blob: 448456467b19bec8e3186561a97ed8667d171aa2 [file] [log] [blame]
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train a linear regression model\n",
"\n",
"In this notebook, we are going to use the tensor module from PySINGA to train a linear regression model. We use this example to illustrate the usage of tensor of PySINGA. Please refer the [documentation page](http://singa.apache.org/en/docs/tensor.html) to for more tensor functions provided by PySINGA. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import division\n",
"from __future__ import print_function\n",
"from builtins import range\n",
"from past.utils import old_div\n",
"\n",
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To import the tensor module of PySINGA, run "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from singa import tensor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The ground-truth\n",
"\n",
"Our problem is to find a line that fits a set of 2-d data points.\n",
"We first plot the ground truth line, "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fee42985f60>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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b2bOHjOkHvAes9S+aYWYPBaumSLV2x37GpKTyzdqd9G3XkMeGdiOhYU2vyxKR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\nQkFERMooFEREpIxCQUREyigURESkzP8DkkYtiInuClwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fedeac59588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a, b = 3, 2\n",
"f = lambda x: a * x + b\n",
"gx = np.linspace(0.,1,100)\n",
"gy = [f(x) for x in gx]\n",
"plt.plot(gx, gy, label='y=f(x)')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.legend(loc='best')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generating the trainin data\n",
"\n",
"Then we generate the training data points by adding a random error to sampling points from the ground truth line.\n",
"30 data points are generated."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fede8b1c588>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fede8c0f438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nb_points = 30\n",
"\n",
"# generate training data\n",
"train_x = np.asarray(np.random.uniform(0., 1., nb_points), np.float32)\n",
"train_y = np.asarray(f(train_x) + np.random.rand(30), np.float32)\n",
"plt.plot(train_x, train_y, 'bo', ms=7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training via SGD\n",
"\n",
"Assuming that we know the training data points are sampled from a line, but we don't know the line slope and intercept. The training is then to learn the slop (k) and intercept (b) by minimizing the error, i.e. ||kx+b-y||^2. \n",
"1. we set the initial values of k and b (could be any values).\n",
"2. we iteratively update k and b by moving them in the direction of reducing the prediction error, i.e. in the gradient direction. For every iteration, we plot the learned line."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot(idx, x, y):\n",
" global gx, gy, axes\n",
" # print the ground truth line\n",
" axes[idx//5, idx%5].plot(gx, gy, label='y=f(x)') \n",
" # print the learned line\n",
" axes[idx//5, idx%5].plot(x, y, label='y=kx+b')\n",
" axes[idx//5, idx%5].legend(loc='best')\n",
"\n",
"# set hyper-parameters\n",
"max_iter = 15\n",
"alpha = 0.05\n",
"\n",
"# init parameters\n",
"k, b = 2.,0."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SINGA tensor module supports basic linear algebra operations, like `+ - * /`, and advanced functions including axpy, gemm, gemv, and random function (e.g., Gaussian and Uniform).\n",
"\n",
"SINGA Tensor instances could be created via **tensor.Tensor()** by specifying the shape, and optionally the device and data type. Note that every Tensor instance should be initialized (e.g., via **set_value()** or random functions) before reading data from it. You can also create Tensor instances from numpy arrays,\n",
"\n",
"* numpy array could be converted into SINGA tensor via **tensor.from_numpy(np_ary)** \n",
"* SINGA tensor could be converted into numpy array via **tensor.to_numpy()**; Note that the tensor should be on the host device. tensor instances could be transferred from other devices to host device via **to_host()**\n",
"\n",
"Users cannot read a single cell of the Tensor instance. To read a single cell, users need to convert the Tesnor into a numpy array.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss at iter 0 = 9.660777\n",
"loss at iter 1 = 8.484187\n",
"loss at iter 2 = 7.452227\n",
"loss at iter 3 = 6.547118\n",
"loss at iter 4 = 5.753264\n",
"loss at iter 5 = 5.056990\n",
"loss at iter 6 = 4.446301\n",
"loss at iter 7 = 3.910676\n",
"loss at iter 8 = 3.440886\n",
"loss at iter 9 = 3.028838\n",
"loss at iter 10 = 2.667435\n",
"loss at iter 11 = 2.350451\n",
"loss at iter 12 = 2.072425\n",
"loss at iter 13 = 1.828568\n",
"loss at iter 14 = 1.614680\n"
]
},
{
"data": {
"image/png": 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24Pb/z9u8N4FZ6/eSnlPA87d34rEBbfGQO3RO4S5PAyJPpfLMygiOpWQy/rq2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\nAQ/3OvWX9jSgtCcB17RtyI7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05s2b+eeff7j11lupU6cOERERVr8uhghg7ty51K9fn4kT\nJ/67bcKECbzyyis8+OCDzJgxo0ztWLly5b//7du3rz4H50pWPGjJSdHO7pw0tzjxSE4cZGGv4h9O\nIWMtGXGDzi5IVhxiUZ/iOXn8d0tO3KCzC5ITh1g7rnhOZp52i8+cy1WWrLjuHd6ProVEK4O3nz/p\nFktGXVStWjUGDRpEvXr18PAo25jR999/n7Fjx/Lcc8/RrVs3PD09eeCBBzCZTPTr14+tW7dyww03\n2LSv8+fPExAQQPXq1Vm+fHl5DsU1lZQTNzvhSE7s7Ptp8M+nV25rEQKP/eKc9lSAZMWODm2BZfdf\nuW3AdLjx/5zTngqoTDlx+MovGUnwTpEJVbe8Cv2etttb2lNlyYpmGf6gr5CQELV79+7y/XFJE9HK\n0YGJjY2lc+fO5WuHTsxmM8HBwaxevRp/f3+ntuVqrP17aZq2RykVYo/3q1BOvh4KR6x0ViQnDuE2\nWYlaBeseK769nBdEkpWycZucZKbA21aWrpScOIzbZEXn4QuSlbKpSE5c4w6v2QRzG1j/nZvdqbtc\nTEwMgwcP5t5773X5ELmNRddA0v7i2yUn4nLHdsCXg4tvd+OcgGRFdwZdJ1VyYgcrHoT9RVYNuLgi\nlBurTFlxboc3LxNea158+x3vQG8rd2XcTJcuXTh69Kizm2EMUath3bgrt3W5B4Z/6Zz26EhyoqOc\nC/BGy+Lb3bwDc5FkRUe7v4CNU67c5kLLFVaE5ERH2anwZusrtz22DVoEW3+9m6lMWbGpw6tp2m3A\n+4AH8KlSquRVjG2VuA8+6nfltom7oHHHCu/6ckop484M1ZE9hrboZlEfSLpsTeU+T8Jtr+v6FpIT\n27lsVs6fgPcDrtxmh46uZMU2LpsTKD4kanIU1G9d8uvLQXJiO5fNStGhLm0HwehvdH8byYptKpqT\nq3Z4NU3zABYBN2NZB/EfTdM2KKViKvTOSgEa9B5v6bzY4aray8uLlJQUGjZsKGEqhVKKlJQUvLy8\nnN0U6zrdYenwTvwHGltZ+L2CJCe2c+msKLOljHivsXDjS3Z51ChZsY1L5wSg3Q1wfAc8tVv3ji5I\nTsrCpbOizFC3BQQMh5vm2OUtJCu20SMnttzh7Q0cVkodBdA0bQVwN1CxDm/TbjAntUK7uBpfX1/i\n4uJISkqy6/sYgZeXF76+vs5uhnU3zrZ82YnkpGxcNisN2sDMk3Z9C8mK7Vw2JwD9nrJ82YnkpGxc\nNiu1G8PUinV1rkayYruK5sSWDm8L4NRlP8cB1xR9kUNK9pWRp6cnbdq0cXYzhIuTnAhbSVaELSQn\nwlaSFcfRrfCEUmqxUipEKRXSuHFjvXYrhBBCCCFEhdjS4T0NXD7t2bdwmxBCCCGEEC7Plg7vP4C/\npmltNE2rBowENti3WUIIIYQQQujDpkprmqbdAbyHZVmyz5VS867y+iTgRJHNjYDkcrbTFRnteMD6\nMbVWStlljEolyQkY75hKOh7JSsUY7XhAzin2YrRjknOKfRjteKAC5xS7lBa2+kaattteJQKdwWjH\nA65xTK7QBr0Z7Zhc5XhcpR16MdrxgGsckyu0QW9GOyZXOR5XaYdejHY8ULFj0m3SmhBCCCGEEK5I\nOrxCCCGEEMLQHNnhXezA93IEox0PuMYxuUIb9Ga0Y3KV43GVdujFaMcDrnFMrtAGvRntmFzleFyl\nHXox2vFABY7JYWN4hRBCCCGEcAYZ0iCEEEIIIQxNOrxCCCGEEMLQdO/wapp2m6ZpBzRNO6xp2vNW\nfl9d07SVhb//W9M0P73boCcbjucRTdOSNE2LKPwa54x22krTtM81TTuradreEn6vaZr2QeHxRmma\nFmyndhgqJyBZkazYRnIiObGVZEWyYgvJiY05UUrp9oWlMMURoC1QDYgEuhR5zZPAx4XfjwRW6tkG\nJxzPI8BCZ7e1DMd0HRAM7C3h93cAmwAN6AP87aR/V7fJiWRFsiI5kZxIViQrkhPXzoned3h7A4eV\nUkeVUnnACuDuIq+5G/iy8Ps1wI2apmk6t0MvthyPW1FKbQfOlfKSu4GvlMVfQD1N05rp3Ayj5QQk\nK5IV20hOJCe2kqxIVmwhObExJ3p3eFsApy77Oa5wm9XXKKUKgDSgoc7t0IstxwNwX+Ft9TWaprV0\nTNPsxtZjtvd7uFNOQLICkhVbSE4kJ7aSrEhWbCE5sTEnMmmt4r4D/JRSAcAWLl0VClGUZEXYQnIi\nbCVZEbaQnKB/h/c0cPmVg2/hNquv0TStKuANpOjcDr1c9XiUUilKqdzCHz8FejqobfZiy/+GjngP\nd8oJSFZAsmILyYnkxFaSFcmKLSQnNuZE7w7vP4C/pmltNE2rhmWw94Yir9kAPFz4/f3AVlU4CtkF\nXfV4iowbGQLEOrB99rABGF04C7IPkKaUStD5PYyWE5CsSFZsIzmRnNhKsiJZsYXkxNac2GF23R3A\nQRJRGcsAACAASURBVCyzBmcVbpsLDCn83gtYDRwGdgFt9W6Dg4/ndWAflpmR24BOzm7zVY5nOZAA\n5GMZ9/Io8DjweOHvNWBR4fFGAyFO+nd1q5xIViQrkhPJiWRFsiI5cd2cSGlhIYQQQghhaDJpTQgh\nhBBCGJp0eIUQQgghhKFJh1cIIYQQQhiadHiFEEIIIYShSYdXCCGEEEIYmnR4hRBCCCGEoUmHVwgh\nhBBCGJp0eIUQQgghhKFVtcdOGzVqpPz8/Oyxa+Fge/bsSVZKNbbHviUnxiJZEbaQnAhbSVaELWzN\niV06vH5+fuzevdseuxYOpmnaCXvtW3JiLJIVYQvJibCVZEXYwtacyJAGIYQQQghhaDbd4dU07TiQ\nDpiAAqVUiD0bJdyXZEXYQnIibCVZEbaQnIirKcuQhkFKqWS7tUQYiWRF2EJyImwlWRG2kJyIEsmQ\nBoHJrJj/0wGi49Kc3RTh4o4kZTD3uxjMZuXspggXppTio1+P8OeRFGc3Rbi4+NRsZn+7l7wCs7Ob\nIlzcsr9PsCUmsdx/b2uHVwE/aZq2R9O08dZeoGnaeE3TdmuatjspKancDRKOlZqVx5gl//Dh1sNs\niTmjxy5LzYrkxH1tO3CWexbt5JuI08Sdz67o7uScYlC5BSamrY7kzc37+T46Xo9dyjnFoKLj0rhn\n0U7Wh53mSFJGRXcn5xSDMpsVb27ez6z1e/km4nS592PrkIZrlVKnNU3zAbZomrZfKbX98hcopRYD\niwFCQkLk9o8biIm/wISlu0lMy+X1od0Z1buVHrstNSuSE/ejlGLx9qO8sXk/nZvWZfHonvjWr1nR\n3co5xYCSM3KZ8PUe9pw4z5Sb/Jl8o78eu5VzigFtiUlk0vJwGtSqxtdPXEPHpnUquks5pxhQboGJ\nGWui+CYingeuacXcIV3LvS+bOrxKqdOF/z2radp6oDewvfS/Eq7s24jTzFgbRb0a1Vg5oQ89WtXX\nZb+SFWPJyTcxY20U30bEc2dAM96+P4Ca1Sq+mqHkxHj2n7nAo0t2k5yRy8IHejA4oLku+5WsGItS\nii92HueV72Po3sKbTx8OwaeOlx77lZwYTFp2PhO+3s1fR8/x7K0deXJgOzRNK/f+rvrJpWlaLaCK\nUiq98PtbgLnlfkfhVPkmM6/9EMsXO4/Tu00DFj0QTOM61XXZt2TFWOJTs5nw9R72xqfpcrK5SHJi\nPL/EWu7W1apelVUT+hLYsp4u+5WsGEuByczcjTF89ecJbu3ahPdG9KBGNY8K71dyYjynU7MZ88Uu\njiVnsmBEIPf28K3wPm25VdMEWF/4QVcVCFVKba7wOwuHS0rPZWJoGLuOnWNMfz9euKMznh66zluU\nrBjE7uPneHxpGDn5Jv73nxBu6tJEz91LTgxCKcWnO47x2qZYujX35n+jQ2jqXfG7dZeRrBhERm4B\nT4eGse1AEo8NaMPzt3fGo0rFL6ALSU4MZF98GmOX/ENWrokvx/SmX/tGuuz3qh1epdRRIFCXdxNO\nE37yPE8sDSM1O0+3q6WiJCvGsGLXSf7v2720qFeD5Y9dg3+TCo+tu4LkxBjyCsy8+E00q3bHcUf3\npswfFqTL3brLSVaMISEtmzFf/MOhsxnMu7cbD17TWtf9S06MY/vBJJ5cFkYdr6qsfqIvnZrW1W3f\ndiktLFzL8l0neenbfTTxrs7aJ/rRtbm3s5skXFC+ycyrG2P48s8TDPBvxMJRwXjX9HR2s4QLOpeZ\nx+NL97Dr2Dkm3dCeKTd1oIp+d+uEgew9ncajX/5DZq6Jzx/pxfUdGju7ScJFrd59ipnromnvU5sl\nY3rr/bRIOrxGlltgYs6GfSzfdYoB/o34cFQP6tWs5uxmCRd0LjOPicvC+PNoCo8NaMOM2zpRVd/h\nLsIgDiWmM/bLf0i8kMv7I4O4O6iFs5skXNTPMYlMWhFO/ZrVWPNEb13v1gnjUErxwS+HWfDzQa5t\n34iPHgqmjpf+N1ukw2tQCWnZPL40jMhTqUwc1I6pN3fUc7yUMJDYhAs89tVuzqbn8u7wQIYG6z/c\nRRjDrwfO8nRoONU9PVg5Xr/VXYTxfLHzGK9sjKFbC28+HR2CT11979YJY8g3mZm13jI06r5gX964\nr7vec4v+JR1eA/r7aAoTQ8PIzjPx8UPB3NatmbObJFzUpugEpq2OpI6XZXZ9kE6z64WxXFxK6tXv\nY+jYtC6fPRxC83o1nN0s4YIKTGZeKRwadUuXJrw3MkiXpQyF8WTkFjBxWRi/HUxi0o3+PHOTvy4r\nAZVEUmggFz+U5v0QS+uGNVkxvg/tffSdcCSMwWxWvPfLIT745RBBLeux+D895Q6MsCrfZGb2t/tY\nvuskt3RpwoIRQdSqLh8doriM3AImLQ9n6/6zjLu2DTPv0HUlBmEgiRdyGPPFPxxITOfN+7ozopcu\nha9KJWctg8jOMzFznaUayc1dmvDu8EC7jIER7i8jt4CpKyP4KSaR+3v68uo93fDy1Hd2vTCG85l5\nPFk4tvuJge149paOMjlNWJWQls3YJbs5mJjOq/d046E++q7EIIzjUGI6j3zxD6lZeXz6cAiDOvo4\n5H2lw2sAJ1OymLB0D/vPXGDazR2YOKi9fCgJq06mZDHuq384fDaD2YO7MKa/n10fIQn3dSQpg0eX\n/EN8ao6M7Ralunwlhs8eDmGggzowwv38dTSF8V/ttswDmNCXbi0ct2qUdHjd3G8Hk5i0PBylFJ8/\n0sthV0rC/ew8nMzE0DCUgq/GXsO1/vos5i2M5/dDyTyxbA/Vq1Zh+fhr6Nm6gbObJFzUxZUY6tXw\nZPXjfencTFZiENZ9G3GaZ1dH0aphTZaM6YVv/ZoOfX/p8LoppRT//fUI7/x0gI5N6vDJf3rSumEt\nZzdLuKDLx3a3a1yL/40OkayIEn3153Fe/i4Gf5/afPpwiMM/lIT7uLgSQ9fm3nz2sKzEIKxTSvHJ\n9qO8sWk/vds04H//CXHKGu/S4XVD6Tn5TF8dyY/7ErkrsDlv3tddZsEKq3ILTMxav5c1e+K4pUsT\n3h0RRG2ZcCSsyDf9P3v3HR1F9fdx/D3U0EvoNfReE7q9Kyjqg0rvJCio2LCgiNh7+dlASiAQiggq\n2BUbFkilhd5DElIgve/e548ESZaELMlupuT7OieH7LCZvRM+zNyZ2+ws3BxBwL8nuKFbE94b3U+y\nIopksyte3BKB/9/HZSYGcUk2u2LB13sJ+PcEt/dpwVv39KZ6FX3GjEhCTeZwbCp+AcEcT0jn2eHd\nmHZFO+mDKYoUm5yJ36oQwk4m8tD1nZhzfSfp2y2KlJSew6zAULYdjsf3qvY8eUtXGV0vipSWPxPD\nLzITgyhBenYuD60J5+d9Z/C7uj1P3txV12uQVHhN5Ie9MTy2fifVq1Ri1bRBDOngqXeRhEGFn0rE\nLyCYlMxcPhnXn1t7yVzMomjH4tOY5h/EqXPpvDGqN/f6tNa7SMKgYpIymeqfN5XUi3f2ZILMxCCK\nEZ+axbQVweyOTGThyB5MHOKld5GkwmsGNrvi3Z8O8uGvh+ndqh6fjveWSd9FsTaGRvLUxt00qVOd\nL+4fKoNIRLH+PhzP/atDqaTB6umDGdhOBqeJou2NSmKafzApmTnlOpWUMJ+jcalM8Q/iTHImn473\n5qYezfQuEiAVXsNLSs/hobVh/H4wjvt8WvPCyB4yZ6ooUq7Nzmvf7WfJtmMMae/JR+P607BWNb2L\nJQxq9fYTPP/VXto1qsXSSQNo4ymD00TRtu4/w+zAvJkYNshNtLiEkBPnmL4iCE3TWDPDWMuPS4XX\nwPZFJ+MXEEJ0UgYv39WTcYOk+UgULSk9h9lrQvnzUDyThrTl2RHd3bYeuTC3XJudl7/dx/K/jnNt\nl8Z8MKafLFIjirXi7+O8sHkv3VvUZemkATSVmRhEMb7fE8PDa8NoXs8D/ykD8WpkrNmApMJrUF+F\nn+bJL3ZRr0ZV1vkNob+B7pKEsRw6k8KMlcGcTszgtbt7MXqg+5doFOaUnJnDg4F5LUZTh7Vj3nAZ\ncCSKVnAmhhu6NeWDMTITgyje8r+OsXBLBH1b12fJRB88a1fXu0gXkfQaTK7Nzqvf7WfptmMM8GrA\nR+P606SO3FGLov0ccYY568LxqFqZNTMG4+MlfTBF0U4kpDFtRTDH49N49e5ejJEbI1GMtKxcHl4b\nxs/7Ypl2RTuekZkYRDHsdsUr3+5jybZj3NyjKe/d148a1YzZ7VIqvAYSn5rF7MBQ/j16lslDvZg3\nvJs0S4siKaX46NfDvP3TQXq2qMeiCTKQURTv36MJzFwVAkCAzPAiLiEmKZNpK4LYF51smNH1wpgy\nc2w8tn4n3+yOZvJQL54b0d3QN0ZS4TWInacSmbkqhLNp2bJuvbik9Oxcnvh8F9/sjmZk3xa8/n+9\nZSCjKNa6oJPM27SHNp41WTZpgOH61QnjKDgTw1JZql5cwrm0bHwDggk6fo55t3Vj+pXGXxPA6Qqv\npmmVgWDgtFJqhPuKVPGsCzrJc1/upXH+NFI9W9bTu0ilJjlxr8hz6fiuDGFfTDJP3doVv6vaG/4k\nI/Rhsyte+24fn/15jCs7NeLDsf2pV0MGp4mi/bo/ltmBodStUZXPZw6lewuZiUEU7dTZdCYt30Hk\n2Qw+HNuPEb1b6F0kp1zOE96HgX2A/C9wkaxcGwu+jmDNjpNc2akRH4zuRwPzTyMlOXGT7UcTuH91\nKDm5dpZZ4OmL3By5T0pmDg+vDWfr/lgmDWnLcyO6U0W6R4lirPznOAu+lpkYRMl2RSYy1T+YHJud\nVdMHmWrubqcqvJqmtQKGAy8Dj7q1RBVEdFIG968KJfxUIvdf04HHb+pi6L4vzpCcuE/Avyd44eu9\ntPGsyWcTfejQuLbeRXIFuTlyg1Nn05m2IogjcWmWWA1Lbozcx2ZXvPRNBMv/Os4N3ZrwwZh+MhOD\nKNbW/WeYtTqMhrWqsdZ3EB2b1NG7SJfF2WS/B8wFij06TdN8AV+ANm1k9O+lbD+awKzAUDKybVZb\n9rXEnIjLk51rZ8HmvQRuP8m1XRrz/ph+1LXAnKlyc+QewcfP4hcQQo7NzoopA7miUyO9i+QKcmPk\nBgVnYpgyzItnhxt7wJEz5ObIfQK3n+TZL3fTvUVdlk0eYMrZo0ps49I0bQQQq5QKudT7lFKLlVI+\nSimfxo0bu6yAVqKUYvlfxxi3ZDt1Pary5axhlqnsOpsTTdN8NU0L1jQtOC4urpxKZ07xqVmMW/Iv\ngdtPcv81HVgyaYAlKrv5zt8c2Yt7g2Tl8mwIiWTsZ9upWyPv3GKFym6BG6MlepfFSs4kZ3Lvon/Y\nuj+WhSN78PztPUxf2c13/uZIuIhSird+OMAzm3ZzVefGrPMdYsrKLjj3hHcYcIemabcBHkBdTdNW\nKaXGu7do1pKRbeOZTbvZFHaaG7o15Z37+lip8gJO5kQptRhYDODj46PKv5jmsOd0Er4rg0lIy+b9\n0X0Z2bel3kVymYI3R5qmXVPc+yQrzrHZFW/8sJ9Fvx9laAdPPhnnTb2aljm3SOuii0VEJTNtRRDJ\nGTksnTSAa7uaeyzAedJq5HrZuXae/GIXm8JOM2Zga14c2dPUYwFKrPAqpZ4GngbIvzg9LpXdy3Pq\nbDp+AXkj6x+9sTOzr+1IJWvcTf9HcuI6m3dG8cSGnTSoWc30s3YUQ26iXSQtK5c568L5KeIM4wa1\nYcEdPSwzd7fcGLne+ZkY6nhYciYGuTlyoeTMHGYGhPD3kQQev6kzs67taPoZgaR3upv9eSiOB9eE\nYbMrllnoblq4ns2ueOvHA3zy2xF82jbgk/HeNK5jvOUZy0pujlwj8lw601cEc/BMCgtu787kYe30\nLpKryY2RC52fiaFb87yZGJrVM2ezdFHk5si1ohIzmLI8iCNxqbx9Tx/+z9sa6wJcVoVXKfUb8Jtb\nSmIxSik++f0Ib/1wgE5N6rBogneFmfBdcnL5kjNzmJM/jdSYga154Y6eVKtijSd1wvVCTpzDLyCY\nrBw7y6cM5OrO1hs3ITdGrmHLX/p16bZj3NCtCe+P7ket6pZ71iU3Ry4SEZXMVP8g0rJyWTF1IMM6\nmn8swHmWS70RpGbl8sTnO/luTwwjejfnjVG9ZaoXUayjcanMWBnMiYR0XhzZg/GD25q+6chZcnN0\n+b4MO83cL3bRrK4Ha319TDc1kCg/eTMxhPPzvjOmWPq1tOTmyDW2HYpn5qoQalevwvqZQ+jW3FJd\nXqTC62pH4lLxCwjhWHyaaZbbE/r5/WAcswNDqVq5EgHTBjGkg6feRRIGZbcr3vnpIB/+ephB7Rry\n6XhvKyxU4xS5Mbp8Z5IzmbYiiIioZKt2eREutCEkkqe+2EXHJrVZPmUAzevV0LtILicVXhf6cW8M\nj67fSbUqlQiYOpChFmoKEK6llOKzP4/y2nf76dKsLosneNO6YU29iyUMKj07l0fX7eT7vTGMHtCa\nhSOly4so3r7ovGbp5Iwclkzy4bquTfUuUrmRm6PLo5Tiw62Hefungwzt4MmnE7ytNoPUf6TC6wI2\nu+K9nw/yv62H6d2qHp+O96ZFfevdHQnXyMyx8fTGvCnqbuvVjLfu6SNdXkSxopMymL4imH3RyTw7\nvBvTrpBWI1G8Xw/EMnt13kwM62cOoUcLy83yIlwk12bn2S/3sDboFHf3a8lr/9fb0jfScpUto6T0\nHOasC+PXA3Hc69OKhSN74lG1st7FEgYVnZSBX0AIuyKTeOzGzsy+zvxTvQj32XkqkRkrg0nPtllq\nzlThHgH/nuD5r/bQtVnealhWmolBuFZqVi6zA0P57UAcD17XkUdv7Gz5a5FUeMtgX3QyfgEhRCdl\n8NKdPRk3qI3lAyNKL+TEOWauCiE9K5fFE7y5qUczvYskDOzrnVE88flOGtepzqrpg+jcVAaniaIV\nnInh+q5N+GCMJWdiEC4Sm5zJFP8g9sek8MpdvRg7qGLMSSz/I0rp651RPLlhF3U8qrDWdzDebRvq\nXSRhYOuDTvHsl3toXt+D1VJ5EZdgtyve/+UQ7/9yiAFeDfh0vDeeta03H7NwjfTsvJkYfoqw9kwM\nwjUOx6YwaVkQ59KzWTLRp0K1GkmF9zLl2uy89t1+lmw7xgCvBnw0tj9N6kqzkShars3OS9/sw//v\n41zRsREfju1H/ZoVY2S9uHwZ2TYe37CTb3ZFM8q7FS/f1ZPqVaSLlCjameRMpq8IZm9UkszEIEq0\n/WgCM1YGU61KZdb5DqFXq4rVv1sqvJchITWL2YFh/HM0gUlD2jJveHdLd/AWZXMuLZtZgaH8fSSB\naVe04+lbu5p6HXLhXmeSM5mxMpjdp5N46tau+F3VXrpIiWLti05mmn8QiRVwJgZx+b7eGcXj63fS\numEN/KcMrJCzAkmF10m7IhOZGRBCQlo2b93Th1EWWWpPuMf+mGRmrAzmTHKW5EWUaHdkEjNWBpOc\nmcPiCT7c2F0qL6J4vx2IZXZgGLWqV+ZzmYlBXML5KTBf+XY/A70asniid4VtZZQKrxPWB+f1v2xc\nuzpf3D+Uni3l5CKK9/2eGB5dH07t6lVY5zuYfm0a6F0kYWDf7Y7mkfXheNaqzoaZQ+newlqrGwnX\nWvXvCZ7/ei9dmtZh6WQfSy4QIFzDZlcs3LyXFf+cYHjv5rx9T58KPYuUVHgvISvXxgubIwjcfpJh\nHT3535j+NKwgKxuJy1dwsFHf1vVZNMGbptK/WxSj4ITv/dvUZ9EEHxrXkcFpomg2u+K17/bx2Z/H\nuC5/JobaMhODKEZGto2H1obxU8QZZlzZjqdv7UYlMwxmVAo+HAAJh+CRCKjX0mW7lv8txYhJyuT+\n1SGEnUzE7+r2PHFTF+l/KYqVmpXLY+vD+WHvGf6vf95go4p8Jy0uLTPHxpNf7OKr8Cju6teSV+/u\nJXkRxUrPzmXO2nB+lJkYhBMSUrOYtiKYnZGJ5hrMmHgK3ut54XVt13btkgpvEYKOn+X+VaGkZ+fy\n8bj+3Narud5FEgZ2MiGdGSuDORSbIithiRLFpmTiuzKE8FOJPHFzFx64poPkRRQrNjmTaTITg3DS\n8fg0Ji/fQXRSJp+M8+aWniaZ7z3EHzY/nPd9rcbw2AGo5NqHAFLhLUApxcp/TvDilghaN6zJmhmD\n6CTzpYpL+PtwPA8EhqIUrJg6kCs7Nda7SMLA9kYlMX1FMInpOXw63kQXI6GLgjMxLJ7gww1mHsy4\nbzOsGw/D5sCNL+hdGksKPXmO6SuCUUoROGMw3m1NMH5EKfh4MMTtz3t944sw7CG3fJRUePNl5th4\nZuNuNoad5oZuTXjnvr7U9aiqd7GEQSmlWPH3cV78Zh/tG9Xis4k+eDWqpXexhIH9sDeGOWvDqV+z\nKp/PHCKDX8Ul/X4wjlmrQ6lVvTLr/UycF6Xgs2shKizvdYO2+pbHon7YG8NDa8JoVs8D/ykDaWeG\n61FSJLzb48Lr2SHQqKPbPk4qvMCps+nMXBVCRHQyc27oxEPXdTJH526hi6xcG899uYf1wZHc0K0p\n743uK4NHRLGUUnzy+xHe+P4AfVrX57MJ3rJYjbik1dtPMP+rvXRuWodlZp6JIeEI/K//hde+v0GL\nfnqVxrJW/H2cBZv30qdVfZZO8jHHyoyhAfD17LzvazSEJw67vAuDowp/lf7zUBwPrgnDZlcslcm7\nRQlikzOZuSqE0JOJPHhdRx65obPcHIliZeXaeHrjbjaGnub2Pi14c1Rvaw1Oy82C170gJx2eOgke\nJn0KaRAFZ2K4tktj/je2v3lvpn97DX57Ne/7Wo3h0f1Q2aTHYlB2u+K17/ez+I+j3Ni9KR+M7keN\nagY/vygFL9S/8PqGBXDFI+Xy0SWmT9M0D+APoHr++zcopZ53d8HcTSnFp78f5c0f9tOxSW0WTfAx\nRxOA0M3OU4n4BYSQlJEjgxlFieJTs/ALCCHkxDkevbEzD17X0VqD0w7+AIH3XnhdXeYPLouCMzFM\nGtKW50Z0N+fMQDkZ8HKBvukj3gWfqfqVx6Iyc2w89nneMuQTBrdlwR09jD9zR2QILLnuwutZO6Bx\nl3L7eGdut7KA65RSqZqmVQW2aZr2nVLqXzeXzW1Ss3KZu2En3+6OYXjv5rzxf72pZda7aFEuNoVF\n8tQXu2mUv/iILA4gLmV/TDLT/INJSMvio7H9Gd7bQjdHdjt8MuTCIJMuw2FMoL5lMrnY5Eym5y8r\nPX9Ed6ZeYdKZGI78CgF3Xnj92EGoI62mrpaYno3vyhB2HD9rnmXIFzi0/jwbC1XKt+tFibU8pZQC\nUvNfVs3/Uu4slDsdjUvFLyCEI3GpPH1rV3zNEBSTsGJrgM2ueD2/yWhQu4Z8PK6/OfpHldX5p3cj\nP4J+4/Uujan8su8MD60Jo7ZHFdb7DaF3q/ol/5BZRAbDkusvvJ6+FVp561ceCzgQk8JU/yDOpWfz\nmZlnYihYoel2O9y3Sr+yWNips+lMXr6DU2cz+GBMP+7o00LvIl2aYxcGgAVJuhTFqceamqZVBkKA\njsBHSqntbi2Vm/wUcYZH14VTpbJGwLRBDOvYSO8iWY2lWgOS0nN4cG0YfxyMY2J+E2NVMzYxXo7c\nrLxRs2lxea8ru35lQSveGMGFNetf/W4/PVvU47OJPjSrZ5HBaUpB4H1w6Ie81017gt+fUMni/x/c\n7I/8mRhqmnkmhhP/wPJbLryetBnaXaVfeSxsz+kkpvgHkZVjI2DaQAa199S7SJe24zP49vELr695\nBq55UrfiOFXhVUrZgL6aptUHNmma1lMptafgezRN8wV8Adq0aePygpaF3a5475dDfPDLIXq1rMcn\n4/vTqkFNvYtlOVZqDTgcm8KMlSFEnkvn1bt7MWagsTLtFnu+gA0F+tr5/uauEdWWujGCvMFpz27a\nw+chkQzv1Zy37ulj/MEjzoo/DB8WeIo77gvodIN+5bEIS8zE4NhM/dQp8JDuXu7w6/5YZgWG0qBm\nNQKnm2CNgIuyof+g1svquKqUStQ07VfgFmCPw98tBhYD+Pj4GKaSk5Sew5x1Yfx6II57vFvx4p0V\neMlXpfIHmWgwOtAtI2ZLag0w8o3Reb/sO8PDa8PxqFqJwBmDGeDVUO8iuVd2OrzWBuw5ea+7jshr\njnRTVx8r3RhB3jKe968KZcfxszx0XUfmWGnmjoIXrWp1YO6Rcu13Z8XWALtd8arZZ2JIPwtvOPQz\n1qmZuiJYu+Mk877cQ9dmdVg+eYCxpzU0UBcGR87M0tAYyMmv7NYAbgRed3vJXGB/TDJ+ASFEJWbw\n4sgejB/ctuL21z30E6wedeG15p6myJJaA4x6YwR5TdIf/3aEt348QI8WdVk8wYcW9U341OVyhKyA\nzQVWtXlgOzTp6vaPtUo3qYNnUpi2IogzyVm8P7ovI/u21LtIruF4vug7Hu78SI+SWKo1ICPbxpx1\nYfyw9wwTBrfl+dtNOBOD45O7Oz+FvmP0KYvFKaV496eDfLD1MFd3bsxH4wx+c/T9M/Cvw3nCIJVd\ncO4Jb3NgRf4FqhKwXim1xb3FKrvNO6OYu2EXdTyqsNZ3MN5tLf6UrjiZyXl34vbcvNdNuoPfH27v\ne3ep1gAjSs/OZe6GXWzZFc0dfVrw+v/1tk6TdFEcV7jpNz5vgFo5MXs3KYBfD8TyYGAYNapVZp3v\nYPq1McEyns5wrNA8EgH19KnIW6k1IDYlkxkrgtl1OonnRnRn6jAv8z2AcczG84luawm6HFZsCcjO\ntfPUxl1sDD3NfT6teemunsYeQ+KYjccOQB1jLZ3uzCwNuwDTLI2Sa7Pzxg8HWPzHUbzbNuCTcf2N\n/fjfnX59FX5/7cJr39+hRV+3fZxZWwNOJ2bguzKYiOhknrylKzOvtvjMHW93g5SoC68f3gkNvHQp\nihm7SSmlWPbXcV7+JoKuzeqyZJJFWgIc58gEQzydsUI3qfMzMZxNy2bReG9u6mGsikCJ/v4QDdOS\n+wAAIABJREFUfpxXeJsBslGApVoCkjNzeGBVKNsOx/PIDZ156HoDz+Ft4C4Mjgz8bPzyJaRm8eCa\nMP4+ksDEIW15dnh3qlUx8B2Ru8QdhI8GXHg9+AG45dXy+GTTtQbsOHaW+1eFkJ1rZ9mkAVzbtYne\nRXKf43+B/22Ft+lwYjLrjRFAjs3O/K/2smbHSW7u0ZR37+tLzWoWOI06Pp25b1Xe1FIGYOZuUnBh\nJoYa1fJmYujVymQzMThmY8KX0OFafcpSDCu1BEQnZTBleRCHY1N5654+jPJupXeRirflUQheWnib\nQSu7YKEK7+7IJPwCgolPy+bNUb25x6e13kUqf7ZceNFhmpK5x6Bm+XTnMFtrwOrtJ3j+q720aViT\nxRN96Niktt5Fch/Hi5bvb3quaW+6GyOAc2nZPLA6lH+OJvDANR14/KYu5h+clnAE/te/8DaDXrDM\n1k0KYM2Okzz75R46NanNsskDzNUSkJ0GrzjM8WrQbIBz4wKM3hqwPyaZycuCSM3KZfmUAVzZqbHe\nRSreRV2f9kI9A1fOsUiFd33wKZ79cg+Na1fni5lDzXcH7QobfWHXuguvRy2HnnfrVx4Dy86188Lm\nvazefpJrujTm/dH9qFejqt7Fco+fnoe/3iu8TeeLltlujAAOx6YybUUQ0YmZvHtfH+7qZ+wTu1Mc\nL1j128KcXfqUpRhmbQ2w2xWv5S9Yc02XxnxotpkYHLNRtSbMi9anLE5yZlyAkVsD/jocz8yAkP/m\nZDb0ap6O+TDwjVBBJvofeLHsXDsLt+xl1b8nGdrBkw/H9qdhLddPlG9oUWGw+JrC256Lh8oWrcCV\nUXxqFg+sDmXHsbP4Xd2euTd3Nf7646XleFIasw663FL0e0Wx/jgYx6zAUKpXqcQa30HmHwCbkwEv\nO/QhNcjgoyKYrjUgI9vGI+vC+X5vjDlnYnA8b8w7A1XNMw7GjC0BG0MjmbthFx0a12b5FAO3BGyY\nBns2FN5mksoumLjCeyY5kwdWhxJy4hx+V7XniZu7mOuk4gqOJ6ZRy6Dn/+lTFhPYG5WE78oQ4lMt\nNoWUo0+GwRmH87yJTkpGoZRi5T8nWLglgk5NarNkko/5F6xxPGeAobNhttaAuJQspq8MZldkIs8O\n78a0K9oZd7CRo+BlsOWRwtsMnI2CzNoSoJTio18P89aPBxnawZNPxnsbt7XR8dwx8y9o1lOfspSS\nKSu8wcfPcv/qUFIzc/lwbD9G9Db4WtKu9mbHC0u/nmeSE5NetuyK4vHPd9KgZjU+nzmE3q3ql/xD\nZmTgwUdmkmPL6/ay6t+T3NCtCe+N7meuJumiOGbjmSioVkufsljQwTMpTFlu0pkYHLMx7GG4caE+\nZSkd07UE5NrsPJc/APbOvi14Y1Qf4w6yN2kXBkemOoMrpQj49wQLN0fQqkENVk0bRJdmBl9ez5VS\n4+CtjoW3TfsJWg/UpzwmYLcr3v7pAB/9eiRvmrrx/WlSxzzNc04z2ZM7I0tKz2FWYN6UQH5XtWfu\nLSbv9iLZcLs/D8XxwKpQPMw2E4PdBgsduuiYMBtmawlIy8rlwTVhbN0fywPXdOCJm7sYsyXg64cg\ndEXhbSbMx3mmqfBm5tiYt2kPX4RGcn3XJrxzX1/jPvp3B7loXbaUzBweWRfOz/tiGT2gNS+M7EH1\nKhZbTMJuh4UOCx7M2AotvfUpj8kdi09jmn8Qp86l88ao3txr9tleLmqG3AbNeulTFosqOBPD0skD\naGnU/peO5Jqii9iUTKb5B7M3KolX7urF2EHGmy0CKGJmH/fO418eTFHhjTyXzsxVIew5nczD13fi\n4es7mX86IGf99hr85jCH7rOx5bqevRkdi09jxspgjsWnsXBkDyZYcVlpuWC51N+H47l/dSiVK2ms\nnj6Yge1MPDgt4G448kvhbZINl7LbFa//sJ9Fv5twJgYTTillBYdjU5m8fAcJqdksmeTDdV2b6l2k\nolmkC4Mjw//v/OtwPLMDQ8m1K5ZO8uH6bgYNiDs4hq5RF5i9Q5+ymMgfB+OYHZhXcVk1bRBDOniW\n/ENmUtT8mA+GgmcHfcpjAau3n2D+V3vp0LgWSycNoHVDEw9Oczxv3PQSDH1Qn7JYVEa2jUfXh/Pd\nnhjGD27Dgtt7mGPQ9LE/YcWIwtssUpkxuh3HzjJjZTBVK2us9R1Mn9YGHEeyeQ6ELC+8zUL5MGyF\nVynF4j+O8vr3++nYpDaLJvjQrlEFGWAhT+5KRSnFkj+P8ep3++jctA6fTfQxd8WlKJINl8q12Xnp\nm334/32ca7s05oMx/ajjYdKuUiH+sPnhwtskGy5n2pkYHM8dbYfBlG/1KUsF882uaB5ZH06r+jXw\nnzKQNp4GvC455mPyN+B1hT5lcRNDVnjTsnKZu2EX3+yOZniv5rwxqje1zNJUVBYxe+DTYYW3TdoC\n7a7Upzwmkplj45mNu9kYdppbezbjrXv6WCszyVHwTrfC2544CrUs9vS6HCVn5jA7MIw/DsYx7Yp2\nPHNbN/MOTnO8WLX0zuvLLVyq4EwMn4735mazzMRg0SZqozv/EOblb/fh07YBn030oYER1wqoIPkw\nXI3gWHwafgHBHI5N5albu+J3VXtz3D2XlTy5K7WYpEz8AoLZGZnEozd25sHrOlorM5INlzuRkMZU\n/yBOJKTz2t29GD3QoANHShK9ExZdVXibZMMtth2K5/5VIXhUq8w6v8HmmNrwf96QcLjwNslHubDZ\nFS9uicD/7+MM79Wct+/tg0dVgw2a3nQ/7AwsvM3C+TBUhfeXfWeYszacKpU1Vk4dxBWdGuldJPcr\napGA+WehksH+YxhU6Mlz+AWEkJ6Vy6IJJnri4oyiVtGTAYtl9u/RBGauCgEgwMx9vOVGqNyszZ+J\noaOZZmJwzMeU76HtEH3KUsFk5th4aE0YP0acYdoV7Zh3WzfjDbSvgCtxGqLCa7cr3v/lEO//coie\nLevy6Xhv869oVBKl4AWHJwTV68LTp/QpjwmtDz7Fs5v20Kyeh/XmZJbKjFusCzrJvE17aOtZk6WT\nBuBlxnEBWanwqsMqgcZdGtjU7HbFGz8c4NPfj3B158Z8ONYEfbzjD8GHPoW3ybmj3JxNy2b6iiDC\nTiUyf0R3pl7RTu8iXayCdGFwpHuFNykjb67Urftjubt/S165q5fxHvu7mlRmyiTXZuflb/ex/K/j\nDOvoyUdj+1O/pgH7RZXG7g3wxbTC26QyU2Y2u+LVb/exZNsxruzUiA/H9jfnPN5y7ig3mTl5MzF8\nuzuGcYPa8MIdJpiJQfKhqxMJaUxatoPopEw+GdefW3o217tIhf2yEP58u/C2CpQPXSu8B2JS8AsI\nJvJchnXnSi3owPew5r7C26Z8B22H6lMeEzqXls3sNaH8dTiBKcO8mHdbN+NfhJwlFyu3SMnM4eG1\neTfVk4d68exwk2bGMR9zj0FNE88VbGBxKVnMWBnMTjPNxOCYj/nnoJIJc25SYSfPMW1FMEopAmcM\nwrutwf5vOuZj7OfQ+SZ9yqIT3Sq83+yK5okNO6lVvQprfQfj42WwcLiaVGbK7EBMCjNWBhOTlMmb\no3pzj9lXwTrvu6dg+yeFt0k2XOLU2XSmrwjmcFwqL93Zk/GD2+pdpMsn545ydehMClP8g4hPzeKT\ncd7c0tPg4wIstvyrGf0UcYYH14TSpI4H/lMG0L5xbb2LVFgF7cLgqNwrvLk2O2/+cIBFfxzFu20D\nPh7Xn6Z1Pcq7GOWnqIuVNFFfth/2xvDounBqVq/CWr/B9G/ToOQfMgOpzLhN0PGz+AWEkGuzEzB1\nIEM7mnAQrGM+JnwJHa7VpywVwLZD8dy/OgSPqpVZ5zvEmIsDFOSYj9s/AO9J+pSlggr45zjPf72X\nXi3rsXTyABrVNtCg4j/egq0vFt5Wga8vJVZ4NU1rDawEmgIKWKyUer80H3Y2LZsH85ujxw9uw/wR\nPahWxaJNLrYceNHhAtu4G8z6V5/ymJTdrvjf1sO8+/NB+rSuz6Lx3jSrZ4EbpMDRcPC7wtsq8InI\n1TaERPL0xl20alCTpZN8jPfEpSQBd8ERh3l0JR9udX5AY4fGtVk62cfYA6ezUuBVh6WAJR/lquDS\n0jd0a8IHY/pRs5ruw6IucLwZujcAut+hT1kMwpl/nVzgMaVUqKZpdYAQTdN+UkpFXM4H7Y5MYuaq\nEOJSs3hjVG/utUpzdFHkqZ1LpGXl8vjnO/luTwx392vJK3dbZECjYz5qesLco/qUxWJsdsUb+Reh\nYR09+XisN/VqmmxwmmM+rpoL183TpywVgN2uePPHA3zy2xGu7NSIj8f1N/ZMDHJ90V1Wro3HP9/F\n5p1RxlxaWrowFKnECq9SKhqIzv8+RdO0fUBLwOkK74aQSJ7ZtJtGtaqxYeYQc0zYXRqhAfD17MLb\n/P6A5n30KU85cmVLAOT1vZyxMpiDZ1LMM2ikJK97Qca5wtvkROQyaVm5PLw2nJ/3nWHcoDYsuKMH\nVY10ESpJ8HLYMqfwNsmHW2Xm2Hhs/U6+2R3N2EFtWGj0mRgcKzJPn4bqJmu9MLmk9Bx8A4LZfuys\n8RbHCl4GWx4pvE3OIf+5rOfvmqZ5Af2A7UX8nS/gC9CmzYVVi1Iyc3j9+/34tG3A/8b0w9NI/Vtc\nSe66XdISAPD3kXhmrQ7FZlf4TxnIVZ0bu7605c0xH/0mwMgP9SmLBUWeyxucdvBMCgtu786koV7G\nuQg5wzEf9VrDI3uKfq9wifjUvJkYwk8l8sxtXZlxpYEqLo6kImMIkefSmbI8iOMJabw/ui8j+7Ys\n+YfKi+M55O4l0PsefcpiUE5XeDVNqw18AcxRSiU7/r1SajGwGMDHx0ed317Hoyqf+w2hVYMa5X7n\nnJOTQ2RkJJmZme77kMSTeX/evP7Ctvr5Ff59+9z3uS7m4eFBq1atqFq1dE15rmgJUEqx4u/jvPjN\nPto3qsVnE33KZWEAt+bEIvkoqKxZcbWQE+fwCwgmK8fO8ikDudqNN0guz4otB1KiLZWP84yWk4Lc\nPRODS3OSeBJoeSEj1WtDjYamz0dBRs7KeXtOJzHVP4iMHBsrp7puhUaXZCXxpCXPIY7KmhOnKrya\nplUlr7K7Wim18XI/RK/VjCIjI6lTpw5eXm542mO3QcwuqNfkwjaP+tDQgKuqlEApRUJCApGRkbRr\nV/byX6ol4FL8/z7OC5sjuKFbE969r2+59aNzW06iwgrno25LqN2k+PebgCuy4sruL1+GnWbuhl00\nr+/BWl8fOjZx72p7Ls1KVFjenw0LZKJFv7Lt0yBcfU5xpb8OxzNzVQjVq7hvJgaX5EQpiA4vfA6x\nSD4KMnJWzvvtQCyzVodSr0ZVvrh/KJ2buu48U6asZJyDc8ctnxFwTU6cmaVBA5YC+5RS75TqU3SS\nmZnpnsru+QtVQSYOmaZpeHp6EhcX54p9XbIloLiuLwB39WtJrk0x7Yp25bruuMtzYrF8FOSirJS5\n+4vdrnj7pwN89OsRBrdvyCfjvGlQy/2r7bkkK8oO0TsLb2ve11JTFbrynOJK64NO8cym3bRvXItl\nkwe4bSaGMuckZg/Ycwpvs8g5xJFRs3LeuqCTPLNpD12a1mH5lAEun0a11FlxvM40aAc1LDo+Ctfk\nxJknvMOACcBuTdPC87c9o5T6ttSfWo5cWtlNjobUmMLbmvaAyuZf1tYVvydnWgKK6/oCUL9mNWZc\n1b7M5SgNl+Tk/BOZghp2AI+6Zd+3gZT1d1XW7i9ZuTYeXhPO93tjGDOwNS/c0bNcpzcs0/Fb+GbI\nkYvOKS5pDbDbFW/9eICP82di+Ghcf+q6uQWp1MfvmBGLXGMuxYh9p5VSvPvzIT745RBXdmrEJ+O9\nqV3dPdOOlbmya9FziKOy5sSZWRq2AcZLY3mrQBeq0jBzS4BLSD5KpTQDYatVrkRtjyo8N6I7U4eZ\naHDaRRWZXlDZQPN2GlOZWwMyc2w89vlOvtkVzZiBbVg40qCzd2SnQ/yBwtvkHKKLHJudpzfuZkNI\nJPd4t+KVu3sZIzMZSXDOYQpLyYjTDPAvaHBRYUXfTbkxZH/++Sc9evSgb9++ZGRkEB0dzYgRIy75\nM1u2bGH+/PluK5MTzrcEXKdpWnj+1216Fqhc2G0X56NJ93I5CZk0J/9xZiCsUspHKeXTuHHjgj/H\nm6N6m2equpjdRZ9DyrGya9asKKWilVKh+d+nAOdbA5wSn5rFmM/+5dvd0TxzW1deuaunMSoujqLC\nCld2PerpUpExa040TWutadqvmqZFaJq2V9O0h0u7r5TMHKb6B7EhJJI5N3TijVG9jZGZqLDCld36\nbXWt7JoxKwb4VzQoW+7FF6m6LcslYKtXr+bpp58mPDycGjVq8M477zBjxoxL/szw4cPZvHkz6enp\nbi9fUZRS25RSmlKqt1Kqb/6XKbq9lFpUWN7AxYJa9IMq5TP1nhlzcl5ZB8KaoqILeRmx51543aiz\nLhcpM2flvOJaAzRN89U0LVjTtGDH/n0hJ86xPzqFT8b1x/eqDsbMTVE3Qw316dpl4pycbwnoDgwG\nZmma1v1ydxKTlMk9n/7DP0cSeGNUb+bc0NkYmSkqIzUb6lOWfGbMilR4ixIVBmd2F97Wot9lj7Cf\nP38+77333n+v582bx/vvX7r72ZIlS1i/fj3PPfcc48aNA+CLL77glltuAeDdd99l6tSpAOzevZue\nPXuSnp6Opmlcc801bNmy5bLKKErBln3xCahZr1JXZCpaTipE95ekyKIvUtXKNmNNRcvKeZdqDSiu\nJQDg5h7N+GPutdzSs3k5ltZJZ4+5rS9mRctJWVsCAA7EpHDXx39x6mw6SycPMMZqsBmJbu+vW5Gy\nUmE6kL2weS8RURe1mhZmy877KqhaLfK6MP9z0du7t6jL87f3KHZ3U6dO5e6772bOnDnY7XbWrl3L\n1q1b6du3b5HvDwwMZPr06Wzbto0RI0YwatQojh07RoMGDahePe+p4cMPP8w111zDpk2bePnll1m0\naBE1a+aNNPbx8eHPP//k3nvvvfRximKVmJPs1Iu3VasNBBX7I5KTi5h6IOx5xWbFMSOVq0PlqhR1\nDnEkWblYWVsDGtfRd7GjInPimJGqNUCrjDMZAcnJpZRmXIDdrpgdmLfY0Tq/IfRsWcRCUuWgUFYc\nM1KlOlRy7jxSkGTlggpT4S1RsRWZ0vPy8sLT05OwsDDOnDlDv379aNu2LeHh4SX/cL7o6GgKPrWo\nVKkS/v7+9O7dGz8/P4YNG/bf3zVp0oSoqKgylVkUQ9khx6EZpoz5OK+i5cSyA2HtuZDrMHm8izJy\nXkXLiuVaA5SCnLTC21ycEah4OTmvtAtkVaqk8b+x/ajjUZWW9WuUW3mL5VgfcUNGzqtIWakwFd5i\n73D+ay4oMLeeC5sMpk+fjr+/PzExMUydOpWUlBSuvPLKIt8bGBhI9+6Fux3VqFHjohVYDh06RO3a\ntS8KTWZmJjVqGOA/q4kVmZP/MlJgzk4XNytJTsynUFb+y0j+halarbz+um5QwbJi+taA/3LyX0YK\n/D7d2J+7guWkzC0BXZvpP33k8ze2grNHcVd9pDgVJSsVpsJ7kdxMiHVYdq+BF9Ro4NKPueuuu5g/\nfz45OTkEBgZSuXLly7pz6ty5M8ePH//vdVJSEg899BB//PEHs2fPZsOGDYwaNQqAgwcP0rNnT5eW\nv0I7v4pNQW5aIEByYlJFnUfcfIGqSFmxTGuAYz/MclhopCLlxBItAY4ZqdMC6jQtl4+uKFmpmIPW\nosKKvki5uLILUK1aNa699lruvfdeKleufNk/X6tWLTp06MDhw4cBeOSRR5g1axadO3dm6dKlPPXU\nU8TGxgLw66+/Mnz4cJeWv8KKCru4stuin9suUpITEyruPOJmkhUTSTpd9KCjchj5X8FyYu5pMYvK\nSDlVdqECZUUp5fIvb29vZQQRERGFN8QdVOp0aOEvu82tZbDZbKpPnz7q4MGDpd7Hxo0b1bx58y75\nnpiYGHXdddeV+jOUKuL3pZQCgpUbMqKMmpPk6IszUg7MlBOlKnhW7LYiziP2ciuDmbJSoXPimJHU\nuHItg5lyolQFzcqRX1XE39+X+/XGkZmyUpacVJwnvFFhF3cEb9EPNPf9CiIiIujYsSPXX389nTp1\nKvV+7rrrLry8vC75npMnT/L222+X+jMEeRlJiS68rRye2ElOTCTxJETvLLytnJ7YgWTFFLLT8nJS\nUIt+UKtRuRVBcmICC+rBypEXXtdupssc3RUpK9bvw6vjkq/du3fn6NGjJb/RCdOnT7/k3w8YMMAl\nn1MhpcXruja55MQkFtSDm9dfeN20Z/50Y+VHsmJwC/KnsyqYEx0qMZITg1vgMO2ZjiumVaSsWLfC\ne2oHJJ6DegUWi2jYATz0H4kpDOS/Soz7R9cLk3q3JySdKrxN1q8XjhwrMc16Q6XL7w8pLCxmD3w6\nrPC2+m2Kfq9wOWtWeA1yly0MbMmNELmj8DbJiHDkWImp1URyIgo7shUC7iq8rX4bqeyKwhzPJdc/\nD1c+Cvv2Ff1+4XLWqvAuvQlOOSyuUg7TvwiTcTzxVK8jlRhR2Lb34OfnC29bkCQXJ1GYVGKEMxxz\nsiBJn3JUcNao8CoFL9QvvK1Fv7y7bKnsivP+5w0Jhwtvk0qMcOR4cRr+DgyYpk9ZhDEVdc2RSoxw\nFHcAPhpYeJvkRDfmn6VhQb2iTzy+v+lRGpfw8vIiPj7esPszpQX1Cld2x643/YlHcuJiJ/4u+kmM\nBSq7khUX8h9h2cqu5MSFFtQrXNm95TXL5ATMmRXzPuE98Q8sv6Xwttkh0KijPuUxAH9/f44fP86C\nBQv0LopxOFZgwFInndKQnBTBMSfdbof7VulTFgORrDhwzMmj+6Fuc33KYiCSEwfShaFYembFnE94\nF9S7uLK7IMlwld358+fz3nvv/fd63rx5vP/++07/fEZGBrfeeiufffYZQUFB9O7dm8zMTNLS0ujR\nowd79uxxel9vvPEGvXr1YuDAgf+thmJpdvvFJx3f3w154pGc6CgjseiLk0Eru5IVnSRHFZ0Tg1Z2\nJSc6STljuspuRcqKuZ7w/vgc/P1B4W3Ohum7pyBmt2vL06wX3PpasX89depU7r77bubMmYPdbmft\n2rVs3bqVvn37Fvn+wMBAunfvDkBqaiqjR49m4sSJTJw4EYA77riDZ599loyMDMaPH39Z61HXq1eP\n3bt3s3LlSubMmcOWLVsu40BN5oP+cPZI4W2SE6dUqJw4XpjaXQWTNjv/85KVipGVF5uALevC68vt\n0y05qRg5cZz5Z3QgdL3MJXQlK27NSokVXk3TlgEjgFillPMldyVbLrzoWXjbHR9C/wm6FMdZXl5e\neHp6EhYWxpkzZ+jXrx9t27YlPDy8xJ8dOXIkc+fOZdy4cf9tmz9/PgMGDMDDw4MPPsir+CckJHD9\n9dcDcPbsWbKzs/nyyy8BCAgIoFevXgCMGTPmvz8feeQRlx6nYeRmwUtNCm+bewxqNtSnPE6SnJQz\nuw0WOmRi/jmoZPwGL8lKOTPZ07rzJCflzKQ5gYqVFWee8PoDHwIrXf7pznBVH8xL3OG40/Tp0/H3\n9ycmJoapU6eSkpLClVdeWeR7C945DRs2jO+//56xY8ei5c80kZCQQGpqKjk5OWRmZlKrVi08PT3/\nC+al+sZoBWarKPi9ZUhOJCfOCLgbjvxy4XXVWjAvqnT7kqwU+b0lhK+BL2cW3lbaSozkpMjvLSE1\nDt5y6EpZlsquZKXI712lxEcaSqk/gLMu/+SSnA65uBLz5AlT3TlB3vrS33//PUFBQdx8883UqVOH\n8PDwIr/Ohwhg4cKFNGjQgFmzZv23zc/PjxdffJFx48bx5JNPXlY51q1b99+fQ4YMcc3BGUFm0sU5\neSZaciI5udiCeoUru0+dKn1lV0eSFTdbUK9wZffhnaY7n4DkxO3Wjitc2b1nhSlzAhUnKy7rw6tp\nmi/gC9CmTRmXynOswLQdBlO+Lds+dVKtWjWuvfZa6tevT+XKl7fyzvvvv8/UqVOZO3cuPXv2pGrV\nqowdOxabzcbQoUPZunUr1113nVP7OnfuHL1796Z69eqsWbOmNIdSonLv/vJmR0iLu/C6VmN4wpyD\nIipSTsrdznWwybfwNpNemECy4jbZ6fCKwyA0yYnkpCgm7sJQlAqTFaVUiV+AF7DHmfcqpfD29lal\n8vubSj1ft/BXGURERJTp513BZrOpPn36qIMHD+pdlBIV9fsCgpWT/+7AVUB/Z7NS6pykxl2cE1tu\n6falJCelUdasXO5XqbPimJP4w6XbTz7JyuUxTU42P1I4Jz8tKN1+8klOLp8pspKR6NI6ilKSlctV\nlpwYY5RGblbeHdPWFy9sm77V9HdNERERdOzYkeuvv55OnTrpXRy3U+XR/WVBPXizw4XXg+7Py4mJ\n162vaDkpF6d2FP0UxrND0e83CcmKGyyoB8FLL7yefw5ueL7495uA5MQNfnoeXivQej3hS9PXUaBi\nZUX/ack+vRJidhXYoMGCRN2K40rdu3fn6NGjehfDUErd9SUrFV5tWXjb84mWWDq6ouXE7V1fXm0N\nWckXXk//BVr5uPxj9FDRsuJWGefgda8Lrz3qw1MndCuOK0lOXMzx5tki1x6oWFkp8QmvpmlrgH+A\nLpqmRWqa5pp1NrPT80JUsLL7bKzLK7t5T7tFScrr96SUWqyU8lFK+TRu3Ni5HwpbXbiye8OCvDtr\nF55wJCfOc8Hvyh+4paQ3XTZbbt45pWBld0GSyyu7khXnGPr3FPF14crurCCXV3YNffwGY9jflVLw\nosN1ysXXnryPMejxG0xZf08lPuFVSo0p0ycUp+Bo6SnfQduhLv8IDw8PEhIS8PT0tN50KC6klCIh\nIQEPDw+9i1K0Lfnz8blpuVfJifNckRWl1B+apnm5rFDnndp+4fuHd0GDti7/CMmKcwwoOR3OAAAg\nAElEQVR/Tvnlhbw/B93vlqmgJCfOM3RW4vaDLTvv+8cOQp2mLv8IyYpzXJET/bo0dL4FHgqDhu3d\n9hGtWrUiMjKSuLi4kt9cwXl4eNCqVSu9i1G0R/ZA5WpQo75bdi85uTzllZXL7v7SZgg8GOrWfrqS\nFee5Iidu6/4y9ce8hUZqNHDZLguSnFwew15/mnTLq6c0aOe2LgySFeeVNSf6VXgrV3VrZRegatWq\ntGvXzq2fIS7I7/5yDdBI07RI4Hml1NJL/5QTajcp+T1lIDkxJqXUYmAxgI+PT8ltWZUquX1QmmSl\n3PnjjoWPanmW/J4ykJxYiNRTLEP/QWvCMtzW/UUIUSG5rfuLEKLCMca0ZEIIIUQpaJrmq2lasKZp\nwdIsLIQojlR4hRDlym0zv4gKqVQzvwghKhzNHdNhaJoWBzjO8dIIiHf5h+nHascDRR9TW6WUW64i\nFSQnYL1jKu54JCtlY7XjARecU/K7NGxxZtBaBckJWO+Y5JziHlY7HijDOcUtfXiL+mBN04KVUtaY\n/R3rHQ+U/zFVhJyA9Y5Jj+OpCFmx2vGAnFPcxWrHJOcU97Da8UDZjkm6NAghhDAk6f4ihHAVmaVB\nCCGEIcnML0IIVynPJ7yLy/GzyoPVjgeMcUxGKIOrWe2YjHI8RimHq1jteMAYx2SEMria1Y7JKMdj\nlHK4itWOB8pwTG4ZtCaEEEIIIYRRSB9eIYQQQghhaVLhFUIIIYQQlubyCq+mabdomnZA07TDmqY9\nVcTfV9c0bV3+3283+rKRThzPZE3T4jRNC8//mq5HOZ2ladoyTdNiNU3bU8zfa5qmfZB/vLs0Tevv\npnJYKicgWZGsOEdyIjlxlmRFsuIMyYmTOVFKuewLqAwcAdoD1YCdQHeH9zwAfJr//WhgnSvLoMPx\nTAY+1Lusl3FMVwH9gT3F/P1twHeABgwGtuv0ezVNTiQrkhXJieREsiJZkZwYOyeufsI7EDislDqq\nlMoG1gIjHd4zEliR//0G4HpN0zQXl8NVnDkeU1FK/QGcvcRbRgIrVZ5/gfqapjV3cTGslhOQrEhW\nnCM5kZw4S7IiWXGG5MTJnLi6wtsSOFXgdWT+tiLfo5TKBZIATxeXw1WcOR6A/8t/rL5B07TW5VM0\nt3H2mN39GWbKCUhWQLLiDMmJ5MRZkhXJijMkJ07mRAatld1mwEsp1Rv4iQt3hUI4kqwIZ0hOhLMk\nK8IZkhNcX+E9DRS8c2iVv63I92iaVgWoByS4uByuUuLxKKUSlFJZ+S+XAN7lVDZ3cebfsDw+w0w5\nAckKSFacITmRnDhLsiJZcYbkxMmcuLrCGwR00jStnaZp1cjr7P21w3u+Biblfz8K2KryeyEbUInH\n49Bv5A5gXzmWzx2+Bibmj4IcDCQppaJd/BlWywlIViQrzpGcSE6cJVmRrDhDcuJsTtwwuu424CB5\nowbn5W9bCNyR/70H8DlwGNgBtHd1Gcr5eF4F9pI3MvJXoKveZS7heNYA0UAOef1epgEzgZn5f68B\nH+Uf727AR6ffq6lyIlmRrEhOJCeSFcmK5MS4OZGlhYUQQgghhKXJoDUhhBBCCGFpUuEVQgghhBCW\nJhVeIYQQQghhaVLhFUIIIYQQliYVXiGEEEIIYWlS4RVCCCGEEJYmFV4hhBBCCGFpUuEVQgghhBCW\nJhVeIYQQQghhaVXcsdNGjRopLy8vd+xalLOQkJB4pVRjd+xbcmItkhXhDMmJcJZkRTjD2Zy4pcLr\n5eVFcHCwO3YtypmmaSfctW/JibVIVoQzJCfCWZIV4Qxnc+JUhVfTtONACmADcpVSPqUvmrAyyYpw\nhuREOEuyIpwhOREluZwnvNcqpeLdVhJhJZIV4QzJiXCWZEU4Q3IiiiWD1gQAUYkZ5NrsehdDGJxS\nilNn0/UuhjCBM8mZZOXa9C6GMAE5pwhnxKdmkZ6dW+qfd7bCq4AfNU0L0TTNt9SfJgxp56lERvxv\nG2/8cMAVu5OsWJTdrnhhcwS3vv8nJxLSyro7yYmFHY1L5a6P/uLZTXtcsTvJioV9uPUQ17/zO3tO\nJ5V1V5ITC4tNzuS+Rf/w0JqwUu/D2S4NVyilTmua1gT4SdO0/UqpPwq+IT9gvgBt2rQpdYFE+fr9\nYBz3rwqhYa1qjB7Q2hW7vGRWJCfmlGOz88TnO/kyPIppV7SjdYOaZd2lnFMsal90MhOWbkcpmDTU\nyxW7lHOKBSmlePOHA3z82xHu7teSrs3qlHWXck6xqJikTMZ+9i8xyZm8clevUu/HqSe8SqnT+X/G\nApuAgUW8Z7FSykcp5dO4sVtmEREutjE0kmn+QbT1rMXG+4fSvnHtMu+zpKxITswnI9uG78pgvgyP\n4ombu/Ds8G5UqqSVaZ9yTrGmkBPnuG/RP1StXIl1fkPo2bJemfcp5xTrUUqxcEsEH/92hDEDW/PW\nPX2oUrlsPSzlnGJNpxMzuHfRP8SmZBEwbSCD2nuWel8lJkzTtFqaptU5/z1wE+CSdiqhD6UUi34/\nwqPrdzKwXUPW+w2mSV2PMu9XsmI9Sek5jF+6nd8OxvHKXb2YdW1HNK1slV3JiTX9dTieCUu306BW\nNdb7DaFjk7LfQEtWrMduV8z7cg/L/zrO5KFevHJXrzLfQEtOrOnU2XTuW/QP59KzCZg2EO+2Dcu0\nP2e6NDQFNuVf5KoAgUqp78v0qUI3drvipW/2seyvYwzv3Zx37u1D9SqVXbV7yYqFnEnOZNKyHRyN\nS+Ojsf25rVdzV+1acmIxP+6NYfaaMNo3qsXKaQNpUqfsN9D5JCsWkmuzM3fDLjaGneaBazrwxM1d\nynwDnU9yYjEnEtIYs/hf0rJtBE4fTK9WZW8tKrHCq5Q6CvQp8ycJ3WXl2nj8811s3hnF5KFezB/R\nvcx31gVJVqzjeHwaE5ZtJyE1m2WTB3BFp0Yu27fkxFq+DDvNY5/vpGfLeqyYMoD6Nau5bN+SFevI\nzrXzyLpwvtkdzWM3dubB6zu5bN+SE2s5GpfK2M+2k5VrI3DGIHq0KHtlF9y00pownpTMHO5fFcq2\nw/E8eUtXZl7d3lV31sJiIqKSmbhsBza7nTUzBtOndX29iyQMKuDfE8z/ag9D2nuyeKIPtavLJUVc\nLDPHxqzVofyyP5Znh3dj+pXt9S6SMKjDsSmM+Ww7drtije9gujar67J9y9mpAohNyWTK8iD2x6Tw\n1j19GOXdSu8iCYPafjSB6SuCqe1RhbW+Q+jYpMwjp4VFffLbEV7/fj83dGvCh2P741HVZV2jhIWk\nZ+fiuzKEbYfjefHOnkwY3FbvIgmDOhCTwrgl/wIaa30H06mpa68/UuG1uGPxaUxctp34lGyWTPLh\n2i5N9C6SMKhf9p3hgdWhtGxQg4Bpg2hZv4beRRIGpJTijR8O8MlvRxjZtwVv3dOHqmUcYS+sKSUz\nh2n+wQSfOCsPW8QlRUQlM37pdqpW1gicMZgOLpg1ypFUeC1s56lEpvoHoYA1voPpK03TohgbQyN5\nYsMueraoy/IpA2lYy3X9MIV12O2K+V/vYdW/Jxk7qA0vjezp0nEAwjqS0nOYuHwHe08n8f7oftze\np4XeRRIGtTsyifFLt1OzWmXWzBiMV6NabvkcqfBaVMEFJVZOHeiSOXaFNS358ygvfbOPYR09WTRB\n+mGKouXY7Dz++U6+Co/C7+r2PHVLVxkHIIqUkJrFhKU7OBybysfj+nNTj2Z6F0kYVPipRCYs3U5d\nj6qsmTGYNp5lXtSoWHJls6CNoZHM3bCLTk3rsGLKAJfMsSusRynFWz8e4KNfj3Brz2a8N7qvK6eo\nExaSmWPjwTVh/BRxhidu7sKsazvqXSRhULHJmYxdsp3Ic+ksmeTDVZ1lgQdRtJAT55i8bAcNalUj\ncMYgWpV9Bc9LkgqvhSilWPzHUV79bj9DO3iyaII3dTyq6l0sYUA2u+K5r/YQuP0kYwa25qU7e1FZ\nmqZFEdKycvENCOavwwksHNmDiUO89C6SMKjTiRmM++xf4lKy8J8ykMFlWBVLWNuOY2eZsnwHTep6\nEDhjEM3ruX/MiFR4LcJuV7z87T6WbjvGiN7Nedu1C0oIC8nKtfHIunC+3R3j6snfhcUkpmczeXkQ\nu08n8fY9ffg/GXQkinE8Po1xS7aTnJlDwPRB9G/TQO8iCYP6+0g80/yDaV7fgzUzBtO0nFqhpcJr\nAQUXlJgyzIvnhrt2QQlhHalZufjlP62T+TDFpcSlZDFh6XaOxqXx8bj+3Cz9MEUxDsemMPaz7eTY\n8ubu7tnSNQsFCOvZdiie6SuDaN2gJoEzBtO4TvVy+2yp8JpcSmYOM1eF8NfhBFlQQlzS2bRspizf\nwZ6oZHlaJy7pdGIG45dsJyYp0+Ur7QlriYhKZsLS7Wiaxjq/IXR28dypwjp+OxCLb0AI7RvVYvX0\nQXjWLr/KLkiF19QKLighFRhxKVGJGUxYup3IcxksGu/NDd2b6l0kYVBH4lKZsGQ7KVm5rJo+EO+2\nDfUukjCo8FOJTFy6nVrVq7B6+iCZDUgU65d9Z7h/VSidmtZm1bRBNNBh6kup8JqULCghnHU4NoUJ\nS3eQmpnLyqkDGSQDSUQx9kYlMWnZDgDW+g522Rr2wnqCjp9lyvIgGtSqSuD0wbRu6N4R9sK8ftgb\nw+zAULo3r8vKqYOoV1OfwfRS4TWhXZGJTFkuC0qIku08lcjk5TuoXEljrZ9UYETxQk6cZfLyIOpU\nr8IqeVonLmHboXhmrMwbdLR6evmMsBfm9O3uaB5aE0avVvVYMXUgdXWcOUoqvCZTcEGJFVMHumX5\nPWENfx2Ox3dlMA1qVWPVtEFuW71GmN/5Ckyzeh6smi7LSovibd1/hpmrQmnfqBYB0waV66AjYS5f\n74zikXXh9Gtdn+VTBug+TapUeE1kU1gkT3y+i85N6+AvC0qIS/hudzQPrw2nXaNarJw2sNymfRHm\n8/2eGB5aE0b7xnlZaVJHsiKK9t3uaB5cE0b3FnVZMWWgLv0whTlsDI3k8c93MsCrIcsmD6CWAVbw\n1L8EokQFF5QY0t6TxRNlQQlRvDU7TjJv0276tWnAskkDdOsvJYzvi5BI5n6xi96t6uE/eaBkRRRr\nU1gkj63fSf82DVg2ZYCuTdPC2NYHneLJjbsY2sGTJRMHUKOaMdYEkAqvwdntipe+2ceyv44xvHdz\n3pEFJUQxlFJ8/NsR3vzhANd0acwn47wNc6IRxrPyn+PM/2ovQzt48tlEH0M8gRHGFLj9JPO+3M2Q\n9p4smeRDzWqSFVG0wO0neWbTbq7s1IjPJvrgUdU41yBJrYFl5dp44vNdfL0zislDvZg/QhaUEEWz\n2xWvfLuPJduOcWffFrx5Tx+qVq6kd7GEARW8MbqhW1M+HNvPUBclYSzL/zrGC5sjuLZLYz4Z7y1Z\nEcU6fxNt1KxIhdegZEEJ4awcm50nv9jFxtDTcmMkLkkpxWvf72fR70flxkiU6KNfD/PmDwe4pUcz\nPhjTj2pVJCuiaEu3HePFLRHc2D3vJtqILdFS4TWgggtKvHVPH0bJghKiGJk5NmYHhvLzvlgeu7Ez\ns6/rKDdGokg2u+K5r/YQuP0k4we3YeEdPeXGSBRJKcXbPx7kw18Pc2ffFrx1Tx+qyI2RKMai34/w\n6nf7ubVnM94fbdwbI6nwGsyx+DQmLdtBXEqWLCghLikpI4cZK4MJOn6WF+/syYTBbfUukjCoHJud\nx9bv5OudUcy8ugNP3tJFboxEkZRSvPxNXveo+3xa88rdvagsN0aiGOdbAUb0bs679/U1dIuRVHgN\nZOepRKb4BwGyoIS4tNiUTCYtC+JwbAofjO7H7X1a6F0kYVB5rQBh/LzvDHNv6cID13TUu0jCoOz5\nrQCrt5+U7lHikpRSvP/LId77+ZBpWgGkwmsQBReUWDl1oKxyJIp1MiGdCcu2E5ucxZJJA7i6c2O9\niyQMKjUrlxkrgvn3WIK0AohLstkVczfs4ovQSGkFEJdUsMvLKO9WvP5/vU3RCuB0hVfTtMpAMHBa\nKTXCfUWqeM4vKNGpaR1WyIIS4hL2RSczcdkOsnPtrJ4xiP5tGuhdJGFQienZTF4exO7TSbxzbx/u\n6idjAUTRcmx2HlkXzpZd0TxyQ2ceul7GAoiiFRz4OnpAa165q5dpWgEu5wnvw8A+oK6bylLhFFxQ\nYmgHTxZNMP+CEnJj5D7Bx88y1T+IGtUq8/nMIXRuWkfvIpWJZMV9YpMzmbB0B8fi0/hkXH9u6tFM\n7yIJg8rKtTFrdV6Xl6dv7Yrf1R30LpIwKKXy1gVYuu2YKQe+OtXhQtO0VsBwYIl7i1NxnF9Q4tXv\n9jOid3NDrDPtIudvjIQL/XoglvFLt+NZuzobZg41fWU3n2TFDU6dTeeeRf9w6lw6y6cMMH1lV9O0\nypqmhWmatkXvslhNRraNGStD+HnfGRaO7CGVXVEspRQLvt7L0m3HmDzUixdHurGyG7sPfnoecjJc\nultnexi/B8wF7MW9QdM0X03TgjVNC46Li3NJ4awqK9fGw+vCWbrtGFOGefHBaGPOWXe55MbIPb4M\nO82MFcF0bFKbz2cOoXXDmnoXqcwkK+5xODaVexf9w7n/b++8w6K41jj8jr2jYq9Yo0ZBBQGTmMSY\nrmlqrFjBlqbpRaOm3eR6028SK4oFLDGmaGLa1ZhKU1HsBRsigigISN0994+FLBCQBWZ2ZnfP+zz7\nZM7s7JzvhJ9nvjPnnO/LzGVdkB83d22mt0lqIAdGGpCRk8/kVZH8djyZRSM8mTjQQ2+TqowcHGmD\n2SyY+9UBVv91hmmDOrHggV7aLXmJ+wU+84c/PoTsNFVvXa7DqyjKMCBJCLH7etcJIZYJIXyEED7N\nm8tNNGWRnp3H1JAotu5L4MV7ezjbLlg5MFKZkD9OMWdjDD4eTVg/zZ9mDWrrbZJalKsVScU4mJDG\n6KV/kWcys2H6QKdY3y0HRtqQlpXHhOAIos9c4cPRfRk1oL3eJqmFHBypjMkseGnLfsIizvLY7V14\n5f6e2jm7e9fBmocsx3e/BQ3VnZ2y5Q3vzcCDiqKcBjYAdyiKsk5VK1yEpPRsxiwLJzzuMu8+6sWs\n27s4zcYAOTBSFyEE7/94lIVbD3F3r5aETPF1liUvNmtFDo5sJ/r0ZcYsC6dOzep8PvMmerVxmq0W\nchCtMpczcxm3PJwD59P4dFx/Hurb1j4VX02AhW7w+wea3F4OjtTHZBY8//k+NkXH89SQbjx/j4aR\nO/73Bnz9uOV41Fq46QnVqyjX4RVCvCyEaCeE8ADGADuEEAGqW+LknLqUycjFfxGXnMmKST7OmD1N\nDoxUojAj1sc7TvCodzs+G9/fcDnJq4hNWpGDI9v49VgyE4Ijad6gNptmDqRTs/p6m6QKchCtPknp\n2Yxe+hcnkjJYPtGHe3vbaX13/G54v6flOOuKVrXIwZGK5JvMPLMphi17z/PsXd155q7u2jm7GwPg\nt3ctx0E7oNeDmlRj7CjBTsK+c6mMXPwnGTn5rJ/u75TZ0+TASB1y883M3rCXdeFnmXFrZxaN9DR8\nMO+KIrWiHttjLxC4OgqPZvXZOGMgbRvX1ceQ3Gta3FUOolUkITWL0UvDOZ+axaopA7jdXs+h/Ztg\nxR2WY//H4a7XVa9CDo7UJc9kZvaGGL6OsSy9fHJIN+0q+7gfHN5qOZ69D9p5a1ZVhRJPCCF+AX7R\nxBInRSaUkNjKtdx8ZqzdzW/HL8nwQJJy2bw7nhc276NfhyasnDwAt7o6LHnJy4K3Ct4SvngG6qqX\nHVII8TLwMoCiKLcDz8mBUeU4m3KNscvDuZqVx9pAX7w7NrVPxT/Nhz8+shw/vBj6jtOqpsLB0f1A\nHaCRoijrpF4qTm6+mSfC9vDjoYvMG9qToEGdtanIbIbXi+wzePE01NV234HMtKYhrppQQg6MKs6V\nzFymhESxPz6VRSM9GeXjNJtIrovUSuVY9ccpXtt6iFu6NmPZRG/q1dKhK79yGj7yspZVdHYl6nEy\nOYNxy8PJyTcTNs2fPu3c7FPxqvvhzB+W48CfoL2vZlXJwZE65OSbeGzdHv53JImFD/Ri8s2dtKmo\n6EAZYF4y1KilTV1FkA6vBjhjQgmJdiSmZTMhOIIzl6+xOMCbe4wSNzXpCNRvDvXd9bZEUoAQgk92\nnOC9n45xd6+WfDy2nz7ru0/8DOtGWI473gJTvtW0OjkwqhyHL1xlQnAEABum+9OjlR02M5Z8c/f0\nQXBzuj0rTkd2nokZa3ez61iytmnIkw5bwo4BVKsBr14CO23elw6vypjNgre+s2QiGebZmvdGeTlF\njF2JNsQlZzAhOJK0rDxCpgzgpi4GiZu6+Ga4eAC8p8ADH+ptjQSLs/v29iMs+zWO4f3a6re+e9ci\n2PmW5XjwPLjtefvbICmX/fGpTAiOpG7N6oRO86OLPZbT5WbCv9pYy68kQC37bqKUg6OKk5VrYvra\naH4/cYl/j+jD6AEdtKnoyHewYay1PD9Fm3rKQDq8KpKbb+a5z/fxzb4EJt/k4WwxdiUqExufxuRV\nkYDl7UvvtnaaarweORnwdpEwRYOe1c8Wyd+YzIJ5X8WyPvIcEwd2ZOEDN+rTtxSdpg74ArreaX8b\nJOWy+8xlJq+Mwq1eTdZP87dPsppzkRB8V0FBgfmXoZpzbbh1RjJz8glcHUXkqcv8Z6SXdhGk1jwM\ncTut5YXqJpWwBenwqkR6dh4z1+3mjxMpvHhvD2be1tmYMXaTj1rW3nW/R29LXJq/TqYwbU00bnVr\nsjbQIJsZLx6CxQOt5VcvQXW5FEdv8kxmntm0j637Enh8cBeeu1vDWJhlYcqHN4osbZm9D5p42NcG\niU38eeISgaujaeVWh7BpfrR2s0Pkjj//Cz/Osxy36gMzf9e+TkmVycjJZ+qqKKLPXOaD0X21i8m8\nsMTLHB2cXZAOryokpWczZVUURxPTee9RL0YYNcbuX5/BDy9bju20SFzyT344mMiT6/fSsWk91gb6\n0crNAJsZ96yBb560HPcYBmNC9bVHAljW1T0eatlE8tJ9PZipR+SOjGR4t6u1PDcRauoU/kxyXXYe\nTWLG2t10cq/P2iBfWjS0Q9/ydnvIuWotS2fXIbianceUVVHEnEvl47H9GObZpvwfVQaDOLsgHd4q\nc+pSJhNXRnApPZflk3yMGWNXCFhyi2VNJljCw0hnVxc2RZ/jpS/249W+MSsnDaBJfQP8HTaMhyMF\nqecf/C/0n6ivPRLA8vYlaHUUEacu8+bDvQnQahPJ9YhcDt89Zzlu3AFm77fbBhNJxfj+QCJPrt/D\nDa0asmaqH03t0bcUdWa63wfjNmhfp6TKpF3LY+KqSA6eT+PTcf24t3drbSoqqo9m3eGJKG3qsRHp\n8FaBfedSmRoShQDWT/enb3sDhuW5dhkWFQkt8lQMNNUo1IjkuizddZK3tx9hULdmLAnwpn5tnf/5\nmfLgjSKb5Gb9BS176WeP5G+uZOYyeVUkBxKu8qGWU43XY/kQOB9tOa5RB+bE2t8GiU18HXOeZzbt\nw6udG6um+NonJnNRZ6bveHj4M+3rlFSZ1Gu5BARHcDQxncUB3tzVq6U2FRXVh98suO8dbeqpANLh\nrSQOkVAi7hdY85DlWKlmWcZQXf7J7Y0Qgne2H2Hpr3EM82zN+6P6UquGzps54qNhxRBr+eXzUNuA\nGnZBkq5mExAcwemUayzR8oF0PYo+rLrdDeM/t78NEpvYGHWWl7bE4tepKSsmDaCB1gPpkjFUx4RB\nj6Ha1ilRhZSMHAKCIzmZnMGyCT4M7qHBjHR+LrxZJIvdw0ug79iyr7cj0vupBFv2xPPC5v10b9mQ\nEKMmlPjuBYhcajkeMA2GvquvPS5KvsnMK1/Gsik6ngD/Drz2YG+q6x25Y+sc2L3KctzsBngiUl97\nJH9z7vI1AoIjSE7PIWTyAG7qaucwdWYTvF4kC9f974LvNPvaILGZ1X+eZsE3B7mte3OWTvDWPiZz\n0fjLAE/thaYaZeKSqEpyeg4BKyI4nZLJiok+3Npdg9TKSUfgMz9r+bFwaNFT/XoqiXR4K4AQgqW/\nxvHO9iMM7OzOsokGTChhyreMroTZUp7wFXQZrK9NLkp2nomn1u/lx0MXeWpIN56+s5v+kTuKvrmr\nVlM6uwbiRFI6ASsiycozERrkR78O2qbZ/AcX9sPSQdbyjF+htVfZ10t0Zcmuk7yz/Qh39WrJJ+P6\naR/vPWQYnP7NWp5/GarJGPOOQNLVbMYuD+d8ahartBpIhy+B71+0luclQY3a6tdTBaTDayMOkVAi\nbhesedBafuEU1LNTznRJMdKz85i2JprwuMvapmi0lZJv7mTCAEMRG5/GxJURVK9WjQ3T/enZ2g4Z\nsYpS0pkx4MNKYkEIwYc/H+ej/x3nAa82vD/Ki5paJyAx0E57ScVITMtm3PJwEq9mEzLFF//OGmTO\n/NQPko9YywbVh3R4bSAn38Tzn+83dkKJNQ9Z1uwWsiBV7qbWiUsZOUxeFcmRC+l8OLovD/fTYcNR\nUU7ugLWPWMuTvwWPW/SzR1KMyFOXCQyJolHdmoQG+eHRzL6ZqaQz4zgUzbb3qHc73hnhqf0SKakP\nhyUhNYuxy8NJychlbaAv3h01eAHmQPqQDm85OERCiaKCc2sPTx/QzxYX59zla0xcGcmFtCxjhKkr\n2RnJZBKG4pejScxct5s2jeuyLtCPNo3tHN/WgR5Wro7ZLFi49SBr/jrDBP+OvPagHbLtSX04LOcu\nX2Ps8nDSsvJYF+SnTRQpB9OHdHivQ1J6NpNXRnHsokETSlw8CItvspYfXgx9x+lnj4tz7GI6E4Ij\nyMo1sS7QDx8PnZeTOFhn5Gp8F3uB2Rv20q1FQ9YE+tKsgZ2XEEh9OAwms+CVLbFsjD7H9Fs78/J9\nPbR/8SL14bCcSclk7LJwMnMt+wE820lnF6TDWyanLmUyaWUkyek5rJjkw+16v+qTYKMAACAASURB\nVKkryUdelhTBhTx7DBrqEL5IAsCes1eYsiqKWjWqsWnmQHq0svMazJI4YGfkShQmIOnfoQnBkwfY\nJ25qIZdPwcd9rWWvcfDIYvvVL6kQeSYzz27axzf7Enjqjq48fVd3bZ3drCvwbw9reeATcM9b2tUn\nUZW45AzGLY8gJ9/i7PZu61b+jyqKgz5fpMNbCoZPKOGgYnNWdh1LZuba3bRoVJu1U/3o4F5PP2PO\n74HlRaJytPWGaTv0s0fyD1b+forXtx1iULdmLJ3gTb1aduyGv3nSkka6kCf3gLsO6YolNpGTb+LJ\nMEuklxfuvYHHbu9a/o+qQvhi+P4la9lgYaUk1+dEUjpjl0dgNgvWT/fX5sWLA/sf0uEtgaETSpQM\n6AwOJTZnZOu+BJ7ZFEPXFg1ZM9WX5g113Nn+n66QmWwtP7Ebmmn8gJTYjBCCj/93gg9+Psa9N7bi\no7F97RvpxYEfVK5Idp6JGWt3s+tYMgse6MUUrSO9lNTH/CtQTecEORKbOZqYzvgV4YDChun+dGvZ\nUN0KTPnwRpEID70eglFryr7egEiHtwiFCSW6tWzIaqMllPj+ZQgvkrpRTkPqztrwM8z/+gADPJqy\nYpIPjfSMySydGUMjhOCtbw+z4vdTjOjfjn+P6EMNrUNJFUXqw6HIzMknaHU04adSeGd4H8b4dtC2\nQqkPh+ZQwlUCgiOoUU1h/XR/uqj9oq7kfqGAL6DrnerWYQekw4vlYbT8tzj+9d0RburizpIJ3vo6\nLyUp2Rm9cgFq6Tht7uIUfVN3Z88WfDKuv/YZjq6HfFgZGpNZMPfLWDZEndMnrKHUh0NxNTuPKaui\niDmXygej7BDWUOrDoTlwPo2A4Ajq1qxO2DR/Oqkd1nDn27DrHWv5pXNQR+c9KpXE5R1ewyeUkJ2R\noTCbBa9vO0TIn6cZ3r8ti0Z42vdNXVHSL8J73Yufk/owFLn5Zp7eFMO3+y/w5B1deUbrDUdFyUmH\nt0tElpH6MDRXMnOZuDKSI4lX+WRsP+7r01rbCuXzxaHZdy6VCcERNKxTk/XT/NXfP+Jk+nBphzc3\n38xzn1t2v0652YNXhxoooUTKSfhv/+LnHFxsjk6eyaKXr2MSCLqlE6/c31M/vSwfAuejreWHPoV+\nAfrYIimV7DwTs9btZufRZF65vwfTb7Xj5rBf3oFf3raWH/gIvCfbr35JhUlOz2FCcARxlzJZOsGb\nO3poHHXHyZwZV2P3mStMXhlJ4/oWZ7ddE+nslke5Dq+iKHWAX4HaBddvFkIs0NowrUnPzmPWuj38\nfuISL93Xgxm3GiihREmhjV4HPR/QxxYJAFm5Jh4LtTgvL9x7A7Nu66KfXuTmEsOTnp1H4Opook5f\n5l+P9GGcn8ZrMItSUh/zkqFGLfvVL6kwF9KyGL88ggtp2ayaPICbuzbTrrKrF+D9HtZyr4dh1Grt\n6pOoTtTpy0xeGUnzhrUJm+avfsIaJ3R2wbY3vDnAHUKIDEVRagK/K4qyXQgRrrFtmpGUns2UVVEc\nSUzn3Ue9GGmkhBJOKjRHJu1aHlNXR7H37BXeHt6HsVpvILkeUh+G53JmLpNXRXIo4Sofju7LQ33t\nmFpa6sPhOHf5GuNWhHMlM4+1gb7aJqwp+eZ/5u/Qqo929UlUJzwuhakhUbRyq8P6af60VHtzvRP3\nIeU6vEIIAWQUFGsWfISWRmlJyYQSuqd+LUQIeK1EvF8HE5ozzgZcvJrNxOBITl3K5JNx/blf6zV1\nZWE2w+tNip9zMH24AhevZhOwIoKzl6+xdII3Q3raMRmMEz+onJW45AzGr4jgWkFGLC8tY76X1MeC\nVDDKrKbEJn4/fomgNVG0b1KP0Gl+tGgond2KYNM8qKIo1RVFiQGSgJ+EEBHamqUN+86lMnLxn2Tk\n5LN+ur9xnN1f3nF4Z7eAwtkAL6AvcK+iKP4621RpTl/KZMTiP4m/co1VUwbo5+z++UlxZ7e9n6Pq\nA7AMjBRFiVQUZZ+iKAcVRXlNb5vU4GzKNUYu+ZOE1CxCpvjaz9kVwukfVM7I0cR0Ri0NJzffzPpp\n/vZ1dhemSWfXwdh1LJnA1VF4uNdn/XR/6exWAps2rQkhTEBfRVEaA18qitJbCHGg6DWKokwHpgN0\n6KDjlG8Z/HI0icdC9xgvoURJkTlw5iNnmg04mJDGpJVRmMxmwrR+GF2Pkvp47jg0MMhArfI43TKp\nYxfTCVgRQa7JTOg0O2ZnPPYDhI2ylnsMgzGh9qlbUmkOnE9jQnAEtWpUIzRoIF1baPg8cgFHxtn5\n3+GLzFq3h64tGrAuyI+m9VVck19y9rB1X5ixS737G4gKRWkQQqQqirITuBc4UOK7ZcAyAB8fH0M5\nOYZNKOGEHZGiKNWB3UBX4NOSswFGHxgBRMSlELQ6moZ1arBm+k3aPoyuhxPqA5xrYASwPz6VSSsj\nqVG9GhunD+SGVipnOCqLkvqYvQ+aeNinbjvhjMukdp+5wuRVkTSqU5OwaX50dFc5bmpRnLQPcSV+\nOJjIE2F76Nm6EWum+tK4norObvJR+NTXWn5kGXiNVu/+BqPcJQ2KojQveLOLoih1gbuAI1obpgZC\nCJbuOskzm/bh26kpm2b4G8PZzUxx2o5ICGESQvQF2gG+iqL0LvH9MiGEjxDCp3nz5qXfREd+OnSR\niSsjadGoNptnSWdXK5xlmVREXArjlkdQv3YNNs/U0dldmOZ0zm4BTrVM6q+TKUwIjsC9fi02zRwo\nnV2VcNZlUt/FXuDx0D3c2MaNtYF+6jq7O94s7uw+e9SpnV2w7Q1va2B1wZu7asAmIcQ2bc2qOoZN\nKFGyE+o9EkYG62OLhlxvNsCofLE7nhe+2M+NbRoRMsVX3WkjW4mPhhVDip9zwgeVMyyT2nkkiZnr\ndtOuSV1Cg/xp5WanwbQLOTLONBuw61gy09dE06FpPUKD/LR7+ZJ8DD4dYC13vxfGbdSmLuPgdMuk\nvtmXwNMbY+jXvjGrpgygoZrZX99oDqZca9mJ+5Ci2BKlYT/Qzw62qIZhE0qUfFC9mgLVnSf3h6Io\nzYG8Ame3cDbg3zqbZRMrfovjzW8Pc0vXZiyZ4E2D2jr8XUrqY+xGuOFe+9thRxx1mdS3+y8we8Ne\nerRuyOopvrg3qG2fil3I2S3EGZZJ/XgwkSfC9tK1RQPWBmqol8+nwMEt1vKsP6HljdrUZSCcaWAE\n8OXeeJ7dtA8fj6asmjyA+mo+j1ywDynEebytAtKz85i5bjd/nEgxVkIJ1xCZw80GCCH4zw9H+eyX\nk9zfpxUfjO6rz0yAa+gDcOyBEcDGqLO8vCUW745NCJ48gEZqvnkpi9Sz8GGJeKlOrJGilDcbYOSB\nEcDWfQnM2RhDn7ZurJ7ii1s9jfTiQn1IaZQ3MCq4xvCDo03R53jxi/0M7OzOikk+1KslnV21cCqH\ntzChxNHEdN571IsRRkgoEbsZvggsfs5JReZoswEms2DeVwdYH3mWcX4deOOh3lTXYybA9TohhxsY\nFVI4E3Bb9+YsCfCmbi07DI42jIcjRf73jFwJvUdoX6/BcMRlUoXOywCPpqycPEC7mSPX60P+gS3L\npIw+OAqLOMsrX8YyqFszlk/0oU5NFfsXqRHncXhPXcpk4soILqXnstwoCSX+kSI4FHoO08cWSTFy\n8k3M2RDD9gOJPD64C8/dfYP9ZwKyUuHfHYufc4FOyNEGRmCZCfjw5+N89L/j3Ne7FR+OsdNMgIsn\nC3Dk2YC14Wd49asDDOrWjGUTfLQbHElHphiOODACWPvXaV79+iCDb2jO4gBv6exqgFM4vPvjU5my\nKgoBrJ9uxxiY10MKzLBk5OQzfU00f55M4dVhvQi8pZP9jQh9FI7/aC17jobhy+xvh6RchBC8se0w\nK/84xUjvdrwzvA81qtuUs6dqyD4EHHQ2YPmvcbz13WHu7NmCT8b1V9d5KYrUCODYAyOAlb+f4vVt\nh7izZws+Hd9f3cG01MjfOLzDu+tYMrPW7TZOQglTHrzRrPg5FxaY0bicmcuUVZEcSLjK+6O8GN5f\nh2UvJTugeUlQw06bniQVwmQWvLxlP5ui4+27AVY+pADHmw0QQvDfHSd4/6djDPVszYej+1JTi8GR\nE6SiVxmHHBgBLPv1JP/67gj39W7FR2P6UauGSnopqZGGreFZh4goqxkO7fAaLqFE2Gg49n3xc67d\nCRmK86lZTAiO4PyVLJYGeHNnLzulfi2KdGQchtx8M3M27uW72ESeGtKNp+/spv2yF1M+vOFe/JzU\niEMghGDRD0dZ/MtJhvdvy6IRntrMBFw8CItvspb9H4N731a/HgfC0QZGhXy68wT/+eEowzxb84Ga\ng6OrCfB+T2v5/nfBd5o693ZgHNLhFUKw7Nc43t5+hJu6uLN0gre6Meoqg3OmgHUaTiSlMyE4kozs\nfNYG+uHbqal9DZBvZByKrFwTM9ftZtexZOYN7UnQoM7aV7pnLXzzhLXsNxPuc5hZWZdGCMFrWw8R\n8udpxvl14M2HemszE/D5ZDj4pbU8JxYaGzPagKRshBB89L/jfPjzcR7p15b/jFRxcLR7NWx9ylqe\ncwAat1fn3g6Owzm8RRNKDPVszftGSCgh39oZmphzqUxZFUn1atXYMMOfG9u4lf8jNdm7Dr5+3Fqu\nWQ/mXrCvDRKbuZqdR1BINFFnLvPO8D6M8bWDQ1GyD3nhFNSz86BMUilMZsHcL2PZEHWOwFs6MW9o\nT21mAuRzxikQQvDej8f4ZOcJRnq3498jPNWLDrR8CJyPtpZdbJNreTiUw5uTb+L5z/fzzb4EJt/k\nwfxhOieUKJmHGmQnZDB+P36J6WujcW9Qi3WBGuetL42SD6kn94B7F/vaILGZy5m5TFwZwZEL6fx3\nbD+GebbRvlLpyDgs+SZLkqOvYhJ4YnBXnr27u3R2JWUihOCd74+wdFccYwa051+P9FHPh5EaKReH\ncXiLJpR48d4ezLxN54QSJcU16DkY8qo+tkhK5btYSzasLs0bsGaqr/3XeMsOyKFITMsmIDiCc5ev\nsXyiD4N72GFJktSIw5Kbb2b2hr1sP5DI8/fcwOODu2pTkdSIUyCE4M1vLbPTAf4deP1BFZe9SI3Y\nhEM4vEUTSrz7qBcj9U4oIcVleMIizjL3q1i8OzQheNIA7bIblYXUiENxNuUa44PDuZKZx+qpvvh3\ndi//R1VFasRhyc4z8VjoHnYcSdI2tKHUiFMghGDhNwdZ/dcZJt/kwYIHeqn3wk5qxGYM7/AaLqGE\nFJehEULw2S8n+c8PRxl8Q3M+G2+nbFiFXDkDH3kWPyc1YmiOXUwnYEUEeSYzYdP88GyncRzvlJPw\n3/7WsgwX5FBcy81nWkEc73890odxfhqs8S65ybVaDZifon49Es0xmwWvfn2A0IizBN3SiblqrvGW\n/kiFMLTDu+9cKlNDDJJQImIpbH+h+DkpLkNRdEPjI/3asmikpzYxMMti8S1wMdZafmQpeI2xX/2S\nCrPvXCqTVkVSq3o1Ns4YSPeWDbWtsGSa4Fl/Qssbta1Tohrp2XlMWRXFnrNXeHekRunrS4YdG/YB\n+ExVvx6J5pjNgpe3xLIx+hyzbu/CC/eomNFTOrsVxrAO7y9Hk3gsdI8xEkqUFNb0XdCmrz62SEol\nz2Tmxc372bL3vD4bGmXn43CEx6UQtDqaJvVrEhroTwf3etpWKDXi0KRey2XSykgOJlzlv2P7M9Sz\ntfqVfPMk7FljLT8fB/XtsLxGojoms+D5zfvYsue8+nG8i/YlLW6Ex/5U575OjiEdXkMllJAPKcOT\nlWviibA9/O9IEs/e1Z0n7uhq3w2NUiMOx44jF5m1bg/tm9ZjXaAfrdw07mOkRhyaSxk5BKyIIC45\nkyVaJa2RGnEa8k1mnv18H1/HJPDMXd15akg3dW6clQr/7mgtD/sQfKaoc28XwFAOb9GEEgM7u7N0\nojeN9EookZMBb7ctfk52QIYjLSuPoNVRRJ+5whsP92aCf8fyf6QW+bnwZvPi56RGDM/WfQk8vTGG\nnq0bsXqqL03r19K2QunIODQXr2Yzbnk451OzCJ7sw6Buzcv/UUWRGnEa8kxm5myM4dv9F3jh3ht4\n7HaVonec3AFrH7GWn4qBphptlnRSDOPwms2WkB0r/zjFMM/WvKdCQom8vDzi4+PJzs6u2A8zkiA/\nG+7ZZCnXqg/13OHw4SrZY2Tq1KlDu3btqFlT54x1FSApPZtJK6M4kVS1mKmV0kl2muVTqJHaDaFu\nE6fWSCGOqJVCNkSe5eUvYxnQsSnBk30qnKGxQloRZkiLt2oELFmxXEAj4Ng6KST+yjXGLY8gJSOH\nNVNtz9BYIZ2knnVZjRTiDFoBS6i6J9fv4YeDF23O0GiTVq6lQG6eVSdu7eFiNlyUOqkIhnB4c/JN\nPPf5fraqnFAiPj6ehg0b4uHhYfsUd8JecGsENLKUW3uBYseNTzoghCAlJYX4+Hg6dXKMEePZlGsE\nBEeQnJ5D8KQB3Nq98m9dKqyThL3gVhsoiBjSyhOq6Zztz044olYKWf5rHG99d5jbujdnSUDlonfY\nrJXMZIuz27hAI407ulTmNEfWSSGnLmUyfnk4GTn5rAvyo1+HJjb/1madJOwFtyKRh9r0q4LFjokz\naAUsfszjoXv4+XASCx7oxZSbbWtLuVpJ2Atu9YGCpEkuqBFQRye6e3Lp2XkEhkSzdV8CL97bgwUP\nqLfZKDs7G3d394o5u0Vp08/pnV0ARVFwd3ev+JtwnTh84SojlvzJ1ew8Qqf5VcnZhQrqpDSNuIiz\nC46nFbB0lO//eJS3vjvM0D6tWT7Rp9Kh6mzSSsJei7NbSGsvl3J2wTF1UpTjF9MZtfQvsvPNrJ/u\nXyFnF2zQiRDF+5JqNVzWkXF0rYAlLvOMtbv5+XASbzzc22ZnF8rRSmnPGxdFDZ3o+oa3aEKJ9x7V\nJsSLTU5MbiZcOlb8nIsJS9esdRUg+vRlpoREUb9WDcJmDKSbSmGkKu3suiCOohWwLJV6fdshQv48\nzWif9vxreJ8q560v19ktiotqBBxLJ0U5cD6NiSsjqV5NYeN0/0r3MWW2PycdUk5Yyy729r80HFUr\nYNk0PX1tNL+fuMTbw/sw1rficZmls2sbVdWJbg5v0YQSKyb5cLteCSVKiqppZ6jjVvq1El3ZeSSJ\nWaG7aeNWlzWBvrRronEYqUKyr8Llk9ZytRrQqo996pZUmnyTmZe2xLJ5dzyBt3RinpoB30tDPqAc\nnr1nrzBpZSQN69QkNMgPj2b11a0g+RjkZVrLrfpY+hOJQ3ItN5/AkGjCT6WwaIQnj/q0V+fGsi/R\nBF3m6/edS2Xk4j/JzDGxYbq/cZzdNv0M4ez+9ttv3HjjjfTt25esrCwuXLjAsGHDrvubbdu2MX/+\nfDtZaH++2nueaWui6daiIZtmDrSfs5uwt7iz27yHYZxdqZOyyck38eT6vWzeHc+cO7tp6+yWnJ4G\nwz2gpFbKJyIuhYAVETSpX4uNM/zVd3YT9hZ3dtv0M5yzK3ViOxk5+UxeGUXEqRTeH+Xlcs6uI2rF\n7g7vrmPJjF0eTt1a1dk8cyBeemRPM/gDKjQ0lJdffpmYmBjq1q3L+++/z7Rp0677m6FDh7J161au\nXbtmJyvtx6o/TjFnYwwDPJoSNs2PZg1q26fi0jRSs6596rYBqZPSuZabT9DqaLYfSGTe0J7MubO7\nds5uTjpciLGWazcyVF9SiNTK9fnteDKTVkXSyq0Om2ZoMKA28POmKFIntnE1O4+JwRHsPnuFj8b0\n45F+Ki3HdBCdgGNqpdzhpaIo7YE1QEtAAMuEEB9VprLChBLdWzYkxM4JJV7bepBDCVchPwfMecW/\nrNUA+KvC9+zVphELHig7Lej8+fNp2rQpc+bMAWDu3Lm0aNGC2bNnl/mbFStWsGnTJn744Qe2b99O\naGgoX3zxBW+++SYAH3zwAbGxsaxcuZLY2FjGjh1LZGQk9erV4/bbb2fbtm2MGjWqwm0xIkIIPvj5\nOB//7zj33NiSj8b0o05NbTeI/a2T3IziX1RSIyB1Yk+uZucxtSD166IRnowaoNJbl1J4bcMuDiXn\nWk/UrAdKNpBU6XtKrdifnw9d5LHQPXRuXp91QeoPqF/b8AuHkos8c6rQlxQidaIfaVl5TFwZycHz\naXwyth/39VEh457ZBIn7ee3XNItWqteE6rWpqk5AaqUotsyn5APPCiH2KIrSENitKMpPQohDtlZS\nMqHEsoneFY5/qQr/cGLqA9qt6Zs6dSrDhw9nzpw5mM1mNmzYwI4dO+jbt/S0xGFhYQQFBfH7778z\nbNgwRo4cyalTp2jSpAm1a1s64dmzZ3P77bfz5Zdf8tZbb7F06VLq1bO8jfDx8eG3335zik7HZBYs\n+OYA68LPqrbZyCaEuQxnVzukTtQhJSOHiSsjOXYxXbvUr4Uk7MUy/i9AY40UIrWiLtv2JzBnQww3\ntrEkIWlcT+UkJCXf2EmdODSp13IJCI7gaGI6n43vz903tqr6TfOzIXG/tVyznl2jQ7mSVsp1eIUQ\nF4ALBcfpiqIcBtoCNjm8RRNKD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"text/plain": [
"<matplotlib.figure.Figure at 0x7fede8baa278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# to plot the intermediate results\n",
"fig, axes = plt.subplots(3, 5, figsize=(12, 8))\n",
"x = tensor.from_numpy(train_x)\n",
"y = tensor.from_numpy(train_y)\n",
"# sgd\n",
"for idx in range(max_iter):\n",
" y_ = x * k + b\n",
" err = y_ - y\n",
" loss = old_div(tensor.sum(err * err), nb_points)\n",
" print('loss at iter %d = %f' % (idx, loss))\n",
" da1 = old_div(tensor.sum(err * x), nb_points)\n",
" db1 = old_div(tensor.sum(err), nb_points)\n",
" # update the parameters\n",
" k -= da1 * alpha\n",
" b -= db1 * alpha\n",
" plot(idx, tensor.to_numpy(x), tensor.to_numpy(y_))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We can see that the learned line is becoming closer to the ground truth line (in blue color).\n",
"## Next: [MLP example](./mlp.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss at iter 0 = 1.427076\n",
"loss at iter 1 = 1.262527\n",
"loss at iter 2 = 1.118196\n",
"loss at iter 3 = 0.991600\n",
"loss at iter 4 = 0.880558\n",
"loss at iter 5 = 0.783158\n",
"loss at iter 6 = 0.697723\n",
"loss at iter 7 = 0.622783\n",
"loss at iter 8 = 0.557046\n",
"loss at iter 9 = 0.499382\n",
"loss at iter 10 = 0.448799\n",
"loss at iter 11 = 0.404426\n",
"loss at iter 12 = 0.365499\n",
"loss at iter 13 = 0.331350\n",
"loss at iter 14 = 0.301391\n"
]
},
{
"data": {
"image/png": 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9vplvNx3lyesHEHlp7+rvzEsPQMLiz73ZRC5Kpk+HZiwMD6KF3uGkzCQjPqOg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\nzCEdKFvaOSer8ZhLG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/u2kcGMwmKKZaa0qy+eZcz8Lew7cZEZQ9oT1KCqgYU5L+nwFuBK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\nwyuEEEIIIdza/wF1kmcwA/JWpwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fede8be6c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# to plot the intermediate results\n",
"fig, axes = plt.subplots(3, 5, figsize=(12, 8))\n",
"x = tensor.from_numpy(train_x)\n",
"y = tensor.from_numpy(train_y)\n",
"# sgd\n",
"for idx in range(max_iter):\n",
" y_ = x * k + b\n",
" err = y_ - y\n",
" loss = old_div(tensor.sum(err * err), nb_points)\n",
" print('loss at iter %d = %f' % (idx, loss))\n",
" da1 = old_div(tensor.sum(err * x), nb_points)\n",
" db1 = old_div(tensor.sum(err), nb_points)\n",
" # update the parameters\n",
" k -= da1 * alpha\n",
" b -= db1 * alpha\n",
" plot(idx, tensor.to_numpy(x), tensor.to_numpy(y_))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "py3",
"language": "python",
"name": "py3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}