++AqBGQU6RYlHTHVuYhA1mXy7lnN7ffBuxtnsi9FPACzeE/1tk7uMT/+imumczVauvn9tbJFE8gGrRYpEypJNn2du2/8ryOVF9mZwkwrCRupKN9pCf+Y53vv9+95vUge/fjOOLZ3xvtfW7/8dP0QwPhIyCnUD4vqcE8ZCAQhM3Uaj/W+fXXpYcfdt/nTbHwPibKQNreG+0FZfqhgegQkFPK7UX+p1SQAWAV/kNLvLelZgiNK5B6IdkLx3GuBcgiepBTiF5kYEnAvbhIJ3/otKyV9+Pg7432xNUPDWQRFeSUaulF1pW4lwPEg/aI4VUq2nvxWxKvKSLj74322iq8+xKVZCAKBOQUy+clsjGAgVUq2jt9SPUb7lBpcjTu1WRGe2+0vyc56n5oIKtosUi52tUxHS6/G/cyACRRve6G4+e3xL2SzCkUWivFXkhmWwkQDQJyinm9yPbsAUIygMGM3BT3CjKrvVJM5RiIDgE55UolSdu2x70MAACAxCAgAwCAxOEoboSJTXoZkM9L9oUDUvmQJiZ3xL0cRG1ysvtJcEx5QDdMr4DBqlX3tEGvT9ub/LFhA33aCAYV5AwoFqX8vdvpRc6qRkPauHHlrVNoBqTm9IrcLqZXwDj+o7i92dDeGLzFRSrJCAYBOSOKRUnbCMkA1kA4huG8iR5jY24onpxsnRnNZkYEgRaLDPEOD6HdAohI0tpbCMdICI7iRtgIyBlDSEaqmRZIvfaWTtdNQzhGgnQ7ipuQjKDQYpFB3ug32i2QOvRbD4ZwjARpP4q7XG62W3g9ycCwCMgZRUjOkFxOmp9fecsxngAiHCNxuh3FPTbGUdwIDi0WGeZvt5iwH1pKzUidqFsLTGtzQHeEYyRUoeBWituP4iYcIyhUkDPOqyTvvPCaZNtxLwdpQJtDMhCOkXAcxY0wUUHGciV554XXdI5KMpKkvVo9Oyt98IG0fr10++3xrcuTy3WvpsfJC8c33EE4BoAOCMiQREhGQrVPiZifl65fl65dc9/2xBVITWwp8Yfj57fEvRogVfxtH53uIzkIyFi2IiSPj7vv4NxOJIVXNZ6fp2WoE8IxEBqOv04XAjJaLIfki9/V+PHvSwsLOlI7SEUZw/vgg84/R71u3uu0+W921g3DJrRTmI5wDITGf/y15IZk/yg6KsnJQ0DGCqWSVKls1Yy2ql6Xdl64N/1tF9Xqymu85B9Mt75babhDMzoduvHBB25LBVbnC8fjjxOOgaB5UzQkNxR7QZnjr5OLgIyOisXm22nvTT5cflfVhSekkZHmxYUFHdHLhORBdKsGp/BnJxHawjE/0kA4OP46XQjIWFMqepM7VYglHT7uHpaibduVzzev189c1v5jIiSbbv36lRvypPinRJiiWiUcAxHh+Ot0ISCjJx17k2eeay01m8q2tf/K860V4iW1xj3Stq0ripvV6hbVjj1ASDbd7bezIa+balV7j/4e4RiIQPvx1/4eZImQnEQEZPSsVJKq1a2amdmq+tk57XzzXp3Tk60ftHt3vGGyUmm9X6+7h6Bs2qT8rpXzXse7LNe9RkhGQhGOgUh1O/5a4vjrpCIgoy+FgpcTR2WXpZ1n/r3yI3PND5ie19TMgXgqy0u9lso1N3LVF0alTTcNdBgCITlgwx6aYeqhG6ZJSThmniyShuOv08VyHCfyT3rXXfc5zz77vcg/L4LX3tpbO3ZZ+asXNbXnQOe/MGiFuVqVZmZW/RDv2Nzxx1rD8LABoVp1n9e43tCRxwnJMJxta+eVv0h0OGaeLICoPPWU9abjOPe1X6eCjKG0/2NVKGyRfVDae/bIyg9eWNTuMzP9V2KrVe0/9oRmtFsa2dD1w+q5LaEcm0slOQCdZhhLvc9ARn9GbkrsjyjzZAGYgICMwJWe36JqtfOs1drRW92QOfNcz4+3/8zvq3Z1TONPbl3144ohBgJC8pA6zTD2riM4lYr2XvyWdEfwLxSjEuQ8Wdo0AAyKgIxQdM+OW1U79kDnCnMXdY0qvyf+XxcTkmE0/0i38bgXM5wg5snSpgFgGARkRMoLme6tN7tlzj9o/pCsmefMWRhQq6l+w9cT3XvsGXaeLG0aAIZFQEbkkv6Pd6Eg1Y7fJNXrcS8FaJXg3mNPEPNkLUvat899m2N/AQxiXdwLAJJq/5Xnu57QB0TK6z1OgW7zZMfGep8nW61KJ082Q7LktrvfeCPhGEBvqCADAxh/bJRe5H4wwzg8S/O/6zfcEcoUlzgMM0/Wa6+YmpL+5m/ca42G9LOfST/4gfTgg9I6SkMA1kBABgbAhr0+ZXWUWxTj7ep1Nxw/33tffxK0h+FeK79ee8Wf/7n013/tDk8ZHZXuvFO6cEH6V/9KmpigkgxgdbyOBgZUKEjjj29RTQ9o/7EnaLfASt54u/Zb0OPtRm4K9vESzrKkfL712u7d8awFQDIRkIEheCF5ZuTza570B5ii/QDVGA5UDd1nP+u+FvnoI+nyZenP/kx6+GGqxwB6Q0AGgAypVt2pEF4o9qZGpOUXIN7zOX3a7Vu+/Xbpk590e5AJxgB6RUAGAlBfGNX+M7+fnpSBVPLPB/ZCsjdCbXExHZVkbwrG/fc3r+Vybkg+dy6+dQFIFjbpAUNiwx56dumSdP26m0RLpeb1IDftrSLIY5xN9uCD0iuvuFVk77l59/s5cARAdhGQgQAQktFR+3i7a9fcZPaJT7gNsp5BN+1VKtp54TVpU+9/JYhjnE1nWdLISGvwf/TRZnU5Tc8VQDgIyEBACMlYob0qXCq1BuNhLM0/1qZNfc0/HvYY56QYZpYyABCQgQARkhGZel313K6BwvEwxzgnyaCzlAGAgAwEjJAMU3U7xlmi9QAA/AjIQAgIySGI4lS6DKD1AADWRkAGQkJIDph3Kl2n60nRvmnPfz1CtB4AwOoIyECICMloQaUbABKBg0KAkHnHUdf0gPYfe4LDRAAAMBwBGYjAcki+OkZIBgDAcARkICKFgjT+5FZCMgAAhqMHGYiQ2368VbWjY/Qk98uQDW4AgPQjIAMRIyQPiA1uAICI0GIBxKBQkPJ7aLcAAMBEBGQgJsVih5BMUAYAIHYEZCBGLSH5+O9o//Hf0eHyu3EvCwCATCMgAzHzQvLMrl/TzK5fk914hpAMAECM2KQHGKBYbL5dqWyVPf2MVH5BE5M7YlsTAABZRQUZMIxXUaaSDABAPAjIgIEIyQAAxIeADBiKkAwAQDwIyIDBCMkAAESPgAwYjpAMAEC0CMhAAhCSAQCIDgEZSAhCMgAA0SAgAwlCSAYAIHwEZCBhlkPy7AFCMgAAISAgAwlULEr5e7cTkgEACAEBGUgoQjIAAOEgIAMJRkgGACB4n4h7AQCGUyxKdn277AsHpPIhTUzuiHtJg5uclBqNlddzOalcjn49AIBMooIMpECpJGlbCirJjYa0cePKW6fQDABASIYKyJZl/QvLst6yLOv7lmV927KsTUEtDEB/vJAMAACGM2wF+RVJn3cc5x5Jb0sqDb8kAAAAID5DBWTHcf7UcZxrS3f/QtKdwy8JwKDyeSW/zQIAgJgF2YP8u5K+0+2dlmVNWJb1Pcuyvjc/fyXATwvAw1QLAACGt2ZAtizrhGVZP+xw+03fxzwr6ZqkP+r2OI7jHHYc5z7Hce7buHFzMKsHsEKxqOT2Iudy0vz8ylsuF/fKAAAZsuaYN8dxHlnt/ZZl/SNJvyFpn+M4TkDrApBFjHIDABhgqDnIlmU9Jul/lvSQ4zgfBrMkAABgCseRLKv7fSCNhu1B/peScpJesSxr2rKsQwGsCQAAGKBalU6ccEOx5P554oR7HUizYadY/ILjONscx9mzdDsQ1MIADC6fl+zGM2zUAzAwx5EWF6VTp5oh+cQJ9/7iYjM0A2nESXpAChWLUn7PVkIygIFZlvTII9LYmBuKJyfdP8fG3Ou0WSDNCMhAShWLku7YGvcyACSYF5L9CMfIAgIyAADoyGur8PP3JANpRUAGUoxeZACD8vccj425Uxi9dgtCMtKOgAykmL8XWbYd93IAJIhlSRs2tPYcez3JGzbQZoF0G2oOMgDzFYuSXeYkOgD9KxRa5x57IZlwjLSjggwAALpqD8OEY2QBARkAAADwISADGbH/yvMcfwUAQA8IyEAGjD82qtrVMe0/9gQhGQCANRCQgQwoFKTxJ7eqdnVMqtXiXg4AAEYjIAMZUShIyjHNAgCAtTDmDciY/Vee15Hqy0uJOWMmJ6X/v707DpH8vOs4/vmaxFxp7iiludBLk3qEUNRSUgwJ5GRVGjWImJRQUAwiHqSBFitWNHeBBi3tKoUgiBADW1AMFUNrEKlQr5asPUjTplxq06Zea1tSK15VYjdIorWPf8xu8lzuLpdkd+eZ3d/rBUN2JsPON7857t559pnn1tbOfHzv3tnfggAAsYIMk3Lopn05nhumuxd5bS255JIzb2eLZgAmSyDDhCwtJYduuSzHc4O9yABwDgIZJmZpKcmeV40eAwAWlkCGiXIuMgCcnUCGCZr8XmQAeBECGSbotL3IJ0+OHmd+9u5Nnn76zNtOO/5udXX2EwAAtoVj3mCilpaS4w+OnmLOdsNRbqurue3BW3M8N+TQTftGTwOwK1lBhinb86rc9vidtlnsJCdP5uSeN+fQLZdN8ihrgHkQyDBh9iIDwJkEMkxYvxdZJAPAjECGiRPJAHA6gQw8F8kn97x5WqdaAMBZCGQAAOgIZOA5p57Z51SLRbaykutP3JtTz+xzggXANhLIQBJ7kRfeRhxfdCBH7rls9DQAu5pABp4jkhfc3kty6BZxDLDdBDJwGpEMwNQJZOAMS0vJ/h+9LNmzZ/QoJM9vr4i/PQ9gHgQycE4nn7li9Aisrs7ieO9VOfLBfaOnAZgEgQyc1dVXJ6dyaa5/4L22WYx08uRs7/FN4hhgXgQycFYbe5FPXXTAXmQAJkUgA+fkA3sATJFABl6USAZgagQycF4ieRCnVwAMIZCBl0Qkz9lGHDu9AmDuBDLwkonkORHHAEMJZOBlEcnbbHU1tz1+pzgGGEggAy+bSN5me/Zk/1XiGGAUgQy8IiJ5m5w8meNrbxk9BcCkCWTgFRPJW2x973H27s3hw6OHAZgugQxsikjeIj6YB7AwBDKwaSJ5k8QxwEIRyMCWEMmv3H1fe5s4BlggAhnYMiJ5E/aKY4BFIZCBLfVcJP/vdSL5pVhZyfJTd4yeAoCOQAa23NJScugdl4vk81lZycFHH0he85ocOTJ6GAA2CGRgW4jk8+jj2N5jgIUikIFtI5LPYeO8Y3EMsJAEMrCtRPILONINYOEJZGDbLS0l+68RyeIYYGe4cPQAwDQcPpysrFye4yeuy20PJn9+/Ddn/2K3fzptZSU5dSpJcv23PyaOAXYAK8jA3Bw+vL6SvOdtObj2WA5++9O57+g3Ro+1fdZXjA+uPZaDa4+JY4AdwgoyMFeHDyfJLBJXVvZl+cS7kqN/nNv3fmT2hN2yomw7BcCOZQUZGGZjRXk5d85WWZ98aHesKItjgB3NCjIw1Bkryo/ekRy9d+etKK+uJsePJ1nfa3zRAXEMsENZQQYWxuHDyf4fu/K0FeUsL48e6/xWV3P9A+/Nwe88/Pxe43suGz0VAK+QFWRgofQrysvL+3LwyYfy9eWfeP4Ji7Ki3K0Y3/ade3LqogM5dMtlWVoaPBcAm2YFGVhYR44kueLKHMzXZ7dFWVFeXc1tD946WzHO13P80reLY4BdxAoysND6BePl5Stz8MmHcuTovac95/arPrmx9LwtXvjBwdVnbs3x3CCKAXapaq3N/UXf+MZr2113fW7urwvsfGcsIK99N/vXvpbPXHPHtkTyfUe/keW1dyUHLj/t8UOHIo4Bdrh3vrMeba1d+8LHrSADO8qZW5D3ZfnoVbn+xL05fPSBLX+95bV3Zf81l2/nAjUAC0YgAzvekQ/uy/LyW7Oct275995/1bbu3gBgAQlkYFdYlMMteHlaS6rOfR9gBKdYADDE6mpy7NgsipPZP48dmz0OMJJABmDuWkuefTZ55JHnI/nYsdn9Z599PpoBRrDFAoC5q0puvHH29SOPzG5Jct11s8dtswBGsoIMwBB9JG8Qx8AiEMgADLGxraLX70kGGMUWCwDmrt9zvLGtYuN+YiUZGEsgAzB3VcnFF5++53hju8XFF4tjYCyBDMAQS0unn3u8EcniGBjNHmQAhnlhDItjYBEIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6GwqkKvq/VX1hao6UVWfqKoDWzUYAACMsNkV5A+11t7SWrsmyd8ked8WzAQAAMNsKpBba9/t7r46SdvcOAAAMNaFm/0GVfWBJL+S5L+S/NSLPO/2JLcnyWtfe+VmXxYAALbFeVeQq+pYVX3xLLebk6S1dldr7Yok9yd597m+T2vtvtbata21ay+55NKt+y8AAIAtdN4V5NbajS/xe92f5ONJ7t7URAAAMNBmT7G4urt7c5InNjcOAACMtdk9yL9fVW9K8v0k30xyx+ZHAgCAcTYVyK21W7dqEAAAWAT+Jj0AAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoVGtt/i9a9Z0k35z7Cy+G1yX599FDTJjrP573YCzXfyzXfyzXf6xFvP5vbK1d+sIHhwTylFXV51pr146eY6pc//G8B2O5/mO5/mO5/mPtpOtviwUAAHQEMgAAdATy/N03eoCJc/3H8x6M5fqP5fqP5fqPtWOuvz3IAADQsYIMAAAdgQwAAB2BPEBVvb+qvlBVJ6rqE1V1YPRMU1JVH6qqJ9bfg7+qqteMnmlKquodVfV4VX2/qnbEcT+7QVXdVFVfqaqvVtWdo+eZmqr6cFWdqqovjp5laqrqiqr6VFV9af33nveMnmlqqmpPVT1SVY+tvwe/O3qm87EHeYCq2tda++7617+e5Edaa3cMHmsyqupnkvx9a+17VfUHSdJa+53BY01GVf1wku8n+ZMkv9Va+9zgkXa9qrogyT8l+ekk30ry2SS/1Fr70tDBJqSqlpI8neTPWmtvHj3PlFTV65O8vrX2+aram+TRJLf49T8/VVVJXt1ae7qqLkry6STvaa09PHi0c7KCPMBGHK97dRL/lzJHrbVPtNa+t3734SRvGDnP1LTWvtxa+8roOSbmuiRfba39c2vtf5L8RZKbB880Ka211ST/OXqOKWqt/Wtr7fPrX68l+XKSy8dONS1t5un1uxet3xa6fQTyIFX1gap6MskvJ3nf6Hkm7NeS/O3oIWCbXZ7kye7+tyIQmKCq+qEkb03ymbGTTE9VXVBVJ5KcSvJ3rbWFfg8E8japqmNV9cWz3G5OktbaXa21K5Lcn+TdY6fdfc53/defc1eS72X2HrCFXsr1B5inqrokyUeT/MYLfpLLHLTW/q+1dk1mP7W9rqoWeqvRhaMH2K1aaze+xKfen+TjSe7exnEm53zXv6p+NcnPJ3lbsxF/y72MX//Mx78kuaK7/4b1x2AS1ve9fjTJ/a21j42eZ8paa09V1aeS3JRkYT+0agV5gKq6urt7c5InRs0yRVV1U5LfTvILrbX/Hj0PzMFnk1xdVQer6geT/GKSvx48E8zF+gfEVpJ8ubV2z+h5pqiqLt04MaqqXpXZB4YXun2cYjFAVX00yZsy+yT/N5Pc0VqzmjMnVfXVJBcn+Y/1hx52isj8VNXbk/xRkkuTPJXkRGvtZ8dOtftV1c8l+cMkFyT5cGvtA4NHmpSq+kiSn0zyuiT/luTu1trK0KEmoqp+PMk/JPnHzP7cTZKjrbWPj5tqWqrqLUn+NLPff34gyV+21n5v7FQvTiADAEDHFgsAAOgIZAAA6AhkAADoCGQAAOgIZAAA6AhkAADoCGQAAOj8Pypdlqs5koS4AAAAAElFTkSuQmCC\n",
      "text/plain": "<Figure size 720x720 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light",
      "tags": [],
      "image/png": {
       "width": 712,
       "height": 712
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 783
    },
    "colab_type": "code",
    "id": "i8kFCidCNL0q",
    "outputId": "82b016b3-4dfd-432d-9780-76c7c0cf631f",
    "cell_id": "00007-93af01f8-8eed-4823-834f-2d2ab4817b98",
    "deepnote_cell_type": "code"
   },
   "source": "# Create a SVC classifier using an RBF kernel\nsvm = SVC(kernel='rbf', random_state=0, gamma=10000, C=1)\n# Train the classifier\nsvm.fit(X_xor, y_xor)\n\n# Visualize the decision boundaries\nfig = plt.figure(figsize=(10,10))\nplot_decision_regions(X_xor, y_xor, classifier=svm)\nplt.legend(loc='upper left')\nplt.tight_layout()\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 720x720 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light",
      "tags": [],
      "image/png": {
       "width": 712,
       "height": 712
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 810,
     "referenced_widgets": [
      "b60b33ead3e045918812744e23f2dac2",
      "5d4516d80e3b4a90b7411e8a81df6d89",
      "d0bb503cf16c463abe504958f9afd476",
      "fac58c96897347929b40dcbf4089d867",
      "3ca6720467454a3b819324c7428930c8",
      "7aabc06a42704880afe3437daf35ac5a",
      "b275f8e448e44caf9a86919d0f3102d8"
     ]
    },
    "colab_type": "code",
    "id": "0F3RJYBZN2uV",
    "outputId": "534da6eb-68f1-4cd0-f921-969c154f548a",
    "cell_id": "00008-0ed236ac-8eed-4461-b2f1-32cd10dfe8ce",
    "deepnote_cell_type": "code"
   },
   "source": "from ipywidgets import interact, interactive, fixed, interact_manual\nimport ipywidgets as widgets\n\n@interact(x=[1, 10, 1000, 10000, 100000])\ndef svc(x=1):\n  # Create a SVC classifier using an RBF kernel\n  svm = SVC(kernel='rbf', random_state=0, gamma=.01, C=x)\n  # Train the classifier\n  svm.fit(X_xor, y_xor)\n\n  # Visualize the decision boundaries\n  fig = plt.figure(figsize=(10,10))\n  plot_decision_regions(X_xor, y_xor, classifier=svm)\n  plt.legend(loc='upper left')\n  plt.tight_layout()\n  plt.show()",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b60b33ead3e045918812744e23f2dac2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "interactive(children=(Dropdown(description='x', options=(1, 10, 1000, 10000, 100000), value=1), Output()), _do…"
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 783
    },
    "colab_type": "code",
    "id": "AZdd86xhN24M",
    "outputId": "517a8c58-4544-4e39-bbda-e10426a825c0",
    "cell_id": "00009-23636035-1af7-4ac2-b70f-b66c68bf87e1",
    "deepnote_cell_type": "code"
   },
   "source": "# Create a SVC classifier using an RBF kernel\nsvm = SVC(kernel='rbf', random_state=0, gamma=.01, C=10)\n# Train the classifier\nsvm.fit(X_xor, y_xor)\n\n# Visualize the decision boundaries\nfig = plt.figure(figsize=(10,10))\nplot_decision_regions(X_xor, y_xor, classifier=svm)\nplt.legend(loc='upper left')\nplt.tight_layout()\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
    },
    {
     "data": {
      "image/png": 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q9+SH5DS9RwCICwEZyIisTTrI2vuVlr6nNL5HAIgDm/SADMnapIOsvd+0K5VMrwBAVhCQgQzJyqQDX9beb5r50yv69VOmVwCIHC0WQEZkadKBlL33m3ZMrwAQJwIykBGZmnSg7L3fLNi/fo80MGx6GQAygIAMZEjWJh1k7f0CAMJBDzKQMVmbdJC19wsAaB8BGQBgtWJRUqViehkAMoSADCA1aqdTvPvu8vfDfsWiVD5yVv0dE9Iw/ccA4kFABpAKpVL1CLdSSXriCemFF7zb/lQLZukmS/nklPo7JrzpFQAQEwIygMQLHis9NuZVjo8elY4dk15+2bvtj3ibnaWSnDTe9ApGuwGID1MsACRevWOlXVfatk2anJQKBe++NB8znUYLvcc50ysBkDVUkAGkQu2x0o4jfeEL1Y8hHCeH33s8nHuc3mMAsSMgA0iF2mOlXdfrQQ7imOnk8HuPh/ZuNr0UABlEQAaQeLXHSg8PS9dc4/Ug9/R4t/v6FnuUCcnJsH/9HtNLAJBR9CADSLx6x0pv3+7dd8st0po1HDMNpNbevfXnZOdy0shI/OtBKhCQAaRC7bHSAwPSH/2RF44ljpkGQmFjGK1UpO7u+teBFhGQAaRGbfhds2b5+9HYvXtv11WV8pLrF3N5PTtyONLXZnqFxQijyAgCMgBgiasqZc10r697PUpMrwBgAzbpAQCsUS5Lw7nHmV4BwCgCMgAAABBAiwUAwArFoqQzp6Ue0ytBouRyjTcOAi0iIAMAjFvoPe55kvYKm9kYRhnlhggQkAEAS1zM5RtOsYgCvccJQRhFRhCQAQBLRD3KDQBsxiY9AAAAIICADAAAAAQQkAEARhUKks6c1lDugOmlAIAkAjIAwCB/tNtwz5OcnAfAGmzSAwAYUy6L0W6w2969jUfbMdUjtQjIAIBQ3bv39oYj4piOgcSpVKTu7vrXkVoEZABAqK6qlDXTvb7u9aCF3uPeA5JorwBgDwIyACB2xaKkc2d1qvfO+HqP+aocQJMIyAAAI/pzL0n9/fG9IF+VA2gSUywAALErlyXNzJheBgDURQUZABArv/d4f+8eaYDeY1gul2vcmoPUIiADAEJ1MZdvOMXCSO8x0A760zOJgAwACNWyo9yKBnqPAWCVCMgAgNiUy9JWU73HfFUOoEkEZAAIietKjtP4dtYZ7z3mq3IATWKKBYDQue7yt9OoVJLGxhbfq+t6t0sls+uyBb3HAJKEgAwgVFkMiq4rzc5KExOL731szLs9O5uNDwjNoPcYQFLQYgEgNMGgKEm7dy8Gxb6+9LYcOI73XiXvvfrvv6/Pu57G97xaRnuP04qTAYHIEJABhCbLQdF/7/57ltL/nptlvPc4rTgZEIgMLRYAQhUMyb4sBEW/rSIo2GqSVaWSpDfP03sMIFEIyABClcWgGOw57uvzvt3u66vuSc6yfNeUlM+bXgYANI0WCwChqQ2KwR5kKb2VZMeROjurW0n8KnpnZzrfc7PGn5tSvjItbd1qeikA0DQCMoDQZDkoDgxUb0L033sU7zkp85YLI1PS5KQO9X6a3mMAiUJABhCqOIOibWrfYxTvuVTyJoX4P1O/at/Z6f3sbVEqSZq5mK7eY9umRnAyIBAZAjKA0LUSFJNSFTUpaWP0Utd7bNvUCEa5AZEhIAMwLilVUdOSNEZvofcYABKIKRYAjOIUutVJwhi9hd7jHQ9Kg4OmlwMAq0YFGYBRSaqK2qDRGD1bflZVvceDKek9BpA5VJABGJeEqqgN/HB86FD1vOVDh+yat5y63mMAmUMFGYBxtldFbeE40unT0jXXSLt2ebd37ZJefdW7bsPPKtW9x3FPjbBtagaQIQRkAKFpZRJFVg8XaYXrSps2eT+bgwe9n83Bg9LkpB1TLBZ6j29Nae9x3KHUtqkZQIYQkAGEotVJFFk+XGS1ktCvferW+9IZjgFkCgEZQNvanc+b5cNFVsv/2fg/a4mfFQCEjYAMoG1hVDbjOIUuDWzt1y6MTClfOWluAQAQIqZYAAgFkyiiV9uvPTzs/e7PkH73XTPr8sMxc48BpAUVZAChsLWymSbBfu0rr/Q26O3a5d3X2endjvv0QcJxhOKemgFgQSgB2XGcfy/pE5LKruveEsZzAkgOJlHEZ2DAqxQfPLj48921a/F2nNMsCMcRY5QbYExYFeT/T9K/k/T1kJ4PQIIwiSJea9bYM83i0A2f5sQ8AKkTSkB2XbfkOM7mMJ4LQDIxiSJeTLMAgOjQgwxkXCuHezTCJIr4mOz5vnfv7frdmfP643fnpMpb3m5BiRPeAKRGbAHZcZwhSUOSdO21m+J6WQDLaPVwD5hluuf7d6+XVXG79aH3/Hfpfdcv3sEJbwBSIrYxb67rjrque5vrurd1d6+P62UBNBA83GNsrDp0zc56t5Osdv1Jfz9BjXq++/pi6vl2XX2o46T0vvdF/EIAYAYtFkBGJeHY4lZloTJOzzeQYHv3Nh7hR5uSFUKpIDuOc0DSf5P0PzqO87rjOMz7ARIgjYd7pL0yHmSi57swMqW1785JV1BfAVpWqUjd3Ut/0aZkjbCmWNwfxvMAiFcaD/dIc2XcNH/u8fuvfKu699gmVOYAhIASAJBRq93oFea0i6gxAi18VYeCnH+/vSe8+ZW5etcBoEkEZCCjVnO4R9J6etNYGTepUBAn5sEOfEOAmBCQgQxrZqNXsKdXqq40x3mscbNMj0ALi20V+8HcU4RjmMc3BIgJARnIuJU2eiWtpzcNx15bV7GvTBl4USDFcjl725QgiYAMoAlJ6+lN8gg02yr2fu/x0I6DkqggA6GgHcR6BGQAK0piT29Sj722qWJftTEvKe0VVOYAhICADKBKbYXy3XelgweT39ObJDZU7BMZjiUqc2FhMxwyjoAMYEG93teDB6XTp5Pd05s0piv2iQ3HCI+tm+H4hgAxISADkLRy7+uuXcns6U0a01M4CMewGtVrxISADEDS6ntfCcfRMDmFo1iU8jOnsxuOaSsAMI+ADGCBDb2vWVNv3rHJKRxbu85IW7dG/0I2srWtAEDs1pheAAB7NOp9dV0z60m7Uqn65+v//EslMxX78skpaWYm+hcCAMtRQQawEND8Xtfbb5fuvntp76tk1+luSWbrvOP9Ox6VBjLYXoFqbIZDxhGQgYwLTq7o7PTCsST9+MfVva8//rFlp7slHPOOYTV6rpFxtFgAGRasYo6NSXfc4V0/fNi7Lnlh7Y47qh8XnLQwO0sLRquCIdlHOAYA86ggAxm2miqmLdXONGHesWVoKwAwj4AMZFyzkyuYcBEu5h1biLYCAPNosQAyrtnJFUy4CFejecd9fdHPOy6VpLzOE44BoAEqyECGNVvFNF3tTCuT844BAI0RkIEMa/bUNpOnu6WdiXnH489NKV+Zjv6FACChCMhAxjVbxaTamQ5Ge485yhlAQhCQATRdxVxNtbPeEcqEabOMb8wzfZQzAR1AkwjIAEIXPHzExkNFshjejYdjG5gO6AASg4AMIFS2HaFcy/bwHoVEhGOquwAsQkDGElmsriE8Nh2hXMv28B6FRIRjieouAKsQkFEli9U1hM/WQ0VsDu9RSEw4BhV0wDIcFIIFweqafwCEX12bneVACDTP5kNFgiHZRziOSS4nTU8v/cVRzosV9NpfVNABI6ggY0HWqmuIhu2HijQK782uKwktSKGH47Cqm8s9dnh49etarVyu8fsAgAACMqrY+tV4FiQheDXD5kNF2g3vSWhBiqRynJb+YFoVADSJgJwhzQSwdqtraE0Sgtdq2HqoSDvhPQkb/Kxsq2gW1V0AFiEgZ0QzAcz2r8bTKgnBqxUmjlBuRqvh3fYWJGvCcavtGFR3AViEgJwBzQYwm78aTzPbg1catRrebW1BsiYcS+lpx4gbFXTAKgTkDFhNALP1q/G0szV4oZqNLUihhePlKr+IHhV0wCoE5IxYTQCz9avxNLMxeKGajS1IhZEpaXJSh24NoXK8XOWX6iaAjCEgZwQBzF42Bi8sZVsLkh+OT916X/RtFVQ3AWQMATkDCGB2sy14JU2c4/FsaUGKNRwDQAYRkDOAAGY/W4JX0pgYj2e6Bcn6cEw7BoAUICBnBAHMfqaDV9KkdTzechbCce+d0mAMJ8+1gnYMAClAQM4QAhjSJGvj8arCcRTHMlP5BYAFBGQAiZWV8XiRh2OJyi8ABKwxvQAAaFWj6Syua2Y9UYglHAMAqlBBBpBIJqezxDU5I7Jw3Opx0ACQEQRkAIlkajpLXJMzIq0ccxw0ACyLgAwYEOfs3jSLezpLXJMzCgXRVgEABhGQgRCsJvCamN2bZnFOZ4ljckahIOnMacIxABjEJj2gTaVS9cYwP/CWSksfG6xA+n/Hr0DOzi6/uaz2vjRtREuSYEj2EY4BIF2oIANtWO1X7q1WIKk626PR5IxWQ3KhELgRcjimlQcAWkNABtrQSuBd7ezeLJ4YZ1qjYBn25Ay/Yjzc86Qkaaj3QGjheLS0TZXZDu3ZfXRh7fvGtivXOachDgUBgGURkIE2tRJ4V1OBzNqJcaatVK0Pa3JG/XaK1YfjemFekiqzHTowsUWStGf3Ue0b264DE1t0f98JucMj/LsBgGUQkIE2rSbwtlqBzMqJcaY1U60PY3JGWL3Gy1WJ9+w+Kkk6MLFlISjf33di4bGZwtxnAKtEQAbasFzgdV3p7rsXg5MfqlqpQIbd92ozk32zzVbr25mcEVY4dt3lq8T+Nf9+/3ZTa01boGTus/3S9m8OiUdABtrQKPD+6lfSqVOLj6v9mn41FUiTJ8bFzYbNiFFW68OcUuE4WrZKLHnV5OC/tX1j2/WlXUe1ZqX5RQRKxC3Mf3OEbYSAMW9AmwYGlgaom26SfvObxqPcVlOBbBTC+/qiPTEubu2MwAt7HfWq9c28/nKj+KIY4RYMyb5gOP7zH96iG655WxPDz+j+vhP65qEt+uQTH9fXStuaf5E33pDOnvV+TU56ax8e9kIIYCM/bNf+4gMeVoEKMhCC2lFufmtFWJvq4j4xzgQbNiO2U61frvo9Pq5I5hv7PcdB+8a2a8/uo+q+ck4fufHv9OvJq/XVg17l+K9efb9eev06/YP/4dfNt65cviytXbv4Z7/KR9gAkGIEZCACUXxNH+eJcaaY3ozYTo947ea+P/sz6fy5y7q6c07rZsoauebJSMKx33Mc7EGWvEry5+44pq8e3F7VgvGnd72czY16ALAKBGQgAlnaVBcmG35urVTra6vf3/uedOni7/Q/XzWmv1z/kPd3Qz4Zz3GkXOdc1WQKv70i1zknx1lswWhpo15SNNNvytxnAKtEQAZClqVNdWGy6efWSrXeD8l+OL5+TVl/ue91OU50R0YPDRxbEuaDAXi5Foxl31MwULqu11ohLbZaRKWVzVXNbO6KY2MWG8Paw4cYWIaADISs1a/psy7pPzfX9doq/HC8fmNnc2G0TY3CfDMtGA3XFQx0w8P1A2gUkjw9I8lrt0GYHyII2wgBARmIQBY21UUhiT+3YlE6f2JKUzNX6u2La7y2in2vNx9GI9JMC0ZTCBvL27tXev11r8r+1lvV9115ZbgfLqhSN4efBUJAQAYikoVNdVFI0s+tWJTKR85qJPe4dJVU6bxaewrrWw+jIVupBaOpwEXYWF7w51d7wo3fmhLma1GlBmJBQAaAFvjheDj3uIb2bpa0tPptw4a4ZT9wELgAoC4CMgCsUr1wLCWr+p0atIAAiAABGcig2kMimj40IsNKJWn82fPejblL6u+YqArHCEErYdeWFhD/dJgg1yWoAwlFQAYyZrkT3wYGTK/OLqXS4p/Hnzqr/o4J7f/wo96FwUEzi0ozW8JuKzo6qm9fviy9973Jfk9AhhGQgQypd+JbcPYwleRFfsU43zUlServeFn7731aGiAYIyCXky5ckC5dqr7uOOFXj2knAWJDQAYypPbENz8oB2cPYzEc9+un2n/zfMV469b0ldgJXO2Ls0JMNRqIDQEZyBg/JPvhWCIcB1WF47RXjJsJXMzeBZBBBGQgY/ye46CxsWyH5Kpe46pwnLKKcSsYBQcggwjIQIb44djvOQ72IEvZDMlLeo31MuEYADKOgIzMy5jGhxwAACAASURBVNLIM8fxplUEe479nuTOzvS+73r8qvH4s+eVnzunQx9+0LuQxl5jAMCqEJCRaVkceTYwsPTEt6xVjgsjU8rPnJYk9eu49u94lLFtAIAFBGRkVpZHnmXxxLdi0fu9fHJK+cpJHdrxoFctltK9EQ8AsGoEZGQWI8+yozAyJVUq6s+9pK0zM7FVjP2D1fx/S7W349B2CxGj4ABkEAEZmcbIs/QKVow1OalTt94Xa8V4tLRNz/9yo3beVNbDdx+VJH3l+e06dCqvuz90VkMDx2JZQ2W2Q3t2H11oIdo3tl25zrnmX59RbmgVIwKRYARkZBojz9KpUJB07uxixfjWeHuMXVeamunQz05t0M9+tWHh+mM/3C650s6bypG38LiuVJnt0IGJLZKkPbuPat/Ydh2Y2KL7+06kuoUIlmBEIBKMgIzMYuRZuixUjMuSzpzWqd47pf5+72LMPcaOo4Wq8WM/3K5/8Z9vkyRd+55ZfXHXUT1899HI/205jheKJenAxJaFoHx/34mFijIAoD4CMjKLkWfpsaRi3LtHGh42uiY/JH/r8Ba99XanJCmfuxhLOA6uYc/uowvhWBLhOIgWAAANEJCRaYw8S67lK8Zmw7Hk/bv6yvPbdb5y1cK1cuUqfeX57bGFZL/nOGjf2HZCsi/YAvDGG9Lly96fJycXP2ARloFMIiAj87I48izpbKwYB737rhdE/Z7jf/mJF+U4XrvFv50PrH5IjqoX2A/Hfs9xsAdZopK8xOXL0tq1i3/2gzP9skAmEZABJEKxOF8tlqQzpzXc86SG9m6ev2BPOPYnR+S65vQHN51X3+ayHEfKdc5p503n9er59yrXOdf6VIkm+a8Z7Dn2e5L91wcixYhAJBgBGYD1ikWpfOSshnOPexd6FAjH9ghOjri/74T+4+cOat/Ydn3r8BZ99vYT2rm5rNd+s07Tv+tYUuGNopI8NHBsSQtRoivHSegZTsIa45K194tUISADlmr7gIeEq6oYn/PCsY2hOGilyRGStGZNvFMlUtVClISxYUlYI4AVEZABC5VK3jHY/oZBfyRdZ6e3sTDtllSMc3ZWjOtZaXLEl3ZV3/elXQmu6CZdsAXAdRc36fm9yAAyi4AMWMZ1vXAcnMccnNdsWyW53Up3qSSNj9f8vXNn9eVu+yvG9Sw3OWK0tE3ffXlT1X2ffOLj+sQtp/X5O6M/WQ81gi0Ata0R09Pe7/TLAplEQAYsE5zHPDGxGJSD85pt0W6lu1SSxp89r/zcOW264tfa3fUT73m6pem7/lijpfA3r0VpuckRriv91avv14/+dqPu2nZW3/nCD/TJJz6uHx7bKEn63B3HtGaN4TeQZfTLAgggIAMW8kOyH44l+8Jxq5Vuv2IsSXrzvD7q/lS/f8s7OvCbj2u675pIjkSOq597pckRf/yR03Ic6dxvr1Zf4VNyXemubWf1ie2nCccAYBECMmAhvxIbNDZmV0hupdIdrBgP5p6SuqShe07LvWNAGjsRyeY1f+ya/1xhjVZrFLpXmhzxuT86pr7Cpxbu+84XfkA4blYSxoYlYY0AVkRABizjh2O/EhuszEp2huRGle7CyFT1X5i5qPzcOR267yuBHozNchTNkcjBsWv+c4ZRnV4pdDeaHOG60lcPVvcnf/UgJ9s1LQltEElYI4AVhRKQHce5R9JjktZK+n9d1300jOcFsshxvB7eYCXWr9R2dtoTjqXlK92PPjKlfOWkDt3w6cU7c/KOg65pUI7qSOSVxq618tythm5OtrMEc4oBNKHtgOw4zlpJj0u6W9Lrkg47jvOfXNf9m3afG8iqgQEt+ZrepsqxVL/SPbxnRv/pmQ4d/N6ctsyd1KHfe1AaXP6Uu6iD40pj11p9Pml1oZuT7SzBnGIATQijgtwn6YTrur+SJMdxviXpk5IIyEAbbD/gobbS/egjU1o3M6lH3vsflFvztoZuOSgNDjb1PFEGxyiq062G7tSdbAcAKRVGQN4o6Uzg9uuSdobwvAAsVShIqnj9xa4rvfgjSZOTem3TnXK/PCzHWS9p5XDsiyo4RlWdbid02/7BBwAQ4yY9x3GGJA1J0rXXblrh0QBsVShIOnNap3rvrL4jJ2l4WK3mvSiCYxTVaXqJAdQT1zhJxCOMgHxWUm/g9o3z16q4rjsqaVSSPvCB29wQXhdAxIpFqXyyZhLF5KQXjoeX7y22RdjVaXqJAdSKapwkzAkjIB+WtNVxnJvkBePPSvrHITwvAIOKRal85KyGc49rKHdg8Y75SnGSNKxOtzjRgF7iBGNOMUIW1ThJmNV2QHZd95LjOP9M0n+VN+bt37uu+0rbKwMQm2JRKr9yvvri3CUvHO/dLClZgbhpbUw0oJc4oRjlhpBFMU4S5oXSg+y67vckfS+M5wIQrVKp+vbx416luL9jQvs/XDPCvIkpFACQdWGPk4R5nKQHZMjCUc9dgb7imVn1dxzX/nuflgYIxGgfm5WQNVEddgRzCMhAitVWi8efPa9+/VT7b66pFG/duuR0O6AVbFZC1jDZJp0IyEBKFUamlFd1X3G/XqZSnGaGj1GOarMSFWnYjMk26URABhKstkLsG39uSvnKSR3a8WD1HVSKq6VtooHhY5Sj2KxERRpJwGSb9CEgAwlVKEh6s6afeF6+Mu2FYzbZLY+JBqELc7MS47OQJEy2SRcCMpAAS3qJx7V4ml0+X/8vEY5hQJiblZI6PouWECD5CMiA5RYqxXpz4Vp+bk6Hej+duAM7YmW4HzeLotisFPb4rKjD62hpm6ZmOvTw3YstIV95frvWddESAiQJARmwQKnkzSOu68xpDfc8qaF7TldfHyAcL8twP24WRbFZaaWK9GoCbtT9zK4rPf/LjfrZrzZIkh6++6i+8vx2PXZwu/7gg+f1uTuOUUkGEoKADBhWKknjT51Vf+6lpXfOzGiw5/D8aXabY14ZEseCTYdhblaqrUh3Xzmn7768aaGa/KVdR/XVg80F3Lj6mXfeVNbPTm3QYwe95y5XrpIc73q7aN0A4kNABmJUr1K8cIrdPU/X/0tMnUCzVtE6EmXYCmuzUrAi7Yfhc7+9Wjdc87a6O+f01YPNB9w4+pkdx6saS9K/+M+36a13OiVJ/+qPX1xouWgV0zyAeBGQgZj4p9j1d/2i6vrWjpn52cQEYcQjSWErWJH2A+43D23RX/z4Q5JWF3CTehww0zyA+BGQgZAVi/Wvl185r/zcOe2/9xtL7yQcIyZJDFu17Rq1AbdWo/cQ9XHA/oa8xw5u17VXz2pD7qLOV67SYwe912y1imx6mgetHcgiAjIQosLIlFSp1O0n3qoZ7b8vUCkOTln4/vcXH8iUhXBY0I9rI9Nhqx31Au59X9ulP/hgeSF8vvuu6vYlx3Uc8KFTecmRvnjX0cVNej/c7l1vg6nqd5K+bQDCREAGVqlhhfjklDQ5qVO33uedWFdPsFLMlIVo8SGjoSS2GtQLuPd9bZfGjt24MDViz+6j+uQTH9dLr1+nP73r5SWbBaM+DthxpLs/dFY7b1oM7H5P8rqu9l4j6up3o9dM2rcNQFgIyMAqFAqSztWfOLF1Zkb7b32UAzpgPRNhq121AVeS/uCDZR06tUHXXj2jAxNb9NjB7XrrnU7dte2svrRr6XuJ4zjgeq/R7ga9uKrftZL8bQPQLgIyUKNYlMqNJjL5p9f19y+9b2BAEuE4UaI6TMTiQ0pMha0w1IZPvzr7rcNb9Ms3eiRJ175nVt/5wg+0Zk3952hnwkazvbhhHzkcR/V7uddO2rcNQBgIyEBAsbg4dm2g6/DSB/SI0+vSJKo2F4vbZ0yGrTAE1+ev/bEfLlbD87mL+urB8Kvhpntx46h+15PEbxuAMBCQkTmlkjQ+3uDOc/Mzife9KQ7mQFqZClthe/dd6ZNPfFxvvd25MDXi/T1v65uHwq2G29KLG3ZleiVJ/rYBaBcBGZnizyLOz53TYO6ppQ/Iaf7UuhgwZQEGxR22wua63rSKl16/TndtO6vvfOEHCweH3HDN2+q+MrxqeFZ7cZP+bQPQDgIyUqVQWOEBb3rh+NB9XzE/e5gpC8m2XJ+xaRb3QIfFD29/etfL+tKuo1qzZjG8dXfO6fMhtz1ktRc3Ld82AKtFQEZqFAqSzpzWcM+TjR/UJQ3de9p8OEbyWdxnbPXaQhRneMtyL27Sv20AWkFARqI0NWFixU10m0NeFRIr7DaXN96QLl3yEsTk5OJ1x5He+95wqssZqA6vRhzhjV5cIHsIyEiMYlEq/7xxhXio9wATJqKS1lAW9tovX/YS2saN1denp5vo/2lSjNVhjhj20IsLZA8BGVYolaTx56aWf9DkpIZ7nlxmEx3hODIZ+coei0yPNbMNvbhAthCQYZw/WaJfP9X+9XsaPzAnKsRADGwZa2YbenGB7CAgI3KFkRUqwzMXvXB879PSAAE4E9LQslGvf9l1pSss+J/VNnurszrWDAB8FvwvOdKsMDIlTU56m+cayck7upnJEtmRhpaNekF+eLj++4pbCB8ysjrWDAAkAjLaUChIqqzcN3zq1vukQSrDMKS2Wn3hwuJus/e+d/F6GNXrOA5/iemAmSyPNQMAAjJaUixqYebwUO5A4wfenJcGB2NbFyKS5FP/aqvVFy54bRCXL1dfD6N6HUd7SAyvwVgzAFlHQMYSq58oQXU49ZLSF4xQMNYMQNYRkFGFiRJIrTfekH73O+/Ply97v58+7f1+5ZV29A4HGd7IWG+smX+ksy+r0ywApB8BOWMKI1PSzMVlH8NECUQul5Nef91LWEGO4wXDKAKgH4prX891699nmgUbGYPhl7nIALKEgJwipdLy948/N7W4aW45W7cyUQLRGhlpPPEhSZMsMoK5yACyhoCcEoWCpDfPK9/VuHc4X5nWoVsfZNMcssXfYFhbrZYWr7luMjYcGsJcZABZQ0C22EoVYd/x41qcKHHzweUfTDhGq5J6uIe/tuFhb4LF2rXV91++7I17a/Qekvq+Q8ZcZABZQkC2lL9ZbrmK8ILKtIZ7npqfKEEARkQs6Ik1Iqvvu0bS5yLXtoHY0hZi67qArCMgG7Jiv7A/SeLmR5t7QirDWEnWK6G5nFdBvnSp+rrj2NleYdHs6aTPRV7tBsO4QisbHwF7EZAN8CdJLFcd7tfL85MkCL4IiW2V0LgDYNI+BFi03iTPRV7tBsO4QisbHwG7EZBD0my/8MIkid47pXy+8QOZJGGPrFdeo2LqZ8d/z5bUm4tse+VYWt0GwzhDKxsfAbsRkENQLErlV5rrF85XpnWo99McspEktlVeaxH4Vsf2/54Wqw1tSQlxzW4wjDu0svERsBcBeRnFYnOPKx85q+Hc4xq653Rzf4EDOBCmuAKfRT2xscrq+06R1WwwjDO0Jn3jI5BmBOQGCiNTys+c1tauMys+djD34/kJEpujXhYQHyrTniy91xRa7QbDuEJr0jc+AmmXyYC8UmW4fHJK+cpJHdrxoNcLvBJ6hZEEq62Epq0VwQ/8k5PeNAvf2rXS9debWxcitZoNhnGG1iRvfARSY5lAmLmAXChIOndW/bmXGj5m68yM9u94lNFpSJesV0L9wF97WMjly+bWhFg0u8Ew7tCa1I2PQCoUCtp57tuSfr/u3akIyKXS/GlyKyiXJZ057U2Q6O9f/sGMV4OPHtR0Wbu2OhS7rjQ9zX/PlGt2g2HcoTWpGx+BRGhUIS6XddOZF6TeTdJr9R+S+IDsnzjX3/WLFR+7dWZG+3v3MEECq2N75ZUAvzq17RTT0/NfLQEeQiuQAsWidh55UsotbRUsz6yTejdoeFj6/Ofr/3VrA3LTEyRemT9x7p6nm/sLTJBA2tge4AEAiFKdVoKdR55UOXez+u9Zt+ThW7Xy9jErA3JhZEqqVJbtE/Zt1cz8iXNslANCRWUaForrGGgACVEqaedTDy+pFJdzGzS8d2k4bpaRgPzGG8t8o1mZP2nu1vtWMUGCcAyEzmRlOooRcwT+xIvrGGgAFmoQHB94c5/KHTeo/54NVdcH24yGRgLy9ZfParjS+P/khnoPSIO0QgCZFcWIOVpREi3OY6AB2GV05DUVKj+pX9DokvrvWRd6I4GRgLx+45UaemTzMo8gHAMAFsV9DDQAQ2oqxaOV+1WoPKT8jo2xTt+1sgcZAIBacR4DDSB+oyOvqTD5gtTTU3U9v2Nd7EdTEJABAIkQ1zHQACLUoJd4tHK/CpMPSr2brJjGS0AGAFgvzmOgAUSkUPAO6KipEC/oXWdFOJYIyABsxMQJ1Ij7GOhGGDMHNKlYnD/CeJEfjtsZvxYXAjIA+zBxAnXEfQx0LcbMAU2aP8WunLu5+nqPEhGOJQIyACRXFPOiLWfqGGjGzAENlEoafW5T1aVixQvHSQnD9RCQASCpopgXHbOktCwwZg6oo1TSA89+RuP6qLQ+cFBHTtb0EreKgAwASeNXjicnpQsXFq+vXStdf725da1S0loW0jJmLikfSmCZOpXi0owXjvvv3RD6QR2mEZABJEcGWwrq8ivHFy54odh3+bK5Na1SElsW0jBmLmkfSmCPB579jMbn+qQbNi5ezEn9/UpdOJYIyACSJAUtBfAkrWUhDWPmkvihBGaNjrwmSSrN3K7xub7YT7MziYAMADAiSS0LtoyZa0fSPpTArNGR11SoPORVjHNSPq/MhGOJgAwAybV2bXVbhetK09OJmRedtJYF02PmwpCkDyWIj18pDipUHspUxbgWARnA6tAHbI/aDXnT04vHuFr+3ympLQt+324wJCepNSFpH0oQvdGR1xaOeA7K35ytinEtAjKA1aEPeKm4w2gzJw1a/t8pqS0LSd7kltQPJQhRoaAH3txXdWl85h6pd1Pix7KFjYAMIDlsPYI67jBqQQU4DElrWUj6JrekfihBm0ol7/fxce+o595NyucX784r25XiRgjIAJIjJcHQ9vaHOJk6Ga8VadjklrQPJWhTsagHXvmy1NWl8cq/lno3UiluEgEZAOJmefsDGkvDJrckfSjBKviVYt/x47rp509JPT3K37yOSvEqEZABAGgSm9xgpUCl2Dde+YjUk9Pw3nUGF5ZcBGQAq2NrHzCq8d8pdGxyg1X8ivHx49p55EmVO25Q/uYNC3dTMW4PARnA6mSsR7YpNoZR/juFjk1usEappAee/YzU1aXjM70qd6zX8L4NK/89NI2ADCA9TG1+I4xmBpvcELva3mJJDzz7GY3rowsV42EqxaEjIANIPj8YT05W7zhau9Y7TMO2zW82VpzRNDa5ITaBSnHQuH5f/fdu0MCAoXVlAAEZQPL5UyEuXPBCsS94DLNNqDgDWE6xKEl64JUva3yuT/kPb9TWrYt390uE44gRkAEAkak9PMP2wzQA00ZHXlNxpih1daqsdcrv2MBmOwMIyACASCT5WGYgVvMV49GTu1SoPKT8Dq9ivFVUik0hIAMAQpf0Y5mBuFRVjGeoGNuCgAwgPdaure47dl1peprNbzHyg68/3cF1k3ssMxCZYMV48kHlb91ExdgyBGQAyedPhag9vjnq8W6oUttS4Xtzukvru2ckMRINGB15TcXKk1Ku26sY30rF2EYEZADJRwg2rl5LxVee367HfrhdchcryxzLjEyarxirXFZh8gWpd5P6+6kY24yADABoW/BUOb+l4nzlKsmVvrjrqB6+m2OZkVGFgnae+/ZCxVi9GzQ8bHpRWAkBGQAQCj8k+yF4jePqf7/rZT18N8cyI2OKRalcliTddIaKcRIRkAEAofDHuPn8vmMfxzIjEwoFLxT39Hi3e9dRMU4gAjIAoG1+OPbHuAXHugWDMeEYqVRbMe7p0fDedYYXhXYQkAEAbXMcr3UiOMaNlgpkQrGonUeeVDl3s3e7R4TjFCAgAwBCMTRwrOoAEFoqEAWrji8PhGNCcbqsMb0AAEB61AYVwjHCNFrapn1j2+W63m2/tWe0tC3+xRCOU40KMgAgVFZV+JAaVhxfHug13nnu24TjFGsrIDuOc5+kfynpQ5L6XNd9MYxFAQCSqfY0Pb/Cl+uc09DAMdPLQ4LVm7UtxXh8eW2vcY5e4zRrt4L8sqRPS/paCGsBACSYFRU+pFrtrG0p4j73Ukmjz22SJBUrtFNkSVsB2XXdX0qSw//iAUDmGa/wIfVqZ21LER5fXipp51MPq9xxg7R+g1cxZp5xZtCDDPP27pUqlaXXczlpZCT+9QBoWewVPmTGcrO2pZD/nQXCcf+9Gzj9LoNWDMiO44xJur7OXY+4rvudZl/IcZwhSUOStOnaa5teIDKgUpG6u+tfB5AosVb4kCmxzdoulfTAs58hHGfcigHZdd3dYbyQ67qjkkYl6bYPfMAN4zkBAPaItcKHTIps1nag17g08xmN66OE44yjxQIAEApO00McQp+1PV8xHtdHF3qN+/vVdjhm3GGytTvm7VOS/h9J6yX9F8dxjriu+z+FsjIAQOJwml44CFcRi7hiXCpJs7PS7t1aGHc4NiZ1drYfvBGPdqdYPCPpmZDWAgBIAU7Taw+zpCPmV4zn+qQbNoZWMfa5rheOJya827t3e+F4YkLq6+PDTlLQYgHzcrnGUywApJIfEhr9nlXMko6eH47zOzZqcDD853ccLxRLXij2g3Jf32JFGfYjIMM8RrkBmeJXSLs75zQ926Ev7Tqqrx7cru4r5zT9u45MV0qZJR2tB/asjzQc+/yQ7IdjiXCcNARkAEBs/ArpNw9t0Q3XvK1fT16tv3r1/Tr326sXbme9Usos6WjEFY6lxZ7joLExQnKSEJABALEJVki/eWiLypWr9Ms3enTt1bNyHCqlErOkQ1UqSYq+rSLID8d+z3GwB1kiJCcFARkATMnoKZLBCmk+d1FvvdOpDbmLkqiUMks6PKMjr6mk/1WSND73kVjCseT9++7srO459nuSOzsJx0lBQAYAUzJ6iqQfAl1XKleukiSdr1ylDbmLma+UMks6HKMjr6lQeUj5HRslSXkplnDsGxjQknGHVI6ThYAMAIiNH479HmTHkXZs+o3O/fZqvb/nbSqlYpZ0u4LhOM5QXItxh8lGQAYAxMavkP7jnSeWnWKR9TBBuGqNLeEYyUdABoC0sby3OVgh9X8PHopBGEQrCMcIEwEZAJZjedisKwG9zcH2gXq/xykYyl23eh0EdssVi5Kk0ZO7CMcIFQEZAJYTZdjkFEnjgsc6/8WPt2lqpkOStK5rTp+74xhHPFtsdOQ1FStPSrlulWfWKb9jA+EYoSEgA4AptlagMyJ4rLNfOX7sh9slV/rirqP6yvPb9a3DHPFso9GR11SYfFDq3aT+fmmrvMkRQFgIyADSK4ntEYhN7bHOrivJleRI3zrMEc+2Cobj4WHTq0FarTG9AACIjN8eUfvLol5cmBUMyY4j5XMXFw4tkcyOV/Or2o1uZ1KhQDhGLAjIAJA2uZw0Pb30F73NSwSPdfYPLjk/f3iJtHigSdxGS9uqXttf52hpW/yLsUWhoJvOvEA4RixosQCA5SRxIx3tI00JHuv82dtPSFrsQfZvmzi4JNgb7b928PjpTPZDE44RMwIyACyHsJlatcc6/8WPt+mLd3ntFv4UC/8xcQbS2t5oPyhnth+acAwDCMgAgMwKHlri/1larNCaCqR+SPbDscm1GEU4hiH0IANIL3px0YRg6HScpbdNCPZG+0z1QxtDOIZBVJABpBftEUigYG+031bh35YyUkkmHMMwAjIAABap7Y0O9iTH3Q9tBOEYFiAgAwBgmWBvtLTYk0w4BuJBDzIAABaqDcOEYyA+BGQAAGAW4RiWISADAABzWgzHHMWNKNGDDKTd3r2NT4JjygMAk1oMx6WSNDsr7d7ttZ64rjQ2JnV2SgMD0S0X2UFABtKuUpG6u+tfBwBT2qgcz85KExPe7d27vXA8MSH19SmbR3EjdARkAAAQrzZ6jh3HC8WSF4r9oNzXt1hRBtpFQAaAqNDeAiwVwoY8PyT74VgiHCNcBGQA6WFbIKW9BagW0rQKv+c4aGyMkIzwEJABpAeBFLBXyOHY7zkO9iBLhGSEg4AMpF0u17iqCgBxCHHOseN40yqCPcd+T3JnJ+EY4SAgA2kXd2uBbW0OAMzyw3FPT2iHgAwMaMlR3FSOESYCMoBw0eYAwBcMx3vXhfrUmTuKG7EiIANIrtpq9eSkdOGCtHatdP315tblo70FWRZhOAaiRkAGkFy11erpaenyZenSJe/PPlOBlJYSZFVGw3HtISUcWpJcBGQA6eFXjaenpULB7FqArMpoOOb463QhIAOIx4ULqrtDp9nNe/U2/01OemHYhnYKAFKxmMlwzPHX6UNABhCuRn23Unub9+pt/rtwwWupAGBesaibfv5U5sKxxPHXaURABhCuRtXgsOY7AbBPhsOxj+Ov02WN6QUAQMvWrvW+u5yerv7FlAggPqWSHnjly5kOx1Lj469d18x60B4qyACS6/rr2ZAH2KCrS/mbCcccf50eBGQAANC68XGNV/618qbXYRDHX6cPARlAPNo9NINDNzKFebIJsTDSLafBQdOLMYvjr9OFgAwgHu0emsGhG5kxWtqmymyH9uw+ujBPdt/YduU65zQ0cMz08uDL6Lzj5XD8dXoQkAFEr94MY6n5GcjIDNeVKrMdOjCxRZK0Z/dR7RvbrgMTW3R/3wkqybbI6LxjZAcBGUD06s0w9q8DAY7jhWJJOjCxZSEo3993YqGi3CzaNCLCSDdkAGPeAABWCYZk32rD8Whpm/aNbV8YseW3aYyWtoW40gwiHCMjCMgAAKv4YTYoGHab+ft+m4b/9/w2jcpsB3Np2zB6chfhGJlAiwUAwBrBMOu3Vfi3peYqyY4jfWlXOG0aqCNHOEb6EZABANZwHCnXOVcVZv12i1znXFPh1p+C8aVdRxfC8fnKVeq+srm/jwYKBRUmX5CYrIgM3ZzRbAAAEaBJREFUICADiB4zjLEKQwPHlsyTbbby67dXfPPQFv3V375fkheO33q7U989ukmfu+OY1tBcuHrBkW7DphcDRI+ADCB6WR3lxni7lrU6T9Zvr/j6f9uq//o3vcp3X9SGdRe148bf6MiZ9+l/+Ytd+suhg1SSV4N5x8ggPkcDQFT88Xa1vxhvFynHkbbmL1RdG9j6a0OrSTjmHSOjCMgAkDG1UxzSONXhDz5YVr77on57sVPHzvfoz3+0XX9611Gqx6tVLhOOkUkEZADIkLTPB/bfz7cOb9E/331UH7p+UtdePau33u4kGANoGgEZADIiC/OB/SkYn739xMK1DbmLuvbqWR06lTe4sgTye48Z64YMYpMeAMTljTeky5e9pBocBRDTpr0wj3G22efuOKavPO9Vkf335t/eN7Y9Ve81MkytQMYRkAEgKrXj7S5d8lLqFVd4m/V8MW7a80OyH46l1R/jbDvHkdZ1Vc9SfvjuowvV5TS910gwtQIgIANAZGqrwsPD1cHYgEbHOKctJLczSznTCMeAJHqQASAzao9xPjzyjO7vO1HVk5wmrc5SzjzCMUAFGQCyIoxjnAEgCwjIAJKBU+lCQesBGvLbK3qpHgMEZADJ4J9KV+96UtRu2gtejxGtB1iCqRVAFQIyAMSFSjdsxMY8YAk26QEAkFXFonae+zbhGKhBQAYAIMty3eq/h3AMBBGQAQDIqnJZ5RnCMVCLHmQAyWDJBjcgNRamVmzQwIDpxQB2ISADSAY2uAHh8XuPezcxtQKogxYLAACyKNet/n7TiwDsREAGACBr6D0GlkVABgAgS/ze4/X0HgONEJABAMiKhY159B4DyyEgAwCQBYRjoGkEZAAA0q5U0gNv7iMcA00iIAMAkAVdXcrnTS8CSAYCMgAAaTc+rvHKR0yvAkgMAjIAAGnm9x7fsFGDg6YXAyQDARkAgLSi9xhoCQEZAIA0o/cYWDUCMgAAaUXvMdASAjIAAGlE7zHQMgIyAAApRO8x0DoCMgAAaUTvMdAyAjIAAGlTKNB7DLSBgAwAQJrQewy07QrTCwCABXv3SpXK0uu5nDQyEv96gAQardxP7zHQJgIyAHtUKlJ3d/3rAADEpK0WC8dx/o3jOMccx3nJcZxnHMfpCWthAABgdUZHXlNh8kHTywASr90e5Ocl3eK67kckvSqJL3QAADBgIRzTXgG0ra2A7LruD1zXvTR/82eSbmx/SQAAoCWEYyAUYU6x+KeSvt/oTsdxhhzHedFxnBffnJ4O8WUBAACA8Ky4Sc9xnDFJ19e56xHXdb8z/5hHJF2S9I1Gz+O67qikUUm67QMfcFtaLYB0y+UaT7EA0JDfXpG/2fRKgHRYMSC7rrt7ufsdx/knkj4haZfrugRfAK1jlBuwasHeY+YeA+Foa8yb4zj3SPo/JN3puu474SwJAACsSoS9x64rOU7j20AatduD/O8k5SQ97zjOEcdxngxhTQAAwAKlkjQ25oViyft9bMy7DqRZWxVk13W3hLUQAACwOqMjr6lQeSiS3mPXlWZnpYkJ7/bu3V44npiQ+vqoJCPdOEkPAIAkKhRUmHxB+Vs3RtJ77DheKJa8UOwH5b4+7zrhGGkW5pg3AAAQp56eSDfmBUOyj3CMLCAgAwCQQKOV+yN/Db/nOCjYkwykFS0WAAAkzELv8Y51kb2GH479nuNgD7JEJRnpRkAGACBJCgUVKj9Rfkc0vcc+x5E6O6t7jv12i85OwjHSjYAMAEDS5HKxHAoyMFA9rcIPyYRjpB09yAAAoKHaMEw4RhYQkAEAAIAAAjIAAAnywJv7TC8BSD0CMgAACTE68prG5/qUvzm66RUA2KQHAEAyxDS9AgAVZAAAkiOm6RVA1lFBBpAde/dKlcrS67mcNDIS/3qAZpVKXu9xl+mFANlAQAaQHZWK1N1d/zpgsQee/YzXe/xheo+BONBiAQCAzYpFLxzTewzEhoAMAIDtOq4gHAMxIiADAAAAAQRkAAAAIIBNegCyI5drPMUCsFGppAde+bLpVQCZQ0AGkB2MckOSlEre9Ap9VP33bjC9GiBTCMgAANjo+HEd77pF/fds0MCA6cUA2UIPMgAAABBAQAYAwDbzvcflGQ4GAUwgIAMAYJOa3mPaK4D40YMMAIBN6D0GjKOCDAAAAAQQkAEAAIAAAjIAAAAQQEAGAMAWpZJ2HnmS6RWAYQRkAABsMD+9otxxA9MrAMMIyAAAWOK4thKOAQsQkAEAAIAAAjIAAAAQQEAGAMAGx4+bXgGAeQRkAABMKxa96RVdm+g/BixAQAYAwKRSSQ+88mWVczdreC/j3QAbEJABADCtq0v5mwnHgC0IyAAAmHT8uI7P9JpeBYAAAjIAAKYEeo8HB00vBoCPgAwAgAn0HgPWIiADAGAKvceAlQjIAAAAQAABGQAAAAggIAMAYMLx4xqvfMT0KgDUQUAGACBuxaJu+vlTUi7H9ArAQgRkAABi9sArX5Z6epheAViKgAwAgAFMrwDsRUAGAAAAAgjIAAAAQAABGQCAOBUKGp/rM70KAMsgIAMAEJdCQTedeUG6YSPTKwCLEZABAIjJaOV+qXeThodNrwTAcgjIAAAAQAABGQAAAAggIAMAAAABV5heAAAAmVAoqDD5gpQzvRAAK6GCDABA1PzpFWzQAxKBgAwAQMSYXgEkCwEZAAAACCAgAwAAAAEEZAAAACCAgAwAAAAEEJABAACAAAIyAAARGh15TYXKQ8rnTa8EQLMIyAAARGQhHO/YqMFB06sB0CwCMgDAGNdd/nYq3EA4BpKGgAwAMGK0tE37xrYvhGLXlfaNbddoaZvZhQHIPAIyACB2ritVZjt0YGLLQkjeN7ZdBya2qDLbkc5KMoDEuML0AgAA2eM40p7dRyVJBya26MDEFknS/X0ntGf3UTmOydUByDoqyAAAI4Ih2ZemcMz0CiC5CMgAACP8toqgYE9yohUKTK8AEoyADACIXbDn+P6+Ezo88ozu7ztR1ZOceLkc4RhIKHqQAQCxcxwp1zlX1XPst1vkOudS02YBIJkIyAAAI4YGjsl1tRCG/ZBMOAZgGi0WAABjasNwKsJxqaQH3txnehUA2kBABgAgLKWSHnj2Mxqf61P/PetMrwZAiwjIAACEZXxc43N9yu/YqIEB04sB0CoCMgAAYWJ6BZB4BGQAAAAggIAMAAAABBCQAQAAgAACMgAAABBAQAYAIAzMPwZSg4AMAEC7/PnH+ijzj4EUICADANCu8XEvHN+7gfnHQAoQkAEACEPXVYRjICUIyAAAAEAAARkAAAAIaCsgO47zfzmO85LjOEccx/mB4zg3hLUwAAASoVjUznPfNr0KACFqt4L8b1zX/YjrujskfVfS/xnCmgAASIZiUTuPPKlyxw0a3sv0CiAt2grIrutOBW5eLcltbzkAACRMrlvD+zaYXgWAEF3R7hM4jvNnkv5E0gVJH1vmcUOShiRp07XXtvuyAABYoTxD5RhImxUryI7jjDmO83KdX5+UJNd1H3Fdt1fSNyT9s0bP47ruqOu6t7mue9v67u7w3gEAAAAQohUryK7r7m7yub4h6XuS/v/27j/E77qOA/jzla4UPTFwE50zZQzpB2ERCq0ksB8johkiJPlHtH8GRQZFtQlJhV0RRNA/dXBCgRSFJv1hYZF4FViarDJ/pIYyK7p+MJqEpPnuj7vB2zHd1rZ73+7zeMDBfb4c3JPX57g997nP5/W98ZgSAQDAQMe6xWJLd7g9ycPHFgcAAMY61nuQv1hVlyR5PsmTSXYeeyQAABjnmApya+3q4xUEAE4qyyveMnP66CTAcead9ADgaB3Yfzyz2f5jWIMUZAD4f8ycma3blGNYixRkAADoKMgAANBRkAEAoKMgAwBAR0EGgKO1uJjFZzygB2uVggwAR2N2NhfvvTtZf26uuGJ0GOBEUJAB4EjNz+fyP9+WbLowu3aNDgOcKAoyAByNmTOzdevoEMCJpCADAEBHQQYAgI6CDAAAHQUZAAA6CjIAHKG5x6+0/xgmQEEGgCMwt/uJzO7baf8xTICCDACHs7CQ+f3X2H8ME6EgA8CRWLfO/mOYCAUZAAA6CjIAAHQUZAAA6CjIAADQUZAB4DDmfnRhFp995egYwApRkAHgJcztfiKz+z+cDZdutP8YJkJBBoAXs7CQhbw1Gy7dmB07RocBVoqCDAAAHQUZAAA6CjIAAHQUZAAA6CjIAADQUZAB4FAWFnLd7VfnF8+8MVu2jA4DrCQFGQAOdqAc583ZetW59h/DxJw6OgAArEqnnZYNm5VjmCJXkAEAoKMgAwBAR0EGAICOggwAAB0FGQAO4dFnNo2OAAyiIANAb2Ehl3/v41nMevuPYaIUZADoXHf71Vlcd779xzBhCjIAHGTDa5VjmDIFGQAAOgoyAAB0FGQAAOgoyAAA0FGQAeCA+fk8+uxFo1MAgynIAJAk8/O5fM/XszizOTt2jA4DjKQgA0CSucevzOLM5uz6wlmjowCDKcgAcMCMcgwoyAAA8AIKMgAAdBRkAADoKMgAANA5dXQAABhudjaz++5OZkYHAVYDV5ABmLbZ2Vy89+7k7LOza9foMMBqoCADMGlz+69dKsf2HwPLFGQAsP8Y6CjIAADQUZABAKCjIAMAQEdBBgCAjoIMAAAdBRkAADoKMgCTNbf7iczu2zk6BrDKeKtpAKbpwNtLb7rQO+gBL+AKMgCTNLf/WuUYOCQFGQAAOgoyAAB0FGQAAOhUa23lv2nV35I8ueLfeHU4J8nfR4eYMPMfzzkYy/zHMv+xzH+s1Tj/V7XW1h/84pCCPGVVdV9r7U2jc0yV+Y/nHIxl/mOZ/1jmP9bJNH+3WAAAQEdBBgCAjoK88uZGB5g48x/PORjL/Mcy/7HMf6yTZv7uQQYAgI4ryAAA0FGQAQCgoyAPUFWfr6rfVtWeqrqzqs4fnWlKqurLVfXw8jn4flWdPTrTlFTVNVX1+6p6vqpOinU/a0FVbauqR6rqsar69Og8U1NVN1fVYlU9MDrL1FTVpqq6q6oeXP7dc/3oTFNTVadV1a+q6jfL5+CzozMdjnuQB6iqs1pr/1r+/KNJXtNa2zk41mRU1TuT/LS19lxVfSlJWmufGhxrMqrq1UmeT/KNJJ9ord03ONKaV1WnJPlDknckeSrJvUmuba09ODTYhFTVFUmeTvKt1trrRueZkqo6L8l5rbX7q2omya+TXOXnf+VUVSU5o7X2dFWtS/LzJNe31u4ZHO1FuYI8wIFyvOyMJP6XsoJaa3e21p5bPrwnyQUj80xNa+2h1tojo3NMzGVJHmut/bG19p8k30myfXCmSWmtLST55+gcU9Ra+0tr7f7lz/cneSjJxrGppqUteXr5cN3yx6ruPgryIFV1U1XtTfKBJJ8ZnWfCPpTkh6NDwAm2Mcne7vipKAhMUFVdlOQNSX45Nsn0VNUpVbUnyWKSH7fWVvU5UJBPkKr6SVU9cIiP7UnSWruhtbYpyS1JPjI27dpzuPkvf80NSZ7L0jngODqS+QOspKo6M8mtST520F9yWQGttf+21i7N0l9tL6uqVX2r0amjA6xVrbW3H+GX3pLkjiQ3nsA4k3O4+VfVB5O8J8mVzY34x91R/PyzMv6UZFN3fMHyazAJy/e93prkltbabaPzTFlrbV9V3ZVkW5JV+9CqK8gDVNWW7nB7kodHZZmiqtqW5JNJ3tta+/foPLAC7k2ypaourqqXJ3l/kh8MzgQrYvkBsfkkD7XWvjI6zxRV1foDG6Oq6vQsPTC8qruPLRYDVNWtSS7J0pP8TybZ2VpzNWeFVNVjSV6R5B/LL91ji8jKqar3JflakvVJ9iXZ01p719hUa19VvTvJV5OckuTm1tpNgyNNSlV9O8nbkpyT5K9JbmytzQ8NNRFV9ZYkP0vyuyz9u5sku1trd4xLNS1V9fok38zS75+XJflua+1zY1O9NAUZAAA6brEAAICOggwAAB0FGQAAOgoyAAB0FGQAAOgoyAAA0FGQAQCg8z+fsTY+GbxrWQAAAABJRU5ErkJggg==\n",
      "text/plain": "<Figure size 720x720 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light",
      "tags": [],
      "image/png": {
       "width": 712,
       "height": 712
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 783
    },
    "colab_type": "code",
    "id": "PsSKUAOmN2-L",
    "outputId": "ff0903dc-77d5-4b1b-855d-601c55c1cc00",
    "cell_id": "00010-2ad49ec1-61e8-4edb-9787-6337414b6acd",
    "deepnote_cell_type": "code"
   },
   "source": "# Create a SVC classifier using an RBF kernel\nsvm = SVC(kernel='rbf', random_state=0, gamma=.01, C=1000)\n# Train the classifier\nsvm.fit(X_xor, y_xor)\n\n# Visualize the decision boundaries\nfig = plt.figure(figsize=(10,10))\nplot_decision_regions(X_xor, y_xor, classifier=svm)\nplt.legend(loc='upper left')\nplt.tight_layout()\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 720x720 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light",
      "tags": [],
      "image/png": {
       "width": 712,
       "height": 712
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 783
    },
    "colab_type": "code",
    "id": "Y1i32tOyN8zG",
    "outputId": "c98565c6-0ac9-4a39-ef03-0c81581066cf",
    "cell_id": "00011-32bab442-4866-46bd-a70a-a94e3139fc88",
    "deepnote_cell_type": "code"
   },
   "source": "# Create a SVC classifier using an RBF kernel\nsvm = SVC(kernel='rbf', random_state=0, gamma=.01, C=10000)\n# Train the classifier\nsvm.fit(X_xor, y_xor)\n\n# Visualize the decision boundaries\nfig = plt.figure(figsize=(10,10))\nplot_decision_regions(X_xor, y_xor, classifier=svm)\nplt.legend(loc='upper left')\nplt.tight_layout()\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 720x720 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light",
      "tags": [],
      "image/png": {
       "width": 712,
       "height": 712
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 783
    },
    "colab_type": "code",
    "id": "4lRfuJ0HN_Zu",
    "outputId": "d8c00e6d-252e-4266-a67f-4cd53d5fb591",
    "cell_id": "00012-87fc72b4-5d53-472d-8360-97a2dbc4b29f",
    "deepnote_cell_type": "code"
   },
   "source": "# Create a SVC classifier using an RBF kernel\nsvm = SVC(kernel='rbf', random_state=0, gamma=.01, C=100000)\n# Train the classifier\nsvm.fit(X_xor, y_xor)\n\n# Visualize the decision boundaries\nfig = plt.figure(figsize=(10,10))\nplot_decision_regions(X_xor, y_xor, classifier=svm)\nplt.legend(loc='upper left')\nplt.tight_layout()\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 720x720 with 1 Axes>"
     },
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      "outputs": [
       {
        "metadata": {
         "tags": []
        },
        "output_type": "stream",
        "stream": "stderr",
        "text": "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
       },
       {
        "metadata": {
         "tags": []
        },
        "output_type": "stream",
        "stream": "stderr",
        "text": "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
       },
       {
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\n",
        "metadata": {
         "needs_background": "light",
         "tags": []
        },
        "output_type": "display_data",
        "text/plain": "<Figure size 720x720 with 1 Axes>"
       }
      ]
     }
    }
   }
  },
  "deepnote_notebook_id": "7cba8147-d1f3-4cfe-bf60-3072e6b27bb9",
  "deepnote": {},
  "deepnote_execution_queue": []
 }
}