From dfbced75b0fa7fe2aff465f1df92a65a337c5883 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Thu, 16 Nov 2023 16:50:25 +0100 Subject: [PATCH] update --- doc/pub/week46/html/._week46-bs001.html | 2 +- doc/pub/week46/html/week46-reveal.html | 2 +- doc/pub/week46/html/week46-solarized.html | 2 +- doc/pub/week46/html/week46.html | 2 +- doc/pub/week46/ipynb/ipynb-week46-src.tar.gz | Bin 294343 -> 294343 bytes doc/pub/week46/ipynb/week46.ipynb | 1799 +++++++----------- doc/src/week46/week46.do.txt | 2 +- 7 files changed, 719 insertions(+), 1090 deletions(-) diff --git a/doc/pub/week46/html/._week46-bs001.html b/doc/pub/week46/html/._week46-bs001.html index b361e0d1e..c964080f4 100644 --- a/doc/pub/week46/html/._week46-bs001.html +++ b/doc/pub/week46/html/._week46-bs001.html @@ -340,7 +340,7 @@ MathJax.Hub.Config({
  • Readings and Videos:
  • diff --git a/doc/pub/week46/html/week46-reveal.html b/doc/pub/week46/html/week46-reveal.html index ac658a1d3..907dae562 100644 --- a/doc/pub/week46/html/week46-reveal.html +++ b/doc/pub/week46/html/week46-reveal.html @@ -221,7 +221,7 @@ MathJax.Hub.Config({

  • These lecture notes
  • -

  • Video of lecture to be added
  • +

  • Video of lecture to be added
  • Video on Decision trees
  • diff --git a/doc/pub/week46/html/week46-solarized.html b/doc/pub/week46/html/week46-solarized.html index cf8b82013..fc1bc203c 100644 --- a/doc/pub/week46/html/week46-solarized.html +++ b/doc/pub/week46/html/week46-solarized.html @@ -301,7 +301,7 @@ MathJax.Hub.Config({
  • Readings and Videos:
  • diff --git a/doc/pub/week46/html/week46.html b/doc/pub/week46/html/week46.html index a2d5c9cbc..15d8ef0ce 100644 --- a/doc/pub/week46/html/week46.html +++ b/doc/pub/week46/html/week46.html @@ -378,7 +378,7 @@ MathJax.Hub.Config({
  • Readings and Videos:
  • diff --git a/doc/pub/week46/ipynb/ipynb-week46-src.tar.gz b/doc/pub/week46/ipynb/ipynb-week46-src.tar.gz index 952a9b04f4be2e0fac81b227465c90856dbd3ca7..87126544bb16b702bdec53840abab86ef72e547b 100644 GIT binary patch delta 30 lcmX^9Tk!aAL3a6W4hB=JutxS)cE(nArdD?5t?VoZY5}A12~+?8 delta 30 mcmX^9Tk!aAL3a6W4u-8OLmSy!*%@2enOfPIx3aSws09GEE($^b diff --git a/doc/pub/week46/ipynb/week46.ipynb b/doc/pub/week46/ipynb/week46.ipynb index aae32ab00..149534914 100644 --- a/doc/pub/week46/ipynb/week46.ipynb +++ b/doc/pub/week46/ipynb/week46.ipynb @@ -2,8 +2,10 @@ "cells": [ { "cell_type": "markdown", - "id": "f4d3b2c9", - "metadata": {}, + "id": "a1dfe9cd", + "metadata": { + "editable": true + }, "source": [ "\n", @@ -12,8 +14,10 @@ }, { "cell_type": "markdown", - "id": "57cda95f", - "metadata": {}, + "id": "89bffd51", + "metadata": { + "editable": true + }, "source": [ "# Week 46: Decision Trees, Ensemble methods and Random Forests\n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", @@ -23,8 +27,10 @@ }, { "cell_type": "markdown", - "id": "0ab525ed", - "metadata": {}, + "id": "1387fd7d", + "metadata": { + "editable": true + }, "source": [ "## Plan for week 46\n", "\n", @@ -44,7 +50,7 @@ "\n", " * These lecture notes\n", "\n", - " * [Video of lecture to be added](https://youtu.be/)\n", + " * [Video of lecture to be added](https://youtu.be/PMswUwhYa7k)\n", "\n", " * [Video on Decision trees](https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn)\n", "\n", @@ -53,8 +59,10 @@ }, { "cell_type": "markdown", - "id": "3c8e0d42", - "metadata": {}, + "id": "95dd37cd", + "metadata": { + "editable": true + }, "source": [ "## Decision trees, overarching aims\n", "\n", @@ -82,8 +90,10 @@ }, { "cell_type": "markdown", - "id": "893c9b6f", - "metadata": {}, + "id": "448f377f", + "metadata": { + "editable": true + }, "source": [ "## Basics of a tree\n", "\n", @@ -100,8 +110,10 @@ }, { "cell_type": "markdown", - "id": "89b0fc63", - "metadata": {}, + "id": "dbd1f3a4", + "metadata": { + "editable": true + }, "source": [ "## A typical Decision Tree with its pertinent Jargon, Classification Problem\n", "\n", @@ -116,8 +128,10 @@ }, { "cell_type": "markdown", - "id": "d4354730", - "metadata": {}, + "id": "292178fc", + "metadata": { + "editable": true + }, "source": [ "## General Features\n", "\n", @@ -137,8 +151,10 @@ }, { "cell_type": "markdown", - "id": "9db7330a", - "metadata": {}, + "id": "a38f83c8", + "metadata": { + "editable": true + }, "source": [ "## How do we set it up?\n", "\n", @@ -158,49 +174,23 @@ }, { "cell_type": "markdown", - "id": "331ebf1d", - "metadata": {}, + "id": "c7e64986", + "metadata": { + "editable": true + }, "source": [ "## Decision trees and Regression" ] }, { "cell_type": "code", - "execution_count": 27, - "id": "122986df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2nd degree coefficients:\n", - "zero power: -4.119685192382436\n", - "first power: 0.1310982182719532\n", - "second power: -0.00043901716252344835\n" - ] - }, - { - "data": { - "image/png": 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2mU7/FAgEgpKCUHxyiCRJhIaGEhUVZW5RBIJ8w8LCAl9fX1E5XCAQlHiE4pND9EpPmTJlsLe3F0kOBcUefdLOkJAQKlWqJL7TAoGgRCMUnxyQmpqqKD0eHh7mFkcgyDc0Gg0PHjxAq9VibW1tbnEEAoGgwBBO/Rygj+mxt7c3syQCQf6id3GlpqaaWRKBQCAoWITikwuEK0BQ0hDfaYFAUFoQio9AIBAIBIJSg1B8BAWOj48PixcvNrcY+cKtW7dQqVScPXsWgP3796NSqcQsP4FAICgmCMWnFDBkyBBUKhUqlQorKysqVarE6NGjiYyMNLdoBcbOnTtRqVSEhoYabS9XrhwVK1Y02nbv3j1UKhV///13ochm+HlYW1tTtmxZOnbsyPLly9HpdDnqa+XKlbi6uhaMoAKBQFACEYpPKaFLly6EhIRw69Ytli1bxtatWxkzZoy5xSow2rRpg5WVFfv371e2BQcHk5iYSExMDNeuXVO2BwYGYm1tTevWrQtNPsPPY8eOHQQEBDBu3Di6d++OVqstNDkEgqKMTtKRqE1U1mOSYrgddZtHcY/MKJWguCMUn1KCra0t5cqVo0KFCnTq1Ik+ffoYWThSU1MZPnw4vr6+qNVqatasyZIlS4z6GDJkCD179uTzzz/Hy8sLDw8Pxo4da5TB+tGjR/To0QO1Wo2vry9r1qxJJ8udO3d4+eWXcXR0xNnZmd69e/Pw4UNl/8yZM2nQoAHLly+nUqVKODo6Mnr0aFJTU1mwYAHlypWjTJkyfPrppxmer6OjI02bNjVSfPbv30+bNm1o06ZNuu3NmjXDwcGBnTt30qZNG1xdXfHw8KB79+5cv34929c5ISGBF198kRYtWvD48eMM2+k/j/Lly9OoUSM++OAD/vzzT3bs2MHKlSuVdosWLeK5557DwcGBihUrMmbMGGJjYxW5hw4dSnR0tGJBmjlzJgC//PILTZo0wcnJiXLlytG/f38ePRI3C0HxossvXaj0RSVikmK4HH6ZMp+VwWeJD2U/L8vyM8vNLZ6gmCLy+OQBSZKIT4k3y9j21rlPnnjjxg127txplK9Fp9NRoUIFNmzYgKenJ0eOHGHkyJF4eXnRu3dvpV1gYCBeXl4EBgZy7do1+vTpQ4MGDRgxYgQgK0d3795l37592NjY8M477xjdcCVJomfPnjg4OHDgwAG0Wi1jxoyhT58+RsrI9evX2bFjBzt37uT69eu89tpr3Lx5kxo1anDgwAGOHDnCsGHD6NChAy1atDB5ngEBAWzcuNFI9nbt2qHT6QgMDOTNN99Utg8YMACAuLg4Jk6cyHPPPUdcXBwzZszglVde4ezZs1mWdIiOjqZ79+7Y2dmxd+9eHBwcsvmJyLRv35769evz+++/K7JZWFjw5Zdf4uPjw82bNxkzZgyTJ0/mm2++oVWrVixevJgZM2Zw+fJlQFb4AJKTk5k9ezY1a9bk0aNHTJgwgSFDhrB9+/YcySQQmAtJkth/az8puhSCw4K5HHGZpNQkZf/B2wcZ1nCYGSUUFFeE4pMH4lPicZzraJaxY6fG4mCT/Rvrtm3bcHR0JDU1lcRE2XS8aNEiZb+1tTWzZs1S1n19fTly5AgbNmwwUnzc3Nz4+uuvsbS0pFatWrz44ovs3buXESNGcOXKFXbs2MGxY8do3rw5AD/99BO1a9dWjt+zZw/nz5/n5s2bSqzNzz//TJ06dThx4gRNmzYFZEVs+fLlODk54efnR0BAAJcvX2b79u1YWFhQs2ZN5s+fz/79+zNUfNq1a8ecOXMICQnBy8uLAwcO8N5776HT6RRr1t27d7l58yYBAQEA9OrVy6iPn376iTJlyhAUFETdunUzvL4PHz6kT58+VK1alXXr1uW69EOtWrU4f/68sj5+/Hjlva+vL7Nnz2b06NF888032NjY4OLigkqloly5ckb9DBv27IZQpUoVvvzyS5o1a0ZsbKyiHAkERZknyU9I0cnW5PD4cMLjw432p10XCLKLcHWVEgICAjh79iz//vsvb7/9Np07d+btt982avPdd9/RpEkTNBoNjo6O/Pjjj9y5c8eoTZ06dbC0tFTWvby8FItOcHAwVlZWNGnSRNlfq1Yto+Db4OBgKlasaBRg7Ofnh6urK8HBwco2Hx8fnJyclPWyZcvi5+dnZHUpW7Zspu6b1q1bY2Njw/79+wkKCiIhIYFGjRrRuHFjYmJiuHr1KoGBgdja2tKqVStAtjT179+fKlWq4OzsjK+vL0C665CWF154gSpVqrBhw4Y81buSJMnIkhcYGEjHjh0pX748Tk5ODBo0iIiICOLi4jLt58yZM7z88stUrlwZJycn2rVrl63zEAiKCmFxYc/ex4cp676uvso2gSA3CItPHrC3tid2aqzZxs4JDg4OVKtWDYAvv/ySgIAAZs2axezZswHYsGEDEyZMYOHChbRs2RInJyc+++wz/v33X6N+0pYzUKlUykwkSZKUbRmR9sae0XZT42Q2tins7e1p1qwZgYGBPH78mDZt2ihKW6tWrQgMDOTo0aO0bNkSOzs7AHr06EHFihX58ccf8fb2RqfTUbduXZKTkzMcB+DFF19k06ZNBAUF8dxzz2XaNjOCg4MVZev27dt069aNUaNGMXv2bNzd3Tl06BDDhw83iqtKS1xcHJ06daJTp0788ssvaDQa7ty5Q+fOnbM8D4GgqGCo2ITFhSnrfho/bkbdNFKMBIKcIBSfPKBSqXLkbipKfPTRR3Tt2pXRo0fj7e3NP//8Q6tWrYxmeuUkqBegdu3aaLVaTp48SbNmzQC4fPmyUY4bPz8/7ty5w927dxWrT1BQENHR0UYusfwiICCA9evXExkZqVg9APz9/dm/fz9Hjx5l6NChAERERBAcHMz333/P888/D8ChQ4eyNc68efNwdHSkQ4cO7N+/Hz8/vxzLum/fPi5cuMCECRMAOHnyJFqtloULFyqWrg0bNhgdY2Njk67MxKVLlwgPD2fevHnKNT558mSO5REIzEk6i89Txae2Z23+uvqXsPgIco1wdZVS2rVrR506dZgzZw4A1apV4+TJk+zatYsrV67w4YcfcuLEiRz1WbNmTbp06cKIESP4999/OXXqFG+++SZqtVpp88ILL1CvXj0GDBjA6dOnOX78OIMGDcLf39/IRZZfBAQEcPXqVXbu3Im/v7+y3d/fn23btnHr1i0lvsfNzQ0PDw9++OEHrl27xr59+5g4cWK2x/r8888ZMGAA7du359KlS5m2TUpKIjQ0lPv373P69GnmzJnDyy+/TPfu3Rk0aBAAVatWRavV8tVXX3Hjxg1+/vlnvvvuO6N+fHx8iI2NZe/evYSHhxMfH0+lSpWwsbFRjtuyZYti2RMIigtGFh8DV1dtjfyAFJscazTVXSDILkLxKcVMnDiRH3/8kbt37zJq1CheffVV+vTpQ/PmzYmIiMhVnp8VK1ZQsWJF/P39efXVVxk5ciRlypRR9qtUKjZv3oybmxtt27ZVYmN+/fXX/Dw1hZYtW2JrawtA48aNle1NmzYlNTUVtVqtBGJbWFiwfv16Tp06Rd26dZkwYQKfffZZjsb74osv6N27N+3bt+fKlSsZttu5cydeXl74+PjQpUsXAgMD+fLLL/nzzz8Vd1yDBg1YtGgR8+fPp27duqxZs4a5c+ca9dOqVStGjRpFnz590Gg0LFiwAI1Gw8qVK/ntt9/w8/Nj3rx5fP755zk6D4HA3BgGLxsGN1dzr4aVhVW6NgJBdlFJ+sAMAQAxMTG4uLgQHR2Ns7Oz0b7ExERu3ryJr6+vEhMiEJQExHdbUNR47+/3+PyorLA3L9+cyxGXiUqMInhsMAGrAgiNDeX0yNM09GpoZkkFRYXM7t+GCIuPQCAQCIochq6uB08eEJUYBYDGXoPGXpOujUCQXYTiIxAIBIIih6FSczfmLgAWKgvc1G5oHJ4qPmJmlyAXCMVHIBAIBEUOU0qNh9oDC5UFnvaegIjxEeQOofgIBAKBoMhhSqnRW3qEq0uQF4TiIxAIBIIihymlRq/wKIqPcHUJcoFQfAQCgUBQpEjUJhKbLGfFr+BcQdmuWHwchMVHkHuE4iMQCASCIoXekmNlYUVVt6rKdk+1HNsjYnwEeUEoPgKBQCAoUugVGk97T8o4PEuAKmJ8BPlBkVd8njx5wuTJk+nUqRMajQaVSsXMmTNNtj19+jQvvPACjo6OuLq68uqrr3Ljxo3CFVggEAgEeUKv0Bjm7NGvA2I6uyBPFHnFJyIigh9++IGkpCR69uyZYbtLly7Rrl07kpOT2bBhA8uXL+fKlSs8//zzhIWJH0dBcOvWLVQqFWfPnjW3KEUOHx8fFi9ebG4xBIJiiV6h0ThoFCVHvw7PFKDHCY9J1aWm70AgyIQir/hUrlyZyMhIDhw4kK5OkSEzZszA1taWbdu20a1bN1599VX++usvwsLCSn2dorlz59K0aVOcnJwoU6YMPXv25PLly4Uydrt27VCpVKhUKmxtbSlfvjw9evTg999/L5TxCxLDczNctFotJ06cYOTIkUpbfY0ygUCQNVlZfDzsPQCQkIhIiCh8AQXFmiKv+OhvJpmh1WrZtm0bvXr1MqrPUblyZQICAvjjjz8KWswizYEDBxg7dizHjh1j9+7daLVaOnXqRFxcXKGMP2LECEJCQrh27RqbNm3Cz8+Pvn37GikGBUVycnKB9q8/N8PFysoKjUaDvb19gY4tEJRU9BYfT3tPJZBZvw5y0LObnRsgApwFOafIKz7Z4fr16yQkJFCvXr10++rVq8e1a9dITEw0g2RFg507dzJkyBDq1KlD/fr1WbFiBXfu3OHUqVNKGx8fH+bMmcOwYcNwcnKiUqVK/PDDD0b9HD9+nIYNG2JnZ0eTJk04c+ZMtsa3t7enXLlyVKxYkRYtWjB//ny+//57fvzxR/bs2aO0u3//Pn369MHNzQ0PDw9efvllbt26pezXarW88847uLq64uHhwZQpUxg8eLCRC7Rdu3a89dZbTJw4EU9PTzp27AhAUFAQ3bp1w9HRkbJly/LGG28QHv7sD1OSJBYsWECVKlVQq9XUr1+fjRs3ZvvcDBf99dS7unx8fAB45ZVXUKlUyrpAIDCNXpnR2Jt2dRm+F3E+gpxSIhSfiAjZ1Onu7p5un7u7O5IkERkZafLYpKQkYmJijJZsI0kQF2eeRZJyda0AoqOjlWtjyMKFCxWFZsyYMYwePZpLly4BEBcXR/fu3alZsyanTp1i5syZTJo0KdcyDB48GDc3N8XlFR8fT0BAAI6Ojhw8eJBDhw7h6OhIly5dFKvN/PnzWbNmDStWrODw4cPExMSYdB+tWrUKKysrDh8+zPfff09ISAj+/v40aNCAkydPsnPnTh4+fEjv3r2VY6ZPn86KFSv49ttv+e+//5gwYQIDBw7kwIEDuT5HPSdOnABgxYoVhISEKOsCQWngfsx9mv3YjJVnVxptf+/v9/Be6G1yWX1+NfA0xsfA1WVo/REzuwS5xcrcAuQnmbnEMto3d+5cZs2albsB4+PB0TF3x+aV2FhwcMjxYZIkMXHiRNq0aUPdunWN9nXr1o0xY8YAMGXKFL744gv2799PrVq1WLNmDampqSxfvhx7e3vq1KnDvXv3GD16dK7Et7CwoEaNGopFZ/369VhYWLBs2TLls1qxYgWurq7s37+fTp068dVXXzF16lReeeUVAL7++mu2b9+eru9q1aqxYMECZX3GjBk0atSIOXPmKNuWL19OxYoVuXLlCuXLl2fRokXs27ePli1bAlClShUOHTrE999/j7+/f4bn8c0337Bs2TJl/X//+x8LFy40aqPRyH/Qrq6uikVIICgt7Ly2kxMPTrDs9DKGNBgCyP9DXx3/iqTUpAyPU6GisVdjqrhVwUPtQQXnCthY2ij7hcVHkFtKhOLj4SEHuuktP4Y8fvwYlUqFq6uryWOnTp3KxIkTlfWYmBgqVqxYIHIWBd566y3Onz/PoUOH0u0zdBWqVCrKlSvHo0ePAAgODqZ+/fpGcSt6JSG3SJKkKDmnTp3i2rVrODk5GbVJTEzk+vXrREdH8/DhQ5o1a6bss7S0pHHjxuh0OqNjmjRpYrR+6tQpAgMDcTShpOr7TkxMVNxiepKTk2nYsGGm5zBgwACmTZumrGf0PRMISit6i4yhZeZJ8hNF6Tk2/Bi2VrbpjtPYayjvXB6A6+9cT9dGb/ERMT6CnFIiFJ+qVauiVqu5cOFCun0XLlygWrVq2NnZmTzW1tYWW9v0P7psYW8vW17MQS4CZ99++222bNnCwYMHqVChQrr91tbWRusqlUpRKqQ8uNZMkZqaytWrV2natCkAOp2Oxo0bs2bNmnRt9RYTvUyGmJLLIY0lTKfT0aNHD+bPn5+urZeXFxcvXgTgr7/+onz58kb7s/puuLi4UK1atUzbCASlGb1FxtAyo39vb21P8wrNs+zDxc4l3Ta920u4ugQ5pUQoPlZWVsoU6QULFihWgzt37hAYGMiECRMKZmCVKlfupsJGkiTefvtt/vjjD/bv34+vr2+O+/Dz8+Pnn38mISEBtVoNwLFjx3It06pVq4iMjKRXr14ANGrUiF9//ZUyZcoYzcwzpGzZshw/fpznn38ekJWnM2fO0KBBg0zHatSoEZs2bcLHxwcrq/RfeT8/P2xtbblz506mbq28YG1tTWqqyDciKH2EJ8gWmcjESLQ6LVYWVkbBy7lFxPgIckuxCG7esWMHGzduZOvWrYA8Q2fjxo1s3LiR+Ph4AGbNmkV8fDzdu3dnx44d/PHHH7z44ot4enry7rvvmlN8szN27Fh++eUX1q5di5OTE6GhoYSGhpKQkJDtPvr374+FhQXDhw8nKCiI7du3Zzs/Unx8PKGhody7d49///2XKVOmMGrUKEaPHk1AQAAgu4w8PT15+eWX+eeff7h58yYHDhxg3Lhx3Lt3D5AtVnPnzuXPP//k8uXLjBs3jsjIyCzTHYwdO5bHjx/Tr18/jh8/zo0bN/j7778ZNmwYqampODk5MWnSJCZMmMCqVau4fv06Z86cYenSpaxatSrb1ygzfHx82Lt3L6GhoRkG2gsEJQ2dTsfde3fhHvAATl44SWpq6rM8PQ55UHxEjI8gt0jFgMqVK0uAyeXmzZtKu5MnT0odOnSQ7O3tJWdnZ6lnz57StWvXcjRWdHS0BEjR0dHp9iUkJEhBQUFSQkJCXk+pUMno2q1YsUJpU7lyZemLL74wOq5+/frSRx99pKwfPXpUql+/vmRjYyM1aNBA2rRpkwRIZ86cyXBsf39/ZTwbGxvJy8tL6t69u/T777+naxsSEiINGjRI8vT0lGxtbaUqVapII0aMUD6LlJQU6a233pKcnZ0lNzc3acqUKdLrr78u9e3b12i8cePGpev7ypUr0iuvvCK5urpKarVaqlWrljR+/HhJp9NJkiRJOp1OWrJkiVSzZk3J2tpa0mg0UufOnaUDBw5kem6mxpKk9Ndzy5YtUrVq1SQrKyupcuXKGfZpLorrd1tQ9EhJSZE2bNgg9e7dW3J1dU33v+Pk5CTVbVlX4lWkTss75XqcnVd3SsxEqvdtvXyUXlCcyez+bYhKkvI5eKOYExMTg4uLC9HR0elcLomJidy8eRNfX98MY4YEhYdOp6N27dr07t2b2bNnm1ucYo34bgvySmpqKsuWLWPOnDncuXPn2Q4V8HTOgm2yLUmJz2Zy2bna8fXnXzN06FAsLHLmgDgdcprGPzTG28mb+xPv58MZCIo7md2/DSkWri6BAOD27dv8+OOPXLlyhQsXLjB69Ghu3rxJ//79zS2aQFCqOXfuHM2bN2fUqFHcuXMHjUbD+++/z5EjR1DPUsNEYCKs/HclZ8+epdUbrcAZEqMSefPNN2nTpo2xspQNlODmuLB8n3whKNkIxUdQbLCwsGDlypU0bdqU1q1bc+HCBfbs2UPt2rXNLZpAUCqRJInvvvuO5s2bc+rUKZydnVm8eDF37txh7ty51G9SnwTds1jCx0mPqV+/PjV61YB3oNvYbjg7O3P06FEaNWpklMk9K/TBzSm6FGKScpB4VlDqEYqPoNhQsWJFDh8+THR0NDExMRw5coS2bduaWyyBoFSi1WqVDO9JSUm8+OKLyqQDvbs0bY4do6ntVvDqsFc5e/YsjRo1IiIigm7dumW7tqLaWo2DtTyrVszsEuQEofgIBAKBIEckJibSq1cvvvvuO1QqFfPnz2fLli3pMpOnnXGVNpmhxkGDr68vhw8f5rXXXiMlJYXXX389W3Xy9MeDSGIoyBlC8REIBAJBtklKSqJXr15s2bIFOzs7Nm7cyOTJk00GJ6e1xCiKj0H1dQA7OzvWrVvH4MGDSU1NZcCAAfzzzz9ZymIY5yMQZBeh+AgEAoEgW2i1Wnr37s327dtRq9Vs376dV199NcP2aS0x+nVTCQytrKz46aef6NmzJ8nJybz88stcvXo1U3lEEkNBbhCKj0AgEAiyRHqaAX7Lli3Y2tqydetWJQFpRugtMR5qD2U9SZvEk+QnQPoEhpaWlqxZs4YWLVoQGRnJ66+/nmmiVZHEUJAbhOIjEAgEgiz57LPPlJiedevW0aFDhyyP0Vti/DR+yrp+m7WFNS626Wtw2dvbs2nTJjQaDefOnWP8+PEZ9i8KlQpyg1B8BAKBQJApu3bt4v333wfgiy++4JVXXsnWcXpLTG1POeVEeHw4j+IeAXJ8TkblZry9vfnll19QqVT88MMPbNmyxWQ74eoS5Aah+AjyxMyZM7MsEiqQWblyJa6uruYWQyDIETdu3KBfv35IksSbb77JuHHjsn2sXiGp5VkLAK1Oy7XH14BngckZ0alTJyZNmgTAqFGjTNa4ExXaBblBKD6lgIMHD9KjRw+8vb1RqVRs3rw5XRtJkpg5cybe3t6o1WratWvHf//9Z9Qmo2Nzyv79+1GpVKhUKiwsLHBxcaFhw4ZMnjyZkJCQPPdvTgzPzXCZPn06ffr04cqVK0pboTQKijrJycn06dOHyMhImjVrxtdff52j4/UuqIouFXG0cQQgOCwYyF6B0lmzZlGjRg1CQkJMFpsWMT6C3CAUn1JAXFwc9evXz/RPa8GCBSxatIivv/6aEydOUK5cOTp27MiTJ08KTK7Lly/z4MEDTpw4wZQpU9izZw9169blwoULBTYmyEqeVqst0DEuX75MSEiIsrz//vuo1WrKlClToOMKBPnJtGnTOHnyJG5ubmzcuBFbW9scHa/k67HXKG6p4PBgZVtWqNVqVqxYgUqlYsWKFRw+fNhov3B1CXKDUHxKAV27duWTTz7JcNqpJEksXryYadOm8eqrr1K3bl1WrVpFfHw8a9euBcDHxweAV155BZVKpazr+fnnn/Hx8cHFxYW+fftmS2EqU6YM5cqVo0aNGvTt25fDhw+j0WgYPXq0UbsVK1ZQu3Zt7OzsqFWrFt98843R/iNHjtCgQQPs7Oxo0qQJmzdvRqVScfbsWeCZFWbXrl00adIEW1tb/vnnHyRJYsGCBVSpUgW1Wk39+vXTJU4LCgqiW7duODo6UrZsWd544w3Cw7MOpNSfm35xdHQ0cnWtXLmSWbNmce7cOcUqtHLlyiz7FQgKiz179vD5558DsHz5cipWrJjjPvSWGI2DRrHOBIUFyduyofgAtGrViuHDhwPwzjvvkJqaquwTCQwFuUEoPnlAkiTi4uLMsuRnUb6bN28SGhpKp06dlG22trb4+/tz5MgRAE6cOAHISkhISIiyDnD9+nU2b97Mtm3b2LZtGwcOHGDevHk5lkOtVjNq1CgOHz7Mo0dyAOSPP/7ItGnT+PTTTwkODmbOnDl8+OGHrFq1CoAnT57Qo0cPnnvuOU6fPs3s2bOZMmWKyf4nT57M3LlzCQ4Opl69ekyfPp0VK1bw7bff8t9//zFhwgQGDhzIgQMHAAgJCcHf358GDRpw8uRJdu7cycOHD+ndu3eOzy0tffr04d1336VOnTqKVahPnz557lcgyA+ePHnCm2++CcDo0aPp2bNnjvtISU0hMlGOyzG0+CiKTzZcXXo+/fRTXFxcOH36ND/99JOyXd9nbHIsidrEHMsoKJ1YmVuA4kx8fDyOjo5mGTs2NhYHB4d86Ss0NBSAsmXLGm0vW7Yst2/fBkCjkf9gXF1d06Wl1+l0rFy5EicnJwDeeOMN9u7dy6effppjWWrVkoMgb926RZkyZZg9ezYLFy5UrFW+vr4EBQXx/fffM3jwYNasWYNKpeLHH3/Ezs4OPz8/7t+/z4gRI9L1/fHHH9OxY0dAdv8tWrSIffv20bJlSwCqVKnCoUOH+P777/H39+fbb7+lUaNGzJkzR+lD/+R75coVatSokeF5VKhQwWhdfx31qNVqHB0dsbKySnc9BYL84OKji3x38jumt51OOUf5O6aTdEzbO40rj69gY2nDpJaTaOzdmD039rDj6g7mvjAXG0sbpk6dyu3bt/Hx8WHBggUZjhGTFMP0fdPp/1x/WlRowYFbB1h6YimpUirJqckAqFDhrnZXApFTJdlik1VwsyFlypRh1qxZjB8/nhkzZjBgwAAcHBxwtnXG2sKaFF0KYXFhVHSRrVL3Yu4xI3AG0UnReKo9md9xPq52rrm5jIISiFB8BAppp5ZKkpThdFNDfHx8FKUHwMvLS7HY5BS9JUulUhEWFsbdu3cZPny4kSKj1WpxcZHzf1y+fJl69eopRREBmjVrZrLvJk2aKO+DgoJITExUFCE9ycnJNGzYEIBTp04RGBhoUrm9fv16porPP//8Y3RN3NzcMmwrEBQEC48uZOXZlVRyqcTk1pMBOPXgFPMOP7PGJqcms6n3Jt7f8z6nQk7RsWpH7B/Ys3TpUgCWLVuW6cPd5kub+er4V1x7fI3tA7YzPXA6h+4cMmrj6+aLpYUl1d2rG22v5l4tR+czevRovvzyS27cuMGSJUv44IMPUKlUeNp7EhIbQlj8M8Vn+ZnlrDi7Qjm2afmmvNnozRyNJyi5CMUnD9jb2xMbG2u2sfMLvcUhNDQULy8vZfujR4/SWYFMYW1tbbSuUqnQ6XS5kiU4WA589PHxUfr48ccfad68uVE7S0tLwLRylpEb0NBCpu/7r7/+onz58kbt9AGcOp2OHj16MH/+/HR9GV4nU/j6+oqp6wKzEvIkxOgVICQ2xHSbp9tvPbrF58PkuJ4RI0ZkmaQw7fH69fdavYevqy8A7X3bAzC+xXgquVQiNjmWco7leKHKCzk6HxsbG2bPns2AAQNYsGABo0aNwt3dHY2DhpDYEKM4H8NzNrUuKN0IxScPqFSqfHM3mRNfX1/KlSvH7t27FWtHcnIyBw4cMLrpW1tbGwUW5jcJCQn88MMPtG3bVnGtlS9fnhs3bjBgwACTx9SqVYs1a9aQlJSkKCwnT57Mciw/Pz9sbW25c+cO/v7+Jts0atSITZs24ePjg5VV/v9UbGxsCvR6Cko3aSuhw7NgYxdbF6KTogmLD0OSJGX72i/Xcv36dSpUqMBnn32W/THijMca3nA4NT1rGrV1sHHgjfpv5Omc+vbty/z58zl//jyfffYZc+fOfTazy2BKu14Ow/MUCPSI4OZSQGxsLGfPnlVmOd28eZOzZ89y584dQFbgxo8fz5w5c/jjjz+4ePEiQ4YMwd7env79+yv9+Pj4sHfvXkJDQ00mE8spjx49IjQ0lKtXr7J+/Xpat25NeHg43377rdJm5syZzJ07lyVLlnDlyhUuXLjAihUrWLRoEQD9+/dHp9MxcuRIgoOD2bVrlzITJTM3nZOTE5MmTWLChAmsWrWK69evc+bMGZYuXaoETo8dO5bHjx/Tr18/jh8/zo0bN/j7778ZNmxYvigsPj4+ymcRHh5OUlJSnvsUCPSkVUYM39fW1FbaxCTFkKJLgVA49Jvspvr+++8Vd3KmYxgoV0naJGKSYoCcBS7nBAsLC2bPng3A0qVLiYyMNJnEMN15CsVHYIBQfEoBJ0+epGHDhoo1Z+LEiTRs2JAZM2YobSZPnsz48eMZM2YMTZo04f79+/z9999GcSoLFy5k9+7dVKxYUekrL9SsWRNvb28aN27MvHnzeOGFF7h48SJ+fn5KmzfffJNly5axcuVKnnvuOfz9/Vm5ciW+vrIZ3dnZma1bt3L27FkaNGjAtGnTlPMyjPsxxezZs5kxYwZz586ldu3adO7cma1btyp9e3t7c/jwYVJTU+ncuTN169Zl3LhxuLi4YGGR959Or1696NKlCwEBAWg0GtatW5fnPgUCPXrXj5ElJE0JieikaB48eQASsB0knUTv3r3p1q1btsbQ95ecmszNqJsAWKosCzSQuHv37tSrV48nT57w1Vdfmbb4pDlPkeBQYIQkMCI6OloCpOjo6HT7EhISpKCgICkhIcEMkgmyyy+//CJZW1tL8fHx5hal2CC+2yWL2KRYiZlIzESquKiisn3QH4MkZiLN/WeuZDHLQmIm0u9Bv0u8igRIlraW0p07d7I9TtMfmirj/B70u8RMpDKflSmIUzJi/fr1EiC5u7tLH2z/QGIm0ogtI5T9mgUaiZlInx3+TGImUv1v6xe4TALzk9n92xAR4yMo9qxevZoqVapQvnx5zp07x5QpU+jduzdqtdrcogkEZiGt20d6OglAb/ko61AWD7UHYfFhnL1zFnbLbb26euUoUaHhODnJyJxXXnvtNWrUqMGVK1e4/PdlcH1m4dJJOiISIgDjqvACgR7h6hIUe0JDQxk4cCC1a9dmwoQJvP766/zwww/mFksgMBuGrp1EbSJxKXHydn0JCYNMyhu/2whPADewbG2Z63Fyk5gwt1haWioFTA/+dhBSn53b44TH6CR51qa+OGpYXFi+Jn0VFG+E4iMo9kyePJlbt26RmJjIzZs3+eKLL/J1ur9AUNxIW8JBCXR++upp7ykHBYdD8FbZUkMXiEiJyPYYCSkJikIFzxSfnCQmzAsDBw7E09OTsPthcCn9ObrYuuDlKKedSNGlKIHXAoFQfAQCgaCEkda1o1eE9K9KCYndIKVKUA2okbPSD2mVq0vhl5S+CwN9iRsAjj07Z+UcHTSordU4WDuYlFdQehGKTy4QJlNBSUN8p0sWaWcxhcWHGVloNA4atDe1cBlQAZ2fvpJ9BSGtcpWgTZD7LiTFB2DMmDFyAtW78PjaY7Q6rVFFeHjmehNxPgI9QvHJAfoMxfHx8WaWRCDIX5KT5bpK+ozYguJN2pt8WFyYss3G0gZHa0dOrn6a6LMRoDFum60xMmhXGDE+ery8vHj99dfllRNyfI9hRXjA5HR3QelGzOrKAZaWlri6uip1qOzt7bNVy0ogKMrodDrCwsKwt7cvkAzVgsLHlMVHUQjsNfzxxx/cD7oP1kA70rXN1hgZtCtMiw/IVp+1a9fCRbh2/1o6i4+pBIeC0o34l8sh+rpWuS3CKRAURSwsLKhUqZJQ5EsI4Qmyu8re2p74lHgji4+HrQdTp06VG7YCnuYoNWybHfTt9MfpKazgZj2tWrXC1tuWpAdJrP1lLZYtLY3kUFxdwuIjeIpQfHKISqXCy8uLMmXKkJKSYm5xBIJ8wcbGJl+yUQuKBoaZi0+FnCI8PlyJ3Uk6nsTVq1dx9XAlqlWUcoxh2+ygb6c/Tk9hurpA/k+u0KEC13++zu8//067+u1kOeyNXV0iuFmgRyg+ucTS0lLEQwgEgiKJ3rrjp/HjVMipZ66uJLj15y0ARr07innJ85RjDNvmZgw9he3qAqjdvjbX118n5FYI185dA0sTMT7C1SV4injEEwgEghJGulpV8U9dXcchKTqJatWqMep/o5T21hbWVHWranRslmPEG4+hp7BdXQDeHt7wtMTfjf03ABHjI8iYEqX4nDlzhp49e+Lt7Y29vT21atXi448/FrOwBAJBqSElNYXopGjAuAr7/bD7cFhuM3PmTLxcvJRjPO09czztW68gVXOvhqVKtn672rlibWmdL+eREzQOGmggv3984jGkGFh8RIyPIA0lRvEJCgqiVatW3Lp1i8WLF7Nt2zb69u3Lxx9/TL9+/cwtnkAgEBQK+lgWC5UFNTxqALIyc/z345AI5XzK0bdvX2wsbXC2dQaeKj45dAnp25VxKKNYVcxh7VHGrQz2GnukRAkuGQQ3C1eXIA0lJsZn7dq1JCYmsmnTJqpWlU227du3JyQkhB9++IHIyEjc3NzMLKVAIBAULMrsLbUHZR3KAhATFUPcNjl5Yb+3+inxiRp7DTFJMUa1u3Ia3Kw/9mHcQ7PE98BT5cYCXJu5Ev9XPJxLn8BQBDcL9JQYi48+uaCLi4vRdldXVywsLLCxsTGHWAKBQFCoGCbwc1O7yW6oY5CakAoaeOnVl5S2hgHAOUn0p9VpeZzwON2xhT2jS49+3OiasouP6xAdHq3IBzkrxyEo2ZQYxWfw4MG4uroyevRobty4wZMnT9i2bRvff/89Y8eOxcHBwdwiCgQCAQDBYcFsubwFkMuF/B78O9ceXwPgUdwjvvr3Kz4/8rmyHL5z2GQ/VyOu8kfwH0bb9JYNT3tPLFQWuOEGR5/uDICyjmWVtoZTvvWuoccJj0nVpSptIhMiWXN+DXHJzwqSRsTLxUxVqHBXu6ebQVXY6MeNc46DioAEa9asAcDZ1hlrC/nBWF+lfWPQRq4/vm4WWQXmp8S4unx8fDh69CivvPKK4uoCeOedd1i8eHGGxyUlJZGUlKSsx8SICr4CgaBg6bupL+cfnidoTBCxybH02tCL1hVbc2jYIabvm86Pp380au9g7UDE5AhsrWyNtg/aPIhj945xauQpGnk1AkiXudjymCUkA2WBWsaKj7eTNwBeTl542HsAICERkRBBGYcyACw4vIB5h+exsNNCJracaDSGu9odSwtLvB2f9uP4LGC6MPFyMhi3AXAXVq1axaRJk1CpVHjaexISG0JYfBj3Yu7x+m+v07ZyWw4MOWAWeQXmpcQoPrdu3aJHjx6ULVuWjRs3otFo+Pfff/nkk0+IjY3lp59+Mnnc3LlzmTVrViFLKxAISjN6a8ONyBs8SX6ivAe4Hinve77S8/i4+rD2wlriUuJ4FPeIii4VM+xHUXwMSlOEh4cTc1B+mGs3tB39XuqHu9pdOX5Sq0m42LowvOFwrCyscFe7K/Wu9IqPXh69fEZjPLX0jGsxDhtLG0Y1eTZFvjAp51iOr7t+zb/3/yW1eiob/97If//9x6lTp2jSpAkaBw0hsSGEx4fzKE7Oun8z8qZZZBWYnxKj+Lz//vvExMRw9uxZxa3Vtm1bPD09GTZsGIMGDcLf3z/dcVOnTmXixInKekxMDBUrVkzXTiAQCPIDwyrp4fHhiuITHh+OJEmKq2ra89PoXK0zu2/sJjQ2lPD4cCPFRyfpiEiIUI7Vo1h8HDR8/vnnJMQl0KhRI/bN2ZeuJEk192rM7zhfWdfYa3ic8NioP/17U9v0ViUfVx+jfszB2GZjGctYAHRbdaxfv56ff/5ZVnwM4pf0sofFy24vUaal9FFiYnzOnj2Ln59fuliepk2bAnDx4kWTx9na2uLs7Gy0CAQCQUGRVknRW09SdCnEJMVkXF08zXTsyIRIdJJO3mcQkKzv3zbRlq+++gqAWbNmZesGbyrZn/69qW3mmr6eFQMGDABg48aN6HQ6o/PSX6tEbaKigApKFyVG8fH29ua///4jNjbWaPvRo3JUX4UKFcwhlkAgEBhhpEAYFA8FObA5rTUlowR8phQRw/dHfz1KfHw8TZs25cUXX8yWbKbG0r83tc1cwcxZ0bFjR5ydnXnw4AFHjhwxsvikvf6C0keJUXzGjx9PeHg4HTt2ZMOGDezbt485c+YwceJE/Pz86Nq1q7lFFAgEAmMFIt74Rnw98jopOrn4cdqkgGktPmn7MdoeD3t/2wvIWZqz685Ja13SSToj11Da8cw1fT0rbG1tefnllwH47bffjLJSZ6QwCkoPJUbxeemll9i7dy/Ozs6MGzeO7t27s2rVKv73v/9x8OBBkcdHIBAUCdLGyhiuB4cFA/IsLrW1Gsi4uripmBt4ejM/BonxiTRs2DBHD31px4pOjCZVSlW2SZJktL+oWnwAevfuDcjuLg87ecZa2ustkhqWTkpMcDNAQEAAAQEB5hZDIBAIMiStxeFJ0hNlPThcVnwMLSkZJRY05bLRSTrCH4fDcXn7Bx98kKPg3bT1ugzH0Oq0RCdF42rnWuRjfMDY3fX4ipxs0TDGB4Srq7RSYiw+AoFAUBxIe+M1VC6CwoIAY0tKRsVDTbm6ohKj0J3QQSLUrFWTV199NUeyKW41E3E9prYXVVcXGLu7zu47C5iI8RGurlKJUHwEAoGgEEkbzKzPggwGio8pi09axSeNxUeSJO6G31WyNH8w9QMsLHL2F592rIzGTJsksajy+uuvA3Bo5yHQQWhsqFJqA4TFp7QiFB+BQCAoRAyViSfJT5QYGoDIxEjA2IWU1gpjqh/9VPgVy1dAHFi5W9GvX78cy5Z2Vpcpi49hrqGibPEB6NSpE87OzjwKfQT3IDop2mi/iPEpnQjFRyAQCAqR7NxsTbm6MgtuBngQ9YA138n1qSp2q6gUbs4JhsHNhgqO4ZjRSdFodVqgaMf4gLG7i//S7xeurtKJUHwEAoGgEMmOe8VI8Xn6Pm3x0LT9/PLLL4SHhoMj1HqhVq5k0ysySjJFE64u/biONo7YWdnlapzCRO/uUgWrQGe8Tyg+pROh+AgEAkEhor/Z6iuGA1hZGE+wNXQhpS0earIfHaz8eqW8oxWUcy2XK9nU1mocrB2U/vVj6OUzDA4u6vE9evTuLilGgvvyNsNq7YLSh1B8BAKBoJDQ6rRKcG11j+rK9uru1Y3aGSoV+uKh8OxGLUmS8r66R3X4Dx7cfoCdkx00zptSYhjno4zxVD5Di09Rj+/RY2trS7du3eSVy/KL/tqLGJ/SiVB8BAKBoJAwnMFV06Om8t5P42fULm3sTNrszbHJsSSlJgFQy70W/CO3q/FiDbDNm1JiOLNLP55ePsNtxcXiA3KCW0BRfGp71gbkYOfk1GQzSSUwF0LxEQgEgkJCb2FwV7tTzvGZO6qqW1Uj11daxSVtRmX9q9pKTerlVHgENvY2eLYzLnORG/THGmY51is+htuKemCzIV26dEFlqYIw4LFswbJUWQLC6lMaEYqPQCAQFBKG1hJDi0kZhzJGikRaa0q6aeYGmZNPbzgNQI3ONYhSRZk8PieYcnXpLSSG24qTxcfNzY1K9SrJK5ehrGNZJXZKxPmUPoTiIxAIBIWEYXyMoVXH095TWbe2sMbZ1tnouHSJBZ/2Y3fHjrtBd8EKynYsmy/xN/qxbkffJkGbAEBtTW1l/KJeoDQjGvo3lN9cNlY8xcyu0odQfAQCgaCQMLTUGFl4HDRG1djT1tdKW69L30/ErqcxQ40gxiomX+Jv9Mfqs0jbWNpQxa0KAPEp8dyOvp3nMcxB245t5Te3wTbF1silJyhdCMVHIBAICglDN1HaXD36dVOWFOUmnWAQ43MXHgc/xtLKElrDrahbJGoTjdrnBv2xhnXDnGycsLG0AZ5VkC9OMT4AfjX8oAwgweVjl9O5DwWlB6H4CAQCQQEjSRIXH13kYthF4Kmik9bVpVd8TFhSTJaSeDqT65U+r4DLMyuQraUtjjaOuZY1bVFUjYMGlUqVbmZZcXN1aRw08HQi3b/7/hWurlKMUHwEAoGggFl/cT3PffscG4M2AvJNuIxDGWW/4bophSLtTfryf5fhCqgsVLz//vvGbZ8qKrnFUC7DsTPaXlwo41AGasjvD+w5gLu1cW4kQenBKusmAoFAIMgLZ0LPAOBq50otz1q8XPNlyjqUZXjD4dhb2+No48hrfq+x+8Zuhjccnu74tBaf478eB6DJC01oXLcxY++MZdf1XahQMabpmDzJ2sirEd1rdOdS+CVsLG0Y1WQUAO80e4f5h+eTKqXSrHwzJe6nuFDeqTyDug1i46aNxETFEHU5CnjmPhSUHoTiIxAIBAWM3lIzudVkpj4/Vdm+7KVlyvvamtocHHrQ5PGGgbiXL18m5N8QAAaMHQDA192+zjdZbSxt2Npva7rtQxsOZWjDofk2TmGjUqlY9eoqrLdb89NPP3Ht2DWoISw+pRHh6hIIBIICRj9zKLdxMXq3UoouhdlzZoME1IQmDZrkl4ilBn0W53MHz4EkYnxKI0LxEQgEggImr0n/lOKhUfDr2l/ljc8XvwDjokD79u2xsbEh9F4oRAiLT2lEKD4CgUBQwOTHTCiNgwYOg1arBV+gQvGbUl4UcHR05Pnnn5dXrkFEQgQ6SWdeoQSFilB8BAKBoIDRWxXyoqi4JLvA6acrbcFSZYmrnWvehSuFdO3aVX5zFXSSjsiESPMKJChUhOIjEAgEBUiSNoknyU+AvE0Bj9kfA6ng5ecFPrISZaESf+G5oUuXLvKb20CKiPMpbYhfjUAgEBQg+sBmKwurXFtowsPDubv3LgBlu5UFlYjvyQt+fn5UrFgRtMAtEedT2hCKj0AgEBQghvW5cptYcPHixWiTtOAFj8o9UvoT5A6VSvXM6nNN1OsqbQjFRyAQCAqQvMb3REVF8dVXX8krbeFB7AOg+GVOLmoYxvkIV1fpQig+AoFAUIDktWL6119/TUxMDN5VvZVaU3npTyDToUMHVJYqeAyXrlwytziCQkQoPgKBQFCAKDl8chGTExsbyxdffAFAv9H9jP6xRYxP3nB2dqZCnQoAnDl0xszSCAoTofgIBAJBAaJkbc6Fhea7777j8ePHVK9enZ69ehrtEzE+eee51s8BcOXfK2aWRFCYCMVHIBAIChDD4OackJCQwOeffw7A1KlTKedczmi/cHXlnRb+LQAIvRBKYmKimaURFBZC8REIBIICJLcxPj/99BMPHz6kUqVKDBw4MN3xwtWVdxo1bASOoEvW8c8//5hbHEEhIRQfgUAgKEByE+OTnJzM/PnzAXj//fextrbG2dYZawtrpY2w+OSdMg5loJr8fvfu3eYVRlBolDjF59ChQ3Tr1g03NzfUajXVq1dn9uzZ5hZLIBCUUnJj8Vm9ejX37t3Dy8uLoUOHAnLuGUN3mYjxyTue9p5QRX6/Z88e8wojKDRKlOKzdu1a/P39cXFxYfXq1Wzfvp0pU6YgSZK5RRMIBKUUfXBzdhUVrVbL3LlzAXjvvfews7NT9hlajYTik3c0DhpF8Tlz5gxhYSKfT2nAytwC5Bf3799n5MiR/O9//+Obb75RtgcEBJhRKoFAUJpJ1aUSER8BZN/VtX79em7cuIGnpycjR4402qe3GrnauWJtaW3qcEEOcLB2wM7VjsSyifAQ9u3bR58+fcwtlqCAKTEWn2XLlhEXF8eUKVPMLYpAIBAA8DjhMRKyxdlD7ZFle61Wq7jmx48fj4ODg9F+vfIk4nvyB5VKJV9L4e4qVZQYxefgwYO4u7tz6dIlGjRogJWVFWXKlGHUqFHExMSYWzyBQFDKkCSJR3FyXS03O7dsWWjWrl3LlStXcHd35+23306331Mtu7eEmyv/MIzz2b17twiNKAWUGMXn/v37xMfH8/rrr9OnTx/27NnDe++9x+rVq+nWrVuGX+akpCRiYmKMFoFAIMgLqbpUWi1vRd1v6wLZU1RSUlL4+OOPAZg8eTLOzs7p2igWHzGVPd/QOGigMmABt2/f5lzwOXOLJChgSozio9PpSExM5IMPPmDq1Km0a9eO9957j7lz53L48GH27t1r8ri5c+fi4uKiLBUrVixkyQUCQUnjTvQdjt07pqx3rto5y2N+/vlnrl+/jkajYezYsSbbtPNph9pKTccqHfNN1tJO56qdwQZ4+te/6vdVZpVHUPCUGMXHw0P2n3fubPwHo6/Ae/r0aZPHTZ06lejoaGW5e/duwQoqEAhKPPop7OWdyhMxOYKvun2Vafvk5GTF2jNlyhQcHR1NtmtbuS0xU2N4q9lb+StwKWZiy4lETYmiYkNZ8zly8IiZJRIUNCVG8alXr57J7XoXl4WF6VO1tbXF2dnZaBEIBIK8oE9aWNaxLO5q9yzbr1ixgtu3b1OuXDlGjx6daVsrixIzGbfI4GLnQtXGVQG4+O9FUlNTzSyRoCApMYpPr169ANixY4fR9u3btwPQokWLQpdJIBCUTnJSnyspKYlPPvkEkC3Q9vb2BSqbwDTVn6sOthAfE8+ZM6Jae0mmxDw6dOrUiR49evDxxx+j0+lo0aIFJ0+eZNasWXTv3p02bdqYW0SBQFBKyElF9mXLlnHv3j3Kly+fLm+PoPAo61QWfIDL8uyuJk2amFskQQFRYiw+AL/++ivjx4/nhx9+oGvXrnz77bdMmDCBjRs3mls0gUBQilDqc2Wh+CQkJPDpp58CMG3aNKMszYLCxTCLs8jnU7IpMRYfALVazbx585g3b565RREIBKUYpT6Xftq5JEFUFISHP1tiY/n+zz8JCQmhkqsrw27dggkTIDUVdDr5GJ3u2Xtra7CzA1tb+VW/ODmBm1v6xcEBVCqzXYPihsZeA3KYD4cOHSI+Pl64HUsoJUrxEQgEgkInJQXu3JGXu3fhzh1679vL63eh+c9LIfZLiIgArdbosDhA/4g2PSoK2wUL8lcutRrKlwdvb3nRv69UCapVg6pVZaVJADxVUj3AytWK5KhkDh8+TMeOIm1ASUQoPgKBQJAdoqLgv//g8mV5uXRJfr1+PZ1S00V598C4Dycn0GjAw4MvwsN5ePMmVRwcGPL66/I+e3vZsqNSgYWFvKhU8pKSAklJkJhovMTEQGQkPH4sv0ZGyvIkJMC1a/KSEWXLykpQtWpQowbUrQv16kHlyqXOWuRp7wkqsKpmhfaklj179gjFp4QiFB+BQCAwRJLg/n04cwbOnn32evNmxsfY2cnKQsWKUKkSSx78wVnbSN59fRF167ZXlB1sbQEIDw9nQRU5oOSTH3/Eul+//JU/NhYePZLP48EDebl/X15u35aVofBwePhQXg4fNu7DyUlWgp57DurXh2bNZIXIxib/5Cxi6OOxkionwUk5wHn+/PlmlkpQEAjFRyAQlG4SE+H0aThy5Nny8KHpthUrQq1aULOmvOjfly8vW2eeMmPeRmKS4P3O3cCzZrpuPv30U548eULDhg3zvxq4SiUrLk5OsjsrI6KiZGuV3ioUHAwXLsivT57A0aPyosfWFho2lJWg5s3lpUqVEmMZ0qcekHzk3G9nzpwhPDwcT09RF62kIRQfgUBQuoiLg0OHYO9e+fXUKUhONm5jaQl+ftCggXyzb9BAXtzcsuw+SZtETJJc889UTa1bt27xzTffADB//vwMk6sWOK6u0LixvBiSkgJXrshK0IULslJ4/LjsSjt2TF70lC8P7do9W6pWLbaKkK2VLc62zsQQQw2/GlwJusK+ffvo3bu3uUUT5DNC8REIBCUbrRZOnIA9e2Rl58gR+eZuSJky0KrVs6VRIzk4OBfoc/hYqixxtXNNt3/GjBkkJyfToUOHohlDYm0NderIS9++8jZJkq1Dx4/Dv//Kr6dPy66zNWvkBZ4pQp06QZcu8nUtRmjsNcQkxdCgZQOuBF1hz549QvEpgQjFRyAQlDwiImD7dti6FXbtkgOADalUCV54Afz9oXXrfHXZ6Keye9h7YKEytuacO3eOX375BaB4pd1QqZ4FQffvL29LSJCtP4GBsH+//N5QEVKpZGtSt27QtSs0bSpb0oownvaeXI+8TvUm1eEnkc+npCIUH4FAUPyRJHmW1dat8nLkiJz/Ro+7O7RvDx06yApPAbpkMsraLEkSU6ZMQZIk+vTpU/wzA6vVEBAgLwDx8bLys3cv7NghB4WfPCkvH38sB3e/+CK89hp07CgHhBcx9K7Jsn5lsbKy4ubNm9y8eRNfX18zSybIT4TiIxAIii/BwbBhA/z6q/zekPr1oXt36NFDtjYUUiyNkrU5TXzPjh072LVrF9bW1kq25hKFvb2sXLZvD59+CiEhsHOnbHn7+2/ZCrd6tbw4OsqfzWuvyS4xBwdzSw88U1afqJ7QrFkzjhw5QmBgoFB8ShhC8REIBMWLq1efKTsXLjzbbmMjWx969JBvqpUrm0U8JWuzgcUnJSWFiRMnAjBu3DiqZjbbqqTg5QVDh8pLSoo8Zf733+Xl/n1Yv15e1GrZEjRwoOwSM+OUef1nFhYXRvv27Tly5Aj79u1j2LBhZpNJkP+UqFpdAoGghPL4MXzzjTyVukYNmD5dVnqsreWb5qpVct6anTth7FizKT3wzOJjWJn922+/5fLly2g0GqZPn24u0cyHtbUc9Pzll3KG66NHYdIk8PWVY4U2boSePeXM0mPHyvslqdDF1H9mYfGy4gOwb98+JDPIIig4hOIjEAiKJlqtHCvSu7dsPRg7Vp6dZWkJnTvDTz/J+Xa2bYNBg8DFxdwSA+ljfB4/fszMmTMBmD17Ni5FRE6zYWEBLVrAZ5/JM8VOnYJ335U/44gIWcFt1UpWcD/+WC4DUkjo3ZPh8eG0bNkSW1tbQkJCuHz5cqHJICh48qz4aLVavvjiC5o1a4azszNWVs+8Z2fPnmXMmDFcuXIlr8MIBILSws2b8P778syrbt3gt9/kPDv16sEXX8hZiHfuhGHDspVXp7BJW6B01qxZREZG8txzzzF8+HBzilb0UKnk1AGffy4rOH//DW+8Icf8XLsGH30EPj6y63Lr1nSlQfIbxdUVH4adnR2tW7cGYO/evQU6rqBwyZPik5CQQEBAAJMmTeL27ds4OzsbmQR9fX1ZsWIFq1evzrOgAoGgBJOaCn/9JbutqlaF+fPl4FhPTxg3Tp4hdO4cjB9f5HPDGMb4BAcHs3TpUgC++OILowdDQRosLeXZXqtXy5a8n3+WY7Z0Ovm78dJLshL00Ueyu6wA0Curendlhw4dANndJSg55EnxmTNnDocPH2bu3LmEhoby5ptvGu13cXHB39+fXbt25UlIgaCoMP/QfMb+NbZAff47ru6gzfI2NF/WnGF/DkMn6bI+qLgSFiYrOdWqyU/127fLsR2dOsGmTXIQ7OLFctbkPLDo6CJGbRuFJElEJkTSc31Pmi9rTvtV7TkXei5d+5ikGF759RWaL2uuLD3W9eBR3KN0bS+HX6bjzx2VdifunwDkeJExY8aQmprKSy+9pNxEBdnAwUEOdt63Ty4EO2mSrATfvy+7v3x94ZVX4MCBfI0F0lt87j+5T/NlzVnzRE7MuHvvbnS6Evw7LGXk6fHj119/pV27dkyePBkAlYm8GFWqVOHMmTN5GUYgKBLoJB3TA6ej1WmZ0HIC1dyrFcg4C48u5PBduWjk8fvHeavZWzTyalQgY5mNoCBYuFBOdJeUJG9zc5PdV//7H1Svnm9DSZLE9H3TSdAmMK75OE6HnObPy38q+3868xNfdv3S6Jid13ay+dLmdH39eelPRjQeYbTt5/M/s+eGcaI7S5UlQfuC2L9/P2q1msWLF+fb+ZQ6atSQ44E++QQ2b4YffpAVos2b5aVBA9kS2LevUgQ2t3g5eeFm50ZkYiTH7x8Ha8AGnkQ/4dy5czRs2DDPpyMwP3my+Ny5c4emTZtm2sbZ2Zno6Oi8DCMQFAmiEqPQ6uQYA70pvCBIa1UoyLEKFUmSs/y++KJcDmH5clnpadIEVqyQn+Y//zxflR6AuJQ4ErQJgOyGSnd949NfX32bNpXasKXvFjpV7ZRl24H1BrKl7xa29N3Cof6H+HS6nKtn2rRpIg9MfmBrC336yAkSg4Jg1Ch5KvzZszBkiDyTb9YseXZfLrGzsuPM/84on+O7bd6FpxMEhbur5JAnxcfJyYmwsMz/lK9fv45Gk75Qn0BQ3DBUQEzdAPNtHH3JA7VHgY9VKKSkwLp1soLTvr3szlKp4NVX5dwuJ07IN65c1sbKCqPPLS4s/fU1oVjqtz1X5jl61OxBg7INMm77tL9WFVrRo2YPetTswZola3j48CE1atRg0qRJ+Xo+AqB2bfj2W7h3D+bNk2uEPXwIM2fKCtDbb8Pt27nqurJrZeVzfKHKC/BUZxWKT8khT4pPixYt2Lp1a4YWnXv37rF9+3batm2bl2EEgiKBoQJSUFYYSZKU6dB+Gr8CHavASUqS3RI1asj1nU6flpWbsWPl6t+bNsnTlgsYo88tPky5nsr1NaFYpk1CqAS9mmqbJlPz6dOnlerrS5cuxTaP7hdBJri7w5Qp8kzA9evlPE+JifD113Lc2NChcimTXKKx1yiKz8GDB0lJW9xWUCzJk+Lz3nvv8fjxY1544QWOHDmC9ulUw/j4ePbu3UunTp2MMpYKBMWZwrD4GLrTannWKtCxCozERFi6VL7x/O9/cOuWPBNr9mx5yrL+plRIZGTxqe1ZO91+pV2aKemG05wzbGuvQavVMmrUKHQ6HX379uWFF17IxzMRZIi1tewG09cK69BBnvq+ciX4+cmlMU6dynG3GgcNlAXsITY2lhMnTuS76ILCJ0+KT9u2bVm6dCnnzp3j+eefZ86cOYDsAuvUqRPXrl3jm2++oXHjxvkirEBgTgxvenqrTEGN4WjjSAXnCgU6Vr4THy/PwKpSBd56S3ZDeHvDkiWy8jN9ulyospBJZ/HRKz4aWfEJjw9PN0svbfZlJaNvJm4xT3tPFi9ezIkTJ3B2dmbhwoX5fCaCLFGpZHfqnj3w779yNmhJkq2LTZrAyy/LaRGyiae9p3yX9JHXhburZJDnBIajRo3i3LlzvPXWWzRt2pSqVavSsGFDRo0axZkzZ9JNcRcIiiuGCkhBWWEMs/5mZmUoUiQlyaUIqlSBCRPk/DsVK8pWn+vX4Z13Cix+JzsYfm7h8eHKut7ik6JLISYpxuQxaV1daZVQrU5LZGIkADEPYvjwww8BWLRoEd7e3vl9KoKc0KwZ/PEHXLwIAwbIGaO3bJFngfXpk76orQnsre2xt7YXcT4ljHzJplW7dm2WLFmSH10JBEWWtC6TghxD46BJl0ytyJGaKk9HnzHjWSCpjw988AEMHmzWYpOGpHVR6tcrulTE0caR2ORYwuLDcLFzMWoHpl1dkiQpqTsi4iPkA3Qw6a1JJCYm0rFjR1HUsihRpw788otscZw1S44F2rBBrg82YICcEDGTorEaew23feXv95EjR0hISEBtRkVekHdErS6BIJukdZkU5BhF2uIjSfDnn1C/vqzg3L4tu7S++04OWh4xosgoPWB8/UKehCgWGqNrbKAc6SSdotCktfgkahOJS4lL17f9WXuOHD6Co6MjP/74o8mcZgIzU6uWPLvw3DnZBabTydmha9aUp8Y/fGjyMI2DBjzAvYw7SUlJHD16tHDlFuQ7eVJ8Vq1aRePGjXnw4IHJ/Q8ePKBx48asXbs2L8MIBEWCQonxMWHxKVIxPv/8I8/E6tkT/vtPTjo4fz5cvSoHMltbm1vCdBh+blcfXwVAhQp3tbtRNW49kQmRpEqpAHjYyzFJDtYO2FnZyW3TWv4eQ8JOOU/QggULqGzGyvCCbFCvnuwCO3ECunaVLZfffy8H3H/yiRyrZoDGXgMqqNGkBiDcXSWBPCk+K1euxMbGJkNftre3N2q1mp9++ikvwwgERYJCcXU9vQF7qj2Vm/LjhMfKTC+zceOGPDOmbVt55oy9vezSunEDJk+W14sohp9VcmoyAO5qdywtLE26E/WfgYutCzaWsuVKpVKZVJJCY0Lhd5CSJdq1a8f//ve/gj0ZQf7RpImcU+rAAWjaFGJj4cMP5QSaK1bIChHPAtsr1JMnG4iCpcWfPCk+QUFBWabwbtCgAUFBQXkZRiAoEhhaXuJS4khISSiwMTQOGjzUHqhIE0tS2MTEyHlSateWZ8ZYWMhugevX4dNPwdXVPHLlAFMWs7SxO2kDoA3bKMeYaPvzVz/DPbBSW7FixQosLET0QLFDr8yvWyfHqD14IJdOadQI/v5b+dzd/NwAOHHiBDExMZl0KCjq5OlXGh0djZubW6ZtnJ2diYyMzMswAoHZkSQpXaxNQcTeGMb4WFpY4q52B8zg7kpNlZMPVq8OCxZAcrJcOfvcOTljbrlyhStPHjD1OSmxOybiqBR3o30axSeNdejgwYPsXLETgLZj2+Lj45O/ggsKDwsLudbXpUty2RRXVzh/Hjp35n+f7MQnEpKdkqlatSqpqan8888/5pZYkAfypPh4e3tz9uzZTNucO3eOsmXL5mUYgcDsxKXEkahNBMDZ1hkoGGUkbRbgzDIGFxgHD8pPu//7n1z3qEYN2LYNdu2CunULT458IEmbpExV139u8Oy6mnJfKe7Gp/uUYwyUpMjISAYOHIikk6A+tOzasuBOQlB42NrCu+/KFs3x48HKihqHggj+GjqtPkz7p1UIRJxP8SZPik+nTp3YtWsXu3fvNrn/77//ZufOnXTu3DkvwwgEZkevkNha2uLj6mO0LV/HSXPTzSxxXr7z6JE8S8vfX37adXWVExJevCgXFi2GM5X0yqmlypJq7s+yRXuq5etqMsYnA4uP/rN4FPuIkSNHcvfuXRzLOUK39EqSoJjj7g5ffAHnz/OoZX3sUqH/H9dov2ULIBSf4k6eFJ/3338fR0dHunXrxrBhw1i3bh0HDx5k3bp1DB06lBdffBFnZ2emTp2aX/IKBGbBMO6jIKeZp0ucVxhT2lNTZfdVzZqwerWs4IwcCdeuwbhxRXKmVnbRX09Pe0/KOJRRtuclxufgrwfZuHEjVlZW1PpfLbBNryQJSgi1a3Nt/VJ69Yb7bpYERMixdmfPniVCTGsvtuRJ8fHx8WHHjh14eXmxcuVKBg4cSEBAAAMHDmTVqlV4eXmxY8cOfH1980tegcAsGOXXKaDEgvEp8cSnyFNpM7sx5ysnTkCLFjBmDERFQcOGcPSoPL3XDOUl8hvDRISGyklmxUfTFihVjnHQwE04vvI4AAsXLiSpbJJRP4KSh8ahDL/7QcNxdpT98EPqPLV87m/bVk6ImJRkZgkFOSXPUxBatmzJtWvX2LBhA++++y7Dhw/n3XffZcOGDVy7do0WLVrkh5w5ZtmyZahUKhwdHc0yvqBkYZRfp4CUEf0YNpY2ONk4KeMZ7ss3YmLkKunNm8PJk+DsDF99JStCzZvn71hmxNBtZaT4pI3xMTGdPa37SoqU4DeQdBJvvPEGb7/9doZKkqDkoPwGiSNxxge0f+MNAPZqtTBzplwCQwQ7FyvypWSFjY0Nr732Gq+99lp+dJdn7t+/z6RJk/D29iY6Otrc4ghKAIY3OFMBsfk9hj7zb4G4uv76S56Sfu+evD5wIHz2WbGaqZVdjCw+DiYsPk9f9ekJ1NbqdAHmAAkJCSyasAjiwaaCDd9//z2QsVtMUHJwsXXBysIKrU5LWFwYHV59la9Wr2aft7fsJr50SZ4SP2KEnMwzi5nOAvNTIpNOjBo1irZt29KxY0dziyIoIRhW4C6ouJu0FcEN3+fLWGFhcm2i7t1lpadKFdi7V07bXwKVHjC4pgYJIeHZdXW2dcbaQo5h0l/jtFYcnU7H8OHDuXLxCqjBpr8NarWaqMQoJbGkCG4uuaRNXunv74+FhQWXHzzg/t69ssID8OOPcr6rX3+Vy7oIiix5tvgkJyezefNmTpw4QVRUFKlPs10aolKpCi178y+//MKBAwcICgpi+vTphTKmoORjVDW9gNxPpqwH+VK2QpLkwozvvAPh4XLOkgkT4OOPi3TG5fzAVFC6fh3k/yaNg4YHTx4QHh9OReeK6T6HKVOmsG7dOqysrNC+riXWPpbk1GSlnaONo1LOQlAy0dhrCI0NJTw+nEZejWjUqBEnT54k8MwZBv7wA7zxhjwh4NIlOR/Q+vVy7TqRyqVIkifF5/bt23Ts2JHr168jZaLhFpbi8+jRI8aPH8+8efOoUKFCto5JSkoiySA4TWTkFJjCVJCsfltobChf/fuVUfHKBuUaMKTBkCz7XX1uNadDTgNwNvSsPIaJINybkTd5d9e7vFH/DRqUa8Dl8MtsubyFt5q9hdpazboL6/j3/r/KcWorNWObjaXCExWPB/fGfe8RAB74ePDrhE48/1pfmhQjpefHUz/yX9h/WFtYM7zRcGp51srWcaaC0vXrhu8fPHlAWFyYUb4mjb2GxYsX8/nnn8syLPuRN2+/SaqUytvb3yYmOSZdX4KSif67s/jYYi6FXyIgIICTJ0+yb98+Bg4cCM8/D2fPwrx5ckbzzZvluJ+lS6F372KZCqIkkyfFZ8KECVy7do033niDYcOGUaFCBays8iVsKFeMGTOGmjVrMnr06GwfM3fuXGbNmlWAUglKAqZifPRP/IuPLWb+4fnpjung24GKLhUz7PN+zH0Gbx6cbnsll0rK+wrOFbBQWfAk+QmLji3iZMhJDgw5wLR909gUvAkvJy+6VOvCgN8HIGHw8CFBrV2nGPzjcdyjo0m2gE/awrw2EaRErqPpX9c4PuJ4rq5FYXM5/DIjt41U1q9FXuOPPn9k61hDhbWis/xZlHEog62VrdLG0I2ht+LZWdmx9fetTJgwAZD/J4YMHsKsJbO4FXWLH07/oBxv+HkJSiaVXeTCszuu7WDHtR0sqb8EkOt2SZIkx+TZ2sJHH8kFfIcMkRWhvn1h40b45hvQCAW5qJAnLWXfvn106NCBVatW5Zc8uWbTpk1s3bqVM2fOKIGh2WHq1KlMnDhRWY+JiaFixYxvVoLSiamq6frioXdj7gLwQpUXaOrdlO9OfkdkYiT3Yu5lqvjci5GDi13tXBndRFbWHawdGNn42U1e46Dht9d/Y8vlLaw6t4q70fJY+jHvRt/lwZMHSEg4WDvwTvN3uHHtBL2+3MPrQXJi0VPlVQx6WSLgxbEM1iax7Mwy5fjiQFpZ9dcgOxjGTVV0qcj6XuvxdjIuqmzoutQrSo63HRn8sayUjh07lilTpgCwrtc6tlzeohxrqbKkb92+OTwjQXFjVrtZVHapzE9nfuJuzF08a3liZWXFnTt3uHnzJlWqVHnWuH59+PdfmDNHtv5s3CgXQv32W+jVy3wnIVDIk+Kj0+myLFJaGMTGxjJ27FjefvttvL29iYqKAuT4I4CoqCisra1xcHBId6ytrS22trbptgsEhhhOcdYXD5WQiIiPUG6uA58byOAGg9lzYw8nHpzIMiBZv7+KWxXmdJiTYbtXa79K3TJ1WXVu1bMA3Lhngbj695VcKjEnsTUJ079FHQ5aC0idPo0W0qdoLeFYh7lEJ0Wz7MwywuPDnz2pFnH056e2UpOgTchRoHfaQOU+dfuka2PougyLC4MrELEhAkkr8dprr7FkyRLlOrWo0IIWFcyTokNgPiq6VOSjdh9xOvQ0d2Pu8kR6QosWLTh06BB79+41VnwAbGzkqe4vvSRbfy5cgNdeky1A33wjZn6ZmTzN6mrZsiXBwcH5JUuuCQ8P5+HDhyxcuBA3NzdlWbduHXFxcbi5uTFgwABziykopiSnJiv1ntIWDw2LD0sXDJvdgOS0WZozQ98mNjmWRG2icmx4fDjh8eE4JMG83yKhe3fU4VEEe0KfdysRMuFNtJZyqQ1HG0elH61OS3RS8Uj1oD9XP42f0XpWpOpSeZzwGMh8urlhXqbd23fDepC0Er169WLt2rVYWlrmRXxBCcLwu9KhQwcgi/IVjRrJebKmTwdLSznouX592QIkMBt5UnzmzZtHYGAgGzduzC95ckW5cuUIDAxMt3Tu3Bk7OzsCAwP55JNPzCqjoPhiWO/JTS0/qRkqN2mtCooFIYtZX6byxWSEq50rVhaygfZ+zH2eJD+R+4gPw/Lf45z7Dl46EApAxP8G0eh/EOjxxGgMlUqFrZWtkhyxUOp/5QP666tXfAwzXGdGZGIkOkkHgIc64yzU+hifc/vP8dWkr0AHlVpVYt26dVgX43IdgvzH0DrYvn17QFZ8Mpvcg40NzJ4NR45AtWpw9y4EBMAHH8BTr4SgcMmTq2vr1q0EBATQp08f/P39adiwIS4uLunaqVQqPvzww7wMlSl2dna0a9cu3faVK1diaWlpcp9AkF30CoKHvQcWKvlZwTDjb7qK6tnM85OTrL/6XCKhsaEEh8tWVgsddP/tPK9s3YWlDiI0jnj8ugWp+XMkfraaxMRIQmJD0o2hcdDwJPkJYfFhVPeonr2LYEb019fX1RdbS1uSUpMIiwujsmvlbB3nZueGtWXGCoynvSccg+O7joME1IWeH/QUSo8gHYYlTpoHNEetVvPo0SOCgoKoU6dO5gc3awZnzsj175Yvh7lzYfduWLMGatQoBOkFevKk+MycOVN5v3//fvbv32+yXUErPgJBQWKqhIEyzTzqJkmpSUbbspt0MKflDvS5RILCgvCKgZ//gA43HwCwri7cmDOaaQEBuOlSsVBZoJN0XAq/JB9rYFXytPfkRuSNYmfx0TjIM+ruP7lPWHw2FJ8MSk8YotVqWTt/LeyU12t0rMGVllco6yzyrwjSY/jAY2trS5s2bdi9ezf79u3LWvEBcHSEn36Crl3lvD8nT8r18ZYsgeHDxbT3QiJPik9gYGB+yVEgrFy5kpUrV5pbDEExx7Dekx79+6CwIECe/mxvLefFyW6CQ1OZmjND3856x9+c+w408RBnDauGN2Zs2VMsKSPnrtLHIIXHhyvymVLaCrTiez5iqMBoHDSy4pMNpS0rV2J0dDT9+vVjx44dAKi7qakxoAZXrl4RuXkEJkn722nfvr2i+Lz99tvZ7+i11+TiwIMGQWCgnP15505ZKTLhNRHkL3lSfPz9/fNLDoGgyJJZRmW928lUfa1sBzdns86Tl407C3fChGN7AThbFvq+Bi4NLOF+esUsPD7cSL60shdYxfd8xihrdg4KxGYWPP7vv//Sr18/bt68iVqtJuGlBBJqJ/Aw7iEgSlAITJP2t6OP89m/fz+pqak5C4SvUAH27IGFC2HaNNi0SXaFbdgAjRvnu+yCZ5TIWl0CQX5iyiWlvzEGhz1VLEwoRfnq6rp+nbkfHmTiMXl1SXNo8SZc1mQug7LPhLWq2Li6TORQyo61ytT11el0zJ8/nzZt2nDz5k0qV65M4P5AVLVlpdWUa1Ag0GP425EkiUaNGuHi4kJUVBRnzpzJeYcWFvDee3DoEFSuDDduQKtWcsZnUe+rwMi3NMt3797lwYMHRuUfDGnbtm1+DSUQFCqZubr0s6tyo1hke1bXn3/C4MFUio4mXA1De8K2ms92ZyaDss9EuYbi4OrSSToiEiKAp1mz1c9iLLIirSvx2rVrjBo1ir17ZYvZ66+/zg8//ICrqyvu+92JSIgweS0FAj3631FSahKxybE42Trh7+/Pli1b2LdvH02aNMldx/rA56FD5d/7W2/JU95//FG4vgqAPFt8tm7dSq1atfDx8aFVq1YEBASYXASC4orJ4OY0yoqpyt9xKXEkpCSY7DNJm6TcZDN0q2i18P77cgr86GhCnvOlwShjpScrGTLbVxwUn8cJj59NSbf3yJXFx9XKlZkzZ1K3bl327t2LWq1m2bJl/Prrr7i6ugLpP09h8RGYwt7aHrWVGjCO84Es8vlkBzc3+OMP+OILsLKC336TXV6nT+etX0E68qT47N+/n1deeYXY2FjeeustJEmibdu2jBw5Ej8/PyRJ4sUXX2TGjBn5Ja9AUOiYjPFJYxEwXHe2dcbawtro2Iz6tFRZ4mrnmr7Bw4fQsSPMf1oDbNw4Dq/+hPuZPPyZCmA2tV6cYnz0MrrYumBjaZOjGJ+w2DAIhkWDFjFr1iySkpLo2LEjZ8+eZfjw4UZZqw2vXYafiUBAxnE+//zzj1ItINeoVDB+/DPX1/Xr0LKlbPkR5Bt5TmDo6OjIqVOnWLJELtoWEBDAt99+y/nz5/n000/Zu3cvL7/8cr4IKxCYg8xifPQYKkUqlSpLy4ShFUmfG0jhn3/kKa7798vTX3/9FRYvxsPFy6iZreWzUivOts5GhTczs2AUpxiftO7A7FirdDodv//+O4emHYJf4dHdR5QvX54NGzawa9cuapjImWL42RrmaxII0pL291OnTh00Gg3x8fEcP55PhX+bN5ctPS+9JCc5HDlSXjIIJRHkjDz9uk+cOEHPnj0pW/ZZzgudTjZLq1Qqpk6dSsOGDYXFR1CsMRWLk06xyMDCkpFyYTK+R5Jg0SI5q2tICPj5wYkT0Lu3yTFredbKcnxT6zlxF5mbdFmxM0kVEBMTw3fffUeDBg3o1asXifcTwQaGjxtOcHAwr7/+eoa1yUzFRwkEpkj7+7GwsFDCOfLs7jLE3R02b5aLnapUstXH3x/u3cu/MUopeVJ84uPjKV++vLJua2tLTEyMUZsWLVpw+PDhvAwjEJiNtMG1euys7HC0cVTWM4r5ycrio/SZkAADB8K770JqKvTvL1d4rpWxclNbUzvD8Q3XLVQWSqkNQ9myW/rBnGSVFVur1XLw4EGGDx+Ol5cXo0eP5sKFCzg7O2PRzgLGw4czP8TJySnTcTJTagUCQwyTGOrRu7v0gfP5hkoFU6fCjh1yDNC//8pxPwcP5u84pYw8KT7lypUjLOzZh1++fHn+++8/ozYRERGkpqbmZRiBwGykDa41JDuur6wsPp72nnDnDrRpA2vXykGNX38Nv/wiu7kM0BdG1VPb85nik1kws4fa2HXjZOOEjaVNpvIVFRSX4NPZXBoHDcRB1Oko3hj0BuXKlcPf35/ly5cTHx9P7dq1WbRoEecvn0fXTgf22VNkhMVHkF1MzYrUFyw9evQo8fEF8DDRubOc5bl+fXj0CDp0gC+/FFPec0meFJ/69etz8eJFZT0gIIDAwEDWr19PXFwcu3bt4tdff6VevXp5FlQgMAdpg2sNyU4wcVbBzS1uaqFpU9mf7+kpJzQbO9Zk6nprS2vc7J5ZbrLr6kp741epVDkKEjYnd0Puwm24t+8ew4YNo02jNvAZsAF++fkXIiIicHNzY/Dgwfzzzz/8999/TJgwgVRb+WHL3tpeyaidGZnNiBMIDDH126latSoVK1YkJSWl4DwcVarIhU7795dnfI4bJ2d+TjA9c1SQMXnK4/PSSy/x1ltvcfv2bSpXrswHH3zApk2bGDBgwLMBrKxEZXRBsSWzXDvZifnJzNU18iSM2/knaHXyk9yff8ozOTJB46AhMjESNzs3vByfBTunVXwyU8r0/ehrXhUkqampxMfHExcXl+417bbY2FhCQkK4d+8e9+7d48aNG4SHyzeXv/nbuGNPGNRrEMP6DaN169ZYWRn/lZnKvZQZmc3YEwgMMRUjp1KpaN++PatWrWLfvn107NixYAa3t5etwU2bwqRJ8vvLl+X/Di+vrI8XAHlUfIYNG8awYcOUdV9fX06cOMGiRYu4ceMGlStXZtSoUTRo0CCvcgoEZiGz7Mr6baamP2ca45OczCtf/k3n3QA6OXh5+XJwcMhSHo29hisRV4yyGEN6xcvWyhZnW2dikmJMKm2m4hQyIi4ujgcPHhgt4eHhxMTEpFvSKjYZJTTNES5Qr249Xmz7Iq1bt2bCxQlcTbzK4EGD8fc1XTYnOwVKDcnMQiYQGJLRxAVDxadA0U95b9AAevWSJ0A0bQpbtkCjRgU7dgkh3zI366latSpLly7N724FArOQqcXHoBp72unPGcb4hIdDr150PngTHXBxXF/qfbE221WZDYN8s4pL0dhrZMUnE6XNUDF7+PAhJ06c4MKFC1y6dIlLly5x5coVoqKisiVbZqhUKuzt7bG3t8fBwQEHBwflveG2cuXKUaFCBcqXL0+lSpUYcmgIFyIvMK//PLpW7wrAgrAFXL19NVOlLdtZsZ8iLD6C7JLRQ41+ZtfJkyeJjo7GpaAzLrdrB8ePQ48eEBwsxwn+/LOsDAkyJc8Wn549e/LSSy9l2Gb79u1s3LiR5cuX52UowVOStEkcu3eMlhVbpos5EaQnIj6CfTf3KQHKKpWK5ys9j5dTerPwzcibHL9vnIfjwO0DwLPgWkP0f4CmrAomXV2XL5ParSuWN27yxFZFv1clJo4bkW2lx1AOT3tP3NRuWKgs0Ek6kzJ42ntyPfK66X1qTwiDnWt2cnTRUY4fP86dO3cyHNfBwYHy5cvj7e2Nt7c3np6euLi44OzsrLw6OTmlU2r079VqdYZTyQ1J1Cay+/pu4lPiucpVQpJClHMxPC+A3Td24+3kTZtKbdL1naM6aBn0LxCYIqOHmooVK1K9enWuXr3KwYMH6dGjBwCnHpyignMFyjqWTddXnqlaFY4ehX795Jlfr70GH38M06eTlJrMv/f/pWWFllhbWuf/2MWYPCk+K1euxMfHJ1PF58KFC6xatUooPvnEF8e+YOreqXzR+QvGtxhvbnGKPAP/GMjOazuNtjX2aszJkSeNtkmSRJsVbXjw5IHJfkz9aZVzLJfhvnTZkffvh1dfxTIykpuu0L2/RFAZmO+Qsz9DZUyHslioLCjjUIbQ2NDM5Xs6RlJSEnv27OH3339n49aNEAa72a20V6lU1K5dm4YNG1K7dm1q1apFzZo1qVy5cpbTwfOL+YfmM/PAzHTbDc+vnIN8Xj+d+YmfzvzEoaGHaF2ptVH7zCqzm8LOyg4XWxeik6IL5gYlKDEY1sFL0iYZJQ7t0KEDV69eZe/evfTo0YNrj6/R5McmtKrYisPDCijo2cUFtm6Vi51+8QXMmAFBQXw5vDaTD3/E4s6LGddiXMGMXUzJd1dXWhITE9MFHgpyT3C4XG1bX0VakDn66uSNvRpjY2nD0XtHTV67J8lPFKXHv7K/kQXB2daZoQ2GpjvmpZovMbDeQAbVG5Run95q8DjhManLf8Jy1GhISeG8rwMde8XhVbU+71T2x0/jl6PzGdJgCNcjrzOm6RgA5naYy4n7J2jkld63/27Ld7G3tMf9vjuDBg1iy5YtREdHP2tgCZ61PJnQfwItW7akcePGODs750ie/Eb//a7uXp3yznKOsOblm1PJpZLSZlSTUdyOvs2/9/8lPD6cS+GX0ik+OY3xAZj/wnyCwoKoo6mT19MQlGBc7VyxVFmSKqUSHh+ufE9BjvP57rvvlDify+GXgUL4v7a0lJOf+vnB6NGwfj2vnvLg85fFvcIUedZIMjJfS5LEvXv32L59O97e3nkdRvAUvXm1OGTdLQror9Ovr/2Kp70nrvNdleKhamv1s3ZPr6u9tT37h+zPVt9uajd+fuVnk/s81B5Y6ODjQLD85015Y+/e9Gp0mEeJcWzr8SNNyzfN8flUda/K2l5rlfUhDYYwpMGQdO3u3btH4KpADv90mHV31inbvby86NWrF671XPnk1ifUqFqDD4Z9kGM5Cgr95/WR/0cMqDfAZJvnyj7Htv7bGLJ5CKvOrTL5W8hpjA/A/5r8LxcSC0obKpUKT3tPHsY9JCw+zEjxadeuHSB7Oh49eqR8Nx8nPEar02JlUcBGgDffhGrVoFcvql6N4Ngy+ML7esGOWQzJcR4fCwsLLC0tsbS0BGDmzJnKuuFiZWWFj48PJ06coG/fvvkueGlF/0Mq6onnigKGmYk1Dhqj4qFpb5Y5jQnJCsukZDZttmHaP083TJuGtHYt97QRijwFwZkzZ+jbty+VK1fmo48+4s6dO7i5ufHWW2/xzz//cO/ePb766is6duoINkXve5QThSWzsiD5/XkKBIZkFOej0WiUvHV79+412h8RH1E4wrVrB//+y50ytvhGwScfBsr1/wQKOVY/27Ztq1h5Dh48SKVKlfDx8UnXztLSEnd3d9q3b8+IESPyLKhARh+7ICw+WaO/VtYW1jjZOClPaiGxIYTFhRm5T/Rt8yWwNSwMXnqJnueTSbaA6/OnUHvSJ8Qlx5KoTcy/cQwIDAxk7ty57N79LGanbdu2jBgxgl69eqFWq43aZ5VnyFzkxEWlbxOekD4JY24sPgJBdsns99O5c2fOnz/Prl27KDOgjLI9LD6s8OLHqlXj5bc9+eb7+7S8p4UXXoDVq6FPn8IZv4iTY8Vn//79ynsLCwuGDh0qipAWIvo/9KKecbcoYHjz0yvrGgcNIbEh6a5fvt0ob96U08tfvUqMgxUvvaZlbNfG1ObZZ2ZnZYeDddY5e7LDyZMnmTp1Knv27AHkB44+ffrw3nvvZZo/S3+eUYlRpKSmFIlZH5Ik5SgoObOyIDkNbhYIckK6yQsGdOnShc8++4xdu3bRpWcXZXth/2dfsYik/WDY+KcNL15Mhr595fI4kyblaCZpSSRPDkd9JXZB4ZCQkkBcShwgm01TdalYWliaWaqiiyl3R0ZPavniGjlzBrp2hYcPoVIlpo2rwYEne+idxj2psddka2p3Zly7do2pU6eyceNGAKytrRkxYgSTJk3C19c3y+Pd7NxQoUJCIiIhQpkBZk6ik6LR6rRADl1daT7LJG0ST5KfAGJquqBg0KeVMKV0t27dGnt7e0JDQ7lx6YayvTDdyoqb3xp69kohucM4VEuWwOTJcOsWLFki1wUspeSpVldGHD16lGnTpvHJJ5/w4IHp6cGCnGP4By8h8TjhsRmlKfqYsuJkZCXIaYmDdOzZA/7+stJTrx4cPUpy9SpGfSvKVR6sSvHx8Xz44YfUqVOHjRs3olKpGDRoEFeuXGHp0qXZUnoALC0slaKrRSXORy+Ho40jdlZ2WbbP8LN8ep2tLKzSZdQWCPIDU2Ur9Nja2irV2m+euKlsL0y3suFvQquSiJgzXZ7qrlLBN9/Aq69CQRRTLSbkSfGZNGkSdnZ2PH787Aa8ceNGnn/+eebOncuMGTNo1KgR9+/fz7Oggoz/4AWmMRUvojyppbl2+jiRXFkI1q6Fbt3gyRMICICDB8HbO92fo1FF9lywefNm/Pz8+OSTT0hOTqZTp06cP3+eVatWmYyzy4qiFueTU6tbRoVWDa9zXi1rAoEpsvrtdO7cWd5//tn+wnzASGfRjguTy1z89hvY2cl5fzp2hMjIQpOpKJEnxScwMJCAgADc3d2VbR9++CEuLi6sXr2aBQsWEBERwcKFC/MsqCD9H7yI88kcU3EeGfnmcx3js3AhDBgAKSly4OCOHXJCMdLfmHMbd/Lw4UNee+01XnnlFW7fvk2lSpX4/fff2blzJ3Xr1s2ZvAZkFqdgDnIaYK5vp09PkLYfEd8jKCiy+u106SLH9iTeTISn5eoK83eW4b2iVy/ZOu3qKld6b9sWSqFXJk+Kz507d6hevbqyfvXqVS5fvsw777zDwIEDmTRpEt26dWP79u15FlSQgRYvyBBT7qt8i/HR6eDdd+VAQZCfptauBdtnWVzT1vTJ6RiSJLF+/Xrq1KnDpk2bsLKyYurUqQQHB/PKK6/k2ZqRk0KlhUFOlc+M0hPkJnmhQJATMkulAFCtWjWqVK0CqcBTb5e5XF3pxm7dWrZKe3nBxYvy+tWrhSZbUSBPik9sbCyOjo7K+qFDh1CpVHTt2lXZ5ufnx7179/IyjOApwtWVM0zF1GQZ45Odm65WC8OGyZlSAT77TH5vkXmh0pyM8fjxY15//XX69etHREQE9evX5/jx48yZMwd7e/usZcwGxd3VpVKpTH6eYiq7oKDJqFCpIc8HPC+/eZo/sFAVn6wekp97Dg4flpMd3rolFzg9fbrQ5DM3eVJ8vLy8uHz5srK+c+dOHB0dady4sbItJiYGW4OnYEHuERafnJGTWV3Zdo8kJUHv3rBqlZwmfvXqDKeHph0ruzf2gwcPUr9+fcXKM2vWLI4fP07Dhg0zly2HZPXUWtjkJsDcVJyPSF4oKGj0SrV+dq0pGj7/9Pd6FZAKOcYnOw/Jvr6y8tOwITx6JCc+DAwsHAHNTJ4UH39/f/766y+WLl3KTz/9xObNm+nUqZOS1RnkabcVKlTIs6CCZ3/uFir5YysqT+pFFVPBxKbcO9me/hwXBz16wB9/gI0NbNoEb7yRYXPDOABJkrJ0wWi1WmbOnElAQAD37t2jevXqHDt2jBkzZmBjY5ONM84Zmc1MMQe5cVGZevLO8ww9gSALPNTyjMjMZtf6NvCV77BRwGPzWHyUe0VGSleZMnIB5Xbt5MkZXbrI/28lnDwpPtOmTUOtVvPOO+8wYsQIrK2t+eijj5T9YWFh7N+/n9atW+dZUMGzL3NVt6pA0QlKLaooVhwTrq7IxEglZ0y2pj9HRUGnTrB7Nzg4wPbt8PLLmY6vvylrdVqik6JNyqPIGh5Oly5dmDVrFjqdjiFDhnD69Gkj62l+o2Q+LiLfo8yuT0aYcnXpZ+gJV5egoLC2tMbNzg3I+PcTp4oDfXL4a88egAoDvUzKvcJEdnMFZ2d5UsYrr0ByMrz2mmzJLsHkSfGpVq0aQUFBLFmyhC+//JKLFy8azTK5ffs2Y8aMYejQ9JWtBTlH/+eur+hdVJ7UiyIpqSlEJspTNQ2f/D3UHqiQ3VL62jmGs4lMBgw/fCg/ER05Is+G2LMHOnTIUgY7KzscbeQYuLC4sAwtEadPn6ZJkybs3bsXBwcH1qxZw4oVK4zi5wqC4h7jY9jWlMVHBDcLCpKs4nzC4sOg2tOVa88egAoDvUzKvSIrN5udHWzYIMcu6nQweDB8/31Bi2k28py60cvLi7feesvkviZNmtCkSZO8DiF4iv7LXNuzNn9e/rPIxGYURSISZKVGhQp39bN0C5YWlrir3YlIiFBq52TqGrlzR65zc/UqlC0Lf/8tJyjMJhp7DbHJsTx48kD50zO0RPz888+MHDmSxMREqlWrxh9//JGnKeo5IbOSD+YgN0HJIsZHYC40DhquPr6a4e8nLO6p4rMHuAWkyNsKI6mm4UPyn5f/zN7DjZUV/PijbNH+6isYNUpOcjhhQgFLW/gUSOZmc7Bv3z6GDRtGrVq1cHBwoHz58rz88sucOnXK3KLlG/ovc21NbXm9iDypF0X018pd7Z6urEe62VYZZVS+elWe7XD1KlSqJFc4zoHSY9hncHgwAJYqS1ztXElJSWHcuHEMGjSIxMREunXrxokTJwpN6QFjpUEnmb/8TK4sPibilMSsLkFhkJXFNCw+DMqCS1kXSAFuFN5/tuFDMuTg4cbCQi5nMWWKvD5xInz6aUGIaFZyZPH5+OOPUalUjB07Fnd3dz7++ONsHadSqfjwww9zJWB2+fbbb4mIiGDcuHH4+fkRFhbGwoULadGiBbt27VJSiBdXtDqt4roxNF9KkiSy05ogs3iRdPl1TLlGLl2C9u0hJARq1pRjeypWzLEc+j6DwoIA8LD3IDoqmldffVUp+Dtjxgw++ugjLCwK9zlEL1uqlEpUYpSRZaywUWoLkcvg5qefYaouVQk2FRYfQUGS1azIsPgwUEG95+vxz8Z/4HLhWFdTUlOISowCjB+Ss32vUKlg7lzZ8jNjBkyfLk/s+PTTElPcNEeKz8yZM1GpVPTp0wd3d3dmzpyZreMKQ/FZunQpZcqUMdrWpUsXqlWrxpw5c4q94qOPR1GhoqZHTQBSdCk8SX6Cs62zOUUrkmRmPcgyo3JQkKz0PHwIderA3r2ymysX6PvUW3xc4l1o1aoVly5dwsnJiZ9//pmXswiSLihsrWxxsnHiSfITwuPDzar46D8DawvrHH2f0z51P054jIQcQGrO8xGUfLLK3qzf3qZjG1nxuQKPYh8VuFyGbn79vSI5NZnY5FicbJ2y14lKBR9+CPb2crqOuXNl5Wfx4hKh/ORI8Ql8Ose/UqVKRutFgbRKD4CjoyN+fn7cvXvXDBLlL/o/dne1O062Tthb2xOfEk9YXJhQfEyQmbsj7ZOakZJ04YIcuBwWJru19uwBTe4tB4riExYM9+DWhlukxKRQoUIFtm/fznPPPZfrvvMDjYOGJ8lPCIsLo4ZHDbPJYfh55cSCmZHb0s3ODWtL63yWUiB4RpbBzU+/k23btuVz9eekxKZw9sxZKOCwV/24HvYeONk6obZSk6BNICw+LPuKj5533wW1GsaOhS+/hIQE+O67dMlaixs5Unz8/f0zXS9qREdHc/r06WJv7YH0N3KNvYbb0be5+Oii8gdfwbmCkrehtJOpxefpNbwZdZM70Xe4GyMrxrXuJ8JrARARISf12r0bPDzyJId+rPvH78MmSNGm0KBBA/766y+8vb3z1Hd+oLHXcCPyBsHhwTQo1wAHG4csj0lJTQFQvncJKQmordXZGi+jtrkNSNa316cnEPE9gsLCVIzPw9iHJKXKxbkexcnWnfJu5fFt4suVf65wKvAUjMi4z+z8lgzbSJJEojZRWU9ISVDc6nr5NA4a7kTf4eKji1hZyLf8HN0rxoyRlZ8335SDn7VaWLasWCs/eZL8/v37LF26lCFDhtC9e3e6d+/O0KFD+eabbwgJCckvGXPN2LFjiYuLY9q0aRm2SUpKIiYmxmgpiqQt4Kh/7flrTyovrkzlxZXp+HNHs8lX1MhsSrN+26pzq6i8uDLbr26n0QPoOeYrWelp2lR2b+VR6YGnic6OAr8CWqjUpBIHDx4sEkoPPLsWI7aOwHuRNw9jH2baXifpaPh9Q+p9V49UXSoHbh3AeZ4znx3+LMuxvj3xLU5znfjryl/p9uVWYXFXuxulJxAzugSFRVpr45JjSyi3sJzyf6x3OXnae9LQX87ifOXolQz7O3L3CM7znPnk4CcZtjlx/wQu81z4KFDOl/f2jrdxX+DO1YirRCdGU3lxZfpu6quMa/j68vqXFdk6/dwpZyc7dCisWSMrOytWyEqQzvwTInJLrhWfjz76iGrVqvHOO++wevVqtm/fzvbt21m1ahVvv/02VatW5ZNPMv4AC5oPP/yQNWvW8MUXX2SaBG7u3Lm4uLgoS8VcBLAWBmn/0Ac8NwAHawdsLW2xsZSz+h64daBIzM4pCihJ7EzcADtX7UwF5wrYWtpia2lL6xBrAn+2wDYmDlq0kC09bm55lkGSJI6vOA675HXbFrZ898t3ODnl0NxcgPSp00dxlcYkxXAm9Eym7cPiwvgv7D8uhV/iYdxDDt05hFanZf/t/VmOtf/2flKlVP6580/6fnNZWFSfnkDfh7D4CAqLtBafwFty6IeVhZXy39KlWhfKOZajdfvWoILIW5Hcvn3bZH+H7xyWf0u39mc45uG7h0nRpXDg9gFlzERtIv/e/5egsCBFFkcbR/rV7QdkcK+4fSDnyRT79jVWfkaMKLbKT67y+EybNo25c+dia2vLG2+8gb+/P97e3kiSREhICIGBgfz222989NFHShr+wmTWrFl88sknfPrppxnmGNIzdepUJk6cqKzHxMQUSeUnba6ZCS0nMKGlnF8hSZuE3ad2pEqpRCdG46bO+027uJPZDbC2pjZ3JzyN+zp2DDp3hoQYuUrx9u1yJtM8otVqGTlyJCtWrABg3rx5TJ48ucjNwHuj/hu8Uf8NXlj9Antv7s0yi7Ph/vD48HQB4tk51lTbbNdKM4HGQSPnZYoLe2YZVYvkhYKCxTDzuSRJyndvfa/19PLrZdTWt7wvVATuwLZt2xg7dmy6/rLzW9Lv0ys4hse42LoA0MS7CSdGnFCOmdhyIhNbyve4RG0i6k/VSjLFHOcU6itbkxgwAJYvlwOdf/ih2Lm9cqz43LhxgwULFuDr68vOnTupXr16ujZDhw5l+vTpdO7cmTlz5jB48GB8fX3zReCsmDVrFjNnzmTmzJl88MEHWba3tbUtFkVUM8w1g/HsnLD4MKH4kM2YkZMnZaUnJgbatoW//oJ8yJacmJhIv3792Lx5MxYWFixbtqzIZy/PbjLDtPly0qYEyPTYNAHIpvblSvGx13CJS4THh2f6OxEI8hP9dyw5NVn57zXcbtTWXgM1gTuwZcsWk4pP2mLGplB+Q3Fh6CSdMtvXcJJLZr8hfTb52OTY3CdT7NsXJAkGDoSffpKVn++/L1bKT44lXbVqFTqdjtWrV5tUevTUqFGDn3/+Ga1Wy+pCqvsxe/ZsZs6cyfTp041qhpUE0sb4pKWoZeE1N1mWLThzBjp2lJWe55+XLT35oPTExMTQrVs3Nm/ejK2tLZs2bSrySg9kv3yF4fcrLD4sW3/Whu3T9pF2X24UFsMkhiLGR1BY2FvbY29tD2Rekgae/g/JM8sJDAw0GUtq+PvIyA2lbxORECFXhpdSle3ZdfPmS6mafv3gl19kZWfZMjnLczFye+VY8Tl8+DB169bNVuHRNm3aULduXf75J71PP79ZuHAhM2bMoEuXLrz44oscO3bMaCnuZPWHXtTqLpkTQ7OzyT+BCxdkpScqClq2lC09DlnPZsqKsLAw2rdvT2BgIE5OTuzYsYOePXvmud/CwFTVelOks/g8bR+bHEuiNjHD4ww/E5MWn1zG+MAzt5bRzUdYfASFgP5/NyQ2REkwa+o7rHHQgCfgASkpKezatStdG/13N0WXQkyS6Uk2+t+JTtJx9fFVo+3ZVfrz7SG5Xz/4+WdZ+fnxRxg9utgoPzl2dQUHB9OtW7dst2/evDk7duzI6TA5ZuvWrQDs3LmTnTt3pttfWFVxC4qs/tCLWqVtcxKVGKU8CaX7EwgOlmtv6Wdv7dgB+RBsHBISQocOHQgODsbT05OdO3cWaGX1/EZJ6phZFWcyjvHRr1dwrmDyuOikaLQ6bbo+0vab2xgfkP/8s7KMCgT5iae9J7ejb3M5/DKQvjagHicbJ2wsbUiumQxH4I8//uD11183apP2t+Ri55KuH8M2+mnrSvunMT5Zfffz9V7Rv7/s9ho0SI71sbSEpUuLfJLDHFt8oqKiTCYLzIgyZcoQFRWV02FyzP79+5EkKcOluJOlxUe4uhT018rJxglbK4P4ratX5eSEjx5Bgwawaxe4pP9zySn37t3D39+f4OBgKlSowKFDh4qV0gM5iPHJwNWV1bGG+6ISo5RcQGn358rVZZCJW7i6BIVJ2lp8HvYe6WoDgly9QGOvAbnaEFu3biUx0dhCavRbyiIpIjxNimqwPdsWn/z2DgwYAKtXy8rOt9/C5MmyMlSEybHik5CQkKNgYBsbGxISEnI6jMCALF03CFeXISZvojduPKu99dxz+TZl/fbt2/j7+3P16lUqV67MwYMHqVmzZp77LWyyHeNjsP9W1C0j91amQZlp9hk+baakpihugrxafISrS1CY6L+vaZMGmmzroIHyoPHSEBsby99//63sS9ImGbm3TD1EGNahg2fKFuQyxieXD8mSJJGSYvzgwoABsrsL4PPPYdasXPVdWBSfMOxSjKGbIMPgZqH4KKSLF7l9W1Z67t2D2rXlMhSeeXeF3Lhxg7Zt23Ljxg2qVKnCgQMHCm32Yn6TmxgfQ1N7Vsem3WfYj2FtodzU19J/968/vk6KLsVom0BQkKRVfDJzM3nae4IKGrVvBMBvv/2m7EvrdjL1Px6REKHUoTMcE+QadaGxoUYyZSizQ+7uFZGRkXz66adUrlwZtVpNgwYN+Oyzz555VIYPl8tagKz4fJZ1UlNzkas8Pr/88ku2A4avXbuWmyEEBuhvGo42jthZ2Zlsk90bV2nAKF4kJER2b92+DdWryxmZc+CqzYgrV67Qvn177t+/T40aNdi3bx/ly5fPc7/mQv9nqC/9oE9tnxbD79ftaONEbLm1+Oj7dFe7m3QTZIX+u6+Xx8HaIdslNASCvKD/3ei/e5lZW/QKSfXnq7NrzS62bNlCUlIStra26RUfUzMf02xL+/vTl97JboxPThSfmzdv0qlTJ6P7+blz5zh37hx3795lyZIlco6yt9+Wi5lOnSq7vBwc5JIXRYxcKT7Xrl3LkUJT1JK2FTey47vNqlJwaUL/B+Gjc4ZOneD6dfD1hX37wMsrz/0HBQXRoUMHQkND8fPzY+/evZQrVy7P/ZoTD7UHKlRISETER1DW0XQ1+uwkV8vOPsM/8azcuFmR9jgR2CwoLNJ+1zL9j366z7GKI97e3jx48IC///6bHj16ZPpgkNk2k+Nk09WV3f6Cg4Np3749oaGhVK5cmU8++YTWrVvz+++/89577/HVV1/h5OTEp59+Kh/w/vsQGwuffioXN7W3hyFDsjVWYZFjxefmzZsFIYcgE7LjuxWurmeExYfhkATvfbofLoXIys6ePVDB9IyjnHDhwgU6dOhAWFgYzz33HHv27MlRsH9RRV/6ISJBrndlSvExjDUzRW5dXXkNSE57nIjvERQW6b572Xk4TQind+/eLF68mHXr1smKTya/j8y2pcXawlqZ3ZWVHNnxDkRHR/PSSy8RGhpK3bp12bVrl1Jn8N1338XV1ZU333yTzz77jBEjRuDj4yMfOHu2bPlZvFh2gdnbQ+/eWY5XWORY8alcuXJByCHIhOxM0RWzup4RFRXK5vVQ+WYIuLvD339DlSp57vfMmTN07NiRiIgIGjZsyO7du/HIh0KmRQXD0g+miE6KVmJoTJETV5fR7LA8BiQbZi4HEd8jKDxyYm00dDGN7D+SxYsX8+effxIbG5v+95FJdvPM8LT3zNLDkt2HZJ1Ox+DBg7l27RqVKlVi3759aDTG5zt8+HDWr1/Pnj17mDVrllKiB5UKFi2C+Hh5mvuAAXKF9x49sjyHwkAENxcDsuXqerovQZtAXHJcochVJNFqefOzvbxwE1LsbeU8PXXr5rnb48eP0759eyIiImjWrBn/b+++w6Oq0geOfyc9k5AeSghICRCqsuiKCooFARtFWJWfrhTXgqisrqiACIuChUXRBQvK6q6ALmBDistKcXXpLIIIKFJDENJ7JmXu74/LvblTkkySaUnez/PkYcqZe89c7p1555z3nPP11183qaAHap/fw5hrZvxVqc3d40rgo5W1yfFxwxB04xeQtPgIb6lLa6Mx4Lj00ktJSUmhuLiYzz//XL8e9GuphtnN7efKMt535dzXyhSXF1NcXlxtuTfffJPPP/+ckJAQVq9e7RD0aLTFyP/+979z5MiRqidMJli8WA16KipgzBjYsqXW+nmDBD6NgCvrGEWGROor7zbb7i6rFe67jyv3nKc0EHYvfhZ++9sGb/a///0vN9xwA7m5uVx55ZVs3LiRWDcMhfc3tf0SNI6WM/6y7Z7QXX3eha4uvayT+X8akptjfK0sUCq8pU45PoZWeZPJxNixYwFYvnx5jdeHxr6MxnjflWuoRUgLggOCbbZp7+TJkzz99NMAzJ8/n0svvbTa7V1++eXceuutWK1WFixYYPtkYCC8/z6MGAEWC9x2G+zZU2sdPU0Cn0bAlXWM9AmyaKYJzooCjz8OH3xARQDcMQYCrr+hwZv95ptvuPHGGykoKODqq6/mq6++IsoNq7f7o9rm9zCOljOeiz0Se9g8X9NrtbLGD3ZttugGtfiYpcVHeF9MWIzNCMga0xHsPp/vuusuAL766itOn1VHZNV0LWnXiVZGY7zvyjVkMplqHAyjKAoPPvgghYWFXHXVVU4XVLX3yCOPAOqM1JWVlbZPBgXBihVw7bVQUABDh8Lhw7Vu05Mk8GkEXF3HqFnn+fz5z7BwIQD3jwrmi9SGfwF+/fXXDB06lKKiIq6//nrWrVtHpBsWMvVXtc3vYczFMX7Aah+82SXZVFornb/2wjb1wMeNOT72r5UcH+EtJpPJ5nO5pnNYK5dTmkN5ZTmpqalcdtllVFZWcmiTOhmhdn04W/tOu06MgU5YUBgdYjpU7d/Fc7+m1t1PP/2UDRs2EBoaynvvvUeAC6uuDxo0iLi4ODIyMpyvzRkWBp9/DpdeCpmZ6mjbU6dcqqsnSODTCLi6jlGzHdm1aBHMmgWA5dX5/K2XmoDbkK6TDRs2cMstt1BSUsLQoUNZs2YNEW5YyNSf1Ta/hzEXx3gudo3vWjUU/sJkhEbGXAKtWd7tOT7S4iN8xObcq+EcjguPw4SaeKxdJxMmTADgzNYzoEDn2M56C1J1I70uir6I8KBwfX/1Ofer+5FssVh48sknAXjyySddnoU+ODhYX5B51apVzgu1aKHmXKamwunT8Ne/urRtT5DApxFweSry5tjis2qVOmkWwOzZ/DpuNAAhgSG0CKnf4qNr1qxh+PDhlJaWcuutt/LZZ58RHt70J8SrravUmItjDCpbR7YmNjzWpoyz1wUHBNMptpO+D6titXm+IQGLza9uafERXqSdtw5rA9oJDAgk3qwOiNDO+TvvvJOwsDAsZy2QDi0jWlb7A8R4nWhl7K9FV3/sVfcj+fXXX+fYsWO0adOGp556yqVtaW6//XYAPvnkE6zVrdKekKCOsp0xA+bNq9P23UkCn0bA1V/EWlJns2nx2bpVHTGgKPDQQ/DsszbHqj4TZ65evZpRo0ZRVlbG7bffzqpVq+q0Nl1jVlvgbNPiY9e1VFNrozFHTftgrlQqyS3NxapY3bKiuvHakAkMhTcZg5Da2P+4iImJYeSokeqT/1O34SzXzjiHVoI5Qb/+EiMcr8W61Nm4j3PnzjFnzhwA5s2bV+du/euvv57o6GjOnj3Ltm3bqi/Yrp06z09g3WdpdxcJfPycsZvA1RafZpHcfOAADB8OZWUwciS88QaYTA1qPVi+fDl33HEHFRUV3HnnnaxYsYKQkBB319xv1dZVapxhWSsbaAokNjy2xnPP2FUbGhRKVKiaHJ5RlEFeaR6VSqXN/utVdxnOLnxEO2/rMpTceI2NHqu2UnMAwqxhTq+lfEu+zTp0+j7r29XlpHV35syZFBQU0K9fP+655x6XtmMUGhrKsGHDADU/0p9J4OPntBMzOCC41q4b+y+uD/Z9wOXvXk56QbpnK+ltp06pIwPy8mDAAFi2TP/1UN98kaVLl3L33XdTWVnJuHHj+PDDDwkODnZ71f2ZcR4ffeFBYM2RNXR+vTP/PvZvwLbFJ94cT4ApQD/e931xH20XtLX5G7taHbar/0o1fOhq/1+1dRPURtumKzPXCuFOxiCkNs5aWrpd2g3iAAv886N/6tt5eN3DXP7u5eSU5OjXibYOnfFaqk+Lj30ANuefc3hnyTsABA0LwopjV9U/vv8HHV7rQNsFbRny4RDKKx0nMw3vpKYEzF0+lxv+fgNllWUOZe774j7avdqOD/Z94FJdPUECHz9nbMGodUZOu66Kd/a+w84zO/nq6FeeraQ3ZWerQU96OvToAV98oc4IekF9uk0WLVrExIkT9WGc7733HoE+bIb1Fe38qbBWkGfJ0x9///v3OZZzjHJrOcEBwfRp1Yc+rfoQEhjCZUmXAej/5lnySC9It/nLKc2xKWP80HVHfg9At4RutAhpwaVJl8ragMKrLmtrew3UxFmranZpNlx46aJFi2yupZ1ndrL5xGaH60S/7tpeRlhQGL1b9iY+PN5mhJer9VAUhb/M+gsoQA/YEbTDZuV3zZK9SziZd5L0gnT+9cu/2H9uv0OZH8J+AMBywsLXv3zN/87+z6FMWn4aaflpNivNe1u9FikV3lOXFgz7pDjtYmkyOT8lJeqU54cOQdu2sGED2E0k6Mpkj0Z/+ctf+NOf/gTAlClTWLBgQbP94gwLCiMyJJLCskIyijKICYsBqo7pSze8xLhLxtEyQl2b7MzjZ/TWlacHPM2I1BEOQ3A1IYEh+jBcZ796G5qXExMWw4kpJ4gIbtoj74T/uanLTZx94iytIpwv7GvkLH8nozgDLoGAzQEcOHCAyyou4/DDh3lo7UN60KON9NKuk0d++wi/6/k7WkeqiyPvuG8HlkoLESGunf/Ga3DNmjXkHcqDQGAwDvWzqWcN9wFKYksgBLAAGbXk/PlwEIIEPn6uLr+I7ftt7f9t1Coq4K674L//hZgY+OorNUnOjiuTPWqef/55nn32WQCmTZvG888/32yDHk2iOVENfIoz6BLfBag6fy5NulQPesA2WDGZTHRPtJ1RtqZ9gPp/pR1vd3wIxoXHNXgbQtSHFoDUxrhQqSajKAPCIfmqZE5tPsUbb7zBypUr6RTbic0nNpNZnElwoNrtrl0nJpPJZp/hweGEB7s+8lRvdc3P4IknnlAfvAJaJbfiXNG5GnP1WkVUXybLkgVtgePAaeffPe5q5W0I6eryc3XputFOpNzSXErKS/Quhkbf4qMo6pD1zz+H0FC1e6tnT6dFXfk1oSgK06dP14OeOXPm8MILLzT7oAect8a4+xeaTY6PH3wICuEtNV1f/Ub0A9Th4EePHrX5geDu60Tbdu43uRw9ehQigIHOZ1YHqLRWklWszj3kbBJSMIw8036PnnZexh9afCTw8XN1OUniwuMIMKn/pUeyqhaLa/Tz+syfD2+9pS56t3w5DBxYbdHa1n2yWq089thjzJ0798Km5zNjxgz317mRsh9RYvzAc9uHrjHHxw8+BIXwFmc5Ptq11q1HN2666SasVivz58/36HUSFx4H+cCWCw9cDwFhAXSJU1t57b8zskuy9Zyc1IRUh/cAak5SubUc2l944LRjmaLyIr073JfTTkjg4+fqkrMSYAogPlydIMuYnNaoW3xWrYKpU9Xbr74Ko0bVWNw45NpeeXk599xzD2+88QagJhLqzbwCcPxgNn7gaeeW2/ZRJIGPaF6czZVl7J7XFgZ9//33CSqumsHZ3ddJYEAgoZtCoQw69+4Ml6jXd6vIVjZ1sq9jbFgsbSLbOLwH4/2IjhFq63k2nEq3XZZC+3wODQwlMsR3y/9I4OPn6pKzAlVRtE3g01hbfLZvB20+icmT4dFHa31JdR8QxcXFDB8+nOXLlxMUFMSyZcuYNGmS26vc2NknX2rHMyYsRs8zaChjEr47VmYXorEwdvPaz1yeYE5gwIABXHHFFVgsFrYs26I+74HrZOvWrVj2WQC4ZtI1EGA7wah94GNMuah2Zmntszc+kVYXqQHU8UPHbcvUYZSyJ0ng4+dcXaBUowVIhzIP6Y81yuTmY8fgttugtBRuuQVee03t6qpBeWU5uaW5gG2gmJOTw4033sj69esJDw/niy++YOzYsR6sfONlP7+Hq+vE1WcfmcWZNbbQCdHUGGcuzytVp4ywn23+ueeeA2Dt8rWQ6/7rpLy8nMmTJ6t3LoXcuFx9/9UtW2OzQHE1k5UaPys6du0IwK/Hf7Xdjp+08Erg4+fq+sWjlTuUURX4FJQVYKmwuL9ynpKTAzffDBkZ0LcvrFjh0vTm2rEKMAUQG6YOcz979izXXHMN3333HTExMWzcuFGfXVQ4Mk5iCJ4ZgSFdXaK5Cg0K1SeidfhxceEau/HGGxk0aBBlljLY4v7rZNGiRfzwww+EtAiB66q+K4xBTXULpNa0PI3xsyK1u5oHlHMqx2kZX7fwSuDj5+r6xaOdlD9n/2y7ncaS51NWpubxHD4Mycnw5Zfg4pox2gdIXHgcgQGBHDp0iCuvvJIDBw7QunVrtm7dylVXXeXJ2jd69h9qnghMtHO5pKJEn1VcWnxEc2EMLhRFccjjNJlMzNMW8PweLOkW0vLTbF5bX2lpaXqLUv97+4O56rsiITyh9qDG7FpwdHHviwEoOlNkU8ZfWngl8PFj5ZXl+pD0uk5FXmGtsHm8UeT5KArcdx9s2QItWsDatZCU5PLLjRfe5s2bufLKKzlx4gSdO3fmu+++o0+fPh6qeNNh/6FW1wkhXRERHEFYUBhQdZ5Ki49oLozBRWFZIZZKtTXeGAz0799fXe1cAb6EioqGXydWq5Xx48eTn5/P5ZdfzsDh6uhY/Ro0tPhkFWfpOUjgfJ2+nNIcm2UrjJ8Vl12izixtPW+ltLxqUlN/aeGVwMePZZWow4hNmFyenK26JsRGkeczZw784x9qt9bKlVDHQEW78KzfWxkyZAi5ublceeWVbN++nU6dOnmixk1OdS0+7myaNplMNtsLCQzx6QgPIbzJ+ONCu77Cg8IxB5ttyr366quYQk1wGtirrkOnLfBbH4sXL+bf//434eHhfPDBByRG2gYfieZEfeRmpVJJTklVN5XxcyAuPA4Tar6l9h1lX6Zfr35qdGGB/T9XLW3hiR9S9SGBjx+z77pxRXUnlN93dS1fDheaYFm8GIYMqfMmzhedh61wZMkRysvLGTNmDF9//TUJCTJiyFVaQFJcXkxxebHHmqZtVpQ2+3aEhxDeZMyjq+n6ateuHW1va6ve+TfElMbU+zrZvXu3vjTPyy+/TLdu3Rz2mRiRSGhQqB5cGX8sG1tqAgMC9R/ixjLG9xIaGkpggvqdtWvfLsftSFeXqE59Ekvty2onqF93de3YARMmqLeffBLuv7/Om7BYLCydtRQ2q/enTp3KRx99RFhYmBsr2vRFhUYRHKAOWzeunu7uX2g2K0pLfo9oRpzOyFzN9dX95u7qEhClULi8EIul7oNUzp8/z6hRo7BYLNxyyy36NB72+9QCMmd5PvbfRTXOR3Th9RFt1XXD9h+oavGpzyLSniCBjx+rz5eOfdnqpiD3G2lpMGIEWCzqAqRaUl8dnD17lmuvvZZ9G/aBCYZNGcZLL71EQICc3nVlMplsm+I9tKSEfYuPEM2FTeBTSwtIq6hWMAYIg5ITJTz22GMoiuurmhcUFDB8+HBOnz5N165d+fDDD/XPRYcWH3P1QY396GJXgqO49uqP7iOHDasISI6PqE19vnTsI+nuCd1ttuVXiopg+HD49Vfo1QuWLXNp2LrRtm3b6NevH9u2bSM4IhjGwtA7h3qows2D0w9mN39QGc9TX//6E8KbnP2wqO4aSDQnQgxwYcL6t99+m6eeesql4KewsJCbbrqJ7du3Exsby2effUZ0dLT+vP0+9dYcu6DGZn2tOrT4tOmszvB8/KeqSQz9ZW0+CXz8mJ4sFu76F0N1gY9xNWC/YLXCuHGwdy8kJMCaNepIrjp45513uOaaazh79iw9e/ak78y+0MX3vyYaO+NCip5qmpYWH9FcGXN8avthoT/e9cIMy8Arr7zCpEmTKCkpqXYfR44coX///nz77bdER0ezceNGunfv7nzbdvWyX0i1oKyAssoy2zLhtvN9aTmBxjIdunQA1EkMFUWhrLKMPEteje/XW5pU4FNYWMiUKVNISkoiLCyMSy65hI8++sjX1aq3+iSWGpPTokKjaBulJsf5XYvPnDnqOlzBwfDJJ9Chg8svtVgsPPDAAzzwwAOUl5czevRotm/fTlGkOmeEr39NNHba8TuWc0z/wHN7V5fk+IhmytiiUtsEtcYfHNf97jp9ncG33nqLyy67jM8//1wf6g6QlZXFzJkzufTSSzl48CCtW7fmX//6F/369XPYtnEyxajQKEICQ2zqYj+JqTnYrI88q26Gd+PIs86dOgNQVlLG+fPnbSeYDY916Vh5SpBP9+5mo0aNYteuXbz44ot07dqV5cuXc9ddd2G1WhvlEgX17WZINCeSb8knwVz9hFQ+tXIlzJql3n7zzRpXW7d39OhR7rrrLnbv3o3JZOKFF17g6aefxmQy+U3iXGOnz/59YdkT4weeu/dhf1uIps7ZcPbqgn+bHwjmRB6a/BApKSmMGzeOgwcPMmLECOLj4+nYsSNFRUUcPnxY7wYbNGgQK1asoHXr1jXWpaCswPZ6dGHZGodpL5yswdUmtg1EAflw/PhxzB3Vz5D48HgCTL5tc2kygc+6devYuHGjHuwAXHvttZw8eZInn3ySO+64g8A65o/4Wn37QxMjEvkl55caZ9n0mT174N571dt//CNMnOjyS1esWMEDDzxAQUEBcXFxfPjhh/ryE1bF6pF1pZoj7fhpC9164nhKi49orrTrqaSihJO5J20eq64sVF0nQ4cO5cCBA8yfP5+lS5eSmZlJVlbVfDp9+/Zl+vTpjBw5stYBHonmRI7lHHMIsMDJ7O1Orln7xYyN9U0wJ6j5SRcCn5YtWzpsx1eaTFfXp59+SmRkJGPGjLF5fPz48aSnp7Njxw4f1az+6jt5nD4s0bDabnZJNpXWSvdWsK7OnlWTmUtKYOhQeOUVl15WUFDAxIkTGTt2LAUFBQwcOJDvv//eZs2t3NJcKhX1/UmLT8Nox08LfDxxPCW5WTRXkSGRerdSbdeYMUgwlklMTOSll14iLS2N3bt38/nnn7Nu3TrOnj3L3r17uf32210a1Wo/hN34mP3s7c6uWfsWH5s6mhPhQo/W8ePH/WZEFzShwOeHH36ge/fuBAXZNmJpyxT88MMPTl9nsVjIz8+3+fOkZ/79DLetuI0D5w7oj+WV5jFlwxR2ntkJwJYTW7hz1Z38kv0LUL+uLlBPQm0mTgWF7JJsd7yF+iktVYetnzkD3bvDRx/xxu7FjFk5hjtW3cH6n9cDsP/cfh5d/6h+IW3ZsoU+ffqwdOlSTCYTM2fOZNOmTSQnJ9tsXisfFRpFaFCoV99aU6N92GqBpCd+oUlXl2iuTCaTfs7Xdo05BBJ2QkND6devH7fddhvDhg2rsVvLGW2/NvuxW33dWcCi3f4l+xfGrBzDgu0LHN5HYsSFEWlcCHz8ZEQXNKGurqysLKfLEsTFxenPOzNv3jxmz57t0boZbTqxiZ1ndjKx70R6t+oNwCeHPmHhjoX8kvMLa+5aw7Svp7EtbRugJou1j25fp32kxKWo/8amEBwYTGxYLDmlOWQUZ/jmpFMUeOgh2LkTYmPhiy/IDVV4dMOjepGD5w8yrMswXvruJZYfWE5yeDLpn6WzcOFCADp06MD777/PNddc43QXnlhaobnSzh/9fmxKNSXrLzY8lrjwOIrKivQEfCGai5S4FM4UnAHU5SqSWjhfkzAmLIb48HgKywpJjkp2WqZB9bhwbRuveWNXl7NFVAHaR7cnOCCYkooSVv24ymF7evkLLT7Hjh8jqVh9j3UZpewpTSbwAWqczru655555hkef/xx/X5+fj7t2rVze9009k2EAGcLz6r/Fpy1uT/1yqnc1u024s3xddrHY5c/RveE7tzY+UZAjbBzSnPUE9gXwfabb8L770NAAHz8MaSkcC7ziE0Rm2NwFOa9M4/c9FwA7r//fubPn0+LGoa7S36P+1zS+hLWjl3LidwThAWFMTJ1pNv3EWAKYNPvN1FSUdKg9YeEaIz+PvLvrPt5HVbFSr82/apdqy7AFMCmezdRVFZEdFi00zINMaX/FHok9tC/K6CqRaa0opSi8iJ9KhTjj+Z4czyb793M9+e+1x8zB5u5vfvt+v248Di9xeeXY7/Qrbibw3Z8pckEPvHx8U5bdbKz1e4dreXHXmhoKKGh3usasR8qCI4JYtr9P/T7g8Ovb1dEhEQwsnvVl1WiOZGfsn7yzciu//wHHntMvf3iizB4MFD1XqNCo8i35JNdks3pM6fZ+8Ze2AO55JKcnMySJUsYOrT2CQn9qRm1Kbipy00e38fFrS/2+D6E8Efto9vz4KUPulS2T6u6LdZcF/bfFQARwRGEBYVRWlFqO3u73Y/Kq9pfxVXtr6p228GBwUS3jiaPPM6cPsP5wvNOt+MLTSbHp3fv3hw6dMhmTgOAAwfUXJpevXr5oloO9GZEJzNeZhRlUFJeQlG5Oh+Nu7ptjBNmeVVaGoweDRUVcOedcGGRPKh6/93iu0EF8B307NGTvD15YIKOwzry448/uhT0gP9MhS6EEI2ZyWSy6ZloSBpByzYtIQAqKipIO50G+MeP0yYT+IwcOZLCwkJWr15t8/gHH3xAUlISl19+uY9qZst+jgSoCkhKKko4lXcKUHN7okPd07TpLNjyuNJSuP12OH8e+vSBd98FQ3djZnEmKFB5oJKARQGwEQryC9QF+e6HVre3qrFry15ti/0JIYRwjbFnoiGt6S0jW+rdXefSzgH+kYfZZLq6hg0bxuDBg3nooYfIz88nJSWFFStWsGHDBj788EO/mcPHWY6P8bY2aVyCOaHGnKW6cBZseZSiwMMPVyUzf/opREQYnlb4dtO38C7sPbNXfbAFPPLMI7xR+gYE1D1I0/qh/eGiEkKIxsw4T09D8if1uXyyIetsFsT6x4/TJhP4AHzyySdMnz6dmTNnkp2dTWpqKitWrODOO+/0ddV0NeX4gGHSODc2B3p99ua33oKlS6uSmS+MtlMUhfXr1zN79mx27lSH7geHBdNmaBtO9ThF6wGtYRP1qqvk+AghhHto3xlp+WkUlBWoj9Xjs9U4sqvwXCH08I/P6CYV+ERGRrJw4UJ9CLQ/sp/x0rjyLXhmtlyv5vh8+y08emGY+oVk5pKSEpYvX87ChQv1nKvAkEAq+1Uybdo09hft59ThU/p7B8i35GOpsLg8J4/k+AghhHvYL1sTFBBUr9QL41w+So66lIY/tMo3qcCnMbBvfSkqL6K0olR/3hOz5Xpt2YozZ6qSme+4g2OjRrF0xgzefvttMjPVoMtsNjNp0iR2ddjF1sytdEzuSPrpdACbwAcgqySr2vkt7EmLjxBCuIez2dvrk3phbPEhB6JDo/VZq31JAh8v006o4vJiisuLHVphDmceBtzbcuGVrq6yMhg9msJz51iVnMz7Z86wNaVqKH779u155JFHmDhxIrGxsfR7R10tODEiUa+f9t41GUUZLgU+xlYzf/g1IYQQjZn2A7Kh30cJ5gR1oVKAAv/5fJbAx8uiQqMIDgim3FpukzGvKakoAdyc42No8VEUxW1J05rs7GzW3HEHn2zfzleAJS0N0tIwmUzccMMN3H///YwYMcJmORHjKCytftp718u4GKgVlxfrrWbS1SWEEA1jXEgV6v99lBiRCNrgXAl8mi+TyURiRCLpBenq5FDVfLl7Isen3FpOviW/wTOAlpeXs3PnTjZu3MjGjRvZsX07lVar/nyXLl249957+f3vf+90FmxjC42xxceeq11z2rZCA0OrnQFVCCGEa+wDnfp+HyWaDYFPJcRYYxpWMTeRwMcHEs0XAp/ijGq/3N3Z4mMONmMONutda3UJfKxWKydPnmTfvn1s376d7du3s3v3boqLi23K9QFGDRzIqEWL6NWrV42tSsa8pgRzQrXv1dVkbGN+j7tbs4QQormxb5mpb0tNYkSiGmWEAyUQXhLe8Mq5gQQ+PuBsjgQtMNG4u0kw0ZzIybyTZBRn0Dmus81zVquVc+fOkZaWxunTp/npp5/48ccfOXjwIIcOHaKkpMRhe/Hx8dwwaBA3fPcdg3/9lYuuuw7+9S9wYb4kLVAJCwojIjjC4b1qx8LVri4Z0SWEEO5j/1naoBwfUFt9SiC4OLiBNXMPCXy8zGKxEBMYAxZIy0jjfNF5KIUuUV34Pu/Cgm8KBBQFcO7cORRFQVEUrFarfru6x8rKyigpKdH/SktL9dvKDgXOwbzj84gllpycHDIzMzlz5gxnzpxxWOrDKCQkhO7du/Pb3/6WK664gv79+9Ota1cC7r4bfv0VkpJgxQqXgh6wXVDUZDI5XFTdE7qz5+wel7u6tO35S/+xEEI0ZrHhsQSaAqlUKoH690DovQ1RxXAeTIX+0SIvgY+XDRgwgN27dwMwbd40/fHv+d6m3DWvXOOR/X/BF04fDwgIICkpieTkZDp16kTPnj3p0aMHPXr0oFOnTjaJyQD89a/w0UcQFAT//Ce0bOlyHYz5PcZ/NT0Se6iBj6stPjKUXQgh3CbAFEC8OV79YU7DWtMTzYmcbHESgIq86n9ge5MEPo1AQEAAJpPJ5s/ZYyEhIYSHh9v8hYWFER4ezqG8Q5wqO8U1qdcwrM8w4uLiiIuLIykpiXbt2tG6dWvH4KY627fD44+rt195Ba6qfoVeZ7RARWuhCQsKIzIkksKyQkBt8YE65PhIV5cQQrhVojmxKvBpwI/KxIiqwMeSY3FL3RpKAh8v27JlC0v2LOGPX/2Rm7veTEZxBjvTd7L89uX836f/h6IoxJpjyXoqy62Juk989QQLti/gsisu46kbn6r/hjIyYMwYKC9X/33ssbpvwkmgkmBOqAp8ErvblKt1e7JAqRBCuJUxdaAhaQTGkV2FWYUNrZZbNJnV2RuLiIgIkhOSIQRyK3PJrsiGIEiOTSYhMgEC1RVt3T06yS0LlVZWwv/9H6SlQdeuDiuuu8rZonfa7ZDAEDrFqmt71XU4u+T4CCGEexhbeRryozLBnKAHPrkZuQ2slXtI4OMDxpmUjfkp1eW8uHuf9TZnDmzcCGYzrF4NUVG1v8YJZzk5+ns3V83rk1WShVWxOm7Ajh5ISY6PEEK4hTHYiTfHN2w7FwKfzPNeWC/SBRL4+ID2BZ1ekE6eJU99zPCF74kumwav17VpE/z5z+rtt9+GXr3qXRdnXV36e49I1FturIqVnJKcem1PCCFE/Wmfp3HhcQQF1D8rxjh7869nf8Vqrf3HrKdJ4OMD2gml5bQEmAKIDY+1afVwtwat0H7unNrFpSgwcSLcfXeD6uKsa8oY9AUHBusrAWtlvzjyBT9l/QRAVnEWKw6s0CdBlFFdQgjhXu76Pko0J0IEYILKykoyMjy8WLYLJPDxgbjwOEIDQ/X7rSNbE2AKIClSXZCzTYs2bt9nvbu6rFa45x51vp6ePeH11xtcF2eBirYYqfbejS1U+8/tZ/hHwxm7eiwAs7fOZuwnY/lg3weUVZbZtJoJIYRoOPvP5AZtJxCCo9TJC9PT0xtct4aSUV0+EBgQyNLhS1l/dD0mTPyu5+8AmNJ/CqFBodzf736371MLJArLCimtKCUsKMy1F770kprXEx6uztdjNje4Ls6Sm++5+B5+LfyVcZeM0587mn2UjOIMskqyADieexyAYznH9PtZxepzWquZEEKIhrupy008M+AZbut2W4O2c0OnG5g+cDorV63kp7yfSE9Pp2/fvm6qZf1I4OMjY3uPZWzvsTaPdYztyMuDX/bI/qJDowkKCKLCWkFGUQbtoh0XD3Xw7bfw7LPq7UWLoEePBtfDpoXG0OKTYE7gpcEv6fe15zKLMzGhjhzLLslW63+h1cq4yGt8eDwBJmnAFEIIdwgLCmPu9XMbvJ3gwGCev+55vu/wPT/98JNftPjIN0UzYTKZ9Jwal7q7MjPhzjvVIex33w3jxrmlHlprT6ApkJiwmGrLJYRfqKvdCvZZxVl6V5n9qDghhBD+KSlJ7TqTwEd4lda1VGuCs6Kogc6ZM+p8PYsX12u+Hme0QCXeXHMLjXHeIeNItIziqkDIeFvye4QQwn+1aaPmCvlD4CNdXc2Iy0PaFyyAtWshNFTN62nRwm11cDVQMSZja11dAKfzTuuj4TKKMhyWvxBCCOF/Wl5Yz9EfRnVJ4NOMuDSya8cOePpp9fZrr8HFF7u1Dq5ONugsxwfgcOZhm205S5QWQgjhXxITL3z/SOAjvEkPfKpr8cnJgTvugIoKdR2uBx5wex1cXVdLz0cqyrBZvuNQ5iH9dp4lj/QCtdlUcnyEEMJ/SeAjfKLGSQwVBe6/H06ehI4dYckSt+X1GLm6rlZ1XV0/ZvxoU04LhKTFRwgh/JcEPsInalyodOlSWLUKgoLg448hOtojdXC1xceYj2Rs8bEPfLT70uIjhBD+Swt8srOzqaioICjId+GHjOpqRqrN8TlyBB59VL39/PNw2WUeq0NmiYs5Phfqaqm06EtTAOSU2q7dpd2X5GYhhPBf8fHx+o/YrKwsn9ZFAp9mxOmorrIyGDsWiovhuuvgySc9WgdXW3zMwWbXZ5d2YXtCCCF8JzAwkLi4OMD33V0S+DQjTicwnDED9u6FuDj4+98hwLOnhKs5PiaTqU7BjHR1CSGEf/OXPB8JfJoRLZDIKcmhwloB//43vPKK+uR770Hbth6vQ11mWjaWCQqw7Q+2vy9dXUII4d8k8BFeF2+OB0BBIef0z/D736tPPPAAjBjh8f1bFau+4KgrrTnGMl3iutg8Z7wfHRpNSGCIm2ophBDCEyTwEV4XFBBEXHgcKBD6wMNw9iykpqozNXtBTkkOVsUKuNZCYyzTPbG7zXM9Ens4LSeEEMI/SeAjfCLRnMgDuyHqq80QEgIrVoDZ7JV9a/k90aHRBAcG11re2OLTLqodkSGR+n1j4CP5PUII4f/8ZdmKJhH4bNq0iQkTJpCamkpERARt27Zl+PDh7Nmzx9dV8zuX5pp59asLd158ES65xGv7rutK6sZyieZEm0DIJvCREV1CCOH3pMXHjd58801OnDjBY489xrp161i4cCHnz5+nf//+bNq0ydfV8x+lpcxZ8gvhFXCqfw947DGv7r6uK6kbyyVGJOqBkAkTXeO7Oi0nhBDCP/lL4NMkZm5etGiR3oSmGTp0KCkpKcydO5frrrvORzXzM9Om0fFUPufNsPLJm3jCw0PX7TWkxSfBnKDn8sSGx9I6srXNc0IIIfybvwQ+TaLFxz7oAYiMjKRHjx6cPn3aBzXyQ5s2wauvAjB+BJwIK625vAfUdSV1Y0Bj7OpKNCfaPic5PkII4fck8PGwvLw89u7dS8+ePX1dFd/LzYVx4wDYP+IK1nWtZr0uD3N18kKNQ1eXFvhEJBISGEJ0aLRDOSGEEP5JC3yysrKwWq0+q0eTDXwefvhhioqKmD59eo3lLBYL+fn5Nn9NziOPwOnTkJLCj09NAKpZod3D6pzjY5fc3DKipc3r9fvS4iOEEH4vIUH90VtZWUlOTk4tpT3H7wKfLVu2YDKZXPrbt2+f0208++yzLFu2jFdffZV+/frVuL958+YRHR2t/7Vr184D78qHVq6EDz9Ul6L4xz+ITVDfn09afOqY4xMXHseD/R7k/t/cT7w5npHdRzKw/UDu+819AEzpP4XBnQZz9UVXe6zOQggh3CM4OJiYmBjAt91dfpfc3K1bN5YsWeJS2fbt2zs8Nnv2bJ5//nleeOEFJk+eXOs2nnnmGR5//HH9fn5+ftMJftLT4cEH1dvTpkH//iSe3QvYLVTqJXVt8QF485Y39dspcSl8M/4b/f6kyyYx6bJJ7qugEEIIj0pMTCQ3N5eMjAxSU1N9Uge/C3zatGnDfffdV6/Xzp49m1mzZjFr1iymTZvm0mtCQ0MJDQ2t1/78mqLAhAmQnQ2/+Q3MnAlUBR2ZxZkoioLJZPJalbTuNRmFJYQQzVNiYiI///yzT1t8/K6rq77mzJnDrFmzmDFjBs8995yvq+N7b70FX30FYWHwj39AsDpTshZ0lFvLybd4L59JUZQ6d3UJIYRoWvxhZJfftfjUx1/+8hdmzpzJ0KFDufnmm9m+fbvN8/379/dRzXzkp5/giSfU2y++CD2qZjkODw4nIjiCovIiMooziA6L9kqVCssKsVRaABmFJYQQzdXo0aPp3bs3ffv29VkdmkTgs2bNGgA2bNjAhg0bHJ5XFMXbVfKdigq45x4oKYHrr1dHdNlJjEikKLeIjKIMUuJSvFItLb8nPCiciJAIr+xTCCGEf7n77rt9XYWm0dW1ZcsWFEWp9q9ZmTsXdu6E6Gj429/U0Vx2tBYXb47s0icvlG4uIYQQPtQkAh9xwa5d8Oc/q7cXL4ZqRqdpeT7eHNml7UsSm4UQQviSBD5NRWkp/P73UFkJv/sd3HVXtUW1VhdvTmJYn6HsQgghhLtJ4NNUzJwJhw9D69Zqa08Nw9R90dUlI7qEEEL4Awl8moL//hfmz1dvv/02xMfXWNwngY+0+AghhPADEvg0diUlMH68OmHh738Pt91W60u0Vhdv5vjI5IVCCCH8gQQ+jd2MGeq8PUlJ8NprLr1ECz4kx0cIIURzI4FPY/btt/Dqq+rtd96B2FiXXiY5PkIIIZorCXwaq6Kiqi6u8ePh5ptdfqkvurqkxUcIIYQ/kMCnsZo2DY4eheRkWLCgTi/Vgo+i8iJKyks8UTsHMo+PEEIIfyCBT2O0dSu8/rp6+913ISamTi+PCo0iOEBdtNQb3V2WCgsFZQWAdHUJIYTwLQl8GpvCQpgwQb19330wZEidN2Eymbya4KztI9AUSExYjMf3J4QQQlRHAp/G5umn4dgxdTmKv/yl3pvxZp6P1qqUYE4gwCSnnBBCCN+Rb6HGZNMmWLRIvb10KURF1XtT3hzZJSO6hBBC+AsJfBqLwkKYOFG9/eCDcMMNDdqcN1t8ZPJCIYQQ/kICn8Zixgw4cQLat4eXX27w5hLCbXN8Jnw+gV6Le1FcXkxafhrtX23PnK1zHF5XYa3giveuwDTbRMDsAGZunulQptJayYClAzDNNmGabWLsJ2MBGcouhBDC9yTwaQy2basaxfXOO9CiRYM3qbf4XOjq+vjgxxzMOMgP53/gm5PfcDr/NKsOrXJ43cnck2xP2w6AgsKqHx3LnCk4w3env7N5zISJGzo1rJVKCCGEaKggX1dA1MJiUbu4FAXuvbdeo7icMeb4FJcXU1xerN4vytC7v5x1g9nnBDnLEdJe1zqyNfsf3A9ASGAI0WHRbqm7EEIIUV8S+Pi755+HQ4egZcs6T1RYE2OOjzHAySjO0Lu/MoszURQFk8lU9fyFsslRyaTlp5FVnEWltZLAgECbbQC0imglCc1CCCH8inR1+bPvv4cXX1RvL1oEcXFu27SWaJxRnGHTapNRVHW/3FpOniXP5nVaUNQtvhugdndll2TblJFZmoUQQvgrCXz8VUWF2sVVUQGjRsHo0W7dvNbVlVmcaTOJYWZxpk0gZD/BofZcUoskfTJC+zLafWntEUII4W8k8PFXr74Ke/aoy1H89a9u37wWlGSXZPNr4a/648auLnDM89Hn5DEnVjsXkCxIKoQQwl9J4OOPfvoJZl4YJr5gAbRp4/ZdxIfHY0LN3TmceVh/PKPYMefHSA9qIhKrnQvIGBwJIYQQ/kQCH39jtcIf/gClpeokhePGeWQ3gQGBxIWrOUM/ZvyoP27M8dHuG+ndWK60+EhXlxBCCD8jgY+/eecd+OYbMJvV24YRVe6mJR8fyjykP3au6BxZxVn6/epyfBLMCdUudCozNQshhPBXEvj4k9OnYepU9fbcudCxo0d3p7XIHMs5pj92Ku8UCop+36E1x7Dult7iY9/VJTk+Qggh/JQEPv5CUdQ1uAoK4IorYPJkj+9SC0ysilV/zHgbak5ctp/9WS8ji5IKIYTwUxL4+IuPPoJ16yAkBN59FwIDa39NA7nSImNszSmtKKWwrFB9bYTzHJ/yynJySnNc3r4QQgjhTRL4+IPsbJgyRb09bRr06OGV3bqSg+NsTp+ggCCiQ6OrJkE0BEdZJWp+kAmTnjwthBBC+AsJfPzBU0/B+fOQmgpPP+213dp3RbWKaOVw29mcPgnmBEwmk/56+wkQAeLC42yWsRBCCCH8gQQ+vvaf/6hdW6CO4goN9dqujV1RJkx0S+im3++RqLY6GVtzjEPZjf9mFGegKIpNecnvEUII4Y8k8PEliwXuv1+9fd99MHCgV3dvDE7izfE2LT5a4FNUXkRJeQngOD+P9m9ZZRkFZQW2ZSS/RwghhB9qkoHPu+++i8lkIjIy0tdVqdlLL8Hhw+rK6y+/7PXdG4MT44SEAJ1iOxEcEAxUBTP2MzKbg82Yg802z0mLjxBCCH/W5AKfM2fO8Kc//YmkpCRfV6VmP/0EL7yg3n7tNYiN9XoVjMnNxgkJQQ1u7JOXjZMX2m9DD460MuEyeaEQQgj/0+QCnwcffJCrr76awYMH+7oq1dPm7CkrgyFD4M47fVINY6uMce0t+/tabo99jo/xtkMZafERQgjhh5pU4PPhhx+ydetWFi9e7Ouq1OyDD2DzZggPhzff9OiyFDUJCwojMkTtDrTv6nK2FpezNbjsFyqVHB8hhBD+rMkEPufPn2fKlCm8+OKLJCcn+7o61cvIgCeeUG/PmuXxZSlqYxyhVV2Lj0P+jpMWH4c8IGnxEUII4YeCfF0Bd5k0aRLdunXjoYceqtPrLBYLFotFv5+fn+/uqtl64gl1wsI+feCPf/TsvlyQYE7geO5xhxyfBHOCnqezYPsC/vnjP9l/br/+nLEcwMIdC/n08KccOHfAoYwQQgjhL/yuxWfLli2YTCaX/vbt2wfA6tWrWbNmDUuWLMFUx26jefPmER0drf+1a9fOA+/qgn//G/7xD7Vra8kSCA723L5c1KtlLwB6tuxJh5gOmIPN+r/ac2n5aWxP205xeTGBpkC6xnd1eH16QTrb07ZTVF5EgCmA1IRU778ZIYQQohYmRZt5zk+cPXuWtWvXulR21KhRhISEkJKSwt13382MGTP05yZNmsQXX3xBWloawcHBREREON2Gsxafdu3akZeXR1RUVMPejFFJCfTuDb/8oi5A+sYb7tt2AxSVFXEk6wh9W/fFZDJxNPsoEcERtGnRhkprJf859R/yLVWtYClxKfocPwCV1kq+PfUteZY8/bHOsZ3p2bKnV9+HEEKI5i0/P5/o6Ohav7/9LvCpqxMnTtCxljyZ4cOH89lnn7m0PVcPXJ1Nnw5z50LbtvDjj+DObQshhBDNnKvf340+x6d169Zs3rzZ4fEXX3yRrVu3sn79ehIS/CDfpKICAgLUlh4JeoQQQgifaPQtPtUZN24cq1atorCwsE6v81iLD8DPP0OXLu7dphBCCCFc/v72u+TmJk2CHiGEEMKnmmzg8/7779e5tUcIIYQQTVuTDXyEEEIIIexJ4COEEEKIZkMCHyGEEEI0GxL4CCGEEKLZkMBHCCGEEM2GBD5CCCGEaDYk8BFCCCFEsyGBjxBCCCGaDQl8hBBCCNFsSOAjhBBCiGZDAh8hhBBCNBsS+AghhBCi2QjydQX8jaIogLq8vRBCCCEaB+17W/ser44EPnYKCgoAaNeunY9rIoQQQoi6KigoIDo6utrnTUptoVEzY7VaSU9Pp0WLFphMJrdtNz8/n3bt2nH69GmioqLctl3hSI61d8hx9g45zt4hx9k7PHmcFUWhoKCApKQkAgKqz+SRFh87AQEBJCcne2z7UVFRclF5iRxr75Dj7B1ynL1DjrN3eOo419TSo5HkZiGEEEI0GxL4CCGEEKLZkMDHS0JDQ3nuuecIDQ31dVWaPDnW3iHH2TvkOHuHHGfv8IfjLMnNQgghhGg2pMVHCCGEEM2GBD5CCCGEaDYk8BFCCCFEsyGBj4cVFhYyZcoUkpKSCAsL45JLLuGjjz7ydbUarS1btmAymZz+bd++3abs3r17ueGGG4iMjCQmJoZRo0Zx7NgxH9XcvxUUFDB16lRuvPFGEhMTMZlMzJo1y2nZuhzXN954g9TUVEJDQ+nYsSOzZ8+mvLzcg+/Ev7l6nMeNG+f0HE9NTXW6XTnOVTZt2sSECRNITU0lIiKCtm3bMnz4cPbs2eNQVs7lhnH1WPvd+awIjxo8eLASExOjvPXWW8qmTZuU++67TwGUZcuW+bpqjdLmzZsVQJk7d66ybds2m7+CggK93KFDh5QWLVooAwcOVNauXausXr1a6dmzp5KUlKScP3/eh+/APx0/flyJjo5Wrr76av0cfe655xzK1eW4Pv/884rJZFKeeeYZZfPmzcrLL7+shISEKH/4wx+89K78j6vH+d5771XCw8MdzvF9+/Y5lJXjbGv06NHKtddeqyxevFjZsmWLsnLlSqV///5KUFCQ8vXXX+vl5FxuOFePtb+dzxL4eNDatWsVQFm+fLnN44MHD1aSkpKUiooKH9Ws8dICn5UrV9ZYbsyYMUpCQoKSl5enP3bixAklODhYmTp1qqer2ehYrVbFarUqiqIoGRkZ1X4hu3pcMzMzlbCwMOX++++3ef0LL7ygmEwm5eDBg555I37O1eN87733KhEREbVuT46zo3Pnzjk8VlBQoLRq1Uq5/vrr9cfkXG44V4+1v53P0tXlQZ9++imRkZGMGTPG5vHx48eTnp7Ojh07fFSzpq2iooIvv/yS22+/3WZK9Isuuohrr72WTz/91Ie1809a03NN6nJcN2zYQGlpKePHj7fZxvjx41EUhc8++8yt9W8sXDnOdSHH2VHLli0dHouMjKRHjx6cPn0akHPZXVw51nXhrWMtgY8H/fDDD3Tv3p2gINsl0fr06aM/L+rn4YcfJigoiKioKIYMGcK3336rP/fLL79QUlKiH2ejPn36cPToUUpLS71Z3SahLsdVO7d79+5tU65NmzYkJCTIue+CkpISWrduTWBgIMnJyUyePJns7GybMnKcXZOXl8fevXvp2bMnIOeyJ9kfa40/nc+ySKkHZWVl0alTJ4fH4+Li9OdF3URHR/PYY48xaNAg4uPjOXr0KK+88gqDBg1i7dq1DBkyRD+u2nE2iouLQ1EUcnJyaNOmjber36jV5bhmZWURGhpKRESE07Jy7tfs4osv5uKLL6ZXr14AbN26lVdffZWvv/6aXbt2ERkZCSDH2UUPP/wwRUVFTJ8+HZBz2ZPsjzX43/ksgY+H1dSs7c4m7+aib9++9O3bV78/cOBARo4cSe/evZk6dSpDhgzRn5Nj7xmuHlc5/vX3xz/+0eb+4MGD6du3L6NHj2bJkiU2z8txrtmzzz7LsmXLeOONN+jXr5/Nc3Iuu1d1x9rfzmfp6vKg+Ph4pxGq1rzn7NeGqLuYmBhuueUW9u/fT0lJCfHx8YDzFrXs7GxMJhMxMTFermXjV5fjGh8fT2lpKcXFxU7LyrlfdyNHjiQiIsJm2gY5zjWbPXs2zz//PC+88AKTJ0/WH5dz2f2qO9bV8eX5LIGPB/Xu3ZtDhw5RUVFh8/iBAwcA9GY/0XDKhSXnTCYTnTt3Jjw8XD/ORgcOHCAlJYWwsDBvV7HRq8tx1fro7cv++uuvZGZmyrlfT4qiEBBQ9bEtx7l6s2fPZtasWcyaNYtp06bZPCfnsnvVdKxr4rPz2S1jw4RT69atUwDlo48+snl86NChMpzdjbKzs5W2bdsql1xyif7Y7373O6Vly5ZKfn6+/tjJkyeVkJAQ5amnnvJFNRuNmoZZu3pcs7KylLCwMOXBBx+0ef28efOa/RBgTU3H2ZmPP/5YAZTXXntNf0yOs3N//vOfFUCZMWNGtWXkXHYPV461M748nyXw8bDBgwcrsbGxyjvvvKNs2rRJ+cMf/qAAyocffujrqjVKd911l/LUU08pK1euVDZv3qy88847Srdu3ZSgoCBl48aNerlDhw4pkZGRytVXX62sW7dO+eSTT5RevXrJBIY1WLdunbJy5Upl6dKlCqCMGTNGWblypbJy5UqlqKhIUZS6HVdtIrJp06YpW7ZsUV555RUlNDS02U/6VttxPnHihHLllVcqr7/+urJu3Tpl/fr1ytNPP62EhYUpPXv2VAoLC222J8fZ1vz58xVAGTp0qMOEedu2bdPLybnccK4ca388nyXw8bCCggLl0UcfVVq3bq2EhIQoffr0UVasWOHrajVa8+bNUy655BIlOjpaCQwMVBITE5WRI0cqO3fudCi7e/du5frrr1fMZrMSFRWljBgxQjl69KgPat04XHTRRQrg9O/48eN6uboc14ULFypdu3ZVQkJClPbt2yvPPfecUlZW5qV35J9qO87Z2dnKyJEjlQ4dOijh4eFKSEiI0qVLF2Xq1KlKbm6u023Kca5yzTXXVHt87Ts55FxuGFeOtT+ezyZFuZAcIYQQQgjRxElysxBCCCGaDQl8hBBCCNFsSOAjhBBCiGZDAh8hhBBCNBsS+AghhBCi2ZDARwghhBDNhgQ+QgghhGg2JPARQgghRLMhgY8Qwm8UFxczd+5cfvOb3xAZGUlYWBjJyckMHDiQZ555hl9++UUv26FDBzp06OC7ygohGqUgX1dACCEACgoKGDBgAPv37yclJYW7776bmJgYTp8+zcGDB3nxxRfp3LkznTt39nVVhRCNmAQ+Qgi/8Nprr7F//34mTpzIkiVLMJlMNs8fP34ci8Xio9oJIZoK6eoSQviFbdu2ATB58mSHoAegY8eOpKamcuLECUwmEydPnuTkyZOYTCb9b9asWTav+eabb7j11ltJSEggNDSULl26MGPGDIqLi23KbdmyRX/9N998wzXXXENkZCRxcXGMHTuWtLQ0h/r8/PPPjB8/no4dOxIWFkZCQgK/+c1veOKJJ9x3UIQQbieLlAoh/MLdd9/NsmXLWLlyJaNHj662XG5uLq+99hqvvfYaAFOmTNGfGzRoEIMGDQLgrbfeYtKkScTGxnLrrbeSmJjIrl272Lp1K1deeSWbN28mJCQEUAOfa6+9liFDhrB582ZuvvlmUlNT2bt3L1999RXt2rVj165dtGrVCoD09HR69uxJUVERN998M926daOwsJCff/6ZzZs3U1ZW5pFjJIRwA7et8y6EEA3w2WefKYASFRWlPPXUU8rXX3+tZGdnV1v+oosuUi666CKnzx08eFAJCgpS+vbtq2RlZdk8N2/ePAVQ5s+frz+2efNmBVAA5d1337UpP3v2bAVQJkyYoD/2+uuvK4CycOFCh31nZGS48naFED4iXV1CCL8wfPhwXn75ZaxWKy+99BLXX389cXFxpKSkMHnyZH7++WeXt/X2229TUVHB66+/TlxcnM1zU6dOJTExkRUrVji8rlu3bkyYMMHmsSeffFIvb9+SEx4e7rCNhIQEl+sphPA+6eoSQviVgoICNmzYwH//+192797Njh07KC8vJywsjI8//pjbbrsNQB/KfuLECYdtXH755ezcuZPp06cTFOQ4hmPJkiXk5eVRWFgIVHV1TZgwgffee8+h/LBhw9iwYQMHDhygV69eHD9+nF69elFeXs6IESMYOnQoAwYMoGvXru47EEIIj5BRXUIIv9KiRQvGjBnDmDFjAMjLy2PatGksXryYiRMncubMGT03pzrZ2dkAvPDCC3Xad8uWLZ0+ruX25OXlAWqi9bZt25g9ezbr169n5cqVgNpiNGfOHL3uQgj/I11dQgi/Fh0dzV//+lcuuugiMjMzOXDgQK2viYqKAiA/Px9FUar9s3f+/Hmn2zt37pxeF02fPn1YvXo12dnZbNu2jZkzZ3Lu3DnuuOMOvvvuu/q8VSGEF0jgI4TweyaTCbPZbPNYYGAglZWVTstffvnlAGzfvr1O+/nuu+8cAqKSkhL27NlDeHi4066s4OBg+vfvz+zZs3n99ddRFIUvv/yyTvsVQniPBD5CCL/w9ttvs2vXLqfPffLJJxw+fJiYmBh69eoFQFxcHJmZmZSWljqUnzRpEkFBQTzyyCOcPn3a4fnc3Fz+97//OTx+5MgRli5davPYK6+8QkZGBnfddZfexbZr1y6nrUNay5CzpGchhH+QHB8hhF9Yv349Dz74ICkpKVx11VUkJSVRWFjIvn37+M9//kNAQACLFy8mNDQUgOuuu47du3dz6623MnDgQEJCQhgwYAADBgygV69eLF68mIceeohu3bpx00030blzZ/Lz8zl27Bhbt25l3LhxvPXWWzZ1uPHGG5k0aRJr1651mMdn7ty5erlly5axePFiBg0aREpKClFRUfz444+sW7eOhIQEh5FhQgj/IaO6hBB+4ciRI3zxxRds3LiRo0ePcvbsWQDatm3LgAEDeOSRR+jXr59evrCwkMcff5wvv/ySc+fOYbVaee6552xmb961axcLFizgm2++ISMjg+joaNq3b8+NN97IvffeS2pqKlA1quu5557juuuuY8aMGezZs4eQkBCGDh3Kyy+/TLt27fTt7tixg7/97W989913pKWlYbFYSE5OZtiwYfzpT3+yKSuE8C8S+Aghmj1j4GO/7IUQommRHB8hhBBCNBsS+AghhBCi2ZDARwghhBDNhuT4CCGEEKLZkBYfIYQQQjQbEvgIIYQQotmQwEcIIYQQzYYEPkIIIYRoNiTwEUIIIUSzIYGPEEIIIZoNCXyEEEII0WxI4COEEEKIZkMCHyGEEEI0G/8P/g4NKzuUVKYAAAAASUVORK5CYII=\n", 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 1, + "id": "562e7450", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -269,7 +259,7 @@ "from sklearn.tree import DecisionTreeRegressor\n", "regr_1=DecisionTreeRegressor(max_depth=2)\n", "regr_2=DecisionTreeRegressor(max_depth=5)\n", - "regr_3=DecisionTreeRegressor(max_depth=12)\n", + "regr_3=DecisionTreeRegressor(max_depth=7)\n", "regr_1.fit(X, distance_list)\n", "regr_2.fit(X, distance_list)\n", "regr_3.fit(X, distance_list)\n", @@ -296,8 +286,10 @@ }, { "cell_type": "markdown", - "id": "967f86f4", - "metadata": {}, + "id": "5e0657c5", + "metadata": { + "editable": true + }, "source": [ "## Building a tree, regression\n", "\n", @@ -316,8 +308,10 @@ }, { "cell_type": "markdown", - "id": "58c89f85", - "metadata": {}, + "id": "dc76aec5", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n", @@ -326,8 +320,10 @@ }, { "cell_type": "markdown", - "id": "8d28defb", - "metadata": {}, + "id": "e938ef05", + "metadata": { + "editable": true + }, "source": [ "where $\\overline{y}_{R_j}$ is the mean response for the training observations \n", "within box $j$." @@ -335,8 +331,10 @@ }, { "cell_type": "markdown", - "id": "3f6c36d6", - "metadata": {}, + "id": "cc6e92dc", + "metadata": { + "editable": true + }, "source": [ "## A top-down approach, recursive binary splitting\n", "\n", @@ -355,8 +353,10 @@ }, { "cell_type": "markdown", - "id": "a89a52db", - "metadata": {}, + "id": "3e081d14", + "metadata": { + "editable": true + }, "source": [ "## Making a tree\n", "\n", @@ -366,8 +366,10 @@ }, { "cell_type": "markdown", - "id": "5feb6c75", - "metadata": {}, + "id": "1357ac9a", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\left\\{X\\vert x_j < s\\right\\},\n", @@ -376,16 +378,20 @@ }, { "cell_type": "markdown", - "id": "8de30cae", - "metadata": {}, + "id": "bdc0c0fa", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "95d5c167", - "metadata": {}, + "id": "42c24160", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\left\\{X\\vert x_j \\geq s\\right\\},\n", @@ -394,16 +400,20 @@ }, { "cell_type": "markdown", - "id": "c5f10599", - "metadata": {}, + "id": "e9a9a69e", + "metadata": { + "editable": true + }, "source": [ "so that we obtain the lowest MSE, that is" ] }, { "cell_type": "markdown", - "id": "ff6f03cb", - "metadata": {}, + "id": "7adc01ac", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sum_{i:x_i\\in R_j}(y_i-\\overline{y}_{R_1})^2+\\sum_{i:x_i\\in R_2}(y_i-\\overline{y}_{R_2})^2,\n", @@ -412,8 +422,10 @@ }, { "cell_type": "markdown", - "id": "9f031abb", - "metadata": {}, + "id": "c9832069", + "metadata": { + "editable": true + }, "source": [ "which we want to minimize by considering all predictors\n", "$x_1,x_2,\\dots,x_p$. We consider also all possible values of $s$ for\n", @@ -443,8 +455,10 @@ }, { "cell_type": "markdown", - "id": "83f9c272", - "metadata": {}, + "id": "c1f0a64e", + "metadata": { + "editable": true + }, "source": [ "## Pruning the tree\n", "\n", @@ -465,8 +479,10 @@ }, { "cell_type": "markdown", - "id": "9f23f5ac", - "metadata": {}, + "id": "c79d6dd9", + "metadata": { + "editable": true + }, "source": [ "## Cost complexity pruning\n", "\n", @@ -475,8 +491,10 @@ }, { "cell_type": "markdown", - "id": "a9b2646e", - "metadata": {}, + "id": "f4676fc1", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sum_{m=1}^{\\overline{T}}\\sum_{i:x_i\\in R_m}(y_i-\\overline{y}_{R_m})^2+\\alpha\\overline{T},\n", @@ -485,8 +503,10 @@ }, { "cell_type": "markdown", - "id": "8c9f1038", - "metadata": {}, + "id": "ca451945", + "metadata": { + "editable": true + }, "source": [ "is as small as possible. Here $\\overline{T}$ is \n", "the number of terminal nodes of the tree $T$ , $R_m$ is the\n", @@ -511,8 +531,10 @@ }, { "cell_type": "markdown", - "id": "1e4cf9ca", - "metadata": {}, + "id": "e2947264", + "metadata": { + "editable": true + }, "source": [ "## Schematic Regression Procedure\n", "\n", @@ -535,8 +557,10 @@ }, { "cell_type": "markdown", - "id": "328198af", - "metadata": {}, + "id": "e43c3d53", + "metadata": { + "editable": true + }, "source": [ "## A Classification Tree\n", "\n", @@ -556,8 +580,10 @@ }, { "cell_type": "markdown", - "id": "338052bc", - "metadata": {}, + "id": "aee5b345", + "metadata": { + "editable": true + }, "source": [ "## Growing a classification tree\n", "\n", @@ -581,8 +607,10 @@ }, { "cell_type": "markdown", - "id": "c96ca06b", - "metadata": {}, + "id": "36e87884", + "metadata": { + "editable": true + }, "source": [ "## Classification tree, how to split nodes\n", "\n", @@ -598,8 +626,10 @@ }, { "cell_type": "markdown", - "id": "970d1672", - "metadata": {}, + "id": "e53f889e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i=k).\n", @@ -608,8 +638,10 @@ }, { "cell_type": "markdown", - "id": "e5a9ac3c", - "metadata": {}, + "id": "0863f784", + "metadata": { + "editable": true + }, "source": [ "We let $p_{mk}$ represent the majority class of observations in region\n", "$m$. The three most common ways of splitting a node are given by\n", @@ -619,8 +651,10 @@ }, { "cell_type": "markdown", - "id": "a0e10726", - "metadata": {}, + "id": "eef6c270", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i\\ne k) = 1-p_{mk}.\n", @@ -629,16 +663,20 @@ }, { "cell_type": "markdown", - "id": "f73a5a66", - "metadata": {}, + "id": "24f8fb88", + "metadata": { + "editable": true + }, "source": [ "* Gini index $g$" ] }, { "cell_type": "markdown", - "id": "c6e5ec5f", - "metadata": {}, + "id": "2c5ea01d", + "metadata": { + "editable": true + }, "source": [ "$$\n", "g = \\sum_{k=1}^K p_{mk}(1-p_{mk}).\n", @@ -647,16 +685,20 @@ }, { "cell_type": "markdown", - "id": "c50e4c55", - "metadata": {}, + "id": "22c61688", + "metadata": { + "editable": true + }, "source": [ "* Information entropy or just entropy $s$" ] }, { "cell_type": "markdown", - "id": "34f4deed", - "metadata": {}, + "id": "06f74650", + "metadata": { + "editable": true + }, "source": [ "$$\n", "s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n", @@ -665,8 +707,10 @@ }, { "cell_type": "markdown", - "id": "b2f791b8", - "metadata": {}, + "id": "16fa9279", + "metadata": { + "editable": true + }, "source": [ "## Visualizing the Tree, Classification" ] @@ -674,119 +718,12 @@ { "cell_type": "code", "execution_count": 2, - "id": "b373a31c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "0 17.99 10.38 122.80 1001.0 0.11840 \n", - "1 20.57 17.77 132.90 1326.0 0.08474 \n", - "2 19.69 21.25 130.00 1203.0 0.10960 \n", - "3 11.42 20.38 77.58 386.1 0.14250 \n", - "4 20.29 14.34 135.10 1297.0 0.10030 \n", - ".. ... ... ... ... ... \n", - "564 21.56 22.39 142.00 1479.0 0.11100 \n", - "565 20.13 28.25 131.20 1261.0 0.09780 \n", - "566 16.60 28.08 108.30 858.1 0.08455 \n", - "567 20.60 29.33 140.10 1265.0 0.11780 \n", - "568 7.76 24.54 47.92 181.0 0.05263 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "0 0.27760 0.30010 0.14710 0.2419 \n", - "1 0.07864 0.08690 0.07017 0.1812 \n", - "2 0.15990 0.19740 0.12790 0.2069 \n", - "3 0.28390 0.24140 0.10520 0.2597 \n", - "4 0.13280 0.19800 0.10430 0.1809 \n", - ".. ... ... ... ... \n", - "564 0.11590 0.24390 0.13890 0.1726 \n", - "565 0.10340 0.14400 0.09791 0.1752 \n", - "566 0.10230 0.09251 0.05302 0.1590 \n", - "567 0.27700 0.35140 0.15200 0.2397 \n", - "568 0.04362 0.00000 0.00000 0.1587 \n", - "\n", - " mean fractal dimension ... worst radius worst texture \\\n", - "0 0.07871 ... 25.380 17.33 \n", - "1 0.05667 ... 24.990 23.41 \n", - "2 0.05999 ... 23.570 25.53 \n", - "3 0.09744 ... 14.910 26.50 \n", - "4 0.05883 ... 22.540 16.67 \n", - ".. ... ... ... ... \n", - "564 0.05623 ... 25.450 26.40 \n", - "565 0.05533 ... 23.690 38.25 \n", - "566 0.05648 ... 18.980 34.12 \n", - "567 0.07016 ... 25.740 39.42 \n", - "568 0.05884 ... 9.456 30.37 \n", - "\n", - " worst perimeter worst area worst smoothness worst compactness \\\n", - "0 184.60 2019.0 0.16220 0.66560 \n", - "1 158.80 1956.0 0.12380 0.18660 \n", - "2 152.50 1709.0 0.14440 0.42450 \n", - "3 98.87 567.7 0.20980 0.86630 \n", - "4 152.20 1575.0 0.13740 0.20500 \n", - ".. ... ... ... ... \n", - "564 166.10 2027.0 0.14100 0.21130 \n", - "565 155.00 1731.0 0.11660 0.19220 \n", - "566 126.70 1124.0 0.11390 0.30940 \n", - "567 184.60 1821.0 0.16500 0.86810 \n", - "568 59.16 268.6 0.08996 0.06444 \n", - "\n", - " worst concavity worst concave points worst symmetry \\\n", - "0 0.7119 0.2654 0.4601 \n", - "1 0.2416 0.1860 0.2750 \n", - "2 0.4504 0.2430 0.3613 \n", - "3 0.6869 0.2575 0.6638 \n", - "4 0.4000 0.1625 0.2364 \n", - ".. ... ... ... \n", - "564 0.4107 0.2216 0.2060 \n", - "565 0.3215 0.1628 0.2572 \n", - "566 0.3403 0.1418 0.2218 \n", - "567 0.9387 0.2650 0.4087 \n", - "568 0.0000 0.0000 0.2871 \n", - "\n", - " worst fractal dimension \n", - "0 0.11890 \n", - "1 0.08902 \n", - "2 0.08758 \n", - "3 0.17300 \n", - "4 0.07678 \n", - ".. ... \n", - "564 0.07115 \n", - "565 0.06637 \n", - "566 0.07820 \n", - "567 0.12400 \n", - "568 0.07039 \n", - "\n", - "[569 rows x 30 columns]\n", - " malignant benign\n", - "0 1 0\n", - "1 1 0\n", - "2 1 0\n", - "3 1 0\n", - "4 1 0\n", - ".. ... ...\n", - "564 1 0\n", - "565 1 0\n", - "566 1 0\n", - "567 1 0\n", - "568 0 1\n", - "\n", - "[569 rows x 2 columns]\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "id": "c5a1f274", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "import os\n", "from sklearn.datasets import load_breast_cancer\n", @@ -825,8 +762,10 @@ }, { "cell_type": "markdown", - "id": "f1649fd9", - "metadata": {}, + "id": "74f9eef5", + "metadata": { + "editable": true + }, "source": [ "## Visualizing the Tree, The Moons" ] @@ -834,20 +773,12 @@ { "cell_type": "code", "execution_count": 3, - "id": "63e625c0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "id": "e5eb312b", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -877,8 +808,10 @@ }, { "cell_type": "markdown", - "id": "119ea0ae", - "metadata": {}, + "id": "faaaf1ab", + "metadata": { + "editable": true + }, "source": [ "## Other ways of visualizing the trees\n", "\n", @@ -888,46 +821,12 @@ { "cell_type": "code", "execution_count": 4, - "id": "711bdf8d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Text(0.5, 0.9166666666666666, 'X[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n", - " Text(0.4230769230769231, 0.75, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n", - " Text(0.5769230769230769, 0.75, 'X[3] <= 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "id": "0561573b", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn import tree\n", @@ -940,8 +839,10 @@ }, { "cell_type": "markdown", - "id": "a80a7f27", - "metadata": {}, + "id": "01480f23", + "metadata": { + "editable": true + }, "source": [ "## Printing out as text\n", "\n", @@ -952,24 +853,12 @@ { "cell_type": "code", "execution_count": 5, - "id": "3aa4b27b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "|--- petal width (cm) <= 0.80\n", - "| |--- class: 0\n", - "|--- petal width (cm) > 0.80\n", - "| |--- petal width (cm) <= 1.75\n", - "| | |--- class: 1\n", - "| |--- petal width (cm) > 1.75\n", - "| | |--- class: 2\n", - "\n" - ] - } - ], + "id": "95da6ba0", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn.tree import DecisionTreeClassifier\n", @@ -983,8 +872,10 @@ }, { "cell_type": "markdown", - "id": "e155d65a", - "metadata": {}, + "id": "f894df4b", + "metadata": { + "editable": true + }, "source": [ "## Algorithms for Setting up Decision Trees\n", "\n", @@ -1001,8 +892,10 @@ }, { "cell_type": "markdown", - "id": "9f64d255", - "metadata": {}, + "id": "1b251f2f", + "metadata": { + "editable": true + }, "source": [ "## The CART algorithm for Classification\n", "\n", @@ -1016,8 +909,10 @@ }, { "cell_type": "markdown", - "id": "c67ea6bd", - "metadata": {}, + "id": "2964dd8f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}G_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}G_{\\mathrm{right}},\n", @@ -1026,8 +921,10 @@ }, { "cell_type": "markdown", - "id": "60be0c2f", - "metadata": {}, + "id": "e96d7f9a", + "metadata": { + "editable": true + }, "source": [ "where $G_{\\mathrm{left/right}}$ measures the impurity of the left/right subset and $m_{\\mathrm{left/right}}$\n", " is the number of instances in the left/right subset\n", @@ -1041,8 +938,10 @@ }, { "cell_type": "markdown", - "id": "68adc691", - "metadata": {}, + "id": "74abd6a0", + "metadata": { + "editable": true + }, "source": [ "## The CART algorithm for Regression\n", "\n", @@ -1052,8 +951,10 @@ }, { "cell_type": "markdown", - "id": "3aa84faa", - "metadata": {}, + "id": "d9816137", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}\\mathrm{MSE}_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}\\mathrm{MSE}_{\\mathrm{right}}.\n", @@ -1062,16 +963,20 @@ }, { "cell_type": "markdown", - "id": "05821fe6", - "metadata": {}, + "id": "66e67465", + "metadata": { + "editable": true + }, "source": [ "Here the MSE for a specific node is defined as" ] }, { "cell_type": "markdown", - "id": "321fb878", - "metadata": {}, + "id": "3177f141", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{MSE}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}(\\overline{y}_{\\mathrm{node}}-y_i)^2,\n", @@ -1080,16 +985,20 @@ }, { "cell_type": "markdown", - "id": "5703ea44", - "metadata": {}, + "id": "3595d01a", + "metadata": { + "editable": true + }, "source": [ "with" ] }, { "cell_type": "markdown", - "id": "6b6cf145", - "metadata": {}, + "id": "6fcc27c1", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n", @@ -1098,8 +1007,10 @@ }, { "cell_type": "markdown", - "id": "57f159ee", - "metadata": {}, + "id": "fba7d697", + "metadata": { + "editable": true + }, "source": [ "the mean value of all observations in a specific node.\n", "\n", @@ -1109,8 +1020,10 @@ }, { "cell_type": "markdown", - "id": "8dba6c9f", - "metadata": {}, + "id": "e66c4c8c", + "metadata": { + "editable": true + }, "source": [ "## Why binary splits?\n", "\n", @@ -1122,8 +1035,10 @@ }, { "cell_type": "markdown", - "id": "54686dd2", - "metadata": {}, + "id": "00afb464", + "metadata": { + "editable": true + }, "source": [ "## Computing a Tree using the Gini Index\n", "\n", @@ -1146,8 +1061,10 @@ }, { "cell_type": "markdown", - "id": "226714bc", - "metadata": {}, + "id": "3aad4efb", + "metadata": { + "editable": true + }, "source": [ "## The Table\n", "\n", @@ -1172,8 +1089,10 @@ }, { "cell_type": "markdown", - "id": "132a6df7", - "metadata": {}, + "id": "7b9360bb", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices\n", "\n", @@ -1187,8 +1106,10 @@ }, { "cell_type": "markdown", - "id": "75ab3e53", - "metadata": {}, + "id": "2c58ee2c", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices, Hours slept\n", "\n", @@ -1199,8 +1120,10 @@ }, { "cell_type": "markdown", - "id": "be9d82ec", - "metadata": {}, + "id": "c6be5cc9", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices, Hours studied\n", "\n", @@ -1213,165 +1136,23 @@ }, { "cell_type": "markdown", - "id": "b502bb89", - "metadata": {}, + "id": "d1dd598d", + "metadata": { + "editable": true + }, "source": [ "## A possible code using Scikit-Learn" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "2e5fc857", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    " - ], - "text/plain": [ - " Grade Trend Hours slept Hours Studied Grade\n", - "0 1 0 1 1\n", - "1 0 1 0 0\n", - "2 1 0 1 1\n", - "3 1 1 1 1\n", - "4 0 0 1 0\n", - "5 1 0 0 0\n", - "6 0 1 1 0\n", - "7 0 0 1 0\n", - "8 1 0 0 0\n", - "9 1 1 1 1" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1 0 1]\n", - " [0 1 0]\n", - " [1 0 1]\n", - " [1 1 1]\n", - " [0 0 1]\n", - " [1 0 0]\n", - " [0 1 1]\n", - " [0 0 1]\n", - " [1 0 0]\n", - " [1 1 1]]\n", - "Train set accuracy with Decision Tree: 1.00\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 6, + "id": "fc74a7ea", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -1437,8 +1218,10 @@ }, { "cell_type": "markdown", - "id": "fe2aa246", - "metadata": {}, + "id": "6963dec3", + "metadata": { + "editable": true + }, "source": [ "## Further example: Computing the Gini index\n", "\n", @@ -1479,87 +1262,23 @@ }, { "cell_type": "markdown", - "id": "46f289da", - "metadata": {}, + "id": "604ca44b", + "metadata": { + "editable": true + }, "source": [ "## Simple Python Code to read in Data and perform Classification" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "38aedbca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (0, 0)\t1.0\n", - " (0, 7)\t1.0\n", - " (0, 9)\t1.0\n", - " (0, 13)\t1.0\n", - " (1, 3)\t1.0\n", - " (1, 5)\t1.0\n", - " (1, 8)\t1.0\n", - " (1, 12)\t1.0\n", - " (2, 3)\t1.0\n", - " (2, 5)\t1.0\n", - " (2, 8)\t1.0\n", - " (2, 11)\t1.0\n", - " (3, 1)\t1.0\n", - " (3, 5)\t1.0\n", - " (3, 8)\t1.0\n", - " (3, 12)\t1.0\n", - " (4, 2)\t1.0\n", - " (4, 6)\t1.0\n", - " (4, 8)\t1.0\n", - " (4, 12)\t1.0\n", - " (5, 2)\t1.0\n", - " (5, 4)\t1.0\n", - " (5, 10)\t1.0\n", - " (5, 12)\t1.0\n", - " (6, 2)\t1.0\n", - " :\t:\n", - " (8, 12)\t1.0\n", - " (9, 3)\t1.0\n", - " (9, 4)\t1.0\n", - " (9, 10)\t1.0\n", - " (9, 12)\t1.0\n", - " (10, 2)\t1.0\n", - " (10, 6)\t1.0\n", - " (10, 10)\t1.0\n", - " (10, 12)\t1.0\n", - " (11, 3)\t1.0\n", - " (11, 6)\t1.0\n", - " (11, 10)\t1.0\n", - " (11, 11)\t1.0\n", - " (12, 1)\t1.0\n", - " (12, 6)\t1.0\n", - " (12, 8)\t1.0\n", - " (12, 11)\t1.0\n", - " (13, 1)\t1.0\n", - " (13, 5)\t1.0\n", - " (13, 10)\t1.0\n", - " (13, 12)\t1.0\n", - " (14, 2)\t1.0\n", - " (14, 6)\t1.0\n", - " (14, 8)\t1.0\n", - " (14, 11)\t1.0\n", - "Train set accuracy with Decision Tree: 0.73\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 7, + "id": "5e62178f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -1632,8 +1351,10 @@ }, { "cell_type": "markdown", - "id": "a6f5da59", - "metadata": {}, + "id": "6198598a", + "metadata": { + "editable": true + }, "source": [ "## Computing the Gini Factor\n", "\n", @@ -1647,74 +1368,13 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "e51855f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X1 < 0.000 Gini=0.408\n", - "X1 < 0.000 Gini=0.408\n", - "X1 < 1.000 Gini=0.394\n", - "X1 < 2.000 Gini=0.394\n", - "X1 < 2.000 Gini=0.394\n", - "X1 < 2.000 Gini=0.394\n", - "X1 < 1.000 Gini=0.394\n", - "X1 < 0.000 Gini=0.408\n", - "X1 < 0.000 Gini=0.408\n", - "X1 < 2.000 Gini=0.394\n", - "X1 < 0.000 Gini=0.408\n", - "X1 < 1.000 Gini=0.394\n", - "X1 < 1.000 Gini=0.394\n", - "X1 < 2.000 Gini=0.394\n", - "X2 < 0.000 Gini=0.408\n", - "X2 < 0.000 Gini=0.408\n", - "X2 < 0.000 Gini=0.408\n", - "X2 < 1.000 Gini=0.407\n", - "X2 < 2.000 Gini=0.407\n", - "X2 < 2.000 Gini=0.407\n", - "X2 < 2.000 Gini=0.407\n", - "X2 < 1.000 Gini=0.407\n", - "X2 < 2.000 Gini=0.407\n", - "X2 < 1.000 Gini=0.407\n", - "X2 < 1.000 Gini=0.407\n", - "X2 < 1.000 Gini=0.407\n", - "X2 < 0.000 Gini=0.408\n", - "X2 < 1.000 Gini=0.407\n", - "X3 < 0.000 Gini=0.408\n", - "X3 < 0.000 Gini=0.408\n", - "X3 < 0.000 Gini=0.408\n", - "X3 < 0.000 Gini=0.408\n", - "X3 < 1.000 Gini=0.367\n", - "X3 < 1.000 Gini=0.367\n", - "X3 < 1.000 Gini=0.367\n", - "X3 < 0.000 Gini=0.408\n", - "X3 < 1.000 Gini=0.367\n", - "X3 < 1.000 Gini=0.367\n", - "X3 < 1.000 Gini=0.367\n", - "X3 < 0.000 Gini=0.408\n", - "X3 < 1.000 Gini=0.367\n", - "X3 < 0.000 Gini=0.408\n", - "X4 < 0.000 Gini=0.408\n", - "X4 < 1.000 Gini=0.405\n", - "X4 < 0.000 Gini=0.408\n", - "X4 < 0.000 Gini=0.408\n", - "X4 < 0.000 Gini=0.408\n", - "X4 < 1.000 Gini=0.405\n", - "X4 < 1.000 Gini=0.405\n", - "X4 < 0.000 Gini=0.408\n", - "X4 < 0.000 Gini=0.408\n", - "X4 < 0.000 Gini=0.408\n", - "X4 < 1.000 Gini=0.405\n", - "X4 < 1.000 Gini=0.405\n", - "X4 < 0.000 Gini=0.408\n", - "X4 < 1.000 Gini=0.405\n", - "Split: [X3 < 1.000]\n" - ] - } - ], + "execution_count": 8, + "id": "f5ca05c1", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "# Split a dataset based on an attribute and an attribute value\n", "def test_split(index, value, dataset):\n", @@ -1780,17 +1440,22 @@ }, { "cell_type": "markdown", - "id": "f6add3e5", - "metadata": {}, + "id": "b630aa6f", + "metadata": { + "editable": true + }, "source": [ "## Regression trees" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "74ecc649", - "metadata": {}, + "execution_count": 9, + "id": "b229a9cc", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Quadratic training set + noise\n", @@ -1803,21 +1468,13 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "04024d89", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DecisionTreeRegressor(max_depth=2, random_state=42)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 10, + "id": "2b4ec8a0", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", @@ -1827,29 +1484,23 @@ }, { "cell_type": "markdown", - "id": "878b4d23", - "metadata": {}, + "id": "743cd43c", + "metadata": { + "editable": true + }, "source": [ "## Final regressor code" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "3c96bff5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 11, + "id": "4a05dc08", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", @@ -1893,21 +1544,13 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "527b27ca", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 12, + "id": "38aadc49", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "tree_reg1 = DecisionTreeRegressor(random_state=42)\n", "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n", @@ -1941,8 +1584,10 @@ }, { "cell_type": "markdown", - "id": "f2a0dd48", - "metadata": {}, + "id": "bc7c4453", + "metadata": { + "editable": true + }, "source": [ "## Pros and cons of trees, pros\n", "\n", @@ -1963,8 +1608,10 @@ }, { "cell_type": "markdown", - "id": "9f896560", - "metadata": {}, + "id": "bdbd2a4a", + "metadata": { + "editable": true + }, "source": [ "## Disadvantages\n", "\n", @@ -1989,8 +1636,10 @@ }, { "cell_type": "markdown", - "id": "3f6f50e2", - "metadata": {}, + "id": "c5c58d30", + "metadata": { + "editable": true + }, "source": [ "## Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods\n", "\n", @@ -2018,8 +1667,10 @@ }, { "cell_type": "markdown", - "id": "24509012", - "metadata": {}, + "id": "2a505793", + "metadata": { + "editable": true + }, "source": [ "## An Overview of Ensemble Methods\n", "\n", @@ -2032,8 +1683,10 @@ }, { "cell_type": "markdown", - "id": "15a871bc", - "metadata": {}, + "id": "43236470", + "metadata": { + "editable": true + }, "source": [ "## Why Voting?\n", "\n", @@ -2054,8 +1707,10 @@ }, { "cell_type": "markdown", - "id": "e6d75533", - "metadata": {}, + "id": "8fd9a72e", + "metadata": { + "editable": true + }, "source": [ "## Tossing coins\n", "\n", @@ -2083,17 +1738,22 @@ }, { "cell_type": "markdown", - "id": "8ecb23d0", - "metadata": {}, + "id": "2acf0f65", + "metadata": { + "editable": true + }, "source": [ "## Standard imports first" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "b42d0a08", - "metadata": {}, + "execution_count": 13, + "id": "b1cb2d4e", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Common imports\n", @@ -2137,29 +1797,23 @@ }, { "cell_type": "markdown", - "id": "e3060cfd", - "metadata": {}, + "id": "702a49d8", + "metadata": { + "editable": true + }, "source": [ "## Simple Voting Example, head or tail" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "59d25264", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 14, + "id": "e397cc8f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "\n", "# Common imports\n", @@ -2188,8 +1842,10 @@ }, { "cell_type": "markdown", - "id": "f8cf0e6e", - "metadata": {}, + "id": "ffd397cf", + "metadata": { + "editable": true + }, "source": [ "## Using the Voting Classifier\n", "\n", @@ -2198,25 +1854,13 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "76fd4c2d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LogisticRegression 0.864\n", - "RandomForestClassifier 0.872\n", - "SVC 0.888\n", - "VotingClassifier 0.896\n", - "LogisticRegression 0.864\n", - "RandomForestClassifier 0.872\n", - "SVC 0.888\n", - "VotingClassifier 0.912\n" - ] - } - ], + "execution_count": 15, + "id": "8da0f182", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.datasets import make_moons\n", @@ -2264,31 +1908,23 @@ }, { "cell_type": "markdown", - "id": "eacefe6c", - "metadata": {}, + "id": "2534a75b", + "metadata": { + "editable": true + }, "source": [ "## Voting and Bagging" ] }, { "cell_type": "code", - "execution_count": 17, - "id": "796dfa6b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n", - " ('rf', RandomForestClassifier(random_state=42)),\n", - " ('svc', SVC(random_state=42))])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 16, + "id": "2c5bbe2c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.datasets import make_moons\n", @@ -2312,21 +1948,13 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "90ec162f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LogisticRegression 0.864\n", - "RandomForestClassifier 0.896\n", - "SVC 0.896\n", - "VotingClassifier 0.912\n" - ] - } - ], + "execution_count": 17, + "id": "6d55e819", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", @@ -2338,24 +1966,13 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "e46dcb77", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n", - " ('rf', RandomForestClassifier(random_state=42)),\n", - " ('svc', SVC(probability=True, random_state=42))],\n", - " voting='soft')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 18, + "id": "8d2a4915", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "log_clf = LogisticRegression(random_state=42)\n", "rnd_clf = RandomForestClassifier(random_state=42)\n", @@ -2369,21 +1986,13 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "67a3b080", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LogisticRegression 0.864\n", - "RandomForestClassifier 0.896\n", - "SVC 0.896\n", - "VotingClassifier 0.92\n" - ] - } - ], + "execution_count": 19, + "id": "7979252c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", @@ -2395,8 +2004,10 @@ }, { "cell_type": "markdown", - "id": "f0d51672", - "metadata": {}, + "id": "e06577c3", + "metadata": { + "editable": true + }, "source": [ "## Bagging\n", "\n", @@ -2415,8 +2026,10 @@ }, { "cell_type": "markdown", - "id": "ab182ea8", - "metadata": {}, + "id": "2e4b2a05", + "metadata": { + "editable": true + }, "source": [ "## More bagging\n", "\n", @@ -2445,8 +2058,10 @@ }, { "cell_type": "markdown", - "id": "998512be", - "metadata": {}, + "id": "bb6af67b", + "metadata": { + "editable": true + }, "source": [ "## Making your own Bootstrap: Changing the Level of the Decision Tree\n", "\n", @@ -2456,63 +2071,13 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "6ac20f8b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Polynomial degree: 1\n", - "Error: 0.06380941468319971\n", - "Bias^2: 0.05160313473529168\n", - "Var: 0.01220627994790804\n", - "0.06380941468319971 >= 0.05160313473529168 + 0.01220627994790804 = 0.06380941468319971\n", - "Polynomial degree: 2\n", - "Error: 0.043464037468677004\n", - "Bias^2: 0.02659851591375224\n", - "Var: 0.01686552155492476\n", - "0.043464037468677004 >= 0.02659851591375224 + 0.01686552155492476 = 0.043464037468677\n", - "Polynomial degree: 3\n", - "Error: 0.020716391693769383\n", - "Bias^2: 0.01159033914386312\n", - "Var: 0.00912605254990626\n", - "0.020716391693769383 >= 0.01159033914386312 + 0.00912605254990626 = 0.02071639169376938\n", - "Polynomial degree: 4\n", - "Error: 0.02063627410934057\n", - "Bias^2: 0.0117496656370668\n", - "Var: 0.008886608472273775\n", - "0.02063627410934057 >= 0.0117496656370668 + 0.008886608472273775 = 0.020636274109340574\n", - "Polynomial degree: 5\n", - "Error: 0.02087627881701288\n", - "Bias^2: 0.01349183949256158\n", - "Var: 0.007384439324451296\n", - "0.02087627881701288 >= 0.01349183949256158 + 0.007384439324451296 = 0.020876278817012876\n", - "Polynomial degree: 6\n", - "Error: 0.02069601123831537\n", - "Bias^2: 0.013918526350129823\n", - "Var: 0.0067774848881855445\n", - "0.02069601123831537 >= 0.013918526350129823 + 0.0067774848881855445 = 0.020696011238315368\n", - "Polynomial degree: 7\n", - "Error: 0.022964339924731444\n", - "Bias^2: 0.01550381208433455\n", - "Var: 0.007460527840396904\n", - "0.022964339924731444 >= 0.01550381208433455 + 0.007460527840396904 = 0.022964339924731455\n", - "Simple tree: 0.5148389267750961\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 20, + "id": "06d3f0ca", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "\n", "import matplotlib.pyplot as plt\n", @@ -2576,8 +2141,10 @@ }, { "cell_type": "markdown", - "id": "c9a44ff4", - "metadata": {}, + "id": "f76207c4", + "metadata": { + "editable": true + }, "source": [ "## Random forests\n", "\n", @@ -2597,8 +2164,10 @@ }, { "cell_type": "markdown", - "id": "74f9056f", - "metadata": {}, + "id": "c60cf5c9", + "metadata": { + "editable": true + }, "source": [ "$$\n", "m\\approx \\sqrt{p}.\n", @@ -2607,8 +2176,10 @@ }, { "cell_type": "markdown", - "id": "9a0166e1", - "metadata": {}, + "id": "e1338d12", + "metadata": { + "editable": true + }, "source": [ "In building a random forest, at\n", "each split in the tree, the algorithm is not even allowed to consider\n", @@ -2630,8 +2201,10 @@ }, { "cell_type": "markdown", - "id": "7e5dd3c9", - "metadata": {}, + "id": "ab90a636", + "metadata": { + "editable": true + }, "source": [ "## Random Forest Algorithm\n", "The algorithm described here can be applied to both classification and regression problems.\n", @@ -2654,63 +2227,23 @@ }, { "cell_type": "markdown", - "id": "b2476d94", - "metadata": {}, + "id": "9e290391", + "metadata": { + "editable": true + }, "source": [ "## Random Forests Compared with other Methods on the Cancer Data" ] }, { "cell_type": "code", - "execution_count": 22, - "id": "0a56d5de", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(426, 30)\n", - "(143, 30)\n", - "Test set accuracy Logistic Regression with scaled data: 0.96\n", - "Test set accuracy SVM with scaled data: 0.96\n", - "Test set accuracy with Decision Trees and scaled data: 0.87\n", - "[0.93333333 0.73333333 0.93333333 1. 1. 0.92857143\n", - " 1. 0.92857143 0.92857143 0.92857143]\n", - "Test set accuracy with Random Forests and scaled data: 0.98\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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IwMbGhiJFimR4jL29fbqNIwHc3NyeeIzIPXq9HicnJ4oUKSKFkIYS9QZWn47Cyt7JFKtfvggfvlVFw6zyr5SUFA4eOkiD+g2wscnar2mHyH/wOvoldrE3M3egomD/8Drw3weQwbYg19+YTYp94SzlYskMBgOn/zpNjeo1sLa21jqdfOP4qb8YM+Fzxo4YRNu33gQgOjoGfmif7cNaLKoQqlevnmqHZYDt27dTu3Zt+RAV4gWsORrK3Vj1ruZjWlSmRqlC2iSUz+n1em4WgOolXbP2u+3eJdgWAIkPXjwZnTXW/j9RrsKbL/5cFkiv13Ph9kMq+DSSz5lcYDQa+frrr5k4cSIGg4FJ036gXZceVKxYkcjIyBx5TU2nz8fGxhISEkJISAiQOj0+JCSE0NBQIPWyVs+ePU2PHzRoENevX2f06NGcO3eOpUuXsmTJEj744AMt0hciT0jUG5i357Iq1qiiO75l8t9f/3lCfBSs9sueIgig9TeQT4sgkbsiIiJo1aoVH3/8MQaDAQBfX1+cnZ1z9HU17QgdP36cpk2bmm4/GsvTq1cvli9fTlhYmKkoAvD29mbr1q2MGjWKOXPm4OXlxaxZs+jUqVOu5y6EOVMUhRl/XGTF4WtEJ+if/lgg7SIa779ZMeeSEzknJRnW9oSoy89+7PNoOApe7Z89zyXEU+zZs4eAgADCwsKA1HG/EydO5NNPP83xS5KaFkJNmjThacsYLV++PF2scePGnDx5MgezEsLy/f5PODN3XszSsfXLu1G7rEwksAgPw2H3FxB1NfV2fCREnFU/pvgr0PhDHh/z81zcyoHHS9mSphBPYjAY+OKLL5gyZQpGoxFInQ0eGBjIm2/mTifSosYICSGezWhM7QZl1fCm5bMxG5Fj4qNgWeund38KFIduweBaIvfyEuI53blzh4CAAHbt2mWKNWvWjFWrVqVbKicnyaarQuQxv/8Tzvnwh5k+TqeDZl5GasvYIPP3PJfAbBwhIEiKIGG2FEXh77//BsDKyorPP/+cbdu25WoRBNIREiJPMRqVdJfEKnkUYGbXms881s3RmiN7/8ip1ER2URTYMgqu7X/yY6xsodMi8Hr2eRdCK8WLFycwMJA+ffqwatUqGjdurEkeUggJkYdsP5u+GzTizYpU9XR55rF6/dMHVQszcehHOLVKHSvoCQ1Hp7b1rGygTH0oWlmb/IR4glu3buHo6KhazLhZs2ZcvHgRBwcHzfKSQkgIC2c0KhgVBQWYufOS6r6KxQrQulr6rWeEhTr3K+z4VB2zdYJua6T7I8zatm3b6NGjB/Xr12fTpk2qRRG1LIJACiEhLNqc3ZdYuO/KE6fIj3izIlZWsrlwnhB2GjYMIHXBg8d0XChFkDBber2eTz/9lK+++gqAzZs3s2DBAgYNGqRxZv+RQkgIC7X3wl2+/f3fJ95foVgBWr8i3aA8ISYMVncFfbw63mwyVG2nSUpCPMuNGzfo2rUrhw4dMsXatm1Lly5dNMwqPZk1JoQFSl0w8cJTHzP8jQpYSzfI8iXHw5qu8PC2Ou7THRqM1CQlIZ7ll19+wcfHx1QE2djY8P3337N582az2+dTOkJCWKD9F+9xKvTBE+/3q12SdtW9ci8hkTMUI2x8D8JC1PEyDaDtjNTB0UKYkeTkZMaPH8/06dNNsTJlyhAcHEzdunU1zOzJpBASwsJk1A0qWdiR5X3qoNNBYSc73JztNMpOZCerPV/COfVG0xT2Br+VYCPnWJiXmJgYmjdvztGjR02xDh06sHTpUgoXNt/1yeTSmBAW5sCle5xM0w0a/kYFKhQrQPmiBaQIyiNKRe7H+tAMddDBFQLWgrN5XVoQAqBgwYKULVsWADs7O2bNmsWGDRvMuggC6QgJYVEURWFmmu0zShZ2pGOtkhplJLLN9UMQfgYUBavEGHxuLFXfr7MGvxVQtJI2+QnxDDqdjkWLFhEdHc0XX3yBr6+v1ik9FymEhLAgBy9Fcvz6fVVsaNMK2FpLc9ei7f4S9n5tupnhXtttvodyTXIrIyGe6fLly9y4cYMmTZqYYi4uLmzbtk27pLJAfnsKYSEURWHmTvXYoBKFHOkk3SDLFrJaVQRl6LWhULtP7uQjxHNYt24dtWrVolOnTty4cUPrdF6IFEJCWIjDlyM5di19N8jORn6MLda1g7B5xNMfU7EltPg8d/IR4hkSExMZMmQIfn5+xMTEEBUVxccff6x1Wi9ELo0JYQFSZ4qpxwaVKORIZ1/pBlmsyMsQ3B2MaVYFL10Po5UN9yKjKFK9JdZNxoFVhhfLhMhVFy5cwM/Pj9OnT5tiAQEBzJ07V8OsXpwUQkJYgMNXIjl6LUoVG9K0vHSDLFXCfVjtn/rfx9UbBi2/wKDXc3jrVlo3aY21ra02OQrxmNWrVzNw4EBiY2OB1P3BZs+eTd++fVX7hlkiKYSEsABpZ4p5uTrQxbeURtmIF2LQw7reEKk+p1RqBc0/0yQlIZ4kPj6eESNGsGTJElOsatWqrF27lmrVqmmYWfaRQkgIM3f4ciR/Xk3bDZKxQRZJUWDrWLiyRx33eAU6LZZLYMKsKIrCW2+9xf79+02xXr16MWfOHJydnTXMLHvJb1IhzFzamWKerg50qS1jgyzSkXlwYpk6VsADAoLAvoA2OQnxBDqdjtGjRwPg5OTE8uXLWb58eZ4qgkA6QkKYtSNXIjlyJU03qEl57G2kc2Bx/t0Gv6eZXWPjAF3XgKsUtsI8dejQge+++47WrVtTtWpVrdPJEdIREsKMpR0bVNzFAb9XZWyQxQn/G/7XD1DU8Q7zoKRlrL4r8r6///6bTz75BEVRf5+OGTMmzxZBIB0hIczW0atRHL4SqYoNaSrdIIvz8A6s6QrJsep400+gWkdtchLiMYqisGTJEoYPH05iYiLe3t7069dP67RyjXSEhDBTaccGebjY41dbukEWRZ8AQQEQnWbl3er+8PoH2uQkxGMePnzIu+++y4ABA0hMTARgyZIlGI1GjTPLPVIICWGGjl2L4uClNN2gJhVwsJVukMUwGmHTYLh1XB0v9Rq0/xEsfO0VYflCQkLw9fVl9erVptjgwYPZtWsXVlb5pzzIP+9UCAuSdmyQh4s9/jI2yLLs/Qr+2aiOFSoDXQPBxl6bnIQg9VLYvHnzeO2117h4MfV3jYuLC8HBwcydOxcHBweNM8xdMkZICDNz/FoUBy7dU8UGNS4v3SBL8tfa9Bup2rtAwFpwdtcmJyGA6OhoBgwYwLp160wxX19fgoODKV++vIaZaUc6QkKYmZk71d2gYgXt6VantEbZiEwL/RN+HqqO6ayhyzIoVkWbnIT4fx9++KGqCBoxYgQHDx7Mt0UQSCEkhFk5cT2K/RelG2Sx7l9LHRxtSFbHW30NFZppkpIQj5s6dSolSpSgUKFCbNy4kZkzZ2Jvn78v1cqlMSHMSNod5osWtCegrnSDLEJidOpGqvHqQpY6A6HOAG1yEvmeoiiqTVHd3d3ZtGkT7u7ulC1bVrvEzIh0hIQwEydD70s3yFIZUmB9X7h7Xh2v0AxafqlNTiLf+/PPP2nQoAF37txRxWvXri1F0GOkEBLCTKSdKeZewJ7u0g2yDL9/DJf+UMeKVoXOS8FaGu8idymKwvfff0/Dhg05fPgwPXr0yFfrAmWW/IQKoRFFUbj7MIlkg5GLEbHsvXBXdf+gxuWkG2QJji6CowvUMSd3CAgGB1dtchL5VmRkJL179+bXX381xeLi4oiOjqZw4cIaZma+pBASQgMRDxPpueQo58MfZni/ewE7utctk8tZiUy7+Af8Nk4ds7aHrquhsJw/kbsOHjxIt27duHHjv5XMP/roIz777DNsbW01zMy8SSEkhAY+3fTPE4sggIGvl8fRTrpBZi3iHKzvA0qaSw5vz4HSdbXJSeRLRqORb775hk8++QSDwQCkDopeuXIlb731lsbZmT8phITIZefCYtj2T/gT73cvYEf312RskFmLuwer/SApRh1v/CFU76JNTiJfunv3Lj179mTbtm2m2Ouvv87q1aspUaKEhplZDhksLUQum5VmwcTHlSjkyNzuvjjZyd8oZkufmLpW0INQdfzljtBkvDY5iXxr586dpiJIp9MxceJEdu7cKUVQJshvWyFy0fnwGH77W90NGvFmRQa+Xg4AJztr1ZofwswoCvwyAm78qY6XqA0d5spGqiLXde3ale3bt7N161ZWrVpFs2aycGdmSSEkRC76cecl1W0XBxv6N/LG2V5+FC3Cvu/gr2B1zLVU6uBoW0dtchL5ysOHDylYsKAqNnv2bGJiYihevLhGWVk2uTQmRC75N/whW86EqWL9GpbDxUFmc1iEvzfA7qnqmF0B6BYEBT20yUnkKzt37qRSpUqsXbtWFXdycpIi6AVIISRELpm1Sz02qKCDDb0blNUmGZE5N0/ApsHqmM4qdcHE4tW0yUnkGwaDgUmTJtG8eXPCw8Pp378/ly9f1jqtPEP68ULkggt3HrI1TTeobwNvXB2lG2T2HtyANV0hJVEdb/klVGqpTU4i37h9+zYBAQHs3bvXFKtXr166y2Mi66QjJEQumLXzIory3+2CDjb0beitXULi+SQ9TC2C4iLU8dp9oe4gbXIS+cbvv/+Oj4+PqQiytrZm2rRp/PbbbxQrVkzj7PIO6QgJkcMu3kk/NqiPdIPMn9EA/+sPd/5Wx8s1gVbfyAwxkWNSUlKYOHEiX331lSlWsmRJ1qxZQ8OGDTXMLG+SQkiIHPbjrkvqbpC9Df0aSDfI7O34FC5sU8fcK0GXn8BailiRM27duoW/vz8HDx40xdq0acPy5ctxd3fXMLO8Sy6NCZGDLkU85Je/bqtifRqUxdVJPkjN2vFlcHi2OubolrqRqmMhTVIS+YNOp+PChQsA2NjY8N1337F582YpgnKQFEJC5KC03aAC9jI2yOxd2QNbP1DHrGyhayC4ldMkJZF/eHl5sXLlSsqWLcv+/fsZM2YMVlbyUZ2T5NKYEDnkUkQsv5xWd4N61y9LISc7jTISz3T3AgT3BGOKOt5+FpSpr01OIk+7fv06rq6uFCpUyBRr2bIl58+fx97eXrvE8hEpM4XIIbN3XcSYphvUT7pB5is+6v83Uo1WxxuOBp8AbXISedqmTZvw8fGhX79+KI+3jkGKoFwkhZAQOeDK3Vg2p+kG9apfhsLO0g0ySynJEPwu3L+qjldtD29M1CYnkWclJSXx/vvv88477/DgwQM2bNjA0qVLtU4r35JLY0LkgNm7Lqm6Qc521vRvKONLzJKiwK8j4fpBddzTB95ZADI+Q2SjK1eu4Ofnx4kTJ0yxzp0707lzZw2zyt/kJ1yIbHblbiybQm6pYj3rl5VukLk6OANCAtWxgl6pe4jZOWmSksib1q9fT82aNU1FkL29PXPnzmXt2rW4urpqnF3+JR0hIbLZ7N3qbpCTnTUDGkk3yCyd3Qx/TFbHbJ0gIAhcPDVJSeQ9iYmJjBkzhrlz55piFStWZO3atfj4+GiXmACkEBIiW127F8fPIeqxQT3rlcVNukHm5/Yp2PBemqAOOi4CzxqapCTynvv37/PGG28QEhJiinXr1o0FCxbIfmFmQi6NCZGNftx1CcNj7SBHW2sGNJKZYmYn5jas6QYpCep48ylQta02OYk8qVChQlSoUAEABwcHFi5cSGBgoBRBZkQ6QkJkk+uRcRmMDSpDkQIyDdasJMfBan94qN7/jZo9oP4IbXISeZZOp2Px4sXEx8czbdo0qlevrnVKIg0phITIJrMz6Aa9J2ODzIvRmHo5LPwvdbxsI2gzXTZSFS/s3Llz3LlzhyZNmphirq6ubNmyRbukxFPJpTEhssH1yDg2nFJ3g3rUk26Q2dk5Bc7/qo65lQe/FWAj47jEi1mxYgW1a9emS5cu3Lp169kHCLMghZAQ2WDObnU3yMHWivdel26QWTm1KnWq/OMcCkHAWnBy0yIjkUfExcXRp08fevXqRXx8PPfu3WPSpElapyWek1waE+IFhUbGs+Fkmm7Qa2Vwl26Q+bh2AH4ZqY5Z2YD/SnCvoElKIm/4+++/8fPz49y5c6ZY//79mTlzpoZZicyQjpAQL2jO7kukpOsGldcwI6ESeTl1+wyjXh1vMx28X9cmJ2HxFEVhyZIl1KlTx1QEFShQgMDAQBYtWoSTkyzGaSmkIyTEC7gRFc//Tt5Uxd6tW4aiBaUbZBYS7qdupJpwXx2vPxx8e2mTk7B4Dx8+ZPDgwQQG/rcieY0aNVi7di2VKlXSMDORFVIICfEC5u5Rd4Psbax4r7GMDTILBj2s7QmRl9Txyq2h2RRtchIWT1EUmjVrxtGjR02xwYMHM336dBwcHDTMTGSV5pfG5s6di7e3Nw4ODvj6+rJ///6nPj4wMJAaNWrg5OSEp6cnffr0ITIyMpeyFeI/N6LiWXdc3Q3qXrcMxQrKL0PNKQps/QCu7lPHi7+SunK0lbU2eQmLp9PpGDt2LAAFCxYkODiYuXPnShFkwTQthIKDgxk5ciQTJkzg1KlTNGrUiFatWhEaGprh4w8cOEDPnj3p168f//zzD+vWrePYsWP0798/lzMXAubuuZyuGzRIukHm4fAcOLFcHStQHLoFg30BTVISeUfnzp35/vvvOXXqFH5+flqnI16QpoXQ9OnT6devH/3796dq1arMmDGDUqVKMW/evAwff+TIEcqWLcuIESPw9vamYcOGDBw4kOPHj+dy5iK/u3k/nvUnbqhiAXVLU8xF/irU3L+/wfZP1DEbR+i2BlxLaJOTsFgnT55UjQV6ZPTo0ZQvL5Mi8gLNxgglJydz4sQJPvroI1W8RYsWHDp0KMNj6tevz4QJE9i6dSutWrUiIiKC9evX06ZNmye+TlJSEklJSabbMTExAOj1evR6/ZMOE7nk0TmwtHMxe9dF9Ib/ukF2Nlb0q1/a4t7H4yz1XKjc+Rub9f3QoajCKe3noBR7BSzkveWJc2HhFEVh7ty5fPjhhyQnJ9OsWTP69OmjdVr5Wk79PGhWCN27dw+DwYCHh4cq7uHhQXh4eIbH1K9fn8DAQPz9/UlMTCQlJYX27dvz448/PvF1pk2bxpQp6QdG7t69W6Y3mpEdO3ZoncJzi0qCdaesgf+2Y3jNPYUTB3Zpl1Q2sqRz8Th7/QNe/3cytvo4VfysZ2cuXrWBq1s1yizrLPVcWLrY2Fhmz57NkSNHTLHZs2dTrFgxdLINi2bi4+Nz5Hk1nzWW9ptKUZQnfqOdPXuWESNG8Omnn9KyZUvCwsIYO3YsgwYNYsmSJRkeM378eEaPHm26HRMTQ6lSpWjatClFihTJvjciskSv17Njxw6aN2+Ora2t1uk8l0m/nMWg/DdI2s7Gii97vI6HhV8Ws8RzYaKPx3rl21jpo1Rh4yv+VGw3m4oW9uFl0efCwh09epSRI0dy7do1U6x9+/b89NNPODs7a5eYyLGJUZoVQu7u7lhbW6fr/kRERKTrEj0ybdo0GjRoYBqxX716dZydnWnUqBFTp07F09Mz3TH29vbY26df08XW1lZ+wZgRSzkftx8ksO6EehXpbq+WomSRghpllP0s5VyYGI2wcTiEnVLHS9fD6u0fsbLgPcQs7lxYMEVR+OGHH/jwww9JSUkBoHDhwixZsgQrKyucnZ3lXGgsp77+mg2WtrOzw9fXN13rd8eOHdSvXz/DY+Lj47GyUqdsbZ06DVZRlIwOESJbzdtzWT02yNqKwU1kiwZN7fkSzv6sjhUuC/6BYCMLW4pni4yMpH379owZM8ZUBNWvX5+QkBDatm2rcXYip2k6a2z06NEsXryYpUuXcu7cOUaNGkVoaCiDBg0CUi9r9ezZ0/T4du3asWHDBubNm8eVK1c4ePAgI0aMoE6dOnh5eWn1NkQ+ERadQPAx9UyxrnVKUdzVsi+JWbTTwbDvW3XM3jV1I1VnufQtns8HH3zAr7/+arr94YcfsmfPHkqXLq1hViK3aDpGyN/fn8jISD777DPCwsKoVq0aW7dupUyZMgCEhYWp1hTq3bs3Dx8+ZPbs2YwZM4ZChQrxxhtv8PXXX2v1FkQ+Mm/PZZINRtPt1G6QTJ/VTOgR2DxMHdNZg99yKFpZk5SEZfr666/5/fff0ev1rFy5krfeekvrlEQu0nyw9JAhQxgyZEiG9y1fvjxdbPjw4QwfPjyHsxJCLTw6kaCj6m6Q/6ul8HR11CijfC7qKgQFgCFZHW/9LZR/Q5uchMVIOymnWLFi/Pzzz3h5eVGihKw1ld9ovsWGEJZg/l51N8jWWifdIK0kRsNqf4hPM4Ok7mB4tZ82OQmLsW/fPl577TXu3r2rir/66qtSBOVTUggJ8Qx3YhJZfVS97Ytf7VJ4FZJuUK4zpMC63nDvX3W8Ygto+YUmKQnLYDAYmDp1Kk2bNuXo0aP06tULo9H47ANFnqf5pTEhzN28PZdJTlF3g4Y0lZlimtj2EVxOs3BlsZeg0xLZSFU80Z07d3j33Xf5448/TLGkpCRiY2NxcXHRMDNhDqQjJMRTRMQksiZNN6hL7VKUkG5Q7vtzARxbpI45F4WAYHCQDzORsV27dlGjRg1TEWRlZcWUKVPYvn27FEECkI6QEE81f+8VktJ2g2RsUO67uCO1G/Q4a3vougYKyRRnkZ7BYOCzzz7j888/N60z5+npyerVq2nSpIm2yQmzIoWQEE8QEZNI4J/XVbHOvqUoWVj2qMtVd87Cuj6gpBnP0WEulHpVm5yEWbt9+zbdu3dnz549pliLFi1YuXIlxYoV0y4xYZbk0pgQT7Bgn7obZGMl3aBcF3s3dYZY8kN1vMl4eKWzNjkJs7dz505TEWRtbc2XX37Jb7/9JkWQyJB0hITIQMTDjLpBJSnlJt2gXKNPTF0rKFo9RotqnaHxh9rkJCzCu+++y44dO9i1axdBQUE0bNhQ65SEGZNCSIgMLNx7hUS9uhs0VGaK5R5FgZ+Hws2j6njJOvD2HLCw3eRFzoqOjsbV1dV0W6fTMXfuXBITE3F3d9cwM2EJ5NKYEGncfZjEqjTdoE61pBuUq/Z+A3+vV8dcS0PXQLCVvd3Ef7Zu3Ur58uXZsGGDKl6gQAEpgsRzkUJIiDQW7Vd3g6ylG5S7zqxP3VH+cXYFU6fJF5AxHiKVXq9n3LhxtGnThsjISPr27cvVq1e1TktYILk0JsRj7sUmseLwNVWsU60SlC4i3aBcceMYbEqz96DOCrosA4+XtMlJmJ3r16/TtWtXjhw5Yoo1btxYdXlMiOclHSEhHrNoX/pu0LCmFTXMKB95EApB3cCQpI6/9RVUbK5NTsLs/Pzzz/j4+JiKIFtbW2bMmMGmTZtwc3PTODthiaQjJMT/i4xNYsVh9digd2pKNyhXJMakTpOPU2+Eyav9oc572uQkzEpycjLjxo1j5syZppi3tzfBwcG8+qqsJyWyTgohIf7fwv1XSNAbTLdTu0EyNijHGQ3wv34QcVYdL/8GvPW1zBATXLt2jS5dunD8+HFTrFOnTixevJhChQppl5jIE+TSmBBAVFwyK9N0gzr4lKCsu7NGGeUj2z+Bi9vVMffK0HkZWMvfagJsbGxMA6Ht7OyYM2cO69atkyJIZAsphIQgdaZYfPJ/3SArHQx7Q7pBOe7YEjgyVx1zKpI6Q8yxkCYpCfNTsmRJfvrpJypVqsSRI0cYMmQIOukUimwihZDI96Likvnp0DVVrEPNEnhLNyhnXd4FW8eqY9Z24B8Ibt7a5CTMwqVLl4iOjlbF2rRpw99//03NmjU1ykrkVVIIiXxvcQbdoOFvyEyxHHX3X1jbGxSDOt7+RyhTT5OUhHkICgqiVq1aDBgwwLRr/CO2trYaZSXyMimERL52P4Nu0Ns+0g3KUXGRsNoPktR/8dPoA6jRVZuchOYSEhIYOHAg3bp14+HDh6xbt46VK1dqnZbIB2QkosjXFh+4QpyMDco9KUkQ/C7cv6aOv/Q2NJ2gSUpCe+fPn8fPz48zZ86YYj169KBjx44aZiXyC+kIiXzrQXwyPx1SzxRrX8OL8kULaJRRHqco8Mv7EHpIHfeqBR3mg5X8OsqPVq5cSe3atU1FkKOjI0uXLuWnn36iQAH5WRQ5TzpCIt9acuAqsUkppts6HQyTsUE558B0OL1GHXMpAd3WgJ0sWpnfxMXFMWzYMJYvX26KvfTSS6xbt46XXpLtVETukUJI5EsP4pNZdvCaKta+hhcVislfoDni7M+w8zN1zNYZugVBweLa5CQ0c+/ePRo3bszZs/8totm3b19+/PFHnJykKBa5S3rRIl9amkE3aLiMDcoZt07ChoFpgjrotBg8q2uSktBWkSJFqFSpEgDOzs6sXLmSJUuWSBEkNCEdIZHvRMfr03WD2lb3okKxgtoklJdF34I13SAlQR1v8TlUaa1NTkJzOp2OpUuXYjAY+Pbbb6lcubLWKYl8TAohke8sOXiVh2m6QSOkG5T9kmJhjT/EhqvjtXpBvWHa5CQ08ddff3H//n0aN25sihUuXJjNmzdrmJUQqeTSmMhXohP0LDt4VRVr84onFT2kG5StjAbYMADCz6jj3q9Dm+9lI9V8QlEUFixYQJ06dejSpQu3b9/WOiUh0pFCSOQryw5e5WFimm7QmzJTLNv9MRn+3aqOFakAfivAWlYHzg9iYmLo1q0bgwYNIikpibt37/LFF19onZYQ6cilMZFvRCfoWXJA3Q1q/YonlaQblL1OroBDs9Qxh0IQsBYcC2uSkshdJ0+exM/Pj8uXL5tiw4YN49tvv9UwKyEyJh0hkW8sP3hN1Q0CGCHrBmWvq/vg11HqmJUt+K+CIuW1yUnkGkVRmD17NvXq1TMVQa6urqxfv54ff/wRBwcHjTMUIj3pCIl8ISZRz5IDV1Sx1q8Up3Jx6QZlm3uXILgHGNXFJu1mgHcjTVISuefBgwf069ePDRs2mGKvvvoqwcHBeHt7a5iZEE8nhZDIF5YfvEZM2m6QjA3KPvFRqRupJj5Qxxu8DzXf1SQlkXuMRiNNmjTh9OnTptioUaP46quvsLOz0zAzIZ5NLo2JPO9hYvqxQa2qFadKcReNMspjUpJhbU+IuqyOV2kLb07WJCWRu6ysrBg/fjyQOi3+559/Zvr06VIECYsgHSGR5/106BrRCXpVTLpB2URRYMtouLZfHS9eHToulI1U8xF/f39u375Np06dKF26tNbpCPHc5LeUyNMeJupZtF/dDXrr5eJU9ZRuULY49COcWqmOFfSEgGCwc9YmJ5HjDh8+zIQJE9LFR40aJUWQsDjSERJ52orD16UblFPOb4Edn6pjNo6pu8m7eGmTk8hRRqOR7777jo8//hiDwUDVqlV5910ZAyYsm3SERJ4Vm5TCov3qmWItXvLgJS/pBr2wsNPwv/6Aoo53XAheNTVJSeSse/fu0bZtWz788EMMBgMAq1evRlGUZxwphHmTQkjkWT8dusaDeOkGZbuYMFjdFfTx6vibk+Cl9trkJHLU/v378fHx4bfffgNSN02dMGECmzdvRifbpQgLJ5fGRJ4Ul5TC4jTdoOYveVCthKtGGeURyfEQ1A0eptkzqkYANByV8THCYhmNRqZNm8ann36K0WgEoGjRogQGBtK8eXONsxMie0ghJPKkFYevcz9NN+h96Qa9GKMRNg6E26fU8TINoN1M2Ug1j7lz5w49evRgx44dpljTpk0JDAzE09NTw8yEyF5yaUzkOXFJKSzcp17TpllV6Qa9sN1T4dxmdaywN/itBBtZLyavGT16tKkI0ul0TJ48mR07dkgRJPIc6QiJPGflEekGZbuQNbD/e3XM3jV1I1XnItrkJHLU9OnT2bVrF5A6KLpp06YaZyREzpBCSOQp8ckpLNynHhv0ZpVivFJSukFZdv0QbB6ujumswe8nKFpJm5xEtlMURTXw2cPDg19++YVSpUrh4eGhYWZC5Cy5NCbylJWHrxMVl6yKvd9MukFZFnUFgrqDUd1ho833UF46BHnFjh07ePXVV4mMjFTFa9euLUWQyPOkEBJ5RkbdoDeqFKN6yULaJGTpEh7Aan9IiFLHXxsKtftokpLIXikpKXzyySe0bNmSEydO0Lt3b1kXSOQ7cmlM5BmBR0KJTNsNkrFBWWPQw7recO+COl6xJbT4XJOURPa6efMmAQEB7N//3z5xBoOB+Ph4nJ1lexSRf0hHSOQJCckGFqSZKda0clFqlCqkTUKWTFHgt3FwZbc6Xuxl6LwErKy1yUtkm61bt+Lj42Mqgqytrfnmm2/49ddfpQgS+Y50hESeEPjnde7Fph0bJAN5s8Lq2EI4vlQddC6WupGqfUFtkhLZQq/XM2HCBL799ltTrHTp0gQFBVGvXj0NMxNCO1IICYuXkGxg/l712KDGlYriI92gTPOIDsEqZIY6aOOQupFqoVKa5CSyR2hoKF27duXw4cOmWPv27Vm2bBlubm4aZiaEtuTSmLB4qd2gJFVMZoplQcRZal+bi04xquMd5kHJ2trkJLLNzp07TUWQra0tP/zwA5s2bZIiSOR70hESFi1Rb2BBmplir1cqSq3ShTXKyELFRmATHIDOmKiON50A1Tpqk5PIVr179+aPP/7g0KFDrF27lldffVXrlIQwC1IICYu2+s9Q7j5M0w2SmWKZo0+AoAB0MTfV8Vf84PWx2uQkXtj9+/cpXPi/Pwh0Oh3z58/HYDBQqFAh7RITwszIpTFhsRL1BubtVc8Ua1TRHd8y0g16booCPw+Fm8fU8VJ1of2PspGqhdqwYQPlypVj82b13nAFCxaUIkiINKQQEhZrzdH03aCRMjYoc/Z8BX//TxVSXEuDfyDYOmiUlMiqxMREhg8fTqdOnXjw4AG9e/fm+vXrWqclhFmTS2PCIiXqDczbo+4GNazgjm8ZGfj53P5aB3u/UoX0Vg7gvxrbAkU1Skpk1aVLl/Dz8+PUqVOmWIsWLVSXx4QQ6UlHSFikoKOhRKQdGyTdoOd342jqJbHHKDorjnsPg6JVNEpKZFVwcDC1atUyFUH29vYsWLCANWvW4OLionF2Qpg36QgJi5PR2KAGFYrwalnpBj2X+9dhTTcwqAtJY/MvibjrpVFSIisSEhIYNWoUCxYsMMUqV67M2rVrqV69uoaZCWE5pCMkLM7a4ze4E5N2ppisIv1cEmNSN1KNv6eO13kP46v9tclJZMmlS5d47bXXVEXQu+++y/Hjx6UIEiITpBASFiUpxcDc3epuUP3yRajjLd2gZzKkwPq+cPecOl7+TWg5TZucRJbZ2dlx48YNABwdHVmyZAkrVqygQIECGmcmhGWRQkhYlLXHbhAeo170T9YNek7bJ8ClHepY0SrQZRlYy1VyS1O6dGl++uknXnrpJY4dO0bfvn3RyXIHQmSaFELCYiSlGJibZqbYa+XcqFuuiEYZWZCji+DP+eqYk3vqRqoOrtrkJDLl3LlzPHz4UBVr164dp0+f5uWXX9YoKyEsnxRCwmKsPX6TsOi03SAZG/RMl/6A3z5Ux6ztoOtqKFxWk5TE81MUhWXLluHr68vAgQNRFEV1v42NdPOEeBGaF0Jz587F29sbBwcHfH192b9//1Mfn5SUxIQJEyhTpgz29vaUL1+epUuX5lK2QitJKQbm7b6kitX1dqNeeekGPVXEeVjXBxSDOv72HChdV5ucxHOLjY2lV69e9O3bl4SEBNasWUNwcLDWaQmRp2j6p0RwcDAjR45k7ty5NGjQgAULFtCqVSvOnj1L6dKlMzzGz8+PO3fusGTJEipUqEBERAQpKSm5nLnIbetP3OR22m6QrBv0dHH3YLUfJMWo46+Pg+p+2uQkntu1a9cYN24cFy5cMMUGDhzI22+/rWFWQuQ9mhZC06dPp1+/fvTvnzptd8aMGfz+++/MmzePadPSz2LZtm0be/fu5cqVK7i5pc4SKlu2bG6mLDSQnGJMN1Osjrcb9WRs0JOlJEFQd3iQZnuFl9+BJuO1yUk8F0VRWLx4MePGjSM5ORlI3SNs4cKFdO3aVePshMh7NCuEkpOTOXHiBB999JEq3qJFCw4dOpThMZs3b6Z27dp88803rFy5EmdnZ9q3b8/nn3+Oo6NjhsckJSWRlPTfmjMxMal/Hev1evR6fTa9G5FVj87B085F8LGb3HqQoIoNa+ItncAnURSsNw/F6sYRVdjoVQtDm1lgMKT+S+N5zoXIWTExMQwZMoS1a9eaYj4+PgQGBlKxYkU5NxqQnwvzkVPnQLNC6N69exgMBjw8PFRxDw8PwsPDMzzmypUrHDhwAAcHBzZu3Mi9e/cYMmQIUVFRTxwnNG3aNKZMmZIuvnv3bpycnF78jYhssWPHjgzjKUb4IcQa+G9acLmCClHn/mTr+VxKzsJUCt9M1bD1qli8rRv73HqTtGP3M49/0rkQOevBgweMHz+esLAwU6x169b07t2bixcvcvHiRQ2zE/Jzob34+PgceV7NpxukXfdCUZQnroVhNBrR6XQEBgbi6po65Xf69Ol07tyZOXPmZNgVGj9+PKNHjzbdjomJoVSpUjRt2pQiReTSitb0ej07duygefPm2Nraprs/+PhNopLOqmKTOtWmvgySzpDu3M/YnFIXQYqdM7Y9N/Kmx9OnWD/rXIicpSgK69atY8uWLbi6ujJw4EAmTZok50Jj8nNhPiIjI3PkeTUrhNzd3bG2tk7X/YmIiEjXJXrE09OTEiVKmIoggKpVq6IoCjdv3qRixfSDZ+3t7bG3t08Xt7W1lW9qM5LR+dAbjMzfd1UVq12mMK9X9pCF4zJy6wRsHpomqEPXaSm2JX2e+2nkZ0M7K1asoH///kybNo3z58/LuTAjci60l1Nff82mz9vZ2eHr65uu3bhjxw7q16+f4TENGjTg9u3bxMbGmmIXLlzAysqKkiVL5mi+IvdtOHmTm/fVY4Peb1ZRiqCMRN9M3Ug1RT2zjpZfQOW3tMlJPNWxY8fYt2+fKubm5saGDRsoV66cRlkJkf9ouo7Q6NGjWbx4MUuXLuXcuXOMGjWK0NBQBg0aBKRe1urZs6fp8QEBARQpUoQ+ffpw9uxZ9u3bx9ixY+nbt+8TB0sLy6Q3GPlxl3rdIN8yhWlYwV2jjMxYUiys7gqxd9Rx397w2hBNUhJPpigKM2bMoEGDBvj5+T1xTKQQIndoWgj5+/szY8YMPvvsM3x8fNi3bx9bt26lTJkyAISFhREaGmp6fIECBdixYwcPHjygdu3adO/enXbt2jFr1iyt3oLIIRtP3krfDXpTukHpGA3wv/5w54w67t0YWn8H8vUyK1FRUXTo0IFRo0ah1+u5c+cO33zzjdZpCZGvaT5YesiQIQwZkvFfrcuXL08Xq1Kliozez+P0BiOz06wiXbN0IRpVlG5QOjs+hQu/qWNFKoLfT2At4xnMyeHDh+natavqj7uxY8fyxRdfaJiVEELzLTaESGvjqVuERqmnSY5sVkm6QWmdWA6HZ6tjjoVTN1J1LKxJSiI9o9HIt99+y+uvv24qgooUKcKvv/7KN998IwNwhdCY5h0hIR6XYjAyJ003yKdUIV6XbpDalT2wZYw6ZmUL/oFQpLwmKYn07t27R69evdi6dasp1rBhQ9asWSMTPIQwE9IREmZlU8htrkequ0EyUyyNexdhbU8wpllZu91MKNtAm5xEOikpKTRq1MhUBOl0Oj7++GN2794tRZAQZkQKIWE2UgxGftylXj23RqlCNKlUVKOMzFB8VOpGqonR6njDUVCzuzY5iQzZ2NjwySefAFC0aFG2bdvGF198gY2NNOKFMCfyEynMxs8ZdINGykyx/6QkQ3APiLqijldpC298qk1O4qm6d+/O3bt38ff3x9PTU+t0hBAZkI6QMAspGcwUq17SlSaVpRsEgKLAllFw/YA67lkDOi4EK/lR1tru3buZMGFCuvjIkSOlCBLCjElHSJiFLWfCuXovThUbKWOD/nNwJpxapY4V9IRuQWDnrE1OAgCDwcDnn3/OZ599hqIovPLKK3Tt2lXrtIQQz0n+jBSaMyowZ4/6ck/1kq40rVxMo4zMzLlf4I/J6pitU2oR5OKlSUoiVVhYGM2bN2fKlCkoigLAunXrNM5KCJEZUggJzZ28p+NqmrFBI96QbhAAt0Ngw3uA8lhQBx0XgZePNjkJIHVfRB8fH3bv3g2AlZUVU6dOlUJICAsjl8aEpgxGhd9vquvxaiVceLOqdIOIuQ1ruoJeXSTSbDJUbatJSiJ1WvzkyZP58ssvTV0gLy8v1qxZw+uvv65xdkKIzJJCSGhqy5lwIhLVnZ/335RVpEmOSy2CHoap4zXfhQbva5OT4ObNmwQEBLB//35T7K233mLFihUULSoD+4WwRHJpTGjGYFTSjQ162cuFZvm9G2Q0pl4OCzutjpdpCG1+kI1UNTRq1ChTEWRtbc3XX3/Nli1bpAgSwoJJR0hoZsuZMK6kmSkmO8wDuz6D87+qY27lwH8l2Nhpk5MAYNasWezbtw97e3uCgoKoX7++1ikJIV6QFEJCEwajwqyd6lWkX/J0oflLHhplZCZOBcKBH9QxB1cIWAtObtrklI8ZjUasHlujydPTky1btlCuXDnc3OR8CJEXyKUxoYmtZ8K4FBGrio3I792gawfhlzTjf6xswG8FuFfUJqd8bPPmzdSuXZuoqChVvHbt2lIECZGHSCEkcp0xg25QleIFaZGfu0GRlyG4Oxj16nib76FcE01Syq+Sk5MZPXo0b7/9NqdOnaJv376m2WFCiLxHLo2JXLf17zAupukGDWtSDiurfNoNSrgPq/1T//u4esPAt7cmKeVXV69exd/fn2PHjpliVlZWJCYm4ujoqGFmQoickqVCKC4ujq+++oqdO3cSERGB0WhU3X/lypUnHCnyu4y6QZ5OCs3z60wxgx7W9oJI9deESq2g+Wfa5JRPbdiwgb59+xIdHQ2AnZ0d33//PUOHDs3fl2yFyOOyVAj179+fvXv30qNHDzw9PeWXhHhu2/4J58IddTforZLG/NkNUhTY+gFc3auOe7wCnRaDlbU2eeUzSUlJfPDBB8yePdsUK1++PMHBwfj6+mqYmRAiN2SpEPrtt9/YsmULDRo0yO58RB5mNCrM/EPd+ahUrADV3R5ok5DWjsyFE8vVsQIeEBAE9gU0SSm/uXTpEv7+/pw8edIU8/PzY9GiRbi4uGiYmRAit2RpsHThwoVl1oTItN//CeffOw9VsWFNy5Efm0H8+xv8PkEds3GAbmvAtaQ2OeVDu3btMhVB9vb2zJ8/n6CgICmChMhHslQIff7553z66afEx8c/+8FC8P/doDRjgyp5FKBlfpwpFn4G1vdDvZEq8M58KCGXYnLTgAED8PPzo1KlSvz5558MHDhQLvULkc9k6dLY999/z+XLl/Hw8KBs2bLY2tqq7n+8zSwEwPaz4ZwPV3eDRrxZMf+NDXp4B1Z3Bb16RW3e+ARefkebnPKRyMhIihQpYrqt0+lYtGgROp2OggULapiZEEIrWSqEOnTokM1piLwstRt0SRWrWKwArat5YjCkaJSVBvQJENQNYm6q49X9odEH2uSUj6xatYrBgwezZs0a2rZta4rLZTAh8rcsFUKTJk3K7jxEHrbj3B3OhcWoYo+6QQaDRknlNqMRNg2GWyfU8VKvQfsfZSPVHBQfH8/w4cNZunQpAL169SIkJIRSpUppnJkQwhzIgooiRylK+pliFYoVoPUrnhplpJE90+CfjepYoTLQNRBs7LXJKR84e/YsXbp04ezZs6ZY+/btZbKHEMLkuQshNzc3Lly4gLu7O4ULF37qgMK0e/OI/GvH2TucTdMNGv5GBazz09igv9bCvm/UMXuX1I1Und21ySkfWL58OUOGDCEhIQEAJycn5s2bR8+ePTXOTAhhTp67EPrhhx9MgwlnzJiRU/mIPERR0s8UK1/UmbbVvTTKSAOhR+DnoeqYzhq6LINiVbTJKY+LjY1l6NChrFixwhSrVq0a69ato0oV+ZoLIdSeuxDq1atXhv8vxJP8cS6Cf26nHxuUb7pB969BUHcwJKvjrb6GCs00SSmvO3fuHB07duT8+fOm2IABA5g5c6bsFSaEyNALjxFKSEhAr1fvmC2zMERqN+iCKlYuP3WDEqNTN1KNv6eO1x0EdQZok1M+4OjoSFhYGAAFChRg4cKFdOvWTeOshBDmLEsLKsbFxTFs2DCKFStGgQIFKFy4sOqfELvOR/D3rTTdoDfySTfIkALr+sDd8+p4hebQ4gttcsonypYty7Jly6hZsyYnT56UIkgI8UxZKoTGjRvHrl27mDt3Lvb29ixevJgpU6bg5eWlui4v8idFUZiRZqZYOXdn2tXIJ92g38fD5Z3qWNGq0HkpWMtEzex0+vRpHj5UL9T5zjvvcPToUSpWrKhRVkIIS5KlQuiXX35h7ty5dO7cGRsbGxo1asQnn3zCl19+SWBgYHbnKCzM7n8jOHMrWhUbll9miv25EI4uVMec3CEgGBzkknF2URSFuXPnUqdOHQYPHoyiqLcrsbGRglMI8XyyVAhFRUXh7e0NpI4HejRdvmHDhuzbty/7shMWJ6N1g7zdnWmfH7pBF/+AbR+qY9b2qRupFi6jTU55UHR0NH5+fgwdOpTk5GQCAwPZsGGD1mkJISxUlgqhcuXKce3aNQBeeukl1q5dC6R2igoVKpRduQkLtOffu5y+maYb1LQCNtZZ+lazHHfOwrreoBjV8Q5zoVQdTVLKi44dO0bNmjVZv369Kfb++++rtswQQojMyNKnU58+fTh9+jQA48ePN40VGjVqFGPHjs3WBIXlUBSFGWnWDSpbxIm3ffJ4Nyj2Lqzxh2T1WBUafwSvdNYmpzxGURRmzpxJgwYNuHr1KgCFChVi48aNzJgxA3t7WZ1bCJE1WbqQPmrUKNP/N23alPPnz3P8+HHKly9PjRo1si05YVn2XrjL6RsPVLFhb1TM290gfSIEd4cHoep4tU7Q5CNtcspj7t+/T9++fdm0aZMpVrduXYKDgylTRi45CiFeTKYKoYSEBHbu3GlqQ48fP56kpCTT/UeOHKFy5co4ODhkb5bC7GU0U6xMESc65OVukKLA5mFw4091vOSr8PYc2Ug1G9y8eZOGDRty/fp1U+yDDz7gyy+/xNbWVsPMhBB5RaYKoRUrVvDrr7+aCqHZs2fz8ssvm1ZsPX/+PJ6enqqOkcgf9l28R0iabtDQvD42aN+3cGadOuZaCrquBltZxTg7eHl58dJLL3H9+nXc3NxYsWIFbdq00TotIUQekqlPqcDAQPr27auKrV69mt27d7N7926+/fZb08BpkX+kzhRTryJd2s2Jd2qW0CijXPD3/2B3msUR7QqkTpMvUEybnPIgKysrVqxYQZcuXQgJCZEiSAiR7TJVCF24cIFKlSqZbjs4OGBl9d9T1KlTh7Nnz2ZfdsIi7L94j5OhD1SxYU0rYJtXu0E3j8OmIeqYzip1wUSPl7XJKY84cOBAuiU43N3dWbt2LaVKldIoKyFEXpapT6ro6GjVQmV3796lbNmypttGo1E1ZkjkfRntMF/KzZF3auXRbtCDG7CmG6QkquMtv4RKLbXJKQ8wGo1MmzaNJk2a4O/vT0REhNYpCSHyiUwVQiVLluTvv/9+4v1//fUXJUuWfOGkhOU4eCmSE9fvq2J5thuU9DB1I9W4NB/StfumbqYqsiQiIoJWrVrx8ccfYzAYCA8P54cfftA6LSFEPpGpT6vWrVvz6aefkpiYmO6+hIQEpkyZItfw85HUmWLqsUElCzvSsVYeLIaNBljfDyL+UcfLNYVW38gMsSzas2cPPj4+bN++HQCdTsenn37K559/rnFmQoj8IlOzxj7++GPWrl1L5cqVGTZsGJUqVUKn03H+/Hlmz55NSkoKH3/8cU7lKszMocuRHE/TDRqaV7tB2yfCxd/VMfdK0GU5WMs07swyGAxMnTqVzz77DKMxdTVuDw8PAgMDefPNNzXOTgiRn2SqEPLw8ODQoUMMHjyYjz76yLTRoU6no3nz5sydOxcPD48cSVSYl4z2FCtRyJFOebEbdHwpHJmjjjm6pc4QcyykSUqWLDw8nO7du7Nr1y5T7M0332TVqlUUL15cw8yEEPlRpleW9vb2Ztu2bURFRXHp0iUAKlSogJubW7YnJ8zX4cuRHL0WpYoNbVoBO5s81g26vBu2fKCOWdlC10BwK6dNThZMr9fToEEDrly5AqROj58yZQrjx4/H2tpa4+yEEPlRlrbYAHBzc6NOHdlMMr9Ku6dYiUKOdPbNY92guxdgbS9QDOp4+x+hTH1tcrJwtra2TJ48mZ49e+Ll5cXq1atp3Lix1mkJIfKxLBdCIv86fDmSo1fV3aAhTcvnrW5QXCSs9oOkaHW80Rjw6aZNTnlEjx49uH//Pt26daNo0aJapyOEyOfy0CeXyC1pZ4p5uTrQxTcPLXaXkgTB78L9q+p41fbQ9BNtcrJQ27ZtY8KECeniI0aMkCJICGEWpCMkMuXIlUj+TNMNGpyXxgYpCvwyEkIPqeOePvDOArDKI+8zh+n1eiZOnMjXX38NgI+PD126dNE4KyGESE9+q4tMSTtTzNPVAb/aeWhs0IEf4PRqdcylBHQLAjsnbXKyMKGhoTRp0sRUBAH8/PPPGmYkhBBPJoWQeG5/Xonk8JVIVWxIk/LY2+SR2T5nN8POKeqYrXNqEeTiqU1OFuaXX36hZs2aHDqU2lGzsbHh+++/Z+XKlRpnJoQQGZNLY+K5pd1TrLiLA36v5pGxQbdPwYb30gR10GkReFbXJCVLkpyczPjx45k+fbopVqZMGYKDg6lbt66GmQkhxNNJISSey9GrURy6nKYb1DSPdINibv//RqoJ6njzz6CKbBnzLFevXqVr164cPXrUFOvQoQNLly6lcOHCGmYmhBDPJpfGxHOZuVM9U8zDxR6/2nmgG5QUm7qR6sMwdbxmD6g/XJucLMyoUaNMRZCdnR2zZs1iw4YNUgQJISyCdITEMx2/FsXBS+pu0ODG5XGwtfBukNGYejks/C91vGwjaDNdNlJ9TnPmzOHgwYO4uLiwdu1afH19tU5JCCGemxRC4pnSjg0qVtCernVKa5RNNto5Gf7doo65lQe/FWBjp0lKlsBoNGL12DICJUqU4LfffqNixYq4urpqmJkQQmSeXBoTT3XiehT7L95TxQY3yQPdoJMr4eBMdcyhEASsBSfZN+9J1q5dS61atXjw4IEqXrt2bSmChBAWSQoh8VQz0qwbVLSgPd0svRt0dT/8OlIds7IB/5XgXkGTlMxdQkICgwYNwt/fn9OnT9OvXz8URdE6LSGEeGFyaUw80Ynr99N1gwZZ+tigyMuwtgcYU9Txtj+A9+va5GTm/v33X/z8/Pjrr//GUjk6OpKcnIy9vb2GmQkhxIuTjpB4orRjg4oWtKd7XQvuBsVHpW6kmnBfHa8/Amr11CYnMxcYGIivr6+pCHJ0dGTJkiWsXLlSiiAhRJ4gHSGRoVOh99l34a4qNvD1cpbbDTLoYW1PiLykjlduA80ma5KSOYuPj2fEiBEsWbLEFKtatSpr166lWrVqGmYmhBDZS/OO0Ny5c/H29sbBwQFfX1/279//XMcdPHgQGxsbfHx8cjbBfCptN8i9gD3d65bRKJsXpCiwZTRcS/O9VfwV6LgQrCy0uMshZ8+epU6dOqoiqFevXhw7dkyKICFEnqNpIRQcHMzIkSOZMGECp06dolGjRrRq1YrQ0NCnHhcdHU3Pnj158803cynT/CXkxgP2/KvuBg1qXA5HOwstGA7PhpMr1LECxaFbMNgX0CYnM7Z//37++ecfAJycnFi+fDnLly/H2dlZ48yEECL7aVoITZ8+nX79+tG/f3+qVq3KjBkzKFWqFPPmzXvqcQMHDiQgIIB69erlUqb5y8w/1KtIuxews9xu0PmtsH2iOmbjCN3WgGsJbXIyc++99x6dO3emWrVqHD9+nF69emmdkhBC5BjNxgglJydz4sQJPvroI1W8RYsWpp2rM7Js2TIuX77MqlWrmDp16jNfJykpiaSkJNPtmJgYAPR6PXq9PovZ511/3Yxmd5puUL8GZbHRGdHrjdn+eo/OQY6ci/Az2PyvPzrU07xT3p6LUuwVkPMPQEREBMWKFTOdg5SUFObPn4+NjQ1OTk7yc6KBHP25EJki58J85NQ50KwQunfvHgaDAQ8PD1Xcw8OD8PDwDI+5ePEiH330Efv378fG5vlSnzZtGlOmTEkX3717N05OTplPPI9bcM6KxxuFBWwUitw/y9atZ3P0dXfs2JGtz2evf0Djfydjq49Txc96duHiFWu4sjVbX88SKYrCjh07WLJkCePGjTNtjZHd50JknZwL8yHnQnvx8fE58ryazxrTpdnPSVGUdDEAg8FAQEAAU6ZMoVKlSs/9/OPHj2f06NGm2zExMZQqVYqmTZtSpEiRrCeeB525Fc3Zw3+qYkPerMQ7Db1z7DX1ej07duygefPm2NraZtOTxmO98m2s9FGqsPEVfyq2m01F2UOMmJgYhgwZwtq1awGYN28ehw8f5p9//snecyGyJEd+LkSWyLkwH5GRkc9+UBZoVgi5u7tjbW2drvsTERGRrksE8PDhQ44fP86pU6cYNmwYkLrnkaIo2NjYsH37dt544410x9nb22e43omtra18U6cxd+9V1W03Zzt6NyiHrW3Of5tk2/kwGmHjcAg7pY6Xro/V2z9iJXuIcerUKfz8/Lh06b+lBPz8/PDw8OCff/6Rnw0zIufCfMi50F5Off01GyxtZ2eHr69vunbjjh07qF+/frrHu7i4cObMGUJCQkz/Bg0aROXKlQkJCaFu3bq5lXqedOZmNH+ci1DF3nu9HE52mjcNM2f3F3D2Z3WscFnwXwU2+XsBQEVRmDt3LvXq1TMVQS4uLqxbt445c+bg4OCgcYZCCJH7NP2UGz16ND169KB27drUq1ePhQsXEhoayqBBg4DUy1q3bt1ixYoVWFlZpVvDpFixYjg4OMjaJtkg7bpBbs529HjNwmaKnQ6C/d+pY/auqRupOufvy6DR0dH079+f9evXm2K+vr4EBwdTvnx5DTMTQghtaVoI+fv7ExkZyWeffUZYWBjVqlVj69atlCmT+gEcFhb2zDWFxIv7+1Y0f5y7o4r1b+SNs70FdYOuH4bNw9UxnTX4LYeilTVJyVz89ddfvPPOO1y5csUUe//99/n6669lmwwhRL6n+SfdkCFDGDJkSIb3LV++/KnHTp48mcmTJ2d/UvlM2m5QYSdbetYrq00yWRF1FYK7gyFZHW/9LZRPP24svylQoAD37qVunluoUCGWLVtGhw4dtE1KCCHMhOZbbAht/XM7mh1n03aDylHAUrpBCQ9gtT/Ep5lNUHcwvNpPk5TMTbly5Vi8eDF169bl1KlTUgQJIcRjpBDK52al6QYVcrKlV/2y2iSTWYYUWNcb7v2rjldsCS2/0CQlc3D8+HHi4tTrJ3Xp0oWDBw9StmxZbZISQggzJYVQPnb2dgy//6PuBg2wlG6QosBv4+DKbnW82MvQeUm+3EjVaDTy3XffUa9ePYYOHZrufmvr/Pc1EUKIZ5FCKB9L2w1ydbSlZz0LmSl2dCEcX6KOOReFgCCwL6hNThq6d+8e7du3Z+zYsaSkpPDTTz/xyy+/aJ2WEEKYPQv401/khHNhMWz7R72YZf+G3hR0sIAFwy5sh23qPeqwtoeua6BQaW1y0tCBAwfo1q0bN2/eNMXGjx9Pq1atNMxKCCEsg3SE8qkfd6XvBvVqUFabZDLjzj+wvi8oaTaAfWcelHpVm5w0YjQamTZtGk2aNDEVQUWLFmXbtm18+eWXz70fnxBC5GfymzIfOh8ew9Yz6m5Qv4beuJh7Nyg2AlZ3heSH6niTj6FaJ21y0khERAQ9evRg+/btpljjxo1ZvXo1Xl5eGmYmhBCWRQqhfOjHnZdUt10cbOht7t0gfSIEBUB0mgU2q3WGxuO0yUkj165do379+oSFhQGpGxdPnDiRiRMnShdICCEySX5r5jP/hj9k699hqli/huXMuxukKPDzULh5TB0vWQfengP5bDf50qVLU716dcLCwvDw8CAwMJA333xT67SEEMIiyRihfGbWrosoyn+3C1pCN2jv1/D3enXMtTR0DQTb/LdRqJWVFStWrCAgIICQkBApgoQQ4gVIRygfuXjnIVvPqLtBfRt44+poxt2gM+thzzR1zK4gBARDgWLa5JTL/vjjDxwcHGjYsKEpVqxYMQIDAzXMSggh8gbpCOUjs3ZdStcN6tvQW7uEnuXGMdiUZh86nRV0WQYeL2mTUy5KSUlh4sSJtGjRAn9/f+7evat1SkIIkedIIZRPXLzzkF//uq2K9THnbtCDUAjqBoYkdfytr6Bic21yykW3bt3izTffZOrUqSiKwu3bt5k7d67WaQkhRJ4jl8byiR/TdoPsbejXwEy7QYkxqRupxqXpgLzaH+q8p01OuWjbtm306NHDtGO8tbU1X3zxBWPHjtU4MyGEyHukEMoHLkXE8ku6blBZXJ3MsBtkSEldMDHirDpe/g146+s8PUNMr9czceJEvv76a1OsZMmSBAUF0aBBAw0zE0KIvEsKoXzgxzQzxQrYm/HYoO2fwKUd6ljRKtBlOVjn3W/XGzdu0LVrVw4dOmSKtW3bluXLl1OkSBENMxNCiLwt736yCAAu343ll9PqblDv+mUp5GSnUUZPcWwx/DlPHXMqAt2CwMFVm5xyQVJSEg0aNODGjRsA2NjY8NVXXzF69Gh0ebgDJoQQ5kAGS+dxs3ddwpimG9TPDLtBuiu7YWuaFaKt7cA/ENzML9/sZG9vz5QpUwAoU6YM+/fvZ8yYMVIECSFELpCOUB525W4sP4fcUsV61S9DYWfz6gYVTLiF9YYvQTGo72g/G8rU0yapXNa7d2/i4uLo3r07hQsX1jodIYTIN6QjlIel7QY521nTv2E57RLKSNw96l6Zji4pzUaqr4+FGv7a5JTDNm7cyIQJE1QxnU7HsGHDpAgSQohcJh2hPOrqvTg2pesGlTWvblBKEtb/641zcppp8i91SN1RPo9JSkpi7Nix/PjjjwD4+vrSsWNHjbMSQoj8TTpCedSPuy6qukFOdtb0b2RG3SBFgV/ex+rGEXXcqxZ0mAdWeetb8/LlyzRo0MBUBAH89ttvGmYkhBACpBDKk67di+PnEPVMsZ71yuJmTt2g/d/D6TXqmEtJ6LYG7Jy0ySmHrFu3jlq1anHixAkgdXD0vHnzWLhwocaZCSGEkEtjedDs3ZcwPNYOcrKzZkAjM5p59c8m2PW5KqTYOqMLCIKCxbXJKQckJiYyevRo5s37b0mAihUrsnbtWnx8fLRLTAghhIkUQnnM9cg4Np5Sjw3qUa8MRQrYa5RRGrdOwMZBqpCCDkOHBdgUf0WjpLLfhQsX8PPz4/Tp06ZYQEAA8+fPp2DBghpmJoQQ4nFyaSyPmb1L3Q1ytLXmPXMZGxR9E9Z0g5QEVfifEl1RKr2lUVI5Y+TIkaYiyMHBgcWLF7Nq1SopgoQQwsxIRygPCY2MZ0OablBPc+kGJcXCmq4Qe0cVNvr04DLNqKxRWjll4cKF+Pj4UKxYMdauXUu1atW0TkkIIUQGpCOUh8zefTFdN2jA62bQDTIaYMMACD+jjnu/juGtb/LERqoGg3oxyJIlS7J9+3aOHTsmRZAQQpgxKYTyiBtR8Ww4qe4GvftaadzNoRv0xyT4d6s6VqQC+K0Aa1ttcspGP/30E7Vq1SI6OloVr1WrFs7OzhplJYQQ4nlIIZRHzNl9iZTHukEOtla893p5DTP6fyd+gkM/qmOOhSFgbep/LVhcXBy9e/emd+/e/PXXXwwYMABFUZ59oBBCCLMhY4TygBtR8aw/cVMVe7duGYoW1LgbdHUfbBmtjlnZgv8qKGIGRdoLOHPmDH5+fpw/f94Uc3V1JSUlBVtby+9yCSFEfiEdoTxg7h51N8jexor3Gms8NujeJQjuAcYUdbzdDCjbUJOUsoOiKCxevJg6deqYiqACBQoQGBjIokWLpAgSQggLIx0hC3fzfjzrjqfpBr1WhmIFHTTKCIiPgtVdIPGBOt7gfaj5riYpZYeHDx8yaNAgVq9ebYrVqFGDtWvXUqlSJQ0zE0IIkVXSEbJwc3ZfTtcNGqhlNyglGdb2hKgr6niVtvDmZE1Syg4hISH4+vqqiqDBgwdz5MgRKYKEEMKCSUfIgt16kMD6EzdUsYC6pbXrBikKbBkF1/ar48WrQ8eFFr2R6r59+7h48SIALi4uLFq0CD8/P42zEkII8aKkELJgc3dfQm9Qd4MGN9ZwEPKhWXBqlTpW0BMCgsHOsqeRDx8+nN27d3Pjxg2Cg4MpX96yB3sLIYRIJYWQhbr1IIG1x9XdoG51SlPMRaNu0LlfYcckdczWKXU3eRcvbXJ6AeHh4RQv/t8GsDqdjp9++gl7e3vs7c1gbSYhhBDZwnKvVeRz8/aou0F2NlYMbqJRlyLsdOrK0aRZQ6fjQvCqqUlKWaUoCrNmzaJs2bJs375ddZ+Li4sUQUIIkcdIIWSBbj9IYO0x9UyxgDql8dCiGxQTBqu7gj5eHW82Gaq2y/18XsD9+/fp1KkT77//PklJSbz77ruEhYVpnZYQQogcJJfGLNC8PZdJNhhNt+2srRikxdig5PjUjVQf3lbHfbpDg5G5n88L+PPPP/H39+f69eumWM+ePSlSpIiGWQkhhMhp0hGyMGHRCQQfU48N6lqnFMVdc7kbZDTCxvcgLEQdL9MA2s6wmI1UFUXh+++/p2HDhqYiyM3Njc2bN/Pdd99hZ2encYZCCCFyknSELMz8DLpBmowN2vU5nPtFHSvsDX4rwcYyiofIyEh69+7Nr7/+aorVr1+foKAgSpUqpWFmQgghcot0hCxIeHQia46qu0H+r5bC09UxdxMJWQ0HpqtjDq6pG6k6W8alpOPHj+Pj46Mqgj766CP27NkjRZAQQuQj0hGyIPP3qrtBtta63O8GXT8Em0eoYzpr8FsBRS1nheVChQoRHR0NgLu7OytXruStt97SOCshhBC5TTpCFuJOTCKrj4aqYv6vlsKrUC52gyIvQ1B3MOrV8TbfQ7kmuZdHNqhQoQKLFi3i9ddfJyQkRIogIYTIp6QQshDz9lwmOSVtN6hC7iWQcB9W+0NClDr+2lCo3Sf38siiQ4cOER+vnuLv7+/P7t27KVGihEZZCSGE0JoUQhYgIiaRNWm6QV1ql6JEbnWDDHpY1xsiL6rjld6CFp/nTg5ZZDAYmDp1Ko0aNWLEiBHp7rey4P3PhBBCvDj5FLAA8/deISlNN2hIbo0NUhTYOhau7FHHPapBp8VgZZ07eWRBeHg4LVu2ZOLEiRiNRpYsWcLvv/+udVpCCCHMiBRCZi4iJpHAP6+rYp19S1GysFPuJPDnfDixTB1zLgbdgsC+YO7kkAU7d+7Ex8eHnTt3Aqmdn88++4xmzZppnJkQQghzIrPGzNyCfepukI1VLnaDLvwOv3+sjtk4pG6kWsg8p5gbDAY+++wzPv/8cxQlde8zT09P1qxZQ+PGjTXOTgghhLmRQsiMRTxM3w3qUrskpdxyoRsU/jes7wuKUR3vMA9K1s7518+C27dvExAQwN69e02xli1bsmLFCooVK6ZhZkIIIcyVFEJmbOHeKyTq03aDcmGm2MM7qXuIJceq400/gWodc/71s+DSpUvUr1+fu3fvAmBtbc3UqVMZN26cDIgWQgjxRPIJYabuPkxiVZpuUKdaudAN0idAUABEq1ew5hU/eP2DnH3tF+Dt7U2NGjUAKFmyJHv27OGjjz6SIkgIIcRTyaeEmVq0P303aGjTHO4GGY2waQjcOq6Ol6oL7X80641Ura2tWbVqFb169SIkJISGDRtqnZIQQggLIJfGzNC92CRWHL6minWsVYLSRXK4G7T3K/hngzpWqDT4B4JtLu9u/wxbtmyhcOHC1K9f3xTz8PBg+fLl2iUlhBDC4khHyAwt2qfuBllb6RjWtGLOvuhfa2Hv1+qYvUvqRqoFiubsa2eCXq/ngw8+oG3btvj7+xMZGal1SkIIISyYFEJmJjI2iRWH1WODOtbM4W5Q6J/w81B1TGcFXZZBsao597qZdO3aNRo1asT3338PwM2bN1m8eLHGWQkhhLBkUgiZmYX7r5CgN5huW1vpGPZGDo4Nun89dXC0IVkdb/UNVDCfxQc3bdpEzZo1+fPPPwGwtbVl5syZjBs3TuPMhBBCWDIZI2RGouKSWZmmG9TBpwRlijjnzAsmRqdupBp/Tx2v8x7UGZAzr5lJSUlJfPjhh8ycOdMUK1euHMHBwdSubZ7rGQkhhLAcUgiZkUX7rxCfrO4GDc+pbpAhJXXBxLvn1PEKzaDltJx5zUy6cuUKfn5+nDhxwhTr3LkzixcvxtXVVcPMhBBC5BVSCJmJqLhkfjp0TRV728eLsu451A36/WO49Ic6VrQqdF4K1tp/WyQkJNCgQQPCw8MBsLe354cffmDQoEHozHgavxBCCMsiY4TMxOI03SArHQx/I4dmih1dBEcXqGNO7hAQBA7m0WlxdHTk888/B6BixYocOXKEwYMHSxEkhBAiW2n/p7/gfgbdoA4+JfDOiW7QpT/gtw/VMWs76LoaCpfN/td7Af369UOv1/Puu+9SsKD57nQvhBDCcmneEZo7dy7e3t44ODjg6+vL/v37n/jYDRs20Lx5c4oWLYqLiwv16tXj999/z8Vsc8biA1eIS9MNypGZYhHnYF0fUAzq+NtzoHTd7H+9TAgKCmLixImqmE6nY/DgwVIECSGEyDGaFkLBwcGMHDmSCRMmcOrUKRo1akSrVq0IDQ3N8PH79u2jefPmbN26lRMnTtC0aVPatWvHqVOncjnz7PMgPpmfDqlnirWv4UW5ogWy94Xi7sFqP0iKUccbfwjV/bL3tTIhISGBOXPm0LNnT6ZOncrmzZs1y0UIIUT+o2khNH36dPr160f//v2pWrUqM2bMoFSpUsybNy/Dx8+YMYNx48bx6quvUrFiRb788ksqVqzIL7/8ksuZZ58lB64Sm5Riup3aDcrmsUH6xNS1gh6kKTBf7ghNxmfva2XCuXPnqF+/Pjt27DDFdu7cqVk+Qggh8h/NxgglJydz4sQJPvroI1W8RYsWHDp06Lmew2g08vDhQ9zc3J74mKSkJJKSkky3Y2JSOyJ6vR69Xp+FzLPPg3g9Sw9eVcXavFKcMoXtsy83RcF68zCsbvypChu9amFoMxNSUp5wYM5auXIlw4cPJz4+HgAnJydmzZpFz549NT8v+dWjr7t8/bUn5yL7GY1G9Ho9iqJk6riUlBRsbGyIjY3FxkaG1eYknU6Hra0tVlYZ92hy6udBs7N67949DAYDHh4eqriHh4dpyvSzfP/998TFxeHn9+RLO9OmTWPKlCnp4rt378bJKYc3MX2GraFWxCX9d8J1KFTT3WTr1pvZ9hqVwn+matj/VLF4Wzf2ufUmacfubHud55WYmMjChQvZtWuXKVa6dGnGjh2Lu7s7W7duzfWchNrjHTqhLTkX2cPa2hp3d3dsbW2zdHzx4sW5cuVKNmclMqLX67l79y5GozHdfY/+cM5umpe3aadDK4ryXFOk16xZw+TJk/n5558pVqzYEx83fvx4Ro8ebbodExNDqVKlaNq0KUWKFMl64i8oOkHPhO/3A/91ZNq84knfztWz7TV0Zzdhc0pdBCl2ztj23MibHi9n2+s8r7///puAgADOnz9vivXu3ZtWrVrRtm3bLP+SEtlDr9ezY8cOmjdvLudCY3Iuso+iKNy6dYuUlBQ8PT2f2G142vFxcXE4OzvL8h05zGg0EhYWhoeHByVKlEj39c6pTbY1K4Tc3d2xtrZO1/2JiIhI1yVKKzg4mH79+rFu3TqaNXv6flj29vbY29uni9va2mr6C2bFHvXYIJ0ORjavlH053TwBvwxTx3RW6Dovw7akT/a8RiaNHTvWVAQVKFCABQsW0KVLF7Zu3ar5+RD/kXNhPuRcvDi9Xk9iYiJeXl4UKJD5SSiPLqk5OjpmuogSmVesWDFu375tukz2uJz6WdDsrNrZ2eHr65uu9btjxw7q16//xOPWrFlD7969Wb16NW3atMnpNHNEdIKeZenGBnlSoVg2TRN/cAPWdIWURHW8xRdQqWX2vEYWLF26lMKFC1OjRg1OnDhBQECAZrkIIfIHgyF1uRA7OzuNMxHP49F5enTecoOml8ZGjx5Njx49qF27NvXq1WPhwoWEhoYyaNAgIPWy1q1bt1ixYgWQWgT17NmTmTNn8tprr5m6SY6Ojha199Syg1d5mKjuBo14M5tmiiU9TC2C4iLUcd8+8Nrg7HmN5/RokOEjpUuXZufOnVStWhUHB4dczUUIkb/JZS3LoMV50rTP5+/vz4wZM/jss8/w8fFh3759bN26lTJlygAQFhamWlNowYIFpKSkMHToUDw9PU3/3n//fa3eQqZFJ+hZckDdDWr9iieVPLKhG2Q0wP/6w52/1fFyTaD1t6kVVy5QFIX58+dTq1YtHj58qLqvZs2aUgQJIYQwG5oPlh4yZAhDhgzJ8L7ly5erbu/ZsyfnE8phyw9eU3WDAEZk17pBOz6FC9vUMfdK0OUnsM6dcQbR0dG89957rF27FoCBAwcSGBgof40JIUQO0Ol0bNy4kQ4dOmidisWSkV+5KCZRz5ID6imYbV7xpHLxbOgGHV8Gh2erY45uEBAMjoVe/Pmfw4kTJ/D19TUVQZA6KD43r/UKIUReER4ezvDhwylXrhz29vaUKlWKdu3amc3Cs4qiMHnyZLy8vHB0dKRJkyb8888/WqeVaZp3hPKT5QevEZOmGzT8zWzYU+zKHtj6gTpmZQv+q8Ct3Is//zMoisLs2bP54IMPSE5OBsDV1ZWlS5fSsWPHHH99IYR4Hkajwv345EweY+RhvB69VVK2zBor7GSHldWzO+TXrl2jQYMGFCpUiG+++Ybq1auj1+v5/fffGTp0qGoZEq188803TJ8+neXLl1OpUiWmTp1K8+bN+ffffy1qj0gphHLJw8T0Y4NaVStOleIuL/bEdy/A2p5gTLNCdLuZULbBiz33c7h//z79+vVj48aNplidOnUICgrC29s7x19fCCGe1/34ZHyn/qFpDic+aUaRAumXdElryJAh6HQ6jh49irOzsyn+8ssv07dv3yce9+GHH7Jx40Zu3rxJ8eLF6d69O59++qlp6vnp06cZOXIkx48fR6fTUbFiRRYsWEDt2rW5fv06w4YN48CBAyQnJ1O2bFm+/fZbWrdune51FEVhxowZTJgwwfQH708//YSHhwerV69m4MCBmf3SaEYKoVzy06FrRCeolwd/4Zli8VGpG6kmRqvjDUdBze4v9tzP4c8//6Rr165cu3bNFBszZgxffvmlTFUVQogsioqKYtu2bXzxxReqIuiRQoUKPfHYggULsnz5cry8vDhz5gwDBgygYMGCjBs3DoDu3btTs2ZN5s2bh7W1NSEhIaYiaejQoSQnJ7Nv3z6cnZ05e/bsE9deunr1KuHh4bRo0cIUs7e3p3Hjxhw6dEgKIaH2MFHPov3qbtBbLxenqucLdINSkiH4Xbivfl6qtoM3Ps3682bCwYMHTUWQm5sby5cvp127drny2kIIkVddunQJRVGoUqVKpo/95JNPTP9ftmxZxowZQ3BwsKkQCg0NZezYsabnrljxvz/IQ0ND6dSpE6+88goA5co9eWjFo+VrMtom6/r165nOW0tSCOWCFYevZ283SFHg11Fw/aA67ukD7yyAXFr9dNSoUezevZuoqCjWrFlD6dKlc+V1hRAiL3u0MWxWZtuuX7+eGTNmcOnSJWJjY0lJScHF5b8/ukePHk3//v1ZuXIlzZo1o0uXLpQvXx6AESNGMHjwYLZv306zZs3o1KkT1as/fdunrG6TZU6kEMphsUkpLNqvninW8mUPXvJ6gW7QwRkQskodK+gF3YLALn0bNbvcunWLEiVKmG7rdDoCAwNxdHSUbQCEEGavsJMdJz55+rZMaRmNRh7GxlKwQIFsGyz9LBUrVkSn03Hu3LlMTYs/cuQIXbt2ZcqUKbRs2RJXV1eCgoL4/vvvTY+ZPHkyAQEBbNmyhd9++41JkyYRFBTEO++8Q//+/WnZsiVbtmxh+/btTJs2je+//57hw4ene63ixYsDqZ0hT09PU/x5tskyNzJ9PoetOHyNB/HZ2A06uxn+mKyO2TpBtzXg4pnhIS/KaDTy9ddfU65cuXTTNl1cXKQIEkJYBCsrHUUK2Gf6n5uTbZaOy+jf88wYc3Nzo2XLlsyZM4e4uLh09z948CDD4w4ePEiZMmWYMGECtWvXpmLFihlepqpUqRKjRo1i+/btdOzYkWXLlpnuK1WqFIMGDWLDhg2MGTOGRYsWZfha3t7eFC9eXLVNVnJyMnv37n3qNlnmSAqhHBSXlMKifepuUPOXPHjZK4vbgdw+BRveSxPUQcdF4OWTted8hrt379K2bVs++ugjkpOT6d69OxEREc8+UAghRJbNnTsXg8FAnTp1+N///sfFixc5d+4cs2bNol69ehkeU6FCBUJDQwkKCuLy5cvMmjVLNaM3ISGBYcOGsWfPHq5fv87Bgwc5duwYVatWBWDkyJH8/vvvXL16lZMnT7Jr1y7TfWnpdDpGjhzJl19+ycaNG/n777/p3bs3Tk5OFrePpFway0ErDl/nfppu0PtZ7QbF3IY13SAlQR1vPgWqts1ihk+3b98+unXrxu3bt4HUb/wBAwbg5uaWI68nhBAilbe3NydPnuSLL75gzJgxhIWFUbRoUXx9fZk3b16Gx7z99tuMGjWKYcOGkZSURJs2bZg4cSKTJ08GwNramsjISHr27MmdO3dwd3enY8eOTJkyBUjd6HTo0KHcvHkTFxcX3nrrLX744Ycn5jhu3DgSEhIYMmQI9+/fp27dumzfvt2i1hAC0CmPRmXlEzExMbi6unLv3j2KFCmSY68Tl5RCo292ExX33+Jdzap6sLhX7cw/WXIcLGsFYafV8ZrvQvvZ2b6HmMFgYNq0aUyaNAmj0QhAsWLFCAwMpFmzzF1ffxa9Xs/WrVtp3bq1XGLTmJwL8yHnIvskJiZy9epVvL29s7TPodFoJCYmBhcXl2wZIySe7mnnKzIyEnd3d6Kjo1UDwF+UdIRyyMoj11VFEGSxG2Q0pl4OS1sElWkIbX7I9iLozp07vPvuu/zxx3+Ljr3xxhusWrVKNSBOCCGEyAukvM0B8cnpxwY1q1qMV0pmYWzQzilw/ld1zK08+K8Em+xdtPDQoUPUqFHDVARZWVkxZcoUtm/fLkWQEEKIPEk6Qjlg1ZHrRKbrBlXK/BOdWpU6Vf5xDoUgYC04Zf84nSJFihAbGwuAp6cnq1evpkmTJtn+OkIIIYS5kI5QNotPTmHBXnU36I0qWegGXTsAv4xUx6xswG8FuGfDRq0ZqFy5MgsWLKBly5aEhIRIESSEECLPk0IomwUeCc2gG5TJsUGRl1O3zzCqZ5zRZjqUa/yCGf5n7969JCSoZ6F1796d3377jWLFimXb6wghhBDmSgqhbJSQbGDBvsuqWNPKRalRqlAmnuR+6kaqCffV8XrDwLfXiycJpKSk8PHHH9OkSRNGjhyZ7n5LWx5dCCGEyCophLJR4J/XuRebphvULBNjgwx6WNsTIi+p45VbQ/PPsiFDuHnzJk2bNmXatGkALFy4kF27dmXLcwshhBCWRgqhbJKQbGB+mrFBTSoXxed5u0GKAls/gKv71HGPV1JXjrayfuEct2zZgo+PDwcOHADAxsaGb7/9VsYCCSGEyLekEMomq4+Gci82SRXL1NigI3PhxHJ1rIAHBASBfYEXyk2v1zN27Fjatm1LZGQkAKVLl2bfvn188MEHskiYEEJYKJ1Ox6ZNm7ROw6LJJ2A2SNQbmL9XPTbo9UpFqVm68PM9wb+/we8T1DEbh9SNVF1LvlBu169f5/XXX+e7774zxd5++21OnTr1xP1qhBBCaC88PJzhw4dTrlw57O3tKVWqFO3atUu3+bVWNmzYQMuWLXF3d0en0xESEqJ1Slki6whlg9V/hnL3YRa7QeFnYH0/IM1OJ+8sgBK+L5TX2bNnadCggWmnYltbW7799ltGjBghA6KFEMKMXbt2jQYNGlCoUCG++eYbqlevjl6v5/fff2fo0KGcP39e6xSJi4ujQYMGdOnShQEDBmidTpZJIfSCMuoGNarojm+Z5+gGPQyH1V1BH6eOvzERXu7wwrlVrlyZmjVrsnv3bry9vQkODubVV1994ecVQgiLZDRCQlSmj9HFPwTrZMiOYQSObs/1PEOGDEGn03H06FGcnZ1N8Zdffpm+ffs+8bgPP/yQjRs3cvPmTYoXL0737t359NNPTXvWnT59mpEjR3L8+HF0Oh0VK1ZkwYIF1K5dm+vXrzNs2DAOHDhAcnIyZcuW5dtvv6V169YZvlaPHj2A1KLNkkkh9ILWHA0lIk03aGSz5+gG6RNSd5OPuamOV+8KjcZkS27W1tYEBgYyadIkvvnmGwoVKpQtzyuEEBYpIQq+LZ+pQ6yALGyO9GRjL4Oz+1MfEhUVxbZt2/jiiy9URdAjT/tdXrBgQZYvX46XlxdnzpxhwIABFCxYkHHjxgGpa8XVrFmTefPmYW1tTUhIiKlIGjp0KMnJyezbtw9nZ2fOnj1LgQIvNkbVEkgh9AKe3A16xvYXRiNsHAS3T6rjpV6D9rOyvJHq//73P0qUKMFrr71minl6erJw4cIsPZ8QQojcd+nSJRRFoUqVKpk+9pNPPjH9f9myZRkzZgzBwcGmQig0NJSxY8eanrtixf/+cA8NDaVTp0688sorAJQrV+5F3obFkMHSLyD42A3uxGRhbNCeL+HsJnWsUBnoGgg29pnOIzExkWHDhtG5c2f8/f2Jispk61cIIYTZUJTUMaNZGcu5fv16GjZsSPHixSlQoAATJ04kNDTUdP/o0aPp378/zZo146uvvuLy5f/+mB8xYgRTp06lQYMGTJo0ib/++uvF34wFkEIoixL1BubuUS982KBCEWqXfUY36HQw7PtWHbN3Sd1I9Rnt0oxcvHiR+vXrM2fOHCC1ol+xYkWmn0cIIYR5qFixIjqdjnPnzmXquCNHjtC1a1datWrFr7/+yqlTp5gwYQLJyf8t9Dt58mT++ecf2rRpw65du3jppZfYuHEjAP379+fKlSv06NGDM2fOULt2bX788cdsfW/mSC6NZdHa4xl1g56xinToEdg8TB3TWUOX5VAs8y3QoKAgBgwYYNox3sHBgVmzZtG/f/9MP5cQQuR5jm6pY3QywWg08vDhQwoWLJg9a645PuOPZcDNzY2WLVsyZ84cRowYkW6c0IMHDzIcJ3Tw4EHKlCnDhAn/Lcdy/fr1dI+rVKkSlSpVYtSoUXTr1o1ly5bxzjvvAFCqVCkGDRrEoEGDGD9+PIsWLWL48OGZfJOWRQqhLEhKMTB3t/qHqX75ItTxfso3eNRVCAoAg3oLDlp/AxXezNTrJyQkMHLkSNXYn8qVK7N27VqqV6+eqecSQoh8w8oq8513oxHFYAfOLtkza+w5zZ07l/r161OnTh0+++wzqlevTkpKCjt27GDevHkZdosqVKhAaGgoQUFBvPrqq2zZssXU7YHUz46xY8fSuXNnvL29uXnzJseOHaNTp04AjBw5klatWlGpUiXu37/Prl27qFq16hNzjIqKIjQ0lNu3bwPw77//AlC8eHGKFy+enV+OHCWXxrJg7bEbhMckqmJPHRuUGA1rukJ8pDpedxC8mrnuzfnz56lbt66qCOrRowfHjx+XIkgIIfIIb29vTp48SdOmTRkzZgzVqlWjefPm7Ny5k3nz5mV4zNtvv82oUaMYNmwYPj4+HDp0iIkTJ5rut7a2JjIykp49e1KpUiX8/Pxo1aoVU6ZMAcBgMDB06FCqVq3KW2+9ReXKlZk7d+4Tc9y8eTM1a9akTZs2AHTt2pWaNWsyf/78bPxK5Dyd8mhUVj4RExODq6sr9+7do0iRIpk+PinFQJNv9xAW/V8hVK9cEda891rGBxhSYHUXuJxmY9MKzaFbEFg/f1MuNjaWsmXLmrbJcHR0ZM6cOfTu3dtiF0jU6/Vs3bqV1q1bm6ZwCm3IuTAfci6yT2JiIlevXsXb2xsHB4dMH280GomJicHFxUW2I8oFTztfkZGRuLu7Ex0djYuLS7a9ppzVTFp3/KaqCAJ4/2nrBm37KH0RVOwl6Lw0U0UQQIECBZg6dSqQuqjW8ePH6dOnj8UWQUIIIYTWZIxQJqSODVLPFKvr7cZr5Z7QWfpzIRxbpI45F03tBDlkrZodOHAgVlZWvPvuuzg5OWXpOYQQQgiRSjpCmbD+xE1up+kGjWz2hJliF3fAtg/VMWt76LoaCpd55mspisLSpUtV13chdV2J9957T4ogIYQQIhtIR+g5JacY080Uq+PtRr3yGXSD7pyFdX1AMarjHeZCqTrPfK3Y2FgGDx7MqlWrAHjttddMg9GEEEIIkX2kI/Sc1p+4ya0HCarYyIxmisXehdX+kPxQHW/8EbzS+Zmv89dff+Hr62sqggD279+fpZyFEEII8XRSCD2H5BQjc9KMDapTNoNukD4xda2g6FB1vFpnaPLRU19DURQWLFhAnTp1uHDhApC6ed6aNWv46quvXvg9CCGEECI9uTT2HDacTN8Ner9ZRfVsLUVJXTX65lH1wSVfhbfnPHUj1ZiYGN577z2Cg4NNsVq1ahEcHEyFChWy5T0IIYQQIj3pCD2D3mBkdppuUO0yhamfthu09xs4s04dcy2VOjja9slrV5w8edJU9DwybNgwDh06JEWQEEIIkcOkI/QMG07e5Ob9NGODmlVSd4POrE/dUf5xdgUhIBgKFHvicyuKwqhRo0y7/7q6urJkyRLTcudCCCGEyFnSEXqKjLpBvmUK06DCY92gG8dg0xD1gTqr1AUTPV5+6vPrdDp++uknChUqxKuvvsqpU6ekCBJCCGEWypYty4wZM0y3dTodmzZt0iyfnCKF0FNsPHmLG1Fpxga9+djYoAehENQNDOpd6Gk5DSq1yPA59Xq96nbZsmXZvXs3Bw4cwNvbO9tyF0IIYbkebZ306F+RIkV46623+OuvvzTLKSwsjFatWmn2+jlFCqEnyKgbVKt0IRpV/P+dixNjYHVXiLurPrB2P6g7MN3zKYrCjBkz8PX1JTY2VnWfj48PdnZ22Zq/EEIIy/bWW28RFhZGWFgYO3fuxMbGhrZt22qWT/HixbG3t9fs9XOKFEJPsOnULUKj4lWx9x+NDTIa4H/9IOIf9UHlmkKrr9PNEIuKiqJDhw6MGjWKM2fOMHjwYPLZXrdCCCEyyd7enuLFi1O8eHF8fHz48MMPuXHjBnfvpv4B/uGHH1KpUiWcnJwoV64cEydOVF11OH36NE2bNqVgwYK4uLjg6+vL8ePHTfcfOnSI119/HUdHR0qVKsWIESOIi4t7Yj6PXxq7du0aOp2ODRs20LRpU5ycnKhRowaHDx9WHZPZ19CCFEIZSMmgG+RTqhCvP+oGbf8ELm5XH+ReGbosB2v1TtGHDx/Gx8eHzZs3m2JeXl5SCAkhhHhusbGxBAYGUqFCBYoUSR2nWrBgQZYvX87Zs2eZOXMmixYt4ocffjAd0717d0qWLMmxY8c4ceIEH330Eba2qZ9RZ86coWXLlnTs2JG//vqL4OBgDhw4wLBhwzKV14QJE/jggw8ICQmhUqVKdOvWjZSUlGx9jZwms8YysCnkNtcj1d2gkY/WDTq2BI7MVR/g6JY6Q8yxkClkNBr57rvv+PjjjzEYDAAUKVKEFStW0Lp165x+C0IIIZ5i+vTpTJ8+/ZmPq1mzJitXrlTF2rdvz8mTJ5957OjRoxk9enSWc/z1118pUKAAAHFxcXh6evLrr79iZZXaw/jkk09Mjy1btixjxowhODiYcePGARAaGsrYsWOpUqUKABUr/rcbwrfffktAQAAjR4403Tdr1iwaN27MvHnzcHB48rIvj/vggw9MW0BNmTKFl19+mUuXLlGlSpVse42cJoVQGikGI7N3XVTFapQqRONKReHybtg6Vn2AtV3qWkFu/w10vnv3Lr169eK3334zxRo2bMiaNWsoWbJkjuYvhBDi2WJiYrh169YzH1eqVKl0sbt37z7XsTExMVnK7ZGmTZsyb948IHWIxdy5c2nVqhVHjx6lTJkyrF+/nhkzZnDp0iViY2NJSUnBxcXFdPzo0aPp378/K1eupFmzZnTp0oXy5csDcOLECS5dukRgYKDp8YqiYDQauXr1KlWrVn2uHKtXr276f09PTwAiIiKoUqVKtr1GTpNCKI2fQ25zLW036M2K6O5dgLW9QDGoD2g3C8rUM93cv38/Xbt25fbt20DqNdWPP/6YyZMnY2MjX24hhDAHLi4ulChR4pmPc3d3TxcrWrTocx37eFGSFc7OzqqFdX19fXF1dWXRokW0bduWrl27MmXKFFq2bImrqytBQUF8//33psdPnjyZgIAAtmzZwm+//cakSZMICgrinXfewWg0MnDgQEaMGJHudUuXLv3cOT661AaYZlQbjUbTf7PjNXKafDI/JqOxQTVKutKklBUs9oOkaPUBjcaATzdV6MiRI6YiqFixYqxatYrmzZvnaN5CCCEy53kvWxmNxnSdncfHfOYmnU6HlZUVCQkJHDx4kDJlyjBhwgTT/devX093TKVKlahUqRKjRo2iW7duLFu2jHfeeYdatWrxzz//5OgOBrnxGtlBBks/5pe/bnP1nno0+8imZdAF94D719QPfultaPoJaY0ZM4ZWrVrRtGlTQkJCpAgSQgiRJUlJSYSHhxMeHs65c+cYPnw4sbGxtGvXjgoVKhAaGkpQUBCXL19m1qxZbNy40XRsQkICw4YNY8+ePVy/fp2DBw9y7Ngx0+WoDz/8kMOHDzN06FBCQkK4ePEimzdvZvjw4dmWf268RnaQjtD/MxgVftyp7gZVL+FCkwtfQOgh9YO9akKH+WBlxY0bN1TXkK2srAgKCsLZ2Rlra+vcSF0IIUQetG3bNtO4m4IFC1KlShXWrVtHkyZNABg1ahTDhg0jKSmJNm3aMHHiRCZPngyAtbU1kZGR9OzZkzt37uDu7k7Hjh2ZMmUKkDq2Z+/evUyYMIFGjRqhKArly5fH398/2/LPjdfIDjoln83jjomJwdXVlXv37pmmIELqukEjg0NUj/3j1eNUOJNmVoFLCRiwC4NTUaZOncoXX3zB9u3bTd+YInP0ej1bt26ldevWqmvNIvfJuTAfci6yT2JiIlevXsXb2ztLs5QeXRpzcXExzdYSOedp5ysyMhJ3d3eio6NfePzV46QjRGo3aFaamWID3f9OXwTZOkO3IMJiFbq/3Zzdu3cDEBAQwJkzZ1SFlRBCCCHMnxRCwK9/3ebK3f/GBr2iu8K4hLTrS+ig02J2/H2Hd99tTkREBJB6KWzYsGEULlw4FzMWQgghRHbI94WQwagwa+d/3aDiRLLcYTrWhkTV41LemMzkVYf48ssvTatClyhRgjVr1tCoUaPcTFkIIYQQ2STfF0JbzoRx+f+7QU4kssTuO4ooUarH3CzdkYBPNrB//35TrFWrVqxYsSLDNSaEEEIIYRnydSH0eDfICiMzbOfwspV6HYY9iS/RedwvREZGAqkj8adNm8aYMWNk4JwQQghh4fL1J/nWM2FciogFYJxNEC2sT6gfUKQCHn7TSUhIAFJXwty/fz9jx46VIkgIISxIPpsgbbG0OE/59tPc+Fg3yM96N4NsflU/wKEQBKylaq16zJs3j/bt23Pq1Cnq1auX/smEEEKYpUfruSUnJ2uciXgej85Tbq7Dl28vjf1xLoKLEbG8ZnWWL2yWmuI7LqfQqKw9Dv6roEjq5nQ9e/akR48epn1UhBBCWAYbGxucnJy4e/cutra2me7mG41GkpOTSUxMlCsBOcxoNHL37l2cnJxydW/OfFsILTxwFW9dGPNtf8BWZyDZoPDhjiRm/JnMkC71mOOtngkmRZAQQlgenU6Hp6cnV69ezXAvrmdRFIWEhAQcHR3lcyAXWFlZUbp06Vz9WufbQuju3Qg2F/yWQro4rt434r8+nmO3U3fMnbtuJ90OHKBhw4YaZymEEOJF2dnZUbFixSxdHtPr9ezbt4/XX39dVvnOBXZ2drneedO8EJo7dy7ffvstYWFhvPzyy8yYMeOp6/Ls3buX0aNH888//+Dl5cW4ceMYNGhQpl/3B9u5lLMK539n9fTbnEB0Umrczs6O6dOn06BBg6y+JSGEEGbGysoqS1tsWFtbk5KSgoODgxRCeZSmFzyDg4MZOXIkEyZM4NSpUzRq1IhWrVoRGhqa4eOvXr1K69atadSoEadOneLjjz9mxIgR/O9//8v0a9dQzjNsawKd1/1XBJUvV860U660QIUQQoi8T9NCaPr06fTr14/+/ftTtWpVZsyYQalSpZg3b16Gj58/fz6lS5dmxowZVK1alf79+9O3b1++++67TL928xVxzDmmN93279iek6dOUatWrSy/HyGEEEJYFs0KoeTkZE6cOEGLFi1U8RYtWnDo0KEMjzl8+HC6x7ds2ZLjx4+j1+szPOZJ/opIXavA3gYWfDWBNes3ZetutkIIIYQwf5qNEbp37x4GgwEPDw9V3MPDg/Dw8AyPCQ8Pz/DxKSkp3Lt3D09Pz3THJCUlkZSUZLodHR1t+v/yhXUs+eFzqrUdRFRUVLpjRc7T6/XEx8cTGRkp1981JufCfMi5MB9yLszHo8/p7F50UfPB0mnH4iiK8tTxORk9PqP4I9OmTWPKlCkZ3nf5vkKT3p8An2QiYyGEEEJoJTIyEldX12x7Ps0KIXd3d6ytrdN1fyIiItJ1fR4pXrx4ho+3sbGhSJEiGR4zfvx4Ro8ebbr94MEDypQpQ2hoaLZ+IUXWxMTEUKpUKW7cuCGXJjUm58J8yLkwH3IuzEd0dDSlS5fGzc0tW59Xs0LIzs4OX19fduzYwTvvvGOK79ixg7fffjvDY+rVq8cvv/yiim3fvp3atWs/sWVpb2+Pvb19urirq6t8U5sRFxcXOR9mQs6F+ZBzYT7kXJiP7F5nSNNZY6NHj2bx4sUsXbqUc+fOMWrUKEJDQ03rAo0fP56ePXuaHj9o0CCuX7/O6NGjOXfuHEuXLmXJkiV88MEHWr0FIYQQQlgwTccI+fv7ExkZyWeffUZYWBjVqlVj69atlClTBoCwsDDVmkLe3t5s3bqVUaNGMWfOHLy8vJg1axadOnXS6i0IIYQQwoJpPlh6yJAhDBkyJMP7li9fni7WuHFjTp48meXXs7e3Z9KkSRleLhO5T86H+ZBzYT7kXJgPORfmI6fOhU7J7nloQgghhBAWQtMxQkIIIYQQWpJCSAghhBD5lhRCQgghhMi3pBASQgghRL6VJwuhuXPn4u3tjYODA76+vuzfv/+pj9+7dy++vr44ODhQrlw55s+fn0uZ5n2ZORcbNmygefPmFC1aFBcXF+rVq8fvv/+ei9nmfZn92Xjk4MGD2NjY4OPjk7MJ5iOZPRdJSUlMmDCBMmXKYG9vT/ny5Vm6dGkuZZu3ZfZcBAYGUqNGDZycnPD09KRPnz5ERkbmUrZ51759+2jXrh1eXl7odDo2bdr0zGOy5fNbyWOCgoIUW1tbZdGiRcrZs2eV999/X3F2dlauX7+e4eOvXLmiODk5Ke+//75y9uxZZdGiRYqtra2yfv36XM4878nsuXj//feVr7/+Wjl69Khy4cIFZfz48Yqtra1y8uTJXM48b8rs+XjkwYMHSrly5ZQWLVooNWrUyJ1k87isnIv27dsrdevWVXbs2KFcvXpV+fPPP5WDBw/mYtZ5U2bPxf79+xUrKytl5syZypUrV5T9+/crL7/8stKhQ4dczjzv2bp1qzJhwgTlf//7nwIoGzdufOrjs+vzO88VQnXq1FEGDRqkilWpUkX56KOPMnz8uHHjlCpVqqhiAwcOVF577bUcyzG/yOy5yMhLL72kTJkyJbtTy5eyej78/f2VTz75RJk0aZIUQtkks+fit99+U1xdXZXIyMjcSC9fyey5+Pbbb5Vy5cqpYrNmzVJKliyZYznmR89TCGXX53eeujSWnJzMiRMnaNGihSreokULDh06lOExhw8fTvf4li1bcvz4cfR6fY7lmtdl5VykZTQaefjwYbZvsJcfZfV8LFu2jMuXLzNp0qScTjHfyMq52Lx5M7Vr1+abb76hRIkSVKpUiQ8++ICEhITcSDnPysq5qF+/Pjdv3mTr1q0oisKdO3dYv349bdq0yY2UxWOy6/Nb85Wls9O9e/cwGAzpdq/38PBIt2v9I+Hh4Rk+PiUlhXv37uHp6Zlj+eZlWTkXaX3//ffExcXh5+eXEynmK1k5HxcvXuSjjz5i//792NjkqV8VmsrKubhy5QoHDhzAwcGBjRs3cu/ePYYMGUJUVJSME3oBWTkX9evXJzAwEH9/fxITE0lJSaF9+/b8+OOPuZGyeEx2fX7nqY7QIzqdTnVbUZR0sWc9PqO4yLzMnotH1qxZw+TJkwkODqZYsWI5lV6+87znw2AwEBAQwJQpU6hUqVJupZevZOZnw2g0otPpCAwMpE6dOrRu3Zrp06ezfPly6Qplg8yci7NnzzJixAg+/fRTTpw4wbZt27h69apps3CRu7Lj8ztP/Znn7u6OtbV1uko+IiIiXdX4SPHixTN8vI2NDUWKFMmxXPO6rJyLR4KDg+nXrx/r1q2jWbNmOZlmvpHZ8/Hw4UOOHz/OqVOnGDZsGJD6YawoCjY2Nmzfvp033ngjV3LPa7Lys+Hp6UmJEiVwdXU1xapWrYqiKNy8eZOKFSvmaM55VVbOxbRp02jQoAFjx44FoHr16jg7O9OoUSOmTp0qVxFyUXZ9fuepjpCdnR2+vr7s2LFDFd+xYwf169fP8Jh69eqle/z27dupXbs2tra2OZZrXpeVcwGpnaDevXuzevVqueaejTJ7PlxcXDhz5gwhISGmf4MGDaJy5cqEhIRQt27d3Eo9z8nKz0aDBg24ffv2/7V3/zFR138cwJ93/DjuhwRhCcLJBSgdBcUPaa4VmBiXuY455KanQMKklQ4Qw9jCP2BFZwIORjQbAzKNH86cEWVY/DhwQsFtJXcKnCDJKFdOQYjf7+8fjk+eIoJfk7h7PTY23u/P5/3+vN73Hnxe+3zen/vg5s2bXF1HRwf4fD7c3Nz+1XjN2YPMxfDwMPh801OnlZUVgH+uRpBH46Gdv+e1tHoRmH4UsqioiOn1epaUlMTEYjHr6elhjDH23nvvse3bt3P7Tz9+l5yczPR6PSsqKqLH5x+S+c7FsWPHmLW1NSsoKGD9/f3cz/Xr1xdqCGZlvvNxJ3pq7OGZ71wMDg4yNzc3FhkZydrb21l9fT1buXIli4+PX6ghmI35zkVxcTGztrZmn3zyCTMajayxsZEFBQWx4ODghRqC2RgcHGQ6nY7pdDoGgOXk5DCdTsd9lcG/df42u0SIMcYKCgqYu7s7s7W1ZQEBAay+vp7bFhMTw0JCQkz2r6urY/7+/szW1pbJZDJWWFj4iCM2X/OZi5CQEAbgrp+YmJhHH7iZmu/fxu0oEXq45jsXBoOBhYWFMaFQyNzc3NiePXvY8PDwI47aPM13LvLy8piPjw8TCoXMxcWFqdVqduXKlUcctfmpra2d9Rzwb52/eYzRtTxCCCGEWCazWiNECCGEEDIflAgRQgghxGJRIkQIIYQQi0WJECGEEEIsFiVChBBCCLFYlAgRQgghxGJRIkQIIYQQi0WJECGEzODw4cOQSqXg8/k4dOjQQoczLzweDydPnlzoMAhZFCgRImSRiI2NBY/HA4/Hg42NDTw8PLB3714MDQ0tdGj3JZPJFlUyMTAwgF27dmHfvn3o6+vDzp07FzokQsi/xKzePk+IuVMoFCguLsb4+Di0Wi3i4+MxNDSEwsLCeffFGMPk5CSsrenfwJ16e3sxPj6O119/nd4mToiZoytChCwiAoEAzs7OkEql2Lp1K9RqNXcLhDGGAwcOwMPDA0KhEM899xyOHz/Ota2rqwOPx8Pp06cRFBQEgUAArVaLqakpaDQaeHl5QSAQYMWKFfjggw+4dn19fVCpVHB0dISTkxOUSiV6enq47bGxsYiIiMDBgwfh4uICJycnvPPOOxgfHwcAhIaG4vLly0hOTuauaAHAX3/9hS1btsDNzQ0ikQi+vr748ssvTcY7ODgItVoNsVgMFxcX5ObmIjQ0FElJSdw+Y2NjSE1NhaurK8RiMV544QXU1dXN+jn29vZCqVRCIpHA3t4eUVFR+OOPPwAAJSUl8PX1BQB4eHiAx+OZjPf24+7atQsuLi6ws7ODTCZDVlYWtz0nJwe+vr4Qi8WQSqV4++23Td4eX1JSAgcHB1RVVcHb2xsikQiRkZEYGhpCaWkpZDIZHB0dsXv3bkxOTnLtZDIZMjMzsXXrVkgkEixfvhz5+fmzjvd+c0iIJaNEiJBFTCgUcgnH+++/j+LiYhQWFqK9vR3JycnYtm0b6uvrTdqkpqYiKysLBoMBfn5+SEtLg0ajQXp6OvR6PY4dO4Zly5YBAIaHh7F27VpIJBI0NDSgsbEREokECoUCY2NjXJ+1tbUwGo2ora1FaWkpSkpKUFJSAgA4ceIE3NzckJGRgf7+fvT39wMARkZGEBgYiKqqKpw/fx47d+7E9u3b0dzczPW7Z88eNDU14dSpU6ipqYFWq0VbW5vJeN588000NTWhrKwMv/zyCzZv3gyFQoHOzs4ZPzPGGCIiInDt2jXU19ejpqYGRqMRKpUKAKBSqXDmzBkAQEtLC/r7+yGVSu/qJy8vD6dOnUJFRQUuXryIL774AjKZjNvO5/ORl5eH8+fPo7S0FD/++CNSU1NN+hgeHkZeXh7Kysrw3Xffoa6uDps2bUJ1dTWqq6tx5MgRHD582CShBYCPP/4Yfn5+aGtrQ1paGpKTk1FTUzPjeOc6h4RYrP/vXbGEkEclJiaGKZVKrtzc3MycnJxYVFQUu3nzJrOzs2Nnz541aRMXF8e2bNnCGPvnzc4nT57ktg8MDDCBQMA+++yzGY9ZVFTEvL292dTUFFc3OjrKhEIhO336NBeXu7s7m5iY4PbZvHkzU6lUXNnd3Z3l5ubed4wbNmxgKSkpXGw2NjassrKS2379+nUmEolYYmIiY4yxrq4uxuPxWF9fn0k/69atY2lpaTMe4/vvv2dWVlast7eXq2tvb2cAWEtLC2OMMZ1OxwCw7u7ue8a6e/du9sorr5h8NrOpqKhgTk5OXLm4uJgBYF1dXVxdQkICE4lEbHBwkKsLDw9nCQkJXNnd3Z0pFAqTvlUqFXvttde4MgD21VdfMcbmNoeEWDJaHEDIIlJVVQWJRIKJiQmMj49DqVQiPz8fer0eIyMjWL9+vcn+Y2Nj8Pf3N6kLCgrifjcYDBgdHcW6detmPF5rayu6urqwZMkSk/qRkREYjUau/Mwzz8DKyooru7i44Ndff511LJOTk/joo49QXl6Ovr4+jI6OYnR0FGKxGABw6dIljI+PIzg4mGvz2GOPwdvbmyu3tbWBMYZVq1aZ9D06OgonJ6cZj2swGCCVSk2u8vj4+MDBwQEGgwGrV6+eNe5psbGxWL9+Pby9vaFQKLBx40a8+uqr3Pba2lp8+OGH0Ov1GBgYwMTEBEZGRjA0NMSNUSQSwdPTk2uzbNkyyGQySCQSk7qrV6+aHHvNmjV3le+1GH2uc0iIpaJEiJBFZO3atSgsLISNjQ2WL18OGxsbAEB3dzcA4JtvvoGrq6tJG4FAYFKePgkDt26tzWZqagqBgYE4evToXdueeOIJ7vfpOKbxeDxMTU3N2nd2djZyc3Nx6NAhbi1NUlISd7uGMcb1dbvp+un4rKys0NraapKIATBJJu5sf2efs9XfS0BAALq7u/Htt9/izJkziIqKQlhYGI4fP47Lly9jw4YNeOutt5CZmYnHH38cjY2NiIuL425lAjN/bg/yWU7vN5O5ziEhlooSIUIWEbFYDC8vr7vqfXx8IBAI0Nvbi5CQkDn3t3LlSgiFQvzwww+Ij4+/a3tAQADKy8vx5JNPwt7e/oHjtrW1NVnwCwBarRZKpRLbtm0DcOuE3dnZCblcDgDw9PSEjY0NWlpauKs3AwMD6Ozs5Mbo7++PyclJXL16FS+99NKcYvHx8UFvby9+++03rl+9Xo8bN25wx54re3t7qFQqqFQqREZGQqFQ4Nq1a/j5558xMTGB7Oxs8Pm3lmJWVFTMq+/ZnDt37q7y008/PeO+D2sOCTFXtFiaEDOwZMkS7N27F8nJySgtLYXRaIROp0NBQQFKS0vv2c7Ozg779u1DamoqPv/8cxiNRpw7dw5FRUUAALVajaVLl0KpVEKr1aK7uxv19fVITEzElStX5hyfTCZDQ0MD+vr68OeffwIAvLy8UFNTg7Nnz8JgMCAhIQG///67yZhiYmLw7rvvora2Fu3t7dixYwf4fD539WPVqlVQq9WIjo7GiRMn0N3djZ9++gkajQbV1dUzxhIWFgY/Pz+o1Wq0tbWhpaUF0dHRCAkJMblteD+5ubkoKyvDhQsX0NHRgcrKSjg7O8PBwQGenp6YmJhAfn4+Ll26hCNHjuDTTz+dc9/309TUhAMHDqCjowMFBQWorKxEYmLijPs+rDkkxFxRIkSImcjMzMT+/fuRlZUFuVyO8PBwfP3113jqqadmbZeeno6UlBTs378fcrkcKpWKW5MiEonQ0NCAFStWYNOmTZDL5dixYwf+/vvveV1dyMjIQE9PDzw9PbnbMenp6QgICEB4eDhCQ0Ph7OyMiIgIk3Y5OTlYs2YNNm7ciLCwMLz44ouQy+Wws7Pj9ikuLkZ0dDRSUlLg7e2NN954A83NzTM+6QX8863Ljo6OePnllxEWFgYPDw+Ul5fPeTzArVtvGo0GQUFBWL16NXp6elBdXQ0+n4/nn38eOTk50Gg0ePbZZ3H06FGTR+v/XykpKWhtbYW/vz8yMzORnZ2N8PDwGfd9WHNIiLnisdtvuBNCyH/Y0NAQXF1dkZ2djbi4uIUOZ0HIZDIkJSWZfJcSIeTB0RohQsh/lk6nw4ULFxAcHIwbN24gIyMDAKBUKhc4MkKIuaBEiBDyn3bw4EFcvHgRtra2CAwMhFarxdKlSxc6LEKImaBbY4QQQgixWLRYmhBCCCEWixIhQgghhFgsSoQIIYQQYrEoESKEEEKIxaJEiBBCCCEWixIhQgghhFgsSoQIIYQQYrEoESKEEEKIxaJEiBBCCCEW638XMJTeojuVTAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 21, + "id": "fa4a3cf4", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -2777,8 +2310,10 @@ }, { "cell_type": "markdown", - "id": "ef32420e", - "metadata": {}, + "id": "b77ba3b2", + "metadata": { + "editable": true + }, "source": [ "Recall that the cumulative gains curve shows the percentage of the\n", "overall number of cases in a given category *gained* by targeting a\n", @@ -2791,17 +2326,22 @@ }, { "cell_type": "markdown", - "id": "5820ebfd", - "metadata": {}, + "id": "a0fd301a", + "metadata": { + "editable": true + }, "source": [ "## Compare Bagging on Trees with Random Forests" ] }, { "cell_type": "code", - "execution_count": 23, - "id": "bb5bea62", - "metadata": {}, + "execution_count": 22, + "id": "ee9649f8", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "bag_clf = BaggingClassifier(\n", @@ -2811,21 +2351,13 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "b879f3ce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9790209790209791" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 23, + "id": "4264d6ab", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "bag_clf.fit(X_train, y_train)\n", "y_pred = bag_clf.predict(X_test)\n", @@ -2838,8 +2370,10 @@ }, { "cell_type": "markdown", - "id": "60160f97", - "metadata": {}, + "id": "b5f04174", + "metadata": { + "editable": true + }, "source": [ "## Boosting, a Bird's Eye View\n", "\n", @@ -2856,8 +2390,10 @@ }, { "cell_type": "markdown", - "id": "b351b1bd", - "metadata": {}, + "id": "776a78b9", + "metadata": { + "editable": true + }, "source": [ "## What is boosting? Additive Modelling/Iterative Fitting\n", "\n", @@ -2868,8 +2404,10 @@ }, { "cell_type": "markdown", - "id": "6e9174ef", - "metadata": {}, + "id": "6356de3e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", @@ -2878,8 +2416,10 @@ }, { "cell_type": "markdown", - "id": "fc319721", - "metadata": {}, + "id": "612f7674", + "metadata": { + "editable": true + }, "source": [ "where $\\beta_m$ are the expansion parameters to be determined in a\n", "minimization process and $b(x;\\gamma_m)$ are some simple functions of\n", @@ -2893,8 +2433,10 @@ }, { "cell_type": "markdown", - "id": "da4ba861", - "metadata": {}, + "id": "42a6c062", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sigma(t) = \\frac{1}{1+\\exp{(-t)}},\n", @@ -2903,8 +2445,10 @@ }, { "cell_type": "markdown", - "id": "f444a5a4", - "metadata": {}, + "id": "e798f7c9", + "metadata": { + "editable": true + }, "source": [ "where $t=\\gamma_0+\\gamma_1 x$ and the parameters $\\gamma_0$ and\n", "$\\gamma_1$ were determined by the Logistic Regression fitting\n", @@ -2915,8 +2459,10 @@ }, { "cell_type": "markdown", - "id": "8a4d8175", - "metadata": {}, + "id": "0c1ee407", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n", @@ -2925,8 +2471,10 @@ }, { "cell_type": "markdown", - "id": "de12bc14", - "metadata": {}, + "id": "3c46526f", + "metadata": { + "editable": true + }, "source": [ "In this case the function $f(x)$ was replaced by the design matrix\n", "$\\boldsymbol{X}$ and the unknown linear regression parameters $\\boldsymbol{\\beta}$,\n", @@ -2936,8 +2484,10 @@ }, { "cell_type": "markdown", - "id": "735bf417", - "metadata": {}, + "id": "34dade71", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\boldsymbol{\\beta}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n", @@ -2946,16 +2496,20 @@ }, { "cell_type": "markdown", - "id": "22b8d82f", - "metadata": {}, + "id": "bdc0d187", + "metadata": { + "editable": true + }, "source": [ "In iterative fitting or additive modeling, we minimize the cost function with respect to the parameters $\\beta_m$ and $\\gamma_m$." ] }, { "cell_type": "markdown", - "id": "661db2e1", - "metadata": {}, + "id": "942467c7", + "metadata": { + "editable": true + }, "source": [ "## Iterative Fitting, Regression and Squared-error Cost Function\n", "\n", @@ -2980,8 +2534,10 @@ }, { "cell_type": "markdown", - "id": "2b14c81e", - "metadata": {}, + "id": "c84274cd", + "metadata": { + "editable": true + }, "source": [ "## Squared-Error Example and Iterative Fitting\n", "\n", @@ -2994,8 +2550,10 @@ }, { "cell_type": "markdown", - "id": "64c44231", - "metadata": {}, + "id": "a0712b34", + "metadata": { + "editable": true + }, "source": [ "$$\n", "(\\beta_m,\\gamma_m) = \\mathrm{argmin}_{\\beta,\\lambda}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2=\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta(1+\\gamma x_i))^2.\n", @@ -3004,8 +2562,10 @@ }, { "cell_type": "markdown", - "id": "1040bdaf", - "metadata": {}, + "id": "7e46dca8", + "metadata": { + "editable": true + }, "source": [ "We start our iteration by simply setting $f_0(x)=0$. \n", "Taking the derivatives with respect to $\\beta$ and $\\gamma$ we obtain" @@ -3013,8 +2573,10 @@ }, { "cell_type": "markdown", - "id": "de59d269", - "metadata": {}, + "id": "3a226e7a", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial {\\cal C}}{\\partial \\beta} = -2\\sum_{i}(1+\\gamma x_i)(y_i-\\beta(1+\\gamma x_i))=0,\n", @@ -3023,16 +2585,20 @@ }, { "cell_type": "markdown", - "id": "5f87e844", - "metadata": {}, + "id": "d0df0158", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "a2f9215c", - "metadata": {}, + "id": "60880d10", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial {\\cal C}}{\\partial \\gamma} =-2\\sum_{i}\\beta x_i(y_i-\\beta(1+\\gamma x_i))=0.\n", @@ -3041,16 +2607,20 @@ }, { "cell_type": "markdown", - "id": "67f71f90", - "metadata": {}, + "id": "033358c9", + "metadata": { + "editable": true + }, "source": [ "We can then rewrite these equations as (defining $\\boldsymbol{w}=\\boldsymbol{e}+\\gamma \\boldsymbol{x})$ with $\\boldsymbol{e}$ being the unit vector)" ] }, { "cell_type": "markdown", - "id": "5410f260", - "metadata": {}, + "id": "122af441", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\gamma \\boldsymbol{w}^T(\\boldsymbol{y}-\\beta\\gamma \\boldsymbol{w})=0,\n", @@ -3059,16 +2629,20 @@ }, { "cell_type": "markdown", - "id": "0485a1f5", - "metadata": {}, + "id": "ca771a46", + "metadata": { + "editable": true + }, "source": [ "which gives us $\\beta = \\boldsymbol{w}^T\\boldsymbol{y}/(\\boldsymbol{w}^T\\boldsymbol{w})$. Similarly we have" ] }, { "cell_type": "markdown", - "id": "3a256711", - "metadata": {}, + "id": "7b10825e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\beta\\gamma \\boldsymbol{x}^T(\\boldsymbol{y}-\\beta(1+\\gamma \\boldsymbol{x}))=0,\n", @@ -3077,8 +2651,10 @@ }, { "cell_type": "markdown", - "id": "fc8cd2ae", - "metadata": {}, + "id": "c014ff01", + "metadata": { + "editable": true + }, "source": [ "which leads to $\\gamma =(\\boldsymbol{x}^T\\boldsymbol{y}-\\beta\\boldsymbol{x}^T\\boldsymbol{e})/(\\beta\\boldsymbol{x}^T\\boldsymbol{x})$. Inserting\n", "for $\\beta$ gives us an equation for $\\gamma$. This is a non-linear equation in the unknown $\\gamma$ and has to be solved numerically. \n", @@ -3089,8 +2665,10 @@ }, { "cell_type": "markdown", - "id": "0a9ecf4b", - "metadata": {}, + "id": "ec72116c", + "metadata": { + "editable": true + }, "source": [ "## Iterative Fitting, Classification and AdaBoost\n", "\n", @@ -3103,8 +2681,10 @@ }, { "cell_type": "markdown", - "id": "ac605ae7", - "metadata": {}, + "id": "e682b778", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}=\\frac{1}{n} \\sum_{i=0}^{n-1} I(y_i\\ne G(x_i)).\n", @@ -3113,8 +2693,10 @@ }, { "cell_type": "markdown", - "id": "b4d530db", - "metadata": {}, + "id": "d839ebae", + "metadata": { + "editable": true + }, "source": [ "The iterative procedure starts with defining a weak classifier whose\n", "error rate is barely better than random guessing. The iterative\n", @@ -3127,8 +2709,10 @@ }, { "cell_type": "markdown", - "id": "0f9fce0f", - "metadata": {}, + "id": "a6bf29e7", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", @@ -3137,16 +2721,20 @@ }, { "cell_type": "markdown", - "id": "73471c17", - "metadata": {}, + "id": "3f0b4035", + "metadata": { + "editable": true + }, "source": [ "will be a function of" ] }, { "cell_type": "markdown", - "id": "d8244842", - "metadata": {}, + "id": "f4a44587", + "metadata": { + "editable": true + }, "source": [ "$$\n", "G_M(x) = \\mathrm{sign} \\sum_{i=1}^M \\alpha_m G_m(x).\n", @@ -3155,8 +2743,10 @@ }, { "cell_type": "markdown", - "id": "be09fe99", - "metadata": {}, + "id": "9df807ee", + "metadata": { + "editable": true + }, "source": [ "## Adaptive Boosting, AdaBoost\n", "\n", @@ -3165,8 +2755,10 @@ }, { "cell_type": "markdown", - "id": "a547cf77", - "metadata": {}, + "id": "8ba840f7", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_m(x) = f_{m-1}(x)+\\beta_mG_m(x).\n", @@ -3175,8 +2767,10 @@ }, { "cell_type": "markdown", - "id": "67b1198a", - "metadata": {}, + "id": "f6c0bb03", + "metadata": { + "editable": true + }, "source": [ "The simplest possible cost function which leads (also simple from a computational point of view) to the AdaBoost algorithm is the\n", "exponential cost/loss function defined as" @@ -3184,8 +2778,10 @@ }, { "cell_type": "markdown", - "id": "f0a75e83", - "metadata": {}, + "id": "916b1302", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}\\exp{(-y_i(f_{m-1}(x_i)+\\beta G(x_i))}.\n", @@ -3194,8 +2790,10 @@ }, { "cell_type": "markdown", - "id": "9d2d96dc", - "metadata": {}, + "id": "d9d05ebd", + "metadata": { + "editable": true + }, "source": [ "We optimize $\\beta$ and $G$ for each value of $m=1:M$ as we did in the regression case.\n", "This is normally done in two steps. Let us however first rewrite the cost function as" @@ -3203,8 +2801,10 @@ }, { "cell_type": "markdown", - "id": "a6c2a558", - "metadata": {}, + "id": "c0e2ff33", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}w_i^{m}\\exp{(-y_i\\beta G(x_i))},\n", @@ -3213,16 +2813,20 @@ }, { "cell_type": "markdown", - "id": "5a582df6", - "metadata": {}, + "id": "5cab6a54", + "metadata": { + "editable": true + }, "source": [ "where we have defined $w_i^m= \\exp{(-y_if_{m-1}(x_i))}$." ] }, { "cell_type": "markdown", - "id": "654c5f13", - "metadata": {}, + "id": "2c2697a1", + "metadata": { + "editable": true + }, "source": [ "## Building up AdaBoost\n", "\n", @@ -3231,8 +2835,10 @@ }, { "cell_type": "markdown", - "id": "efecb2bc", - "metadata": {}, + "id": "7d6bea43", + "metadata": { + "editable": true + }, "source": [ "$$\n", "G_m(x) = \\mathrm{sign} \\sum_{i=0}^{n-1} w_i^m I(y_i \\ne G_(x_i)),\n", @@ -3241,8 +2847,10 @@ }, { "cell_type": "markdown", - "id": "da78bb27", - "metadata": {}, + "id": "5c659add", + "metadata": { + "editable": true + }, "source": [ "which is the classifier that minimizes the weighted error rate in predicting $y$.\n", "\n", @@ -3251,8 +2859,10 @@ }, { "cell_type": "markdown", - "id": "dc1c118f", - "metadata": {}, + "id": "e5896e0d", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\exp{-(\\beta)}\\sum_{y_i=G(x_i)}w_i^m+\\exp{(\\beta)}\\sum_{y_i\\ne G(x_i)}w_i^m,\n", @@ -3261,16 +2871,20 @@ }, { "cell_type": "markdown", - "id": "1b77640d", - "metadata": {}, + "id": "e59ed848", + "metadata": { + "editable": true + }, "source": [ "which can be rewritten as" ] }, { "cell_type": "markdown", - "id": "d7944742", - "metadata": {}, + "id": "c810dab7", + "metadata": { + "editable": true + }, "source": [ "$$\n", "(\\exp{(\\beta)}-\\exp{-(\\beta)})\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i))+\\exp{(-\\beta)}\\sum_{i=0}^{n-1}w_i^m=0,\n", @@ -3279,16 +2893,20 @@ }, { "cell_type": "markdown", - "id": "94ffa0c4", - "metadata": {}, + "id": "0bc6c147", + "metadata": { + "editable": true + }, "source": [ "which leads to" ] }, { "cell_type": "markdown", - "id": "eae46622", - "metadata": {}, + "id": "3ce040a0", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\beta_m = \\frac{1}{2}\\log{\\frac{1-\\mathrm{\\overline{err}}}{\\mathrm{\\overline{err}}}},\n", @@ -3297,16 +2915,20 @@ }, { "cell_type": "markdown", - "id": "099f71b5", - "metadata": {}, + "id": "9180bf9d", + "metadata": { + "editable": true + }, "source": [ "where we have redefined the error as" ] }, { "cell_type": "markdown", - "id": "11e5f200", - "metadata": {}, + "id": "1340606f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}_m=\\frac{1}{n}\\frac{\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i)}{\\sum_{i=0}^{n-1}w_i^m},\n", @@ -3315,16 +2937,20 @@ }, { "cell_type": "markdown", - "id": "77b52ed2", - "metadata": {}, + "id": "f754a0ce", + "metadata": { + "editable": true + }, "source": [ "which leads to an update of" ] }, { "cell_type": "markdown", - "id": "e8fe5df6", - "metadata": {}, + "id": "a440027b", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_m(x) = f_{m-1}(x) +\\beta_m G_m(x).\n", @@ -3333,16 +2959,20 @@ }, { "cell_type": "markdown", - "id": "4c1ea9b7", - "metadata": {}, + "id": "19078953", + "metadata": { + "editable": true + }, "source": [ "This leads to the new weights" ] }, { "cell_type": "markdown", - "id": "a61b875a", - "metadata": {}, + "id": "fb878359", + "metadata": { + "editable": true + }, "source": [ "$$\n", "w_i^{m+1} = w_i^m \\exp{(-y_i\\beta_m G_m(x_i))}\n", @@ -3351,8 +2981,10 @@ }, { "cell_type": "markdown", - "id": "a0df6e36", - "metadata": {}, + "id": "f604fbd2", + "metadata": { + "editable": true + }, "source": [ "## Adaptive boosting: AdaBoost, Basic Algorithm\n", "\n", @@ -3369,8 +3001,10 @@ }, { "cell_type": "markdown", - "id": "862806de", - "metadata": {}, + "id": "70ed70ae", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{err}=\\frac{1}{n}\\sum_{i=0}^{n-1}I(y_i\\ne G(x_i)),\n", @@ -3379,16 +3013,20 @@ }, { "cell_type": "markdown", - "id": "60c6b96e", - "metadata": {}, + "id": "510e54a7", + "metadata": { + "editable": true + }, "source": [ "where the function $I()$ is one if we misclassify and zero if we classify correctly." ] }, { "cell_type": "markdown", - "id": "d4cf16bb", - "metadata": {}, + "id": "ae7981eb", + "metadata": { + "editable": true + }, "source": [ "## Basic Steps of AdaBoost\n", "\n", @@ -3401,8 +3039,10 @@ }, { "cell_type": "markdown", - "id": "91e907b9", - "metadata": {}, + "id": "b87f1fec", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}_m=\\frac{\\sum_{i=0}^{n-1}w_i^m I(y_i\\ne G(x_i))}{\\sum_{i=0}^{n-1}w_i},\n", @@ -3411,8 +3051,10 @@ }, { "cell_type": "markdown", - "id": "cc913a38", - "metadata": {}, + "id": "aad16e11", + "metadata": { + "editable": true + }, "source": [ "1. Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.\n", "\n", @@ -3437,8 +3079,10 @@ }, { "cell_type": "markdown", - "id": "87e49535", - "metadata": {}, + "id": "e7639342", + "metadata": { + "editable": true + }, "source": [ "## AdaBoost Examples\n", "\n", @@ -3448,8 +3092,11 @@ { "cell_type": "code", "execution_count": 24, - "id": "a48ac6a2", - "metadata": {}, + "id": "5358dca1", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.ensemble import AdaBoostClassifier\n", @@ -3476,25 +3123,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.10" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/src/week46/week46.do.txt b/doc/src/week46/week46.do.txt index 4401f9a49..51324e72d 100644 --- a/doc/src/week46/week46.do.txt +++ b/doc/src/week46/week46.do.txt @@ -15,7 +15,7 @@ DATE: Week 46, November 13-17 * Thursday: Basics of decision trees, classification and regression algorithms and ensemble models * Readings and Videos: * These lecture notes - * "Video of lecture to be added":"https://youtu.be/" + * "Video of lecture to be added":"https://youtu.be/PMswUwhYa7k" * "Video on Decision trees":"https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn" * Decision Trees: Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from "STK-IN4300, lecture 7":"https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf". Chapter 9.2 of Hastie et al contains also a good discussion. !eblock