diff --git a/Solutions to Chapter 6 Exercises and Problems.ipynb b/Solutions to Chapter 6 Exercises and Problems.ipynb index 4520a09..146443b 100644 --- a/Solutions to Chapter 6 Exercises and Problems.ipynb +++ b/Solutions to Chapter 6 Exercises and Problems.ipynb @@ -18,7 +18,7 @@ "import math\n", "from sklearn.preprocessing import normalize\n", "from functools import partial\n", - "\n", + "import h5py\n", "from scipy.spatial import distance\n", "\n", "nb_dir = os.path.split(os.getcwd())[0]\n", @@ -1090,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -1155,9 +1155,8 @@ " print('ERROR: Found inconsistency in training set!')\n", " print(dfd)\n", " else:\n", - " #print('Found a training set consistent CNN!')\n", - " #print('Size of CNN: ', len(condensed_set))\n", - " pass\n", + " print('Found a training set consistent CNN!')\n", + " print('Size of CNN: ', len(condensed_set))\n", " return df\n", " \n", "def cnn(df, do_plot=True):\n", @@ -1169,6 +1168,9 @@ " df = is_training_consistent(S_idx, df)\n", "\n", " if do_plot:\n", + " Xs = X[S_idx, :]\n", + " ys = y[S_idx]\n", + " \n", " x1s = [Xs[ys==1, 0], Xs[ys==-1, 0]] #[xsp, xsn, Xs[:, 0]]\n", " x2s = [Xs[ys==1, 1], Xs[ys==-1, 1]] #[ysp, ysn, Xs[:, 1]]\n", " myplot.plt_plot(x1s, x2s, 'scatter', ['b', 'r'], ['o', 'x'], ['y=+1', 'y=-1'], \n", @@ -1184,6 +1186,9 @@ " C_idx, C, Cy = inf_nn.find_cnn(X, y)\n", " df = is_training_consistent(C_idx, df)\n", " if do_plot:\n", + " Xs = X[C_idx, :]\n", + " ys = y[C_idx]\n", + " \n", " x1s = [Xs[ys==1, 0], Xs[ys==-1, 0]]\n", " x2s = [Xs[ys==1, 1], Xs[ys==-1, 1]]\n", " myplot.plt_plot(x1s, x2s, 'scatter', ['b', 'r'], ['o', 'x'], ['y=+1', 'y=-1'], \n", @@ -1202,20 +1207,31 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Final S_idx: [779 0 172 237 267 268 511 18 277 512 164 513 289 292 514 270 327 328\n", + " 347 387 516 3 20 129 473 517 520 324 521 524 528 530 290 310 531 493\n", + " 556 560 562 567 194 569 287 309 311 570 107 118 571 361 575 582 329 597\n", + " 604 605 326 487 501 609 629 649 650 7 28 76 199 220 652 656 663 71\n", + " 149 190 667 34 148 189 672 690 704 130 262 718 373 449 490 724 339 345\n", + " 375 477 508 733 738 9 140 200 276 741 744 466 745 6 747 428 759 272\n", + " 427 764 770 773 774 775 776 40 261 777 31 51 778 780 782 5 22 24\n", + " 178 786 793 263 796 68 799 340 380 395 818 824 834 836 837 266 307 308\n", + " 392 838 376 855 271 874 99 249 890 895 50 125 901 919 300 921 923 925\n", + " 931 470 932 937 943 355 379 947 275 291 337 341 953 964 128 188 244 976\n", + " 997 458]\n", "Found a training set consistent CNN!\n", - "Size of CNN: 186\n" + "Size of CNN: 182\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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c9k7ZcccdOfzww+nt7WXatGmdFwg88sgjLFq0iM2bN9PT08OyZcu49957ecELXhBL+X4s2qoJzeyduaa+gW+nwU9jvIgNZjRyzsAALF8O/f3uNervd+txdB4rlQo33ngjJ598clv/X7t27ZRtu+++Oxs2bGDz5s1MTEywYcOGRAQHmPBoSpL2zjwxJRRxZQrjRWwwo1EQBgZg7VqoVNxnHILj3nvvZd999+WII45gv/3267zADDCzVROStHfmhUDT3EcFLhhlYBAnMKoO97jGi/gHM0KtY39w0MxjRuk54IADeOCBB7KuRkeY5tGEpUudfdNPXPbOvNDQNPcPPmd7lbiitfI4mNFMaIYRCRMeTUjS3pkXGprm1jUYLxJXoyoJCqeomAnNMCKTqfAQka+LyO9F5O4Gv4uInCsi94vInSLyqrTrmIS9M08Em+CUC2f7zEiVyqSWEJcAaTSYMe0ev+UDM4y2yFrzuAQ4usnvxwD7ecti4N9TqFNXEWyaExYdGTBeZHAwnvEi9YMZkxBOYcmjCc0wCkCmDnNV/T8RWdBkl7cCl6qqAjeKSK+IvEhVH06lgl1AVZOqHwj5ioGR+MaL1JO3wYyWD8woKEcffTQ33ngjr3vd6/j+97+f6rHzHm31YuBB3/oGb9sU4SEii3HaCX1lCodKgYGBBua4OMaLNCLOwYydYvnAjGbkeLDs3/3d37F161YuuOCC1I+dtdmqFUF3KNCmoarLVXWRqi6aN29ewtUyYiFJ4RSWPJnQjPyRUDBFXCnZjzjiCGbPnt1RXdol75rHBmBP3/p84KGM6mKUkbyZ0Iz8kOB4JEvJnjxXAJ8SkcuBQ4BN5u8wYidPJjQjP/g7EjEPlm03JXueyFR4iMg3gTcAu4rIBmAY2AFAVb8K/AA4Frgf2Ap8KJuaGqUnDyY0I38kGEwRNSX7li1b+OhHPwrAWWedxfHHH99xHToh62ird7f4XYFPplQdoxU5dhwaRiIkGEzRTkr2PGkmeXeYG3nBRmEb3UbCwRTVlOzvfOc7207Jfthhh3HiiSdy3XXXMX/+fK6++uqO6hSFvPs8jDxgiQyNqJRBS004mKKakv3b3/5222X89Kc/7agOnWDCw2hNgo5DIwGybrhHRlxno/psVHvwvb3F01QTCqa49957Oe644zjhhBMKm5LdzFYFZMr8G+MpHDRPiQyNxmRtXixorrCNG+HOO2HVKve5caPvxwSCKaop2f/1X/+147KywoRHwajOv7FunXsPq1PjJi5A8pLI0GhMHhruguQKU9+12LjRvUfPPOPWn3nGrdcIkBKhMT0HJjwKRsP5Nz6X4HwUNgq7GOSl4c65ljpjxgw2bty4vRH93e/cI+2nUnHby4aqsnHjRmbMmNFxWebzKBhB828MM8LO6ydAO7cxj49PTZI4MGCjsAtDHpI8ppArLPg5Dfff+fPns2HDBh577DEAHmqSs2KHHWKobM6YMWMG8+fP77wgVS3dcuCBB2pZ6e9XdW9ndanoKIOqoKuPGtT+voou861rpRK67JUrVWfNqi1/1iy3XVWnlhWh7FxRlvMIolJRHRysvYmD0Z6D2I5fPW79eoe0fE4jMvWdckt/f8dVLRTAKo3Qzmbe0CexlFl4BL44Myt63Z/XNhijDOqsmZVIL1RXvETDw7WNWLVxGx7OslbxkELDHYqEr3Hcz2ncwqiomPAoufBQdQ91f7+qiPtcuVK1v69S8/RDJfILJRL8UookdCJpk5fGNUl8DXf1uVjGoI7OGU63MUxQu0viOQ16p7oNEx5dIDymUJk0Vfk1D6hEeqG6QvPI2qyTBp7gmOxNb6vtTRf8XLviOc2AqMLDoq1SIrGxGeqck4OMsYxBhArLGORUxhhliL49w0dCBU9J67aXhpxHAnWMugFt1ai8YYYZ5TRAJ6PyCp5Wpiue0yIQRdIUZcmb5pG4TXV4WFcf5Xwcfif62dOjmypKr76XWfPwmaycaWeb3srCGk10u4Za8HMu/XOaAZjZKn/CIxU1u2rj7vdeqL5ozvKuoMw+j7pz6e+bjMKrCpBCC8syR8jlhKjCQ9x/ysWiRYt01apVWVdjOz09wePoRKYOTjLixz8m4JwXjHD0qyfY/6oS5F2qp3ouvjEeyxhkiHNQfFlbK5VimenykCtLtfiJHlsgIreo6qLQf4giaYqydKXmYUzi65WuXKk+c55uD22u0crK1Iut1Ebd9e+5bUowRaE0jzxoi2UO7/aBma3yJzza8nkURU3PWz3rXvSq+WaY4fIL7iB/zsKF6Ta8STwPWfqp8iC8UsKERw6Fh2pEB19Rejp5q2fAi13tdVcdxqUbu1KlWSO3cKHqtm21+yVxj5J8Huo0qlQb7TIHWfgw4ZFT4RGGlSsqdelFTslvTyeuHlncPdWAF71ecJRW82jUeJ9xRu1+SWkcSfXQ89B4xyy8QnUmU9bqTXgUVHjcccKwnje92shVdJRTsn1ZwtDpS51UT7XuRZ/i82g3TDpvJrogsqxjEo18HsxGMZ9XKDN2hHcjrrBlEx5FFB6Vil40u9a8MkV45LGhUm2/R5ZUoxDwolcTRnb0cuXNRJdXkjAvZXntE3hOWwbQRDhmnGPITHgUUXioqviy4wYug4OTduu80GmPLO6eahoCqYxO07i0lSTNS1lqVDELr1C5uUJeyzgjOU14FFR4uIegttd2Kwu1f89ttY7Pevt1VjRpUCP19OPuqSZpCsva7p4EcV0vE7ChCd3gh3g34kwSacKjoMJj5YqKnjd9quax+k2nOI3DC7mMxQQTFwENz+qjXFqUUGp0Ug1yUr3Uupe58KP4427wzbQXilCmJtM8THiEwvegXDR7UIWKXvT8v5kqSGryV7VomNOsu4/+vkq4hzlsw5WXHqsnGP0n1c6cKa2O0XQ9CVo0UpGdsUUIKsgBTa+r+TxMeEQiqNd2Sq3TPHTDnCGR1Oj6cz7jjFrTXF56rr6XdzKgYXK9v6+4TuGguWD8gsMmScoIi7Yy4REJfy8tQHgsCxiv0K59Mykiq9H1L0debebDw3XXf3LkesfXP6Nzr6ZuqQ/UqE5fbGl1smXlikqtUFiR7DtgwqPIwqNKk8Yk7wPeOuqt5twpPVXziz5bY0MyOHd/5t16jUoHnfk0tBZpxEoWWp8JjzIID9XOndEZ0pEancQ4gXYIsN0n/UKvXFF77s16mnGYKkRUhxmu65B4GQ6Gh03zyJAsrn2hhAdwNHAfcD9wesDvHwQeA273lg+HKbcUwkO1YQNWhklwAs8jL5pHE3tzUtc/KNruvOmDgQIkLiE22UDVabJ95vPwk8U7l8Q87a0ojPAApgG/AfYGdgTuAA6o2+eDwHlRyy6D8CiLkAiitlFyDdesmb5opix9Hln4HypBGQYmo++SCs8MIxzy/hwmXb+sBKhpHs2Fx6HA1b71zwKfrdunK4VH2Xt81Rej3mQy+oKcRFtloAGNBJiPRhnUEYan7BtnrzQvwqGdeqTxnmRlujOfR3Ph8Q7gQt/6++oFhSc8HgbuBL4D7NmkvMXAKmBVX19fvFc1Zcpua3aN31RnbeD82ln6PFL0vVQzDNQ75IPuedmej3YbyjSuQxbmoyppC/YiCY8TA4THv9XtMxfYyfv+MeD6MGUXXfPI8oFNA7+tfUo+rzxEV2WgeURpQMummTYVAk0GHabxnpRNUDejSMKjpdmqbv9pwKYwZRddeJT9ga33eaTZw29JhuNNovQ082JuiqM+jYTACMNNB8ql8Z6UTVA3o0jCYzrwALCXz2H+srp9XuT7fgJwY5iyiy48uuGBrY5szuX82pajKRKdPq/BQmAyiKCREE/rPcmboE6KwggPV1eOBX7lRV0t8badBRzvff8CcI8nWH4E7B+m3KILD9ViPrCR6pz3EeWWoyk0nWoADYXAitbmwyK+J3klqvAQ959ysWjRIl21alXW1egqxsdh8WLYunVy26xZsHw5DAw0+NPICExMwOgoiLjmYWgIenvdb92IqrsWjdZzSE+Pq2Y9IlCphCtjfByWLIH166GvD5Yu9Z4bVXeAKpVK7q9HURGRW1R1Uej9TXgYcbBgAaxbN3V7fz+sXdvkjwVsLOMgsLH89UghhWnb974V1fMfG5vcNjg4eX2MWIkqPHpa72IYrVm/Ptr27dQ3AmVqFOo7Zt56VUtbt85tWrcOFn9EWXPjhGsoh4ZqG86JieCufQvGx13D3tPjPsfHOz+lIJYudVqmn1mz3Pa28Z3/mqMGWdBXYYxBt37MUFvXw4iZKDauoixl8HkUjbJHiEWmidO94bXqiy9EOO2gi0R8D8PDdXPYuNDus6cPm28jAUjSYQ7Mi7J/VosJj/Tphgix0LQIBmiarTamwYllEeaJZjI2aogqPEKZrUTkEBFZB9zqrS8SkeWJqUNG4RgYcM7x/n5neervb+EsLzMizi4/6Mws9PS4T89e39cfbJrr29Mz1fgZamGiqf/NW2/bjJgz1j9Yf63cetHOo4yE9XmcAxwDPA6gqquA1yZVKaOYDAw4B2ml4j67UnBUqQoQP56jN9BHMFP54UuHJoVMpTIpfBoJkJGR2t/UEz4jI/T1BVer0fa8UpbzKCNhhceOqnpv3bZn4q6MYZQGbaxFBGppXxP2f3VvbTRRVXvp7Z0aSKDqHOkNHOxLz9b4ndgZkIgz3oiHMLYt4GfA84FbvfUDgBui2MfSXMznYWRKJwMgowxObJGDqywD6MpyHnmHJAYJisgxwOdxc2/8EDeJ03tV9dpkRFpn2DgPI3PSGgCpNojOiIeo4zymh9lJVa8SkfuAo3Aeq7NV9f4262gY5WdkpHbAY9UMFWfD3sg0ZoPojBQIG231GVV9QFX/XVW/oqr3i8hnkq6cYUB6g91iJ8kBkH4fR1gHu2HESCjNA3gX8KUQ2wwjVupzZq1b59bBornoDXCwQ7CD3TBipqnPQ0TeCLwJN1HTpb6f5gAHqeqrkq1ee5jPIx80THYXgcTyJpUFv2ksaN0wQhK3z+MZ4AlAgSd92x/GpUs3jEDi0hjKMtgtMcqcG8zINWGjrV6uqnenUJ9YMM0je+LSGEzzMIx0SCra6m4ReROwEJjh235W9Coa3UBcGsPSpcHzhNggMcPIlrDRVv8M/D1wGvBi4BPASxKsl1Fw4kor0TJnVoPcTmWgsFFmRlcQNj3Jm3FjPB5V1Y8CB+JGnBtGIHGmlWiYM6tJbqeiEzjnx2ITIEZ+CCs8nlbV5wAVkR1U9XfAngnWyyg4iWfZbZHbqegayJIltaY6cOtLlsR/LNNwjHYI6zC/HjgO+DLQi4u2ep2qHpJs9drDHOZdgl9gVCnJNKVxzAsehrbmnjdKSSJzmIvIC4EJYBrO79ELnKuquQyYNOHRRZQ0t1NaUWYWzWZUSWQOc1V9VFX/pKpbVfVsVf10XgWH0UU0SXtedNJKRW7jaIx2CRtt9WciskJEfiEiv6wuSVfOMBpS8txOac3MaJMtGe0SNrfV5cC3gYuBbclVxzBC0gW5nQYGkvc72Dgao13C+jzuUNVXplCfWDCfRxdhuZ06Jo4cZEbxSWSEOXCDiLxCVe9ss16GkQyW26lj0tBwjPIRVngcAnzImxDq6epGVT04kVoZhmEYuSas8Dg10VoYhmEYhSJsqO5PgpakK1cGbPSuYRhlpKnwEJEvep/fFpH/qF86PbiIHC0i94nI/SJyesDvO4nIt7zfbxKRBZ0eM00sP5FhlA/rEDpama1+5n1+P+4Di8g04HzgjcAG4GYRuUJV7/XtdjLwR1XdV0TeBXwR+Ou465IUzfITmYPSMIqHTYs8SahQ3UQOLHIoMKKqR3nrnwVQ1S/49rna2+cGEZkOPALM0xaVzkuoblr5iYwMsBDhrqTM6VxiDdUVkW/jpqANRFXfGaFu9bwYeNC3vgEX1RW4j6o+JyKbgLnA4wF1XQwsBujLyfDYvr7gBy0n1TPaZWTEZe6tDk6sjnbv7S1FOnijMZbOZZJWDvPvA1cCjwF7AT/3ln7gtx0eO6ibVi+owuzjNqouV9VFqrpo3rx5HVYtHtLKT2SkSEAq+DXHuDQpY2dOsKBfu9YG3g1YOhcfqtpyAa4FZvrWZwLXhPlvkzIPBa6osyBzAAAew0lEQVT2rX8W+GzdPlcDh3rfp+M0DmlV9oEHHqh5YeVK1f5+VRH3uXJl1jUyOqZSUR0cVHWiRBV0lEGFioLqrFl2n8vKypXu/vpufWnuN7BKI7ThYdOTrAEOUNWKtz4NuEdV929XaHk+jF8BRwC/A24G3qOq9/j2+STw56r6Mc9h/nYNYSrLi8/DKDF1qeCFCn5FuQw2cCOYsqZzSSo9yU+AH4jIN7z193nb2kadD+NTOO1iGvB1Vb1HRM7CScArgIuAFSJyP/AH4F2dHNMwYiEgFfwoQwwxSlWAdKMNvFuwdC6OsNPQfgrn/3gHcCLOD/KpTg+uqj9Q1Zeo6j6qutTbdoYnOFDVp1X1RFXdV1UPVtUHOj2mYXREXSr4BX0VljHIqYwxyhBVl1xcNnAbU2DklZaah2ei+gdVHQbOS75KhpFj6lLBLz1IWPyRUXgKJugFJLagCBtTYOSZsD6PH6nq4SnUJxbM51FgijJ+wlev8XFY8jll/YMSqw28zGMKjPyRyDS0wJUi8mkR2U1EZlWXNutoGMGMjNTOAlg1EeVx7IRPoA0MwNp1QqXiGvW4tIKsxhSYqcwIQ1jh8SVveQR4wlu2JFUpI33abTBia2gCxk9s9y1MTBR+Wtl2yGJMgeVjM0ITJa63KEuexnkUgXZj12OPeQ8YP6GDg257HqivR8L1ymJMQX9/7fGqS39/csc08gERx3mE39GlBXkzcCywS5SDpL2Y8IhGuw1GHA3NlEGUKyq1heVFcAwP1wqyqqAbHk70sGkPMhUJvqciyR7XyJ6owiOU2UpEjgLW4CaFGgJWi8gbE1GFjNQJbVvXWtPR+nXBpqSwNvmpJhJl4kO14ydqfCBZodmZ1AYGnB8lbn9KIyz9hhGaMBIGWAW81Le+PxGlVJqLaR7RCKVBBPS8L5o9qMMMt6151B63oqM4k9VFswdrTVh5MF3l3aQWE2VOv2E0J2qbHtZhvoOqrvYJnDXADjHLMSMjWiZwbNDzPmnLGPOmT+DPVRlljEOthiJM0MsyBvnwFi9b7eioG0/R25t9uG61Pn6qWXVLxMAALF/uwoFFYO5cmDkT3vc+i7wy6ggjYXCJET/oW/8AcG0UKZXmYppHdFra1hv0vFeuqLRtkw/WeCq1mkteevZF0jxicuw31EJWpBs4YKQDSTjMgX2AG4GngaeAG4C9oxwozcWER+cECpNKrTO7E8FRPUYeTSSBTvx6E1qeTGp+YnTsBwn3YYYnzYodll+PZaDOlkSEx/ad4fnA7Cj/yWIx4dEZgY36zIquPqq2533e9Mk05O02/HlrMBoJtDtOGM4k2ioSQUKtAyE3NfJq0i8VtxDNa0eim0hMeABHAf+CGyz4xigHSXsx4dEZU3ucUxuNi2a7df88FmUYD9A0eCDlcR5tEaN5rZFZsXrv4zTf2fiS7IkqPMLmtvoM8H7gm96mdwHfUNUvd+51iR/LbdUZQXOvDzPCzkwwWHFO4h5RzmGICXo5k5Ht+xV9fvZSzDuvWjPXCJVKW479+sSM4AIill+gDLyv8/L9lOK6F5ykclu9Fzej31J1qdNfgxMmRgkJiuk/kxFG+yaji/r6hSFGawRHo/8WiY7HOdS3gCE6Z7GiOmWukXbHytRHXvX3e4JjVfxjcWx8SQEJo54Ad4XZlpfFzFadEcb+XFYbdUfnldEo9O3E7PNIs/yyPk9FgoTGedwsIheLyGtE5FARuQg3cNAoIYE9zuW1o5vD7FNE2j4v1ewTO9bNNRL7WJkEyy/r81Rmwvo8ngd8HjgSN8/mNcA/quqTyVavPcznYURCNZ45RPwCo4q/oU2LuM4nq/KNTEjE56GqT6rq6aq6SFUP9L7nUnAYRiTinEOkySj0VOfIqG/I427Yky7fKARhEyOeKyK7+Nbnisiy5KplGCkQt6mpgbN6fKXaHBlG6Qjr8zhMVf9QXVHVjcBfJlMlw0gJv81+bMypBWNj7Zma/IJncBC2bdte7p8+McTWrbXxplu3wpIlMZ+PYaRIWJ/Hnar6irptd6vqyxOrWQeYz8OIREzjIhgZcRrLnDmwaROccw6cdhpnjs1hDpuSGxNjPggjBpIa53GziIyJyItFZL6IjAE3t1dFw8gRMY6LYGTECYxNm5wGctppcM457Dl7E6cyRi+1GYhjGcNQpHnfjVIRVngMAbOB24BbcTmuTk2qUoaRCvWmpkpl0oTVrgDp6ak1hU2bxklbxjh/+iBDjOKCFaOlrm9a/6zDg42uJWy01WZVPUlVd/OWk1V1U9KVM/JHqlFDSZPUuIWAqKvei0fp75d4xzDE6bMxjKhEGVHo+Ue+GfU/aS82wjwZSjsKOO6EhzEmJwyVdbguVX7eEjZGyZyctyzL3QRJpmR35XNr1P+kvZjwSIZMMp8WIZOtnxhTeIQS1jmfpCpKhyPvnZOyC7Y0hMdtUf+T9mLCIxmmzu/gFpGEDph1rqh2ianeLYV10rmsYiBUh8Or5+S+lcb7ZkTeBVscpCE8don6n7QXEx7J0LAh6EtAOyhAw9iUGDSmUMI65wK25Tn46u/2dXPHDDOcfOckAt0w30jswgPow6Vg36lue9sTQgG74PJj/dr73LnBftuA273lirDlm/BIhqDe19nTh90Mg0k0Xjk3ySRN6AYrSFDlxNzXcnItX4egv29y0jH/JGN5aKBT17ozIFbhAQwAjwF3Autwc3pUf2vb94GbjfB07/vpwBcb7PdEO+Wb8EiOGrtvn29q2qS0gzpncH9fpVSmgma0bSrJkTbS8hwCOgh+wZEX05BpHtGFx+3Ai73vb/AEyJu89bZ9H8B9wIu87y8C7muwX6mERykdbklqB5Wp86aPMqizZjYQIDnpbcdJ5GcmZ+a+lStV586dvIVz5wacQ0AHQcTtO3duPt4X83lEFx531K0fAPwWOK5DzWOibv2PDfZ7DjdvyI3A21qUudjbd1VfX1/c17VjSv3wJREq6mv0qj1Rv0ljip8lR73tzMmJua+TaLGVKyq5e19K2fnzEbvwAObUbdsP+E2jBt+337XA3QHLWyMIjz28z72BtcA+YU4qj5pHadXeJBuq4WFd5jNh+J2pNbbmnPW2c0EOxn50Ei120Wz/fS/R+5Jj4hYeHwZeH7B9b+A/oxyo7v+hzFZ1/7kEeEeY8vMoPPLicIu195RCo93fV9+ANHCi5qS3nQtyci2Cn/lK7TN/xhmBGuOIL9qqrA7qvBFVeDRNT6KqF6rq/4nIZd5sglWeBnZt9t8WXAF8wPv+AeB/6ncQkZ1FZCfv+67Aa4F7OzhmpjRKghdLcjyPVqlDxseJd16JFuk9xi+TjlOZLP0nYdasmoMG54VqMhFTV6Eaf76uNql/tocZYZQh+vbUybpu2uSyEFfvk3cfL+4fCVWmkSFhJAwwAqwGFgLH4BznH4oiperKmwtchwvVvQ5v7AiwCLjQ+/4a4C6c6ewu4OSw5edR80ja5xEmqqXWjDDZo+/YFBDgqI7zfEOn6MhBbzsXtPD/pGW7r30GJn1W20O7m2ippfYR5hSSGiQIvBF4EtgAvDTKQdJe8ig8VJN9aZval73GRAJ8B0mZAlL18RTd55FElFiDMttulNusY8PQ7hACvuwO6ryRiPAA9gRuAC4Gfgl8BdgxyoHSXPIqPJKkoU+FqU7I+oFYSTTolsokJCnXuy2hHmcdc+DIN4JJSnisBwa87zsAY+Q4x1U3Co/QI3m9xT8Q6+MfT7k+SVG0cR4ZaEyRhXqcdWxkWty2bep+RuokJTz+LGDbW6McKM2lG4VHqJG8vh+d4KiLXIrxpQ0d469N1pMm6+NXj5mir6YtoR5HHZsJoYULJwVIUTTGEpKYz6NISzcKD9UmNuKAl/8GDtFRTtHtoZMJvLRNbdZZm5myPr6fFE05Hfk8Oq1j/TXfts0JjqL6qkqGCY8uFh6BBAy8GuWU7Y3AKKe40dppvrRZO7izPn6jusSteTRxmred8sRbVh81uD2NSCRndn2dtm2Lr2yjI0x4mPCYitfjm0z54ATIDRySTKMVhqxDa7M+fn0d4hZicWlWAXWqRkxVNdft2suKNutbp9XMmlk7MNRCdNPBhIcJjymsXKlTk81RcRpHlpEvWUfeZH181WTMZ3ELpbo69vdV9AYO0Rs4RP2pYy6a3aFwCgjmSCXQwlBVEx4mPFRrGoeVKzW4J7eiy3v+WR+/vi7N1tstM87z8/1PfKbP+qSVkY4RINSW1ZVbFSJ5S0tSxjEoJjy6XXgE9BLrZ2bb3kvsVp9D1sdPi4Q0Kxex5RMY3nLR7A61muFhvej5p+gop3jP6+SzmyfNo6yj36MKj6a5rYxkaJWDqm1UYWKiJo/R0PohTmWMXiYA9XYUHtzSOCdV4vmgWuTEKv3x00DVPQN+YspttXQpzJolDFGbS2ynr7SRS2xkZDIn2cQEJz1xLjtMgzMZZhT37M6bPsHSszuvd1wsWQJbt9Zu27rVbe8qokiaoix51jwS77UE2pAnHZtVzWP74MH6/6ZJnMePWlalUrtP/XreiHJ+KWhWK1f4tNe4zH4Bz+5Fswfbd8QnRF4yZMcNpnnkm8R7LQHZZXeY5l9Tzps+xBWvGpnaS0y7xx3X8UdGanvV1V73yEjg7ne+fYSvzxma1PxWevufeWZ7x0+Q8XFY1jvCWM8QC/rVaaktzi9xzUqVgVVDnLQl5sy9Ac/uSZtGGXhvvjTBNDJkF4IokqYoS541j8R7LUGOUtCLZp+iEuTrKDoRe9krV1T0vOm1ub2q63m7JtVgB38uslkzA+aNb0SSmmXSUWJZBzE0wXwebsm8oU9iaVd4pBFBkWjOp6CG85RTph4shy9kR0RodGJ19ibM5LMytb65uIdxmx0LFMRg0VaKuP+Ui0WLFumqVasi/ac6UZLfpDRrFixfDgMD8dUt8eOMjDinedVcoQqnngrnnju5T6VSDqewH1UXgVClwTn29FStKorfattDhYrm65pM1hXq61vIe6haW+f69aBnd2jImdoameiM2BCRW1R1Ueg/RJE0RVna0TzSzAKbeK+l3hFcr33ksCfXEUXWPJr03hPVPNIOlghr5so6iKOLwcxW7QmPUkZQ5MUUkGSDUGSfR4gZ/zryebR53NjJy3NoNCWq8LBoK49SRlDkYTxDxEioyEQ8x4H3Coe9pZevzx7kNEbp7xd6L85gjIfqlDE52+cen5hwEU0DsPxrAnN6GWOQZX2jLP+asP9VHdS3/riVSu1xK5X4z9V/T8bGnD2uOse6b575xMY/GckQRdIUZWlH8yhrBIWqZmcKSLPH2c44jyj7J0GU6KKYndP108GuPmpwMsNtkhqI/1zr0+gU7f3LwzMUI5jZKt/RVl1HQcIvMyOhFCLNGraVK1VnzdhWc9xZM7Z1bg5rVZ8mz0EmM092Qp7mg4kJEx4dCA8jIZJqIItOUoK1RcNWzXcW2FonLTgaaKCF8jmW1IdjwsOER74wzSOYpBqgVuVu26bLfA54qNVApswnHhetBFp/sPDIreZRwufahIcJj/xQ0h5abCRl+mjRsI3OGd4uOFIdfNjKlFZEn0eJNGoTHiY88kUJbcOxkpTTtZVzeqfnakOAd3ouWZ9HCIric6xOrrYsj6P+OyCq8LAR5kbyqDYfWWzEi/rCfqv4w2JHRlhz4wQ/vGEOsnkTo3ueww8POI39D5kDmzalP6K7QM/H+Dgs/oiy9CmXLn4ZgyyZOcotrx9i/6unhh8XiagjzG2ch5E8WWfv7Sb8giMo422lAhMT7H/1GKd+aBOD285h7dtPcw3fpk1wzjnpCo6kxwHFzJIlsPUpYYJeljHIEKNsfUo4enXJ5oMJwfSsK2AYRow0GjQJbntPz+T62NikdpJFj1l9AxbBHd8v+HKogaxf7z7PZARQwNVv/YNSWI2jXcxsZRhlpPpeVxuzoPUQiSQTp5WJLWcsWADr1k3d3t8Pa9emXZt4MbOVYRhuYqv6iZmq5qBqg+0npilqIxMwAVReBQdUp+Ct3TZrltvebWQiPETkRBG5R0QqItJQ0onI0SJyn4jcLyKnp1lHwygsfnNQfd6sP/7Rpehv5BNJW4DkSZCFYGDATZ/Q3+/kW39//NM2FIYooVlxLcBLgT8DfgwsarDPNOA3wN7AjsAdwAFhyrdQXaPraTbWIy/h0zYOKFcQMVQ3E4e5qq4GkOaq6cHA/ar6gLfv5cBbgXsTr6BhFJ2qOcjvS/CF6tY4oyUbZ+/4ZcJjl/QiDDL63VGWHiQM+J37OTVdGY48R1u9GHjQt74BOKTRziKyGFgM0FfoPOqGEQONzEFVIZFx+PTkjJojgMJ6YfFiAE+AmODIPYn5PETkWhG5O2B5a9giArY1NISq6nJVXaSqi+bNm9depQ2jDLQa6xGHP6G+jIhlLlnin4rZvepbt7rtJjiKQWKah6oe2WERG4A9fevzgYc6LNNoRYFG+xoNaDXWo9P7GcNc49XxEmG3A/Zs5ow8m61uBvYTkb2A3wHvAt6TypHz9JCmWZcYGgUjJyTl14hpYF9fX/B4iYYWZ3s2c0dWoboniMgG4FDgShG52tu+h4j8AEBVnwM+BVwNrAb+Q1XvSbxyvnQJ4+OwoF8Z6xliWe9I+tNippm6oVl4pzctqlEwkvBrhJxSthWRxkvYs5lPooRmFWVpO1TXFyq4+qhBnTWzUpt5dGYlvUyfWYQxlnCOAiMhYkhHHimLrj2biYOlZO9wnEfAQ+rmPqikPzlNFi9MyeYoMBIgq4bcns1EiSo8LD1JPQHpEoYYZXsCtGYOvRTqkmg8vhZrtK+RAdVnJO0R6vZs5g4THvUEPKSjDFGNEk51CEmaL0xWjYJRLBpFciWZjjzFZ3N83CU/7Olxn6n7OYtEFDWlKIv5PNokL2krsiKpWf3KSNrXKoVns5BT4cYINpNghynZfSGB45cJSz6nDK0fQuf0Mu/8kXQToGURnqg5ClNOEwsFzT8JP5tlTrcehqgp2U14BJGnBjRPdSkr9WaR+rELli6jK+jpCbaAiThLWdmJKjzyPEgwO/I0bWqe6lJW/IEJWc+uZ2RG5IGLXY45zA0DCjcpkRGRepUiQMWwiZ6iYcLDMMBCQctMyEwNNtFTNEx4GIaFKXdOiJ59Jmi01CYDA845Xqm4TxMcjTGfh2EknYW27OQ5Us38WYlhmke3o1o7MKpfu3Ng1MhIbWNSbXSybvzyTsSefSaYPysRTHh0MyMjrDlmiMUfUdatcwNGT10/xNoPZpBBOA9YZFt0Ysqymyjmz0oEEx7ditdj3P/qMZY+5dKvjDLEqYzxvOcmWPI5za8d28gXee7Zmz8rMUx4dCveCz/GIKcyhtLDqYyxjEGGGOVD689Mby4Ro9jkuWefRS6ubiFKLpOiLB2lZO8y+vtq01y71PMVvWh2ynm1jGKSRQ62drC8ZS0hYm4ri7bqZlT54UuHwJdmfpQhlswcZaevjMIqLELFaE5RItXMnxU7ltsqSfKcl8pnC15z1CBHrx5laP0Qg7j1/a/yGoAen2WzUslP/Y18kedn3QhF1NxW5vNIijTnH28HX49x/6tGWbtOGKw4W/D+r+51++TVjm3kD+vZdx1mtkoCf+w7TM3Smpde2chIbV38JodGWWbBTFeGYZjwSIQijWpt1GMsgh3bMIzMMJ9HkqgW22dgdmzD6BrM55EX8hz7HhazYxuG0QATHklgo1oNwyg55vNIgqLEvhuGYbSJ+TySxHwGhmEUBPN55AnzGRiGUVJMeBiGYRiRMeFhGIZhRCYT4SEiJ4rIPSJSEZGGNjYRWSsid4nI7SKSAyeGYRiGAdlFW90NvB24IMS+h6vq4wnXxzAMw4hAJsJDVVcDiDmQDcMwCknefR4K/K+I3CIii5vtKCKLRWSViKx67LHHUqqeYRhGd5KY5iEi1wK7B/y0RFX/J2Qxr1XVh0RkN+AaEVmjqv8XtKOqLgeWgxvn0ValDcMwjFAkJjxU9cgYynjI+/y9iHwXOBgIFB5+brnllsdFZF2nx0+YXYFu8OXYeZaLbjlP6J5zrZ5nf5Q/5TY9iYg8D+hR1S3e9zcBZ4X5r6rOS7RyMSAiq6KM5iwqdp7lolvOE7rnXNs9z6xCdU8QkQ3AocCVInK1t30PEfmBt9sLgZ+JyB3AL4ErVfWHWdTXMAzDqCWraKvvAt8N2P4QcKz3/QHglSlXzTAMwwhB3qOtyszyrCuQEnae5aJbzhO651zbOs9SZtU1DMMwksU0D8MwDCMyJjwMwzCMyJjwSIkIySCPFpH7ROR+ETk9zTrGgYjsIiLXiMivvc+dG+y3zUt4ebuIXJF2Pdul1f0RkZ1E5Fve7zeJyIL0a9k5Ic7zgyLymO8efjiLenaKiHxdRH4vInc3+F1E5FzvOtwpIq9Ku45xEOI83yAim3z384xWZZrwSI9qMsiGgxxFZBpwPnAMcADwbhE5IJ3qxcbpwHWquh9wnbcexFOqutBbjk+veu0T8v6cDPxRVfcFRoEvplvLzonwHH7Ldw8vTLWS8XEJcHST348B9vOWxcC/p1CnJLiE5ucJ8FPf/Ww5ps6ER0qo6mpVva/FbgcD96vqA6r6DHA58NbkaxcrbwW+4X3/BvC2DOsSN2Huj//8vwMcIcXLAFqG5zAUXrqjPzTZ5a3Apeq4EegVkRelU7v4CHGekTHhkS9eDDzoW9/gbSsSL1TVhwG8z90a7DfDS2R5o4gURcCEuT/b91HV54BNwNxUahcfYZ/Dv/JMOd8RkT3TqVrqlOGdDMuhInKHiFwlIi9rtXNu05MUkRiSQQb1UHMXS93sPCMU0+clvdwbuF5E7lLV38RTw8QIc38KcQ9bEOYcvgd8U1X/JCIfw2lb/y/xmqVPGe5nGG4F+lX1CRE5FvhvnKmuISY8YiSGZJAbAH8Pbj7wUIdlxk6z8xSRR0XkRar6sKfe/75BGdWklw+IyI+BvwDyLjzC3J/qPhtEZDowh5jNBSnQ8jxVdaNv9WsU0LcTkkK8k52iqpt9338gIl8RkV2bTcRnZqt8cTOwn4jsJSI7Au8CChOJ5HEF8AHv+weAKRqXiOwsIjt533cFXgvcm1oN2yfM/fGf/zuA67V4I3Fbnmed3f94YHWK9UuTK4D3e1FXrwY2Vc2yZUJEdq/65kTkYJxs2Nj0T6pqSwoLcAKuF/Mn4FHgam/7HsAPfPsdC/wK1wtfknW92zjPubgoq197n7t42xcBF3rfXwPcBdzhfZ6cdb0jnN+U+4PL9ny8930G8G3gflxCz72zrnNC5/kF4B7vHv4I2D/rOrd5nt8EHgae9d7Pk4GPAR/zfhdc5NlvvGd1UdZ1Tug8P+W7nzcCr2lVpqUnMQzDMCJjZivDMAwjMiY8DMMwjMiY8DAMwzAiY8LDMAzDiIwJD8MwDCMyJjyMrkJEForIz0Vkq4h8J+v6+PEym67yvi8SkfEOy1soIu+Mp3aGUYsJD6Pb+D1wGjCUZSW80ecNUdVVqjrQ4WEWAiY8jEQw4WGUEhHZX0QeFJF+b31ERC5X1YdU9SbcYM1WZewoIl8Wkbu9hHHf9bZP822/2/s+zfvthSLyXS9h4F0i8n5feWtF5PMi8iPgAm/b2d5cET8B3uzb16+FLBCRx0VkqYjc5s2z8Trvt+kicrWXZPIeEbnYq/dc3KC+I735Gc719j9ERH4kIrd4y/ZjGkYULLeVUUpUdY2IfA74ljexzbuBgyIW81lgb+BVqvqMl0oF3LwOC4HqxEBXMTnXw7nA3ap6gpfC41YRuVVVq5PwvEhVDwcQkbfgUnssBJ7CJaNrxFzgBlVdIiIDuFxSrwW2Ae9R1Y1eeolvACep6le98z5OVd/hHa8X+CpwrE7mHrtZRF6uqhMRr43R5ZjmYZQWVV0BrME1yu9RX/K3kBwHLFM3pwU6mSTuSOASVX3G++1ib1v1twu8/R8GrgQO95V5qe/74bgJlZ5Q1W3ARU3q8oSqft/7fiOwj/e9B/i0iNwO3InLbLuwQRmvAfYCrvL2vwqXIXbfJsc1jEBM8zBKi5fU72XABPDCEPvfBOwEbFHVwwhOx423vT6vjzb4Xr/+RF05YfGb2bYx+e6+B3gdcJiqbvG0rZc0KEOAO1X19RGOaxiBmOZhlJl/AW4B3gh8VUTmN9tZVQ9RNwXnYd6m7wGnekIIn9nqGuCDIrKDiOyAy6J7rffbtTgTFiKyOy7B4I8aHPI64J0i8jzPZ/KhNs6xF3jcExxzcMKkymZcSvgqv8Bly92uCYnIQdVsqoYRBRMeRikRNzvhG4BTVfUe4EzgmyKyr4hsAM4BjhWRDSJycoNi/hlYC9zumXm+6m1fjjMR3eYtd+LmtAA4BXiliNyJEzKne8efgmeG+j5wO3C9V1ZULgVmi8g9uGy+P/X9dh3wPM/Zf66q/hHnYxn2tq0GRoimARkGgGXVNQzDMKJjmodhGIYRGRMehmEYRmRMeBiGYRiRMeFhGIZhRMaEh2EYhhEZEx6GYRhGZEx4GIZhGJH5/5UeJIM3XV50AAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -1255,7 +1271,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1263,12 +1279,12 @@ "output_type": "stream", "text": [ "Found a training set consistent CNN!\n", - "Size of CNN: 157\n" + "Size of CNN: 162\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -1346,6 +1362,418 @@ "print('Average number of condensed set in Influence set generated CNN: ', tot_S/N) " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Problem 6.14" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Problem 6.14 (a) Prepare Zip Code Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def split_zip_data(zip_data_path, splits = 1, train_size = 500):\n", + " # Split the raw data into train and test\n", + " # splits: specify the number of random splits for each train-test pair\n", + " X_tr, y_tr, X_te, y_te = data.load_zip_data(zip_data_path)\n", + " train_size = train_size\n", + " splits = splits\n", + " data_splits = data.sample_zip_data(X_tr, y_tr, train_size, splits)\n", + " return data_splits\n", + "\n", + "def set_two_classes(y_train, y_test, digit): \n", + " # Classify digit '1' vs. not '1'\n", + " y_train[y_train==digit] = 1\n", + " y_test[y_test==digit] = 1\n", + " \n", + " y_train[y_train!=digit] = -1\n", + " y_test[y_test!=digit] = -1\n", + " return y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train: (500, 256) y_train: (500,) X_test: (6791, 256) y_test: (6791,)\n", + "Frequencies of the digits: \n", + " {0: 0.164, 1: 0.138, 2: 0.1, 3: 0.09, 4: 0.09, 5: 0.076, 6: 0.092, 7: 0.088, 8: 0.074, 9: 0.088}\n" + ] + } + ], + "source": [ + "zip_data_path = './data/usps.h5'\n", + "data_splits = split_zip_data(zip_data_path, splits = 1)\n", + "\n", + "X_train, y_train, X_test, y_test = data_splits[0]\n", + "\n", + "print('X_train: ', X_train.shape, 'y_train: ',y_train.shape, 'X_test: ',X_test.shape, 'y_test: ', y_test.shape)\n", + "unique, counts = np.unique(y_train, return_counts=True)\n", + "freqs = counts/len(y_train)\n", + "print('Frequencies of the digits: \\n', dict(zip(unique, freqs)))\n", + "\n", + "y_train, y_test = set_two_classes(y_train, y_test, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Problem 6.14 (b) Performance of 3-NN" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------ Prediction with the raw pixels -----\n", + "1-NN E_{in}: 0.0\n", + "3-NN E_{in}: 0.01\n", + "3-NN E_{out}: 0.010455013989103226\n" + ] + } + ], + "source": [ + "print('------ Prediction with the raw pixels -----')\n", + "k=1\n", + "cls = nn.NearestNeighbors(X_train, y_train, k)\n", + "y_pred_1 = cls.predict(X_train)\n", + "diff_1 = np.abs(y_train - y_pred_1)\n", + "diff_1[diff_1 !=0] = 1\n", + "E_in_1 = np.sum(diff_1!=0)/len(y_train)\n", + "print('1-NN E_{in}: ', E_in_1)\n", + "\n", + "# N.B. This prediction is from the raw pixels\n", + "k=3 \n", + "cls = nn.NearestNeighbors(X_train, y_train, k)\n", + "y_pred_3 = cls.predict(X_train)\n", + "diff_3 = np.abs(y_train - y_pred_3)\n", + "diff_3[diff_3 !=0] = 1\n", + "E_in_3 = np.sum(diff_3!=0)/len(y_train)\n", + "print('3-NN E_{in}: ', E_in_3)\n", + "\n", + "y_pred_te_3 = cls.predict(X_test)\n", + "diff_te_3 = np.abs(y_test - y_pred_te_3)\n", + "diff_te_3[diff_te_3 !=0] = 1\n", + "E_out_3 = np.sum(diff_te_3!=0)/len(y_test)\n", + "print('3-NN E_{out}: ', E_out_3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Problem 6.14 (c) Test on Image Flip" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "id = 5\n", + "X1 = X_train[id]\n", + "X1 = X1.reshape(16, 16)\n", + "plt.imshow(X1, cmap='gray')" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Xf = np.flip(X1, axis=1)\n", + "plt.imshow(Xf, cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Problem 6.14 (c) Condense the zip code data with CNN" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "X_tr, X_te = data.compute_features(X_train, X_test)\n", + "x1s = [X_tr[y_train==1, 0], X_tr[y_train==-1, 0]]\n", + "x2s = [X_tr[y_train==1, 1], X_tr[y_train==-1, 1]]\n", + "myplot.plt_plot(x1s, x2s, 'scatter', ['b', 'r'], ['o', 'x'], ['Digit 1', 'Non Digit 1'], \n", + " title = 'Digits 1 vs non-1', yscale = None, ylb = None, yub = None,\n", + " xlb = None, xub = None, xlabel = 'Average Intensity', ylabel = 'Symmetry',\n", + " legends = ['+', '-'], legendx = None, legendy = None) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---- Prediction with Condensed Data: In-Sample Error\n", + "3-NN with CNN E_{in}: 0.06666666666666667\n", + "------ Check if CNN is traing set consistent ------\n", + "Differences between 3NN using original data and condensed data: 0.0\n", + "------ Test error of CNN with condensed data ------\n", + "3-NN with CNN on test data E_{out}: 0.036371668384626715\n" + ] + } + ], + "source": [ + "#%%time\n", + "k = 3\n", + "cnn = nn.CNN(k)\n", + "S_idx, S, Sy = cnn.find_cnn(X_tr, y_train)\n", + "\n", + "X_cnn, y_cnn = X_tr[S_idx, :], y_train[S_idx]\n", + "cnn_cls = nn.NearestNeighbors(X_cnn, y_cnn, k) # 3-NN with condensed data\n", + "cls = nn.NearestNeighbors(X_tr, y_train, k) # 3-NN with original data\n", + "\n", + "print('---- Prediction with Condensed Data: In-Sample Error')\n", + "y_pred_cnn_3 = cnn_cls.predict(X_cnn)\n", + "diff_cnn_3 = y_cnn - y_pred_cnn_3\n", + "diff_cnn_3[diff_cnn_3 !=0] = 1\n", + "E_in_cnn_3 = np.sum(diff_cnn_3!=0)/len(y_cnn)\n", + "print('3-NN with CNN E_{in}: ', E_in_cnn_3)\n", + "\n", + "\n", + "print('------ Check if CNN is traing set consistent ------')\n", + "y_pred_orig_train_cnn_3 = cnn_cls.predict(X_tr)\n", + "y_pred_orig_train_nn_3 = cls.predict(X_tr)\n", + "diff_orig_train_cnn_3 = y_pred_orig_train_nn_3 - y_pred_orig_train_cnn_3\n", + "diff_orig_train_cnn_3[diff_orig_train_cnn_3 !=0] = 1\n", + "E_in_orig_train_cnn_3 = np.sum(diff_orig_train_cnn_3!=0)/len(y_train)\n", + "print('Differences between 3NN using original data and condensed data: ', E_in_orig_train_cnn_3)\n", + "\n", + "print('------ Test error of CNN with condensed data ------') \n", + "y_pred_cnn_te_3 = cnn_cls.predict(X_te)\n", + "diff_cnn_te_3 = y_test - y_pred_cnn_te_3\n", + "diff_cnn_te_3[diff_cnn_te_3 !=0] = 1\n", + "E_out_cnn_3 = np.sum(diff_cnn_te_3!=0)/len(y_test)\n", + "print('3-NN with CNN on test data E_{out}: ', E_out_cnn_3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Problem 6.14 (d)\n", + "\n", + "The results show that the average $E_{in}$ are the same for original and condensed data, but the average $E_{out}$ is larger when using condensed data." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---- Working on iteration: 0\n", + "3-NN with original data mean E_in and E_out: 0.021820000000000003 0.03381976144897659\n", + "3-NN with condensed data mean E_in and E_out: 0.021820000000000003 0.035780444706228834\n" + ] + } + ], + "source": [ + "def get_cnn_cls(X, y, k):\n", + " cnn = nn.CNN(k)\n", + " S_idx, S, Sy = cnn.find_cnn(X, y)\n", + " X_cnn, y_cnn = X[S_idx, :], y[S_idx]\n", + " cnn_cls = nn.NearestNeighbors(X_cnn, y_cnn, k) # k-NN with condensed data\n", + " return cnn_cls\n", + " \n", + "def calc_with_nn(cls, X, y, X_test, y_test):\n", + " y_pred_in = cls.predict(X)\n", + " diff_in = np.abs(y - y_pred_in)\n", + " diff_in[diff_in!=0] = 1\n", + " E_in = np.sum(diff_in!=0)/len(y)\n", + "\n", + " y_pred_out = cls.predict(X_test)\n", + " diff_out = np.abs(y_test - y_pred_out)\n", + " diff_out[diff_out!=0] = 1\n", + " E_out = np.sum(diff_out!=0)/len(y_test)\n", + " return E_in, E_out\n", + " \n", + "\n", + "k=3\n", + "tot_exps = 1000 \n", + "zip_data_path = './data/usps.h5'\n", + "data_splits = split_zip_data(zip_data_path, splits = tot_exps)\n", + "digit = 1 #we classify digit '1' vs. non '1'\n", + "nn_Eins, nn_Eouts = [], []\n", + "cnn_Eins, cnn_Eouts = [], []\n", + "for it in range(tot_exps):\n", + " if (it + 100) % 100 == 0:\n", + " print('---- Working on iteration: ', it)\n", + " X_train, y_train, X_test, y_test = data_splits[it]\n", + " y_train, y_test = set_two_classes(y_train, y_test, digit)\n", + " X_tr, X_te = data.compute_features(X_train, X_test)\n", + " \n", + " nn_cls = nn.NearestNeighbors(X_tr, y_train, k)\n", + " cnn_cls = get_cnn_cls(X_tr, y_train, k)\n", + " \n", + " nn_E_in, nn_E_out = calc_with_nn(nn_cls, X_tr, y_train, X_te, y_test)\n", + " cnn_E_in, cnn_E_out = calc_with_nn(cnn_cls, X_tr, y_train, X_te, y_test)\n", + " \n", + " nn_Eins.append(nn_E_in)\n", + " nn_Eouts.append(nn_E_out)\n", + " cnn_Eins.append(cnn_E_in)\n", + " cnn_Eouts.append(cnn_E_out)\n", + "\n", + "nn_mean_Eins = np.mean(np.array(nn_Eins)) \n", + "nn_mean_Eouts = np.mean(np.array(nn_Eouts)) \n", + "\n", + "cnn_mean_Eins = np.mean(np.array(cnn_Eins)) \n", + "cnn_mean_Eouts = np.mean(np.array(cnn_Eouts)) \n", + "\n", + "print(\"3-NN with original data mean E_in and E_out: \", nn_mean_Eins, nn_mean_Eouts)\n", + "print(\"3-NN with condensed data mean E_in and E_out: \", cnn_mean_Eins, cnn_mean_Eouts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Problem 6.15 TODO\n", + "\n", + "Let $N=2^k$, For a data set of size $N$, let $T(N)$ be the expected time to find the nearest neighbor in a cluster with $N$ points. At the beginning, it'll take $2d$ operations to compute the distances from $x$ to $\\mu_1, \\mu_2$, which are the means of its sub-clusters. It then need use $T(\\frac{N}{2})$ time to find the nearest neighbor in one of the sub-clusters. Once it finds the nearest neighbor from one of its sub-clusters, it needs to apply the bound condition, if it satisfies the bound condition (with probability $p$), then it's done. Otherwise, with probability $1-p$, it needs to find the nearest neighbor of another sub-cluster, which takes expectation of operations $(1-p)T(\\frac{N}{2})$. After this, the algorithm then compares the two nearest neighbors to get the nearest neighbor for the original cluster.\n", + "\n", + "So the total cost for any $N\\le 2^k$ will be $T(N) \\le 2d + T(\\frac{N}{2}) + (1-p)T(\\frac{N}{2}) = 2d + (2-p)T(\\frac{N}{2})$. \n", + "\n", + "We apply this recursively, i.e. for $N=2^k$, take $T(\\frac{N}{2}) = 2d + (2-p)T(\\frac{N}{2^2})$ into abvoe equation, and notice that $T(\\frac{N}{2^k}) = O(1)$, we have \n", + "\n", + "$T(N) = 2d \\frac{1-(2-p)^k}{p-1} + (2-p)^k T(1) = O(d(2-p)^k) = O(dN^{\\log_2(2-p)})$\n", + "\n", + "I am not sure where does the term $O(d\\log N)$ come from. It appears that if $p=1$, for $N=2^k$, we have $T(N) = O(d\\log N)$ from the recursive relationship. The term $O(dN^{\\log_2(2-p)})=O(d)$" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Add lib input sys.path\n", + "import os\n", + "import sys\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import sklearn\n", + "import matplotlib.pyplot as plt\n", + "from scipy.optimize import minimize\n", + "import math\n", + "from sklearn.preprocessing import normalize\n", + "from functools import partial\n", + "import h5py\n", + "from scipy.spatial import distance\n", + "\n", + "nb_dir = os.path.split(os.getcwd())[0]\n", + "if nb_dir not in sys.path:\n", + " sys.path.append(nb_dir)\n", + "\n", + "from matplotlib.colors import ListedColormap\n", + "import libs.linear_models as lm\n", + "import libs.data_util as data\n", + "import libs.nn as nn\n", + "import libs.plot as myplot\n", + "\n", + "%matplotlib inline" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/data/usps.h5 b/data/usps.h5 new file mode 100644 index 0000000..309999e Binary files /dev/null and b/data/usps.h5 differ diff --git a/data/usps.h5.zip b/data/usps.h5.zip new file mode 100644 index 0000000..9fe1e39 Binary files /dev/null and b/data/usps.h5.zip differ diff --git a/libs/data_util.py b/libs/data_util.py index 2122e33..45457fb 100644 --- a/libs/data_util.py +++ b/libs/data_util.py @@ -2,6 +2,8 @@ import numpy as np from sklearn.preprocessing import PolynomialFeatures import functools +import h5py +from sklearn.model_selection import StratifiedShuffleSplit def generate_random_numbers01(N, dim, max_v = 10000): @@ -242,3 +244,78 @@ def generate_gmm(means, covs, probs, N): gs = np.random.multivariate_normal(mean, cov, count) gaussians[ix] = gs return gaussians + + +# USPS Zip Code Data for Handwritten Recognition +def load_zip_data(zip_data_path): + """Load the USPS zip code data + https://www.kaggle.com/bistaumanga/usps-dataset/data + """ + with h5py.File(zip_data_path, 'r') as hf: + train = hf.get('train') + X_tr = train.get('data')[:] + y_tr = train.get('target')[:] + test = hf.get('test') + X_te = test.get('data')[:] + y_te = test.get('target')[:] + + return X_tr, y_tr, X_te, y_te + +def sample_zip_data(X, y, train_size, splits): + sss = StratifiedShuffleSplit(n_splits=splits, train_size=train_size, random_state=0) + sss.get_n_splits(X, y) + + data_indices = [] + for train_index, test_index in sss.split(X, y): + X_train, X_test = X[train_index], X[test_index] + y_train, y_test = y[train_index], y[test_index] + data_indices.append([X_train, y_train, X_test, y_test]) + return data_indices + +def calc_image_symmetry(X, img_w, img_h): + """We define asymmetry as the average absolute difference between + an image and its flipped versions, and symmetry as the negation of asymmetry + + X: Nxd: where N is the number of images, d is the number of pixels + img_w, img_h: Image width and height, e.g. 16x16 + Then we have d = img_w x img_h + """ + + N, d = X.shape + if d!= img_w*img_h: + raise ValueError("Image width and height don't agree with data.") + Xf = X.reshape(N, img_w, img_h) + Xf = np.flip(Xf, axis=2) + Xf = Xf.reshape(N, d) + asy = np.abs(X - Xf) + asy = np.mean(asy, axis = 1) + sy = -asy + return sy + +def calc_image_intensity(X): + """Compute the average intensity of an image + X: Nxd: where N is the number of images, d is the number of pixels + + Return + ret: Nx1 matrix + """ + + ret = np.mean(X, axis=1) + return ret + + +def compute_features(X_train, X_test): + # Compute the symmetry and intensity for images + img_w, img_h = 16, 16 + X_tr_sy = calc_image_symmetry(X_train, img_w, img_h) + X_tr_int = calc_image_intensity(X_train) + + X_te_sy = calc_image_symmetry(X_test, img_w, img_h) + X_te_int = calc_image_intensity(X_test) + + X_tr = np.hstack([X_tr_int.reshape(-1, 1), X_tr_sy.reshape(-1, 1)]) + X_te = np.hstack([X_te_int.reshape(-1, 1), X_te_sy.reshape(-1, 1)]) + return X_tr, X_te + + + diff --git a/libs/nn.py b/libs/nn.py index 984424b..33c06a2 100644 --- a/libs/nn.py +++ b/libs/nn.py @@ -64,11 +64,11 @@ def __init__(self, X, y, k, problem_type='classification'): self.k = k #number of nearest neighbors self.problem_type = problem_type - def find_nn_idx(self, x): - # Find the indexes of nearest neighbors for x + def find_nn_idx(self, x, k): + # Find the indexes of k nearest neighbors for x distances = dist(x, self.X).ravel() order = np.argsort(np.array(distances)) - return order[:self.k] + return order[:k] def find_nn(self, x): # Find the nearest neighbors for x @@ -120,18 +120,19 @@ def init_cnn(self, X): S_idx = np.random.choice(N, self.k) return S_idx - def find_inconsistency(self, X, y, cnn): + def find_inconsistency(self, X, y, cnn, onn): #Is the condensed set training data consistent? - found = False - + found = False for ix, x1 in enumerate(X): # It can be a point in S as well x1 = x1.reshape(1, -1) y1 = cnn.predict_one(x1) # O(K) - if y1 != y[ix]: + yo = onn.predict_one(x1) + if y1 != yo: found = True + #print('Found diff:', ix, x1, y1, yo) break inconsistent_idx = ix if found else None - return inconsistent_idx + return inconsistent_idx, x1, yo def setup_cnn(self, X, y, S_idx): # Build a NearestNeighbors classifier based on @@ -142,15 +143,14 @@ def setup_cnn(self, X, y, S_idx): cnn = NearestNeighbors(S, ys, self.k) return cnn - def augment_S(self, X, y, inconsistent_idx, S_idx): - N, d = X.shape + def augment_S(self, X, y, inconsistent_y, neighbors_idx, S_idx): # The purpose is to find a point different from # and nearest to inconsistent_idx - nn = NearestNeighbors(X, y, N) - inconsistent_x = X[inconsistent_idx, :].reshape(-1, d) - inconsistent_y = y[inconsistent_idx] - # Find the neighbors from nearest to farest - neighbors_idx = nn.find_nn_idx(inconsistent_x) + + #inconsistent_x = X[inconsistent_idx, :].reshape(-1, d) + # inconsistent_y = y[inconsistent_idx] #This is wrong, should be the y prediced by onn + #inconsistent_y = onn.predict_one(inconsistent_x) + found = False for ix in neighbors_idx: if ix in S_idx: #Find x' not in S already @@ -159,18 +159,34 @@ def augment_S(self, X, y, inconsistent_idx, S_idx): found = True break if found: + #print('Found a new idx: ', ix) S_idx = np.append(S_idx, ix) + else: + print("Can't find a new idx.") return S_idx def find_cnn(self, X, y): - - S_idx = self.init_cnn(X) + N, _ = X.shape + S_idx = self.init_cnn(X) + onn = NearestNeighbors(X, y, self.k) while True: + old_s = len(S_idx) + #print('Size of S_idx: ', old_s) cnn = self.setup_cnn(X, y, S_idx) - inconsistent_idx = self.find_inconsistency(X, y, cnn) + inconsistent_idx, inconsistent_x, inconsistent_y = self.find_inconsistency(X, y, cnn, onn) + #print('inconsistent idx: ', inconsistent_idx) if inconsistent_idx is None: break - S_idx = self.augment_S(X, y, inconsistent_idx, S_idx) + # Find the neighbors from nearest to farest + neighbors_idx = onn.find_nn_idx(inconsistent_x, N) + #print('Input inconsistency: ', inconsistent_idx, inconsistent_y) + #print('NUmber of neighbors: ', len(neighbors_idx), neighbors_idx[:10]) + + S_idx = self.augment_S(X, y, inconsistent_y, neighbors_idx, S_idx) + if len(S_idx) == old_s: + print('No new point added into S. Exit.') + break + #print('Final S_idx: ', S_idx) S = X[S_idx, :] Sy = y[S_idx] return S_idx, S, Sy diff --git a/libs/plot.py b/libs/plot.py index aa57219..4559cb0 100644 --- a/libs/plot.py +++ b/libs/plot.py @@ -111,4 +111,7 @@ def plot_decision_boundaries(xx1, xx2, num_cats, classifier, transformer = None, y = y.reshape(xx1.shape) plt.contourf(xx1, xx2, y, alpha=alpha, cmap=cmap) plt.xlim(xx1.min(), xx1.max()) - plt.ylim(xx2.min(), xx2.max()) \ No newline at end of file + plt.ylim(xx2.min(), xx2.max()) + + + \ No newline at end of file