solutionsEX/Ex4_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# <span style=\"color:rgb(213,80,0)\">Hawkins data: BIC monitoring.</span>\n",
    "\n",
    "This file creates Figure A.13\n",
    "\n",
    "## Beginning of code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "source": [
    "load('hawkins.txt');\n",
    "y=hawkins(:,9);\n",
    "X=hawkins(:,1:8);\n",
    "n=length(y);\n",
    "p=size(X,2)+1;\n",
    "logn=log(n);\n",
    "prin=0;\n",
    "Xwithintercept=[ones(n,1) X];"
   ],
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare input for Figure A.13\n",
    "\n",
    "Monitoring LXS Find LMS and LTS residuals\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "source": [
    "bdp=0.5:-0.01:0.01;\n",
    "lbdp=length(bdp);\n",
    "BIClxs=[bdp' zeros(lbdp,1)];\n",
    "RES=zeros(n,length(bdp));\n",
    "\n",
    "for j=1:length(bdp)\n",
    "    [out]=LXS(y,X,'lms',2,'bdp',bdp(j),'nsamp',100000);\n",
    "\n",
    "    hh=out.h;\n",
    "    if hh<n\n",
    "        resj=(y-Xwithintercept*out.beta).*(out.weights);\n",
    "        RES(:,j)=out.residuals;\n",
    "        % Apply Tallis consistency factor\n",
    "        vt = norminv(0.5*(1+hh/n));\n",
    "        factor = 1/(1-2*(n/hh)*vt.*normpdf(vt));\n",
    "        RSS=factor*resj'*(resj); % /hh;\n",
    "        BIClxs(j,2)=-n*log(RSS/hh)-logn*(p+n-hh);\n",
    "    else\n",
    "        beta=Xwithintercept\\y;\n",
    "        res=y-Xwithintercept*beta;\n",
    "        RSS=res'*res;\n",
    "        BIClxs(j,2)=-n*log(RSS/hh)-logn*p;\n",
    "    end\n",
    "\n",
    "end"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total estimated time to complete FASTLTS:  0.61 seconds \n",
       "Total estimated time to complete FASTLTS:  0.60 seconds \n",
       "Total estimated time to complete FASTLTS:  0.54 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.53 seconds \n",
       "Total estimated time to complete FASTLTS:  0.56 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.54 seconds \n",
       "Total estimated time to complete FASTLTS:  0.63 seconds \n",
       "Total estimated time to complete FASTLTS:  0.68 seconds \n",
       "Total estimated time to complete FASTLTS:  0.63 seconds \n",
       "Total estimated time to complete FASTLTS:  0.63 seconds \n",
       "Total estimated time to complete FASTLTS:  0.65 seconds \n",
       "Total estimated time to complete FASTLTS:  0.63 seconds \n",
       "Total estimated time to complete FASTLTS:  0.62 seconds \n",
       "Total estimated time to complete FASTLTS:  0.54 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  1.30 seconds \n",
       "Total estimated time to complete FASTLTS:  0.59 seconds \n",
       "Total estimated time to complete FASTLTS:  0.56 seconds \n",
       "Total estimated time to complete FASTLTS:  0.58 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.58 seconds \n",
       "Total estimated time to complete FASTLTS:  1.33 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.62 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.53 seconds \n",
       "Total estimated time to complete FASTLTS:  0.54 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.54 seconds \n",
       "Total estimated time to complete FASTLTS:  0.64 seconds \n",
       "Total estimated time to complete FASTLTS:  0.56 seconds \n",
       "Total estimated time to complete FASTLTS:  0.66 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.60 seconds \n",
       "Total estimated time to complete FASTLTS:  0.54 seconds \n",
       "Total estimated time to complete FASTLTS:  0.54 seconds \n",
       "Total estimated time to complete FASTLTS:  0.54 seconds \n",
       "Total estimated time to complete FASTLTS:  0.60 seconds \n",
       "Total estimated time to complete FASTLTS:  0.54 seconds \n",
       "Total estimated time to complete FASTLTS:  0.57 seconds \n",
       "Total estimated time to complete FASTLTS:  0.57 seconds \n",
       "Total estimated time to complete FASTLTS:  0.56 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.55 seconds \n",
       "Total estimated time to complete FASTLTS:  0.56 seconds"
      ]
     },
     "metadata": {},
     "execution_count":  2,
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Figure 4.43\n",
    "\n",
    "LTS figure\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "source": [
    "subplot(2,2,1)\n",
    "plot(BIClxs(:,1),BIClxs(:,2))\n",
    "xlabel('Breakdown point')\n",
    "ylabel('BIC')\n",
    "[~,indmax]=max(BIClxs(:,2));\n",
    "ylim([min(BIClxs(:,2)) max(BIClxs(:,2))])\n",
    "xlim([0 0.5])\n",
    "\n",
    "set(gca,'XDir','rev')\n",
    "\n",
    "% FS\n",
    "outpre=LXS(y,X,'nsamp',10000);"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total estimated time to complete LMS:  0.06 seconds"
      ]
     },
     "metadata": {},
     "execution_count":  3,
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "source": [
    "[Un,BB]=FSRbsb(y,X,outpre.bs,'init',round(n/2));\n",
    "BICfs=[Un(:,1), zeros(size(Un,1),1)];\n",
    "for j=2:size(BB,2)\n",
    "    boo=~isnan(BB(:,j));\n",
    "    hh=sum(boo);\n",
    "    Xb=Xwithintercept(boo,:);\n",
    "    yb=y(boo);\n",
    "    beta=Xb\\yb;\n",
    "    resj=yb-Xb*beta;\n",
    "    if hh<n\n",
    "        % Apply Tallis consistency factor\n",
    "        vt = norminv(0.5*(1+hh/n));\n",
    "        factor = 1/(1-2*(n/hh)*vt.*normpdf(vt));\n",
    "        RSS=factor*resj'*(resj);\n",
    "    else\n",
    "        RSS=resj'*resj;\n",
    "    end\n",
    "    BICfs(j-1,2)=-n*log(RSS/hh)-logn*(p+n-hh);\n",
    "end\n",
    "\n",
    "% Plot BIC FOR FS\n",
    "subplot(2,2,2)\n",
    "plot(BICfs(:,1),BICfs(:,2))\n",
    "xlabel('Subset size m')\n",
    "ylabel('BIC')\n",
    "[~,indmax]=max(BICfs(:,2));\n",
    "ax=axis;\n",
    "\n",
    "xlim([n/2 n])\n",
    "ylim([min(BICfs(:,2)) max(BICfs(:,2))])\n",
    "\n",
    "if prin==1\n",
    "    % print to postscript\n",
    "    print -depsc Hbic.eps;\n",
    "else\n",
    "    sgtitle('Figure A.43')\n",
    "    set(gcf,\"Name\",'Figure A.13')\n",
    "end"
   ],
   "outputs": [
    {
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jjvu6LABBhOAbhKCBICs2bBhw2OPPRZfftNNN5WVlXX48GnTpv37v/97fPnjjz/+z3/+s/vNS4nm5uarrrqqrq4u2w0hxXK39z722GPxR3Ttvffed91114gRIzpTw3777XfvvfcmPDX4r3/9awqaSPrlbgdeunTp22+/HVM4dOjQ++67r5MduE+fPmeffXbCHR+bm5vvueeeFLSSdMrd3tuWpqamlStX3nDDDSeeeOLtt9++bdu2rDSDPFBRUdHc3BxTOH78+P/8z//scJ/m4uLiX//61/H7oTY3N998882pbCUA5J0nnnhi+/btMYUlJSV33313YWFhhw/v06fPRRddlHDD7xdeeGHTpk0d1uA9APRYra2t77zzzkMPPTRz5swzzzxzzZo16Xuu7A4FBiJIq8wMJq2trffdd198+be+9a1zzz23k5Xsv//+9957bygUir9r0aJFb731VvsPN5gAdJMQJABkzV133dXS0hJTeOqpp375y1/uZA3f+MY3Ep4i/dOf/rS7jUuRX/3qV88++2x0SXFxcbYaQwrlbu/9y1/+El94+eWXd+kc2NGjR1966aXx5a+//nqHExn0BLnbgRPusHLVVVe1vxA53je+8Y2pU6fGly9dujR+oo0eJXd7b8Sjjz562223/cd//Mcll1xy0kknHXHEEeecc84DDzwQn1CHzqupqfnTn/4UUzhgwICbb755wIABnalhyJAhCbfaraysjHk3CwBEq6ysjC+8+OKLEy4dbMull16a8IDIp59+uv0Heg8APUpVVdXdd9994403XnXVVdOnTz/qqKOmTJlSXl7+j3/8I63Pm92hwEAEKZeVwWTVqlWvvvpqTOEBBxwwe/bsLtVTVFT0i1/8Iv64ntbW1mXLlrXzQIMJQPcJQQJAdmzcuPHxxx+PKSwsLPyP//iPzlfSt2/f66+/Pr78+eef7wlHmr7wwgt33nlndMnIkSO/973vZas9pEru9t4dO3bErxMdOnToGWec0dWq/vf//t977713fPmqVauSbByZkrsdeOPGjStXrowpHDVq1JlnnplEbVdeeWW/fv1iChsaGnrOXsLEy93eG+2uu+665557fve73y1dunTDhg2tra0ZeFLy3q9//ev4fPDMmTMPPvjgzldy7LHHnnbaafHljz76aLcaBwD565NPPlm9enV8+Ve+8pUu1VNYWHjyySfHl69bt679B3oPAD3KunXrbr/99vvuu+/xxx9fs2bNRx99lJnnze5QYCCClMvKYLJo0aL4wksuuaSgoKCrVf2P//E//vVf/zW+POHSkQiDCUD3CUECQHY89dRT8Z9nvvKVr3RpoXwQBKFQ6IQTTogvX7x4cfKNS4UtW7ZceeWV0TuK9enT5+abbx46dGgWW0VK5G7vfemll5qammIKTznllM4c0RVjwIABxx9/fHx5Z87qIrtytwO/8MIL8YVnnHFGfJaxM8aMGTNhwoT48hUrViRRG5mRu70X0qq5ufmpp56KKezTp8/Xv/71rlY1Y8aM+MKlS5fGn/IJAARB8O6778bvJR8KhUaMGNHVqhLuVt7+R2zvAYAg20OBgQjyRvzUa1uLNDrj9NNPjy9cuXLljh07El5vMAFICSFIAMiOv/3tb/GFSXyeCYLg7LPPji988sknk6gqVVpbW7///e/X1tZGF1588cUJZ7TJObnbe1966aX4wsmTJydX20EHHRRf6DjXni93O3DCHVZOOeWUpCtMGOR99913k66QdMvd3gtp9cILL2zZsiWmsKysbNSoUV2t6ogjjjjkkENiCj/++OO///3vybcPAPLX5s2b4wv33XffJKraZ5994gvff//9dh7iPQAQZHsoMBBBfvjggw/++7//O6awrKxsr732Sq7CiRMnxm8huWPHjvr6+oTXG0wAUkIIEgCyoKGhIX5VWWlp6WGHHZZEbVOmTInfXvGll17auHFjku3rtgcffHDp0qXRJZ///Oe/853vZKs9pFBO9966urr4wokTJyZXW3FxcXzhhx9+mFxtZEZOd+D4EOSwYcMOPPDApCscM2ZMfGHCbzHpCXK690JaJcwHJzx5qkN9+vSZNm1afPmSJUuSqA0A8l7CT9kDBw5MoqqEO9xv27atnYd4DwAE2R4KDESQH9asWRNfmPQXB0EQDBw4cOTIkfHlbU29GkwAUqJ/thsAAL3R8uXL408LSnqXxH79+h155JHLli2Lf5bkdofqppdeeumnP/1pdMngwYNvvfXW/v298cgHOd17GxoaYkoGDBiQcLeJzkh4fkTSa0PJjJzuwPHptCOOOKJPnz5JVzh8+PD4woTfYtIT5HTvjTZ69OgO3xLU1dV99NFHaW0GeaO1tTXhVwWTJk1KrsKEL6vKysrW1tbuDLkAkJcGDRoUX/jee+8lUVXCTMDee+/d1vXeA0APtNdee332s59t/5rm5uYUrr7L7lBgIII0yfxgkrCqCRMmdKfO4cOHV1dXxxQmfMNjMAFIFVkEAMiCZ599Nr7wyCOPTLrCo446Kj7H8NprryVdYdI+/vjjyy+/fMeOHdGF5eXlHX5kJVfkdO+ND0Huv//+ffsmuTl6wqxYSUlJcrWRGbnbgXfu3BmfCQuFQt2pM+GuKi0tLd2pk/TJ3d4b48EHH+zwmh//+Me/+c1v0t0S8kNtbe1bb70VUzhmzJgRI0YkV+Ehhxyy1157ffzxx9GFdXV1mzdvTrpOAMhXCT8Cv/HGG/X19QkPT2jHP//5z/jCAw44oK3rvQeAHmjq1KlTp05t/5r6+vrJkyen6hmzOxQYiCBNMj+YxB9FHaRn6rW1tTW+0GACkCqOwwaALHjjjTfiC7uZY4gvzHwIsrW1tby8fMOGDdGFp5566le/+tUMt4T0yeneO3fu3Gf3tGjRoqRrW7duXXzhuHHjutFA0i53O/DWrVvjC/fbb7/u1JlwiXM7W62QXbnbeyGtUv7S6NevX8LNHl5//fWk6wSAfDVu3Lj4Y6xbW1v/9Kc/dame1tbWp556Kr780EMPbesh3gMAQbaHAgMR5I343RMKCwuHDBnSnToTTr0mrNNgApAqQpAAkGmtra3xH2n22muvkSNHJl3nwQcfHF/4+uuvJ1xVlj5//OMfY6a5R48eXV5eboP9vJHrvXfIkCHFexo6dGhyVdXX169evTq+vJtnZJBWOd2Bm5qaxsU58MADu1Nnwpk4u5n2TDndeyGtEn5VkLB7d15br47u1AkAeamwsDDhQY333HNPwnVcbVmyZMkrr7wSX/6lL32prYd4DwAE2R4KDESQNwYPHhwz7zpp0qTufLHV2NiY8CCp4cOHxxcaTABSxXHYAJBptbW1n3zySUzh6NGju/OBavDgwcOGDYvZsX/r1q2bNm3ad999k662S9588825c+dGl/Tt2/fWW2+1qVg+ydfem4SKioqdO3fGFE6YMGGfffbJSnvojJzuwKNGjfrzn/+cwgpbW1v//ve/x5cfcsghKXwWUiWney+kVcKvCkaPHt2dOkeNGhVf6KsCAEho+vTpzz77bExhXV3dNddcU1FR0bdvxztxbNiwoby8PL788MMPP+igg9p6lPcAQJDtocBABHnj6quvvvrqq1NY4YoVK+IL99prrwMOOCC+3GACkCp2ggSATKuqqoov7ObnmbZqSPjZKR22bdt22WWXNTY2Rhd++9vfnjhxYmYaQGbkZe9NwlNPPfXggw/Gl1944YWZbwydpwNHW7Vq1fr16+PLDz/88Mw3hg7pvdCWdHxV4KUBAJ03derUI444Ir582bJlV1999fbt29t/+Lp1684777yEWyVdeeWV7az58R4ACLI9FBiIgLYsWLAgvvCwww5LuD7EYAKQKkKQAJBp1dXV8YXdzzEkPBAz4SRyOtx0002vvvpqdMnEiRMvueSSzDw7GZOXvbdLmpub77///ssvv7ylpSXmruOOO+5//a//lZVW0Uk6cERLS8svf/nL+PIRI0YceeSRmW8PHdJ7oS3peHV4aQBA5/Xt2/cXv/hFwm2N/vznP59xxhlPP/10c3Nz/L21tbXz5s07++yza2tr4++96qqrjj322Hae13sAIMj2UGAgAhJas2bNypUr48u//OUvJ7zeYAKQKo7DBoBM++ijj+ILR4wY0c1qE55cGX90ZjosXrz4t7/9bXTJkCFDbr311n79+mXg2cmk/Ou9ndTa2vree+899dRTjzzySEzeN+yAAw649dZbu3MuLRnQaztwvJ///OfPPPNMfPlpp51m6O6Z9F5IqLW1Nf7V0a9fv2HDhnWn2n322Se+0EsDANqy3377LViw4Oyzz960aVPMXa+//vq3v/3tYcOGTZ48+YADDigqKtqxY8fmzZvXrl378ssvt7a2JqzwrLPOav+kBe8BgCDbQ4GBCEjo7bff/s53vhNfPnDgwJNPPjm+3GACkEJCkACQaR9//HF8YWFhYTerHTRoUHxhBj7SbNy4cc6cOTGFc+fO7f72VPRAedZ723LJJZc0NjbuvffegwYN+uijj7Zu3frmm2/W19e3df1hhx32y1/+cvjw4ZlsJEnoJR24Qw888MCvf/3r+PLBgwc70r3H0nshoW3btsXvzVxYWNjNZQkFBQXxhV4aANCOz3zmMw899NANN9xQWVkZf++WLVsWL17cmXoGDRp06aWXnn/++e3/a+49ABBkeygwEAHx6uvrL7jggg8++CD+rnPPPbekpCS+3GACkEJCkACQaQk/ZiRMIXRJVj7S7Ny584orrvjwww+jC6dNm3bqqaem9XnJlnzqve145ZVX3nnnnc5cWVRUdOGFF86aNat/f++rc0Av6cDt+OSTT2666aaHH3444b2zZ8/u/s6CpIneCwkl7K4JO3aXtPXSaG1tteszALTlM5/5zC9/+cvly5ffeOONNTU1SdRw9NFH33DDDWPGjOnwSu8BgCDbQ4GBCIjx7LPP/vu///u7774bf9c+++zzrW99K+GjDCYAKdQ32w0AgF4nTR9pEiYhEm4clUJ33HHH2rVro0s+85nP/OhHP0rrk5JF+dR7u2/gwIGXX375ueeeKwGZK3p5B163bt20adPaSkCecMIJF1xwQYabROf18t4LbUlTPrh///79+vWLKWxpadm2bVs3awaA/LZjx45Nmza1dch1hzZt2vTmm2925uHeAwBBtocCAxEQsW3btptuuum8885LmIDs16/fbbfdNnTo0ISPNZgApJAQJABkWibXdaU1x/DMM8/86le/ii7p16/fz372s7322it9T0p25U3vTYnt27dfd911X/ziF+fPn79z585sN4eO9doOvHXr1nnz5p199tkbNmxIeMFhhx3285//vG9fHw97rl7be6F9aXppBCLCANB1q1evPu200+bMmdPW544OVVdXX3zxxd/73ve2bt3a/pXeAwBBtocCAxEQ9ve//33atGnz589PeG+fPn2uv/76SZMmtfVwgwlACvmWCwAyLeFCq+5HTzK8F90HH3xwzTXXxBReeumln//85zPZDDIsP3pvatXX1990001f/epX169fn+220IFe2IFbWloWLVr05S/WlFOFAAAgAElEQVR/+f77729ubk54zec+97kFCxYMGzYsw22jS3ph74XOSNNLI/DqAICuaG1tveeee77+9a9XV1d3v7bFixdPmzat/QO1vQcAgmwPBQYi4J133vnud797/vnnv/nmmwkv6Nev309+8pN/+7d/a6cSgwlAChn4ACDTEi7h2r59ezerbWpqii9M06aMLS0tV1999ebNm6MLJ02a9M1vfjMdT0fPkQe9tzOGDRu2Y8eOQYMGDRw4cOfOnR9++OGHH37Y/l6PVVVVZ5999q233nriiSdmrJ10VS/pwBEvvfTSj3/847Vr17Zzzde+9rVrr722+weskG69rfdCJ6XppRG08eoYPHhw92sGgPzzk5/85L777mvr3sLCwgkTJhxxxBH77rtvUVHR9u3bt2zZ8vbbbz///PPr169vaWmJf8jGjRtnzJhx//33l5aWJqzTewAgyPZQYCCC3mzbtm3/9V//9ctf/jLhCzZsn332ueOOO4488sj2qzKYAKSQECQAZFrCbEE7n5Q6KZOfZ371q1+tWLEiumTo0KG33HJLv3790vF09Bx50Hs7449//GNMSUtLy3//93+/+OKLy5cvX7JkScLVmY2NjZdffvlvfvObz33ucxlpJl3WSzpwEAQNDQ233Xbbww8/3Nra2tY1I0aMuPbaa08++eRMNoyk9Z7eC12SppdGS0tL/PcNffr0SclxVACQZx555JG2EpCjR4++8MILTz/99MLCwoQXvPfee//1X/+1cOHC+E/ZH3zwwSWXXPL73/9+7733jn+g9wBAkO2hwEAEvdayZcvmzZv31ltvtXPNqaee+oMf/GDEiBEd1mYwAUghx2EDQKal6SNNwlRWOnIMq1ev/sUvfhFTeMMNN4wcOTLlz0VPk+u9N2l9+/YNhUKnnXbarbfe+swzz1x44YV9+vSJv2z79u2XXXZZSpZpkg69oQM3Nzc/9NBDJ5100sKFC9tKQPbr1++cc8558sknJSBzSG/ovZCEDL80Ev7rDwC92caNG3/84x8nvOuss8564oknvv71r7eVgAyCYL/99vvhD3/46KOPfvazn42/d8OGDddff33CB3oPAATZHgoMRNALbdiw4Vvf+tbFF1/cTgJy7Nix/+f//J+f/exnnUlABgYTgJQSggSATEv4kSbhB5IuycxmTlu2bLniiiuam5ujC//t3/5t6tSpqX0ieqac7r2pMnTo0KuvvvqBBx5I2MKNGzf+9re/zXyr6Iy878Br1qw588wzy8vLt2zZ0tY1kydPfuyxx6699tqE+6nQY+V974XkpOmlIR8MAJ102223JVwH+N3vfveGG25oJ/4Y7eCDD37kkUcS5iAfe+yxl156Kb7cewAgyPZQYCCCXqWxsfH222//yle+UllZ2dY1Q4YM+f73v//4448ff/zxna/ZYAKQQkKQAJBpCT/SbN26tZvVJqwhtR9pWltbf/jDH9bW1kYXjhkz5tprr03hs9CT5W7vTblJkybdc889CY+Ab/8MYrIojztwQ0PDD37wg+nTp7/88sttXTN69Og777xzwYIF48aNy2TbSIk87r3QHf379x84cGBMYVNTUzd3ZfbSAIDO2LRp0xNPPBFfPm3atNmzZ3epqqKiooqKivh/1oMgWLBgQXyh9wBAkO2hwEAEvcfSpUtPPvnku+++e8eOHQkv6NOnz5lnnrl48eJZs2YNGDCgS5UbTABSSAgSADJtyJAh8YXvvPNON6tNWMO+++7bzWqjPfzww08//XR0Sf/+/W+77TYfnHqP3O296XDMMcfMmDEjvvzNN9+sqqrKfHvoUF524NbW1ieffPKUU05ZtGhRW9cUFhZ+73vf+8tf/nLSSSc58SRH5WXvhZRI+OqIWbTTVV4aANAZTzzxREtLS0zhkCFDrrnmmiQ+d4wbN+7ss8+OL3/qqacaGxvjy70HAIJsDwUGIsh7mzdvvvzyyy+55JJ2XtoTJkx49NFH582b18nzr+MZTABSpX+2GwAAvU7C8326n2PYuHFjfGFpaWk3q422du3amJLhw4fPnz+/S5UkbOfdd9/9u9/9Lqawb9++N998c5cqJ91yt/emybe//e3f/OY3MQfEB0Gwbt26gw46KCtNoh3514G3b99eXl7++9//vp1rTj311Kuvvnr//ffPQHtIn/zrvZAqn/3sZ+vq6mIK33nnnTFjxiRdp5cGAHTGc889F194xhlnDB8+PLkKL7roov/P3n0HNHWFjR9PCCCoLAEXbqt1UBeIA8RRXFXrqhbraBW1bq2z7m21Tlw4qrbuUbXOom1d1ToQcOKsFhUERRSQTcjvD96XH2/uTcgiIfj9/GWfe8+5B/Jwm5w895xffvlFKZiamnr79m0vLy+lOO8BAEhMfSvgRgQUbeHh4SNHjnzz5o2qE0qXLj158uTOnTvr+dg5NxMAMBSKIAEAMDbRfUj1fKgrKysrNjZWKWhvb6/zvLOGYmNjjx07pn8///zzjzAok8kogixsilL2GoSjo6OXl9fly5eV4o8ePTLJeKBeEUvg+Pj4UaNGhYaGqjqhVq1as2bN8vDwKOiRwAiKWPYCBlSjRg3hgzp6lgjzVQEAAJq4f/++MPjpp5/q3KGrq2udOnUiIiKU4qJFkLwHACAx9a2AGxFQhB0+fHjmzJmq9r+2tLQMCAgYNmyYQfZJ42YCAIZCESQAAMZWunRpOzu7pKSkvMHY2Nj09PRixYrp1mdUVJRwB6Lq1auz7SkMy6yz986dO1lZWXkjVlZWdevW1bPbhg0bCosg3717p2e3KAhmncBK4uPj+/fv//jxY9Gj1tbWY8aMGThwoKUln/iKiKKUvYBhiZYIP3/+XJ8+nz17JgxWq1ZNnz4BAChi5HK56HfzderU0adbDw8PYRGk8NEdCe8BAEgkElPfCrgRAUXV7t27586dq+po/fr1Fy5cKHoH0A03EwAwFL4SAwDA2KRS6UcffRQeHp43KJfLb9++7enpqVufSr3l4PMMDM6ss3fixIlPnz7NG7G3tw8JCdGzW1dXV2EwJSVFz25REMw6gfNKTEwcNGiQqgrIxo0bL1iwoEqVKgU6BhhZkclewOA++ugjYVA0vTUn2pz1EgAAyCs5OVkYtLKysrOz06db0VXJ379/LwzyHgCAxNS3Am5EQJF08OBBVRWQtra23333Xb9+/WQymQGvyM0EAAzFwtQDAADgQ1SzZk1hUJ9iLNG2tWvX1rlDQBXzzd5KlSopRRITE5XWhtSBjY2NMGhvb69ntygg5pvAubKzsydMmHDv3j3hIZlMNmPGjO3bt1MBWSQVgewFCoLon8aNGzdUbVmVr5iYmBcvXigFHR0dy5Ytq1uHAAAUSaL/q7W0tNRzWXEHBwdh0NraWhjkPQAAialvBdyIgKInNDR05syZoofq1at3/Pjxr7/+2rAVkBJuJgBgOBRBAgBgAg0bNhQGr1+/rnOHom1bt26tc4eAKuabvRUrVhQGX716pWe3b968EQZF165AYWC+CZxry5YtFy5cEMYdHBx+/vnn/v37W1jwKa9oKgLZCxQEFxcXNzc3pWBaWtrdu3d161D0T6NVq1bsFA8AQF6Ojo7C/zmmpqamp6fr063ooo+iH7F5DwBAYupbATcioIh5+/btd999J5fLhYe6dOmyc+fOChUqFMR1uZkAgKGwHTYAACbw6aefWlpaKi1Bd/369cTERB0WkHv69Ol///2nFKxZs6bBP49NnTp13Lhxenby119/zZs3TykYGBjYoEEDPXuGcZhp9krEVoKUSCS3bt0qX768Pt3GxMQIgxRBFlrmm8C5V1y5cqUwXr169Q0bNogmOYoMc89eoIBIpdIOHTps2bJFKX7mzBnd3l6eOXNGGPz00091GRwAAEWXTCZzcnKKj49Xij9//lx0S0cNvX79WhgU/YjNewAAElPfCrgRAUXMDz/8EBsbK4yPHz9+6NChBVdByM0EAAyFNUIAADABe3v75s2bKwXT0tKOHj2qQ2979uwRBv38/HQZmVoODg5l9ebo6CjsuVSpUqInG/xHgP7MNHslEkmNGjWEwdDQUD27vXjxojCoz1c+KFDmm8A5Vq5cKXwW2dXVdfv27VRAFnnmnr1AwenQoYMwePDgQaWiYU28efPm1KlTSkFra2sfHx8dBwcAQNHl4uIiDP7zzz/69Cn6IV3Vgzq8BwAgMfWtgBsRUGQ8ePBAdJJtzJgx3377bUGvocjNBAAMgiJIAABMQ/Qjzd69exUKhVb9pKWlHT58WBjnoS4UHDPNXk9PT1tbW6XgyZMnMzMzde7z4cOHwqXUihcv3rhxY537REEz0wSWSCR37twRTmDJZLLAwEDRbx9R9Jhv9gIF6pNPPhGu6xwXF/fXX39p29WhQ4eEXzA0b968ePHiuo8PAIAiqn79+sLgyZMn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width=\"562\" alt=\"figure_0.png\"></center>"
      ]
     },
     "metadata": {},
     "execution_count":  4,
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "source": [
    "\n",
    "%InsideREADME"
   ],
   "outputs": []
}
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "MATLAB (matlabkernel)",
   "language": "matlab",
   "name": "matlab"
  },
  "language_info": {
   "file_extension": ".m",
   "mimetype": "text/matlab",
   "name": "matlab",
   "nbconvert_exporter": "matlab",
   "pygments_lexer": "matlab",
   "version": "24.1.0.2628055"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}