{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest from scratch!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from fastai.imports import *\n",
    "from fastai.structured import *\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from IPython.display import display\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in our data from last lesson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = \"data/bulldozers/\"\n",
    "\n",
    "df_raw = pd.read_feather('tmp/bulldozers-raw')\n",
    "df_trn, y_trn, nas = proc_df(df_raw, 'SalePrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_vals(a,n): return a[:n], a[n:]\n",
    "n_valid = 12000\n",
    "n_trn = len(df_trn)-n_valid\n",
    "X_train, X_valid = split_vals(df_trn, n_trn)\n",
    "y_train, y_valid = split_vals(y_trn, n_trn)\n",
    "raw_train, raw_valid = split_vals(df_raw, n_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_sub = X_train[['YearMade', 'MachineHoursCurrentMeter']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic data structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TreeEnsemble():\n",
    "    def __init__(self, x, y, n_trees, sample_sz, min_leaf=5):\n",
    "        np.random.seed(42)\n",
    "        self.x,self.y,self.sample_sz,self.min_leaf = x,y,sample_sz,min_leaf\n",
    "        self.trees = [self.create_tree() for i in range(n_trees)]\n",
    "\n",
    "    def create_tree(self):\n",
    "        rnd_idxs = np.random.permutation(len(self.y))[:self.sample_sz]\n",
    "        return DecisionTree(self.x.iloc[rnd_idxs], self.y[rnd_idxs], min_leaf=self.min_leaf)\n",
    "        \n",
    "    def predict(self, x):\n",
    "        return np.mean([t.predict(x) for t in self.trees], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecisionTree():\n",
    "    def __init__(self, x, y, idxs=None, min_leaf=5):\n",
    "        self.x,self.y,self.idxs,self.min_leaf = x,y,idxs,min_leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = TreeEnsemble(X_train, y_train, n_trees=10, sample_sz=1000, min_leaf=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.DecisionTree at 0x7f645ec22358>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.trees[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecisionTree():\n",
    "    def __init__(self, x, y, idxs=None, min_leaf=5):\n",
    "        if idxs is None: idxs=np.arange(len(y))\n",
    "        self.x,self.y,self.idxs,self.min_leaf = x,y,idxs,min_leaf\n",
    "        self.n,self.c = len(idxs), x.shape[1]\n",
    "        self.val = np.mean(y[idxs])\n",
    "        self.score = float('inf')\n",
    "        self.find_varsplit()\n",
    "        \n",
    "    # This just does one decision; we'll make it recursive later\n",
    "    def find_varsplit(self):\n",
    "        for i in range(self.c): self.find_better_split(i)\n",
    "            \n",
    "    # We'll write this later!\n",
    "    def find_better_split(self, var_idx): pass\n",
    "    \n",
    "    @property\n",
    "    def split_name(self): return self.x.columns[self.var_idx]\n",
    "    \n",
    "    @property\n",
    "    def split_col(self): return self.x.values[self.idxs,self.var_idx]\n",
    "\n",
    "    @property\n",
    "    def is_leaf(self): return self.score == float('inf')\n",
    "    \n",
    "    def __repr__(self):\n",
    "        s = f'n: {self.n}; val:{self.val}'\n",
    "        if not self.is_leaf:\n",
    "            s += f'; score:{self.score}; split:{self.split}; var:{self.split_name}'\n",
    "        return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = TreeEnsemble(X_train, y_train, n_trees=10, sample_sz=1000, min_leaf=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.trees[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,  18,  19,\n",
       "        20,  21,  22,  23,  24,  25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
       "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,  57,  58,  59,\n",
       "        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,\n",
       "        80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99,\n",
       "       100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
       "       120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
       "       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179,\n",
       "       180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199,\n",
       "       200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
       "       220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239,\n",
       "       240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279,\n",
       "       280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n",
       "       300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319,\n",
       "       320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339,\n",
       "       340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359,\n",
       "       360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379,\n",
       "       380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399,\n",
       "       400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419,\n",
       "       420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439,\n",
       "       440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,\n",
       "       460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479,\n",
       "       480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499,\n",
       "       500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519,\n",
       "       520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539,\n",
       "       540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559,\n",
       "       560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579,\n",
       "       580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599,\n",
       "       600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619,\n",
       "       620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639,\n",
       "       640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659,\n",
       "       660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679,\n",
       "       680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699,\n",
       "       700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719,\n",
       "       720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739,\n",
       "       740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759,\n",
       "       760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779,\n",
       "       780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799,\n",
       "       800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819,\n",
       "       820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839,\n",
       "       840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859,\n",
       "       860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879,\n",
       "       880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899,\n",
       "       900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919,\n",
       "       920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939,\n",
       "       940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959,\n",
       "       960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979,\n",
       "       980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.trees[0].idxs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single branch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find best split given variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['YearMade', 'MachineHoursCurrentMeter'], dtype='object')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ens = TreeEnsemble(x_sub, y_train, 1, 1000)\n",
    "tree = ens.trees[0]\n",
    "x_samp,y_samp = tree.x, tree.y\n",
    "x_samp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
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       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 160.6)\">\n",
       "<title>Tree</title>\n",
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       "<text text-anchor=\"start\" x=\"8\" y=\"-96.2669\" font-family=\"Times,serif\" font-size=\"14.00\">YearMade ≤ 1974.5</text>\n",
       "<text text-anchor=\"start\" x=\"32.5\" y=\"-81.2669\" font-family=\"Times,serif\" font-size=\"14.00\">mse = 0.47</text>\n",
       "<text text-anchor=\"start\" x=\"20\" y=\"-66.2669\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 1000</text>\n",
       "<text text-anchor=\"start\" x=\"26\" y=\"-51.2669\" font-family=\"Times,serif\" font-size=\"14.00\">value = 10.08</text>\n",
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       "<text text-anchor=\"start\" x=\"178\" y=\"-104.767\" font-family=\"Times,serif\" font-size=\"14.00\">value = 9.66</text>\n",
       "</g>\n",
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       "<text text-anchor=\"start\" x=\"172.5\" y=\"-29.7669\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 841</text>\n",
       "<text text-anchor=\"start\" x=\"175\" y=\"-14.7669\" font-family=\"Times,serif\" font-size=\"14.00\">value = 10.16</text>\n",
       "</g>\n",
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       "<g id=\"edge2\" class=\"edge\"><title>0&#45;&gt;2</title>\n",
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      "text/plain": [
       "<graphviz.files.Source at 0x7f645ec7c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=1, max_depth=1, bootstrap=False)\n",
    "m.fit(x_samp, y_samp)\n",
    "draw_tree(m.estimators_[0], x_samp, precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_better_split(self, var_idx):\n",
    "    x,y = self.x.values[self.idxs,var_idx], self.y[self.idxs]\n",
    "\n",
    "    for i in range(1,self.n-1):\n",
    "        lhs = x<=x[i]\n",
    "        rhs = x>x[i]\n",
    "        if rhs.sum()==0: continue\n",
    "        lhs_std = y[lhs].std()\n",
    "        rhs_std = y[rhs].std()\n",
    "        curr_score = lhs_std*lhs.sum() + rhs_std*rhs.sum()\n",
    "        if curr_score<self.score: \n",
    "            self.var_idx,self.score,self.split = var_idx,curr_score,x[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76.6 ms ± 11.8 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:681.0184057251435; split:3744.0; var:MachineHoursCurrentMeter"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%timeit find_better_split(tree,1)\n",
    "tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:658.5510186055949; split:1974.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_better_split(tree,0); tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Speeding things up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = TreeEnsemble(x_sub, y_train, 1, 1000).trees[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def std_agg(cnt, s1, s2): return math.sqrt((s2/cnt) - (s1/cnt)**2)\n",
    "\n",
    "def find_better_split_foo(self, var_idx):\n",
    "    x,y = self.x.values[self.idxs,var_idx], self.y[self.idxs]\n",
    "    \n",
    "    sort_idx = np.argsort(x)\n",
    "    sort_y,sort_x = y[sort_idx], x[sort_idx]\n",
    "    rhs_cnt,rhs_sum,rhs_sum2 = self.n, sort_y.sum(), (sort_y**2).sum()\n",
    "    lhs_cnt,lhs_sum,lhs_sum2 = 0,0.,0.\n",
    "\n",
    "    for i in range(0,self.n-self.min_leaf-1):\n",
    "        xi,yi = sort_x[i],sort_y[i]\n",
    "        lhs_cnt += 1; rhs_cnt -= 1\n",
    "        lhs_sum += yi; rhs_sum -= yi\n",
    "        lhs_sum2 += yi**2; rhs_sum2 -= yi**2\n",
    "        if i<self.min_leaf or xi==sort_x[i+1]:\n",
    "            continue\n",
    "            \n",
    "        lhs_std = std_agg(lhs_cnt, lhs_sum, lhs_sum2)\n",
    "        rhs_std = std_agg(rhs_cnt, rhs_sum, rhs_sum2)\n",
    "        curr_score = lhs_std*lhs_cnt + rhs_std*rhs_cnt\n",
    "        if curr_score<self.score: \n",
    "            self.var_idx,self.score,self.split = var_idx,curr_score,xi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2 ms ± 148 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:658.5510186055565; split:1974.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%timeit find_better_split_foo(tree,1)\n",
    "tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:658.5510186055565; split:1974.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_better_split_foo(tree,0); tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DecisionTree.find_better_split = find_better_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:658.5510186055565; split:1974.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = TreeEnsemble(x_sub, y_train, 1, 1000).trees[0]; tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full single tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=1, max_depth=2, bootstrap=False)\n",
    "m.fit(x_samp, y_samp)\n",
    "draw_tree(m.estimators_[0], x_samp, precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_varsplit(self):\n",
    "    for i in range(self.c): self.find_better_split(i)\n",
    "    if self.is_leaf: return\n",
    "    x = self.split_col\n",
    "    lhs = np.nonzero(x<=self.split)[0]\n",
    "    rhs = np.nonzero(x>self.split)[0]\n",
    "    self.lhs = DecisionTree(self.x, self.y, self.idxs[lhs])\n",
    "    self.rhs = DecisionTree(self.x, self.y, self.idxs[rhs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DecisionTree.find_varsplit = find_varsplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:658.5510186055565; split:1974.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = TreeEnsemble(x_sub, y_train, 1, 1000).trees[0]; tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 159; val:9.660892662981706; score:76.82696888346362; split:2800.0; var:MachineHoursCurrentMeter"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.lhs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 841; val:10.158064432982941; score:571.4803525045031; split:2005.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.rhs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 150; val:9.619280538108496; score:71.15906938383463; split:1000.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.lhs.lhs"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 9; val:10.354428077535193"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.lhs.rhs"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['MachineID', 'YearMade', 'MachineHoursCurrentMeter', 'ProductSize', 'Enclosure',\n",
    "        'Coupler_System', 'saleYear']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 288 ms, sys: 12 ms, total: 300 ms\n",
      "Wall time: 297 ms\n"
     ]
    }
   ],
   "source": [
    "%time tree = TreeEnsemble(X_train[cols], y_train, 1, 1000).trees[0]\n",
    "x_samp,y_samp = tree.x, tree.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<graphviz.files.Source at 0x7f645ec0f208>"
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     },
     "metadata": {},
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   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=1, max_depth=3, bootstrap=False)\n",
    "m.fit(x_samp, y_samp)\n",
    "draw_tree(m.estimators_[0], x_samp, precision=2, ratio=0.9, size=7)"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(self, x): return np.array([self.predict_row(xi) for xi in x])\n",
    "DecisionTree.predict = predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if something:\n",
    "    x= do1()\n",
    "else:\n",
    "    x= do2()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = do1() if something else do2()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = something ? do1() : do2()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_row(self, xi):\n",
    "    if self.is_leaf: return self.val\n",
    "    t = self.lhs if xi[self.var_idx]<=self.split else self.rhs\n",
    "    return t.predict_row(xi)\n",
    "\n",
    "DecisionTree.predict_row = predict_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 156 ms, sys: 4 ms, total: 160 ms\n",
      "Wall time: 162 ms\n"
     ]
    }
   ],
   "source": [
    "%time preds = tree.predict(X_valid[cols].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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UH+7NEcLxxjhFCMeHe3Pim1VlXq/413w5OHY77M1qehuCmWkcjHRn3Bmjcdmx\n6s4s88+jC4vAtJb8+r0hB2VP3fUsIk2hPfPO8nTeoJXkne3V/n1zLsVy2vZkicB6RRErHB4pPdPW\nsjMU3B6nbLsEYy1lYxhn7kqFaRdJNvQeNooE5xzWLaUMZHj9RaxK5xxmCd+6DQdVR9U5Ii1JlaDp\nHVpJYiW4darBS6IUdwYJi87SGMcoUcQiRQlYtM+qcvGhTuIYpSR3NlJ+9NmEaWMYp5rfeHuDIomI\nlWBamZPGJLeShGyFlEJnYbuI2Ft0zMqKJJbcHsR0p74qt8YZv/XmiJ8+mbE3qYiV5K2Nggezmp1R\njli6kLYHQWbj0dHVu2idZlp3CA+bxdmdw+Gi4dGkoWoNXoDwUPWWWd1xZyOhbh3704ZUS+5sJGd2\nRzfB2vCv+dIQKclWkdIeV9ueBCo90Uv81avGuoxryC19zeM8xVmPlyUOS6igCSqdVbd6txEpRWvs\nie/58aymNY5xphlnKZKGp3XL40nJt28/C/YVSXRSgPU6JBtyLZm3drnCD//n8QxfUni2Kp1TCOic\n4Ju3xygpaHrL3qxmmCdsZvGJwNwxW0XM/qJja5BQpJqyMTw6soxSjbUeLzx1YwEfmokvUxkP5w0P\nDyvubxd8N4tY1D0PDytSLWmMpes9SoTPr/qe77wxxpy7/kXbU1vH/c2CYRYxr3vmbXcmC0t4iCPN\nn3pnm0ebBdOywXrQs5aNPEECWRLxxijDEuQmroNZajZtFGe73H+8X9L3jo08OcnXL5fprO/eLoiU\nY7Q8NollkAa/QdaGf82XijSSSCnojT0JgmWxJlY34/OMtaS3nl54JlW3DOh6Wgy9cxRCcXRBNef5\nc207w2Te8lHVY/0UBRSxwmYhzU8iiKMwoSRacJU44ou0jPIk4oO9BZ8/LU+Evt7aKbjz1otlmVcJ\nrDnnwYdJRgCLpkdLyUYasVGESeq0ztAoj/nGdsbutOPhYckgjbm/kZGlEgTMyp401mEH5J9NVo9n\nLUWkEVIwLTu0lhSR5sPdBd++O8I6hxdBAsI6ycPDkjvjnFndnBSKddajheSzgwVlZylixd2N7ERC\nGsAARao4WHTsTUt66xgkMV4KOhue27QLPQiKRDLOr6fVoxXcGubPSfAdlg1KKSZ1S+9CtlKiFVXT\nMa8TqtacTIq51ZdqVn8d1j7+NV8qslgjBeRJxGYRkycRUvDStomvyjiPWbQGhUCroOUzb1vSKOSw\nP5hUlO0gpSvcAAAgAElEQVTqVeD5c+2M4+dPSxA+yBZLz+dHFVXTkkSKIgvyu61xQfP/Cn99F7Uq\nNA4mi4b3H8+oOkMkBVVneP/xjMmieeGYq9o5dsaSJVHwz/gQeIyXDVjOfy5AHmmklPzKrQF/+pu3\n+JVbA1DgvKRqDUms8N5T9X3I1jnW6vEei8AYh1Lhp0VwULVESjDKE7aKmFGeECnBx08XoU+AF+Sx\nxntB07V8cliSJ4pffWNEnig+P6g5nT1knKUxljdGGW/vjBimCV54bucxk6rHWMutYYoTlgdHNd+8\nfT2tnjvjPOzmzsWkhBDszWqchTRSOAt7sxqtNY9nNVLCzihFSng8q0nXPv41Xyeu2jbxuhr+WaS4\nv5nxeFazN28pm55Bpuk6y4wOBxe2wTt/rkelYZwrms6zaEq0lOSJoncS4xwYxziLibUKLfcGl69N\nuKhVYawEP3o8C8YYaIxDyNBL4EePZ3z3BW2cIiVx3vPxkymLNuTVJ0owLjLi5awkZaia7k65T04X\n1CklEHh++PkRk7ZnI4nYKKITwyIJDm3vJZ11J1WteSQpq44egek8Wgk0wb0jhDyRVj6udzhadKg7\noUANONmZDRNJ3zs+OyhJlWBnGNG0z85VS0WhNdOq5WjR4KwLKqDAOIv4fH/Go6OKO8OYP/mN2+TJ\n9Vbae7OKPI6Q4qzlHucRjyctT8sGX4b6aiUkqYZbo5yf78354MmCYaL55u0hnbvZrJ614V/zpeOy\n/vnXoeEvEAgheHtryL2NCY0xVK3B2DBekSjMJWWZhQBjOdHjsS40Xs8ixXfvjk8UR9VSDuEqE9RF\nrQrvbmTsThp6Y3EurCy9dTgJu5MXr/indcdnT0uyKGKzCFk7jyYVSkrGRbqcXBR13xHLYCqOC8eO\nXRFHZcv7j0uGacTdrZx51fPB4xnffmNEnmqsD9erBHjnTyaNnWHKZwcVwzRmOIqYVz3zpuPNzZzO\n2OVni5A+aiwbgyQY7FOSFVpJtI7YKWKyOKLuejrjQjL9kiSS9N4xzGLujQX7iwqHh7mnaQ13dwbs\nDFIiJdidVXx+sLj0M1l5TyvDpOp599ZZH/8wiShSTd1ahPR4J8gSRdX2PJk3bA8S7m1mtL3jybxh\nuEKN9HWyNvxrfml5HRr+Hs8gibDeU/eOvndYA5U1SBn83d0LnPGndxxN19OanlhFOAdaieVuwLCo\nexatQQhIInVGL+gyHLcqDPGE0Kowy2PMUqPnybRCnkpFdM6+VGJ4d1KRRpp0mW2TxuG8DqsepeSJ\nL30jj5HiWYvE0xPrg8MK7ywfPK6Yt4ZhopESHh7UbBYJi7YnkYoslcG9s5w0hlnM3WHK//fRPtPK\nMM41f+ZXbrGzkWCtYG9a0xlLrBWbRcy72zmJlljvMctVex5rBk3H/qyh7iuySHJ7nJHrZ/c2URLT\nO3745IjH04badMRC88nBgghIIsGismwNY3It+fjJ9Qz/7qRkM0uYNWdrPzxwfyvDOk58+UrCZ08X\nGON5bAydccRaMtCaWXuzlbtrH/+aX1pe5Pe+LFoptFr2QfXBVZInmlujlDyKmBtzxnVwmt66oPvf\nhF1CpDRNJ5DSszFICLJACo1kUgcft1hKMje9u5LUtHHBNzzMQkOUYRaHpugOci05qi2dtUuDbTmq\nLflLggh1706M/jGDLOKobMniiFvDlCwOBWebxdLnvuygdczuUcXHT0uEFNzbyhBS8OCo5tGkRAtB\nHmkWXcfDw4py2cg9UpKDWc0PHh1xa5TzJ97Z4tYo5wePjpi3ljgSjIuYu5s54yImjgRvbOQ470m0\nYpzHJFqRZxH7lSFLJN9+Y0SWSB4e1YzyZ+d3WHX86NGUOFJsDxJmlWV3ssB4mNZwWHuUDruKx7Oe\n6QXxnMuSRRFPy46jRXvudR3kq/OYNzdzNvKYNNI46zhsGqSA7SJFCjhsGpp2ddHg62K94l/zS8uq\nrJRjV8Jlff9ZJPE+6NzMWssgllgJVRtK7neyjO4C3ZRZHWScnQuFU0kk2C4SHkzm9LamSBS/sjlk\nlGuqrudgEQqNxmloyn7VauTmWEbhVCOWWAmyNOaNUcLupGJv1pJqwd2NnCx98dhZFCQpThv/Rd2z\nWSTUnWFWL7NM4tCspendc/fycNFjjGW/NXz+NIxlTM9TZ3m81PZXSjLKIpJI0fSWSEk+3CtpjGd/\nUdL1lngpU/3+gxn/+Z+4T92ZE2nrEIT23N3IOCpbytaQaBFy/FPFjx5NKPsjikjwG3c26Myz78PP\ndxfsDFOkkvzs8YxUQSQT9qYtsYRFAx88ari3qSgiaPvrqXPOOkOmFbP6rOEe5xFl3fH+/oJ53THM\nYr61neOEZ5DEzOqeg8WcLNKMsphFs9bqWbNmJatExqxzpJG6tO9fK0nZtmSxZjvXdC5GOBgVMSFs\n6cmj1X8mR1VPb0KRk1r67J/ManaGGe/sDDkqWx7NGvI8QskgMw1hUprWHUUSMcpWDv0cL/LxS+Vo\njWVURGyisFhaY5HqxTuKuxs5P38yBzjpGTBtDPc3MgZpdJLHf1R15LFmnMvn7qUQ8HDeEgvIkphp\n3bE/7bi7mRArRTxQCA922QDnOKvn0WHJYdkRCRhkMV3Xc1h2tF1weXjEspmNOHG1vbmpz1Q7P5k1\n/GRvDs6zmWZ0fc1P9uZsbhQnx9TWkkRhHOs8PRHSh1X9zEEK5GmIjTxtHO9eJdVqBcZYDlvL/eTs\ng/Xe87P9klEScW8jY1b2/Gy/RCCZVg2DLOKNjdA8flp13BpeXx76RawN/5ovHZddrQc/qWB3UlL3\njiyS3N3IMdZd2vdvrKNI9LJfrgZncUimVUeWSBKhuDVcnX3TGUNvPdOmpzeWw6olS+Bg0TJvgqsl\n05550/DJ04q6M0sBtYiNLF42V7kcxobV7/68eVbROkww1uF6qOqeRWfpHMQSBrHCvcRbMM5ivnln\nyO6kYlp3ZJHk7c2UPI1P7l1vLH3v2K3qk9X6aNmfNspiWuMY6JC1VDb9cifiMf5YukGACD7lxvqT\nrJ5J3VFWDaXxtH1FEgkKLei15rOnC/YWzckzvT1IeXvn+XjFB48OmZahW5tva4QHgeGDR4cnxwwT\nzYOjirq3HEyrIL4Xhfz+FGiApoHN3HNnqK/kfltFbxxIQaTOJgQ8mTXcGSQc1R1H+y1ZHCqef/Lg\niI1UUxnHo8OKJFZspJpE3axpXhv+NV8qrpKpU3WGp/OWIonZLMIq+Ok85OCP87MGflVzDDittyO4\nPY75cD+sxmOlaHrYSjNuDVavvrwXPJ5WZHHoslV2hrJ2FLFkZ5xhjWHeWPamHfO6D/18jefBYQmb\njre3i5XjrmLeGI6qnizWjPNQM3BU9QghmDQ909bisaQ6orM90xYmzcv9xOMsZnxqMtybNWeqkWd1\nz2HVkSeaLNZ0xrE/b0/8/XEiiWPFdpKdVO5WjSVZPivnQCzz6mP1zBXnvePTo5pEQp5lzMuapw7u\nb2f8aHfKOA21EbOy40e7UzYGz5uq/XlNb0AqyBJJ3bjQK2D+THbhza2UP/rkkM1cszGK+Xy/oy4h\ni6HtIBVwZwz3tgvq2pFH10vnHKQRxlnSc3r6B4uQGrxZpNweh2wl4xxSSISCzUSRb2mqxtAZy9YF\n37nXxdrwr3klrps/f9GYe7OaeR0aYSMERawYZfHK1fpR2a5sOThvuhNXxTEXNXNpuj70yRXwdN7h\nEORaEUWKcZaQ6NDEZRXeB0VMLUKpfts5Ui3IkphYKbTWTBdTeilw3rM3qZFaMoyipYjX5cvy502H\n957OWpo+XCs+vL4/q4mVo+4Ec9MTCUEWu5NmKefv8YueWxqF3P5Z3dFZz/68JosiBsvm37GWOOeX\nTWRytpeT0KdPF3xyEGoBbm+kDNIgh112x+fqSZQ6yepZtI7IOyY1HFQlSsAohum8ZSePKY3j0VFF\nrBU7eczDo+dTU1vjQ5aMCz2Jw76OMzo3Askff2uDHz44Yv+oQ3sYDOBwESYiHNQtWOPZHoUmKddB\nK8GdYU56TlvIOUfZWbwQoZZASYT3pJHk3lbO01nLvOzJI8WbO9mV2nK+0nne6OhrvpK8jvz5i8ac\nN4ZZ0yGXRTzB+DRsFwmjc4a/bO3KloMSnpPwPS/HfEzTO+rekkSKeduTaYWINcMkZquIaY3lyWx1\nPnysNaPUUzYG4zxpojioFW3Z4LzHG0fZeYZ5CKRuFAXWuGVnMXsl/SHvoV66Wo59/CEtUNMaz/7C\n4IxFLiuOF51iszg7sax6bkEUz+F8kJFIIkXZmtAfVksOFm1Qw1zOoWEn8KyJzPYw4d8+OGJnkPHu\nraCXsz+veXtriPcOCSzqDiUlW4P4JKun7XvmPUQKiljTdYZ5D14aSuOwNhRpWQslDtmZ5yatRAVX\njQKyJBjwDjhtM2e1oex63r09ouwtB/OOSEGqPWXX0trwXi+g7eFbt65XuRtJSWkMd6KzPv5xpvn4\noAwN15e6Qou25+5minGejSLhViToe49xnu0rNB56FV76VyqE+H0hxJ4Q4oenXvsrQogfCSGcEOK9\nF7z3zwsh3hdCfCiE+Fuv66TXfLGsyp8/Dtpdd8y6M3TL4OfupObxpKIzjtmKNoUXtRy0+OckfC+a\nlKx3REpwuGh4MqkwzoKDsu3pbFiRXaRyGesQePRCIARkSuNsi/Oetnd00tMJg5eao6rjs/0Z+2WL\nIBQfnV8VvgghQjqnlsGPrGX4XYgQa6jamqo3lI2l6sPvnTn7PM4/t9aETlp78xZjHLPa8NnTBd45\nns5rfr43Dxr5cVip98t7GWlFsRSA01Kwk6csmpZP9+YsmpZbRUIeqVAb0VnySHF3nLIzSE/aac6q\nLrTErGBvYphWx5MbHM5aZm3LtOyZtS2HsxbpxXMS0mmiEUALTNrwUwLFqWymsjPMK0PdWYxzeBuy\nqw7qlt6A66E1oUYhT0BdUQzwPE/mDXXrnttdxpHm/kZB2Vh+/mRO2VjubxSkccQ4i0OAvg8B+XEW\no6/w3XgVLrPi/zvA7wJ/99RrPwT+MvC3L3qTEEIB/xvwZ4EHwB8JIf6h9/7Hr3y2a74UrFJ1vMiH\nftUx294yKTtipYPPuDUclh2xen4FFFoLnq3mBEEsBdO649OnixMpgm8sKzTP4x0cLkLe+p2NnM+e\nLqhFx2aeYq1l0jq+dWf19l9Lwby16KUxanqDdYrbo4StIqMxhsXc0PY9sQpxh7qzlJ3hbpReSX9o\nlMYclC2R0qdW/JZRGtMZj3MKpQSRCgqY1j7/PM4/t6fzJqyeRVAhFc4zqXqeLhre3h4xziWzSLE7\nrdAq5e5GcOt0xp40kZlXPVGs2coznATpwOKZ1C1KjNgaJgjvaWyoYj6e3BtrqdqluykX1JWnamGk\nYd51+NqjtMYag1ACJYtl05v+ZMXvHaEIS0KcQNcCy+rlZw84BJpZFk3VdukSEuFYHYVUy3Ee01nB\nowt2d5clUiGuMa3PTrqS0DHsGzsFeaKoWkvd9+Acm8OEe5v5SaVy3fX4V+g8dxVeOq147/85cHju\ntZ94799/yVt/G/jQe/+R974D/j7wl175TNd8aVjVpPuqDdEvGtM4T6Y1USRpjSONdHAFrMi2SOOI\nW6MEKcJqVgq4NUqoO8u/+3yCsbA9SDAW/t3nE56uEC1z4pm8shYSB+DCTqC3IXB4e2O14W+MZzON\n0Dr0e9VKcnccevoeB0h3RjHbRcYoCw1ltIStIrmyW2yQam4NU6TwVJ1BCs+tYcog1eBhVCTESuOA\nWGlGRfJcCOH8c5vWJkhOLB9cmDyhat2JVs8oj7kzzKh7Q9mGz727kZ2kVc6aHmccRR4xTDVFHlF3\nHbV1+KVmkFSSWEnKZXc148BaS6IAAXXjQQTXTS+CcJ5SMshzK7kU0utDf4VTK36kZJTCMA0r2GEK\nGzlo+ey+CiG4Nc4okoiucwwjzcYwpe8hjkC7kILprMc7z5NJdelnsooijSkSxaQ6W8DlgJ0iJdGS\nurMkWrJTpEgp2B4kNJ1hb9rQdIbtQYLWv7ySDW8Cn5/6/QHwp2/w89a8IlVnOCpbWuNJdMg3f1F3\nqIvy518kJfuyoOLxmFopGulouxDcFSKU6CulOCy7M+8dJIp5A7dH2cl5dMZxWHUUcdBGAU5+fvp0\n8dyqXwlBHEm8h6084s1hzoNZxaTsGaUR39oYkF0wo3XGUKQRG8v/3ykynHVU1hEpRSQ8LlYhj9wv\nNditpTfwzmUezLl7bpznzjh/rtl6kUjUzNHhEF7ihUM5KBL53Binn5sS/kSm+Bjvg2Da6V1UkUYM\nsigob55DK820KbHNs8+e1j2bWcpmFmOco7Oetre084a744xYCZI4RqoW24HQ4C3IGI7t3TiNcUIg\nvcfhKdvQla01oWBOSsEg0UyjDnyYvK21RBEMTmV0bQ9iyranGESMswgnBVXd4D1hxxFD1TkWvSMS\nfnl/Xp29o4Y3xulzWT15rDEOBudiNJtFzLw2xJEiT8IznteGu5vXCzK/jJt0JK2Sl7tw/yKE+B0h\nxPeEEN/b39+/wdNac5qqM+xOapwXoeWdF+xOaqoX+OuPVSkv40OH1S3+5k1/Jmf6eMwiVUjvT1aJ\nUgqMteSxeu69oywmi0MWSm+WgmhxMOLFuayIItUsVuifSBFS8EaZxmuw0vHWZs537464t5kz6y3N\nBY1YEq1pe8Os7pnWHVrB47LFGkcaSSpjOSo7lIPdaYPDkWuF8ZbdWc30Ap3/q97zcZFx2LTMypaq\n75mVLYdNy7jIXjjGVhHjl9r7s6rjqAzxiTujDAEYE9wikZYnPv3zaOnorGVed8zqjnnd4REIFfL3\nZ7Wh6x2KEAeaVh1aSYpUYg0IBUqHn9ZALIJsdU/wk/e4pYy1ZNEajA3xEWM9gywBGxYSxzLX0ilu\nn1I9vbuRMcxi6towa3sWs4o4ikjjEMwtyxBbaJuOo3mH0tdbCzvl+fQwZCmdZpRFbObxmR3bZh6z\nPUjonAuuHR+E7DrnyC4oGnxd3OToD4C3Tv1+H3h00cHe+98Dfg/gvffeu1kH1y8pryuF8vQ4e7OK\nWOuTrf3xz6OyfW09YS8rphYpya1BQtkY+j4UAFWdRauQ1nn+vaMsZmspL3D6nmzkEWVjKFKNscFw\nzOueWIsTgaxjhmnM/KgKPV/nHb1xeBXcEd4Fka/ygmbrRaLYnzf0vcPBsn+sYL9sebzoKLQgixRl\n3zHOFLFUGOfII02iFLuT6kwO/cu4SLW06w2pVOhE4IUilqFBSbeim9TpMdJI0vSOprN0hB7Ht4bJ\nsoeuQ4nwUzh4Y7y65qDpPfO65XBh6L0nEoI8Fni3lJC2jt15TWdCj9z7mxnGOrIkRsqa2oS4gPOQ\naUgSSKVi1nZMbE+sBKMkFJQlWlJ1hnkTnuFmokljcMtJxSKIFIzjZ/donEWM0ohESTb3IuZ1izWG\npZ2lBmwJkW7YzCIicb0CLuGC3tH5BlqbRcKkmocqZBGSAiJlGaQxgzTm6aKh6g1pJHlrnJ9pJnMT\n3KTh/yPg20KId4GHwF8F/usb/LyvNK8rhfL8OJ31CCy9lifjxFpe2HzkVc7lKsFguww2tiKsNo1z\nFEl0Rnjt9HtXGcNv7AyCjz9UD9G2js46fuX26LnzVBKc90ixLDNaVpqOM0UkBca7F2Tbh367UapD\nm0BjmVc9O+OU24OMsu14MmnxhGsyzpElikEaEWt9pQbyL2LetGwNE7TSCCHx3mGsYd60L3yfsY6d\nYcqs7p91f4oVi7YPNQPOo8SL+yE8ntU8mfUkOhjcpuuYNI5Z4zDL78lGmlBkilgrHkwqpBC43qMU\nJD6s+K0BpQAPVdeTaMVmHlF1PVXXc3scUmuDlo2kN47OezaLDOfAConyijzVuFObEyFCo/vWeQZZ\nxEae01sDvqMHcmCYwyCOqXrwZpWj4vKM05goCiqi5/EepAjaUOE5Qe890sPt0TMXXm8s/RfdelEI\n8feA/xjYEUI8AP4HQrD3fwVuAf9ICPFvvPf/mRDiHvB/eO//gvfeCCH+BvBPCKm2v++9/9FNXchX\nndchQbxqnFQrjPU0p8bpjCPRF/8BXPVcXiSmdp550yGkCK31TEin9B4WbSgYetF7j9kZpPzWWxv8\n+OGERRv6v37zjTHbg/RM20AIwmd5otkaaG6Pc/JE0zuP9TDII4QTRPIiH79lq0hYND2tcRyWDeNc\n03Q9jw49aQypFLTe8sZGQRKHcdreUXc996Kr+XEv2vFJHZEljqN5S+MglbA5TJD6xVWode/orT8p\ndrPOM6laEq24M37mJjp/z07zeNKwU0TEUdDeL+KMrjfMqlABPEgiBpkOqahKUjaG/UXDvPn/2XuT\nH0uzNM3rd4ZvurNNPsWYkRlZXVVdXdVFLmhBS0hsAKlhxaJXLGi1+A9gx7bEf0AvEGxAggXQi5ZQ\nq1st1IICqqSCGrIyM4aMCA93t/lO33gmFue7166Zm7lbhEeoCdIfKWRh5mb3fnd6zznP+7zP0zJI\nQQ9TQuizDExH08HhNKOuPbWx5EpRjBKcj1nFvpeVKgkCz2QYg9+liKeGtrN0O9ScD57ZKKqpvIO9\nkWKQFjydlxxp6AzgYTzMUDJQ2jfj+B/sFZjWIW7U7cuyZTrIONp543bW8+xyTao1nYsnXIEghHAn\ntfZd4bWFP4Tw9+/4p//xlt99Bvx7O9//E+CffOure4stvisJ5c3bmQ1TjhcNtQmMC7Zyvcezu93D\nbrsW3ztc3kZD3bcZXHWWry5qfn2yxNhAXigSoZEibIMtdo3YlvXG3tihlaJI5PZ+p0XKh4cjhJBR\n897TVjefMx9iM7vpLLNcU3cds2FGIiWjNKE2lgfT2wu0cYFF1fUa8Ti5+/lxGdUyaQz6ng4z3t8r\nWHcGSJBS0LSWLI2+QvfFq05Zh4Xm519doLWg6HfdT89K/u5PX20JYV0cfNtdwJ2HdWdxl9X2FDAb\npteUMrtIlaRCcFHVhKAQwjFMUqQQlI3BBUGoA1rGwJu09/UZ5ikvVi2rVUeaQtdBkUOeAUGR54KJ\njpbQBEmRxhOpStT2PXQwKmjmFeu2QwuJDZ5MCQ4nVxy/FJKzVcWqNoBgvjKcig7noW3jYpEVkaYb\n5wnjwZtZJbStxYXAe+Prz31rXy7mqZb4AM55SuPYzHIPE4V+w3mC1+Ht5O4PBN9k1/y627lp77s3\nSFi3Zmt3uyvXu8+1GOdZVNGga5PPukup3CdOcdNkXpUtK2MhSJqVYVh4OhsIJyuKTFMkksNxHATy\nvWyS3sFRSYHtPd9XTXw8nQtY55BCcjCKGb67we2ZFpStpepM9GwfpFyUFocnTwQf7A95eIeFZmdj\nJm+mFImUnK5rvlhaNDBSYFtYVy0PZxkf7g94vmiwxnM4yfngYPCN+P1XnbIyrcgzjXcB6wISSZ7F\nMO9XYSNj3VXwdNayrCyP9wZbb57nlzUPpxnw8vVOi4RPz5axkCgJznPW1IyOxgQEwdPPLlQ8mRVk\niWacanQio4lcgMaADHGYKs0l40xS20idZUpRaEGRaopUYexmxy94d3/Eug+F90FSiJgY9mRn02Kt\n5dlFhQmxCb1uDD4EtIDaAgISGWcBmtYyfsO+1mVleO+w4OCGsV/WD/ulN3b8UsSpYS0ELgS0EASx\nWZS/P7wt/D8QfBsJ5W24zd63s573D0b3bubevJbYDwjbicnbqJ/XxSleli2pVqyMJ3hJogVSRJMw\n5wKjXPFkNqCznq8vKx5OCkI/EHTFjcYQlZNlDYh4e03083HBM68NxoVrp5ksUfz82QIp4n2erAzG\nGj5+PGGUZTTWMbrjOS47SyYVUkqCgNN++CdTUGSaTljqFl5crPnxoyk/fTL71q/bq058xsNPD8fM\nOxO5cg2zNOF1LYQ8iaqpmOoVF2QpoMjUtQVG9puF27A/TFFIslSSpwlNB03dMk5jAf7qsiaRCiEC\ni8pwMJI8PhyTSYHxRDosS2najs5ClqSYEBikkr0kpTVxkntvmPXB9nr7ej8aJ5yMchpjCUgEsR/0\neGehPl23WBFVRRLQiUarQGkCvuhAwHCQMh4kWBsYZm+24388zVk3lpsU/94w4/k8eiddfe4ceaqp\n2kg3Jjr2LhrjvrP+z114W/h/IPimIeR34SrCz/ej/2Ib4fdtr8X7WHCbzlF6i5KCPFUvvflfhdYG\nUi1YtwbrHcuVQwgo644n+6PtJGOqJUpKLsoW6AeEZFTP+BAL1bKxHI5yqtYyzhOMjwlLxjqmvW3z\nBmVjmBYpnQucrQxaCbxQnK0atIyZrnVnb92dGxfYH6e4jQzPxS7EwsFqEXf++xnU1mOdY1HFAaTR\nt+BvX3XiUwJaEe0rahMoEkGRyZckhTdRpJq6bHFxfgoXYmbw4SijbM02CGU6SLlrkFQnkmEBf/LZ\nmhoogJ88lCRpilSSSap4sW4xNvoM/e0P95kWKdYKjiaK05WjKju0gKOJwgXPw1HOybJh2TYUUvJw\nkjPK9Evv/8kwI1eKT04W1F2gSAV/8/E+amdXfVEaCqVjk15IDocJi9bRmJK9YUrnIlWnlODHB1Oy\n7M2au4um43CQsmiuiyMGqWaYKX75YsmysUxyzU8fTWitpUgVdedY1oZUS4pU4cPbwv8WPe4bQv4q\nbCL88uR68XlVruzrriVqwR2pjlOgzsfd3Ti/f4GTIkS3TQSr2pD1OmYvJKvGcrBjU5tIwcmy5cEk\ni+HiITpojjN9rTh6H8gSRQa4JFoZ5Im69lgXjWM2jBLGRdVyNMkIQVAbx7RI8cDxsuXRLfm1g1TR\nGE/R8854WPb/NtLQWDhu4b0k6syng2y7W/2miqwi1VyUbVR7RGcKEiXYH2ZkmeYXvyiZ5JLDccay\navnFs5KP/tb0fk9+6NuKIaBkYNVYRnnCuNhcq2Vyx2v5F08v+dPPKhRwNIC6gr869hTJCX/vD96h\ntLQ8LU4AACAASURBVI66cSyNYZQkzMsuLrTW0RrP4TQlVdFKum1jPGVn42syk4CPlNo1G4Yen5+V\n/PzFnFRJBiONdZafv5jzcJbzd37yAIi9p9Y5RlnKMFEsW0MgkOqkt5DQDPOEw2FBbQ1a3DMZ5w4c\njgoa4zi/MSW+qDueXdYcjgre3Y+hN88uayDEwclEMS5iT6PuHHny1pb5LW7gTfT831Wv4GXc3BJ+\ns6ZznigWVVRxIGBeNTgnIHiMswx2FqrWeVItyFNN0/VEbWAb5n0wTGlMlGFuLAqM84wz9dJj1TLg\n++ejbG30j5cxRhEpMcaxrG+XRT6c5Hx9UWFdwIeA2Lld52LYRyAqVr4LRRZwrUhvZiTXdcejaYrz\ngrrzZEnCoyywfs2A2KZv4GXY5hcXacJFGRO3lBT4nprKktvdIv/vLy+oiKEmVQOO+N+vnrZ8frrk\ns9MSJWXksrXjr57O0UpQaEUIgbazWClw3kIIpImic55V2+GIcsBxpvHevdTc/ssvL6gai1QSKS3e\ne0II/OLZfHt9h+OMuvNY7/ECFmUHIdpznK9bdAL745yy6yjbwN96780+CFJGVc5NS5Pbgu0BXiyr\neJJpDasGtIA0iUKF7xNvC/8PDG+q5/+uegW7ECLSAc3OMXw6SL8R1ZNqzdEEmuBwziOkZJYniODJ\nksjRQzyZOHcVYuK1wriY9brRnEPCRdkSCL2/jIiKnyx56bHuD3OeXpRIIamM32bLTgoNQTBI5EuN\nug028tBlbXABigSmLSwDVD19sqch1QofAlVtt4vuN6XC6s5G36Ls+oJddxYTBB8djjldGzpr43M5\nSjDh1bRFY3yk1nZOEW0fAiL6CdNUCd7ZG6DuUPUsylhEGoiGND1WwCfHJY11DDPBMNVMiwwhJb96\nsWQ81KRaM28s3rdICbNco4WMjfhxTiIlxnus8cxLE11a63arNjpeNnQERP8cJCKgU8nJ4mqhfjId\ncLZo+ORkyaenC6quJU80XbAMMqgb+OJ0jVaSHx+Nqd4w43xVd6RaMiuul9ba+JfowjxVWBfpVpUm\niN54TojwVtXzFtfxpnr+REnyRL3kzfMmISpaQkAw3rl/5wNiR8z8ulNKCJ7zVUtVttgQUAiCD0wG\nKYNMYh3XVEdFGrXn42Ln/gjb29wfZhSJjR4pvfd9qgRaxedqtb0ORZFqytYgJRwvKugXitNljVSC\nR3fIORMleTApGOdxwGyUaZaVZUJUaoi++CcKFpV5IyrM9gWh2pHM5qkmBBglkl9flKRKk6gU8Jys\naz7cH7/yNqs+hAbBVkpYNg4lJe/t+PJsntvbUId4snnpeonuqaNxRtt5Fo1DyJZ3ZwNWjcV7QWMt\nMoDWAu8CjbUEkaGVZFEaOueiq2mRsGii6iue2AR1F3sal1XDMM9QWlEZh6k68oOd95X3PJs3W3WZ\nlppEKMaZYtk0JKlnlCtGiebFsmV/9GbunJuZhL0bfvpFIlnXBqTA+RgNig99WlyG92HrQSTlNwvp\n+TZ4W/h/YHhTPb9xUTUwylOm/Y5/k6X6bYv/604R9zmlrBrDr46XLCrHojS0xhKE5LEreCxzPjzM\ntiZhm9u7z6klUXKr79/cz+51zKuWQarorGPZWEI/SXlRGgotGeb6tb5FmwV3Nkz59ZklUTAaaMrS\n0loY5AlvSoWFEP3yoxpL9ItHxzjXvHs44i+fL5CFjxYUTUPZet49fNlUbRet8ZwsKi7KbhsaPykS\nkhsmbY2xaCleMsmDWEBue3YE8aRzsmwY5zGasnOe54uah7OcZdvRGrABpA/RQsFA3bYs6ra35VYY\nE1jUcfp5UXaYELaT1lLDug0o5ciF6mXKlmF2Vda+OFuTppJZkjLME4LzdEFwtiyjhXMAYxzGgekM\nl+WbbfmljAZo+Q2vncNxzp9/NY9N3k1EZWd53Du/xuCguChI+VbH/xY38KYc/Xc1AbyL1ymO7nOf\nX16sMS7QBsu6NVStBQn23DFMNM2O78zuqaVsHT7EgqelgL7AX5Rt9N2Js5DUJvroaHVdqlh3jq/O\n11gfeHZecrZq6CwUWcdkoHEknKzubvhdO8kkCXsDuKzgrNfz7w9hmGUMMs28bOlcIFWC2TDbpljd\nHzepm/j9OJX8ax8e8BdfXfDFesWo/36cvvr2z1c1X11WUeqoZFRldZZxpnojt0DoKbbN83Zz0c7o\naZ4bSIHxQPFi6VnXlqJ3LB2liqNhxmVlMBYqE3l8BwwSKGuLQLBsOmwVd/dFomhcDLNXUqKljIE5\nQjJMoWpNXNAFHI6i980Gx2XH0TBj0Vhkr7wCT1XHAq0kZAmsu45ESdwbWiWEEJgOU5obYolUSX7y\ncMzpqmZZG/JE8JO9Mc57ms5SGb89CQxCYFp8v6X5beH/geFNOfrvI0QFXq04us99nizb/oMHxglS\npRBC0lrL5brl2WWz3XFqJWmMI0uimyhE9UbnAraJXPCqMZFD77lrYaIy5NH0+lv+ZNXw9LzmYJRx\nUbacVy0yxABxFSSny4ZRWt76uG6eZILxeAmjQWwQE6LrpDWO81XLqom+Mz4IVlXHOwdDbhuKug2x\njxKL82aAaTpIot+LDyRK8K//+AFSSbzztM5hXhPmcbxsUEIwLjK0jrbRq7rlvOy2MZfLukMrceei\nvbrjtltgWmT89JHgxbJh0RimWeBHD/bYH+UsqpLOQCJAZ3HYrTOwJvLhZW2QStK6uBPurEP28t1A\nDMFRPlAamOUSoTKCa2l9NErbPm8+8Gxeo4REyKgY8iEgexmscTE4Jd7L1UL3bbE/ygFB2V5vrFsf\n6Z+bFNDXFyXrzkEIaClx3rPuArNvIK/+Nnhb+H9geFM9//en6nmz+4xOkIFlZ8hUQCiF9YHUS4QK\n/YBX5MdPlw3DPNkWwF1zq0GWcLxck6hIRxjvuFi1lMahCIwyfW0g68WiBkHc8RoXOVop6IxDJBJv\nFavmdqrn5knGSId1MM4VeZbTdR3r1tCEjq/nFRJBkSVY6zldGbK04WCU3+u1u+qjXF37po+Syhjm\nnieaNJV0nae2tk8ouxvGB4QQ/Pq0pLGWXGsOx8m1BeN1i/arypOWgjRRfHAwRClJpiVaRKqqT7gk\nS3oP/gTaDoyNnP/RNN/2STofWDUerSVZordJVTYEcgV7k2F/CtDUTUvtrl6v2Sjnr48X7I8KtIpD\nfY21DAvwFqYDwWxS8GCUcVl2d/oy3RdXsxPXn7O7PgONdaRK4IJAAErF+YvyFgvx7xJvC/8PEG+i\n53+VHvz7QpHGadqN+kWJ6E/+YGfC8t1Zwafna9rOYb3AGENAUmSCTGvk1k1TEBDR8teHa+lRnY0f\nrKrz7A0ExjlOli2pkoxSSdkanl1WPNkbMMqTbXDL3iClcTGTVQpB00W7hzxTzPKENLmdb71ZFAuV\nMEwFpwuHUCUiwP5IQJCcLWs+P6sw3jPNUn7ycEhl/L0ptiLVPLssOV1d0UVH44wne0Omw4xJY/n0\nZEVjHblWfPgg/vxV0FLwxUVFpkW0Q3CWLy4Mv/fOZPs7nbUcLzpcYEtRZVrda6NwOMkozz3CebwK\nGB9ojCFPRr13kuWiIyakA0MgzcE7z2VtsUGgRaBINaMs2it3LvaklJBkWcL+GJrOYr1Ay8DeaIBS\nV2VtlCl+dDji16drns9rhIxxlWVrCCowXwUuVhXracdHD8bs36Hgui865xknilF2/QS+/dzt0I/x\nvRtQIkqYN1SPAlp7d1/pu8D3KxZ9i//vIoTYXvyefb9hQ4lEvX0qJSBYNfZaEMvjvQGzPhClNR2I\nQK4lhVSs6g7hPV+crXl2WRG8o3N+u9OHqxOE84FBGuWTl+u2D3OJUYrTQcJsmDGv2m2YycNxxqqx\nOOMYZ5pF1TCvuygdXHecrVumd9BoN6MMBbBuwlWma4BFGVhXDX/21SUhBPaGKUIF/uLrBaeLmvvO\nzVWd5cWywQcY9FPKL5YNVWcRBKommsn9+MGIB9OcqrF3KnG21y/inAQb2WeI3+t+WKrqLPPKRBdP\n67isDL96sWBetttm+V02cwnQdS7SKXi8E2RCopXCeQjecZNAKwEsrNqORW1ZNSZ+bTtmA80gj1kL\nD8c5s0HCKE3wrosZwz4mWlVNzWBnVfLeU9aW1gUsnrLyJFIwznLqLkZvjotoCf3s8n6vx6tI1XGe\nwqvcNUXc2dM/x1rEKfRVZaJSqTJclC3yG/d/vhne7vh/w/AqPfibTgXfhcuyZZglLxlU7Ya9FKnm\nnb0hjycD5rXD+7hzDUQPnvEw2ZqGnVeGXFtGecq6iRYRWSKZFCnOex5Ool9K2Toq03K+bnE+8GSW\nczDMSLVmfxgf66O9ASeLBusl3kPTBryPZmGN9XRVh5C3V4Obu7h53dC0IHUsJJ2NKU/LitizQLIs\nY9iGkIIvL2r+7j0/38/nFeMs3Q7+ADSd4/m8orMe60P00LdxARLi9mns3Wb02lgeTAacrRrKLlIO\nj2dDvLh63bwPXFYttYkFc5ho5nXHk71Y8scpVLfMiY0FPF91tJ3BIwk4hFQUiWLVdJxd3L4onZsY\nhTgZpGQ6msitmq7PM85ZNjF6MZOCSab43EkmuWSQZVRty7J1qJ35heNFw18+nyOlJJGKtWv44tyw\nNhAs5AkMsoRJnlE1hheX69e+Fhlwl/anbA17g/Ql+u6uz5310SvLeb+1ZVYyNp2/T7wt/L9h+KaW\nyt8F7rKk3Q17sT4GVR9OCz60nqqzJFJRtl2UA+qrBC4pYjzfTMa4yMZYrNvEEaZAgvMNrXOxOZsl\npKnCB8lfP1vw44dX+vZhovjp4wnz0tJax8E4Bl1rrZlmEiklx/PXaLuFQPQBIknMPmejxisS6FyM\nfuysIxGS0lhmRYoPfrtzfh3uGgBa1B3rzm1nETbldJAmsWm4g5dktUHS2pb3DgYkUuEINK3bNswX\nteHFoiFVivE4wdiY0dvuUFRpCqqLqpxdJBnMBppjE3f9SsTch1VjKTLF+R07a0vMLjbO9Q3dPssY\n+pmJq1mQQZ7w4eGQVetorSNJNB+MMsLOU/rZeY0LklQKEqUYZDGxzawdD2cKlCTRiiJP2BumLJvX\nc+t3kTAC+tCgQHPDZO2uXkljI+WTCYHqHTp9iJuP7xNvC/9vGG7aMhM8jQ0UibzVUvkufBPbiLss\naXfDXjY2tJJA8J6TdUPdWCQwLVKOFzV//tUFg1QxGyRkaaRtNrg5wJUnEsXGRkGQKonqI+86e/Xh\nVkqxP8pJtUUowcE4JwSB8SHaHeeS8g5nyrqzfWi3oXOBzkSaZ2HBrWMh2O8dLyFQNoZVY0kSyThV\nPJwO7+/Vc8cAUJFEpQ/EvolzfbSfcZwuW5Z1t31tbjajp7nkbB296CeFxBmPwzPQUc3zYlGzqAx5\npllUmySyAOwkooVo17BL22RcpU3N8gRD5KwrA3uD6ED5KuSJ4KKyVK1lkGmeTDV5nxB3LapTSB7P\nRuRVh7UOrRV7g/Tajn9R1cyGCZ111I0hSwSj4Yjz1YLZKKNuHc46lBAoFcjU60viXduAAMzLjlEe\nTzW7RNhdzV0pAqmWnPanrmGqOBrn2O+Zgn3L8f+GQasYeB13IJJV61hULZ3zzMuOqo15pPUtQ0vG\neZZ1x8my4dm8ik3GO8LTd7E3zOis21IPm+J7XdomOJ5XfP5ixb/85AWfv1gwrzqO5yX/8pPnLFrP\nOEsJQfDpSYmx14uxkuIaP+s8KK34ydGEyTDBuOiQeTjO8DuKi0wLni8qXlxWeB94erbkxXxNax2n\nZcfX5zXDOwyz1o3ldNXgg2CQaqQPnNur3W8AzuM4Ar94vuJi3aCUxFrH08uKd2b3byQejnPOy5a6\ntbGIt5bzsuVwnDPMJGfrlkVlaJ3nxbLmdN0wGehrr83Gj2eDSZHyYJRRdY7zdUtjPfuDFCElyyYG\nm//6vOT/+OUJf/F8zifHK45XTbRB7mWPRc5LXH1LtFvujOWkrFmVHRKBtYbSWBIpmXA7FPBnX53z\n5fmKRdXx5fmKP/vqnOKWBvujWc6zyzUhBEZFSgiBZ5drHs2uJq1TGem4EAQH4wyPZlHWTIZwPI+v\n+cO9Id47vr5o+cmDV087w6s5/mXV8sVFGedQdlCkGuf9tZ6U68UEn56sWNQG0ZsNfnqyuvOz9F3h\nbeH/DcPGllnLqIzx/ZvvsmypWsuyNixr85If+IYmCJvpQiGpOxdDuWUMu75tsYBoSft4ViDFxjsn\nvBT2sqw7zsqWzy+WOBv6sBSBB+om8OX5CqUFQQiM93H3u4Ob8lDjHKlWKCk5Gme8uz/gYFzg+tPN\nBk3nOFu3OOIxvfGBtl/88J62s3f68S+bDiXk9iRzfgc9fGJi2I2HaL0rFYfDnFV9f8neZgAo0fE2\nEg0/eTgmVRKtNQ+HGUoJlrUlE5KH44ysH7ravDY3m9Gp1qRK8fGDEX/zyR4/eTACAt7HLAQbPPOq\nZW065pVhXrc8u6ivUUiL20ccWKwgCMmgjyJbVC1ZmpAlEqHEnTJQB1RdtC8IvY1B1QWOly/vsx9M\nc4ZZigie1jpE8Ayz9Fpi2ruHQ7SP1uEIiZYe7QMH4wHvHUxItaCsDEoIfvvxhHcP75+KdhvSVKOF\n4PLG+3Mjw46DcRtaMmHVGqwNaKFQSqCFwto4Uf594i3V8xuGm7bMy6ajtg5rA1LGqcmqi8U5HuIj\ndmmC6BsDi6rjZFkzSDWjTJMmijvCqhik+pVBL5dVR64VF6UhT2MGaWsMxsFsmHC2rDldNuSJ5oP9\nEZdlw1fn6x15aMqDydX1aqXYH6R8+mKFl5BJiQUGyfXIw5N1y6NJAQhyKTga5pyuG57P1+wVKe/O\nhtylVpdCInaO8MudX8uJXPDGoXOcZzFqjxg/mOcJL5b1q16qa7A+hsaMsoRUx4UxSxTWRxpIZ4qJ\n7gebhEAqQZZe9UU6Gxjn14f/MhWDTYaJJksUzkfVzbCIC8blqkNpwZ4qaG2g0JLKGuarls2GtG5v\nt20wQCpjr2OWpuRakWWKXGtSKXDEnXO8mvgMb77fG2jq1mNdINeaIpN8dVG99JwkUvGzHx3w7KKi\ntp5CS57sx37FBh8cFFTtPuerlnVnECHj/f0RPgQ+OBxzuqjJU82DccY7ferY61BIbg25yYBMq3ie\nvCdVU7Ux8B5CnFvQMM41Vft2gOstvkPc5Bpb61jWFh0Cdc/xIzyDG+P+u82pEDwnyziJ6n3ksBsT\nQ833hy8rGiBKA28aw+0uBF2/mnSd4byKgzxSRBOw1hiOJopBplm3luNVRdU49vrhp8ZYLqoOJdkO\nRMUYSMVkmPJi0bC0HUWieW86fOl+JXBRdjxfN3x90XDexkK0WncY1/H+/u3ZtcNM8eXpmr9+sWTe\n2GvFb7M/3dht/fp8zsm8pfOBRMK7+wN+793De79uUU/fkCW69xbyPL+seDjNybSmKjt+ebZkUVqK\nVPLxozFZv7hvTkM3h/+yLOHjByl1T70NtCYIj3XwyfGCz87WtG20xdZKko1S9scpKxMnkCGPdti3\nXK8kDiMJAufrjjxVzELC44cFearJC+j6dW9T4jYk1GVtaI2PT5yFxkdt+833kA0xpzZPFUEJciX7\nIaironswyvnddwXH84bjZYtznrR38Gxby+FkQJ5K9oYZPgSG+etL4iCNrp63KXvOy5aDcXLNNgLi\nifk2GxEl4bLs+ORkybJxTHLFTx5MOLrDGPC7wmupHiHEfyWEOBFC/MXOz/aFEP9UCPGr/uveHX/r\nhBB/1v/3j7/LC3+Lb4ebXGPVOUQI5JmCfvcshaS+0YDbpQmMC1yWBhFE/0ERMQOX23sDmzxdH6IK\nxwfB83l9zfwsVYJ1bZAqNkcDoJL4dWHBO8eiNrR907JIxDYOcDbI0EJyuu6u9RpaGxUkHx0N+fjR\nNLpsKslyx6deC/j5syWr2vD0bMVxX/T3BpBo+PwC/vzZxa3PZd0a/vjXFwgEPzoaXTNf2LD3mxL0\nyckKKeBwUiCBX54sOJm/vIu9C41xyBt+R1LGaePTZc0vT1ccjHJ+7709xsOUXx2vuFzWWy55s5NN\nVJS97g9THk9y0kRzNM55/2DE0ThHCMHxosJYmOYp666hsZ6Dccok19SdZZgk2x3/XYRECXTO4b3g\naJwzKxJ0oiib6LD6sNjObTHsK35HNLA4Oa+QQjApEqQQnJxXOOdfeg8tKsMvni8QSvBokiOU4LPT\nFXJn+vbBJMda+OBgzG89HvN4VjDJE37vnTFBSAKeYaYRwOmqY3aPdLQn+5H6m6oYPrP5i3cmMEo1\nZ4uOgxteO8s6Jn1JGalBKSNVWjeGP/7sDA+8dzjAA3/82Vmcr/gecR+O/78G/p0bP/vPgH8WQvgY\n+Gf997ehDiH8Qf/fv//tL/Mtvivc5BolgXGRMhlkTAYJwzxBS/mSHdjugmGcY5AppIpveiUFs2HU\n0N8m2Njk6W648FRLUh1N1jaYFH3foTftqoFFF4uwJGrhP3mx4qyfxN0fFiBiVGOiJXmqqFq75bOF\nkBgbuFi3vFg2XK5bvIC2c8yvma4HfHC0xnLSG8944LyC0sYC/uXp7QX6k5M1R4OoLf/l8yW7e7Sb\nBXGUKBZtx69Pl6yNZZRonq7ubwHsg4jDX4C1sT29N0zxQfD1ZcW4SHh+WfHnX1+yXLcM04TPz8ot\nl3zbKWxSpNEyuu44WzUs6o7OeR5MB6SJZH+cIlF4HF/Pa04WFZ0N/OhoSNJ7E9xV+AMxxlFKWHeW\nZWOQIaCUiOqcnYZ5ucOKpP3jKpuO40VD2XTsDVOU1i+9hxCBQZZQN5avLirqxnI0yXE7QeWTPOGd\nvYLWWp4vGqo2NpeXrWc20BjrOb6o8CHwNx6NsfcYnPqtx/u8M40+P/MqPtb9BB7uj0kSyYeHA/SN\noPuytX3P6WrhTrXi6WXDwTin7Byfn64pO8fBOOd4/a+Y4w8h/K9CiA9v/Pg/AP6t/v//G+BfAP/p\nd3hdb/E9YlcW92BSsG6jHbG10UA+TySjTL/0N1uawAVyrSjS9Bqd0Fh36yj/fXT8g1TxcJbT2Mj1\npkCqoHGx8NsAv/3OFOc8X56XNMYyEyndjkpCCbHls0PwnJd1lBQOdDxaW4f3XDPQamzgnYMR68qw\nO4e0KUuOGKF4G57Pa1wQTEcpD7REKkhcpAAKYkEYJHBhwHhJscmvdRbjoW5fnZC1i0wLfOCaV89G\nEnteWhaVZZClzIYxyLu1nsayNVu7C1pJxpne0g/PfAwMtwH2RxlP9ocs6hbnYsbttEgp0uSa9fGd\n16wE6SClMw6PQCnBdJAihGTZdUwltP6K48967vxoVlB0jhCil9AoVWglr8mBIaacFVmUcG7sR7SU\nNOZqJXEeZoOUQZZElVXvx7NqDVma8GiacDhOeXd/iHEeY+5S6V9hf5Dz04czjiaWZWMplCDLFB/s\nDfnocBh7VDd2QOGlrVTEojGMck2h40bKO1BacPmvuvDfgYchhOcAIYTnQogHd/xeLoT4EyIN+Ech\nhP/prhsUQvxD4B8CvP/++9/ysv7/jTeJXLwLsz5IO/iw/fALKZgN7i4Yw1SzdnF3qPv7XzcGKUKv\nBuquXdt9dPxSCpwLBM+28bcJF+yIhfR83SCQCBl4elnT+YASARkEiRZ8eDDc8tkX646nZw2LpsN5\nQZ5JRqnmYJASJrt788B82fLZ2fpaC3e3JI/u8DpzHr44W/J8VbNuPQt3Re1oHacvN06RnTX4AMvO\noIj9i4Px/VPP9oYZz+eRFE+13EpiH88KnLOcLyrWxtI4yBWMEs2seDVtUXeW5+dr/vTLBYumY5qn\n7E9SikoyKjK6zvJgnNG0jrnpqDpHoS1dZ7cLysZS+TZIqfBEqaUCfGDbf+nq2DdKACQoH793QG0N\ni7rDOEGiAkqmPNkb9tGYV4El1sWoRSkknqg065xjstPcNc4RECgB3gVyKSlyzarVWON4XpY8u5Ss\nGsN+kfL44PWqnr1BAkKwV8SmtQ0eIULvkCteem9D9Aza5FJvrEY666M5oXGkqcZ7j04kXWdJvuf2\n6/fd3H0/hPBMCPER8M+FEH8eQvj0tl8MIfwj4B8B/OxnP/v+DWR+YNiduhQiTtqerqKHSTyyf7sF\nYFKkWB9eMo+6uVPcvf/9UYaxsVm1bk0cMxfwweGIYR94vjsE9qqitYH1gbOyZVTAadc3znYqigD+\n6usFUgTGRYIxAi09s1HOvO6YDpI+3crjQ+Dzs4oiFbxYO2pjWVXAtMAGz4/81Ye76hz//JcvCK8Y\nlbxL6OGC50+/uKRIYTIoto1ciDTR5hYnwGINbYBMQ2shE/CHH97fImMjiY0ZBFdJZINUI4Pglyfz\n6DGfgu9gUMDHD+9Sy0f86tmcf/rzE7Ikplwta8Ovz5a8tz/m99/fo3Pw+emK42XH0SSnMZbjpWOa\nqW384YMhPL9F0rmNeQ9grGdtHcNEkihBkWoGA3jaM13aXzWIZ8DXFy2daciSgqquqdvAH7ynebEo\naTu/tZ72Pk5wX5YtWSJpTQxS/62Hu1p8QdmamB2QKryAqrUE53m6iL2EPIsT0Iuq453914etD3ON\n83CyKGk9NF3LME1Zziy/eD5nXCT8/vv71/4mfs6iOaLvT9ZFKvn44Zj//dMTijQ2hNdlQ90Z/s2P\nH772Ot4E37bwHwshHve7/cfAyW2/FEJ41n/9TAjxL4C/Ddxa+N/i1dgGY4fAui/ARRpH+YW4f+bu\nTSRKsj/MXnuS2JVzehdDsfeGGUIEQojyyQ3tc9Oz/VVFa4NF1XEwzNEqvik1kQLY1BQh4dE0cqHP\nLisOhwkfPTiIFsiJouvTtB5OCz49WTJMFS7AKE2YFTkWhzMx5Hu5o0P/+bMFwQfSV4RbH9+huvzi\nbM3hKKFFsmrcrZZoGlgCjxLY3EWiIfHc6DW8HndJYn91PqexkGZxEtkJR2Pjz1+FP/liTiIFIqB/\nKgAAIABJREFUs2Fsos6GCVVnuFw3JBqeX65Zto4Hk4JJrslSRdtZLirDybLmw8Mx1R06/oZor7yu\nLZ1zJFqyP87Y72M+hbg6LWyKviK+3pNMEvIxHhjlY0RwfPpiwR9+sI9SMooQlERqyYNRFh1VnWeS\na8Z50kcXbhCtuF2Ik9hKCEZZwtfziofjAUKCDLA3zBEhZhT87mteh+eXDUoGPnwwpmkd55WkM5Zl\n1aKEou3iJmnXYuOuz9neKOXjowlP52XvxyT5+GjCZPDtM7Dvg29b+P8x8B8Bf9R//Z9v/kKv9KlC\nCK0Q4hD4N4D/4tte6G86NnLKqjbXxu69DduG5pskaL3ub3flnE3nyBPFMLviMpUUNJ0jKa7bJG/w\nOh2/DzH/VmvNJLdoATpRuKXbsqNZEht8q7plmGWMi4TOOY6KhESpPpBEUhvPqEhYNjEyUApPIRSN\ncjyaFez0/nh2UTEqEupX+J/f9S/LxjAd5OSZJtOSP/tquS1iTyYK5xw2wGkFRSFIfCAIiQgxYLv+\njrTaX1+05BpE73wqtAAb+OqsumbZcBPzqmN/HKehERCCYFQkrCrDTx/N+PSkZNk4XAicrDe5tZBr\n1c8DxOZuSlyktYoNXUOkykapYponW4uJo/FV9oDzgpEMpFmcT2iNo2th6aHI06jqQtATNTxbNuwP\n860NN8DxquZk0bE/0mgvkVKQJuKas2WiFD44Mql4b3/AZ2cr6sayqg17RcYw1TzeK0ilxLhAdYc9\nxy7WTUeepKSJYNU4RlkGWYrSgncPBzSd4+vLinf3rsuAb/ucJVJQ5JrfebJHkkiM8RgfDfG+T7y2\n8Ash/jtiI/dQCPEU+M+JBf+/F0L8x8CXwH/Y/+7PgP8khPAPgN8G/kshxEaY8UchhL/6Xh7FbwA2\nckrrQYqwtTVWIkoq38Ta4z69g139/8YHf3daNnB9KvSbhruMc01nAkWeMWssJ0twtcMAB0lMsjpZ\nNeRKUSSa1sXQFC2jZcSyiXF2AFpFqd+00DQmZgwvTcf+MGOQaNJkxyPIey7rhlx/80VzMkiYVx0v\nFh2N8df07CfLaDC2aSe0PtB1IJQnOEjTEInt7wDBOyoTM3RDiPr3NIFBGraWDbedCGfDhMbEhrz1\nHi0lxrhtmpuSgao1nK5bBIIijUEoSrTbRT0XMN+87Ds1syAWutZ6Mr3JPL56f4wGKU3bclnBsnZo\nooS2qaL3kRPgnUCqgApcO+1uOP4QAk1nwScoIQk+sGw9RztSyDyJC4LpLUNsZ1l28bOzbDp02sct\nqihlHqSvl3OqJEEIw/OLiuNVS64F40JjvWLdRFrJ3DPRTitJrmWUNxvfDxgW297Z94X7qHr+/h3/\n9G/f8rt/AvyD/v//N+D33ujq3mKLTeSic46ViTYLkji9uagM4/zbhTPfJwh99/43U5+LqmPedKT9\n3wkE4343c1sc5OsWl8ezAX/9fEGuA8frWCLSFLoOzgz8eBSHnqzzfHHeIvA8m1cUaeR7tZbsDyN3\nv1ckPD0vWbeWL88qrPCMdYIo4HhZ84cfXvGvB8OcLy8qQn63muMu95bffjDhv/2/viCVUOTXB25y\nDZWFk57HXqxjmpRKwXVQd/Cjw2/24b5rCE4pz1kTpaeJgCbAuoODkaNqDYlWt54If/bBHv/Dn36F\nFDBIU6quw7jAv/u7j/pFXjKv2i1t0XlPvWoYJENEX1zfOYD52cvX+mAYE75cCHy9qPEh8HiSb695\nf5Tz2ddtdNnsh7mWFcySaHSWp5I8zWm6hlXn+Z3HU14saxIR5xe895wu49/LyP4gRRwutDsbkCLV\n2MYwyBJO1y1eKB5NEsZ5wucnK5z11MYyTDS1sfzOu9OXH8wNDLTk64uKvWHCI62ZryvOy4539sY4\nH/16jqb382HqbDwdBARFpnAu8HU/mPd94q1Xzw8EGzklIioVpIhNplg8v/12/7Yg9Nt8dzYB5+um\n43RV8/nJCoWITeAgWTYGgr/mQ7Ip7Ls+P3eZug1SzaNJjjfQ9T/WKqo+PCCDiN4tNjBMFMM8I1H0\nMr3YVs16L2TvQfRWyV4EutZTdQYbPINUxVD2Hk/2Mt6ZDV5pg3vnP0nJ40nWB7Bcl9+5Xoaa0MsV\n+wlX0ee7EiB8g4/fq4bgVB96bgDX30/CVVpZ3dmXbIIBDsY5Hz2coIiqFoXk48dT3j8cIwg0nUEI\nwd4wQQqwJiB1lGTSD0kV2e0FKs/j636+6lBICqUwxm+vOVeaNAWd9DRREhf64RCO9gpEgLKLxmVH\newUHozhZ63qplwvgg2eQaehjCwlxRmD347A7t/JiUTNMFbNByjuzAR89mJAmgq/OK4Lw/PTxhHf2\nbp/SvvbYUsnRJAUEIsQFcpglyBBtq7UWHI3uV/gXVYcV8f2qEQghsL0dyveJt5YNPzAIERU3xsbQ\n7TyJ2vBvQvXs7hwbYxhl8ci/ifSbDTP0jexR4yJlMspTytZyNM5Zt47ORa+e/UGGEGIbcLKLaBHg\nWdbN9j4mRXptF1p3lizRrL3jx4dJlO4B2TBaHFzWgacXFcNE8HAyYG+g+enjafSlkQICVL0G+3Td\nYFzg6byi7hxaiii7s9EXZd5Y3u2vbVjkvH9gSZUktmFfxh39S46XHe8ejNkbDTHe8cVJQ+sjv12G\nWNAeFfCshlkOlYnhLKmGYQLL8v5a7duG4DY/t0ZwMICyibr4EdE107tNHnGMobyJk3XHbz0Y82SS\n05j4XhrnCRe14SePpgSheH9/QGkDbedIlGSQSoII20b+ZdXysIB1fTV5m2oo2yifHBcaawPGOCoV\nC9xl2RJ84G+8M+FyHR1Fs4Fkb5Tw9HzJk2nBUytojCVPcp5MczySDw9GrBtLZx3DQcrhZIBpLcNB\ngndR7aMAd+PDsOHWx3kSYyP7DcmTWcEk01TG84cfHNxbHq205PffPeDLy4rLVcv+UDNIMwKBaZHw\ncJqTJ/crrYvGkcs4dd7ZaCx4NEpZ3CMX4E3wtvD/QLClZERsWGVa4XwgTzVSCIS4pylUv3NMtWKY\nKRZVx69P5jzZHzAp0mseMOyYEOyeDKrOIaRglGu0EhRJlHDeFRC9bqKFcKLU1mfmdNVwMMy2stFV\nY7koO4TxZFoxGeYIIVlXFScrQ57C3/n4iFVt+OTFnEfTfWQfX+d8iBGLfVPvdN3y/3x1wWXVkKdx\nkvO8sujUsqgtu2vaJJOxyTm5e4d2F4nWmpbWOKZZglQp+Cv9//tTzbq0nPeKoMrEtKeDScq66qgM\nHIr7c/yvGoLLU42tDPsTjZSK1nRUXSBJ9bbvotXLj2JVddTWkycJ4yIGmK/aGGoDkTZCCFIJ+3tD\nnPesmpbUBQ76Bd66QNnE3fo0U9Sto+kg91Bbh3MBpSTDYUJwMcLROkGWaowNvHOgtwMb3gbSRPB8\n3jAbKAZZQdV2PJ83/PiBjtm7O/Th8bzi15VBIygGKXXrqIzl4R2v5dEo5cWyBXQ0zOv5oY+OBq8d\ndNvFOE/57GTNg0nB0bigc5aqs/z4cMzHjybbbIj7oLWWp/OKYZIwHOW0bfy+uId1xJvgbeH/gaDu\nolWwB+ZlQ6JijF3ZWgapusanvwqXZYsUgtY66i7mmkolWDeGSZFe84DZxXWTtoDz0RzL9rbMdWcx\nznFRdi9x+MsmZtiuG7Pd8adasWw6nvRhFaumo2oN43HGr56uMa5BaGib2HQ7ONR8crxCS0GWJVws\nW9iROrfWbwvji3nMzV3UjmeXS0QISAWts3x0OODB9IrjD8RhtS8u7trXw8EdNWGSF3x1ecG87AgC\ndo0dvlxcp8qCg8pD3XVIAYUGqe//8cu0YFl1VMZtd4aDRDHIFAeTOCdRthaLRYbYT5gNIw1VpKq3\nub4OIQXLteFpW2O8I5GKSabJpmn0M5LxNFXWlotyiRSSSaZ4b3/IwShSPMNE8kWIK97ljkz2oYLT\nVcu87EhU3CR8cDDEeZDS86P9Ef/L8y9ZVpYgBcIHJgPNKEvJtaBsHfO6JpGQa4Hwgqq1uLAh9iAo\nyTuzgnlteDYvGWQpj2dppH92sOkvFWmCpOaibLe3MUgERZq8Uv10E49nGX/97JJVHV/3tjPkqeLB\ntLi1v/Uq+D4k3gaPa+IcgpbRGvv7xNvC/wNBbSJ/rqVklCWcrxvO1p5xpth/OL23hr9sHT5E/lcr\nie0lkJdlR5rEAjEdZPgbG5ZdVc8oT7goO5rOkSaxH7CqDYfj7FqKV57EheF81XKybEgSzSBRLDpH\nwPCOuhqWqTrLlxcl+3nKuo1FVGzM2gIIL/jViwVKRFO2rvPMqy5SPUKgNeRJLEarpuOri5KLVU3d\neYILZJlGS8HpypCKqw/VqrGcLOuXJi13Ye+gWw/GmstPDMeXK7yQd8o+Icoe274gCwAFD0ev55M3\nyBLFXz1boEXstbyYrzkvGz44HFFIRZJK2rVHpmBbmEwkH0xHDDJ9zaRtF1rCZ2crWhffV9Z7TiUM\nBgdRChsCx4uGF8saISRSBTI94N3plSwz3PG0WQvP5hVtF6dYjfMMEo2Wkid7AwoNl2tLWYNKA64D\n5y3v7ml8gLJpCULRBYce5mglsN4TAigh8CGgvGPRWRIJ++McZx0XpeG92dXzuiteGGSaIkm4KKvt\ne/lgMGCQ6Veqn25CET8jTy9KrAsEAgjBs8uK5/OSx7PBvT+Pg1SAV5yWFc7FFLD9oog//x7xtvD/\nQBCjCeMbvjWOSZEx9PFN1xjXa6xf/2bzweO9uOKKleRi1ZJnisNRjvNRMje+YU+7q+oZZQkhRHMv\n7wM2OI4m+Y4MUGAcnK4aZoMM4+JUsLMe05t0Geeod04Vl30YxotVTZLA1AOijzEE1sbwYDqkM5an\nFyv2hjlVa8lTRSIFSqhtg/b4sqZrHcFHfxevPY211J3i0TRO+m7w/LJGKsH4jiYlwO3enPDleUPT\nNBxMCtIk4cvl7T0CiA3Xjd49AM5AZe8/wLVuDNMiiSH1TTRVG+aaRCoMgdR7Hs0SVJIQRhYZPDbY\nV5q0na1akgRyneAFyKBYdx0vLivkjw55sYqpXqMsYZAlqD4+8uudHIFnO+G5G88dgK9reDQu8MFv\nTyllZ0mVYJxr/s8vzhnlgr1xGi0XgseYjqfnDR/lmr1BgZCxoV9ZQ1l3HI7zbWSolnCy1PjSkqXR\nA1+o+HW54wG1S1FeLBoa53k4HWCdQytF4zzzdcvRtGB36PBVeDGvEQJ++9EUG6IKqXXRnXaYpfF5\nVfKVcysbeA8+OA7GBYLoFuqMe5u5+xYRiVK01lE1dms7K4g7wW8ywDXO094XXJDoaP9gvGeWXEkx\nfR/Wcv3+r0zapBRkWvKjozF5ojhbtSgpyHc00DEaMTYXhRJkiSSRimRjtiVA7BpXhUCeaM4XNYdj\nRSo1HoE/awjA2TJK36x32CCx3pIlis45BmmGlHJLHZXWMBqkBAFKxR1v2xp0onkwya+ZeEFgmKWM\n7qHfvokvzxccTIcUmY4ZuDvN4ZTrfj8FMB1BqhNaE3Xq6+r+7pyrxrA3zOOg3MmSh9MhnsDzRYV3\ngelkwChNOJwWdJ2jNIYkSV7JXV+WHbMi65uicViqW0SeXEnB04uS6Sgl1QoRola9bg2fn17RYl2I\njzXXkKRgOlj2J7XHs4Kqc+z3BbzpOhzRsuF43XIwjieSzWxI1aYcX65IpSbNJEWSUBuDbaLx0W6A\nEETb51GuORjnKBm9k1rjWDdXz/wuRXledmR9g/x8bRkXis4KzsuOo2nx0tDhXaiNxRnPV2XNZdmS\nSJjkKU1jrzXd71P4/1/23u3HsjRN7/p9h3Xaxzhm5Lmquqq66J4ZZmxGHnwBBiEbXyD5DokrX4D8\nLwASkiVAiL8BgbG5wAJxg++QsWRZGiF7xu72TM/0TPfUMY+RcdqndfxOXHxrR+yIjMyMzOoyUzgf\nKVW9o3esWHvttd7v/d73eZ8HIcgyzXaRUfQ9krO6jU5H3yHeB/7vCdaDKKvWUjUtJ6vogjTKFJmW\nN2YRjPP4oC3qjqqzpIniszsTjAtx8VCSO9vFS6weuGBHTIqY7T+bVdQm2jceTIpLWWXkmsfXg775\ne7ToKJuOvUnOwShjtDF7MCpStLQYB9Y6Zv2AVksMIgb4v//oKYWGe9sDaltQtQ5PYI4BAmlfOpoU\nGSEInp6tqLoaSRSjK5RAAUV2cZ5b4xxVdczegmFzDqEYZ/Bi1XDVhuBqdShL4MkK1vYdH4whSW/u\nuSukpLUO76MNoRKC3ledJFGMMsmj0yVPZjXDTPLD2xOmb6gzZ4miqTvmq+7cwcoRzh2srIvmJlVt\naF1AiCjQtyk8OUygMbC0EGxMRhRxMfAhsj5PV20cyvKeX7sfd6bDLC4iUYwtkChBqgVFQXROe7Fg\n1TlGqeLjWxMmRfqSWblAkevQ95eidlCh1drdHrgyeBgCiZQ9GUCc/3xNK77p0KHxgqNVi0okSim8\n9xxXLVv99b6qPPs6jHLNp/tjvjwqeb5oGCaKT/fHFDdQP/02eM/j/56gSHW8n0PgZNGRSc2kSEik\n4vPny94R6WbHSbXkYDrgw70RdyYFozzlw/0RH+yNuLs9iJS319wZVWc5XrYMs5S7WwO2hzkvFg2r\nfox/zWoQApa1wTnP09OKQis+2p8wzVJOVt2ljOhglLJqDHvjlLMSmiYGjo4Lp6M8kZQd/OxRxaJd\nMStbTlZ11JHv/Pm4/d4w4+ujFaM80uy0glndkWUpj87qS8qjn+4XHM4ruvD29LmDYcovDhe0jb2i\nDwNX87UTE39WEAPj10uwr2oeXIOtXHO6ammtR0jBWdWxbC2TYcokkfzpsxVd4xnkCcZYfvbN6SUJ\n5+swGSR8dbRiUbeIIFjULS9mNYM8LtRbg4QXs4rG+zhcFBzP5yt2N3RkPr6TsyIuzmspagfcm8Ks\nbjme13TOYbxnmCZ0xlF1lh/f3uLRYcOTo5ZZ3fHkqOXRYcOdqear4yXTQcInB1Omg4SvjpcUqaYx\nllnZclbGhXqQQGMdiZRsD1MSKZnVHTsbqqSbPhKjXLOsTOTIiziEuKyiLPJVw5rXwfuAUnFRkQRM\niHLOJ5Xh+bxmUXWv7RltYpprns8btFJMigStFM/nDdMbOIF9G7zP+L8nWJdafvl8TmNdHLU3klRK\npBLMyvYlbZDXHWdtv1ekisa4mKH0tLpECXaGr85Gz8oWIQStsVRtlB/OE8WTs5LtYU6mo4zEvDKk\nWpFoxXSQUbeWPJMMEsXe5HJNfVzEoZpBJgnE5m5xJS421uNtNGl5+qLlrO0Yp5qqsVTCstXLHA9S\nTaY1x4slSxunf7MUhHBsjdJL2c6oyBAI/uxwfqPvYRO3JjnGWqraI/UVFs817w/Ec19jXt98l1H0\nvrCV8SiiEflkoMmU4LRusSZy+MOqIrhAmsLpNQblmxPUrY1SxsZ5rDeEEBikCamKk7EH04w/fS7w\nxpElirq2CC95sHfRlL83HSNpovvaxt+5szciEJi1lrqz5Eqyt5fTucDRoqZIJEFB6aGtYw9komL5\nL00UrQu0dQchCgLadWLTD+YhBOMiwxN5+00XRfK2BwnT4cW9tXm/j1LNoa/JtSLXiqa1HK4agvAo\nwY2bskUmyFTKsu5orOesaphkKeOBpukcp6uW33iwdaPvNU0kLxY1XgQyldA6gwyCNNl58y9/C7wP\n/N8jJCqadQ8yTfBxGx2bQ4Hla0TGrjvOuh8QH/oNL9CwVsF/Nco2mmRIKUl0pIYuW4tWgluT2CCe\nlS3DTBNC6KmWklnleXSyYm+U84P9EX6DEmJ9NPpAp3xyy0ZqYpBUGw7meaJAOyhhUcM4Tfop3N7D\ntN9xzOqO/WnC6UoySATjxDPIMtrGkylxaTjmjx/PaKzjB/tT/unXN7dCBDhtDA92J8zqDucE17nP\nFsSAuFnzl8BYxYGrm0IIycHWgKazdG7CvW1P2Rmq1vHktGZrqBBSUWRJ5I4i+OXJ6tIxrspznJWG\nO9s5oAhEjaOiD/qTImFUpPzFD3d5elbRWM/OIOOjvSGJuggb38xX7KfRMEfI6BiYKygbyzCNhvfj\n7QE74wwpJIvWEhbwh09n7IwEW16wdiCRMvD01PDXfjOyiqyL09uTXHNWdeSJZpht3DMueuRa589p\nwtvDrO+3XGDzfv+hnHC0bDirWsrO8cHuiJ1xTqbVjZuyUiryRKJ0CnVHlgzAO4yDLBFsDwe0NxB7\nAzicdTzcHVF1UWpiKlMGqeRw9n5y9z02YKxjXnacrAyVMQyShN1Rci1P+5XH2Mj66s5grOesMjQm\nqm7uj7PXNovXzKCk384aGx1I1oaNa6mAEGImr8WKL19UfelVsGosP3004y88mBLnTCNrKVESYQKi\nb9StB4nWqDtHEND0rk1JEhvFbReprovWY5znbNUyLw1CKoL1mCBZNQ1Iz9N5zWBjOOYXhyvqzvD5\n0eUgeROcLBvGhSLXAzrn4PlF07PgwmgdYtCfSEDGunxrIL1ZGRiIZYXOxaMNU0UNbA2H5InC4Sly\nzSjPo6qjSGlMd8lbOF6/9QR1GxeBKpqgT4uUUZHgfWDVRibQpIgBaO2CpWT8PqvO4TZE0M4WNXkG\nAxVlsKPhiWNeNlFHaKSwIXC66tBKooWgEoajeUmuEpIiib0AAaYzPOs6WhdI0wRpA1qLONmbqEv1\nfehnG2p/qQ/qfOBV5fHGxPtjkCUsG8Mkj4Yqnb0QprtJU3asFa03JEJRaIUPEuclD7Zz7vRU0pvW\n+M/qFglUXUfZOYapYpTmscH7HeJ9jf97Bus9P38+o3GG7WFK4ww/fz7D3pD/dVU3Z1Z2/PzZnNY6\nxkWC9YGvTkrOyldnHOM8xYVwLslcdx4EjDeeuEwLuj5wHy9bVl08XpFqpJSUbcfh8uLmTpQCAkp7\nHr+wtB0MsssLj1TQ1LHhu5XBk9OKR6c1VWvJVMzul43Be8HTeUVjDEFIOutYNNEQ5NlpdUne4nBW\n8kdPFrQ37JFsItOKJyctjTUMrtRksyxq0m+WP6q+GVub+Bn2t26uua6VjLouPrpxJSrWs0MIbA9z\nytphnEMpQW0M81XH/uhyOW3ZWB6dlDyfNxyvWpTWHC+jQb11cWdG8NzbikN1q8by8+dzahMZVLWJ\nr1cbXpSpVszryKbJdEJrHPOac3/Zo7LBWE+WKoz1HJVNzwDTzJuOuovNj7ozzJuOrRSO5i1l08ae\nTtNyNG+5vzW4pP4K0bznxaLBuljesw6enlWvnJmtO8OqdUgRE5MskZSdpe4Hz1ItaW/A6lGJ5PZ0\ngJRx0QghsDcuzjWgrnPgehW8Dfzk0Smnqw6B4HTV8ZNHp9Gs5TvE+4z/e4ZZadgd5pytOg4XLUOt\n2B3lzMqbBa6romxnlSHb2LoXqcL7wPEqDgddh3EeSzhHy4bTMrJ6JnnSTxV3kcIpBBCbarMqap8/\nWzSEEBimitv9xOUaeRJZK8vK4jyctXB2xZP2uE+oBVHUq24tufeERNJ6z7jf5ttgCd7xaAG+bw3v\n9El+nqpLWi4ndUvZOE7Lt2/ubhUp4yI+/C+u1NNnVxK2EbAC5v1H3lXwYOfNNn9rWOeZDlKMjdd0\nMkjQbdSz+dGtLWblc746WuF87LncnqT8+t3LdeajRcVXxytWbYex4L1lVbc0XUvTObSS3N3K+OGd\n6Nz1ZNaiRNTtf7ZoUTIwSjSPTi8+697WiOfHc55ZCGWDILpo7U2GdC6QSEHnHK4JpFqwlackWvGD\nvSkv5i84XnQI1RFclLS4vZdxf5rz5WnJ83nLQEs+2hmyO8lwPiq8r+mfi7pjb5TReU+5iv2kcZ70\nMw8v71aNjxajZWtpjaVpY/D3/YJy04CtEGgRxerSVFPVkXsm1fXucq+DcYHGOeZNx9qaJksUxr0P\n/O+xgboz1CawM8q41/N+K+Nj5nQDbPKaATwh6tfXlhBiaSJR4qXJ3U3oXmd9b1yQ6piJPjmruJdq\nkg1rxb1xhiBQmWjTOMkjo8j6wPNZxf7k4uEIwNGioXIeraEIoFM43UiZd/JoK2j7z3HWtGyFjGkR\nXcBSEctMi1XL0bI38CZm16cOtnsP2c35gbKxLLp32/oebOU8n6fMW0chLh9hs8wDMfNPgEHPdUdB\n8hZ/1fazFXkSbQ9DA8UoslEOtgvqP/VMMsjyAtPWGBfYGV3eUTw5q3iyqNlKEybDaKKz6Dw7A8WD\nvSHee/JEn3PlZ1UdM1ofF2vvPMZ6Xiyqc/rkVqpogQFQDKCu4vVOVLxXrQsgYqlmlEWRtEme8HB3\nyB881mhhEVoTrCVNNVvDDC8F93cGJEmKMR1eCoL1l0gJcRREIKRkoAXjPOmZOa/WjJIEgojBf1yk\nnCxbpBX4LLxVwE6UYFk7bIjsHikVde0IWx4pwkvucq/Dom7RAqxWJCgMoEX8+XeJ94H/e4RYmw84\nZzgzgpNloMg1qQrcNHRpCY1x55mjdzEDShPZl0ACTRfYGr1chlj3Bs4qg/eeRRO59sY6bk9yIPSy\nElAMUgTRa3RnkvBsVpFKSRDQtZ5la7m3oUe/agzbw4y2NWD7ss6V5memIS80i5XFOTCt43FTMcxT\n7g5SyjZ+pq9O59T9s7/5+JwuQSIpNoa1llV3PifwtkgTzWiQYnxHZy6vlFfXTdf/m3dxURhYmJub\n/9WL7831U86Qp5pUCb45WXFne8CsNljnGQ0H7BSaL15c1h86XrYMtaY0jnljOCtb9gYpLvjIdEmi\nTv2zeU2qJLPSsKwsUknaLhq2iOBorT/vAR2tWvYHMDMx6OsEhgqezSqGaRpNcirD8bJhkCrujQcM\nM4V1gR8eTDhrYiBPtWA71xyVDYWWWDTWe/I0ReM5Le1LDlZSVpStobMBYx2JVqRavCRmd/F+wbLq\nOF41tDbSjZ20FJl6q4DtvGdQCLyPu4VipJAysDfMbiTrvIl568j6iWNjA4VWpCr+/LvDRxZ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b3GTLSZXDWWQKx17wxSUhm/HyUke6OU8SC9pDM/yqKWznr9VEJgvWBrnLM/ztkax4Z7KgSIaAL+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\nB4gpiakLpeoyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f645ec0fbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(preds, y_valid, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50371522136882341"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(preds, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8 ms, sys: 0 ns, total: 8 ms\n",
      "Wall time: 8.48 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f645dee8860>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pSo1BYHUgUgIp4H4vbeWxNed0c/PeQD+N8b5djU2rpl3Zeo8UkkgJTucNjWk3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\nWcn8uU8yencnpb5lP7fZtd9W8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UlZdXz8YsYffHzEedXSaTguW3qRIg8lf/ZkzH/w/Udb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\nazrkDIUw8EFAq+tR0HUcP2JkRYjfaNGk0faQ6UJoEEva3R7DaZueH1G0t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SkXpZRLwJeA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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f645df531d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=1, min_samples_leaf=5, bootstrap=False)\n",
    "%time m.fit(x_samp, y_samp)\n",
    "preds = m.predict(X_valid[cols].values)\n",
    "plt.scatter(preds, y_valid, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47541053100694797"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(preds, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Putting it together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TreeEnsemble():\n",
    "    def __init__(self, x, y, n_trees, sample_sz, min_leaf=5):\n",
    "        np.random.seed(42)\n",
    "        self.x,self.y,self.sample_sz,self.min_leaf = x,y,sample_sz,min_leaf\n",
    "        self.trees = [self.create_tree() for i in range(n_trees)]\n",
    "\n",
    "    def create_tree(self):\n",
    "        idxs = np.random.permutation(len(self.y))[:self.sample_sz]\n",
    "        return DecisionTree(self.x.iloc[idxs], self.y[idxs], \n",
    "                    idxs=np.array(range(self.sample_sz)), min_leaf=self.min_leaf)\n",
    "        \n",
    "    def predict(self, x):\n",
    "        return np.mean([t.predict(x) for t in self.trees], axis=0)\n",
    "\n",
    "def std_agg(cnt, s1, s2): return math.sqrt((s2/cnt) - (s1/cnt)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecisionTree():\n",
    "    def __init__(self, x, y, idxs, min_leaf=5):\n",
    "        self.x,self.y,self.idxs,self.min_leaf = x,y,idxs,min_leaf\n",
    "        self.n,self.c = len(idxs), x.shape[1]\n",
    "        self.val = np.mean(y[idxs])\n",
    "        self.score = float('inf')\n",
    "        self.find_varsplit()\n",
    "        \n",
    "    def find_varsplit(self):\n",
    "        for i in range(self.c): self.find_better_split(i)\n",
    "        if self.score == float('inf'): return\n",
    "        x = self.split_col\n",
    "        lhs = np.nonzero(x<=self.split)[0]\n",
    "        rhs = np.nonzero(x>self.split)[0]\n",
    "        self.lhs = DecisionTree(self.x, self.y, self.idxs[lhs])\n",
    "        self.rhs = DecisionTree(self.x, self.y, self.idxs[rhs])\n",
    "\n",
    "    def find_better_split(self, var_idx):\n",
    "        x,y = self.x.values[self.idxs,var_idx], self.y[self.idxs]\n",
    "        sort_idx = np.argsort(x)\n",
    "        sort_y,sort_x = y[sort_idx], x[sort_idx]\n",
    "        rhs_cnt,rhs_sum,rhs_sum2 = self.n, sort_y.sum(), (sort_y**2).sum()\n",
    "        lhs_cnt,lhs_sum,lhs_sum2 = 0,0.,0.\n",
    "\n",
    "        for i in range(0,self.n-self.min_leaf-1):\n",
    "            xi,yi = sort_x[i],sort_y[i]\n",
    "            lhs_cnt += 1; rhs_cnt -= 1\n",
    "            lhs_sum += yi; rhs_sum -= yi\n",
    "            lhs_sum2 += yi**2; rhs_sum2 -= yi**2\n",
    "            if i<self.min_leaf or xi==sort_x[i+1]:\n",
    "                continue\n",
    "\n",
    "            lhs_std = std_agg(lhs_cnt, lhs_sum, lhs_sum2)\n",
    "            rhs_std = std_agg(rhs_cnt, rhs_sum, rhs_sum2)\n",
    "            curr_score = lhs_std*lhs_cnt + rhs_std*rhs_cnt\n",
    "            if curr_score<self.score: \n",
    "                self.var_idx,self.score,self.split = var_idx,curr_score,xi\n",
    "\n",
    "    @property\n",
    "    def split_name(self): return self.x.columns[self.var_idx]\n",
    "    \n",
    "    @property\n",
    "    def split_col(self): return self.x.values[self.idxs,self.var_idx]\n",
    "\n",
    "    @property\n",
    "    def is_leaf(self): return self.score == float('inf')\n",
    "    \n",
    "    def __repr__(self):\n",
    "        s = f'n: {self.n}; val:{self.val}'\n",
    "        if not self.is_leaf:\n",
    "            s += f'; score:{self.score}; split:{self.split}; var:{self.split_name}'\n",
    "        return s\n",
    "\n",
    "    def predict(self, x):\n",
    "        return np.array([self.predict_row(xi) for xi in x])\n",
    "\n",
    "    def predict_row(self, xi):\n",
    "        if self.is_leaf: return self.val\n",
    "        t = self.lhs if xi[self.var_idx]<=self.split else self.rhs\n",
    "        return t.predict_row(xi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ens = TreeEnsemble(X_train[cols], y_train, 5, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = ens.predict(X_valid[cols].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kpTx7/+M06rrQ6HgWw1lKJSW745imY2Jpgh/fG5OWJXuTmIt9nziX+LaOZQg0TdD0a/97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UAAAg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\nFqxM/wYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f645df0c518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_valid, preds, alpha=0.1, s=6);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.71011741571071241"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_valid, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fib1(n):\n",
    "    a, b = 0, 1\n",
    "    while b < n:\n",
    "        a, b = b, a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "def fib2(n):\n",
    "    a, b = 0, 1\n",
    "    while b < n:\n",
    "        a, b = b, a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "def fib3(int n):\n",
    "    cdef int b = 1\n",
    "    cdef int a = 0\n",
    "    cdef int t = 0\n",
    "    while b < n:\n",
    "        t = a\n",
    "        a = b\n",
    "        b = t + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "698 ns ± 10.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit fib1(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "291 ns ± 13.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit fib2(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49 ns ± 1.1 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit fib3(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}