From df3fb42ae95ddce71c69de62c1b9a60e61e3daf6 Mon Sep 17 00:00:00 2001 From: qiaoyue <1174693941@qq.com> Date: Sat, 1 Jan 2022 10:59:59 +0800 Subject: [PATCH] numpy_tutorial has repaired --- .../1-numpy_tutorial.ipynb | 873 +++++++++--------- .../random-matrix.csv | 6 +- .../random-matrix.npy | Bin 200 -> 200 bytes 3 files changed, 450 insertions(+), 429 deletions(-) diff --git a/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb index 9f9d2a9..f175432 100644 --- a/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -127,7 +127,7 @@ "array([1, 2, 3, 4])" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -146,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -172,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -227,7 +227,7 @@ "(numpy.ndarray, numpy.ndarray)" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -245,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -254,7 +254,7 @@ "(4,)" ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -265,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -274,7 +274,7 @@ "(4, 3, 2)" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -292,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -301,7 +301,7 @@ "24" ] }, - "execution_count": 13, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -319,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -328,7 +328,7 @@ "(4, 3, 2)" ] }, - "execution_count": 14, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -339,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -348,7 +348,7 @@ "24" ] }, - "execution_count": 15, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -375,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -384,7 +384,7 @@ "dtype('int64')" ] }, - "execution_count": 16, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -402,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -410,9 +410,9 @@ "evalue": "invalid literal for int() with base 10: 'hello'", "output_type": "error", "traceback": [ - "\u001b[0;31m------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'hello'" ] } @@ -430,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -440,7 +440,7 @@ " [3.+0.j, 4.+0.j]])" ] }, - "execution_count": 18, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -483,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -506,7 +506,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -519,7 +519,7 @@ " 6.00000000e-01, 7.00000000e-01, 8.00000000e-01, 9.00000000e-01])" ] }, - "execution_count": 22, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -539,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -548,7 +548,7 @@ "array([ 0. , 2.5, 5. , 7.5, 10. ])" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -560,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -571,7 +571,7 @@ " 7.25095809e+03, 2.20264658e+04])" ] }, - "execution_count": 24, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -589,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -598,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -611,7 +611,7 @@ " [0, 1, 2, 3, 4]])" ] }, - "execution_count": 26, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -622,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -635,7 +635,7 @@ " [4, 4, 4, 4, 4]])" ] }, - "execution_count": 27, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -653,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -662,39 +662,39 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[[0.34933999, 0.78232989],\n", - " [0.07449912, 0.57488499],\n", - " [0.28079982, 0.65921106],\n", - " [0.71455261, 0.88375022]],\n", + "array([[[0.57397454, 0.12434228],\n", + " [0.74835474, 0.01034541],\n", + " [0.91383579, 0.02807574],\n", + " [0.14217509, 0.64698341]],\n", "\n", - " [[0.00794753, 0.41466795],\n", - " [0.21029866, 0.12968518],\n", - " [0.98595403, 0.47316115],\n", - " [0.50330171, 0.87038751]],\n", + " [[0.65606545, 0.84787378],\n", + " [0.31064031, 0.70205451],\n", + " [0.30486756, 0.34702889],\n", + " [0.47537986, 0.91154076]],\n", "\n", - " [[0.10672402, 0.09192073],\n", - " [0.48656172, 0.16710676],\n", - " [0.46217936, 0.09035176],\n", - " [0.19623019, 0.73555862]],\n", + " [[0.32192343, 0.77700745],\n", + " [0.80485914, 0.85919158],\n", + " [0.29751565, 0.27228179],\n", + " [0.57796668, 0.18255467]],\n", "\n", - " [[0.75468369, 0.76685125],\n", - " [0.68205367, 0.99455825],\n", - " [0.23566499, 0.431837 ],\n", - " [0.86997877, 0.52098775]],\n", + " [[0.50020698, 0.58134695],\n", + " [0.14200095, 0.97556272],\n", + " [0.32948647, 0.35170435],\n", + " [0.27768833, 0.75059373]],\n", "\n", - " [[0.99353122, 0.72868516],\n", - " [0.74724343, 0.63273805],\n", - " [0.16946554, 0.06170885],\n", - " [0.28687951, 0.6671094 ]]])" + " [[0.23972627, 0.08461662],\n", + " [0.1929383 , 0.80565903],\n", + " [0.2627892 , 0.73361884],\n", + " [0.18415944, 0.44976198]]])" ] }, - "execution_count": 31, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -706,19 +706,19 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.47727318, -2.11212891, -0.10766674, 0.88444896],\n", - " [-1.66157402, 1.80598739, 0.20359836, -1.1118912 ],\n", - " [ 0.24731274, 0.0396289 , -0.54177391, 0.38118806],\n", - " [-0.15762081, -1.05826785, 0.91565702, 0.79167261]])" + "array([[-1.74300737, 1.94689131, 0.18922227, -0.20440928],\n", + " [ 1.31664152, -0.01176745, -0.43956951, 0.53571291],\n", + " [ 0.02140654, -0.09635041, -1.84205831, 0.64951045],\n", + " [ 0.35682903, 0.96657395, -0.50099255, -0.80044681]])" ] }, - "execution_count": 33, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -737,7 +737,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -748,7 +748,7 @@ " [0, 0, 3]])" ] }, - "execution_count": 34, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -760,7 +760,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -772,7 +772,7 @@ " [0, 0, 3, 0]])" ] }, - "execution_count": 35, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -791,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -802,7 +802,7 @@ " [0., 0., 0.]])" ] }, - "execution_count": 36, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -813,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -824,7 +824,7 @@ " [1., 1., 1.]])" ] }, - "execution_count": 39, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -856,7 +856,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -882,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -893,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -902,7 +902,7 @@ "(77431, 7)" ] }, - "execution_count": 43, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -913,12 +913,12 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAEWCAYAAACpAjzFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAClmElEQVR4nO2dd5jcxPnHv3Pn3nsv54aNO9iY3k01vQXSKCGEQBIIkMQQAiRAMIFAyi8JJdQQaugY0003NsYNjHsvuOPerszvj5V2R7Oa2Vfa2Xb3fp7Hj/d2pdFIGo3m7UJKCYZhGIZhGIZhmLpKWaE7wDAMwzAMwzAMU0hYKGIYhmEYhmEYpk7DQhHDMAzDMAzDMHUaFooYhmEYhmEYhqnTsFDEMAzDMAzDMEydhoUihmEYhmEYhmHqNCwUMQzDMLUaIcQEIcSFhe6HDSHEUiHE6Ki/EdqVQoi+2fWOYRim9sNCEcMwTBGTzYK4kAghbhFCPFHofgCAlPIkKeVjcfYVQhwmhPhUCLFFCLFJCPGJEOIA77eLhBAfu+0twzAMUwjqFboDDMMwDKMjhKgnpawqcB9aAHgNwE8BPAugAYDDAewpZL8YhmEY97CliGEYpkgRQvwHQA8Arwohtgshfu19f5BnvdgshJgphDhK2ed9IcRt3u/bhRCvCiHaCiH+K4TYKoT4XAhRoWwvhRC/EEIsFkJsEELcJYQoU36/RAgxRwjxrRDiTSFET+W3vwohVnjtfiGEONz7/kQANwD4jteHmd73AauXak0SQlR4ffmREGI5gPdsxxcJ7hVCrPOO/6UQYrDhOr4vhLjU+3yREOJjIcTdXptLhBAnGW7BPgAgpXxKSlktpdwlpXxLSjlLCLEvgPsAHOyd42av/ZZCiMeFEOuFEMuEEDdq1/PH3vlsE0J8LYTYP6S/+3r9ukD5ergQYpZnsXpGCNFIa3OhZ8l6RQjRxXAdHhVC/NNzJ9zuWb06CSH+4l2LuUKI/QzXgmEYplbDQhHDMEyRIqX8AYDlAE6VUjaTUv5JCNEVwHgAtwFoA+A6AM8LIdoru54P4AcAugLoA2ASgEe87ecAuFk71JkARgLYH8DpAC4BACHE6UgIN2cBaA/gIwBPKft9DmC41+6TAJ4TQjSSUr4B4I8AnvH6PSzCaR8JYF8AJ2Q4/vEAjkBCcGkJ4DwAG4nHOBDAPADtAPwJwENCCBGy3XwA1UKIx4QQJwkhWvs/SCnnALgcwCTvHFt5P/3d609v71x+COBiABBCnAvgFu+7FgBO0/vsCUlvAvi5lFK91ucBOBFALwBDAVzkbX8MgDu83zsDWAbgacu5nwfgRu/c9yAxNqZ5f/8PwD2WfRmGYWotLBQxDMOUFt8H8LqU8nUpZY2U8m0AUwGcrGzziJRykZRyC4AJABZJKd/x3NGeA6BbA+6UUm6SUi4H8BcAvoXicgB3SCnnePv+EQmLRU8AkFI+IaXcKKWsklL+GUBDAP2zPL9bpJQ7pJS7Mhy/EkBzAAMACG+bb4jHWCalfFBKWQ3gMSSEiY76RlLKrQAOAyABPAhgvWeJSdsWAIQQ5UgIpNdLKbdJKZcC+DMSAioAXArgT1LKz2WChVLKZUoThwN4BcAPpZSvac3/TUq5Wkq5CcCrSAijAPA9AA9LKadJKfcAuB4J61WF4dxflFJ+IaXcDeBFALullI971+IZpI8NhmGYOgELRQzDMKVFTwDneq5zmz23rcOQWNj7rFU+7wr5u5nW5grl8zIAvvtVTwB/VY6zCYBAwgIFIcR1nivYFu/3lkhYHLJB7Yvx+FLK9wD8H4B/AFgnhHhAJGKAKKzxP0gpd3of9Wvi/z5HSnmRlLIbgMFIXJu/GNptB6A+EtfQZxm86wWgO4BFln5dDuBTKeX7tj4D2Kn0t4t6PCnldiSsT10RTtSxwTAMUydgoYhhGKa4kdrfKwD8R0rZSvnXVEo5LotjdFc+9wCwWjnWT7RjNZZSfurFD/0aCXes1p772BYkhJawfgPADgBNlL87hWyj7mc8PgBIKf8mpRwBYCASbnS/inLSUZFSzgXwKBLCkd5XANiAhAWrp/JdDwCrvM8rkHBnNHE5gB5CiHsjdGu1ejwhRFMAbZVjMgzDMARYKGIYhilu1iIRn+LzBIBThRAnCCHKhRCNhBBHCSG6ZXGMXwkhWgshugO4Cgk3KiCRSOB6IcQgIJlE4Fzvt+YAqgCsB1BPCHETEnEyar8r1CQDAGYAOF8IUV8IMRLAORn6ZTy+EOIAIcSBQoj6SAhbuwHUxDl5E0KIAUKIa/1r612fCwB85m2yFkA3IUQDAPBc0J4FcLsQornn5ncNEvcMAP4N4DohxAgvUURfoSSuALANibihI4QQVCH3KQAXCyGGCyEaIuFiONlz3WMYhmGIsFDEMAxT3NwB4EbPhew6KeUKJJIh3ICEQLICCQtJNvP5ywC+QEJoGQ/gIQCQUr4I4E4ATwshtgL4CoCfqe1NAG8gkYxgGRJCier69pz3/0YhxDTv8++QsJR8C+D3SCRnMJLh+C2QiPP51jv+RgB3RTvtjGxDIinDZCHEDiSEoa8AXOv9/h6A2QDWCCE2eN/9HAkhbTGAj5E4x4e983kOwO3ed9sAvIREkookUsrNAI4DcJIQ4tZMHZRSvoPEdX0ewDdIXN/z45wswzBMXUZIGebhwDAMw9QFhBASQD8p5cJC94VhGIZhCgVbihiGYRiGYRiGqdOwUMQwDMMwDMMwTJ2mYEKRFxw8RSSqsc8WQvze+76XEGKyV537GT+AlWEYhnGPlFKw6xzDMAxT1ymkpWgPgGO8SufDAZwohDgIiaDae6WUfZEIoP1R4brIMAzDMAzDMExtp16hDiwTGR62e3/W9/5JAMcA+K73/WMAbgHwL1tb7dq1kxUVFTnpJ8MwDMMwDMMwpc8XX3yxQUrZPuy3gglFACCEKEciDWxfJKqSLwKwWUpZ5W2yEoaq3EKIywBcBgA9evTA1KlTc99hhmEYhmEYhmFKEiHEMtNvBU20IKWsllIOB9ANwCgAAyLs+4CUcqSUcmT79qECH8MwDMMwDMMwTEaKIvucV6xuIoCDAbQSQvgWrG4AVhWqXwzDMAzDMAzD1H4KmX2uvRCilfe5MRIVvOcgIRyd4212IRKV1hmGYRiGYRiGYXJCIWOKOgN4zIsrKgPwrJTyNSHE1wCeFkLcBmA6gIcK2EeGYRiGYRiGYWo5hcw+NwvAfiHfL0YivohhGIZhGIZhGCbnFEVMEcMwDMMwDMMwTKFgoYhhGIZhGIZhmDoNC0UMwzAMwzBMXpm2/Ft8vXprobvBMEkKWryVYRiGYRiGqXuc9c9PAQBLx40pcE8YJgFbihiGYRiGYRiGqdOwUMQwDMMwDMPUWrbtrix0F5gSgIWiOsK3O/ZiypJNhe4GwzAMwzBM3pi0aCOG3PIWPpi/vtBdYYocForqCN/992Scd/+kQneDYRiGYfLOrr3VWLR+e6G7wRSAL5YlFMJTlmwscE9SfLVqCyrGjsfUpaysLiZYKKojzPkmkeFFSlngnjDFzKYde1FTw2OEYZho7NxbhZ17qwrdDSM/e3Iajv3zB9hbVZP22/hZ37DAxOSVjxZsAAC8PWdtgXvCqLBQVMd4b+66QnehZJBSYvysb1BdR4SEDdv3YP9b38Y9b88vdFcYhikxBt70Jgbe9Gahu2Hk44WJRWhNiGLwSk9gYmonxagL3l1ZXegu5IR73p6Pf0xcWOhuxIaFojrG7sp0LRkTziszV+PKJ6fh3x8tLnRXjFSMHY9rnp3hpK0N2/cAAN76eo2T9nLNo58sQcXY8di8c2+hu8LkiPs/WMTuJUwaC9dtx5ad0QLni2VdPG/NNmzfU7wWtdqMgCh0F5L89d0FAIAZyzcXtiOO+du7C3DXm/MK3Y3YsFBUx5A5ejW88/XaWrd4Wb8tISSs3bqnwD2x88K0VU7aKaYXBoWnpqwAAKzZurvAPWFyxR0T5uKc+zgWkgky+p4PMObvH2Xc7uUZqzB3TXEVBz3hLx/ikkc+L3Q3aj2bd+7F/R8sKvqQgWISkO/7YBEG3fRGobtRUFgoKkEe+ngJfvnMjJy0LaXE81+sRGV1NIvSpY9PzdviJWrfmNpNkb/zGCaAlBJVPIdlzcpvd2Xc5qqnZ+DEv2QWnvKFv0CfUssUiMXI9S98iTsmzMXkHGTdlVLipemrnLjAiSLSRY6bMBc79tZOtz4qLBSVILe+9jVenO7GOqDzyszVuPa5mbjv/UU5aT9bvly5Bf1+OwETOTYqZ7gUMuav3Za0uDEMA9zw4pfo+9sJztqTUuLTRRuKXiPO1E2e/XwFXsrResXGtt0JC4yqRHXlKfPRgg24+pkZuPONuVm3tXozezoUEywU5YHj7vkAlz0+NeN2W3ZVYuP2wi4gv92RiM/YUOB+mPjc07BxvYH4rNi0Ew9+mJ84qePv/RCH/+m9nB4jTNO2evOupBZv9eZduK8E3CiY0mbdtt247rmZGceZ7/bpiglfrcF3H5yMJz5bZt2upkbiL+/Mr7sxeMTHf/ueKtzz1rycWPPq4hT06+dn4eocebZExZWL+JZdiXi2dQ5c6zftiP88Vowdj4qx47ErgnVnd2U1KsaOzzhf7K2qwZ/fmhep7doAC0V5YMG67Xjr68xpF0fe9jZG3PYOgMTC9W3CPlHJx6RcUyNzlprV737YQvjZqStw2v99nJPj1iZ+8NBk3P76nEgC+JadlXhx+spYxytEco9Dxr2Hnz7xBQDgsv9MxbgJc7Fs486894OpO4y6/V3874uVeOSTpXk97spvE+M60/ieOG8d/vLOAtz8yux8dKtoyeSudPeb8/C39xbipRmr89MhpuTwMxgWi+tbFCX2Zi9Byd/fW2Dd7snJy/D39xbin++Xbia5OLBQVERUVqcklpP/9hF+TLAuRSUfiqo7JszBwJvejORvO3XpJvzmf7Myaln938M2+/X/ZmHWyi2R+loX8d0K9EvoT/Bhd+AXT0/HL5+ZmbGWR02NxLXPzsSMFZuz7me2TJyXsCb657uX4zjyyh0T5uCdHCh28s3uympc/p8vsGzjDtL2SzbQtss3fn2eYk4FvGDtNizPsfIik2Jwhxf4Xl2TA0uR8xYZCv51/9aRldQfQ+VlRSIVhXDjS19i0qJEsdqaGomJ89aFrq9em7Uaf3knvQzHnhKYL3IBC0VFir+Qy8Sbs9egYux4fLMlc9BpvnjwoyUAUi8XCufdPwnPTF2RsSaQ7zb36KdLrdtJKTFvzTby8esSpitsm9798ZUpycXGHXvx/LSV+NGjxZNdydeg3zkhe/9vhs79HyzGpTlQ7OSb9+auwxuz1+BX/5tF2r5YtMcAsGbLbvz5rXmQMhVN4bsQrdu6OyvXHSpLN+xAxdjx+JDg8nzcvR/iiLsm5rxPNvxXkMjBjYzrwvvpwg14/ot4lnqXVIwdjz+8+nWhuxGb/05e7qSdpKXISWsJNu3Yi3UOM6k+8dlyXPDgZwCAxyctxcWPfI5XZ32TVNr4WXV/9uR0/OWddKtRMc1j+YSFohLnmc8T/umzV9HSjuZznEepeZpyi7P3kOrD+9/Jy3HCXz7EJ17BPiaFjDGh+/eyTAhIKVExdjwOuP2d9LbhuxXkb6RR1xnLNkXTQI+87R089PGSGD1iCsnuymqn7rtPe3PsFGIWq0KtJcIeuZ8/NQ1/f28hvlq1Nfmc+NuN+uO72P/WtzO2+9qs1QHrzbcRBSk/09rLnjvajj1V2FNVvNpnfw4rK6JV4Xf/PRnXPjez0N0AADz8SW7mxI8WrMeXJeLpIZX3oSv2v/VtjPrju6G/vTBtZVap5Vd4mRrXbtmNyUs2kvYZP+sbAEEPJp/Pl25C/xsnRJ4LSgEWikocGdG31ZX5XkqJN75aYw1GjaIVS76wM21HPIPZqxMTyFKiy0tdxCS4hN03/7sykZokw7LK+R4nxZioo14EV4dVm3dhw/Y9uPW14tGKVlbX4I2v1uQlYcTarbvzVnds5bc7nbpbHnfvBxh405vO2juwVxsAQL8OzZy16RLbcNiluL6kFBbR2v/Zk9Nx0l8/BJAQDPe79W288VWEAs/JBWTi/0E3v4mT/pr/NNnUd8cqbwG5dVe04rC0PmRHLhIMVVbX4MKHp2Bmhmcw12nkf/DQFJxaIjHBNdQFS0yWb9yJ79w/Cdt2V0JKiWuenZlVavn3vGy9QtCV1TM9ATXM5fCfExdiT1UNpq/4NnafihUWikocW+KBXPL212tx+RNf4B8Tzam7o1iKfDKdR5w2axNTlmzCI1lq6jYbXva2a091KdlVAP9j6tivX06f7lYRaqDkm7+/uwCXP/EFJs7LfTr64+/9MHLdsd2V1bjp5a+wdXe0xeRhd07EGf/4JO17agwPAOxz4wRc8EDCVWTFptS9q6yuwUvTV0Vy5dVp27QBAGC/Hq1I2+fTSpoJX0khhKp4it4/v3aJv3D+PIPA/Jbn1r10w47kAvLtOan4ssXri1dZ5de1eXWW+0QL2eozpi1zvwid881WfDB/PX6dwT00zGIQxp6qanz3wc+werO7OXT15l2RXctyVag+0XaCXFkT731nPiYv2YS3Zq/FEw5c/tQ4x6ixcrYYSSnjz/vFCgtFJY7tRbd9T1XOqiVv2J7QHthimWocabRXb96FJ72JgdJmJk368o07UTF2PL5aRTPVZ5r3npqyHPv8dkLGeCidtTH8h8+7fxJ+n6VPdyYlV9hZ+BPji9Ps9Sbi3vNj//w+7pgwJ9I+UV969cuLZ7Eah5XeImPj9mguC0f8aSLueWtepH22xNCSPzVlOR6ftAx/f9ee1YjKjj10AXtvVQ0mLU53Cxk3YS6ufmYGjrzrfSd9KjXUOcmFAq1GsRgDwH8mLQ1drL4yMyFQzFq1JXlcP+tVoaFOUcXkPueTiy797qWvAABrvPu4u7IaW7K4Vy9MW4VPF23EIePclWI4ZNx7RtcyCq6N61J7Duav3ZYc83e+MRcVY8dn5Qqo3ubZxHUKhV17qyNbG/3kVeu27lY8k1I9/O/kxLz/f+8Fs9RFXQ8VCywUFSGR3M78DyGT5eCb38Tgm925kYRhm6TnrY2e6CBMy/r9hybjhhe/xJadlSQNY+Lyma/hu3MTGsvnptLqhWS6Hbe8Mht7q2uS2Z0ovDBtJQ7847v4wqD5q6mROXeTEiIx0aUCoDO/cd+fv84qjMTt86L1O3D/B/FqJ/kKgZoMk3CUOboI10NWDf/6bXvwvsGCtHzTTvzNe2FJKTNep7j4L8FiSvLnu+WZ3DlzcS0KNXbC5s5NnutLwlKUfbyfGlu4fONO/O7l2fiJl/o+rC9SSquiREppXTxFvT/VNRLn3T8pNKlD1KmpPJvrZJi/s7VeuKqzo+K7SW3zNP0n//UjDPvDWwAS76nL/5N+f4FEaYc3Z6e7UbpShpqgvhs3bMtdvIv6HAAJy/ovnpoOAPiXV/j+ySmJOkDZvsv9mEYb+lA1He/ed+bHmp8XrtuOUX98NzTG1rc86c9qpqRMxQoLRTlizjdbM2pbpZS49LHP05IBvByjPgJ1qsz0cK7ektAWVTlYLGSyKlDxNePUydbVpOy7C2QKLPWP9tsXv0z7bcvOytAASd/9xJQhr/cNr2cMrHUhNJ3xj0/ww4enkLdfumGndXERJSX6lCWbnCxKJSTenbMWvW94HXO+MQejRolbKRaZaPXmXWkpisOu2Ln3fYqLHsmc8e/CRz5H7xted9Q796z8didO/8cn2L6nKqfuLwDwxldr0PuG17Egg/JGz9r25cotVne8XCxc49K6SX0AQL2yMnLcpo3kPRGpFPdh7zlfgy6lXRh57NOl6HPD66E106Yu3YTeN7xOTnABJOIfpizZFFos1H+nUZNwZCPc9r7hdZwb0f2UQi4Fbv82LVbcpa55dibe8AQfPYb0owUb8JMQgSnXFoLeN7yO657LnAkyjlKWSg1BweCP+943vI6bXs5vbTDTM1cj460blm9KjAlb4qqwSzF9+beRvRQKDQtFOeKkv36E79xvnxQ37diLd+asw/f+PTntewpV1TXOrQkPfJjQ1k/O8CJassFerwYwx674TFq0MVKBWuoLITEnBzeesWJz0ry9k1iheZf28hzwuwn4c9gD7t2CF6anC4HD/vBW5ABJPzPTCxmEShe33heCgw0n/rv55a9wkOaysH1PlfW41zxLy5A0ce46nHf/JDySIbU6lbdmJ8ZRruojXfDAZ/jBQ5MzbvfW7DXJ2hBU9lRV496354fWgzhk3Hs44q6J+GrVFuv4X0qs7UJJixyFf3+0GBVjx1vnoSgaw8PunIiZKzbjYUdZ/2zH9rXcX2ZwT1Gztu3YU4VT/+9j/OzJacbtw+7TgrXb8PSUYGxA1GQkFz0yBefe92mkfcLIZmGtZt2yZbH0v5Owa8mf9+a4VSHxJ35M2+tffmPt0/Tl3ybndt9ab3uHPvEZLUYj2xo0Uz0vgF17q7FuW2KezXbOzqW4nalvfyAmnclHXZvnp6XSk6/8dmfOBDH1mduxpyr5zPqHsyeaSn3+z2fLctK/YsA0boQAzvznp0kvhVKBhaIcMjfECrBw3XYcfMe7WL9tD/6ape/9ciXFsBAC3+7Yi0PHvYevV8dP3Whj9uot+GJZQljyaxHZyGQFuODBz0gFaqMKfgltZnCfM/7xSdK8fdebNM2F/jDvrqzB30MecIpG2/SiCNv30U+WkvqXLfpl9RdLvqbwsUnLkn7mgf0caPD9+JhF67db03rW1Eg88/ly6+LWlWZeSmm8T5MWb8RHC8K1ZLsrq5Nj9LL/fJGsDUHlsU+X4q/vLrCm/x6vLgyLyFX7tvGJODDbI+pKb5OYf6IFmtssl77wuieC26u/6PYLA1M57t4PMfaFoCX5uudmoqq6Bifc+yHe81x67/tgEX7quaN9uXILDrvzvaQl5v156/H50vTzp15e23O7cfueZArewD7azdNjKYBwbfnrXyYEzpqaVJ2wMPx2bK+KReu3o7K6Bs98vjz0nXLmPz9Nzu2+Br9Ly0bG9vTrYHq/6DFFuyurcfTd7+PTRdHKPJz/wCSMuj1+PEw27K6sdmKNX7V5F/k9bJqPoyRKWLZxByn2ZfXmXTjszom4O0fWCPWZO+mvH2HkbYkyFL6l+LkiqB0Vhu1OZeNJY9vTZjUrpfgiForyzEMfL8E3W3bjra/XYBfRYmFizZbdiuYO+HDBeqzavAv/+iCYES5Tqs0wtocUjx3zt49x9r9065f5QXDtWxw22U5b/m2a+wb1sK6yRKlZeaprJK57bibma6b7het0y5r52LsrMy/SVm3elfXaOO7+cW/ryzNW4YEPE2MzqUmWwMcWk/zz01biN89/ifs/MGc5zIa1W3cnXe7+MXEhBvzuDWzOUPV8x54qXPnfaVi/bQ+27KzEgN+9gZtfCbpHVFbXYPysb5L+5Tb8+23TsAoUj0ufiv8IRX0Jz1yxOXKNi8T8k52lZNH67VjhKZN8gd+WXUmHkl2RGlu4Y08VNu3Yi3lrt+E3zycEpnET5mKCl+76L+/Mx8pvd+HzCO5jQMIC5SeRUS+9zX3ux49PxZVPTktqwr9YtinUKhyoV+Z9lz63pVzraqQMVaz4+PEsHy8wL4CFEHjgw8X4zfNf4n+WRSjVIqmfk2k+K9MsRYvX78CSDTsiFy+dqQjm2b4S9T7Z2F1ZjQG/ewN3vpkoWr1g7bZQixyFQzMkTdiyqxLTlieEB9NrdZM2r66w1I078q73cSHBrdt36ctHPUJVCX3nG5kLgatxfJlwbV2zHTfOGDQJumpbutCr7vMsMX67GGChqIDYXhY6T05ejlkrNwe+k0gtOFRBXNcMqS5FvsQ+8rZ3cOV/zS4gUfpmIo52IKyon6mVyuoanPXPT/GjR4OxFFKmYnVmLN8cuQ/ZsGj9dvzvi5XWawsoi8mQk6PIanur7K6Tlz72ecYXWVzivtevenoG/vh64mWSOkdpTTHtC7ybdqS7YoZeu4h9OnTce8maKS967o8btu+x3oNBN7+J8V9+g7+8Mx/rtyeek8cnBd0jZq7YjCufnEZ6efovxLDFZRiuYm2qayT5mCZSwq29T1JK/GPiwuRC6PR/fIJzM7gX54Jj//wBDv/TxMB3Ybf67a/X4l0vhbRa3+fVmcF4z/MfmITj7/0g8B3VVUZA5MTod9JfP8Ipf0+v92IrOLnSS0FfVS2xZVclzv7XJFzx32lp/fOFQgHa4irdZh/OpxaX0zKRcjW0pf29m+gB4Pdn6YYdiXnUclwgka00bNH61JTlqBg73hpfFjUVvBrftmTDjqyC1f1jP+sF6h9374dZvRNsFtWLH5mCs/75aaR3/l4Hgfg7NBf3D+avT3O3lJI+Z0opsXBd5lgk0tiXwDbi/ddjuPXzysTmnZWocTCfZ+L9EAu53/OXLLHwYTGDxUrBhCIhRHchxEQhxNdCiNlCiKu879sIId4WQizw/m9dqD7mhuivwddmrcYNL36J0/4vWMtDytTL5NpnZ2DphsSCY3zapJB+zA3b9yS3+9f7i3BOlhrYMOIIRdZaCdq73G9fjwmokRLTPGFogqXIoC2deLZkOveUz308BIBqy8z8zpx1GbWC+rigChQuLICqFomSmtwmpAgRfOlFcbdUX0b+p+3EVNASblzD/ulZk2xjFUi/Bm98tQZLI1g5dG56+SuMvucDa6KBTDFa/gI7k6Vo9uqtuOvNeQGBxPYCzxSgDyQWyE5iKkVCwfLYp0tRVV2Dyuoa/PjxqfjRY1OTffE31I/22eJNmL82twuRTOgWIDUgXn1WbQUn/a2EAPZ4AsDXq7emXV/f8klNxLN1VyVpXrEJRVICO71n0rYwXxzhWdi0Yy+Ouvt9/Ob5WcYxVC4EKqtrcMRdE/GLp6anLaz9a+Ff7+emrkizfupxY7bF+dtfr8Vx936Il2eswrptu3H03e/j969mDtA3CYq+J0Sm2F6qw4TNou+/g6tqasipzLP1lAGA7z4YjPO88OEpuCJEIbnA8oz+/tXZuOK/CZfVhz5egtH3fOgsNnXoLW+RttPfqW/OpsdaA4m5/B8TF2L0PR9Ykw0BMdcc2i1VlQR5qCWeVwppKaoCcK2UciCAgwBcKYQYCGAsgHellP0AvOv9XSuhTh43vJCe1QwIugts2L43Gcyps3xTcHGsa0LufGNuMijUJXEWz6FxG4ZmUjUzROj3mfrwbUgthjnfbA1daEZJtw2Ev6DfnL0mWRMijA/mr8euvdVk4eTxT4Ma6Uc+WUJy1/Jx5T43ce46jH0+czag1P6qAGOPCcrnhOune7/pZfM90qF0z4XwrR7HvyaXP/EFjvnz+7Hb/K9X+2uOIQMikBC8bPiPnvqc6dNajQTemRPtJf+fz5Zaf1+/bQ+G3vIW/jEx+yBeAYFHP1mKm1+Zjf98tsxYT8d15q9te6rcukSGNLbIG9N7qqrTsuhlbE6Yx3fCApR59N82fk7WbsoSQJOG5QCARvXLjW5XZM8yKXGf54774vRVgbNQlVlCiKQw9paSECiZbtzbs6pGYvH67fjV/2ZljJG1zWe+y/XcNduw1RNk9KQt+qWcOG8dht7yVmick5/1NM0F0kXGT61R/x1cU0O/D7qLeTbYDlkjJaq0gqUfL9iAa56dAQB45JOlyRg43w3P5tqXC0zjgqpY3ltdgy8898U1W3Zbn8xYiiRtF1+JUcvkIQAFFIqklN9IKad5n7cBmAOgK4DTATzmbfYYgDMK0sEcI6X5JUt9h+gV300DdOW3qQdcSlrMShRMD9k0outa3CA8fz99ElbnP5tQFObacNJfP8JRd7+f9j3FDQqwv/R+8p8v0l1rvB2uf+FLXPjwFFz9zHTS/RciPWPT71/9Oq2f1PSzkdDO8eJHPw+tpWBKN/7KzNUB90HS+SKhda0YO94aV0ChYux4VIwdb/w9SnFUyvvlnxOzj4dKxBSlX6iwR0dKiT+9MTfw3Ntws0gK/wwknsG/vBMtqcyGDPfAty6O/9IutFHYXVmddNPcttucCpxivYqCqtHV0x0DbhccO/dWJxvM9LylhCfz/Fkm6NciW2ve7srqZFp6KaXR7apMCNwxwXfPFZryJWgRVouTqt17RCm/UCaAKUvT47l8y+oKT9l43v2Tku6HNuVimBBQMXY87ng9rGi12QK7u7I62Zafrjws46UpTiVOYeZM+Ba8yUs2orw8fFmpz19UC6up/lqwcfug1ofg9x+aHJrd1U+gEqcsStoxIzzBpmekfj26QoH6mC0i1Hp0QdB7Iy+HdEJRxBQJISoA7AdgMoCOUkrf/2sNgI6GfS4TQkwVQkxdv95tmtlcMnlxYhJ7lJiKeP22PUZNm76WMS1uVEuKa22nEJlTR4e98FVUi5eUElXVNagYOx4ne7Eevl+uHsinF1BLfa9YiizyX5QHNZNJOir6fXjKS9f75uy1JM2qgLDG4vjY3FzSs8/RBgd1sjdlaVq/bU8gZa/NYqreS9+C99SU9JS6krrgczw7U6yh9b1Fwobte0gCyBtfrUmrbbXi25QAnKmFeWu34Z/vL8JPn7DHtaXay9yn3ZXVyeKOP39qerKgo59kRHWF2bY7XuKTKLgsDKjOxWHDR3Uti8IPHpocyYKq801YuvwMmFySWjaun4qNCvld9axL1WCxJCEQgnxfs3W3nb92G96dm5jrbC7BZUIEMuip7pl6im61ho06/lcqz5k+L/mnUVUjA8lYNu3YizZNGwAAKto2MfYvUXsrnfs/TC9aLVITZBpjn5+F4+/9EAvXbUelJ4yEZUWl4nJJ8ObsNeT2JhneD7rw+OpMe0p2IPM5RB2BUS3boceMcFDTa4Fq1VUTn+QiI4/NnZD0/giJFS9WCi4UCSGaAXgewNVSysBKQCZWMKFXXEr5gJRypJRyZPv27fPQUzq2BbTvVkUNiJsaoqny0V82JhcvdW6P+n5atXlXxuJ58y2BiZMXb8QBt78TWtjUxE7fp127jrprnb/A1YMZ1etii7uJgu7zft8Hi3DHhHQNH3XhJCyaQAoS0pjqVtUEWlMlK0d/N8JLgGpYUI9do7mlqNfJds38tPXrFMHav+9h3bjvg/QFhqlPNraGZF/U+XbHXjz4kf14QCImcN3W3Rh52zuh/u46lz/xRVptq0079gSsazZ8RUCm+jtROPru9zHE849/debqZEFHn2WKu4mepta1IAoA37k/WtrzTNhe7H6yFltNkjA+WrAhYzV625Xx3yMZi4ArrazaHD4nqBbqnaEWBEUq8thdWWMca6/MXE2O4zP9pLsoTZwbruSpUrJ7CpiFMX0eMbr+yeAiLzBPKX+UlZnPy5SFsEmDeoajBms72bC9QgREMqD90sc+tyq9xs/K3opqYp1B0VlVnX0amJdnBJWsJne8TFlCfVzPPrlIL53tHCmUNqhJUKKw2qKMoIQW/MOBt0S+KKhQJISoj4RA9F8p5Qve12uFEJ293zsDINhOi4tF62kCDzWmyAR14KvHsVkXwmKSjvzTRJyXRZYoX5Dx4xfCILthaH+b5iZVELJNYNnMG+MmzMX9IQtw6rlQF7gmXrGY97drQuLyjTtRMXY8Xp25OpmqV2fGis2BiXmNRUtNncCl4bMAcNtrqfo2tqfAL7Q7e3WqeKl+dHX/JRt2ZBAEMyMESLW+Zq3cEpqNR2fdtj3JRYQvTCxctw1jn58VaaFNnS58X3kqW3dVQUqJqUs3Ge9tJqtFdU2N0SKQixIVlMxVYZksTfh9XLV5F3bvDbbtF4qcv3Y72UrqMtvSjr1V1kVJkPBBsmtvNeatSbyXwuoRpfYW+NJLIb1lV6Xxni7buDPwnMXJJKZbfS7WsoiGtZ1YIGf2iLAKaZZ7GBCKLA+cSYNve0bLY7zv0+Y6pYktuyqtx5uylFZEOs7zaXo/VNdk72Naryy4LDWdo5qYxnppHc8/+rO93KCcnB4hWUOUsgBhzFy5JbnWkrC/v+Ngu74uY8OKgUJmnxMAHgIwR0p5j/LTKwAu9D5fCODlfPctW9Q5IVu3KwnzgPxQq+1gevbV2gbvzjELRXtCYo0yZRnKNM3XK8/8IlBfRHryA5NfOGAWeErJfzUMyrtzo6XGi65Ze3VWQoC64/U5QYFJu05fKYKATbCnXl71fq0IFBpOWfckaMqBhB90cDvf2vrAh4vJRfTUPplc2YQIFvg1JUrYtrsymakr83GDf9/62hw8/fmK0ALPmfa3Lep2V1aT2/S5+815eGXmapxz3yT0uv71wG97q2oCcRb6sXyqaxCqJADc1ytTsc2vNsWBjl/u4OnPV1gX+NRT8YvaxkUdp49PWkaqjwTY5w6qZl21krq4d6Y2qCKCup2tTk+58put17ZTUm99ueVY+k+Uy1RWRp87KdemvExY3atMruMunkZTG9WSbikybadbRtV3VhyLioTM6ZrgyidT1n/VAhklPfZPPHdknTh685oambW72uTFNIG6NlJIS9GhAH4A4BghxAzv38kAxgE4TgixAMBo7++SYrESyPbarOwC9rbsqjS6T6z6NrhYM1fmTn2uVy6SRdbywXRDsgVVQ26br9TA1bAA7jCoL3LXcUIA8JGlCGEYYbERVD9iEwEhQ6YW+Ku37A60rF+lPyjpX62KtxgvGHUM67JI1OxU+v5xq4pTT2PrrnBXOiEEdhDTyupLBb9a+9tfB90WTbEyNVImiwfarn+cIOpte6qMmsqHP1mCP7wWXqzyPcXdqaqmxiio6/3Va/3kCl3Y1i2oKqqCpUWjcBeoKMOUGpNgupf695RnzrZEFgBemG6O/QwmylAUBzGe9c+0BdXHYRlFY/LRgg0k97lvlMLmid9SP+qJWkxuvoE4jbSDZe5DYrPUF+Vl9jgs3yI5WXFXtwkBuhuynlihxqIA+njBBpz8149ix+aZ3rHVNTKZOS8us1cHvRleV5KpmBK6ZH403UlFekvq3KHHPVOhprgHEmsnF8lxbOiePfUNyTNslKpyupDZ5z6WUgop5VAp5XDv3+tSyo1SymOllP2klKOllNHKeRcBD3xI85+0vcB8/u+9hcZJW5+YbD7UPvXLy3CrYZETZxAvXLfdWivFX/zpfBvI/hM88KwVqUlR9SfXz9dkKfKTWRQCPQ4qDDXRwG3j56S9zCjmaGvdHuXzCmIGMiBzxi8f6qI2uChJfVaL623N4AISxswsakgEXPoMAz5tQWp01zEfJy2+wfBs6XE/938QPnfUSOCThYnFZjGmi33LUldDv87UGiDZvlT13U2lDXRMQvqOPVWBczGVQAASWewo/DE081j6XKcvFFVcLj7STt3mimpYoOp1r3ZHLGeQdhzl85xvthq7lCnhj48eD6M+33pqeaMApr29KfaRTBZxX5n6xbJvk2Nw6cadgTGntqAnMNLHnHouen2nsS/MwtffbI3tZmValO/cW42735ofvo/Xn69WbbFaLVULne6aFmeoS82j7wvH5Uek4bPOrr3VqBg7HkfdNTHttyjKrL6/nYALH5li/N1WyysuR+wTHrdfXSNzks2wkBQ80UJdZq3lpeqjF39T/bD1JAKmKfdzJVlD+2YNzdrJGFPO7qoa7IqR4lszZgT43JBcwqahUbn6mRmR++MMgub36LvfT2ZTAtIzRr1o0ej62KxJ6qLO6gan9Ok+fTFueX/rQfYmTNpE9aW0ccdeen0RIrZR7KooHxDdwhWGLjCsNixS1O3CslWltgv+rS569MKSFJo3tAeO+9iyacZVaupFqKMyV1NQ2OpFUQSL+Wu3B85l6tLsF1evGBQMeneuenpG4O9HP1mCirHj0+YO05i0JcOxHdtmdTe9Lz7TEvNk64JXT5sgTAkZ4hJMtJD6XFUtjYoTk9Ljq1VmpVh5mSCv6tXm1UVuuiUqxRtfBZ8Xhwka01BPQ1XSvJfh3ny9eitO+fvHOOtfnxrHxZCuLZOfL340uPgPplZXBVhbBtPg35OXhLuG5SIhjMq1z80AkBB0VeK8/0LrOXr8/tXZsXxN1PeDfiVMAv19HyxKKutqCywU5QCqS406gZo0NvrgDFqNgr/NWhmuTVQ1Sof1a0eyKNnQTcRxHsCAG5fluOqzuHOv2T0gG3bsqUJNjXSSVcZ4bZVftu+pCqR+zXTU7Xuq8Mzny4MaQ8tFp06yap8qq829SNPqES8T5XJW18g0AW/9tj3Y7w9vpSU7cJFO/tFPliY/Z3u3bd3RFySmY6W5EhK3o6IKgYs3pARk3dpkepT0zI4q1HGmP6em3XQFULZFb8sJ8Yw+QW2vRRCI5LITH7XuTNh1vuXVhLVfndt3V5oLP2/YFqX2VrjlxI+7Ct8n9VlP5GB2d6NdwWaaYP7E5GWGLWlcdEhF4G+T9VgIYL0hYUacJEmzV2+NpXi0zvXa4FizZXfy+u9V4kpsrqMqeu1DE6qlyGZ5UZ9pIYCT/5bIqrl4vTkhTu/2zZKf9Zo6pqtnd/eWViHCJ06yEJ0qy3vUJDC7UK6prN0aL8mLqkC1eYOoLpc2JePamK6EhYaFohxDXbfrWp5UA8E/awwvLCCV7ltHNUc/9PESWocsqObStVt2G4vEhfHs1BX4x8SFwYlAXxgqP6nTxd/eDRaAjOKHa2Lb7koMuvlN3P3WvKRPdy7IRn47/p4P8Jvnvwy8fNK8XIhZk+Kgv1CpL/aAK4rhtbWnqgZasiFMnLsO3+6sxMNagL+Ls1LdkEz3RC/eatpOF9JV9LSyRlc9YwtB9No/Jqi3XhdAVKgFf6nH0s/RtN997wetlXpsSlQaRPGDJ/rABLXTqe91l7FsURN92KzCEjLpbvuqUhRZ57B+7azHU09ZVdioU6xfrDTsN9ucYLoP5PIF2na22nOm/QLJGtLqD6X6rnpfvPP1Wtz08myEEWcu+sVT02O9B9T+ZspSdsGDn+HKJ6dhb1VNIGGGnnXUtz7p7b1myUyoslpRWNjuY6AUg+5yaHRNNEOJJ0vbBzSB54EM5RwofGtxC1wew+15s5Z4ynUsUaZyK2HoNb9MuKwll09YKMox1Do56gJfzQKmv2zUhSYlsxuQnkXHtEDT3dZMlhNVe7Zm6+5AYGgmfv2/WbjrzXmBiU93I1RdQmxalPeUTHqmVNOZ8AW8l2esjhUgSjW5W91QLL9t2VmZdKlSM1DpAuFyLbubCTVeKa6gRp2Xg7WJwreprqlJu8e2IpPZQrEI6Jm+1OukZuWxZQRboFVrN1tn9ec7fDv95WhCv6em627LrEW1mFI1nPqL3LTfPE2wUDWeVKFQRU/taxvvVEE/2EbqPOavpWeaoqAuKPTrovdn384tAABn7d816yQtQLAQqDo+ddnmrdnhAfA6jeqHLzOoyrT0xXSce6V8r91r9a+XlYyFVI8PvQ0besZYE89OTdW3Us9+t+aqHrgyQmClF0OaNp5VHaSUSQvjza8EhT7qtdX7YcI2lZh+sin1TM/phu17yYtwU1IRF7ExcfSRtrlYL4Aep+6i7Z5S46uCWYBpx3WhtC4ELBTlGJs5VUUtDqouznW3JvWvVo0bkNpO04wZtntLy4Rl8r3Vs97FQe2Snr5WjZmwZcpThbM3vsquUN2qzbtIBTszYZqAbFYF29yh3gO1aT2GQ71O6iJC747qRhB3ylLP0aYhp8yJe6pqzAlHciAV1cSY3NUX8QTiONtZqVnXlGMFMi9qffhy1ebQ9ly8X9RjubYmUo8LmJMQ2BYH97wdHrxtg6o00gPqbZeaGmOSLeRU7Uitd/VsZCo2pczarbtJbpu6MKtaVG3XzDR243otZPso6MIYeR5QtluoxWpShQmqlv2fitVU3UdXWOj1w/z1AlU4TrPCURVeMZSBZPd0m9VHhn9esmEH7nh9bsZ9bJjCD6K0F0cp0bRBufG3T7V4Heq5NG9UP/I+NijKRCBRf8+HuvYtNlgoyjHqxD/VkEAACErVtsWByd/bRmPLQ6dOuPphTZPYwC4tSMe1oU4eHxoy1AF2H2d1Qndiqo0xe5hcFnX+8Gp4xj/APsmoiTXsBVVTn/dUp176NkujDfX+LNDqLahWC5v/MuVYyzbuTFtsmbLgUS0T1sKNWc7T1HT26cdJfbHToiE31haJaZHMpcBJvZZ6n742ZFLTA+pVbAsqk0uJLQYmEOsA+kI7KCQon4n7u8bm6qi6Ri1eb56nPl6wwZhOXdVM6wVke7ZrkvxsG5+me0ddNPVo0yTwd7bP8FNTVgS/iNGeLUPoFotV1zbGjSi76Bnn3leLsRNjTtV5VN+MLOwo93SZoXgpEEwSQXUppips9L2/MM7NwS1Na4UplvWZiq4sjpOgIRhrZT7fK47uG/hbvT/Uwt8uRBP1GbadrloeI0rx7GKChaI8YkvXrAoGtgJ16vuFqnjRK2mrg3rTjtTDaRPGVOJqmeNMHnurzPsE3N1cLPJi7KMLbQGNinK+m2yFE4kH1rWCJlRXQj24MxAHQLwf+oJKFQRtL1GqVUa/dWo8hUqswn3aPnEWVOo+egptE9SU6Xu19Kmm/WzFelUes2SBU09drx0TzOrkls2aW4ravvritM0rtsdbdTVS0V3a1Oafm7pS+T7Yui39tRo8rO6VR8NbwNLxu5e+Sn7Wx7paKsEWEK16GaYtkrW6PSrDu7dKHdvS3zjJBdRzOah3m2CfHGcJq6IGKal9sLx8X1IW/9RxYXMlVBVUkxwX1UyPr6Ltpy6SbW5SqmJC95ZQj6Wmt6fGFOnu7qpheKMidOjnlCkuKxN6Jkj1vKhjXbWyphcCTrXRsJ7ZBXgNMZGBVUmI6PO+9flTfrKt3YoZForyCPW9adMmmfxhrcclHlifID83xAplSrtpQs0kQ62fY7MAqRq/uL702b5fbe/TbYFK3ObtqPWB9uvRitirFLqLClXjQ8XuNkOzaprG55qYWXSoZCsg2RjQKWhNDWQtU05Y16bFWVyr8wA1488dE4KuJjOJriPakUlb/fmtoJCrXosde1Lnb1PK2LSppphGvT01BkxdDFTXBFMv22ILVE1o3JiqbFEXodOWb06OmQc/WhK0Xlm6t1WJ0bLNnep80aCeeblgey5McyRZuNFORK8zlC13vxWuhNHRx0zwtxS2WMM/KQofdcztsGSHI88Jyob6pd1tcN+2JZ2woZ6+rnSlol7PJcrawJ40IbWPno5efd7VZCF/1RI0qaEKLt6BawKKEqqVK3Vg/f2v9qlF4/qB33QlGqV99V7Z1pa2epMmt8X046Zwkc23ELBQlE+Ik4dNY0pdaFLbU9Gzozz9ebgG9k1inZp0FMsJUfNtc1MY2Dm18MynO5CK7lseaC8wf5kbN8WR6NiKslEnrTgTld0dzWYpUj9HP67uVhlnitWDpVV3RPXFsdGSjS0O//simMlQ7bs6VNNTckcfyJ9b6uWoC/d5ljgVqlWvJoZQrY/bxcozEwzkp7kN65isaPoCwObqmf3r29z3SkUqoKZGtqEXtFTHTMA1yjIp7jZot3VvBlUAs2bzs42ZLFeeLoxw6qUY0bN14DdqLImK7vlnmn9t8XRqvRrb2Cd7ZigH09/lav0zW2kHk6eDjnpPbQ4mQWWQ+Tc1O5xVKFL20a1rZYGxn/pej8/TXRAp2NxUqYKKClWwaKKFPnzngUm0NtQxqLSop6On9mnV5p2k7dSMoUuJ6d2LDRaK8gjVnZguFGXboyDUl0PrJuYED/oLW+VbYgYtFWrAcVymK24lcdw8KrUJUb0n6gLcZg1yEkSvfra09/HCzPUa4h5XjzlQxyo11astVXScPtmy66jX6dbXLDFfxPuz1ZIhzRTIH2fM6dgys0nLQikO24npulX0tK/VhjmM6r6ro+41R1nU27K26cRZt1N7q7ZtKkwdBWr9JmrMgcoPHgoWy1TjQOprlqLJi1PnYhvHcYQiN1bs8Eb26dgs9PsoVBNd7qYus8QRK/fHLhTR++Vz40tfBv5WC95OX745+dkmOL8wzVxEXFWu2Vx7beNCjVVdqHy2Z59LobvtqbGuVOWSnvnWxJ4YBeptWBVPFgWVuh4yJawBgoqoud+k9mlqKcZte07//NZ80nbqdXJR/qUQsFCUR6gPqlUhl6X2PS6qhtN21JWbzQuvc++bZPwtDibtuw1dU/uLp6ZHPq5avNaWInMBsYo82YvE2oYykdKaIx/XqqlU2pijx8wpv9nqN6jYrGFxoLqDuPDVt7lJmRKk2F5sVH702NTkZ91qpArjh/a116pJYrlk6kiIO/uo6XzVRZPVUqR8ti32VY1ulOnRpFSwBQu3aZZSDuUzpogan2d1cyb2V1VS6JYi1S3JvsgzdIHYh55tm2TeKAM2600cTX96HE34SdqEYHWPuAqBAMoF1d9zqjJM/W3Vt+Yizjalgnr6eibUwHbKpbWVzbC5DwaPmzqwrvCKogTxoQo7VAtvnBp+OtQYJVtowetfppSQajxhfS0jp6rEHty1pbE99b1sLS+i9DdOrF4xwEJRHqHOe7YJstKSztdE66b1M2+UgZXK5GnLFqfXBjHhQpxTF5rvzFlr3E41l+vWjGB7tOOqk4yLWCHqREpdRFDTk7s4bsDHW3NncO2qR90ujv+z9VjU60RcaaoLAD1zk3qtdZ95Cqu08R3HEvXYpKWk7ezuG8TjKpvNUDTYtmNd+vjUwG9OBBLlAO/PS81vt2vlAlSaK1pXas0dW2YyKrYrGy8mzfyb+gzrCypqn0zoRZJV1MQ0ce+v6bz0RZ2eXdOEKszrChCTu1JapjsD9iQj5t9MZSRsRkK1f/r+geLE5iZipeSeZnm+1f7aBBD1sEf3b0/qgw3beagWWUrxV4C+hrAdVy1Sb40vt7VPfPeqIRJUwZz6rFdzSm4mE1TfYJtJW11o2xadqvBkc3dTOTxD1XMKVMHPdfCxrXgidXGubmVLBa5up09u6mJwriXboAq5fAPNYBO7kG2wvVSLj09aZtxOVQbpKXbV82rRKHvBnC7syNDP6e1RBTDSZvY2lM+2VMTqsRYSF2vW4yrtPW7R6Ko894V5IafOTfZrSzpUoAVbens1RksVWoB4MSd6/9Q/VYunbeyri2Q1C5yOOkdc/cwM43bUuDbdXVK1VgdqlJFas6NaRGxZUeNkhrS5Rv9Hue5xnz+Ty2rcBC53vpFKTkItpmxDVY5Y1wYxBrjr92vc9ls2ps376hj+7YvmZ0m9kbbxSMV2Fqf87ePkZ2oyCepVt2XsU5PllFuUzLZnTk0+Qy6S7GDtpv5UyYkWmIxQLUUOEi2ofsMfLaDFkejbxRnS1IxM974TvRijjgutjIkbLYscmwuj+rfNHB1sj2qxoUlFlUSzNfWy2FKYqk1s0zR86rWgZ1Sk9cmuJUt9tgogymebNpY8eogBwi7cXuP4a+8gxgOt2GS2plKXIdSaH9RLYcvoNdWSaEKFqvmmVo2/4cVU3Iat8PNjn5oFq2AfSJvhmmdnavspQpFykk6EeaUN23vJ9fKHao23oQrtao03m6eDDbWN/05eHvjtbcVTgdrfV2auTn7euMMsqFEXqy9MSykObGOYfD0tx6WO1S6tGpO2o8Zoqff0L+8ssGxJw3Yt1Fipz6ju1VQLmuUCqvG3trWBbT2gukuqyYbsrrfxEnKpqAJdHE+HYoCFojxCXVDodT1U1IepXwdzwOj2PTRNlm1yi5OlhWqCjZPxRyeO/24cs3/acdUFlDaXv/5lynWNWlvGRUaq3UrsQxzLWNpvxJfeCmWCfFhbqJMtMbRDkdv+dFHqhWBbTKtNuHDpt2nktuxKjQXb7Qm4r1j6ZEsMoaK24SJ+ScXuOkqbO5wkmiCmvo+RxMuKLZufCrX8QKzCngi+E9QaS7bEHwGLkuV81XnQNre7jm/9QBFc4rb8rOIa9JWl9hQV9Rz1eeUJxbJFHWdqAPyJf/kou84hqAi1xzfS2tPdnwYpRdupJRao7yKb5UTFfRg1rUHqu5zsWubAikJ+5ojbUYutPvyJWSGnDn0X65pCwEJRHqFaUV5VNEg6ag2NfZWU1DprttAWJXGCTG3kMd6YTEBLb/O1VrabucL8En1didm574NF2XQNAPCnN8x1MtQ+fbbIrK1S6zK4WKBQm1DTs+uaoUChWEsbeoE6ChPnmbW9quvoOK0eTwBi/1RscRW2Og8PfLg4+dn2QtSL7bqE6spiQw1uVs9Cvy6O39dkbDWG7DFQqc9k4Yk4alwI3DbUBeUqZR5Q3bOs6bQtqGPVnjKd1l5aMhYD6j2wWXZs93v26tSx4rj36dieW6o2XkVN7mI7jziucHHfAY3rp1JAP/6Z2cJJr2eU2m6klgo9sJ0DRZ4K9RnetZfqVeFWaLMp6wLtWX57bw6tXiT1PUy1FH2y0LwOceHSWGhYKCoxxitZRfSgahVbXv1cYtNOuob6rlAnZmuiBWXKsGk5dhI1IONnmYVbKuopTltOcxOiJn2hVrq2oS6U9GBUau0b+jSaasRmlVGPZbNYUM9RvU6N6pWbt7O0oQYZ5/PZVK/Fwb3bkrazoV539bpUalpl6uLFtfXKFrOjoi/CqAUOVfRzLhRq39UsUer9tgWK67VQgm3TXGCpi3DVLc6G2twrFiXhc1PN8W9qnI6L8IZCFaO84r/TIu9js7w0qm++3yq6wlQ9/y+o7yLlRrZpao5t/tSi8FMhZxMltve7ly3xS4HjkjYj989WKoJ6XKo79EfEMhwuEtaUvkjEQlFecTFggosS83ZUc7RrVL/rXEOdgLbuSk0eeuaqOIwe2JG0HTX7nA1JFCxitW35jboAUCdSPe222oStCG8c7K484Z91JigWP9uzqV4L2+KSen+oGalcY3vpURe1qobzqqdnGLejPpt/cRBbqEIVsvRFnWrBcP2cUbXRVBcdHdU6dJuSLa9Xu6ak/bu2Nsd9qAqWDywWG+rr5j+WxBVxoCY8cGE9p8aauU7PPtMiSHZvE37vvrG8h+euiWeNVtcUaup/PWW6evrquHjra3OGWFsmQhXqXXzra1oGVlu8rAp1fFP752J9Rh3StvGjst5xAfNShYWiPGJLG01FrcruelHrAif1FhQa1TcP0WHdW5HaoPr0U2c0m2ZVxVZUMw627gWFJ9qJfGLRIFGL5tqSJKja+HXEInk2NxJVHrHXtEkd1/YcXP9CKlDe5tqqCk+2lxnV8hSnqKYLbOdI1dTaEleoqLU2urRsZG4vj8ob9fT1DHYqcYUTE88rAfA2qFYUavs2a/dsYoyNKkz8/b2F5u3IsSOO3bUtrxs12Ufc5Aoq1BTDroXqHZaA9RE9wl3SbPOemobZRv+OzY1t2pJuqPNMY6JVynVMEdUVjPqupFr3qf0jJ27IY1HkTx0UdqdmWC5mWCjKIzZNCZXTh3dJfrY90IUy9bsWimyncTzRYvOIJTAwl9SL6cevEijkZrnfgRoaxFt/08uzjb+5iDWzZVRSUbv7hMWPXbVE2V7K6tiPG7yusidGEgsbVI2zCwrl4KUmaenexlx8820Hc6JrXMSfxMF1sgLbYlpN925PtEA7FtUdiIrJAqJDtcbHFThV9OyapQo1tq6hppBUBdpdFo+VOHMkNfvcl6s2k7bbvIs2Llwvk6jP8HRLzSYV2/iOM128P98ch+RCoKkFMhELRfnExYKqe+vUAsPWGjWTCBW65cmxUORiEeo4iJM6GVEX5K2amAPg73k75V5kO43/KMJEPhMt2HiHGAiq8vIMWhwWNRMW1arnAnKK+BKt3wDQX3ovTV+V/Hz68K456k3t4n9TaRYlKm6SC9AWq9RMpdShT12gPTVleeaNkF8lYT6fb9fvWxU9Q+zZ+3cL3c72vqG+i6j3h+oeu3ozzTOBnCGtQIt9a2mQGCovmzDmZiyVvlTEQlGJodag6WbxBX/mc7cvWNUSYcN18hEX7jXUJqiZkcjphonHpfrF5zLFtQ71ZeY6yxg1hfarliQW05ZtTn6m3ntb0hLXUOPzv16dfSa63Yq1oIfFYkPFFvSuohZiVev5lALfOijMGQdb/Y842BaawSK8ljaIY1WPJzRhe2epUN00qeTTTfPvE81uhq6JowCzrX1tcSVxFEzUy164lNw06O54Oe6Igush7aJYOFuKmLzz0EcpV7B9OjQ3btenPS3Ilv4QF592m6yZJ25IfRFTFwCu3WFcWLKGE+OwCuVCZCfVJ1uMgPqCzWetBNeWoqUbaUHANm5/PRV4Hyf1uY4tFqfYKcohrUDNJkXFNszUtYtNeKLOA00b1iNtZ8saqVLpOO6OqnhygesyFzaolnUqVIWfiloOQmfCV98Yf1MZ3KUlaTtqYhbX769XZqzKvBHiXb+4PKkUED5in/ZZt+ci5r041w3RYKGoxCAXESOOzR3EReOcb2iB9w8q9ViKBdfCyfNf0CZI1/NDM+LCo10zc+pTsmBF3i5/rnqq6T+fixzX95Fe2NQttSEINhuK/XXtOj25LaGHKggtsGSGpLo12ep3qXy2eFPmjSIcl4mO7U6pxcd14syDWy2Jc1RsLuQqSzfSrKmu4zZXE7Pq3l+g9U/f9s0KclwdFxl3Cw0LRY7JtaRMbZ+a5UfNOnV4v3bG7d6bS4sPman5IecS6pWmWoCowtOarbQJ0vXETE1r3aKR+QUzj5iOlXotqGuXBQ5M86p534XVg8oeoua3kvjMTV5CWxi6FubLiQvX2kpt0GJG4a/vLjD+piZw2WSxfK8jxgq5vrT5TEZS14grb95hK4RtgCrcukhCpVLXZGoXykkmAQtFtRSqS/b/vnAbe5RPHvt0KWk7qr8ydbFaKKhaGDUVrQ41Noz8UnEwF1Mn9HcVwdyWWcu1QYT6jDz6yVK3B3b9nuP3Zp1ip+UZmbosNUf86Y15xu2ocQauhxY1/TUTnTguXicMomV61SlUvcR8ujAWA6xDcAcLRSWGOslc+9xM43ZxtKKuNdO5hirEuAggjEPTPGY+cw05gQKvtJPYaizF4aMF2deNYFLkM74sX8RVAFDdpqm4fnUUajFdDFCL7uaTJg1orts6LsZFc6LbeF2G3U3dkVEoEkJ0EEKcKYS4UghxiRBilBCChSkDuZYrqHEBejpNE8Gin7TtGBqdW9IyLRUjVGFnk+NCl6VMZ0uR0jh8TcyGyNCgWklLiZ8e2SfWfq4NMa6VI3V5kdemqTkmtFC8OJ0WR6vjYt1QW+pD5RIXCm0XNf1qA0bhRghxtBDiTQDjAZwEoDOAgQBuBPClEOL3QogW2RxcCPGwEGKdEOIr5bs2Qoi3hRALvP/DSzbXUVxbc+qyRi7XlLJffKN6NCvXovXZZ0hzbWzq0Lyh2waJZJvIwJYgwwVqmmymbuNayeV6qttrSRLhukB4sVGbFJC8vMgPLtaFjXPo2dK2CAV9EzaLz8kAfiylPEBKeZmU8kYp5XVSytMADAMwHcBxWR7/UQAnat+NBfCulLIfgHe9v0uGXM8BrudL1aWk1NznCkVvontDKRfpzGvRU8ft1S8vjCE72+cn14/fvW/T0tkypUPcIVPKc31ttyLVprMb/yUtJTeTHU6eiRwOvI079mJLgeq/RcW4epBS/kpKGVouWkpZJaV8SUr5fDYHl1J+CEAPDDkdwGPe58cAnJHNMRg7ajCu7T1pcrdoVL/ueVKS6x6V2Mv7tGFdkp/z2XX3WuvCXPfKLH2Scq9QKa3xSMVFUdq6hmvXXmrqZSYzaumBUqe2C7DFwkYHqbBz7aa4dhsta2+hsbnPXSOE+FHI9z8SQlydwz51lFL66oU1AELTngghLhNCTBVCTF2/vngKCuY8JXcOl062rpuKZbZtWhhXpUJCdYsrZW1sPjmwd1un7X28sDAJCrItfldbhZZc07qEXDNcE3fIuHbVLFR9FqY0cR1/Wdd5l1gyhcmMTc3/PQCPh3z/HwCX5KY7QWRilRA67UspH5BSjpRSjmzfPvtqvqUCr5sKD7EcTUm7z+VToLPVVYpDsaRjPbRvNGEv55aiHLdfKGau2FzoLhSMpz8PdebIyN6q2joamFKgtseFMemUytrVJhTVk1Km2cSllHthL4qcLWuFEJ0BwPu/pETgXN93aiHJOMSxQtVF7Tb1nEst0YLa23ymMa+tab0/Wbgx80YKuRaiS2w4MgQ2x/TTr7QkMig2hnZrWegulARdW5VOtlOei+oepfKetwlFZUKINNe1sO8c8wqAC73PFwJ4OcfHYzxK2LCRV1ZvofnGlrI/ddwUrHFw/YIUrqu35olcj5ZdleZinkzdoopq7i4CqOUl6jq92xdffSOG8SkWD45M2ISiuwCMF0IcKYRo7v07CsBrAO52cXAhxFMAJgHoL4RY6cUwjQNwnBBiAYDR3t8lQylrQOJYfUr4dHNOKQtF+eTRT5cWugvFAQ8XJk9kmxSEKT5KqdhzXfQwqevc9ea8QneBhLFUsJTycSHEegB/ADAYiVf2bAA3SSknuDi4lPICw0/HumifiUYh1/ADOjXH3DXbCteBHFBq2ecKxRfLvnXa3qpvdzltL1/waGHyRacWHOjOFA6e6+oeLjLk5QNrPmUp5QQp5ZFSyrZSynbeZycCUW2lkH6To3q1yWr/GTECll0pfNrmuHBlIWBlWGFYs7U0Un/qsPbULUO6ciyKiW+ILsAMkwt4/NU9ykqkeostJfeNQgjjKlsIcYwQ4pTcdIuJRQmvqWrjenB7jvP+u+br1ey7X0hq4SNQUL7i8Wwk2/TxDMMwUSgrkVhfo/scgC8BvCqE2A1gGoD1ABoB6AdgOIB3APwx1x0sNQq5uC9EXRxXlrHaKBSVGovW7yh0F+o0/Ay4ha8nwzBMcVAqCVNsMUUvA3hZCNEPwKEAOgPYCuAJAJdJKUvTcb8WU4g1wNqte5y0UyrpGhkmV/AzwDAMwzCFw2YpAgBIKRcAWJCHvjBZUsoxCSXcdYZxwu7K0khZyjAMwzC1kRIJfWIolLJcMXnJpkJ3gWEYhmGYOkr3NqVTAJfJDSwUOaYQcT0+bG1hGIZhGIaJDq+hGBaKHPPh/MIVUOPnmWEYhmEYJjosFDEZhSIhxD5CiHeFEF95fw8VQtyY+66VJnurCxcXMDNGnSGGYRiGYZi6TqmV0WDcQ7EUPQjgegCVACClnAXg/Fx2qpQp5WQHDMMwDMMwdZFChj8wxQFFKGoipZyifcfiNMMwDMMwDFMrKI3yokwuoQhFG4QQfeCFrAghzgHwTU57xTAMwzAMwzB5Yhu7z9V5MtYpAnAlgAcADBBCrAKwBMD3ctqrEoatrwzDMAzDMKUFr98Yq1AkhCgHcIWUcrQQoimAMinltvx0rTThqvQMwzAMwzAMU1pYhSIpZbUQ4jDv8478dKm0YU0DwzAMUyiE4PcQwzBMHCjuc9OFEK8AeA5AUjCSUr6Qs16VMDX8MmIYhmEKBAtEDMMw8aAIRY0AbARwjPKdBMBCEcMwDMMwDMMwJU9GoUhKeXE+OlJb4DpFDMMwDMMwDFNaZBSKhBCPAOnZA6SUl+SkRyUOi0QMwzB2ygS7GjMJfnx4Lzz40ZJCdyNvDOnaEl+u2lLobjAMEwLFfe415XMjAGcCWJ2b7tQC+EXPMAxjhQUixqdR/fJCdyGvCK4QyjBFC8V97nn1byHEUwA+zlmPShxOyc0wDMMwNEQdkxLq2vkyTClRFmOffgA6uO5IbYFDihiGYRiGRl0TEWau2FzoLjAMY4ASU7QNQaewNQB+k7MelTivzmLPQoZhGIah8L8vVha6CwzDMABo7nPN89GR2sInCzcWugsMwzAMUxJUVtcUugsMwzAACO5zQoh3Kd8xDMMwDMNE4fwDuhe6CwzDMAAsliIhRCMATQC0E0K0Rsr1twWArnnoG8MwDMMwtZj12/cUugsMwzAA7O5zPwFwNYAuAL5ASijaCuD/ctsthmEYhmFqO5yNjWGYYsHoPiel/KuUsheA66SUvaWUvbx/w6SULBQxDMMwDMMUKb3bNS10F5wxpGvLQneBicGoXm0K3YVIZIwpklL+XQgxWAhxnhDih/6/XHdMCHGiEGKeEGKhEGJsro/HMAzDMEx+mfPNVqftdW3V2Gl7pUxZWe2xwjVuULeK/NYWfnhwz0J3IRKURAs3A/i79+9oAH8CcFouOyWEKAfwDwAnARgI4AIhxMBcHpNhGIZhmPyyZVel0/ZaN63vtL1SpnmjjAmGGSanlJWYeyyleOs5AI4FsEZKeTGAYQBybcccBWChlHKxlHIvgKcBnJ7jYzIMwzAMk0dcp+SOU0B9ePdWTvtQLAzq0qLQXXDGlCWbCt2FAI3r123LVS+ia2ZpiUQ0oWiXlLIGQJUQogWAdQBynUOzK4AVyt8roWW8E0JcJoSYKoSYun79+hx3h8mG2jQxMwzDMO4QRbBsKjFlNpm9VVwDKldIxJC+axE92zYhbVdqzxZFKJoqhGgF4EEkstBNAzApl52iIKV8QEo5Uko5sn379oXuDmMhjuaOoXFARetCd4FhmBxx45h9C92F2DSoR1leMHUNF+Pi6P685is09HVdaUlF1tEpErky75BSbpZS3gfgOAAXem50uWQVgtaobt53TAlSapqCUqJji0aBv284eUCBesIw+aUuWKAb1gEXHdfvh6YNo8fR1NZXVDG6eJ0ypHPWbfRp38xBT5hsaN2EFrtXaus/q1AkpZQAXlf+XiqlnJXzXgGfA+gnhOglhGgA4HwAr+ThuHln3861/8XO5A69xkf75g0L1BOGyS+lFsAbhwbltHPs0rJR5o2KiHNGdEt+dn0Xf3JE78j71FZnhsYNii/Rwun7dc28UQZaNi58Mo1i9IDp37F53o7VumkD0nalNk9T7JjThBAH5LwnClLKKgA/A/AmgDkAnpVSzs5nH/JFaQ2XeBTj5FEocp0htRj8811wWYyFjY1ieIkybimxd20shnZrVegu5ISRPXPn9tuwHs06UoxWFNcUY0ZuF32iPvtN61ga72KMc/Jjj+oTFTyFhiIUHQhgkhBikRBilhDiSyFEzq1FUsrXpZT7SCn7SClvz/XxCkUborRdylAf0wGd8qflKBTljt9Sugm7tiwUOzi2eNWW68LukSlqyS21Ql24F99SCLj3vOHG3w7t2y75Wbd2Z0uPNrQA8OMHdUz1wWkPioc4WvoWDtJ4XzCqh/G3cgf3mzpmqnOokS3GdOfFqIAWALq3aYxTh3YpdFdIUISiEwD0AXAMgFMBnOL9zzigGDXYrovfyWJ8UgtEvTK3wcdXj97HaXs6xTg+VXKpcS5G3pu7rtBdyDnFqByhpp91reSqIB63GDl6gDkYvpEi7B3dv4PT45aXiEY6GxqU094jcXRwLgq+2gTTfBaUza07WfGNs3yutKgCd0tPcVsqq8CMT5aUchkSSQ+O8T7vpOzH0CjGitPUuBTXi5cflFjl4zi4uN2H9Gmb/NxEcw9QJyoXC7T6xJdvoWhFDPasLUxfvrnQXYgNdSE3kJhAwbWFwQZ1cTWwQDGixfcWsaPeuo4tHFuFLb/p82UuyWX2vbP2p8XlxHlGXMSA1FgUofmMMWnfPBVrd81xuVUgxmG/Hq2ctpfP+Yd6Fzs0b1RSbv0Zn1ohxM0AfgPgeu+r+gCeyGWn6hL1i1AoonaJakWwTZAqtbWAnooLIfgnR/Yx/qa+b1yMrFy+v0ZVtDH+5tq46Po0msXIcOWCQmnbbAqQhsTF3/49W5G2s71A1cVgPqfO3VXVpO16ty9dy45r9lSmauSM3tdsDXI9pm1zlmqhUueYaTGVDUfsY7aGXXVsv1htUmhEdKuMM39v2rE3+k4aeyz1kVzo2ejnlbrJVY6LBLt4N1KbOExxN7Vx86kD43eGAUCz+JwJ4DQAOwBASrkaQPH5N5Qo+dR2UqHGvVBfZtQFrm27IrxMsaC+zOKyYO325GfqNbPF7+Tysjesn/3bsc55Zhbh+T754wNJ21E1xLbNvtm8W9mu+CYF167HpYx6v1s1CVqtTXfu/AOyrwtvE6o374y+4LfFjhSqXs55I1PX6dRh5liNQmnon56y3PKrg5giYhvq+8H1fHGMxe2TagGiTucdLNbUnx3dN/k511a4OpGchLDNXi81twQAIQSrwhziYgw3crC4VMnnYkNdkOfT5H6Wg7SgKu2a0VxAXEwqK7/dafxt/JffJD9T4yDaWvqey0n2jOHZ3wNq91zLEtRgbtfkM7vQqF4pS55tThjR02zxU6HeK9tmkxZvJG3nmmJPK9vXQeyE63T+Qnkt2fRs6k9HWiwv+3TMvjaNGqNFvaW2e9+5pVkIDljtHQ+florb8NkWV7pCeT/vqjRbVl1YeKnzoFrLzPUzfNuZg5Ofda+Zpy87KPn5glE0Qf/XJ/aP1Y84p/XIRfESSndSUv8X+ZQYG8oj86wQ4n4ArYQQPwbwDoAHc9utuoOLcTVmiNusHq7dUmzT1wM/HJn8XGOzFLnrDoD8umyouLi2i9btIB2X6t5YqEQYw7q3NP6mvvRsrmqFshSNGZp9AcI45PN81RSqzR24C1K1u9TFSz4FFepzS709B/dum3mjCFx1bN/MG+UZEfhMtRKat+vemqaIsA2L04dFV8TY2mvakKbk+s2JbrNGUkd+oaypUpoX+S4ysFLHkyrou17XqPG2Y08K3l81LfwxAzrChDqfX3FU8BkOCFOWiSXOO2FwV/O7d8JVh5Pa6NC8tGqjUaEkWrgbwP8APA9gHwA3SSn/nuuO1RVcvNhPcbxAI2cc0x7GIZYHzYTqmpDPxTk1zsk1Ll5StgDewEKEeCxb8HFu36nmxqm3x4X1oZRwPWptz7o6N+3TKXstPTWejnxP83pT3R7MdYKQcmJWS1sMTC5dwfJ5T10Pi6YxC6CqC/fLLXGg2VKMbqT7dGyGPu3D5wwXax5yE8qG1H2OivEc2BISubGM2X6L/lawXYt6SocP7GX2ArjwkIrIxy0FqMbVLwF8BOBD7zPjiCKcz3DjmHjBeqrJWMUmgKg1C1o3MWdLUyfSMUPyp6Wn1suJMUfH5vB+5qDLOIkWbIVS43SX6rZnQz0Pm2ZRj1UwUYShOLGgZnyk+rRTX44uYhNcPyNDu0VXwsTFtZa5UFlH1WzVeheu0tL7m9yyrx4dPYGAfk/VhXyZ4bMO+Rm2ueoRL/vFh1YkP193gjlrme25yOUt3rGnirRdHKuMPn/HOY8RPVtjmKHwsOtafTbURFZU4fG2MwZn3kjD1rLtsCbBUce1Epd6B3Rhr62S0TbKffzn9/bPaeIRl1Cyz10KYAqAswCcA+AzIcQlue4YUziaxnSVMe1XVW0RipQHy1qTQ3n+XMdV2IQCataXfFosgoJPsMV9lNgCap9sWq44Wsg/njmEtB3V9e93p5iF9G6taYHtNTbfzBLiIqJ2jpq623Z3DyWOfSr0oUTb8NrjzT741HFBxUVSmZ8fk3KPiVPA0hYHSV2fHDMgFRyuL2r0Nlo0CrdmUd1mmiv72+aRkxVPB9tpUD0JTP2OgtpGo3rx4kDjKMCoiTq27q5MHcfWh+hdSOu3LebURuum4ffBhWJw2cYdmTdCvLTocWoJ2s7JNvap87k9tCB3QmaLxsE1nXo91aNmKmQ7uGvLkqm5Rrn7vwKwn5TyIinlhQBGIJGim3FAMVqKqF3atzNNa22bmKjaBnWz5g2zf+mp79cfHdYr8FvTPNWyqBdXY2aZILsoL1XqZGmbcE0vNhsussqpfW/ruCBmsRAnsN21q4ytvcALMI9uTVQtuC2T47ED3BYEpS7Izx5hFlzUhUOc1N3dLMk9qM+66manz71UZXSceUvfQ/2bWr+KKpjq40LtLrXn1GMNtcRFxkmqc8PJ+xK3zN4V1fTutd0rKlKax2R7RciyeT3YmLx4E2k79fz7daBZZeLMdVYXdMt+Q6zW7tSePR0n9qG6MO7fo3Xg71tOGxS63c2nhn9filBmo40Atil/b/O+Y5xQfFIRdVKo56Cqtmlirq9VJVcn2MuONFt2qMxfu834m/pCzKWbSy4E4ngvMPMS4PqTUi9p19XBnVjNivD5odIig3YtDNdxd9asYKo/voNjUQW6PZaaQCN7tjb+pvKDgytI21HRL3trQ0xQq8Y0Ad51UWR6FsbUiejPjj6yzvQsU3r8Uxz3J30RZsrMNkK5v7r7XtyhXx5woYrXhgmbQBfH0kq1bFDdXuPE76RbELO7aDbLk80V0zbMqoiWf3WMH9THnNxEzSZqO1t9XeJz/MBOxn26aQlC4hQQ7qsIdIMsxa2p48d2T9WfqrXrbPJEqU2puilXcCGAyUKIW7xCrp8BmC+EuEYIcU1uu1f7yWd6SipxagDE3c70cO6jLcDVzaiaRRu7LSlD1f5SH/Y4i3N9n8Fdo1ejTrt8hsWGDdv7pbEygeczHXQ+j1VKvDJztdP2bM+m6uKVz2Bu27NEde2N012bkKr79B9sWGC5SPNswvUdyPTu8d1dTtAWfP201NiUU9G3UWMB1Z9aK1bhrq0aBwSjuDOCMIxj29jv2ormIhjXbcoEVelha7kv1SJi+F5P2hHnWVLPwh5vY/7VdiXUxbottlCNCdaPdMKgVFa4bNdhNuWpLtzv25n6nk+d42mWWlQq5NAHy/mqQ9AmfAbjAmmHLQUoq8tFAF5C6g69DGAJEgVcuYirgVGWrB1FD3GA92xLM+naJjeT1lF/N7h+5qgv2JMGmzVAKi7WjOSsf5ZjLVmf8rWmLrwa1LNojZTPBQvLsZxGy8bURXLtmLU3bs++2nxDRZu40VK93kVdmDjYXPqpVoo4d/viQ4NutKP3TbngDdGCxtXxRK1ZpS4w4gxHF0P4aKXgpL6Q0xfk/p9CBBc9ak22Zg3rkeYZ230zPZvfPbAnvn9gT2P/qJgObXMDVIujxoV6u9RrQz3DoKAX/O0SZRzHSVyh6xzpLn1K2zHfFWoiGVsbway15u0OsVjrVItVYAwSBYa4dLQUYjXhuuYVWWFKfOnripJShpKS+/e2f/noZCmSS//VXEOV+l24lpkCjnXNbJnlJRCHfh1o8rxtUnWNizou789frzZIwl6AMNUIdYKkvjjIGXUsm53jYPFCpXPLwtdlcDH2fzuGtsjpQVR6UKF23fYcUNtQLRFUy7Ie56NqXQdrLivqvDV6X3MdEhX1+XE97VPHhRpvo88jpgQKQphdwVo3rU96X1jddYz7BB/9uBm4zj+gR/KzmpWxhUUJRVWiuHDfzXZe0fuQrbvgoM5By0sXS/IHm0IgzrGvOS6V6e+4gebn6gcH9yRtF+wPbQy6dslOcx2N4YnjuoKI2oMrjgqmi1e7S3VT7NWuDglFQoiRQogXhRDThBCz/H/56FwpQ89Glj+piGqJyKdWnRyUb9BURkF1Twu4heXRAqIuoFQXsWuPM6d91VF9/PU79dMc1sOgL0po263btid+ZzziZPGKi+r/7iK2R12gmnzVbeixHtSEFHGumBvtJK2R0QPNSRKo/WijXAtqgc2GWpaxwKJE2zYQvE+d6x0rdgJtx7irujCjxyOk5icR6LtaOqFMCNKx4+jPEsH6wb/joC7c42Smi++2R9vOtSul2lycto8fFBQybE30Vyw7qgUkELsWoQ/qtrZyGOXE7egHpm0WZyykCZVZuyO6nTxstyfsnR9WlLeuuc/9F8AjAM4GcKryj7GQz2rr1JcFNf9+tj3/7oE9An/bFpCmDFL6Lur1tGWdstGmqTJpW/q0yxJvZIJ6zdRYKbULrZrUdyJIq24F1D7Zxs82JfVra8dZ4MjPiGUzcoC5AyFG7W9zB2l/1UQlPdsGrRQUwb+Ndj+o12J499a0DR1ArU1xppJuun9Hs899HIWNbZ/jLVrmg3qn4obSEi0YngVb76j1eExYa+JE1y1ldEVU3ec6t0hZM1T3SylBmmhsRWOpxLUUmQrlupgT8unKpBZHt8cyhX/W033rQfQmXK5loiQZsM2BrvWYVE8UF1ciThuuE+xQ59GwMXLFUX2xdNyYWO2VApSpdL2U8hUp5RIp5TL/X857VoLs2ptaTJNdRRyMJerz4iKzTWC7kO8W3n4SbteErziTqh5o7+I6qcr4QIa5PGrBbZs5N5ET+2RbbCzbuDP5+ULHGb3sLjVuJ1lViDm4tzkLETVV8qF9zW2onDeyW/LzuSO6BX6rrKpJfu7UIuhCQ7p1McfLPp2Irg5K+3HvRyfFNcgWw6G6uNkW+HGeVdsuQfef4AUdEch0F/xNfS7I/chySNuy8tnujzmGIYNQpHwO0w77UO4Jtd6brQ9x58cjDQLZtt201O+5hjosXvnZoco+5kV84Dfl+2GW9OE2qP07cVAq/lYG5o4UUQTbnyl1vXTixOfZFRapz3HHxX3fHxH4++1fHoEXrjgkvR/EDlMvFaW5X2qFmanzKFVwrk1QVsk3CyH+LYS4QAhxlv8v5z0rQQqVMcv1UakPo+oL3a5ZQnNar7ws7aHvqhVSpLj55CLRgtov1Y2iQ4vcxoqYXG0GdY33klLRr3VQGx3ehyiot2GgJRVoHGx9cv0sqRY0m4bc5o4XvNa0C6pmgjrvgGD809ffbE1+1uPzTF20vwCJgnkeXXbVI3W2ZPRS77dNWI5lYcmB4um9ueuSn9WYBtuoDYyeGJ2qrK4x/lZVY/7NdL8zdsE7aQFzzSrqcxpXk5xtXMX5B3Q3HrvahaVI+dxMy/zlIkY00F4MxVswJpS0e1p/dIu0inoJVU8MKrYzsqWtf3fO2ujHshxMPeede81Cka0NvWB0v47Nk3V+ju6fEszjPAm28iemYdy9Tao/urKvSQPVoyTYI9Xdj4WicC4GMBzAiUi5zp2Swz7VClyYE7s4Dux2bYlQC3vp/vgqenptCnpXqYUubZld1IVmnODWC0b1yLxRCAdUpDIRDuqSEoTGnjggsB09NsH8m6lQoZ6mtRexurRqtm9FzY5HxMUz4tpqb82SleVx9UWTin5Y0/2xulA5vhYBP3YHbdvqXFHnJlvQt4mrRpvj9Y7Z1xy/pC741e7pVeh7E5+lYKwHaZcAtsXz3G/MdddM7lS2gpDDu7dKRRQJPR4q9dlWpJOK6dbrpxvXfc7UO9frvbguTup5UpOC2N3nwq1INiHwJ0ekav/pbXe3ZldUn5Hw7IWZvovD1l2VmTfScFHKI+5YV+O5yZnfDBfrHM3jwKSYUIVKa3FZrT+qwORCcVBqUEbJAVLKkVLKC6WUF3v/Lsl5z2oxFx9akfxsL6JlcTchusLFQT2sza9X3e6o/maf8Y6aJYbymOkTwilDaXn6bZmgqDUqTNxx1pDoO2moi6E4wfWAtjjX2y8LX7zoqC9w27WIc52o++j1G0zYrlIuMwXtr2Sq0iG7Nij9s9Wn0OeBv52/X8a29S7EFarV+SjQvuPsR7rFOHAsw3F14gjmav2hEVrx10P6hLt1jejZOtAPNY5mZEWwjWAQtBl1LMSzeJn3sQkMgXgJpQ9tm5ktAOr52p6xHm2ahAp4UU7PJkyoC744C7Qrjza7YMUVstT4N2tGM/LzqHgwEEsMBPbP8LeP7TqriYds5zSQWGNHSpm8d7kIN4lz51RrS8cWDZ0riRta1mRxDqUL7f77Mk66eCmB//xolPH3doa5gC1F4XwqhBiY857UMtSJQC8ASrVSWINnY41VtwNcnVSaaYUPf+bgZaTGbVA1qzahaJNSk6Vd8+AkcJmiKYtDnBdgLo4V0BKq32uvSvUFoS489MkxjvaTuod9sUXTvsfB5vJDDWB2cSwVveYXJZGDq+Db3xrqkKjzVtxhe6SiLMln8hkV9TLpsVsm9PinYwaYLUqBY1l+M1lsqOQzw5MQwUQLJiraNQ3c14N6J6ziruIPVXfWY4n3QKV7mybJ/qfFtMV4fA7s1YaczZBKnNtKdQVTcZEchvru7t8pJTyp83ymvalzWpSEDWG8eMWhgb/7KVZs/b0UUEBbrns/B5bwwD4O12sSQKvGZiXIx785JvR7ForCOQjADCHEPC8d95eckjszQa2g+TdqGyE/JnGxODK99OOuY+rFtIIk0WOKHCQy2LA9lQJa91cmF041UFVN9K3X/m7RKFwzOEpxuYuCMPyhX5frjk+5FKnD56j+wXEQZ2Q1N5yTjs1V7eA+5kQGcRaX6kKhUwuzxSLOwssF+ks+znNHn1e0vw0Ha2nI2hUFtQaWrdq6y6DitLaJo1jvg+ruqF4jekV6M5ssRXNN6M+LmkDAVk8t7hzuv1cy7a66Nrf3ah3tZ7Gy0o8PnDIs5SHQl1hbzoT+jMVZ8P3qhP4BDb56beK+hYNFg6PfLP00AvOj0p6eujqg9LCsV1ROGBQsZq4+M6qy94JR3VHP0+pecbRSJsLRGlu9d3GWP7pVR00SYSpK//51R8X2S1CFSXIb2nmZzpMy70spjfOAgDmjb1xrailDEYpOBNAPwPFIxRNxSu4M7NiTCtaLqyGlpoakDlvb+DZNhLZ9RMw3wk+OSEySoy3+/DVSWjPsxCHQhONnPTDxa6iH0lMvnzY83C3Qlu0JluticpXRURNNqP3TEw3EURTZ4svUxVIm90FTGl89ixsF9bRsQtvQbq0it61zYK+UQGsTBIL70LLZqcR1n4uDPg9Qi1OrdLe6z9EG2uQlmyIfN857XQhzLEWf9tkXKrQlTaByrDJ/2hOERG97ePfWZLdAahHvAzS3Qx9bTJF6XuSadhZUV26qO14gk5owKyHjrh/1EhYU1EK76dnnaJiSZ2yxxOscYFHWqW5dQgiUlwksHTcGVysxfX4GPKrizEQuDRim69mpZSMnc2xbap1F7Vh+xr14iiHbccwNVhKVvbWJjLOMl367O4BjvM87KfvVRQLaFtXiqj9kWbpR6PvlU5gnp/U2pAUFUi5aLUPMuX86eyiA9IfYJlje+51hpD7l0n1HfUldqQlIw7qlkiuYqo3r52uqrZGJOEUl1YWHSUhzwaWH9YqUgSts8XH5kX1ymj3N5rJKfW4rFMH3pMGdjdupmlpdADRq9ZTvmzYILiyo9bviuHDqQsv1JyeShJjSHcc4AIkVm6Knws71/Ki2b0s7rqLORaoQTT0OENTak92piO+NAypaJ63pmcYV1a3P5L5l2l0gKLhUx1ygqfOFatWK52Eh0oQkH2qMpLV1w8XQlRCmGJBEGzRlomFYYI6SFVNHdzsPJmPJPBD85Ex+chJ1Hz3MwEouhSLDiMxmHvn5MYl6bTefOhDXHW9TeKbQr4dvHdMtnCaFUkBnHbPvNew+l44Q4mYAvwFwvfdVfQBP5LJTtQN1Yoq3iHO9iG/novKzAzcXWxOjvAVClJTc6nXSs6yphLlr+e5SLi91ubay/uVx5uxXrglailLo7oHq5R3cNeUOpKcVVRcOLYguhqbFRqP65bGeBXWfsScNsGxJbY92rLjPrWmxoXPCIEtSEILg94tjg3F7V4+mFUpNc58j7RPcyr82cV+Zp+dQ+C4k9sD78LH16MWjSPOPrpBSrR623YOKiNTnTNk0zxvZHVcc1Qe/yFCAV51zbILGDSdHe3bLykTAfSfb5Zm+f9z1nsntjmoVtmG6j+lW4eiWQZui0eRyp9OttS0TXWb0PqjjpUG9MvI9VsdFBTH7o603qmBhVWLHVMj179QcS8eNwcWH9rImZKDyqxNSglWnluEW+OAYiTfYOftcOGcCOA3ADgCQUq4GkJ1zbx1AHY9+8GnodpaHzPb4xRmrNtN38LjR3dbSJm3lc1q8jrdxmIaxLLnYCvrAHkDUpra11EpQM0/5k6BfgyHOZHeq4u+uL4SbKv7rTRqYXpbmY1Kz/ukvR5M1wyYsBpMzmPtEtUTkGvW8qJp5lYq25peoTRGhuhzmOmkApfkTNSuUOs6aZhmInIlsz97XnPrIwGfz5KbHNFAItJaD2xYnIFodto2J98oW90gVxlSOtsTPVdVINKhXhl+fOCBjLFjYs+Af85ShqTHawmApMrrPIbhojmRJ0BsKOVCmeImTh4SPNb1mjCklu+mW2BQCpnklSmwHdYjHq4WWHdZMgxHOUb0eB1mKcZvYGCOmT0KmWcrioJ/l2fuHu4Pr2/njX4hgTNTRlsy/ybZsYRCW/aprpDHuubZCEYr2ysRolQAghMhdWqhahDrQbPECcSegMgdaAK0nod+m5fY3aaEsT52awhRILSLCzt3/Tm9OrYmkY9J46QxR3Nj8uBf/he/6RXDm/l0zb6ShLl5aNbG4RxDbsAk4w7q3Mv52y6mpZJNxAi1te7h2MYmTgMMWA2GTsdRkJLnOBKaOadWSZ0N9Bi9UaunoqeRdJCtIWooijA/b4p+aIj5OKQK1bauiKXLLfvu07dRjd4+gcY8j+JuOS21pd2V1sA3LjqphXL8Ufzt/P8y/7aTQ3zL1qUmDegFrTtyYIlPfM1mKfn5MP3Rv0ziQBVUIYN/OSqYyIfDwRQeE7q+XovAxK8ksVp4Ii1p1TlSHjouETPqxoraZ5gES88Ubxcr3qxP6p61B0q3l5n74iph6ZWW47Ahz7HBc7jpnaPIZUXHpuha3peoaiR6GxBO1Fcos86wQ4n4ArYQQPwbwDoB/57ZbpY+TRbbVzYfWhCn9N0W7EEoM9zl9EUqZR6PMtdSgTXWheeqwLrjmuH0CZmgfcjFGw/cvzVhF2j9OfARgf5GUBa67uk9wO1MhUSGCGuQRPcMtdLqbHYURFa2DwbiRW3DDEf3MmbpM7ocA/QVuijnQsbWnunraguhNqNrsTG5SceYqyi56Io0mDeolM4DpxxzYhSb4ZSukx1kc2AT5ds0akPuknrO+wLddT0rRaOs9zOGDJkTwmfE/+9+UlYmMgqz+HPjvpmaN6pGzjNlcR33F3v5ajapMi859O7fAR78+xqig6t0+8Z4wWXf0DKc+qpBFxTbG+mheAMG40ugDw0XZCBNNHLgYApkKyga58ui+uPc7wwPfCRGMDbNZe+86Zxg+u/5YNKhXllFBMeOm4zDrluOt2+iCpOkZocYOUY4TVx7u2KJRTmN4ixFKooW7AfwPwPMA+gO4SUr5t2wOKoQ4VwgxWwhRI4QYqf12vRBioZcC/IRsjlNIqNaL4wea4wqoxMuBT8OUnjITlIk17GHz3dm+e2AP8sKbHFiqfC4vE/jFsf1CXUNsqaIpbW/dVUmaSPZW1TifcFo3VV/gNKuRin79OhmEattCp70Wu9bfq9/QqUUjY6IJa59om2VoI9WKngEwsJ1yMP1FSoWeApq6mE516ihiYgP9ml16WK/Q9nJJmOBtqhPURnF7tcYjGi6ZLU32acPixy+9e+2Rxhoefn/izL/6Qtrm0ulDndsyHcslUgbH002nDMQFo3rEcnP0UWMyAzFFlgtti3VpVL8cr/38MPzze/sHvo8jYAsA547ojvMP6I4Xf3qo136iv5R7mKmvpnk6LI7Jd6HS3Sptip3AsQzCE3WfKPjpwId2TXhrhF35KHfDFo9JoV6ZICWzARLvOtN7UKdVkwZGN9GomGR2fYzYsr366OEIgfYs9/SQPm2TiaNs3iW1CUqihTullG9LKX8lpbxOSvm2EOLOLI/7FYCzAHyoHWsggPMBDEIiFfg/hRDFEchAwDjo0rZLfaP7J9v287niqD7Gyu661eQnhqKk6e+D8CfwX98bYeyfvT0zJw7uhIGdW+CnR6Wbops2rIcld5yMK4/ui5tPHYTvHdgDU244lt64BdviwOWygbwIEalYqUy1T9RgZ1vrh/QJunr4jDSkw03rkhCBSde0ELH1oUWj+sk4uj7tzQsFam0oJ86hxFui3jtVG+nLcq/87FC8c80RDnpER5XRdW23eZ/gCedqXWxP2Z9+0OM8JVDrJuYiwbY2TT99Z2S6X/7ScWOw5I6TA7Fw1MvgPwN92jezxvc1aVgey31O17vcde4w434UIVu/1ocr1lBh2c5EFIFBPZf2zRvijrOGhCpNosogAiA//GdncFce3LVlmgLM9u419kkING5QjnFnD03W8erQohEeunAk/qEIXfY44nQGeZbSOO5ztgOo9zsXMfPUJgd7wpB1+EXon+lcWhOztsZxw7UdNxdUEBXSuhIyDFu/B1rWHjVSJrNGNimSmOJcQxkZx4V8l+4AGQEp5Rwp5byQn04H8LSUco+UcgmAhQBGZXOsYsc2SZgWyucf0AN/Oif8Jao3Z/IHTc8CE94HFwUcdVo1aYDXrzrcmDXGn8gb1S/H7WcOQQdiFXogg0XEgTtitvsE9kdCiz3lt8dmTIJxtSIU2YW7cI3f6cOjxzjZyLS4+s4BCTe5wV1bGq+TLW4qw9Ej79HPK/x4mFfk0mQNzCTQDu3WKmMRSXohUtp5qH3Vi+ua26YfK15GwPDvM1m+f33iAEy9cXTSIhyVqNp9/dyoCotMm53j1cq69LDesRIt6P1SrWqPXRL+yoviCPXvC0fiyR8fiPu+PyJonY3aUQKuLVG+26UQIpD9Sr3KN47ZN7BPnDGsxgZly7H7dgwoeaxzdMhPmYY11aqsZzazWxBN39PeL9mQK9sldRy0baYrZZQ2LL2L86zrUD1RyjPU8ctEQCAO+d1PoGCLeQ8UKq4jXnRGoUgI8VMhxJcA+gshZin/lgCYlaP+dAWwQvl7pfddWP8uE0JMFUJMXb9+fY66Ex+bdk79a7ilWOQPD64I/V5CBurY2B7TuuYPql5qXRuUzysRRbvr1ziyvsCUibR7G7NboQtBzcV2/ktGaN+ZxqPNxcnFffMXsTecnFhMXW1IM6y/r4ohdXScRad+HtmOC9OaTB/nvkYYCL9v5WXCankB7JpPcz/s+Mkq1IBrPd4yEGOQoUF/4V6vXJCDvoMB8GZLXhdLv8xtB/9uWK8ch/RphxMHdwrck7jY3iNRvY07ZNBs33rGYPzosF44un97Y/prWzZNMnnU+quEPc+ZuhIl5j649jAfw5RltnMruhKSii7UmU4nW0sMVWlCcTnLFXFiU7NFSpn2DFOar66RebWOFQM2S9GTAE4F8Ir3v/9vhJTy+5kaFkK8I4T4KuTf6S46LqV8QEo5Uko5sn17RwUEHWIb+M0UFzdqNXCbdiHOoG2jWYCoi6azR4RbHAr53AjDZx3bNQy4jGXbH0ETRvVAdHr78bTdeoHQMMqE2dfa1rZOUigi9jVKLac4C/yD+7TF0nFjMgb068/jnw1uTV1b5U4w1YlTmymuhYTcPmF8U+ta6diEprjpYX0tvhr8nqUeNvmJHhuW+hwroVxMa59+rFHE8gY0ZGRXpExDsV2zhvjdKQNRr7zMKKi6eN/EaSNXijV/DJkz5dl729yzNJ40uBM5nlkfF76V1xYTo7enF5A24fc+zYJt/CMecRfwwfgq83bZZoKMgkkhQEXvq/G8LKekjrs6bymSUm6RUi6VUl4gpVym/NtEaVhKOVpKOTjk38uW3VYB6K783c37ruSwPWSmvPQ2DlZy8cfxude59YzBwf2IO952xpBQ4aIUtAlRXRoyYZxjiIJFtoXwQo8d+BzshG0hn9xH67fpvpITN5C2yrzdFUf1Sbq+5XJuTn+RhB/NFo/g+lkYarEmq3S1JCZxfc38OSAtZT9S89vPju6b9puJI4kJJH5MiJHMFJ9nQj2VehkVFqkDmtYuNmEhzVJkOdIPDu6JBvXKMHpfs+tk2H1ItZ1qvVH9cjzxowMx+/eJHEajPLddff/01Mnm1tt7Qmym4qz+o6XXO7Odu2qJdOG6FGhbO0nbNYxK1PeJf01MirtMZ97Gcwf7xbH9QrMBhvcx+1nhtIiWdFeeKybhJJOC4oh92ifjrKNYhn3863lGHjwIdEE4an/VOSyREEXbgNAG5TiZLL+lhrtZwA2vADhfCNFQCNELQD8AUwrcp6zRx2Kc7Ga2F4LpN90NQ6W5pg2ivm7Ky0TS/70YBSFTjYbEb8Q2Yhwrzv7p7cXcMdBGqpFTs8i6lfk49t+TQ0MAAzolfPdNacBN7anj69cnDsATlx5oPeYIYhICG9TkDzaiPhbfOzBDymxiO4O6KK5rxJ10y8tQr5aXf89MHNirDX52dN/Q2MabThmIy4/sE8n10BQ3qAesmxau6jW/WKnRZEOv1j563444aXAia9rxEbKnmRZiE687yrwTYW7y5+8BnVpg/m0nobOhcj1gF8DUtsu99L9+0gE/nfNvtRgd/ZT09NIHhlibKG6RvzqhPx73Yqb8NNq24rANyhUByvH7Rsqgtb6bxS3Zx8UcHSao/PN7++OqY/slM3XqZEof7t+vMiGCiS+aNQw8Q00MtcKCwkqUJBvBczn/gO4kK+hF3jOant02eOywcQakErboqHuHKUcev2QUrvdcqM0KPzO+lfWcEd0tW7nBdM+pY1DN4igh0bG55pZLaK9GSuva89Oxx+Dta46kdahEKIhQJIQ4UwixEsDBAMYLId4EACnlbADPAvgawBsArpRSVptbKl5cu6v0bhfdh7pNswZ5C6TRHxwXheKy5dShwUWZy1TEB1uqaAsBDCH48VOtMlFQm4zjJuPqCiXdQSBwx1lD8fRlB8UuAkd1A8y0kCcdy7sCceow2dqzYStMHJcO2gvQnI41+MNvTkxo+/XscDplZQLXndA/Lf5HykRylrEnDYiV3UvnlKFd8MIVhxj7e/GhFbHbXrt1T+DveuVlSaE4s5tM6nfTelW3zFLdfP1TbGQpdhsFyrSnK+r0U9J/Vy0EdHdegSuP7ptMiX/16H2wdNwYq0BHjZ2Ng0R0y4ULS0eoANyqMX553D7Ga0k9dyGAkUrSnkP6tsPofRMChJTBLKZhNfqyZdzZQ7H4jjFp3+vvtrP274al48YEk+2EnKRJkWaaW/7zo5Ti7Luj7IJLHMtjz7ZNsXTcGBxmqXXnioMsawy9ztmEqw7HpOuDpQP0OcykfAgbcZ9dfyxOGtwJJw7upGyXvmWXVo2dKBKLiYIIRVLKF6WU3aSUDaWUHaWUJyi/3S6l7COl7C+lnFCI/rlAHY8uFgejFc2ILXOcMHyficAL2/tDz2STtk+R+JgGXBWV7y/TXG16RCj4lgndKqEXk8smuPn2Mwdn3shA1vdE0NqIImA2blBuneCBcOupr9XUf/Nf8j5/OH0QAODyI+nVxs1CQuL/bARUalFAukUyeh8oaVrjtm0jbqpbE0LQhEaqEibTQsh3YzIV3ozTZhLrxVaFcdcTawzPhAzXs2/7hJJuePfUPJdXPZiDY8mEVOQU35JpC+SP8sz5LpNqghCfe78zHAM6NU+zntqeQXWh3FGxzgb7RO9gZOVnxqZ19+XE/9RaY8Mj1NJRu+67ZieOmfuFzX9+NArvXWu3sBzStx0uOqQCZ3np5lXvHl/pdYBXamPfzi3SLMmqEFRTE6aENd+7Ti0b4V/fH4EmDerFeq5PHhK/TlmhcVNemAmlaYNy7NhbjUP7ZK9VsPncS8NnILcWm6g1SvKN3gWbJcHW3yYNyrFzb9BgKSExpGtLvDg9EfKmmuoFiMkKDP3tYnGTyTWuLJy+EHRuSP2YMMKu/7izh2JI12VpVjl9yx8eXGHM1BiVlFBUeEvn9N8dh7IyEetZOrxvvDnHP1Lc+I2zMtSLcU2Y9jKs71QN/3Un9EfLxvUjxQzEGSqmexpY0BLafePqwzFj+Wary5J9+IQH+GcK7D+wd1t88Kuj0KNNE0xeTAoztnLS4E5YsmGHdRu1S1U1Eh2aN8S6bXvMO2RAQgZHRYwsfzp3njMEv3xmJhpbarpEsTb1aJOwqumZEgHg5CGdcfKQzgCAnx3TF7/+36xQZcjRAzrgtvFzcNrwLpi6lHKvEtltN++sTO97Ll/roQq5xBdjhnbGKzNX5+zQrWOWCIjL4f1oMZS3nDYo+fl/lx+M9+etC8TkhVn09+nYDPPXbsfPj+mLD+cnMjPb5H9/LppxU1j1HXU7UpcBIBlnWIoUW0xRrcI3DcedSEwLsygLtpqazNscHtEUHHY+1PopucZ2ra2/ef9fdEhFQIMGAB/86mi8/ctgwU4pgQ4twh/8bBPUZBNQnK0wKhB0+/GHmu5Oluko3ds0wdJxYzJaiGy0adoAPz+2X04E7EmLN4Z+7wuFMvl34v8LiXEqiX2j3T/T1q2bNgh1TaBcDmpWy/TGw78+P4Mrik+ceEkb+S4p0KxhPfzyuH0iWfd1AeInR/RGu2bpixWS+1xYQLSFAZ1a4PxRPazPCKU5/ToP757ZOtezbdOMtVCo/Ov7I/DG1faiyOpzJRDM4hqHXOg96pWZx81vvViWjob3RhhUy/V5I7tj6bgxaBKSDa5P+2ZYOm5MmhWll1InUCA47qiL2lypjkwxQy4x9b3w6txwKto1xUWH9gp8F3YO/hi0CeYA8ANPkehbGls1aRBaO7Dw6sH8wkJRjoircbdlCOtlKHZqE5Ioeft1ISAOuiWrGDTtOrYFlnq7fL/rYd5LpH3zhuhnCIANb0vE0x5H3yUnhC2w9K8s7/4MbRu+j9dcbPR4kmQ/vI74z40QAov+eDJuOmWgsS1bgHkuFvUf/upoPPlje9IJaj/Ub08YpLjoauO3EbGuR5SzvTZCGnbj8SJeXlfTUrOGievRsF55mnb++pP3xdQb07WutoykfjHbYwaklEtxu6pXqLddI9P1MMmE6YHxyNvDa4vR0rn73GEZCwkDwWvTpGHmMZ45K6HfcPpXlx7eC4v+eHKkotW+jsFV5r3GSpxaJ8X6dMZ+XYNu+MZ5OvgD1V3W1vtD+yYUZz8+POXuno/lQxEuUZK4jtORUobEj/bHoj+e7NzludThq5EriLEZOiMrCP7zEdqjCjzhGofECeiWJF8TUcRzSijU+0GpHxQnSDe9P25WE43qUx/jzHeMHDRdNCKcW5KWIuVSlWdwYYtyG/34p8D+9N0BJKxwh0R0yTX10fdDn3rjaPz9gv3zOqZ9YYKyeM1EpnS1ro2N1xzXH786oT/O3K9rcj5s3ST+QqZN0waYcsOxuP7kfa13oE/7cMWYiq6wsBZe9eZ43cAXdg1n3HQc3rjKbs3JJb3bB5MN2cbaOSO64YEfjrS2pxe0vPiQhBbeNh5N2eHSG0//SggR2ZLqn6OfzOPQvm0xZmjnSG2onDcy3OKrp1WmzgNHEt3AUu2m41ulKto1TboJ+vXkcumuZxI0i8DznzxOwrbKlMk1tV308VgX4Jgih7h+mPRxnPT3t61tZaJOyPPTVkLKlLXDht7tRvXL8Ytj+yWD5fSEC0/9+EC8MnO1dRFQKC1Mtm4kVAtX26YNYrvqmThz/26YOG89+nfKXGdlYOcW+GbLLgDA704ZiN+++FVSQ3ztcfvg6AHR3BlbNq6PLbsq0xdHRNG3d7umWJwhJgAoHu2c6T77p59N3bzmWpafT8cekxwPPzy4Aje9PDvYl/iHio0QiXvhK2F0a1dabGKO+gAEM4zpZLJ0R33MXJ1H4wbluNKrw9TUszBcrLm2qFyvFeANW3TqacnDxuivThiAy5/4wto3eo0h4Ncn9Ee5EDhTiwULu05RLByuoLg8u2rf15jXt6Y3z+8i8tgBHfDAh4txSJ+ENeW/lx6UVXv1y8vwydhjQrMr2iyZpu9ju+kq/O6UgWjRuD5OHNQJDeqV4eUrD01LVBTrvRHzXhVDPLTr7MVp8XNR9i2Wl3aeYEtRHnA2poijOmqa2rDuXXPcPhjgLc51bUK/js1x7fH9i2LyAOharWwFJiC1gDt9eFdL4CKtLX2z04Z1wdJxY0hFVl+/6nBMv+l4r51ES8O8TFA/P7YfKfvdR78+Gi9deSiAYArt8L4Gv9ffhVGydYW2XxxDKRmncEoEbez/XbBf4G/V1VKUJdKWhtaYMZyzrZ5TXPRD3ey5A+rfJ+9DzDkrzm20zY9+ApMxQzvjoQvNmv8o7kX/+t7+ABI1VVzQsF45lo4bE0h3rHPJYb1w+vCU4NHdUhPHX5y3DXHL3K9HKwDp9aVU0oQi45YJQefWMwYns6Xt0zFhjaG4XacfN3+LJxHTEwMA/vHdxP2/8phgcWGnvXcwn504qBMO7N0WS8eNIRdwptC1VeM0D5JCvsvbNmuIP5w+ODnuh3Vv5cSCMdkQN5qkiNf6xwyIZoHLB1HGSBFf2oywpShHmDKQ6Vpkexs0Au+iGHNJkaxHYxGlQKntPP0HXiKlNR8RkgpY1bD591evNF8sC/xMdG/TJJlK3DSJGdc52kn6PtDnZcg2R/VVz5ZRvdpgyhJaVqyz9u+KF6Ylsgg2aVAPM246Lq24sY0DLckkWkRox+ePZw2JvE9UenmuSANCChzmC8o991Pf+wtZHX8hpWpWM72QTxzcCbecOhDnGlyJcoFAUNj1a/WE0a11E9x59hAcM8DsxtUoQxC1ygmDO+GxScuI/Yz+HObSldbUdjby15ihnTFm6Biv/bBjFgeFeo9IaV4Ax+1Sg3LfTTN/J7V8007r78W8cL9Uia+Ki+5SHDvhV9Y9KS1YKMoRvntK+g8uWpfaX4YsdSHfTb1xdFql5ERf3Q79jTv2Om3PRBSFEsXdTcqEsPD2L49ARWhiC194Sl0vfaIvIyZayDRJHdS7DT6LkO6Wegv/eGbIotvfl+g2oR/M1+yp2vAoRMnIROHHh/e2CkXtmzfEovUJdz/dlSQbN6FbTjUnZKBiswLERruBR+7THhOuOpxc8JY6P0R58R7gFReOotjw8WM7rjy6L/ZW1eD7B/XEs1NXYP7a7aHbXzCqBz5asAEDOjWHECIti9MPDuoZuQ9RiKqJ/84BPay/2+5GOy0+pHvr6PXZ4rwO8rl4cuZ8EXJfnLSdRSOnDeuCV2audmodiopr0eWOs4ai78dLcGjEUgHZ9CNTncVidgvrFJKCnUpoTFEWfUm2G2HbIr60GWH3uSLGFFOkM7BzZlcpn3bNGqb5rmcyVcfRBGaakLLFL+aW6eHzqzy3b94w0sKkX8fmdpcwCaQEpNxAiQcD3GgUkzJRWkxROLqWPds+RLk3Ls73CCVboosJfFi3xDM4vEfrrMdDlGtx3/dHAAB6ZwjAvygkpfi+nVukHct05FyM8V7tEtXhj7DUYAvj+Z8ejKcvS8RWNG1YDzeeMhCN6pdbi7yePKQzlo4bE5p4Zum4Mbj1jPgFk4uN35+WnsyDCjUFdNg+ucCkLDEdMmp5CUqb2TT27rVHZizSqeMviAtlKcrGNdFE++YNMfakAUUV2F/M6/Y4HgY+Yc+uzfqXiYN7t8XxAzuS5xVyMpIihYWiHCEQPrFkGpZxFmhxJxp/kJcJ4VwrFVYvwSVq6lrbs+5fG2qcRqbYBEowaqbfkttkGA2utS229vxgcZN7g/6tKWVopj4XzyvRMcmsdbl71Y6qaJP23YmDO2HurSfizQw1XtpELE7oPweDuybc6/Lp9pKJET3bhBZb9F3KwoLIC43rHtnaM5VuILWbzHZGH8e+8iab45oIi6sCgAGdm6fVYZl764l45KIDIrX//RxbCPu0b5aWNa/YsblaFUscsc7cW09MKz5aihlS7zlvGPp1cDNe1FuVTUr3RvXL8cAPRxo8Z8KP6yqFfCFg97kcYZrMXUwqmbLPUQ+hZo45d2Q3/PXdBdl1rABkOtcOzRvh+pMGJCt/G9uJeFwJYJgnSH53lNnVJRu3MN3N0cTBXkzLmfvRXNfCrtkzlx2Md+aszUmQv0q+pspMwkmuKm4nUrVH3Efra9j+n449JrR6ORAtviQTydg6r0sP/GAkXpy+ChVh9WmKjOtO6I9mDevhDOJzwKQT5+30/QN74NA+bfO6+G/aoB4uOawXrnp6RvK7OM8BJcV5ocj3kl4VIqIIFPXLBSqrcz2z29tvVL888v3XXxFzbz0xb27/Js7avxvO2t8el0tFPb+2EZVidRkWihyiTiSqFitQiTvCTFehBeOaBarsJyRXdViy2cc1/qTwkyP7ZNy2pbfgbNvUvlhOJueSCTeHpePGpG2za2918nOrxqnJ6JwR0SY76l2t8NyQyO2GNFzRrqmT4M5sGNa9FWau2OykrUyWDd+SefKQTk79raWk3zfTwqNpiGDahZCR0AX6ZevSqnEy9TRp/wJqZ5s1rIfrTuhfsOPbcDUfZmOI7NY6N2NICFEQa0hDYkFhG+o80cKzfus1e3LBsG4tMXPlltDfrjiqD9Zv24PvZWHFevLSAzGZmGgmjChFtj/5zTH4dmdl7GPF6YcL9EepUf1yUubXYke9ZuN/cRiueWYmjh/YKb99KEErnQ8LRTkkbGBEGSqmYDvqezGTtvykwZ3w4IeL8aPDelm3izMx5TqmiEKUfp86tDMqq2pw2nB70HfS595yF4Z1b4WKtk0xoFNz3HxqwkVxwe0noTzihXTtiZXLF4yLtk8a3MmZUNSQXNDWDanzl5FvnL61n3GtkBTa+eEXx/bDhu17CtyL3DCcGCvoik4tG2Fg5xa4/uQBGbf1C7+WQqC0izlHLXR7RL92uOe8YRm9ClxwZP8ORqGoVZMGuPc7w7Nq/5C+7XBIxKQGV43uh3lrt+GQPu1w34eLyft1aNEoLU65FIiTdr7UGNSlJd78Zf4LLrP7HJNGwJ8zwviwbapqo8N/8f+ivS3aNWuID399NABg1eZdpH2oHD+oE+6YMNdpm2FI6cbNQAiBswmWHMq1PWFQJzSqX443lDiPsKQNmV7qrieWWBmlIi/w4xfbdCmzRem2X5slG/w4liiWolzz48N74bVZ30Tax/eojR2W4+gmXnPcPm4aKhJ8S/zUG0dn5aIaRxCoX16G1686nNY+oscUFQon877qMiZE1q5LnT1FZikGm+/buQUmXncUAPO1LWSa8MhkDODO3MQfTo+ftKSuonodnTgovxYqF7BQlENcTyCm9nS/6CYNEm4FPQsYBxDVKkJh3FlDIgeM5wrbJO0qzjtX65J47pDBnfT4FhfmcpdDhm5NBS49rDf++Hp2Avxfzt8PD364GPv1aI3Zq7dat/31if0xqqINnpqyAkDuYgd+O2YgfjsmWorw4d1b46JDKnDp4XbrMROPdlnGsuVaVikGt+d84vp8R1a0wQtXHJKMNy1VimUcZPNeySwTZX6YaoM7Xb6RUibnqUP7mmv4FSuF93GqpTSuXx7rBfbL0eaq6D76w6wvWCvaNcW/fzgSd587LHoHHJGLSfX8UT1wvKZ5yPfkTcoq56hPLrS1L195aOSUsBQaNwj68x/ppVXuFqMmSi6IYuEqcyDFdm3VGLecNgjlZSLjsa84qi9GKpnkikknX14mcMtpg2Lfx2JZTNV2cn2di2lM5hJqVsUoRdf379E6Y0bYIkyQGMBsKcpvx/0sp1HcoZt7dd6G92hl3S7fbqz54vYzhuCAitbYp1MRZD0swRcCW4pyxKnDuuCet+dH3s8WsErRmtTzKkePHpiqhn7kPu3RPovg0dINmstdv+1ujm6O66R+TpYTv6kL+lx36eG9cMZ+XTOOM1vCAOp1s72Yu7ZqjFWbd9GTHeRgiBTy2AyTDfv3aI1ZK7egTRYFjPOFiwW6qbSAzmu/OAzTln+b9fFKhWJJvf27UweiX8dmOLp/h8wbe3x2/bH4+3sL8esMSVcuGNUjo4dACXiRpjGseys8d/khBe3DgM6JUg492xSHkjQKLBQ5RJ1HystEeJ2iiJPN45eMwtqtu9O+N2USCgvQfOySUZGOWXLkcf5OxXWZZ0vqLc60XTH59Wc6JSEESfC2CWku3sN9OzRLxsft36MVpi3fnH2jDJlsbuF/Lz0QK7/d6awvTHR+O2ZfnD+qO3qUQAp2F9N+B69kQqYaSz3bNkXPtsWbvts1xSESJTJKRs2K2rRhPYw9KXNSEb3OVSnh149zlb7bJVIm0vTv170VBndtWejuRIaFojwTdbIJq/YuZSJDTRimjHXZMKhrC4z/MlrAtskikIt0p/nM1pVPDVoxiEQUuewXx2Z2+aTg4trefOpA/OG1r3Fw77b458SFDnoVHaos27pJQkvdNMeFjkuFQyNmy6qLtG/eECcP6ZSz9Pn1y8swoFOLnLTtGhfzY4fmifflacPsWUfrGmFlAWob9UKSH5UK3Vo3iVSGI98IIUpSIAJYKMoL6uSdzbqPFM+SAx3P0K6tAEQLmjP5THd0lLrT12QO7dYqcr2Kod2yf1htC1/q4j7TvdKP8cvR+2Dyko2ktintRUKY/9zPkW921Ex3YfRu3wyPXlxYyyj1LK49vj96tGmCkwaXXoYenaYNyrFDqc/F5IbyMoF/fm9E6G8vXXko9lbV5LlHhcPFm65N0waYdcvxaJZnxUSxu6TfefZQHHTHu3jhisK6YWWifnlxX8e6xCWH9sLDnywpdDeyhoUih+hruvDH1fwQjxma+/oIJmxTSzHmnN+/R2u8c80R6BOxaODHvzk65xnsqNN01wzFFHUh4arR/QBkb5WJWC0p6+MVigtG9cDnS/MfB0AV7hrVL8cPDq7IbWfyTLHEIuSC135+GKYXcVxJbQ0cN+FqqLVoRIsrqkuYCpOruChlkA1TbjjWSQFfxg3nHdAND3+ypCjXi1EoXfthERNXezEygxvY6cO7AsiNCxplGGej3frX9/ZPtOFwzdS3Q/PIi7BurZugSRZaQb+WUasm2b9IMwX5llJMkZNjOF5QHzMgEZwbdp2beRmKsk2RXErcevqgnCleimek5o7BXVvWOiGWKQy1QXdATVKRKzq0aISWDt7DjBuK3fpJhS1FOcBfy/7h9MH4/auz0aWVG5exy4/sjYsPrUCjPAcIulibd64l+f5/ObofrjiqT17uQa5kIhfNBgQYR3NhPtPUHtGvHe46ZyhOzUEsQRHJsgF+cHBFzhf1teO1mB/Y5TA+LRrXR4N6pavTrQ3PSW1ZBNs4oFcb6+9CFO98byLn6fxL7HrosFCUQ47Ypz3evfaowHe2AZlpMAkh8i4QBY8fZduozoPuycXDn897UAxzSz4nONe3K1Pc17kjuzs+onfcAt+5KDVVXFHqL8JC8NkNx6Kymi9cHMrLRLLG0CF9Sq9AZKnd9WHdWmLmyi3BL2u/TJTRGjbz5uNRXULP8B1nDcnokRSX2mD9BFgoygm2wZHrcRN3QWaPKcotk284Fjsdakx7t2uKxRt2OGsvKq4mh4N7t8X/vliJAyrcTmJxuqcLuU3V4q2l807IC4UUEN655ghjZsp8EDb2G9cvR+ccZMUsdZpzLEtkzhvZDc9OXVnobmRNqa0f/3PpgVixKZguv9TOIReUWjzaBaN65Kztvu2b4aJDKnDhIRU5O0Y+YKEoz1gtRfnrRiQaeKkrW+TIh9hFRrrXfn5YxiripYZfiLdTy8K5HprGZL+OzfPajzgUSnNVyOe4b4fC3BebMubrP5yQx54wtZk7zx6KcWcNLXQ3sqasxN5VLRrVx6AuiaytR/dvj4nz1uPGMQPz2odc1J07Y3gXHNi79CyNxUhZmcAtpw0qdDeyhoUih1CsNDYtsouUxHGxHfmg3m1w45h9ce6I3LgbuUDNiX/N8fvgZ09OR5cCCRNlRWpHjmJFfPTiAwK1KvJxRqW2UDBRl13JwuIManNGOib3PP/Tg7F1dxWAxFjSh1Ndft4Kwf0/GIltuyvRNs9Jap6+7GDsc+MEp7Gnfzl/P3eNMbUCFopyQNQAxGLP7y6EyLpYYLOGCXerigyVw11wytAuOGVo4YrxHVwLNE9H9U9kbpu/dpvztv93+cGYsWJz2ve1ZelcTFkD80UdPGUmT4zoaQ92L0WaNyrdpVeDemV5F4gA1DpPEKY4KciTKYS4C8CpAPYCWATgYinlZu+36wH8CEA1gF9IKd8sRB/j4AtDHVqYJ4y6qjTt26E5Hr5oJA6qBQKDieaN6mHb7irnFg9XrcXJFtSrXVMc1b89fjl6H0e9AEZWtMHIitwvdHihnn/q6vzGFI5SHHM92yaUgyNyFPReG/Fv85n7dStoP5jaTaFyWr4NYLCUciiA+QCuBwAhxEAA5wMYBOBEAP8UQpRMda4G9crwl+8Mx7M/Odi4Tbh7SeL/Qi7i8vFeOWZAx6xqBBU9Rb4Ij5OEo355GR69eBSGhRSGPKp/+9jthlGvPDfTUSkumkqNP545BO2bN0zGHzJMvihF5YevN2tcwGyypUZZmcDMm4/HnWcPKXRXmFpMQd5gUsq3pJRV3p+fAfBF/9MBPC2l3COlXAJgIYBRhehjXM7Yryu6WGryhC3Q/K+iLi4fufgA/OO7+0fah8k9rhfhrt/52fTv9V8cjt+dkgiwdS1r5KJmUCEoZGxgLrGNm7NHdMPnvx1da+LCXHHZEdm5HTO1E185WhddbbOhZeP6OVOeMQxQHDFFlwB4xvvcFQkhyWel910aQojLAFwGAD165C7NoGvClgxxF6lHe3EfLsjV1HxghuJnTGkxsEsLDOzSIidtN21QjvMP6I5zRpS2e0RNLV3ndGjeEGu37inpopn55oaT98UNJ+9b6G7UakrRElwM3iEMw6STM6FICPEOgE4hP/1WSvmyt81vAVQB+G/U9qWUDwB4AABGjhzJU4tHRdvcJzKgMvfWE1GPNcdpNKhXhr1VNeTtXV1B31WjXllxLmqFEBh3dumn262t3HbGEPz48anYL8SVkmEYOr5QxJYihikuciYUSSlH234XQlwE4BQAx8qUv8kqAGre527ed7WGsPS0pwztggc/WuLE8tMopo9yXJef6b87zqipi9uX2s7rvzgMkxZtzPtxf3XiALRsXB+nDy8eN7VXfnYo5q/dnpO2/ZTiLpNE1GWaehkkS1EzzzDFhF+2gWUihikuCpV97kQAvwZwpJRSLZP8CoAnhRD3AOgCoB+AKQXoYl4Z1r0Vlo4bU9A+xJ2cWzdt4LYjJUzPdk3w1aqtKM+wauzboTmpyKbrF2azhvVwzfH9nbXnF5Vt1jB+Ud+h3VphaLdWjnoUpEG9soI8V728tPP9Ixa4ff6nB+P9eetz0SUnNPOEzM4FLCbMMLWBuHHEDMPklkLFFP0fgIYA3vYsJ59JKS+XUs4WQjwL4Gsk3OqulFJWF6iPDBOJRy8ehenLNweKnrqgWDXzN50yEAf1boNRHDcWwNcC9+kQzZV1RM82RV2TZWi3VvjbBfvh2AHuYhkZpi7iJyRhSxHDFBcFEYqklH0tv90O4PY8dodhnNCuWUMcN7Cj83aL9cXZuEE5Th8emgel5DhuYEdUVdPjvGx0atkIANC/Y24SUhSS02pJhkCm9lCs86MNX8/FMUUMU1wUQ/a5OoWv9X/8klFYtmmnfeM8wnNz8ZHMUFTYbtQJHvzhSGdtjejZGi9deSiGdG3prE2GYWoPfmwxz+0MU1ywUJQnzt6/G56fthLHeK4nR+zTvsA9CsK+zcVHPy/u6NA+bQvcEyYqwzlDG8PkhWJ1L7aRyj5X2H4wDBOEhaI80bt9Ir6gmeN4E1ewpaj4GNilBabeOBptOZlFGu2aNcRVo/sVuhsMwzCRKSuSQkVvXH04lm8sHo8Vhik0xblCZ/IOy0TFSbtmDQvdhaJk6o3WjP8MwzBFSyqmqKDdwIBOLTCgU+2LfWSYuBRnFUcm7/Ro06TQXWAYhmGYWk/nVolkLCcNCatvzzBMoWBLUZ6IWxw1X5SXlaBjNsMwDMOUGB2aN8KXtxxftO70DFNX4Scyz5RiUCjDMAzDMO5o3ih+0WuGYXIDu88xDMMwDMMwDFOnYaGIYRiGYRiGYZg6DQtFDMMwDMMwDMPUaVgoYhiGYRimpPBr/50xvGuBe8IwTG2BEy0wDMPE4O5zh+HD+esL3Q2GqZN0btkYS+44GYKzFzGOOX5gR7z19dpCd4MpACwU5YmTh3TG3W/Nx+lErdagLtEKql14cE/MXLklTtcYhonBOSO64ZwR3QrdDYaps7BAxOSC//vu/tixp6rQ3WAKAAtFeaJ3+2ZYOm4Madv3rj0S7Zs3jNT+708fHKdbDMMwDMMwjEeDemVoUK9BobvBFAAWioqQ3u2bFboLDMMwDMMwDFNn4EQLDMMwDMMwDMPUadhSxCS5+dSBOLBX20J3g2EYhmEYhmHyCgtFTJKLD+1V6C4wDMMwDMMwTN5h9zmGYRiGYRiGYeo0LBQxDMMwDMMwDFOnYaGIYRiGYRiGYZg6DQtFDMMwDMMwDMPUaVgoYhiGYRiGYRimTsNCEcMwDMMwDMMwdRoWihiGYRiGYRiGqdOwUMQwDMMwDMMwTJ1GSCkL3YesEUKsB7Cs0P3waAdgQ6E7wTAGeHwyxQqPTaaY4fHJFCs8NqPRU0rZPuyHWiEUFRNCiKlSypGF7gfDhMHjkylWeGwyxQyPT6ZY4bHpDnafYxiGYRiGYRimTsNCEcMwDMMwDMMwdRoWitzzQKE7wDAWeHwyxQqPTaaY4fHJFCs8Nh3BMUUMwzAMwzAMw9Rp2FLEMAzDMAzDMEydhoUihmEYhmEYhmHqNCwUERBCPCyEWCeE+Er5brgQ4jMhxAwhxFQhxCjveyGE+JsQYqEQYpYQYn9lnwuFEAu8fxcW4lyY2kXEsXmUEGKL9/0MIcRNyj4nCiHmeeN2bCHOhal9GMbnMCHEJCHEl0KIV4UQLZTfrvfG4DwhxAnK9zw+GadEGZtCiAohxC5l7rxP2WeEt/1C790vCnE+TO1CCNFdCDFRCPG1EGK2EOIq7/s2Qoi3vXXk20KI1t73vPZ0gZSS/2X4B+AIAPsD+Er57i0AJ3mfTwbwvvJ5AgAB4CAAk73v2wBY7P3f2vvcutDnxv9K+1/EsXkUgNdC2igHsAhAbwANAMwEMLDQ58b/Sv+fYXx+DuBI7/MlAG71Pg/0xl5DAL28MVnO45P/5eJfxLFZoW6ntTPFe9cL791/UqHPjf+V/j8AnQHs731uDmC+N0f+CcBY7/uxAO70PvPa08E/thQRkFJ+CGCT/jUAX8PZEsBq7/PpAB6XCT4D0EoI0RnACQDellJuklJ+C+BtACfmvvdMbSbi2DQxCsBCKeViKeVeAE8jMY4ZJisM43MfAB96n98GcLb3+XQAT0sp90gplwBYiMTY5PHJOCfi2AzFe7e3kFJ+JhMr0McBnOG4q0wdREr5jZRymvd5G4A5ALoiMfc95m32GFLjjdeeDmChKD5XA7hLCLECwN0Arve+7wpghbLdSu870/cM45qrET42AeBgIcRMIcQEIcQg7zsem0w+mY2UUHMugO7eZ547mUJjGpsA0EsIMV0I8YEQ4nDvu65IjEcfHpuMc4QQFQD2AzAZQEcp5TfeT2sAdPQ+8/zpABaK4vNTAL+UUnYH8EsADxW4PwzjYxqb0wD0lFIOA/B3AC8VpntMHecSAFcIIb5Awi1kb4H7wzA+prH5DYAeUsr9AFwD4Ek1Fo5hcoUQohmA5wFcLaXcqv7mWSe5ro5DWCiKz4UAXvA+P4eEiwcArEJQu9TN+870PcO4JnRsSim3Sim3e59fB1BfCNEOPDaZPCKlnCulPF5KOQLAU0jECwE8dzIFxjQ2PZfOjd7nL7zv90FiHHZTmuCxyThDCFEfCYHov1JK/52+1nOL890313nf8/zpABaK4rMawJHe52MALPA+vwLgh14mkIMAbPFMnW8COF4I0drLFnK89x3DuCZ0bAohOvmZkbyMdGUANiIRXNxPCNFLCNEAwPlIjGOGcY4QooP3fxmAGwH4mbxeAXC+EKKhEKIXgH5IBLHz+GTygmlsCiHaCyHKvc+9kRibi713+1YhxEHe3PpDAC8XpPNMrcIbTw8BmCOlvEf56RUkFJ/w/n9Z+Z7XnllSr9AdKAWEEE8hkbmrnRBiJYCbAfwYwF+FEPUA7AZwmbf560hkAVkIYCeAiwFASrlJCHErEi94APiDlFIP8mSYSEQcm+cA+KkQogrALgDne+b3KiHEz5CYKMsBPCylnJ3fM2FqI4bx2UwIcaW3yQsAHgEAKeVsIcSzAL4GUAXgSilltdcOj0/GKVHGJhKZ6v4ghKgEUAPgcuX9fQWARwE0RiL714S8nABT2zkUwA8AfCmEmOF9dwOAcQCeFUL8CMAyAOd5v/Ha0wEisSZiGIZhGIZhGIapm7D7HMMwDMMwDMMwdRoWihiGYRiGYRiGqdOwUMQwDMMwDMMwTJ2GhSKGYRiGYRiGYeo0LBQxDMMwDMMwDFOnYaGIYRiGYRiGYZg6DQtFDMMwTJ3GL8zJMAzD1F1YKGIYhmFKBiHEH4QQVyt/3y6EuEoI8SshxOdCiFlCiN8rv78khPhCCDFbCHGZ8v12IcSfhRAzARyc37NgGIZhig0WihiGYZhS4mEAPwQAIUQZgPMBrAHQD8AoAMMBjBBCHOFtf4mUcgSAkQB+IYRo633fFMBkKeUwKeXHeew/wzAMU4TUK3QHGIZhGIaKlHKpEGKjEGI/AB0BTAdwAIDjvc8A0AwJIelDJAShM73vu3vfbwRQDeD5fPadYRiGKV5YKGIYhmFKjX8DuAhAJyQsR8cCuENKeb+6kRDiKACjARwspdwphHgfQCPv591Syuo89ZdhGIYpcth9jmEYhik1XgRwIhIWoje9f5cIIZoBgBCiqxCiA4CWAL71BKIBAA4qVIcZhmGY4oYtRQzDMExJIaXcK4SYCGCzZ+15SwixL4BJQggA2A7g+wDeAHC5EGIOgHkAPitUnxmGYZjiRkgpC90HhmEYhiHjJViYBuBcKeWCQveHYRiGKX3YfY5hGIYpGYQQAwEsBPAuC0QMwzCMK9hSxDAMwzAMwzBMnYYtRQzDMAzDMAzD1GlYKGIYhmEYhmEYpk7DQhHDMAzDMAzDMHUaFooYhmEYhmEYhqnTsFDEMAzDMAzDMEyd5v8BgT8yOH4j7u4AAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -950,18 +950,18 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.81040687, 0.69606128, 0.42944323],\n", - " [0.99033137, 0.60317056, 0.82435046],\n", - " [0.70689463, 0.05604562, 0.53930095]])" + "array([[0.34743109, 0.34666094, 0.67796236],\n", + " [0.37775535, 0.7452935 , 0.44639271],\n", + " [0.7097024 , 0.54721637, 0.96400871]])" ] }, - "execution_count": 45, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -974,7 +974,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -983,16 +983,16 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "8.104068706347091755e-01 6.960612833436454761e-01 4.294432296218351208e-01\r\n", - "9.903313699879116028e-01 6.031705643128616456e-01 8.243504620080480683e-01\r\n", - "7.068946259685966460e-01 5.604562284444569720e-02 5.393009542524886957e-01\r\n" + "3.474310879390657414e-01 3.466609365910759966e-01 6.779623624489031775e-01\r\n", + "3.777553531256817587e-01 7.452935047749419395e-01 4.463927097637667707e-01\r\n", + "7.097023968559375007e-01 5.472163711854115542e-01 9.640087120207403437e-01\r\n" ] } ], @@ -1002,16 +1002,16 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.81041 0.69606 0.42944\r\n", - "0.99033 0.60317 0.82435\r\n", - "0.70689 0.05605 0.53930\r\n" + "0.34743 0.34666 0.67796\r\n", + "0.37776 0.74529 0.44639\r\n", + "0.70970 0.54722 0.96401\r\n" ] } ], @@ -1037,14 +1037,14 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "random-matrix.npy: data\r\n" + "random-matrix.npy: NumPy array, version 1.0, header length 118\r\n" ] } ], @@ -1056,18 +1056,18 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.81040687, 0.69606128, 0.42944323],\n", - " [0.99033137, 0.60317056, 0.82435046],\n", - " [0.70689463, 0.05604562, 0.53930095]])" + "array([[0.34743109, 0.34666094, 0.67796236],\n", + " [0.37775535, 0.7452935 , 0.44639271],\n", + " [0.7097024 , 0.54721637, 0.96400871]])" ] }, - "execution_count": 50, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1085,7 +1085,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1106,7 +1106,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1115,7 +1115,7 @@ "48" ] }, - "execution_count": 53, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1126,7 +1126,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1135,7 +1135,7 @@ "2" ] }, - "execution_count": 54, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1167,7 +1167,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1176,7 +1176,7 @@ "1" ] }, - "execution_count": 55, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1190,7 +1190,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1219,7 +1219,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1230,7 +1230,7 @@ " [5, 6]])" ] }, - "execution_count": 57, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1241,7 +1241,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1250,7 +1250,7 @@ "array([3, 4])" ] }, - "execution_count": 58, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1268,7 +1268,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1277,7 +1277,7 @@ "array([3, 4])" ] }, - "execution_count": 59, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1288,7 +1288,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1297,7 +1297,7 @@ "array([2, 4, 6])" ] }, - "execution_count": 60, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1315,7 +1315,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1346,7 +1346,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1357,7 +1357,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1368,7 +1368,7 @@ " [ 5, -1]])" ] }, - "execution_count": 63, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -1393,7 +1393,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1402,7 +1402,7 @@ "array([1, 2, 3, 4, 5])" ] }, - "execution_count": 64, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -1414,7 +1414,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1423,7 +1423,7 @@ "array([2, 3])" ] }, - "execution_count": 65, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1441,7 +1441,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -1450,7 +1450,7 @@ "array([ 1, -2, -3, 4, 5])" ] }, - "execution_count": 66, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -1471,7 +1471,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -1480,7 +1480,7 @@ "array([ 1, -2, -3, 4, 5])" ] }, - "execution_count": 67, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -1491,7 +1491,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -1500,7 +1500,7 @@ "array([ 1, -2, -3, 4, 5])" ] }, - "execution_count": 68, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1511,7 +1511,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -1520,7 +1520,7 @@ "array([ 1, -3, 5])" ] }, - "execution_count": 69, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1531,7 +1531,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -1540,7 +1540,7 @@ "array([ 1, -2, -3])" ] }, - "execution_count": 71, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -1551,7 +1551,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -1560,7 +1560,7 @@ "array([4, 5])" ] }, - "execution_count": 72, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -1578,7 +1578,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -1587,7 +1587,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -1596,7 +1596,7 @@ "5" ] }, - "execution_count": 74, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -1607,7 +1607,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -1616,7 +1616,7 @@ "array([3, 4, 5])" ] }, - "execution_count": 75, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -1634,7 +1634,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -1647,7 +1647,7 @@ " [40, 41, 42, 43, 44]])" ] }, - "execution_count": 76, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -1660,7 +1660,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -1671,7 +1671,7 @@ " [31, 32, 33]])" ] }, - "execution_count": 77, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -1683,7 +1683,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -1694,7 +1694,7 @@ " [40, 42, 44]])" ] }, - "execution_count": 78, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -1720,7 +1720,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -1748,7 +1748,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -1757,7 +1757,7 @@ "array([11, 31, 24])" ] }, - "execution_count": 81, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -1776,7 +1776,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -1785,7 +1785,7 @@ "array([0, 1, 2, 3, 4])" ] }, - "execution_count": 82, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -1797,7 +1797,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -1806,7 +1806,7 @@ "array([0, 2])" ] }, - "execution_count": 85, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -1818,7 +1818,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -1827,7 +1827,7 @@ "array([0, 2])" ] }, - "execution_count": 86, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -1847,7 +1847,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -1857,7 +1857,7 @@ " 6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])" ] }, - "execution_count": 87, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -1869,7 +1869,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -1880,7 +1880,7 @@ " False, False])" ] }, - "execution_count": 88, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -1893,7 +1893,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -1902,7 +1902,7 @@ "array([5.5, 6. , 6.5, 7. ])" ] }, - "execution_count": 90, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -1913,7 +1913,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -1922,7 +1922,7 @@ "array([3.5, 4. , 4.5, 5. , 5.5])" ] }, - "execution_count": 91, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -1954,7 +1954,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -1963,7 +1963,7 @@ "(array([11, 12, 13, 14]),)" ] }, - "execution_count": 93, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -1979,7 +1979,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -1988,7 +1988,7 @@ "array([5.5, 6. , 6.5, 7. ])" ] }, - "execution_count": 94, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -2013,7 +2013,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -2022,7 +2022,7 @@ "array([ 0, 11, 22, 33, 44])" ] }, - "execution_count": 95, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -2033,7 +2033,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -2042,7 +2042,7 @@ "array([10, 21, 32, 43])" ] }, - "execution_count": 96, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -2081,7 +2081,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -2092,7 +2092,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -2101,7 +2101,7 @@ "array([0, 2, 4, 6, 8])" ] }, - "execution_count": 98, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -2112,7 +2112,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -2121,7 +2121,7 @@ "array([2, 3, 4, 5, 6])" ] }, - "execution_count": 99, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -2132,7 +2132,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -2176,17 +2176,17 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.04434218, 0.59817098, 0.78569806],\n", - " [0.03270677, 0.93918254, 0.01270568]])" + "array([[0.12684531, 0.88008175, 0.00646408],\n", + " [0.56140088, 0.06651575, 0.79145154]])" ] }, - "execution_count": 101, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -2199,7 +2199,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -2208,7 +2208,7 @@ "array([1., 4.])" ] }, - "execution_count": 102, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -2227,7 +2227,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -2236,7 +2236,7 @@ "((2, 3), (2,))" ] }, - "execution_count": 103, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -2247,18 +2247,39 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.21057582, 0.36170027],\n", - " [0.77341514, 1.93822861],\n", - " [0.88639611, 0.22543893]])" + "array([[0.35615349, 0.93812672, 0.08039952],\n", + " [0.74926689, 0.25790647, 0.88963562]])" ] }, - "execution_count": 104, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.35615349, 1.49853379],\n", + " [0.93812672, 0.51581293],\n", + " [0.08039952, 1.77927125]])" + ] + }, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -2269,7 +2290,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -2277,9 +2298,9 @@ "evalue": "operands could not be broadcast together with shapes (2,3) (2,) ", "output_type": "error", "traceback": [ - "\u001b[0;31m------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,3) (2,) " ] } @@ -2304,20 +2325,20 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[1.03315792, 0.6905855 , 0.84353203, 1.45096566, 0.87872966],\n", - " [0.38571022, 0.35776393, 0.43959241, 0.58357586, 0.47922741],\n", - " [0.92430843, 0.58422879, 0.78342705, 1.42096537, 0.66931662],\n", - " [0.53165197, 0.29194347, 0.4070023 , 0.71839424, 0.36541866],\n", - " [1.07740213, 0.81283748, 0.74676416, 1.1225275 , 1.16903802]])" + "array([[2.59833251, 1.8189686 , 1.32946437, 2.15441681, 1.55219543],\n", + " [1.4561364 , 1.26875236, 0.97855704, 1.35013248, 1.05524471],\n", + " [2.38061437, 1.70445667, 1.16297305, 2.27888345, 1.66499116],\n", + " [1.08602725, 0.76015292, 0.46415646, 1.38753125, 1.00011024],\n", + " [1.82122991, 1.34175794, 0.92375387, 1.74770416, 1.27559765]])" ] }, - "execution_count": 106, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -2331,20 +2352,20 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[1.44646503],\n", - " [0.6220085 ],\n", - " [1.17066732],\n", - " [0.68703402],\n", - " [1.71817279]])" + "array([[2.0139906 ],\n", + " [1.41657535],\n", + " [2.09784627],\n", + " [1.2752073 ],\n", + " [1.6253844 ]])" ] }, - "execution_count": 109, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -2355,16 +2376,16 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[2.28303296]])" + "array([[2.08466462]])" ] }, - "execution_count": 110, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -2382,7 +2403,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -2392,16 +2413,16 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[0.73710128, 0.81515879, 0.7439564 , 0.16244929, 0.70382519]])" + "matrix([[0.45282687, 0.64874757, 0.70028245, 0.91412865, 0.36429705]])" ] }, - "execution_count": 113, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -2412,20 +2433,20 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[1.03315792, 0.6905855 , 0.84353203, 1.45096566, 0.87872966],\n", - " [0.38571022, 0.35776393, 0.43959241, 0.58357586, 0.47922741],\n", - " [0.92430843, 0.58422879, 0.78342705, 1.42096537, 0.66931662],\n", - " [0.53165197, 0.29194347, 0.4070023 , 0.71839424, 0.36541866],\n", - " [1.07740213, 0.81283748, 0.74676416, 1.1225275 , 1.16903802]])" + "matrix([[2.59833251, 1.8189686 , 1.32946437, 2.15441681, 1.55219543],\n", + " [1.4561364 , 1.26875236, 0.97855704, 1.35013248, 1.05524471],\n", + " [2.38061437, 1.70445667, 1.16297305, 2.27888345, 1.66499116],\n", + " [1.08602725, 0.76015292, 0.46415646, 1.38753125, 1.00011024],\n", + " [1.82122991, 1.34175794, 0.92375387, 1.74770416, 1.27559765]])" ] }, - "execution_count": 114, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -2436,20 +2457,20 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[1.44646503],\n", - " [0.6220085 ],\n", - " [1.17066732],\n", - " [0.68703402],\n", - " [1.71817279]])" + "matrix([[2.0139906 ],\n", + " [1.41657535],\n", + " [2.09784627],\n", + " [1.2752073 ],\n", + " [1.6253844 ]])" ] }, - "execution_count": 115, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -2460,16 +2481,16 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[2.28303296]])" + "matrix([[2.08466462]])" ] }, - "execution_count": 116, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -2488,7 +2509,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 98, "metadata": {}, "outputs": [], "source": [ @@ -2497,7 +2518,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -2506,7 +2527,7 @@ "((5, 5), (6, 1))" ] }, - "execution_count": 118, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -2517,7 +2538,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -2525,10 +2546,10 @@ "evalue": "shapes (5,5) and (6,1) not aligned: 5 (dim 1) != 6 (dim 0)", "output_type": "error", "traceback": [ - "\u001b[0;31m------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/anaconda3/envs/dl/lib/python3.7/site-packages/numpy/matrixlib/defmatrix.py\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 216\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 217\u001b[0m \u001b[0;31m# This promotes 1-D vectors to row vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masmatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 219\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__rmul__'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/matrixlib/defmatrix.py\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;31m# This promotes 1-D vectors to row vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masmatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 221\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__rmul__'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: shapes (5,5) and (6,1) not aligned: 5 (dim 1) != 6 (dim 0)" ] @@ -2554,7 +2575,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 101, "metadata": {}, "outputs": [ { @@ -2564,7 +2585,7 @@ " [ 1.5, -0.5]])" ] }, - "execution_count": 120, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -2583,7 +2604,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -2592,7 +2613,7 @@ "-2.0000000000000004" ] }, - "execution_count": 121, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -2613,7 +2634,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 103, "metadata": {}, "outputs": [ { @@ -2622,7 +2643,7 @@ "(77431, 7)" ] }, - "execution_count": 122, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -2644,7 +2665,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -2660,7 +2681,7 @@ "6.197109684751585" ] }, - "execution_count": 123, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } @@ -2673,16 +2694,16 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.5275611748380306" + "0.4931528475182218" ] }, - "execution_count": 126, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -2708,7 +2729,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -2717,7 +2738,7 @@ "(8.282271621340573, 68.59602320966341)" ] }, - "execution_count": 127, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -2735,7 +2756,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -2744,7 +2765,7 @@ "-25.8" ] }, - "execution_count": 128, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -2756,7 +2777,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -2765,7 +2786,7 @@ "28.3" ] }, - "execution_count": 129, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -2784,7 +2805,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -2793,7 +2814,7 @@ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, - "execution_count": 130, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -2805,7 +2826,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -2814,7 +2835,7 @@ "45" ] }, - "execution_count": 131, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } @@ -2826,7 +2847,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -2835,7 +2856,7 @@ "3628800" ] }, - "execution_count": 132, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -2847,7 +2868,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -2856,7 +2877,7 @@ "array([ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45])" ] }, - "execution_count": 133, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -2868,7 +2889,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -2878,7 +2899,7 @@ " 40320, 362880, 3628800])" ] }, - "execution_count": 134, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -2890,16 +2911,16 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.9739601910770402" + "1.4446600641166332" ] }, - "execution_count": 135, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -2927,7 +2948,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -2955,7 +2976,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -2964,7 +2985,7 @@ "array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.])" ] }, - "execution_count": 137, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -2975,7 +2996,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 117, "metadata": {}, "outputs": [ { @@ -2993,7 +3014,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -3020,12 +3041,12 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 119, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3065,19 +3086,19 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 120, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.27954547, 0.39434877, 0.4540566 ],\n", - " [0.98942566, 0.36000135, 0.33399721],\n", - " [0.88427852, 0.87776476, 0.80368109],\n", - " [0.54534545, 0.12886051, 0.29466367]])" + "array([[0.85882078, 0.0838741 , 0.4529751 ],\n", + " [0.32355282, 0.23641565, 0.37693805],\n", + " [0.06769945, 0.30438005, 0.9780961 ],\n", + " [0.46162058, 0.42681981, 0.71106984]])" ] }, - "execution_count": 143, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } @@ -3091,16 +3112,16 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 121, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.9894256638866764" + "0.978096099540799" ] }, - "execution_count": 144, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -3112,16 +3133,16 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.98942566, 0.87776476, 0.80368109])" + "array([0.85882078, 0.42681981, 0.9780961 ])" ] }, - "execution_count": 145, + "execution_count": 122, "metadata": {}, "output_type": "execute_result" } @@ -3139,7 +3160,7 @@ { "data": { "text/plain": [ - "array([ 0.7859115 , 0.77223336, 0.50666828, 0.55104521])" + "array([0.85882078, 0.37693805, 0.9780961 , 0.71106984])" ] }, "execution_count": 123, @@ -3175,17 +3196,17 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 124, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.77814945 0.11415817 0.62327907]\n", - " [0.78832786 0.9410027 0.3151632 ]\n", - " [0.0368383 0.24516094 0.74711683]\n", - " [0.81727139 0.76195462 0.19487213]]\n" + "[[0.58458652 0.95489874 0.76873658]\n", + " [0.79144906 0.35559767 0.96031963]\n", + " [0.55942317 0.78723157 0.3650356 ]\n", + " [0.04685468 0.43444695 0.33839966]]\n" ] } ], @@ -3198,7 +3219,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -3216,18 +3237,18 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 126, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.77814945, 0.11415817, 0.62327907, 0.78832786, 0.9410027 ,\n", - " 0.3151632 , 0.0368383 , 0.24516094, 0.74711683, 0.81727139,\n", - " 0.76195462, 0.19487213]])" + "array([[0.58458652, 0.95489874, 0.76873658, 0.79144906, 0.35559767,\n", + " 0.96031963, 0.55942317, 0.78723157, 0.3650356 , 0.04685468,\n", + " 0.43444695, 0.33839966]])" ] }, - "execution_count": 148, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -3239,25 +3260,25 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 127, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.77814945]\n", - " [0.11415817]\n", - " [0.62327907]\n", - " [0.78832786]\n", - " [0.9410027 ]\n", - " [0.3151632 ]\n", - " [0.0368383 ]\n", - " [0.24516094]\n", - " [0.74711683]\n", - " [0.81727139]\n", - " [0.76195462]\n", - " [0.19487213]]\n", + "[[0.58458652]\n", + " [0.95489874]\n", + " [0.76873658]\n", + " [0.79144906]\n", + " [0.35559767]\n", + " [0.96031963]\n", + " [0.55942317]\n", + " [0.78723157]\n", + " [0.3650356 ]\n", + " [0.04685468]\n", + " [0.43444695]\n", + " [0.33839966]]\n", "(12, 1)\n" ] } @@ -3270,18 +3291,18 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 128, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[5. , 5. , 5. , 5. , 5. ,\n", - " 0.3151632 , 0.0368383 , 0.24516094, 0.74711683, 0.81727139,\n", - " 0.76195462, 0.19487213]])" + " 0.96031963, 0.55942317, 0.78723157, 0.3650356 , 0.04685468,\n", + " 0.43444695, 0.33839966]])" ] }, - "execution_count": 150, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -3294,19 +3315,19 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 129, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[5. , 5. , 5. ],\n", - " [5. , 5. , 0.3151632 ],\n", - " [0.0368383 , 0.24516094, 0.74711683],\n", - " [0.81727139, 0.76195462, 0.19487213]])" + " [5. , 5. , 0.96031963],\n", + " [0.55942317, 0.78723157, 0.3650356 ],\n", + " [0.04685468, 0.43444695, 0.33839966]])" ] }, - "execution_count": 151, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -3324,18 +3345,18 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([5. , 5. , 5. , 5. , 5. ,\n", - " 0.3151632 , 0.0368383 , 0.24516094, 0.74711683, 0.81727139,\n", - " 0.76195462, 0.19487213])" + " 0.96031963, 0.55942317, 0.78723157, 0.3650356 , 0.04685468,\n", + " 0.43444695, 0.33839966])" ] }, - "execution_count": 152, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -3348,7 +3369,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 131, "metadata": {}, "outputs": [ { @@ -3365,23 +3386,23 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.15246604 0.88310479 0.72114104 0.13062197 0.34755784 0.23403431\n", - " 0.92416609 0.02256229 0.5800577 0.74899696 0.92496278 0.98122918\n", - " 0.48526406 0.61784995 0.74526605 0.36587567 0.53935338 0.48663655\n", - " 0.46254169 0.60397314 0.20181377 0.4031567 0.91153399 0.550312\n", - " 0.05484349 0.73570383 0.52350869 0.29877126 0.64086659 0.74656651\n", - " 0.4431057 0.26241441 0.8531424 0.44721405 0.25633508 0.68545314\n", - " 0.90912925 0.91285705 0.95180396 0.74203516 0.57374628 0.02461786\n", - " 0.65634028 0.3354893 0.89448353 0.42890204 0.09926527 0.93041583\n", - " 0.65132991 0.40817373 0.39369988 0.38511924 0.59963332 0.36952358\n", - " 0.22752502 0.56710332 0.95845757 0.97355957 0.43180755 0.20890057]\n" + "[0.88616566 0.11474399 0.49426839 0.86496944 0.44553257 0.01731081\n", + " 0.26391484 0.81714822 0.9077824 0.45350327 0.34418481 0.30680307\n", + " 0.22397584 0.96490185 0.25766897 0.1628303 0.35022665 0.87266285\n", + " 0.14436895 0.2987234 0.04567582 0.62524215 0.03006832 0.15222984\n", + " 0.86554462 0.30036796 0.66637188 0.51245662 0.46296801 0.53384373\n", + " 0.90012971 0.00319531 0.48428543 0.24703543 0.53384405 0.48024175\n", + " 0.17175873 0.1834814 0.43739033 0.64565657 0.49266811 0.72123815\n", + " 0.57728476 0.76663343 0.68360823 0.34881945 0.64329004 0.79011718\n", + " 0.7055079 0.32594224 0.48795517 0.43684614 0.32047664 0.63067622\n", + " 0.24496431 0.25019593 0.57181523 0.38889906 0.53574819 0.02653888]\n" ] } ], @@ -3393,18 +3414,18 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([10. , 10. , 10. , 10. , 10. ,\n", - " 0.3151632 , 0.0368383 , 0.24516094, 0.74711683, 0.81727139,\n", - " 0.76195462, 0.19487213])" + " 0.96031963, 0.55942317, 0.78723157, 0.3650356 , 0.04685468,\n", + " 0.43444695, 0.33839966])" ] }, - "execution_count": 155, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" } @@ -3417,19 +3438,19 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 134, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[5. , 5. , 5. ],\n", - " [5. , 5. , 0.3151632 ],\n", - " [0.0368383 , 0.24516094, 0.74711683],\n", - " [0.81727139, 0.76195462, 0.19487213]])" + " [5. , 5. , 0.96031963],\n", + " [0.55942317, 0.78723157, 0.3650356 ],\n", + " [0.04685468, 0.43444695, 0.33839966]])" ] }, - "execution_count": 156, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } @@ -3454,7 +3475,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 135, "metadata": {}, "outputs": [], "source": [ @@ -3463,7 +3484,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -3482,7 +3503,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 137, "metadata": {}, "outputs": [ { @@ -3504,7 +3525,7 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 138, "metadata": {}, "outputs": [ { @@ -3525,7 +3546,7 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 139, "metadata": {}, "outputs": [ { @@ -3534,7 +3555,7 @@ "(3, 1)" ] }, - "execution_count": 162, + "execution_count": 139, "metadata": {}, "output_type": "execute_result" } @@ -3546,7 +3567,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 140, "metadata": {}, "outputs": [ { @@ -3555,7 +3576,7 @@ "(1, 3)" ] }, - "execution_count": 163, + "execution_count": 140, "metadata": {}, "output_type": "execute_result" } @@ -3574,7 +3595,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -3604,7 +3625,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 142, "metadata": {}, "outputs": [ { @@ -3625,7 +3646,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 143, "metadata": {}, "outputs": [ { @@ -3675,7 +3696,7 @@ }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -3694,7 +3715,7 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 145, "metadata": {}, "outputs": [ { @@ -3703,7 +3724,7 @@ "array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4])" ] }, - "execution_count": 172, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -3715,7 +3736,7 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 146, "metadata": {}, "outputs": [ { @@ -3725,7 +3746,7 @@ " [3, 4, 3, 4, 3, 4]])" ] }, - "execution_count": 173, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -3737,7 +3758,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 147, "metadata": {}, "outputs": [ { @@ -3747,7 +3768,7 @@ " [3, 4, 3, 4, 3, 4]])" ] }, - "execution_count": 176, + "execution_count": 147, "metadata": {}, "output_type": "execute_result" } @@ -3791,7 +3812,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 149, "metadata": {}, "outputs": [], "source": [ @@ -3800,7 +3821,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 150, "metadata": {}, "outputs": [ { @@ -3811,7 +3832,7 @@ " [5, 6]])" ] }, - "execution_count": 186, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -3822,7 +3843,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 151, "metadata": {}, "outputs": [ { @@ -3832,7 +3853,7 @@ " [3, 4, 6]])" ] }, - "execution_count": 187, + "execution_count": 151, "metadata": {}, "output_type": "execute_result" } @@ -3850,7 +3871,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 152, "metadata": {}, "outputs": [ { @@ -3861,7 +3882,7 @@ " [5, 6]])" ] }, - "execution_count": 188, + "execution_count": 152, "metadata": {}, "output_type": "execute_result" } @@ -3872,7 +3893,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 153, "metadata": {}, "outputs": [ { @@ -3882,7 +3903,7 @@ " [3, 4, 6]])" ] }, - "execution_count": 189, + "execution_count": 153, "metadata": {}, "output_type": "execute_result" } @@ -3907,7 +3928,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 154, "metadata": {}, "outputs": [ { @@ -3917,7 +3938,7 @@ " [3, 4]])" ] }, - "execution_count": 190, + "execution_count": 154, "metadata": {}, "output_type": "execute_result" } @@ -3930,7 +3951,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 155, "metadata": {}, "outputs": [], "source": [ @@ -3940,7 +3961,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 156, "metadata": {}, "outputs": [ { @@ -3950,7 +3971,7 @@ " [ 3, 4]])" ] }, - "execution_count": 192, + "execution_count": 156, "metadata": {}, "output_type": "execute_result" } @@ -3964,7 +3985,7 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 157, "metadata": {}, "outputs": [ { @@ -3974,7 +3995,7 @@ " [ 3, 4]])" ] }, - "execution_count": 193, + "execution_count": 157, "metadata": {}, "output_type": "execute_result" } @@ -3992,7 +4013,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 158, "metadata": {}, "outputs": [], "source": [ @@ -4001,7 +4022,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 159, "metadata": {}, "outputs": [ { @@ -4011,7 +4032,7 @@ " [ 3, 4]])" ] }, - "execution_count": 195, + "execution_count": 159, "metadata": {}, "output_type": "execute_result" } @@ -4025,7 +4046,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 160, "metadata": {}, "outputs": [ { @@ -4035,7 +4056,7 @@ " [ 3, 4]])" ] }, - "execution_count": 196, + "execution_count": 160, "metadata": {}, "output_type": "execute_result" } @@ -4062,7 +4083,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 161, "metadata": {}, "outputs": [ { @@ -4085,7 +4106,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 162, "metadata": {}, "outputs": [ { @@ -4121,7 +4142,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 163, "metadata": {}, "outputs": [ { @@ -4150,7 +4171,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 164, "metadata": {}, "outputs": [ { @@ -4160,7 +4181,7 @@ " [ 9, 16]])" ] }, - "execution_count": 200, + "execution_count": 164, "metadata": {}, "output_type": "execute_result" } @@ -4186,7 +4207,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 165, "metadata": {}, "outputs": [], "source": [ @@ -4202,7 +4223,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 166, "metadata": { "scrolled": true }, @@ -4212,10 +4233,10 @@ "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", "output_type": "error", "traceback": [ - "\u001b[0;31m------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTheta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36mTheta\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0m阶跃函数的普遍版本\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \"\"\"\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTheta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mTheta\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0m阶跃函数的普遍版本\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \"\"\"\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" ] } @@ -4235,7 +4256,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 167, "metadata": {}, "outputs": [], "source": [ @@ -4244,7 +4265,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 168, "metadata": {}, "outputs": [ { @@ -4253,7 +4274,7 @@ "array([0, 0, 0, 1, 1, 1, 1])" ] }, - "execution_count": 204, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } @@ -4271,7 +4292,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 169, "metadata": {}, "outputs": [], "source": [ @@ -4284,7 +4305,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 170, "metadata": {}, "outputs": [ { @@ -4293,7 +4314,7 @@ "array([0, 0, 0, 1, 1, 1, 1])" ] }, - "execution_count": 206, + "execution_count": 170, "metadata": {}, "output_type": "execute_result" } @@ -4304,7 +4325,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 171, "metadata": {}, "outputs": [ { @@ -4320,7 +4341,7 @@ "array([0, 0, 0, 1, 1, 1, 1])" ] }, - "execution_count": 207, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" } @@ -4334,7 +4355,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 172, "metadata": {}, "outputs": [ { @@ -4343,7 +4364,7 @@ "(0, 1)" ] }, - "execution_count": 208, + "execution_count": 172, "metadata": {}, "output_type": "execute_result" } @@ -4369,7 +4390,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 173, "metadata": {}, "outputs": [ { @@ -4379,7 +4400,7 @@ " [3, 4]])" ] }, - "execution_count": 209, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } @@ -4391,7 +4412,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 174, "metadata": {}, "outputs": [ { @@ -4400,7 +4421,7 @@ "True" ] }, - "execution_count": 210, + "execution_count": 174, "metadata": {}, "output_type": "execute_result" } @@ -4431,7 +4452,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 176, "metadata": {}, "outputs": [ { @@ -4465,7 +4486,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 177, "metadata": {}, "outputs": [ { @@ -4474,7 +4495,7 @@ "dtype('int64')" ] }, - "execution_count": 212, + "execution_count": 177, "metadata": {}, "output_type": "execute_result" } @@ -4485,7 +4506,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 178, "metadata": {}, "outputs": [ { @@ -4495,7 +4516,7 @@ " [3., 4.]])" ] }, - "execution_count": 213, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" } @@ -4508,7 +4529,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 179, "metadata": {}, "outputs": [ { @@ -4517,7 +4538,7 @@ "dtype('float64')" ] }, - "execution_count": 214, + "execution_count": 179, "metadata": {}, "output_type": "execute_result" } @@ -4528,7 +4549,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 180, "metadata": {}, "outputs": [ { @@ -4538,7 +4559,7 @@ " [ True, True]])" ] }, - "execution_count": 215, + "execution_count": 180, "metadata": {}, "output_type": "execute_result" } @@ -4583,7 +4604,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.3" } }, "nbformat": 4, diff --git a/1_numpy_matplotlib_scipy_sympy/random-matrix.csv b/1_numpy_matplotlib_scipy_sympy/random-matrix.csv index 5e031a7..295c447 100644 --- a/1_numpy_matplotlib_scipy_sympy/random-matrix.csv +++ b/1_numpy_matplotlib_scipy_sympy/random-matrix.csv @@ -1,3 +1,3 @@ -0.81041 0.69606 0.42944 -0.99033 0.60317 0.82435 -0.70689 0.05605 0.53930 +0.34743 0.34666 0.67796 +0.37776 0.74529 0.44639 +0.70970 0.54722 0.96401 diff --git a/1_numpy_matplotlib_scipy_sympy/random-matrix.npy b/1_numpy_matplotlib_scipy_sympy/random-matrix.npy index 1df9863b6c763783cc8ef930a2776c96d9265692..36d736eab45e07872fade6018ebff7ab7578f628 100644 GIT binary patch delta 79 zcmV-V0I>ha0muQ6fJmD66Piyv)<4u{U0Sg()<4T()F0llqa;XiBzX3Qwr?mthdDc=A9 delta 79 zcmV-V0I>ha0muQ6fJlsux?|ez=|8?8|4$-D=0Ci+t#to;+dpri$!W{5?>~P=^ja)U l<3GELSU416>Oa&Ph