[
  {
    "path": "Matplotlib 学习笔记.md",
    "content": "---\ntitle: Matplotlib 学习笔记\ndate: 2019-03-13 21:23:56\ntags:\n    - python\n    - Matplotlib\n    - noteBook\ntoc: true\n---\n\n## Matplotlib\n\nMatplotlib 是Python2D绘图领域使用最广泛的套件。能导出多种格式的图片。\n\n### Matplotlib中文显示\n\nMatplotlib默认是不支持中文的，如果出现中文会以方框代替。\n\n![ch_err_pic](data:image/jpeg;base64,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)\n\n但是经过测试国外字体中部分可以支持。\n\n```python\ngoodfont=[\n 'Adobe Heiti Std',\n 'Arial Unicode MS',\n 'DengXian',\n 'SimHei',\n 'STKaiti',\n 'STXihei',\n]\n```\n<!-- more -->\n使用fontproperties指定字体\n\n```python\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport matplotlib\nzh_cn_font = matplotlib.font_manager.FontProperties(fname=\"F:\\ProjectFiles\\SimHei.ttf\")\n#第七行的作用是为了消除更换为unicode字体之后0、负数之类的显示异常。之后所有使用中文字体的地方只字符串都以u\"\"的形式出现，并指定fontproperties属性为我们的指定的myfont就行了\nmatplotlib.rcParams['axes.unicode_minus'] = False\n#matplotlib.rcParams['font.sans-serif'] = ['SimHei']\nx = np.arange(0,9)\ny = 2*x+1\nplt.title(u\"Matplotlib 标题\",fontproperties = zh_cn_font)\nplt.xlabel(u\"x 轴:\",fontproperties = zh_cn_font)\nplt.ylabel(u\"y 轴:\",fontproperties = zh_cn_font)\nplt.plot(x,y)\nplt.show()\n```\n\n一劳永逸法\n\n1. 找到matplotlib 配置文件：\n   \n```python\nimport matplotlib \nprint(matplotlib.matplotlib_fname()) \n#我自己的输出结果如下：\n#%Python_Home%\\Lib\\site-packages\\matplotlib\\mpl-data\n```\n2. 编辑器打开此文件 matplotlibrc删除font.family和font.sans-serif两行前的#，并在font.sans-serif后添加微软雅黑字体Microsoft YaHei\n\n3. 下载字体:msyh.ttf (微软雅黑)放在matplotlib 字体文件夹下:# %Python_Home%\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\n\n4. 删除.matplotlib/cache里面的两个缓存字体文件C:\\Users\\你的用户名\\\\.matplotlib\n    ![font cache](data:image/jpeg;base64,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)\n\n5. 重启Python\n\n![ch_pic](data:image/jpeg;base64,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)\n\n### 绘制一次方程\n\n使用matplotlib中的pylot包进行简单绘图。\n\n```python\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nx = np.arange(0,9)\ny = 2*x+1\nplt.title(\"Matplotlib Demo1\")\nplt.xlabel(\"x axis:\")\nplt.ylabel(\"y axis:\")\nplt.plot(x,y)\nplt.show()\n```\n\n![\"pyplot\"](data:image/jpeg;base64,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)\n\n### 绘制离散图\n\n向pyplot的plot()函数添加特殊的格式化字符串可以显示离散值。\n\n| 字符       | 描述         |\n| :--------- | :----------- |\n| `'-'`      | 实线样式     |\n| `'--'`     | 短横线样式   |\n| `'-.'`     | 点划线样式   |\n| `':'`      | 虚线样式     |\n| `'.'`      | 点标记       |\n| `','`      | 像素标记     |\n| `'o'`      | 圆标记       |\n| `'v'`      | 倒三角标记   |\n| `'^'`      | 正三角标记   |\n| `'&lt;'`   | 左三角标记   |\n| `'&gt;'`   | 右三角标记   |\n| `'1'`      | 下箭头标记   |\n| `'2'`      | 上箭头标记   |\n| `'3'`      | 左箭头标记   |\n| `'4'`      | 右箭头标记   |\n| `'s'`      | 正方形标记   |\n| `'p'`      | 五边形标记   |\n| `'*'`      | 星形标记     |\n| `'h'`      | 六边形标记 1 |\n| `'H'`      | 六边形标记 2 |\n| `'+'`      | 加号标记     |\n| `'x'`      | X 标记       |\n| `'D'`      | 菱形标记     |\n| `'d'`      | 窄菱形标记   |\n| `'&#124;'` | 竖直线标记   |\n| `'_'`      | 水平线标记   |\n\n以下是颜色的缩写：\n\n| 字符  | 颜色   |\n| :---- | :----- |\n| `'b'` | 蓝色   |\n| `'g'` | 绿色   |\n| `'r'` | 红色   |\n| `'c'` | 青色   |\n| `'m'` | 品红色 |\n| `'y'` | 黄色   |\n| `'k'` | 黑色   |\n| `'w'` | 白色   |\n\n例如：要显示圆来代表点，而不是上面示例中的线，请使用 ob 作为 plot() 函数中的格式字符串。\n\n### 绘制正弦函数\n\n```python\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport matplotlib\nm = np.arange(0,4,0.1)\nn = np.sin(m*np.pi)\nplt.title(u\"Matplotlib 正弦函数\")\nplt.xlabel(u\"x 轴:\")\nplt.ylabel(u\"y 轴:\")\nplt.plot(m,n,'-')\nplt.show()\n```\n\n![sin](data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAIAAAC6s0uzAACJgUlEQVR42uzdd1gT2dcHcAEFpdiQYkEQFLBQXKSISFHA1WVXBXvZtWBZOyB2BWwoqGsvrAV7FxVFRAREEBu9W+ggFhBEpCa+B2ef/HhRkBJyZ4bz+cMnhMnkZi7eb25y54zQ169fWyGEEEJIsIQwgBFCCCHBwwBGCCGECMAARgghhAjAAEYIIYQIwABGCCGECMAARgghhAjAAEYIIYQIwABGCCGECMAARgghhAjAAEYIIYQIwABGCCGECMAARgghhAjAAEYIIYQIwABGCCGECMAARgghhAjAAEYIIYQIwABGCCGECMAARgghhAjAAEYIIYQIwABGCCGECMAARgghhAjAAEYIIYQIwABGCCGECMAARgghhAjAAEYIIYQIwABGCCGECMAARgghhAjAAEYIIYQIwABGCCGECMAARgghhAjAAEYIIYQIwABGCCGECMAARgghhAjAAEYIMUNZWZmQkJCoqCjphiDEHxjACKGmSk9Pv3v37ty5c5u+q/v375uYmLRu3VpbW9ve3v7PP/8MDg7u0aOHsrKyh4eHi4tLdnY26ZeLEH9gACMkUEFBQWZmZidOnJgxY0bj9qD0DewHbh84cGDdunX+/v46OjppaWm9evVycnJydnauz36SkpLq+Yxt27aFZzx9+jQELe9OU1NTIyMj6vajR4+GDh3q4+MzYsSIGo/NyspSUFCoe/8VFRWQuNQNLS0tCwuLPXv2DBgwYPny5RDG8NLgRS1dunT27NkwCT5z5gx/ewQhUjCAUYvj6ek5c+ZMcXHxjIwMaWnp7zf49ddfYT73119/wZY/3RvEnpeXl52dXT2fvaEB/P3+qwfwsWPHNm3adPPmTU1NzYYGsJCQUD3bDKEYFRUFSR8XF0fdExYWtnjx4smTJ0NkUvesWLFCWFh427Zt1I9weHv27Ak3KisrX7169f0+379/7+7u7uvrC7F68OBBXmMiIyPHjBnz7NmzYcOGQQD7+fm1a9cOXiaMVDAP3rp1K/RLPZuNEM1hAKMWhwpgERERyKr169fX+G1MTAzkjaioKKRLfQIYcvTkyZP1/3/U0AD+fv/VA7i6hgbwDy1btgxC8aeTY3iPAtPfo0ePVp8TVzdkyJCQkBDej9B+XsR++PBhx44dELpWVlbw7kFFRYW3WXR09MuXLyHU27RpY29v/8cffwwaNAj6QltbGx4Fs2HoterPEhoaqq+v3+gXixBZGMCoxaEC2MDAICUlBfKjbdu21X87bdq08PDw3Nzc0aNHYwBXx+Vyy8vLqdsQjcbGxjAnpp63b9+++/fvh7lsjYfs3r0b5rIwZ4W57NmzZzU0NODO4cOHFxcXHzlyBGbt8AZIVlb23bt3X7582bVr16FDh65evQrbvHnzJiEhoV+/fl27doUf//zzzxs3bsBmp0+fhh+h42AnDx48gPQVExNr9ItFiCwMYNTiUAF87NgxCAwPD485c+bwfpWRkQETsn/++QdyCJKYCmCYkEEwwND/6tUrYWFhmNvBBrAZFXjV9xwYGGhqakoF5L59++zs7MLCwiDgx4wZs3Pnzo4dO7b6UQAHBwe7uro+fvwYQggeCM+7atUqmALWvX8qgCFrXVxcUlNT4R5eAEPObdy4MTk5WV5efv78+StXrqzt02aYVsJE9vDhw7Bb6p46AvjcuXNTp07l/QiTVyqAx44d+/nz53v37sFteNXw7+LFi6lt4IVAOwMCAuBQX79+HQ7CggUL4LnMzc3h4EyfPt3Pz+/UqVM3b96EPVy5coV6FMxrx40bV1BQAG+S5s2bN2nSJHhLpKenB3H76NEj6hgOGzYMDleNN08IMQsGMGpxqACG0Bo/fjyM+zDT4uUTxA8ELcSwpKQk7zvgrKwsQ0NDW1tbmLHBjHnDhg3KysoQCWVlZTAJg1Dx9/e/c+cObAkh0blzZ8hCUVFRmCzCQ9TV1WGbAwcOjBw58vbt262+C+CLFy9OmTJFW1t77ty5HTp0gF0dP37cysoKMqmkpKS2/dcRwLDNx48fId5gbzB1hoRzd3eHCegPDwVM9GGK6e3tDc/IOwK1BTDsB6ah1BooiF5Ibkh3eCxMT6Ojo3v27AkHB6bCDg4OkM3UQ6gAhvcWcHvHjh1bt26F4wYHHwK4tLQUEt3Ly0tLSwsOBRXAcNDgWTZv3gxHDCbEurq6cBwggAsLC6k3KDk5OVQPrl69GmbJpP+UEGoSDGDU4vAC+OnTpxMnTuTFT35+PqQIJBAEAEQyL4AhaCsrKyUkJKiHr127FoIkJSWFmp7+8CNiiHAIHmNjY+oeyCSIk2fPng0aNKh6AEPEKigodOvWDWIJprzUxmvWrIEJMUwoIaVa/ewj6O8DWFZWNjExEXK61bdvXnV0dOC379694+2/uh8G8J49e2psRs28IdRhxkx9CEyBgwCv6ODBg5CR8KO1tfWLFy8iIiJ4p+pWD+BW3ybc0CR43wMvDQ4vHFKqnbwAHj58OBylY8eOwTbUKughQ4ZMmDABmiQjIwP3ZGdnw7QeWgJvEag3JQgxFwYwanF4AQzhp6qqCv9SYQbzti1btsA0Tk5OrnoAUyDVIiMjIXIgGoO/GTp0aKtaAlJMTCw5OZl3T1hYGMyhqY+7qwdwQEAARM6hQ4dgKsnb+PXr171794boghhu1fAAhpn0kSNHeBvDTiDRIRf79Onz/aH4YQDDpNPHx6f6ZvC+RFxcHN6sSEpKOjo6UnfCAxctWnThwgXIxbJv4NDBK4VpK++B1QMYJtawc01NTXg3MHDgQJjo29jYwPuSjh078gIYMhXaCS8fshzu/OOPP1pVW8AF0+uNGzdCNuvr61tYWMD7JNJ/Sgg1CQYwanF4AQyhdeDAAUgRmHVBiigqKsKI/++//7b6dooOL4Dj4+Nhhge5CLEB2ZCXl3f79m1qUtiqfoukEhIS+vfvD/Pm1atXVw/gs2fPTps2DdJu5MiRvI1hWgxpB1ENgf3T/f/wO+Dqi7Bgerpw4cLQ0FB4B/D9oWjQR9DwhqP6wuZz585Rc3eYxUL6Ll68GB7r5uZW/SFUAO/du3fdunXwlmXp0qVw4/fff4cZMByEqVOnVlRUwMu8fPkyFcCFhYVwD0xz4VB7eXnB9Jfaj7S0NPQIdR7wP//8A5Ng2C31Bggh5sIARi1O9QCGtIPpnaWlJcxEbW1tISnV1dVb/f8AHjx4MEzanjx50qVLl1bfal9AZtcdwLDl8+fPeffcunULUgf2BvusHsAQdRC9NRaCUTNgCCrqm9SGBrCDg8OOHTt4Gy9fvnznzp3UBt8figYFcHUSEhJXr1799ddfW32bof72229v37599OgRvJmQkpKiPjxv9S2AYZYPmQpRvWfPHnhdrb5V8IAN4AUWFBTMmzePOpUIDjgEMPzqwYMH3z8dbNmhQwf4FTzjkiVL4Ei+efNGWFiY9J8SQk2CAYxanOoBDD9CgLm6uvbt21dBQeHmzZvUNtUDWFJSEhL62rVr1K8gMiGieAEMEQIJChM43pfEsFuIh7i4ON6nvqNGjbp37156ejrkUPUA/vTpU48ePVRUVGAKTpWCAjBL3rZt29OnT6nPcn+4/zoCWE1NLTo6mjo5p6ioCDIPZqiJiYk/PBSNC+Dy8nLYP7wj0dPTgx+3b98ODYapsLKysr29PcQk3Ka2pGbAfn5+7du3h3ktvEYIY3hD4+joCDlKbQNvblasWMFbBQ0pbmFhERkZqaqq2urb9wLQPDga1Mbw9ig5OXnDhg3wqkn/HSHUVBjAqMWpEcB5eXkwCf7y5QskB2/ZVPUANjExCQsL27x5M8Tb5cuXY2JiIAN4Abxv3z7IEpgTw2aDBg2i0hGSD/ISckVaWvrixYs3btzgzWhrrIKGcIWI1dHRgX8hpSCnjx8/vnDhQup8ntr2X0cAy8nJQRD+/fffZWVle/fuhTk9vKuAdwA/PBSNC2B4ewHvJKhlaLBza2trmA2PHj2a2iHcef36daomZfXvgKdMmXL+/PlW3748hnuoOlkU3nfAcHvu3LlwY82aNXBAIIah5TCrpvqFy+XCbv39/eHN0NixY0n/HSHUVBjAqMWpEcCtvp23CvM53jSr1f8PYNgSEjEkJKRdu3Zz5syBgLG1teUFcElJCewNQkJUVBSyQVtbmwpIyA9IRwhFRUVFePjSpUupPX9/HvDt27dhEhkREcHhcGAiDtlZ/RPp2vZfWwCvX78eGn/kyJGCggLYGH77fX3mVt+SsrS09P379zCLPXbs2LBhw6j7N27cCG9E7t+/X2N7mEbD+wPqNjQV3goUFxfHx8fD24IFCxZs3boVfoS3HYWFhRC6cIP6JLl6AH/48AGeTlhYmFqkVn3n1QOYOkTwuqKiouCAwFwZ5ru8bjp06NCAAQPg9cI2AwcOJP2nhFCTYAAjxGe1Vaqildq+ba2Nu7t7v379IIbFxcV37NgRFxcHMTx9+vQzZ85ApsLcFLYRERHp2LEjbPP69euHDx8aGhpCAMN7i/rs38bGhhfAwcHBW7ZsefbsWe/evaOjo+fPn+/m5rZkyZJ///0X3rjAlpD6r1692r9/PzSA9IFEqPEwgBHiM0YEcHl5OZWa9dSmTRsrKytfX1+43adPn1OnThkYGEA6Jicnd+3aVV5eXlpaulOnTtT5QiNHjoScvnr1Kkx58/Lyfrrz1atXQ3hDAK9cufL8+fMwV541axbM3WGfXl5e2dnZJ0+ehMj38PCgEhd2O2HCBDjCEPO8KzIhxDgYwAjxGSMCuHFguOBwOLz1YrVJTEyUkZGhFo03CMxxRUVFra2tpaSkqt+/c+fO4cOHa2tr8+6BZkDAQwyTPiQINR4GMEJ8xuIARgjxEQYwQgghRAAGMEIIIUQABjBCCCFEAAYwQgghREDLCmAul5uTkyMlJVXb9ckRQggxAoRXUVFRt27dmFsVvGUFcFZWloKCAulWIIQQ4o/MzMwePXqQbkUjtawALiws7NixI3QYr6geQgghJvr06RNMqKgrZZFuSyO1rACGDoOughjGAEYIIUZjwXiOAYwQQoh5WDCeYwAjhBBiHhaM5xjACCGEmIcF4zkGMEIIIeZhwXiOAYwQQoh5WDCeYwAjhBBiHhaM5xjACCGEmIcF4zkGMEIIIeZhwXhOowCOjo6eOXPm3r17jYyMavwqODjYzs4uPj5eRUVl165dI0aMoO6Pi4tbsGDBs2fPunbt6uLiMn369LqfggUdhhBCqBUrxnNaBHBERMS2bdtu3bpVUlLy8OHDGgGcmpqqoaGxcOHCadOmHTp0yNPTMyEhQUlJCY6+mpqapaWlg4PDzZs3nZycQkNDDQwM6ngiFnQYQgihVqwYz2kRwOvWrUtPT4fp7/Dhw78PYHt7+4CAgKioKLhdWVkJ0Tt79myY78JcecuWLZmZmaKiovAriF7I45MnT9bxRCzoMIQQQq1YMZ7TIoChDUJCQmlpab169fo+gAcOHDhixAiYIlM/TpkyJS8v7+7du2PHjhUTE7tw4QJ1/5o1a65evZqcnFzHE7GgwxBCCLVixXhOiwCm1BbAcIjd3NzmzZtH/bhq1Spvb+/4+HgtLa1Ro0a5urpS9x8+fNjBwaG4uLjGbsu+oW5TV89gdIc1SMjLDwFJ72YOUVLoLE66LQjVy+v3n888Th85oKter86k24JoDQOYn2oLYBEREU9PT94Cq40bN546derVq1cqKiozZsxYv349dT/cOWvWrMrKyhq7dXZ2dnFxqX4PozusnkorONt9k06EpsFtKbHWm8cOGK3dnXSjEKoLjEUXnmVu9E4oqeAICbVaYKqyzFy1jQhTr7WOmhsGMD/VFsDi4uL79u2bPXs29eOaNWtu3boVExPTr18/GxubTZs2Ufd7eHgsX74cuqTGblvgDDgp99PS81HJb4vgtqK0eHreF7gxRrvbxjED2rdtQ7p1CP1AfnH5qqsxfglv4baStHjatz9azR4d9kwa2KuLBOnWITrCAOan2gK4T58+U6dOhYks9SNMhYuKiq5fv25hYdG9e3eYHFP3w1TY29ubWqtVGxZ0WN2gNz0fpbneSSqv5HaRFHUfpzW0T5cDga/3BrzkcL9279hu9yRtXSX8ZA/Ry8OX7x0uRb8rKmsjIrRihPpso16+8bmrr8UWllS0ayPi/Ee/CYMUhGBSjFA1LBjPGRDAM2fOfPHiRWhoKNzmcDhKSkorVqxYvHixi4vLsWPHUlNTRURE4FeGhoaDBw/euXNnHU/Bgg6rw7uiUsfLMQ9evIfbZmoybuO0ZKTEqF+Fp39cdjEyM79EWKjVIrPei4f3wU/2EB2UVXLcfZOPhqTCbRUZCZjvDujegfrVm8IS+4vRYSl5cPvX/vKu1hqdJERJtxfRCAvGc5oGMJfLHTVq1Ny5c62trSMjI/X09GCCC7cPHjx448aNpKQkKSmp7Ozsvn37wuR44cKFXl5ebm5ucXFxioqKdTwFCzqsNv4Jb1dcjckvLhdrLbz2t77TDRRrzBiKSiucbyZcjciC29oKHfdM0laUxk/2EEkv3xYtPh+ZlFv1Xck0g55rR/VrJypSfQMO9+u/D1N2+iVXcL7KtRfbNUF7SO8upFuN6IIF4zlNA7isrExVVdXR0XHRokXwqytXrqxatSorKwuS+NChQ/3796ce8uDBA9gA5sdwz549e4YOHVr3U7Cgw75XUs7Z4pNw5nEG3FaXl9o7eaCqnFRtG3tH56zxii0qrZQQFXH+o/84nR74yR4SPBh2zjxO33w7sayS21lC1M1G07yfXG0bx2YVLr0YmfK+GP5U5w5VtrdUFWst0pBnQ+zEgvGcRgEsACzosBrisguXXoh8/b7q5Ctbo16Ov6r9dGzKLiixuxj1NDUfbv+m0XXrWI0O4rgyCwnOh89lK67EBCS9g9vGqjI7xmvKSrWt+yFfyishrc89qXqX2b9b+z2TtHvLStXnuRCLsWA8xwBmKui4YyGp232TKjhfZaXEdk7QGtpHpp6P5XC/Hgl+vcvvRSX3a9cObfdMGojnXCLBCH7x3v5S1IfP5aKthVePVP9rsJKwcH0/g/GLz115Nebjl4q2bYSdfu8/Wa8n6VeDSGLBeI4BzFT3Et7OOfUcblj2k9tmo9m54etTYrIKll6ISv1QLCXW+uFKs47iuMIFNa/cwlJj98DySq66vNTuSdrq8g3+b/juU6nD5eiHLz/A7fNzDAarSJN+TYgYFoznGMCMBL02+kBoTFbhDEMlp9/7Nfp73OKySuuDj5LfFi0z77PMXJX0y0Ist9E74Xho6sCeHSE727Zp5Pe4XO5XxysxVyOyhvSWPmtr0LidIBZgwXiOAcxID168/+v407ZthENXDpOWFGvKrm7F5Cw6F9mhXZuQlWZSWKYDNZsPn8uMtgeUVnBPzdIzVq3v1yU/lPXxi6l7UCX369W/DXUUO5F+ZYgMFoznGMDMA102/nDY8/SPs416rbfq18S9cbhfLf55kPK+eOWv6n+bqpB+cYi1tvsmHQp6rdWjw/WFQ5q+9n7FlehLz7OGqcsen6FL+pUhMlgwnmMAM8/jlLxJHo9FRYQfrjSTa9+26Tu8Gp7lcDlaWkI0ZOWwGidiIsQXBV/Kh2wLKC7n/PvnIIvazziqv9QPxcN3BnG/trq12IhXuwO1KCwYzzGAmWfa0Schrz5MM+i5eYwGX3ZYyeGa7QzKzC/ZYNVvllEv0q8PsdBu/xe7/V+qy0v5LBla/2XPdVt6IfJGVM7IAfKHpumQfn2IABaM5xjADBOR8dH64KPWwkKBy035eJHB808zVl+LlWsvFrzCDKscIP4qKq0w2h5YWFKxf8pAK81u/Nrti7dFlv8Eww0/O+M6is8gtmLBeI4BzDCzPZ/dT3o3XqeH+3gtPu62rJJj6h70prB0y9gBU/UVm75DhHgOBb3e7pukLCNxz85EhE/TX8r80+G+8bljtLvtnjSQ9KtEgsaC8RwDmEnisgut9oXACOZvb6IsI8nfnXuGpjp7J/To1A7m1nipBsQvJeUco+0BecXlO8dr2ej04O/Oef8jAhxMlfCqhS0M08fzVhjAzLLgbLhPbO4fWt32Tub/+/3SiqqB8sPn8h3jtcbxe6BELdbxkNSNtxIUOreDjGyON3YzTzwNTH4/cZDC9nGapF8rEiimj+etMIAZ5OXbIsvdwdBdzfeN15EHr13vJCl3kbhnz+ePClHLVFbJMXYLfPupzNVao5kqR4anf7Q5VLUq4sEKs+4d25F+xUhwGD2eUzCAGcPuYpRXZPav/eUPT2+uNZ+fyyphElzwpWLf5IG/a/FtsQxqsc4+SV/rFde1Q9sgR9PmW9w39ejj0Fd5fw5W3Dh6AOlXjASH0eM5BQOYGdLzis12COKsx733X+6690JNTurOUr6dLoJapgoOF/5osz6WOP/eb8aQZjy9Lex13uR/H4u2Fg5ZYSbLjzPjESMwdzznwQBmhlVXYy48yzRTkzkxU69Zn6iwpMJoW0BRWaXHdB3L/vKkXzdisCvhWcsvR3eRrCrw0ujKz/XBqw03Z2ivtb81tTYcYgrmjuc8GMAMkF1QYuoeWMERUOVb97tJBwJfa/bocIMfJQNRy1RV4nTXg5QPxatHqs8zafYSp0HJ72aceNaujUjoqmGNuDIYYiKGjufVYQAzgNONuJNh6YYq0ufmCOLaL3lVRfMDSyo4J2fpmTStaD5qsbyjcxafj+wo3gamv5JirZv76WAc+2N/aGx24SKz3stHqJF+9UgQGDqeV4cBTHfvikohDssruefm6BuqdBHMk26+lXA0JHWQYqfL8wfjJBg1FJf7deSeh8lvi+wtVJcM7yOYJ70bnzvvdLiUWOuQVcM6tMPrerEfE8fzGjCA6W6rT6JHcIqOYqcrAszCt59Kh7pVpf6FuQYGynjNc9QwfvG5cwWehbzUd7BQXSyo1EcEMXE8rwEDmNbyi8uNtgd8KeecmKlrpiYryKdefz3u9ON0o95dztjqkz4MiElgSBl9IDQmq3ChmYrjCHVBPvXN6Jwl3z73Dl05TKL5P/dGZDFuPP8eBjCt7fRL3hfwakD39t6LjAT8UXB2QYmJW2Al9+u1BYa/9MRrnqP6evDi/V/Hn7ZrIxKy0kxaUkyQT83hfjXf9SD1Q/GaUepzjfHi1izHuPH8exjA9MU7I+jwNJ1fBxA4I2jllZiLzzPxmueo/nhnBNka9VpnReCMoMvPMx2vxHSRFIP4b9ZznxBxzBrPfwgDmL723X+5894LVTlJ36XGRGpipH0oHobXPEcNwauJ8XCFmRyJmhgVHK6pe1B2QbNX/0DEMWs8/yEMYJoqLqsc8q0q5J5J2qO1u5NqxrILkdfxmueo3qiqkNMNFDeNIVYV8szj9HXXm73+JSKOQeN5bTCAacoj+PVWn6ReXST8iV4X4eXbIgu85jmqH951ESD5enQSJ9WM0gqOiXvzXgEC0QGDxvPaYADTEXSKsXtgZn6Jm43mBF0Fso2hrnn+12BFF6x0j+pkfynqWkT2eJ0e7uO1yLbk6MOUzbcT+3Vt77N0KOmjgpoLU8bzOmAA01Him08j9zwUay0cucFCXJTw2RT3E9/OPvm8W4e2oauGYVEOVJtKDldns39hScWleYP1enUm25j84vJBm+9xv7YKWWlGcC6OmhVTxvM6YADT0R7/l//4vzDvK3v0L/LLj0srOAM33iup4OBSLFSHR68/TPn3SWcJ0WdrzelwMekJR8KepuY7/d5vJi7FYimmjOd1wACmI6t9D+OyP9Hh82cK9Sn0kuF97C1USbcF0ZSLd/yJ0DQ6fP5MoT6FFlgFdSR4TBnP64ABTDvZBSVDtgXAFAJmEgKuY1CbaxFZ9pei1eWlfJcZk24LoiMYRoy2B8Kf7r9/DrLoJ0e6OVUy8r4YuwfCXDx8nXlHcbw+EgsxYjyvGy0CGNqwcePGI0eOwKG0tLT08PCQkfnfRXiUlJTS09Orbz9s2LD79+/DjZ49e2ZmZvLuf//+fZcudV2ugBEd5hma6uydoKvU6fJ8Q9Jt+U/Bl3Kdzf4c7teHK8wUOuM3aqim+JzC3/aGtG0jHLnesp0oXc78+XV3cFJu0a4JWta/9CDdFsR/jBjP60aLAHZzc9u+fbunp6e0tLStrS0kro+PD++3kL4VFRXU7crKShMTk82bN8+ZMwd+lJSUhLTW0/vvGvXKysrCwsJ1PBEjOow6k3LtqL5zjJVJt+V/Jns8DkvJW2/Vb7YRfqOGatrt/2K3/0vLfnIefw4i3Zb/2eWXvDfgFZ7FzlaMGM/rRj6AuVyunJzc2rVrly1bBj/6+vqOHDkyJSWlV68fDPTHjh3btGnTy5cv27RpU15eLiYmFhMTo6GhUc/non+HFX6p+GXzPZhrBi03VeoiQbo5/3M8JHXjrQT9Xp0vzhtMui2IdkbteZjw5pP7OM3xg2ixaoESm1X4+/4QcVGRiPUWWJaSfeg/nv8U+QCOjo7W1tZOTExUV6+6cEpJSQnMa8+cOTN58uQaW8L0V1VVddWqVXPnzoUfc3Nzu3btmpOTA//W87no32FekVl2F6NV5ST97ExIt+X/ycz/MtQtUFio1fN1Fp0l8Bs19D+0/duAwc1wW8CbwtJjfw0a3pcW30wjPqL/eP5T5APYy8vL2tq6tLQUprPUPfLy8g4ODo6OjjW2vHDhwoIFC7Kzs9u1awc/xsfHDxgwAB4lLS2to6OzefNmTU3N7/df9g11GzpMQUGBzh224Gy4T2zuIrPey0eokW5LTdQsZ8d4rXE6+I0a+p8Toaku3jT9dMTpRtzJsPRJugrbbDSbvjdEKxjAfHD69OkZM2ZwOBzePcrKyrNmzVq3bl2NLY2NjXV1dXfu3En9WFRUlJCQIC4unpaWtm3bNrgdGxvbo0fNbHB2dnZxcal+D207rLSC88ume1/KOTcWDtFS6Ei6OTX9c+/Fnvu0+54PEUetD1j3W1/boTRatUAJeflh2rEnXSRFn6yhxdnJiI8wgPng0qVLEydOrKioaN36v5JP3bp1W758ub29ffXNIF/79+8P//bt2/f7nUAY9+zZc82aNd/Pmxk0Aw5MejfT85l8+7aPVg0jcvmjutFzpSsii7dCPtjRrKc07VbIV3C48Ka2qLTy6t+DdRQJ1+dC/IUBzAehoaFGRkapqalKSkqtvuWlpKTk5cuXx4wZU30zmMVevHgRAri2/cDkWF9ff//+/XU8F807bPW1mPNPM8leSaYONDzXExFH/3PEl16IvBGVM89YefWovk3fG6IPmo/n9UE+gEtKSqSlpffs2UOdWeTn52dlZZWbm9u58/97u6qtrT1q1KitW7f+cCcFBQWKiooQ0tRS6trQucO43K96W+9/+Fx2apaesapM03fYHJxvxns+olG1I0Tcf1XShvW2t6TdqgXK7Zg3C89F9OoiEeBggsXM2YTO43k9kQ9g4ODgcO7cOU9PT5j72trampubQx5D3M6dO9fa2rrVt3zt1KmTj4/PyJEjeY+6fft2cnKyqanpx48fnZ2dYQ4dGxsLm9XxRHTuMOpSblJircPXW4i2Fm76DpsD3er9IrJ4qxboXCf8c1nlLxvvlXO4/vbGvWXxkprsQefxvJ5oEcBlZWV2dnZnz54VERGZNm2au7s73Kmqquro6Lho0SK4ff/+fUjlN2/eyMvL8x4VGhoKaZ2WlgYTaIhhNze3bt261f1EdO4w1zuJRx6k/KHVbe/kgaTbUitaXfEGEceUK2XNOPE0KPm94wi1hWa9SbcF8Q2dx/N6okUACwydO2zYjqCUD8X7Jg/8Xatb0/fWfOwvRl2LzLY16rXOqh/ptiDCVl6Jufg8k/7Xij73JGONV6yWQscbC4eQbgviGzqP5/WEAUwLr959Nt/1oI2IUMR6C6m2bUg3py6+cW/mn4lQlBYPWm5K50kPam4c7lf9rf4fPpeftdUf0rtL03fYfN4VlepvvQ9D3ZM1w+XatyXdHMQftB3P6w8DmBYOBr1y8002UZU5OUuPdFt+oriscuCme+WV3LvLjNXk8Ru1lut5Wv64w2Ht21atWmgjQtNVCzxjD4ZGZhRsHjNgmoEi6bYg/qDteF5/GMC0MOZAaFQmY0aH2Z7P7ie9c7BQXTy8D+m2IGK2+iR6BKeM0e62exJ9Vy3wHAp6vd03yVhV5hTt3+OieqLteF5/GMDkvftUqre16uqKTPl87MLTjFXXYjV7dLi5yIh0WxAZMG6Y7QhKy/tyYMovv2nWtxg7QQz6lgfVEz3H8wbBACbv7JP0tV5x2godrzNkhcj7ojK9rf7whxO2eljXDu1INwcR8PJtkcU/waIiwhEbLCTFWpNuTr0M2xmU8p4B6xxRPdFzPG8QDGDymHiOxLhDj56nf9w0uv/0wUqk24IIOBD4yv1uspmazImZjPlEd9udpMMPXtP8TD9Uf/QczxsEA5iwotIKnU3+jKsScOTBa9c7SUP7dDk9W590WxABow+ERmcWbB2rMUW/J+m21Bcjat2g+qPheN5QGMCE3YrJWXQuknF18lI/FJvtCGotLARjWYd2+I1ay/L2U9VZPfDX+mTNcFkpBqxaoDCi2iuqPxqO5w2FAUwYcyvFm+968Ord5z2TtEdrdyfdFiRQZx6nr7seN7BnR68FzFi1wEPz652gBqHheN5QGMAklVdydTYz9Vppbr5JB4Ne/6bZ9cCUX0i3BQnUn8efBr94v/JX9b9NVUi3pWFofsVP1CB0G88bAQOYpIcv308/9pShVwuPyiwYcyBUQlQkYoOFWGu8PHBL8alq1cK9Cs7X+w4mKjKSpJvTMKUVHGh8cTnnxsIhWgodSTcHNQndxvNGwAAmacONuFNh6ZN0FbbZaJJuS4NxuV8NXO+/KyrznKlrqiZLujlIQLyjcxafj1SWkQhwMCXdlsZYcDbcJzZ3kVnv5SNoev1EVE90G88bAQOYGDjyg10Dcj+VHp8xaJg6I69vv9Yr9uyTjCn6PbeO1SDdFiQgkL6QwfNNVFaNVCfdlsa4Hpm97GKUqpykn50J6bagJqHVeN44GMDExGQV/LE/VFxUJGK9Rds2jPwINyj53YwTz2SkxJ6sHo7fqLUEVasWNt0rKqu8+rehjmKnpu9Q8Aq/VPyy+R6H+zVoualSFwnSzUGNR6vxvHEwgInZ6Ze8L+DVyAHyh6bpkG5LI5VVcnQ2+X8uq/RaYDiwJyOHY9QgwS/e/3n8aRdJsadrGPyWa+rRx6Gv8taO6jvHWJl0W1Dj0Wo8bxwMYGJG/BOc/Lbon4laYwf2IN2Wxlt0LuJWzJu/TVVW/srIDyRRg6y7HnvmccZkvZ6u1gz+0uHkozSnm/G6Sp0uzzck3RbUeLQazxsHA5iM9LxiE/cgEWGh8HXmHcVFSR+YxrsRlb30QlRvWUl/e/xGjeW43K+G26pWLZyYoWumzuBld9kFJUO2BcAE/ulac5jNk24OaiT6jOeNhgFMxtGHKZtvJxqqSJ+bY0D6qDQJo09KQQ0SnVkw+tuJZ+GMXbXAY7XvYVz2JzcbzQm6CqTbghqJPuN5o2EAkzHhcNjTtHzn3/vNGNKL9FFpqunHnjx8+WHVSPX5Jgwry4AaxP1u0oHA179pdD0wlfGlV/bef7nr3gvzvrJH/9Il3RbUSPQZzxsNA5iAvM9lulv8uV9bhaw069FJnPRRaarTYWnrb8TrKHa6+jd+o8Zmlv88ePH28+6J2mMGMr74aOKbTyP3PBRrLRy5wUJclBmXU0Q10GQ8bwoMYAKuhmc5XI7u17W9z9KhpA8JH7wpLBnsGiAk1Cp8nUVnCQZ/n43qkJn/ZahbYNXlN9ZZdBBn/OU3YNwzdg/MzC/5989BFv0YeRY+osl43hQYwAQ4Xo6+HJ7F3FIG36PmRoen/fLrgK6k24KaxaXnmSuuxLDpc441XrHnnmTMNuq13qof6bagxqDJeN4UGMAEGLsFZuR/OTFT14wtFRzXX487/Th9hqGS8x/9SbcFNQuHS9FXI7IWmKqsYMv5ZtQC/gHd299azIYPologmoznTYEBLGg5BSWG306BiHaylGrL+I/yKNRVjdXlpXyXGZNuC2oWQ7YFZBeUnJylZ8KWK+nyrmoctcESr2nNRHQYz5sIA1jQvCKz7C5Ga/bocHOREenjwTfvikr1tlSNZZHrLRh9WjP6oayPX4y2B4oIC8U4WUqIsWfJktmOoNQPxcf+GjS8L34NzDx0GM+bCANY0FZdjbnwLHOusfKaUX1JHw9+Gr4z6PX7Yo/pOpb95Um3BfEZtWxQW6Hj9YVDSLeFn9j6n7GFoMN43kQYwILG1jfduKSFxVZcib70PGueifLqkawKKurjKK0eHW6w6OOoloMO43kTYQALFIu/dqKWtPTv1v72ElzSwjb/LRtkeAXK77FyQUbLQXw8bzoMYIFicUqx+L1FC0ed583WlBrqFpCZX8KmUxJaDuLjedNhAAsU9TntrCG9NvzOws9pTd0D0/K+sO/T9RaOuoI9y5YN8rDvpPyWg/h43nS0CGBow8aNG48cOQKH0tLS0sPDQ0bmf6c6cDicNm3a8NopISHx+fNn6nZcXNyCBQuePXvWtWtXFxeX6dOn1/1ExDuM3SuVcEkLK62+FnP+aeacob3W/sbCd41XwrOWX44e2LOj1wJWrS9rCYiP501HiwB2c3Pbvn27p6entLS0ra2tkpKSj48P77cfPnyAPA4KCurevaoCrbCwsLJy1WW04eirqalBYDs4ONy8edPJySk0NNTAoK6LC5HtsPdFVSWgWXyuDi5pYaVhO4JSPhQf/XOQORtLNvJKbEaz6wyrlgADmA+4XK6cnNzatWuXLVsGP/r6+o4cOTIlJaVXr/8uE/TixQsIWjjWUlJS1R+4d+/eLVu2ZGZmiopWhRlEL2x28uTJOp6LbIfdjnmz8FwEi6tV4JIW9nn3qVSP7V/tUzVGTs3SM2ZLjZEWAgOYD6Kjo7W1tRMTE9XVq76DKSkpkZSUPHPmzOTJk6kNHj9+bGJiUlZWVuOBY8eOFRMTu3DhAvXjmjVrrl69mpycXMdzke2wllCvEZe0sMzN6Jwl5yNZuWyQx/5S1LWI7IVmKo4j8GtgJsEA5gMvLy9ra+vS0lJIU+oeeXl5BwcHR0dH6sfbt29bWVm1bdtWVlZ2yJAhW7duVVJSgvu1tLRGjRrl6upKbXb48GF4VHFxcY39l31D3YYOU1BQINVh1BULDk39ZaQGa69YsPxy9BVc0sIia71iz7J32SDl0rPMFVdjBil2usKW60y0EBjAfHD69OkZM2ZwOBzePcrKyrNmzVq3bh3144cPH1JTUyGek5KSXFxcvnz5EhMTIyUlpaKiAg9cv349tdmpU6fgUZWVlTX27+zsDI+qfg+RDssvLv9l0z24Eb7OXFpSTMDPLjC4pIVlzHc9ePXu85HpOiPYuGyQkp5XbOIe1EZEKMZpRDtREdLNQfWFAcwHly5dmjhxYkVFRevW/62A6Nat2/Lly+3t7b/fOCMjA6a/Fy9eHD9+fL9+/WxsbDZt2kT9ysPDAx4FXVLjITSZAfvGvZl/JkJVTtLPzkTATy1IuKSFTT58Lhu02R9uRK636MTeKz3DGDjYNSD3U+k5W33D3l1INwfVFwYwH4SGhhoZGcEcl/pgGcJSUlLy8uXLY8aM+eH2MjIyK1euhKy1sLDo3r27p6cndT9Mhb29vaOioup4LoId5nwz3vNR2nQDxU1jBgj4qQUMl7SwBuuXDfIsvRB5IypnybDe9pZqpNuC6gsDmA9KSkqkpaX37NkzZ84c+NHPz8/Kyio3N7dz587fb5ySkqKiouLl5QXx7OLicuzYMUhuEZGqT40MDQ0HDx68c+fOOp6LYIeN3PMw8c2n/VMGWml2E/BTC5j9xahrkbikhQ023Ig7FcbyZYOU808zVl+L1evV+dK8waTbguoLA5g/HBwczp07B3NZmPva2tqam5tDHo8aNWru3LnW1tYnTpyorKzU09PLzMxctWoVxG14eHjr1q2zs7P79u07derUhQsXQiS7ubnFxcUpKirW8USkOqzgS/nATffgSD9dO1xWqi3hw93MLj7LWHk1Fpe0sMCIf4KT3xaxe9kgJeX952E7H4i2Fo5xsmzbBr8GZgYMYP4oKyuzs7M7e/YshOu0adPc3d3hTlVVVUdHx0WLFl27dm358uU5OTldu3aFVN6yZUvHjh2pBz548AA2ePHiRf/+/SGzhw79yZkSpDrMLz537ulwFRmJ+w6mZA6xAOGSFnZoIcsGKTAM6m29/76o7MJcAwNladLNQfWCAcwwpDps062EYyGpU/R7bh2rQfoYNDtc0sIOvnG588+E95GVvGfP5mWDPIvORdyKeWNnrrrUvA/ptqB6wQBmGFIdZrXvYVz2pz2TtEdrdyd9DAQBl7SwQMtZNkg58zh93fW4wcrS5+caNH1vSAAwgBmGSIcVllQM3OjH/drqyZrhcu1Z/gUw5dyTjDVeuKSF2VrOskHKq3dF5ruCxVoLxzhbirXGr04YAAOYYYh02P3Et7NPPleSFg9yNCN9AATk9fvPw3FJC5MVfqnQ3uTXQpYNUmAk1N3i/+Fz+eX5g3WVOjd9h6i5YQAzDJEO2+qT6BGcMklXYZuNJukDICC4pIXp7iW8nXPqubKMREALWDbIs+BsuE9s7nJL1UXD8GtgBsAAZhgiHTZ6f0h0VuE/E7XGDuxB+gAIDrWkZZl5n2XmqqTbghqMWjY4Wa+nqzX7lw3ynHyU5nQz3qh3lzO2+qTbgn4OA5hhBN9hn8sqtVz8ONyvj1YN69axHekDIDinH6evxyUtjNXSlg1SknOLRuwObtdGJMbZso2IMOnmoJ/AAGYYwXdYUPK7GSeeKXRu93DFMNKvXqBevi2y+AeXtDDSp9IKbZeWtWyQwuV+1dl87+OXiqt/G+oodiLdHPQTGMAMI/gO23Yn6fCD1+N0euwYr0X61QsU/F0N2uyfV4xLWpgnIOntLM+WtWyQZ97p53fj3674VW2BaW/SbUE/gQHMMILvsLEHQyMzCiB9IYNJv3pBwyUtDOXqk3gkOGXiIIXt41rKskGe4yGpG28lmKjKnJylR7ot6CcwgBlGwB32pbxS09mvkvv14Qozhc7ipF+9oOGSFoZqmcsGKQk5n0btfSghKhLtZNkavwamNwxghhFwhz18+X76safdO7YLXdWyvgCmJOV++nX3Q1zSwiwtdtkghcv9OnDTvcKSiusLh2grdCTdHFQXDGCGEXCH7bibvD/wlfXA7rsmapN+6QTAWPbL5nsFuKSFUVrsskEe25PP/RPfrh6pPs9EhXRbUF0wgBlGwB02/vCjZ2kft9toTNTtSfqlkzH31HO/BFzSwiTbfZMOBbXEZYM8Rx+mbL6dOExd9vgMXdJtQXXBAGYYQXZYSTlH0+VuBefrA0dTRWkJ0i+dDFzSwjjWB0MjMgrcx2mOH6RAui1kxGYV/r4/REqsdZSTpYiwEOnmoFphADOMIDvs0asPU44+kW/fNmz1MCGhFvrfOD6n8Le9IbikhSla+LJBCof7VdvFr6is0nuRkUaPDqSbg2qFAcwwguywXfde7L3/crR2tz2TBpJ+3cTAWDZwo9+n0kpc0sIILXzZIM8sz2cBSe/W/dbXdqgy6bagWmEAM4wgO2zikbAnqflbx2pM0W+hXwBTbE8+8098h0taGKGFLxvkOfLgteudJPO+ckf/GkS6LahWGMAMI7AOK63gaLr4lVdy7zuYqMhIkn7dJP0bnLLFB5e0MAMuG6REZRaMORDaoV2byPUWwvg1MF1hADOMwDrscUreJI/HMlJiT9cMb7FfAFNwSQtTlJRztOBdI4cbtNxUqUsLXTZIqeRw4VAUl3N8lgzt142pgzvrYQAzjMA6bI//y3/8X1hpdt0/5RfSL5owXNLCFLhssLq/jj998OL9Bqt+s4x6kW4L+jEMYIYRWIdN+ffxo9d5m8YMmG6gSPpFkzfzxNPA5Pe4pIXmcNlgdQeDXrn5Jo/oL3dkOn4NTFMYwAwjmA4rr+RqutwtreDeszPuIydF+kWTd/jB6224pIX2cNlgdeHpH20OPeok3iZ8HX4NTFMYwAwjmA57npY/7nCYtITo83Xm+FEeiMz4OPbgI1zSQme4bLCGCg5X09mvpIJzd5mxmjy+jaYjDGCGEUyH7Q94ucPvxSgN+YNTdUi/YlqAsUz725KW20uM+nfDr4HpCJcNfm/6sScPX35w+aP/X4ZKpNuCfgADmGEE02H4//Z7fx5/GoxLWmgMlw1+D99J0xwGMMMIoMMqOVxNF78v5Zw7S4f27crUPwu+OxD4yv1u8sgB8oem4VhGR9OOPgl59WHT6P7TByuRbgtdPEvLH384rIuk2LO1+KkAHWEAM4wAOoyqfiwl1jrayRK/7+ShPuGUay/2eDWOZbTD4X7VcvH7XFaJp71WV1rB0XCuup5KS66MTWcYwAwjgA47HZa2/kb80D5dTs/WJ/1yaeRLeaWGc9Vl3kNXDeve8i7zTnNJuZ9+3f1QQlQkxnkEFkupbvSB0OjMgj2TtEdrdyfdFlQTBjDDCKDD7C5GeUVmLxnex95ClfTLpRerfQ/jsj/tmzzwd61upNuC/p9zTzLWeMUaqkifm2NAui304uIdfyI07a/Bii6jB5BuC6oJA5hhBNBhJu6B6XlfPGfqmqrJkn659LLhRtypsPSZQ5Scfu9Pui3o/1l+OfpKeNYis97LR6iRbgu9eEfnLD4fqdG9g/diI9JtQTVhAPMHtGHjxo1HjhyBQ2lpaenh4SEjI8P7bUlJyZo1ay5cuFBUVKSpqenq6mpiYkL9qmfPnpmZmbwt379/36VLlzqeqLk77MPnskGb/eFG9AbLDuJtSB9Xerkemb3sYpSWQscbC4eQbgv6f4btDEp5X3x8xqBh6nKk20Iv2QUlQ7YFtBYWinUe0U5UhHRz0P+DAcwfbm5u27dv9/T0lJaWtrW1VVJS8vHx4f123759AQEBS5culZKS2rlz582bN+Pi4mAb+JWkpCSktZ6eHrWlsrKysHBdV31v7g67l/B2zqnnvWUl/e1NSB9U2snI+2LsHthGpGosa9sGxzK6+FhcPnDTPbgRud6ik4Qo6ebQCwyPBq73334quzjXQF9ZmnRz0P+DAcwHXC5XTk5u7dq1y5Ytgx99fX1HjhyZkpLSq9d/J4zCHFdBQYG6XVFR0bFjx127ds2bN6+8vFxMTCwmJkZDQ6Oez9XcHbbdN+lQ0OsJg3q4jdMie1RpCP7SdLf4f/hcfnn+YF2lzqSbg/4TkPR2ludz5S4SActNSbeFjv4+E34nLnfFr2oLTHuTbgv6fzCA+SA6OlpbWzsxMVFdXb3Vtw+cYV575syZyZMnf78xtBYC2NXVdcGCBbm5uV27ds3JyYF/6/lczd1hVDXdbdYak/Swmu4PzDn1/F7C29Uj1eeZqJBuC/rPjrvJ+wNf2fzSY+cEfNf4A9QFrbGSOQ1hAPOBl5eXtbV1aWkpTGepe+Tl5R0cHBwdHb/f+OLFi5MmTUpISOjbt298fPyAAQPgUdLS0jo6Ops3b9bU1Pz+IWXfULehw2Ay3UwdVsnhanwrHutnZ6yK12D4kUNBr7f7JuEVZmiFunLXlrEDpurjlbt+IDw93+YQlnanIwxgPjh9+vSMGTM4HA7vHmVl5VmzZq1bt67Glrdu3YL0hbmvm5sb/FhUVARJLC4unpaWtm3bNrgdGxvbo0ePGo9ydnZ2cXGpfk8zdVhcdqHVvhCptq2jN2AJjh97kpI3EQsO0wmH+1XT+W4xFm6rHa8cxwNHU0VpCdLNQf+DAcwHly5dmjhxYkVFRevWral7unXrtnz5cnt7e942XC4XcnTr1q2Qyk5OTt+P3RDGPXv2XLNmzffzZoHNgE+FpW3AEhx1KimvGssquVhaiC4Scj6N2vtQ8lvhNizBUZsxB0KjMgv+mag1dmCPpu8N8QsGMB+EhoYaGRmlpqZSC5shLCUlJS9fvjxmzBhqA0jfyZMnBwUFnTlzxsLCorb96Orq6uvr79+/v47natYOW3Yh8npUztLhfeywBEftft8XEptdiKWFaOLM4/R11+OG9JY+a4slOGq10TvheGjqdAPFTWOwHAeNYADzQUlJibS09J49e+bMmQM/+vn5WVlZ5ebmdu7830JZyFSY/j59+lRZWbm2nRQUFCgqKrq4uFBLqWvTrB1m7BaYkf/l5Cw9E1WZpu+NrZxuxJ0MS59hqOT8B5bjIM/+UtS1iOzFw3o7WGIJjlrdislZdC6yf7f2t5cMJd0W9D8YwPzh4OBw7tw5T09PmPva2tqam5tDHo8aNWru3LnW1taGhoYQrps2beJt37Zt2x49ety+fTs5OdnU1PTjx4+Q0DCHjo2N7dSpUx1P1Hwd9r8SHE6WHdphCY5a3YjKXnohSrNHh5uLsLQQeWY7glI/FJ+YoWumjoXbapVTUGK4LUCkqhyHpbhoa9LNQf/BAOaPsrIyOzu7s2fPioiITJs2zd3dHe5UVVV1dHRctGgRTHwhXKtvr6Oj8/z589DQUEjrtLQ0mEBDDLu5uXXr9pMiw83XYX7xuXNPh/eRlbyHJTjqlJn/ZahbYGthoTgXLMdBWH5x+S/fSnBEbbDoKI4lOOpisPV+7qfS83MMBqtgOQ66wABmmObrsG13kg4/eD1xkML2cZpN3xuLfSvHcf/D57JL8wbr9cJyHCTdT3w7++RzZRmJAAdT0m2huwVnw31icx1HqC00w3IcdIEBzDDN12ETjoQ9Tc3fbqMxURdLcPzE3FPP/RLerhqpPh/LcRDlfjfpQODrcTo9dozHEhw/cfRhyubbieZ9ZY/+pUu6Leg/GMAM00wdVlFVguNuaQX3np1xHyzB8TNHHrx2vZNk2U/O408sx0HSZI/HYSl5rtYak7Fw289EZHy0Pvios4RoOJbjoA0MYIZppg6LzSr8fX9I+7ato7AERz08S8sffzisi6TYs7VYjoOYSg5X08XvSznn7jJjNXl81/gTZZUcDSe/cg43aLmpUhcsx0ELGMAM00wddvJRmtPNeGNVmVOz9Ei/RAYoreAMcMJyHITF5xT+tjdE6lsJDnzXWB/WB0MjMgp2TdCy/gXLcdACBjDDNFOHLb0QeSMqZ5l5n2XmWIKjXkbvD4nOwnIcJJ1+nL7+ehwWbqu/zbcSjoakTjPouXlMfS+/hpoVBjDDNFOHDXULyMwvgemvMZbgqB/nm/Gej9L+GqzoMhpLC5FhfzHqWmT2kuF97LFwW/34xL5ZcDaiX9f2PkuxHActYAAzTHN02PuiMt0t/kJCVSU42rfFEhz1cjM6Z8n5SI3uHbwXYzkOMkzdA9PyvnjO1DVVwxIc9ZJbWGrgel9YqFWs8wgJMSzHQR4GMMM0R4fdjc+ddzpcTU7qrp0x6dfHGFkfvxhtryrHAWNZO1EsxyFoeZ/LdKjCbRssO4jju8b6MnS9n1NYem6OvqFKF9JtQRjATNMcHeZ6J/HIg5TJegqu1liCo77gr05/6/13RWUX5xroK2NpIUHzT3hre+p5b1lJfyzc1hALz0XcjnmD5ThoAgOYYZqjwyYcDnualu82TnPCIAXSr49J5p8O943PXfmr+t+mWI5D0Nx8kw4GvZ4wqIfbOCzB0QDHQlI33UoYri57bAaW4yAPA5hh+N5hvBIcMJOA+QTp18ckHsGvt/okWfST+xfLcQjcJI+wxyn526w1JmEJjoaIzPg4Fstx0AYGMMPwvcNisgr+2B/aoV2byPUWeDJlgzxPyx9XVY5D9NlaHMsEqrLqXaNfSQXHz85YFQu3NUR5JXeA8134N3C5aS8sx0EaBjDD8L3DPENTnb0TTNVkPGdiCY6GKa3gaDjfreB8DXY06ymN5TgEJy670GpfiFTb1tFYuK3hbA49Ck//uHO8lo0OluMgDAOYYfjeYUvOR96MzrG3UF0yvA/pF8c8ow+ERmcW7J6oPWYgluMQnNNhaetvxGMJjsbZcjvh34epU/V7bhmL5TgIwwBmGL53mNH2gKyPJWdm6xv1wdMSGszFO/5EaNqfgxU3YjkOAbK7GOUVmb10eB87LMHRcHdi3/x9NqJv1/Z3sBwHaRjADMPfDntXVKq35b6QUKsYJ0spLMHRcN7ROYvPRw7o3v7WYhzLBMfEPTA978vJWXomWLit4d5+KtXfWlWOI8Z5hCSW4yAKA5hh+NthvnG588+Eq8tL+S7DEhyNkV1QMmRbgEhVOQ5LcVEcywThw+eyQVQJDifLDu3wXWNjwB8t/Omes9U37I2fe5GEAcww/O0wV5/EI8Epk/V6ulrjt0GNAX97Bq73334quzDXwADLcQjEvYS3c0497yMreQ9LcDTWonMRt2LeLLdUXTQMV36QhAHMMPztsPGHHz1L++g+TnM8luBorL/PhN+Jy13xq9oCUywtJAjbfZMOBb2eOEhh+zgs3NZIx0NSN95KGKYuexzLcRCFAcwwfOyw8sqqEhxlldz7DiYqMliCo5H+DU7Z4pNo3lfu6F9YjkMQJh4Je5Kav91GY6IuluBopKjMgjEHQjuJt4lYb4GnsBOEAcwwfOyw6MyC0QdCO4pXleDA/4SNFp6eb3MoTFpC9DmWFmp+vBIc9+yM+2AJjsbivfkOcDBRxjff5GAAMwwfO+xEaKqLd4KZmswJLMHRBLxyHA8cTRWlsbRQ86JKcLRv2zoKS3A0zbhDj56nf9wxXmscluMgBwOYYfjYYYvPR3pH5zhYqC7GEhxNM+ZAaFRmwT8TtcYOxLGseZ0KS9twI95YVebULHzX2CRbfRI9glOm6PfciuU4yGlZAWxjY6OmprZ161bePWvWrElJSblw4QLpV1FffOww6lSEs7b6Q/BUhKbZ6J1wPDR1uoHipjFYjqN5LbsQeT0qZ5l5n2XmWIKjSXzj3sw/E4GnIJLVsgJ4woQJvXv3rhHAr1+/vnjxIulXUV/86rB3n0r18GR8PrkVk7PoXGT/bu1vL8FyHM3L2C0wI/8LTH+NsQRH0+AIQActK4BZgF8dhu9/+SinoMQQy3E0P6oEh5BQVQmO9li4rcnwMzDiWlAAu7m5lZaW1rHBhg0bSL+Wn+NXh+E3QPxlsPV+7qfS83MMBqtgOY7m4hefO/d0uKqcpJ8dluDgA1wFQlwLCuBOnTotW7aMuu3u7u7o6Mj71fbt21etWtWiAhjXQPLXgrPhPrG5jiPUFpphOY7msu1O0uEHryfpKmyzwRIcfIDnQRDXsgL448eP1G0ZGZn379//8Fc0x5cO412UG88C5JejD1M230407yt79C8sLdRcJhwJe5qa72ajOUEXC7fxAVYCII79Abxx48ZW32r2UtNc6k43N7cVK1bwttm9e3d+fj7pF1IvfOkwrIPDd+HpH20OPeosIRqO5TiaR0VVCY67pRVcf3vj3rJYgoMPsBYecewPYMhd6oaLi4uTkxN1e9OmTevXr6++TRMDGNoASX/kyBE4lJaWlh4eHjDJrr5BcHCwnZ1dfHy8iorKrl27RowYQd0fFxe3YMGCZ8+ede3aFVo4ffr0up+ILx2GlWD5rqySo+HkV87hBi03VeqC5Tj4Lzar8Pf9WIKDz7AaPFnsD2Cezp0781JWVlb23bt3P/xV48CUGlLc09NTWlra1tZWSUnJx8eH99vU1FQNDY2FCxdOmzbt0KFDsFlCQgJsA0dfTU0NAtvBweHmzZvw/iA0NNTAwKCOJ+JLh+G1UJrD2IOhkRkFuyZoWf+CX6vz38lHaU43401UZU5iCQ7+weuhkdWCArhTp04wB6VuQ1iuXLmS9ytXV9c1a9ZUnxM3CJfLlZOTW7t2LbXIy9fXd+TIkSkpKb169aI2sLe3DwgIiIqKgtuVlZUQvbNnz4b57t69e7ds2ZKZmSkqKgq/guiFPD558mQdz8WXDjN0vZ9TWIpXA+WvTbcSjoWkTjPouXkMjmX8t/RC5I2oHDtz1aXm+K6Rb/CK4GS1oACG0K37NCTeB9QNFR0dra2tnZiYqK6uDj+WlJRISkqeOXNm8uTJ1AYDBw4cMWLEtm3bqB+nTJmSl5d39+7dsWPHiomJ8epwwZuAq1evJicn1/FcTe+w3MJSA9eqE/BjnUdI4An4/HM75s3CcxH9urb3WYrlOPhvqFtAZn7J6dl6Q/tgCQ6+eVdUqrflvpBQqxgnSyk8tVrgWkoAw4zTysoKAq/Vt4+LFy9e3K5dux07dsDktfpqrMbx8vKytraGdIc0pe6Rl5d3cHDgnekEhxiedN68edSPq1at8vb2jo+P19LSGjVqFMy/qfsPHz4MjyouLq6x/7JvqNvQYQoKCk3pMJ/YNwvORvTt2v4O5gRfvSksGewagO9smsP7ojLdLf6YE83BaHtA1seSM7P1jfrg52GCxv4AhvkoZC1E7/jx47t37x4eHn7q1KkJEya0bds2NDQ0JSWFt+5p7ty5MHNtRAtOnz49Y8YMDofDu0dZWXnWrFnr1q2jfhQREfH09OQ90caNG6ENr169UlFRgQfyPvqGO+FRlZWVNfbv7Ozs4uJS/Z6mdNjmWwlHQ1Kn6vfcgiU4+G2w6/03haXn5ugbquBYxk9343PnnQ5Xk5O6a4eflPLZkvORN6Nz7C1Ul2A5DoFjeQDn5uZCyBkaGqanp8PkEuaODx8+vH79+siRI2G2Cr+9du3aggULqI1hYgrHohEtuHTp0sSJEysqKlq3/m/e061bt+XLl9vb21M/iouL79u3D2bh1I9r1qy5detWTExMv379bGxsNm3aRN3v4eEBj4IuqbF//s6AE3I+PXz5XrNHR6zZxHcLz0bcjn2D5Tj4zvVO4pEHKZP1FFytsQQHn3mGpjp7J5iqyXhiOQ6BY3kAt/o2WQwKCoLMy8jI0NXVnTNnzvnz58+cOQMvODs7u1evXsXFxW3aNOlDLZhJGxkZpaamKikptfqWlzCTvnz58pgxY6gN+vTpM3XqVJjIUj/CVLioqAjeB1hYWMCkHCbH1P0wFfb29qbWatWGBR3GYlQ5juHqssfw/C6+mnA47Glavts4zQl4tgy/xWQV/LE/tEO7qnIceH6XgLFgPK/Xd8Bjx46Fuaa8vPzx48d9fHwyMzOlpKrO5W/Xrt2zZ88GDGjSVeRKSkqkpaX37NkD6Q4/+vn5WVlZwfS6c+fO1AYzZ8588eIF5DTc5nA4kNMrVqxYvHixi4vLsWPHILlFRETgVzBTHzx48M6dO+t4LhZ0GItFZHy0PojlOPisWgkOk96yWC+Cz/DwEsSC8bxeAZyTkyP1TY37AwMDdXR0mv7iHRwczp07B3NZmPva2tqam5tDHo8aNWru3LnW1taRkZF6enowwYXbBw8evHHjRlJSEjQGpuB9+/aFyfHChQu9vLzc3Nzi4uIUFRXreCIWdBiL8cpxBC437YXlOPgEp2jNDT9gIIUF4zktLkdYVlZmZ2d39uxZmMtOmzbN3d0d7lRVVXV0dFy0aBHcvnLlyqpVq7KysiCJDx061L9/f+qBDx48gA1gfgz3QGYPHfqTlcks6DB2sz4YGpFRsHO8lg1e5YJP8EvK5oZfsZPCgvGcFgEsMCzoMHbDReZ8h8t0mxsuMieFBeM5BjCiETzNmu/wRNXmxjvNOtrJsj2eZi1ALBjPGxDAaWlp1EJl5mJBh7Ebr9BYjPMISSzH0WRYqkkwsNAYESwYzxsQwMLCwkZGRn/++ef48eMbd8ovcSzoMNbDUtt8hMWKBQNLbRPBgvG8AQEcGhp65cqVq1evvn//3srKavr06aNGjeJVz2AEFnQY6y08F3EbLzbFJ3i5HsHAi00RwYLxvDHfAT969OjKNyUlJRMnToQk1tfXJ/1C6oUFHcZ6x0JSN+HllvkEL1grGHi5ZSJYMJ43chFWaWmpj4/Pli1bIiMjhYSE1NTU1q9fz7t+EW2xoMNYLzLj49iDjzqJt4lYb4HlOJqivLKqRkRZJfe+g4mKDNaIaEbVynEY95aVavoOUX2wYDxvWAAXFBTcunXLy8vr7t278Mph+jt16tQePXqcOHFi586dixcv3rBhA+lXVBcWdBjrQWwMcL4L/wY4mChjbDRBdGbB6AOhHcWrSnDgW5nmNuFI2NPUfDcbzQm6+GGDgLBgPG9AAFtYWAQHB4uJiY0ZM2batGnm5ubCwsK83/r6+k6ZMiU/P5/0K6oLCzqsJbA59Cg8/eOO8VrjsBxHE5wITXXxTjBTkzmBJTia37Y7SYcfvJ6kq7DNBstxCAgLxvMGBPCoUaMgd8eOHduuXbvvf5uUlLT/G9KvqC4s6LCWYMvthH8fpk7R77kVy3E0weLzkd7ROQ4WqouxBEfz84vPnXs6XFVO0s/OhHRbWgoWjOdYiAPRzp3YN3+fjcCTZ5poyLaA7IKSs7b6Q/CErub34XPZoM1YjkOgWDCeYwAj2nn7qVR/K5bjaJJ3n0r18BgKlrFbYEb+l1Oz9IxVsRyHILBgPMcARnSEs7cm8o17M/8MfoogUMsuRF6Pyllm3meZuSrptrQILBjPMYARHS06F3Er5g1+f9loW30SPYJT8Ht0QToVlrbhRjxMf09hOQ6BYMF4jgGM6Oh4SOrGW7iCt/HGHXr0HFeSC1ZcdqHVvhCptq2jsRyHQLBgPMcARnQUlVkwBs9hbSw8l5qIyqpyHH4lFZx7dsZ95LAcR7NjwXiOAYzoCKs4NQX19gWriQnexCNhT1Lzt9toTNTtSbot7MeC8RwDGNEU9SEq1jFuBOoDfKynLXjbfZMOBb2eOEhh+zgsx9HsWDCeYwAjmqKWEeGVfBqBWsKGV5QSvHsJb+ecet5HVvKePZbjaHYsGM8xgBFN4Yk0jUadxIXXVBY8qhwH3Ih2suzQDstxNC8WjOcYwIimqFISQkKtYpwspbC0UL3xypjEOo+QwBIcAmfiHpie9+XkLD0TLMfRzFgwnmMAI/qiZnJnZusb9cGZXH1RhTz7dm1/Z+lQ0m1piewuRnlFZi8d3sfOAstxNC8WjOcYwIi+qMsJ2FuoLsFyHPVGXcpiqn7PLViCg4TTYWnrb8QP7dPl9Gx90m1hORaM5xjAiL6oC+qZqsl4YjmOeqMu5rhzvJYNluAgActxCAwLxnMMYERf1CXlO7SrKseBY1l9lFVyNJz9yiu5gctNe3WRIN2clqiSw9V08ftSzvGzM1bFchzNiQXjOQYwoi9eOQ5/e5PesliO4+ciMj5aH3zUWUI0fJ05luAgZZJH2OOU/G3WGpP0sBxHM2LBeI4BjGhtwuGwp2n5buM0J2A5jno4+jBl8+3E4eqyx7AEBzluvkkHg15PGNTDbZwW6bawGQvGcwxgRGuudxKPPEiZrKfgao2lhX5u4dmI27FvHEeoLTTrTbotLZd/wlvbU897y0r6YzmO5sSC8RwDGNHa3fjceafD1eSk7tphOY6fG+x6/01h6bk5+oYqeOIWMXmfy3SochwbLDuI4ynszYUF4zkGMKK190Vlulv8hYSqSgu1x3IcdXpTWDLYNQBLcNCBqXtgWt4Xz5m6pmqypNvCWiwYzzGAEd0NdQvIzC85PVtvaB8sLVSX2zFvFp6L6Ne1vQ+W4CDN/mLUtcjsJcP72GM5jmbDgvGcFgF89erVdevWpaamamhoHD58WEdHp8YGcOeuXbsyMjKUlJTs7OzmzZtH3f/nn3+ePn2at9nly5fHjRtXxxOxoMNaoKUXIm9E5diZqy41x3Icddl0K+FYSOo0g56bx2AJDsJOP05ffz0Oy3E0KxaM5+QDOCwszNjY2N3d3czMbOPGjSEhIa9evZKS+t/5c7GxsVOnTnV2du7du/edO3dWr1598eLF8ePHw6+srKxUVFQWL15Mbdm1a1cJibrOfWRBh7VAJx+lOd2MN1GVOTkLy3HUZezB0MiMgl0TtKx/wRIchMXnFP62N0RKrHW0E5bjaC4sGM/JB7C1tTWXy71+/TrcLigokJeXP3To0MyZM3kb5Ofni4mJ8ZLV0tJSWlr6/PnzcNvQ0HDy5Mm8AP4pFnRYCxSbVfj7/pD2bVtHYWmh2lWV4HDyK+dwg5abKmEJDtJ45TjuLjNWk8dyHM2CBeM5+QDu1KmTq6vr/PnzqR8hUzU0NI4cOVLb9n/88Ufbtm0vXboEt9XV1Z2cnCCD6/lcLOiwFqiCU1WOo7SC629v3FsWx7IfC0//aHMIS3DQyGSPx2Epea7WGpOxHEfzYMF4TjiAP3782LlzZ19f3xEjRlD3TJo0qaio6Pbt2z/cPi0trV+/frt27aICW0ZGBo6+lJSUmpraihUrxowZ8/1Dyr6hbkOHKSgoMLrDWqYJR8Kepua72WhO0MVyHD9GleAw7yt79C8swUEL7neTDgS+Hq/Tw308luNoFhjATZWZmdmzZ8/g4OChQ/9btzlr1qyMjAx/f//vN37x4oWVlZWsrGxAQICoqCjc8+TJE3Fx8by8vJMnT3p6enp7e8MGNR7l7Ozs4uJS/R5Gd1jLtO1O0uEHryfpKmyzwXIcP7bgbLhPbC6W4KCP+4lvZ598riIjcd/BlHRb2AkDuL5gjjty5Mgad/71119ubm5ycnIQt8OHD6funDJlyufPn2/evFljYy8vr5kzZxobG589e7b6Ei0ec3NzMTGx76fOOANmAb/43Lmnw1XlJP3ssLTQjxlsvZ/7qfT8HIPBKtKk24Kq5BeX/7LpHtyI2mDRUVyUdHNYCAO4vjgcTklJSY0723wDU9jDhw/PmDGDuhOmwjo6Ort3766+5f79+x0cHFxdXe3s7Gr7fsvR0fHOnTtxcXF1NIMFHdYyffhcNmgzluOoVU5BieG2ABFhoVhnS3FRLMFBF2Y7glI/FJ+YqWuG5TiaAQvGc/KLsMzMzLp16wbz2lbfPhyWl5e/dOnS77//ztsgOjp60KBBly9f/uFXvDwmJiadOnWiVlPXhgUd1mIZuwVm5H85NUvPWBXLcdR0KyZn0bnI/t3a316CJThoxP5S1LWI7CXDettbqpFuCwuxYDwnH8De3t5jx449cOCAgYGBi4tLSkpKeHi4iIjI6tWr4bcw6/2/9u47ronkbQA4HWkqoFIELJxiQ0A6Il0RxVOxwHkq9rOeYkHEAthQsJcT9RQr9o6ejY4iSC+KiCBdEekdEt7B3MtxCHg/SJjdyfP9g0+yLMmT2fA8O9nJjLOz89WrV58+fdr8r5SVlXNzcz09PWfMmCEgIHDmzBlvb++goCADA4N2nouAA8a11lyNuRubu8Zi0BoLmFqope0P3px9kT5Hr9+OKSNwxwL+celVxpa7iYY/9bq0CKbjYD8C8jn+Aoyg6rt79+6ioiIzMzMvLy8FhcZpBGbOnIl+ot7wggULUHFt8SdlZWW1tbWooxwfHy8oKKihoYEeQVf3B+9yAg4Y17oQ9nHbvSTU/b0A03F8Z8rxF7FZxQdt1aZqwBQcFPImt3TCkRDxb9Nx8MNX2NmNgHxOiQLcZQg4YFwrMafE+mioRDeBOJiO49+q6xiqrk/qGA1BG0z6ScMUHBTCYDaMdH1SUct4vGbMEFnIOWxGQD6HAgzoob5xOo6nVXWMZw5Gg2RgOo5/RGUUTjsRJi0mFAlTcFDPrNOvXn74unuq6ixdmI6DzQjI51CAAW3YngwLTy/cO03VVhty2T9OB6ftevTWYqjMn/ZauGMBLe178u5YQOp0TYV9MB0HuxGQz6EAA9rY+zj5ROAHWy3FvdNhOo5/LLsU9VfiJ8fxKstNYAoOyvFP/rzgXOTA3mL+MB1HM1ciMu/G5NjpKHZm1AIB+RwKMKCN528+L7oAuexf0P+vzm6/L2U1V5fo6Q2EKTgop6iiVuPbdBxRWyykxYVxh0MVrInb1o8bvNKs42uMEpDPoQAD2iiprFPf8RS9YSM2m/eR6IY7HEr48KXcfH+QkABfvMu4boL8uMMBrbA8GPzuc9mJX0dZqcrhjoUSUNHR2vn8a0XtjaX62v2lOvw4BORzKMCATqwOh7zNKz02S8N6pDzuWCjBJzzT+U6CzgCp67/p444FtG7bvcQLYRnzDPq7/jwcdyyU8P5z2diDwcLorNF1nLBAx88aCcjnUIABnbjeTzr38iPMONFk9dWYe7G5MNcSlT2Mz1vhEz1EVuLxGiPcsVDCxVcZW+8m6g+UvrJErzOPQ0A+hwIM6ORxYt7SS9GwKgML+ufVd/f/VFrts0jX4KdeuMMBrWuayTxmK6zK0GilT7RvfJ6DxeDVFh2/AMxDRD6HAgzopGmFGRjSgnwsqDDZFyjIzxvvYikiBBeAqcviQFBqfvnJOZqWw2Vxx4IZG4cNEpDPoQADmoEhLU2uvc7ceCtBq5/kzWUGnX80wDmb7yRcDs9cMHrAtknDcMeCWdqXcjM2DRskIJ9DAQY0A0Namqy9Fns7JmeFqfIGyyG4YwHtuR+X+/sVWK6qERuHDRKQz6EAA5qBIS1NRu/xzymugiUaqS+/tFpntx8vL0/stnE9RLh6QWs2DhskIJ9DAQY086WsRnsXDGnhySqsHOMRIMDHG+cyTkxYAHc44AfM9gWmFVT8OVfLYpgM7liwYe+wQQLyORRgQD8wpAW5EZm14Wa8hlLPO8tH444F/Nim2/FXIrIWjxmweSL3XgbO+Fph7Mm2YYME5HMowIB+YEgLsv5G3M2o7KXGyk5WcAGYBu7G5Ky5FjtSocf9lYa4Y8Hm+ussx1vx7Bo2SEA+hwIM6AeGtCBjPPyzCqu852ubqvTBHQv4sbySKn13fz5enjiXcRLduPQy8Nrrsbej2TZskIB8DgUY0A8MacktrjLYw+3ZnHaMPAIyCyu5+ZyJvcMGCcjnUIABLZnuC0wvqDhjr2U+lBuHtNyJyXa4Fqem0OMeF3+eSTuON+OuR2b/Zjxwk9VQ3LFgwBo2yM/HG8+mYYME5HMowICWuHxIy8ab8dcis5YYDXSewI2pnKZuRWWvuxGnrtjz7gpuHDd3Myp7/Y04Ng4bJCCfQwEGtMTlQ1pMPAM+fq3k2g8AaCq7qNJwLzu7gPSy4UbcDbYOGyQgn0MBBrTEzUNaPpdW63L3JXD64ua5U9h+CZyAfA4FGNAV1w5puRebs/pq7Ii+3X1Xce8gcJpadz3uVnT2chNlx/Hc9eUxTgwbJCCfQwEGdMX2T7TowvlOgk945kLDAVutufH6N61dj8xyvBmv2U/yFpetn8EaNsjea0YE5HMowICuuHZIi9n+wLQvFafmaI7j4onAaCrza6WRZwAXriDpdCv+6ms2DxskIJ9DAQZ0xZ1DWvLLqnV2+cFU2DSF8q3BHv+8kurLi3RHd3oyZBrhxPcGCcjnUIABjXHhkBbf+NyVPjGwGBR9rbkac5dNywHRBYdmziEgn0MBBjTG3pntaGHr3cSLr2A5ZBq7GpHpdDtBp7/U9aWdXRCXLjg0dywB+RwKMKAx9s7tTgvjDgalfC73mj1q/Ag53LGAjkgvqDDdFyjEzxfvOq6bIFdcBmYNG2T76ikE5HMowIDGuG1Iy9fyGs2dz9GN6K1jpcTgAjAtoZSru9svv6zmymI9fWVp3OF0BfP9gR84MGyQgHwOBRjQGLcNafkrIW/Z5ejBMuJPHYxxxwI6btWVmAdxuWssBq2xGIw7Fo77Ulajves5J4YNEpDPKVGAb926tWXLlvT0dFVVVS8vL01NzRY7GBkZhYSENN19/fq1lpYWulFcXLxy5cr79+8LCQktWLBgz549fHx87TwRAQcMtMBVQ1pc7yede/lxjl6/HVNG4I4FdNylVxlb7ibqD5S+skQPdywc9zA+b4VPNCeGDRKQz/EX4LCwMFRfPT09TU1Nt2/fHhoampqaKiEh0XyfESNGzJ0718bGhnVXUVFRWFgY3ZgwYUJ+fv6xY8dycnLs7e23bdvm6OjYznMRcMBAC1ciMjfdTtAZIHX9N/KHtIw/FJz8qez4rFETR8IFYBpLzS+3OBAkLNB4GVhYgPBLJ9vuJV4I48iwQQLyOf4CjMoqk8m8e/cuz7cerays7IkTJ+bPn998H3l5+ZMnT06aNKn5xvj4eDU1tZiYGHV1dXTXycnpypUrGRkZ7TwXAQcMtJD2pdxsf5AQymUuhA9pKa6s1djxDP2/vt5s0VtCGHc4oONQ1tXe5VdQXoPOGtG5I+5wOIs1bPDEr6OsVNl81khAPsdfgCUlJd3d3ZcuXcq6a2BgoKqqispt831ERET8/f319f/VxTl8+DD6w0+fPrHuPn361NLSMjc3V06uzcNMwAEDLaA3sM5uvy9cMKTlSdKn3y5GKfcW81tngjsW0FkrLkc/TMhbN3bwKvNBuGPhoMKK2lE7nqEbUVsspMXZfNZIQD7HXICLioqkpKQeP36Maidri52dXVlZ2cOHD5v2qaioEBcXFxISQm2NarOrq+uYMY1fJlu9enVERERYWBhrt+Tk5KFDh6It2trazZ+i5hvWbXTAFBUVaX3AwPe4ZEjL9gdvzr5In6WrtHuqKu5YQGddCPu47V6S4U+9Li3SxR0LBz1OzFt6iVPDBqEAd1ZWVpaSklJwcDCrpiILFizIzMx8/vx50z51dXVRUVFiYmKos3vkyJEnT56Eh4draGgsXLjw48ePfn5+rN3QX/Xr1y8kJMTQ8F+TfaOC7ebm1nwLrQ8Y+B6XDGmZeCQkKbf0yC8aP6vJ444FdFbK57JxB4O7CfLFu1gKCfB1/gGpiaPDBqEA/1eoj2tlZdVio729vYeHh4yMDCq35ubmrI2zZs0qLy+/f/9+q49TX18/YsQItPPx48eXL1+ekJDQNDo6JSVFRUUFlepRo0Y1/xPoARMvNb/M4kAw2UNaSqrq1Lc/Rf+sEc7mfbp3wx0O6CyUeDV3Pi+sqL21TF+zH7GXga0Oh7zNKz02S8N6JPvPGqEA/1cMBqOqqqrFRsFvREVFvby85s2bx9qIusKampqHDh1q66FmzJiBHsrX13fXrl2nT59GnWDWdtQVtrCwKCwslJSUbOtvCThg4HvfhrQ8LyivvbFUX7s/mbnM7+3nhecjB/QSC1hvgjsWwB5LL0Y9Tvq0wVJlhelPuGPhiKZhgxGbzftIsP+skYB8jn8Qlqmpqby8/OXLl3m+fTgsKyt7/fr1FgOem9TV1Q0dOtTa2hpV6KCgIBMTE9TxHTSocRSDs7PzkydPUA+4neci4ICBVi2/HPUo4RPBQ1p2PXxzOiTdTltxz7SRuGMB7OH9It3twRujwb0vLNDBHQtHPE36tORi1MDeYv6cGTZIQD7HX4AfPHgwderU48eP6+npubm5paWloSLKz8+/adMm9Ft3d/fIyEjU350wYUJtbe3+/ftRTzcmJkZZWbmx36OtLSIicvDgQdQPRn3o8+fPT5s2rZ3nIuCAgVYRP6Tl52Oh8dklB23Vpmoo4I4FsMeb3NIJR0LEhPhjXcYJ8hN4GXiH75szoRwcNkhAPsdfgBFUfXfv3l1UVGRmZubl5aWg0JhiZs6ciX6i3jDq49ra2qKfYmJi+vr6Hh4eKip/z3mUlZW1aNEi1BWWkZFBBbvpu0xtIeCAgVa9+1RmeShYRJA/3pXAXFZWXafm9pTZwPPSyUy+pwjucAB7MJkNGjuelVTV3VluoKEk2fkHpBrroyGJOaWH7dQnq/flxOMTkM8pUYC7DAEHDLQK5TLNnc+KKutuLTPQ7EdaLgt4lz/f+7WSlGiwoynuWAA7Lb4Q+ezNZyerIUuNlXHHwmal1XXq384aw53NZTgzbJCAfA4FGBDit4uRT5I+O45XWW5C2pCWPX8lewV9mKGp4DlDDXcsgJ3+DEnb+fCtqUpv7/mkXQb2T/684Fxkf2nRwA2cOmskIJ9DAQaEOBuavt2XzCEtU46/iM0q3jdDbbomXAAmSmJOifXRUAlhgZhtYwXIunSy+9HbU8FptlqKe6dzatggAfkcCjAgRNOQljiXcSTlsoqa+pFuTxnMhhBHU0UpUdzhAHZCh1V9+9Oy6vr7K0ePVOiJOxx2mnwsNI7DwwYJyOdQgAEhmoa03F0xWl2RnFwWnPJl7tmIvj1FXjiZ4Y4FsN/Cc6/9kvM3Txi62Ggg7ljYprymXu3bWSNHhw0SkM+hAANyLDof+fzt501WQ34jaEiL55Pk4wEfbEb1PTBTHXcsgP1OBX/Y/SjZYmifP+21O/9oFBH4Ln+e92tFKZEQRw6eNRKQz6EAA3KwhrSYDelzdh45uWz6iZeRGUUe00bO1FbEHQtgv7is4snHX3TvJhCzbRw/Hy/ucNhj7+PkE4Efpmsq7OPksEEC8jkUYECOhOySSccah7TEuhCSy6pqGSPdntQxGoI2mPSTFsMdDmC/egZTffuz8pr6h78bDpfvgTsc9pj6x4uYzGLP6SNnaHHwrJGAfA4FGJCjcUiL29OymvoHKw1VFUjIZS9SC379M1yuR7eXTma8vCScUoDvzfOOCHz3Zav1sIWGA3DHwgaVtfUjXZ/Wc37YIAH5HAowIArrMjAxk0KzpoC20eh7wBYuABOLdRl4zKBeFxeSMJEqawpoBUmR0I2cHTZIQD6HAgyIcu115sZbCap9ezxYZdj5R8ML/W+a7AvM+Fp54tdRVqpyuMMBnJL2pdxsf5AgP2/U1rHduwniDqez1t+IuxmVPX90f5dJwzn6RATkcyjAgCgF5TXau543EDFtMmvNdiEBvpitY8WEBXCHAzjIfH/ghy8VR37R+FmN/evmdqV6BhP9AxZV1l1ZrKevLM3R5yIgn0MBBqRhDRvePnn4XP3+uGPplGP+7/c9TSFsUDdoFWvYsPVIuWOzRuGOpVPC077annrVU1QwcrMFp+fDISCfQwEGpGFdUSNgaULWEoR7bFTtdJRwxwI4KyazaOofL8WFBaK2WggL8OMOp+NYSxB2zdfWCcjnUIABadILKkz3BQrwNV5R6yFC1ytqeSVV+u7+vLw8Ec4WvSWEcYcDOIvJbNBz98svqzm/QMd4cG/c4XQQqibGnoGZhZVes0eNH8HxUQsE5HMowIBAYw8Evc8v59xCpF3gYtjHrfeSNPtJ3lpmgDsW0BWc7yT4hGf+qqu0izPL13eB5E+l4w+FCAvwxWwbKyrE8VELBORzKMCAQKzpGyeqyh3/la5X1OacCQ95X0DYtJqgHazpG2W6C4c5mfPRcxqZI37vDzxL6bJpNQnI51CAAYFis4qnHH8hJsQfvW0sHa+olVTVae54Vs9s8F9nPLC3OO5wQFeoqWdo7nheXlNP39VEJh0NTcgp2TtN1Va7K0YtEJDPoQADAjGZDfp7/D6X1njP1zZV6YM7nP/Zvdic1Vdjf+oj/nytMe5YQNdZ4RP9MD5vuYmy4/ghuGP5n+UWVxnsaRy18HqzRS/xrhi1QEA+hwIMyLTlbsKlV5m/6Ci529DvitpKn2jf+LxlJsobaZiIQYexTrwG9RF/RsMTr/MvP7rcT9LuL3ljaReNWiAgn0MBBmQKSvlifzait4Rw+CaaXVFr+ijyznIDDSVJ3OGArkPrSw+z/wwPTS1wnjBkiVEXjVogIJ9DAQZkqq1nolxWVlN/e7nBKFqVMdZgnD4Swq/oduoAOo+mg+9KKus0dzaeOgSsNxnQq4uW7SIgn0MBBsRadSXmQVzuUmNlJys6fZC7+U7C5fDMWbpKu2n7dRTQYRfCPm67l6TVT/Imrb5+djcmZ8212MEy4k8duu7DcwLyORRgQKz7cbm/X4lR7i3mt84Edyz/VdOEDOfma5vQcPgY6KSmoUz0moBlxeXohwl5K0yVN1h23ckuAfkcCjAgVml14xW1OkaD3zpjZZpcUWN9gYqAKQlBh7GmIO2yL/N0Xk09Y9T2ZxW1jHsrRqt14ReoCMjnUIAByeaejQhO+eJkNWQpTa6oeTxO/iPww8SRcsdpPik/6LCjfu/3P0sxH9LnDE0W4Qh4lz/f+7Vs924vncy6ctQCAfkcCjAg2cVXGVvvJo5S6nl7+WjcsfwnFgeCUmk+iSbopHefyiwP0WkZyk23E65EZM7WU9o5pUtHLRCQz6EAA5J9KqnWc/fj5eUJdzbvI9ENdzg/wFqYne7LSIBO6uIlDTqJyWzQdff7UlZzYYGOUdcuI0FAPocCDAg3+fiLuKxidxvVXyi/qN/JoA/ufyWPGdTr4kJ6L6QIOmmn75s/Q9NtNPoesOX4on6dFJ1ZZPPHS4nGUQtjUa+9K5+agHwOBRgQ7nhAqueTd6Yqvb3n6+CO5QemnXgZlVG0Y/LwOfr9cccCcIpIL5x5MqyHiGDkFgtBDi9r30l7/kr2CvowSU3+6C8aXfzUBORzKMCAcO8/l409GCzEzxe9baw4ha+ofSmr0dn9HP07hm0yk+shgjscgBOD2aC963lhRa3PYl0D5V64w2mP2f7AtC8VqPqiGtzFT01APqdEAb5169aWLVvS09NVVVW9vLw0NTWb/3bevHnnz59vvoWPj4/BYKAbc+fOvXjxYtP2GzduTJ8+vZ0nIuCAgf8Veoeb7Q9KL6j449dRE1Spe0XtakSm0+2EkQo97q80xB0LwG/DjbgbUdnzDPq7/jwcdyxtSs0vtzgQJMjPG711rES3rh61QEA+x1+Aw8LCjIyMPD09TU1Nt2/fHhoampqaKiEh0bRDfn4+auimu6tWrZKSkrp8+TK6bW1traysjLawfiUnJycm1t4saAQcMNABux+9PRWcNkVd/pBdV39K9t8tPPfaLzl//bjBK80G4Y4F4PfszefFFyL79hQJ3WjKy0vRGUlPBH7Y+zjZaHDvCwswXN8hIJ/jL8A2NjZMJvPu3bvodnFxsays7IkTJ+bPn9/qzikpKaiXHBcXN2RI43wrBgYGv/zyS1MB/iECDhjogMiPhdO9wrp3axwnQs0rahU19Ro7ntXWM5+sMVKRlej8AwK6q6plaOx4Wl3HfPi74XD5HrjDad3UP17EZBbvnDJitl6/rn92AvI5/gIsKSnp7u6+dOlS1l1UU1GJPXnyZKs7z5kzh8Fg+Pj4sO6iMuzi4oJq8H98LgIOGOgABrNBd/fzgvLay4t0R/9ExStqfyXkLbsc3U9aNHC9CWW7O6CLLbkQ+fTN59XmgxzGDsYdSyvyS6t13f1QAQl3NpfpjuE7fgTkc8wFuKioSEpK6vHjx5aWlqwtdnZ2ZWVlDx8+/H7nnJyc/v37v3jxQkfn7487evfujVpfQkJCRUXF0dFxypQp3/9VzTes2+iAKSoq0vqAgY7ZeDP+WmSWvX4/t8kjcMfSirXXYm/H5CwyHLDFehjuWABV3IjM2nAzfphc90erx+COpRU+4ZnOdxLUFHveW4FnlhsowJ2VlZWlpKQUHBw8Zszf77AFCxZkZmY+f/78+523bdvm6+sbHR3dtCU8PFxUVPTr16/nz58/d+7cgwcPrK2tW/yVq6urm5tb8y20PmCgY/zefl54PlK+R7cXTmZU62LWMZhaO5+XVNVd/01fZ4AU7nAAVRRW1GrtfMZs4AlxNFWUEsUdTkvzvCMC333ZYKmywvQnLAFAAf6vUB/XysqqxUZ7e3sPDw8ZGRlUbs3NzVkbZ82aVV5efv/+/RY7MxgMBQUFFxeXpg+rW7CwsBAWFv6+6ww9YIBU1zE0tj+rqmP4rjIc0ZdaV9RefiiYdTpcSkzo9WYLflgAGDRjezIsPL3QZdKw+aMH4I7lX8pr6kdtf1bLYD5zMBokg2fUAhTg/wqVz6qqqhYbBb9BXVgvL6958+axNqKusKam5qFDh1rsHBQUZGpqmpubKysr2+pTbNiw4a+//kpMTGwnDAIOGOiwpRejHid9+t3sp7XjVHDH8i+u95POvfw4Q1PBc4Ya7lgAtfwZkrbz4Vv9gdJXlujhjuVfHsbnrfCJHtBLzH+dMa6PlAjI5/gHYaGyKi8vz/paEWpKVF+vX78+adKkFrutWbMmIiLi5cuXbT2OsbGxpKQkazR1Wwg4YKDDbkdnr70eN0RW4vEaI9yx/AP9AxruDcgprjo9V2vsMBnc4QBqyfxaaeQZwM/HG7XFoqeoEO5w/rH6asy92NzfjAZumjAUVwwE5HP8BfjBgwdTp049fvy4np6em5tbWlpaVFQUPz//pk2b0G/d3d1Zu6mrq1taWu7du7fpD3Nycjw9PWfMmCEgIHDmzBlvb2/USzYwMGjnuQg4YKDDiitrNXc+ZzAbgjeYKklT5YpaUm7JxCOhIoL8MdvGdhOEBYBBS+MPBSd/KjswU81mlALuWP5Wx2CO2vGsrLr+5lJ9rf7YRi0QkM/xF2AEVd/du3cXFRWZmZl5eXkpKDS+z2bOnIl+ot4wz7fruOLi4hcvXrSzs2v6q8LCQtRRjo+PFxQU1NDQQI+gq/uDKewJOGCgM3459Sos7euWiUMXjRmIO5a/HXyWctjv/bhhMqfmauGOBVDRgafvjvinjh8u6zVHs/OPxhah7wtmnwnvJS4U7oxz1AIB+ZwSBbjLEHDAQGd4v0h3e/BGZ4DU9d/0ccfyN6vDIW/zSvfNUJuuSZX+DaCUxJwS66PU+oxk273EC2EZdtqKe6aNxBgGAfkcCjDgItlFlYZ7A9Ape+SWsVJi+K+oZRVWjvGgUDyAglCKHr3HP7ek+oy9lvlQ/KMEUDwGe/zzKBAPAfkcCjDgLhMOh7zJK/WcPnKGliLuWHjOhqZv932jO0DqGmV65ICCXO4lng/LsNVS3DsdZ4+TJSG7ZNKxUFEh/uitmHvkBORzKMCAuxx6nnLo+fuxw2ROU+CaK+ua9FbrYQsNqfUtT0ApL1ILfv0zXFpMKIIC3xRnXZO2GiF7Yjbma9IE5HMowIC7vM0rtTocIsDHG+Ro2rcnzmV3338uG3couIGq8xwB6qhjMLV3PS+urMO+pGZNPcPII+Bzac0hW/UpGn3xNgsB+RwKMOA6s06/evnh61z9ftuxzgvtcC32TkwOpUa3AspijZYfKtf90e+GGOdSvRyesflOomz3bkGOJsICmEeEEZDPoQADrsOa+lFIgC/U0bQPjlVckIyvFab7ApkNPBScGhNQUHFl7eg9/hW1DIxDn1BHHL1ps4uqKDI1JgH5HAow4DroPT/dKywqo2jxmAGbJ+JZfcjpVvzV11mmKr2952NYyRzQ0Z6/kr2CPqgr9ryz3ABLJ/hmVPb6G3G9xIVCHM1EhPB/IYqAfA4FGHCjgHf5871fiwjyv3Ay6/rv/+QUV5l4BtQxGm4tM9DsJ4m7MQA9FJTXGO71r65jXlqoazioq5e1ZjAbxh4ISiuocLIastRYGXdjNCIgn0MBBtwIve0nHQtNzCldafrTesuuXpuBNY+BgbK0z2JqzbAPKM7tQZL3i49YZpJ5EJe76kpMDxFBdM4qLiyAuyUaEZDPoQADLvU48dPSS1ESwgKhTmYorXTZ8+aXVht6BNTWM30W6xood3U/BtBaXkmVsUdgLYN5bYme7kDpLnteJrPB6nDIu89lDhaDV1sMwt0MfyMgn0MBBlwK5ZTxh4NTPpevGzt4lXnX5ZRdD9+cDknX7Cd5c6k+xuGsgKac7yT4hGeOGdTr4kLdzj/af/Q06dOSi1Go4/tio1kP0a47W20fAfkcCjDgXvdic1Zfje0pKojSiliXfKpWWNE4lrWqjuE9X9tUpQ/uBgD0k1VYabIvkMFsuLtitLpizy54RlQjJh9/EZ9dstxE2XH8ENwN8A8C8jkUYMC9UBazOBCUXlDhPGHIEqOuGFey78m7YwGpI/p2f7AS57c5Aa2tux53KzrbYqjMn/ZdMZtbUMoX+7MR3QT50HmqtLgw7lf/DwLyORRgwNWuR2Y53ozvJS4cutGU0xPbllTVGe7xL6up95qtOX6ELO6XDujqw5dydOKIMvej38cMk+dsKkMFYoZXWGRG0ULDAVut8Xxnry0E5HMowICr1TGYJp6BOcVVbj8Ptzfoz9HnOur3fv+zlMEy4o9XG/HhntEX0NpKn2jf+LyJI+WOzxrF0Sd6lfbV7tQrIQG+EEdTGUyz1rSFgHwOBRhwu0uvMrbcTZTr0S1ogylKNBx6loqa+tF7/Ysr6w7bqU9WxzyJLqA71pTmvLw8zxyMf+ojzrknmv1neGhqwRy9fjum4Jy3tVUE5HMowIDbVdcxjD0b55ffY6Nqp6PEoWc5Ffxh96PkAb3Enq81xr6gDSDA4guRz958thnV98BMdQ49RXRmkc0fLwX4eAM3mChIUm69EALyORRgAHjOhKbv8H2jJCXqv85YgJ/9nWBU4w33BhSU13hMHzmTAusQAwLEZRVPPv4CncwFrDNRkuZIdVx47rVfcv5MLQWP6Wq4X24rCMjnUIAB4KmsrR+zN+BrRe1BW7WpGgpsf/zzLz+63E/q21ME9SQEOVDgAXeaezYiOOXLLzpK7jaqbH/wxJwS66OhfLw8futMBvQSw/1aW0FAPocCDECjPwJTPR6/U+4t9szBmL0jpGrrmcaeAXkl1TumjJij1w/3CwXkeP2xcIZXmCA/b7CjqVwPNi9uvfxy1KOET5PV5Q/baeB+oa0jIJ9DAQagUVl13eg9/qXV9Wxf8/xqRKbT7YQ+EsIoS3L6m06A29ieDAtPL5xn0N/15+FsfNj3n8vGHQpGxeGpg9FgGQncr7J1BORzKMAA/I0Ta57XM5hm+4MyCyu3TBy6aMxA3C8RkCb0fcHsM+HCAnyhG816S7BtlgyHa7F3YnLGD5f1mqOJ+yW2iYB8DgUYgL9xYs3zOzHZDtfipMSEQjeaigpRYg0ZQBKUwKf+8TI2q/g344GbrIay5TEzvlaY7gtkNvD4rjIc0bcH7pfYJgLyORRgAP7B3jXPmcyGsQeDPnyp2GCpssL0J9wvDpDJ7+3nhecjxYT4USdYkh2LWzvdir/6OstUpbf3fB3cL649BORzKMAA/IO9a54/Sshbfjm6ezeBF05mEt2osoYMIAzK4ROOhL7NK/3dfNDasYM7+Wg5xVUmngF1jIZbyww0+0nifnHtISCfQwEG4F9Ya57rDpC61rk1z9mbFgFoR9OpXqiTWffOneq53Es8H5ZhoCzts1gP98v6AQLyORRgAP6lac3zznwnGP1bXQjLcLmfxMYPBgFoC5PZMO5QcGp++Ww9JddJwzs8mUx0ZpHdqVe19UyfxboGyp39BIjTCMjnUIABaMn1ftK5lx/RjZlaCi6Thv+vSwWXVNZtvpvgG5+Hbq8y+2ndOBXcLwiQzzc+d6VPDLqh2U/ykK26otT/NjdWPYP5R+CHw37vGcwGvYFSVxbrUX+5TALyORRgAFqqYzAPPU9B+Qj9c/SXFj1kp/HfVz5/lfZ17bXY3JJqAT5eh7GDlxorw8zPoGvcjcnZejexrKZeXFhgx5ThU9T7/scimlVY6XAtNjKjCN2erC6/ffKIHiI0GLJAQD6HAgxA65pKKaqgDhaDlpn81H4pra1vLNsngjpStgFgi+al9Gc1+R1TflxKm5ftnVNGTNGgzVJdBORzqhTg/Pz833//fcSIEVu2bPn+t1lZWcuWLQsICEDNvXbt2vXr17O2FxcXr1y58v79+0JCQgsWLNizZw8fX3sXPwg4YKArNf8wWae/1AFbtbbWhEn7Ur7mWmx8dgm6bauluG3SsP/1g2sA2KKewTwR+OHQtw+T+/YUOWirrjNAqtU9S6rqtt1LvBeby9PRD67xIiCf4y/Anz592rt379mzZysrK11cXL4vwAwGQ0NDo2/fvrt27YqKilq6dOmVK1dmzpyJfjVhwgRUuY8dO5aTk2Nvb79t2zZHR8d2nouAAwa6GPoHuROTs+1eUnlNvQTqIkwd0WI1X7TDtddZbg/eVNUxUG9jj42qFVtnsgSgA2Iyi9AZYcbXSj5enuUmP622GNRiFZCI9ELUV84pruLn411tPmi5iTIn1gHjKALyOf4C/OjRo3379qHaOW/evEWLFn1fgFEHd/r06dnZ2X369EF37ezs8vLygoKC4uPj1dTUYmJi1NUbl8N0cnJChTkjI6Od5yLggAEsMr9WrrkWE51ZjG5P1ejrNnk468seRRW1TrfjnyR9RrcNlKX3z1Rj+5z4AHQMOmV0u590Iyob3VZT6HHIToO1qFEdg3nE7/3xgFRmA4+SlOghO/VRSpT+vm9bCMjn+AswCoA1UqB///6tFmAHB4ewsLBXr16x7p46der333+vqqo6cuSIu7s76kCztj99+tTS0jI3N1dOrs3+BwEHDOBSz2Ae9U896v8epS0FSZFDturVdcx1N2I/l9YI8vNusFRZZDiQvcsoAdB5D+PzNt2OL62uFxXid500XGeA1OprsXFZjaeS00YpoFNJcdpeKyEgn+MvwE3aKsCTJ08WFRVFvVvW3cePH1tZWeXn5+/cuTMiIgLVZtb25OTkoUOHoi3a2trN/7zmG9ZtdMAUFRVpfcAAXlEZhauvxmYXVaFSy/z2r6PcW+ywnQaVp8wFXC63uGrt9dhXaYXotgAfbz2zoXs3gV1TVSepyeMOrVOgALNTWwXY3Nwc/erMmTOsu8HBwcbGxllZWS4uLh8/fvTz82Ntz8zM7NevX0hIiKGhYfM/d3V1dXNza76F1gcMYFdWXedyL+l2TA66PVtPafOEYSJCsMggoDQGs+F0SNq+J+9Q9dUdIHXAVr1vT9pfK4EC/F+xuq0tNtrb2587d67pblsFeMKECdLS0hcvXmTdZX3U/PXrV7RnQkICqris7SkpKSoqKlFRUaNGjWr+59ADBpwQ+r5AkJ9Xd6A07kAA+K9SPpel5pdbDpcl47vpUID/KwaDUVVV1WKjoKCgsPA/C1i2VYAXL178/v37wMBA1l3UFV63bl1xcfGuXbtOnz6NOsGs7agrbGFhUVhYKCnZ5oACAg4YAAAAHiLyOQ0+gj5//vyyZcvy8vJQW6O7s2bNQj3aW7duBQUFmZiYoI7voEGD0HZnZ+cnT56gHnA7T0HAAQMAAMBDRD6nbgHetGkT+unu7l5ZWamioqKtre3i4hIeHr5q1arg4GBdXV0UOdooIiJy8OBB1A+eN28eKtXTpk1r5ykIOGAAAAB4iMjn1C3ArKk2rl+/jn4mJiYuXrw4Ojp6wIABu3fvtrGxYe2TlZWF/gR1hWVkZFDBXrp0aftPQcABAwAAwENEPqdQAe4CBBwwAAAAPETkcyjAAAAA6IeAfA4FGAAAAP0QkM+hAAMAAKAfAvI5FGAAAAD0Q0A+hwIMAACAfgjI51CAAQAA0A8B+RwKMAAAAPohIJ9DAQYAAEA/BORzKMAAAADoh4B8zl0FGB2qnj17ZmVl0feAAQAA4Pn/5WWLi4tZ6/TQEXcV4OzsbHTAcEcBAACAPVCHSkFBAXcUHcRdBZjJZObm5kpISPDydnw9atZpFy260TQKFaKFaOkYLY1CJS9aVLzKysrk5eX5+PhwB9tB3FWA2YJGFx5oFCpEC9HSMVoahQrRUhAU4P8Zjd4WNAoVooVo6RgtjUKFaCkICvD/jEZvCxqFCtFCtHSMlkahQrQUBAX4f1ZTU+Pu7r5p0yZhYWHcsZATKkQL0dIxWhqFCtFSEBRgAAAAAAMowAAAAAAGUIABAAAADKAAAwAAABhAAQYAAAAwgALcJtQy27dvP3nyZElJybhx406dOtW7d+/mOwQHBzs4OCQlJSkrKx84cMDS0pKy0TIYDEFBwaZjLSYmVl5ejjFalri4uPnz5x85csTQ0LDFryjVtu1HS7W2jY+PX79+fWhoaPfu3a2srPbt2yctLd18B0q1bfvRUq1tr127tmvXrtTU1F69ei1atGjr1q0t5tSjTtu2HyrVGpblwoUL9vb2Fy9enD17dvPt1GlVtoMC3CYPD4+9e/eeO3cOZQT0Du7fv/+jR4+afpuenq6qqrpixQr0Xjlx4gTa7c2bN2gfakZbUFCA6nFgYGDfvn3RXT4+voEDB2Js2+jo6D179vj6+lZVVYWEhLQoaVRr2/ajpVrbGhkZjR8/3traOisra9WqVUOHDn348GHTb6nWtu1HS7W2dXNzU1FRGTZsWFhYGGrDo0ePLlu2jJpt236oVGtYpLq6esiQIRkZGS0KMKVale2gALeOyWTKyMhs3rx5zZo16O7jx4/R6XlaWtqAAQNYO6xdu9bf3z82Nhbdrq+vR2+IhQsXojc9NaNNSUlB/42lpaUSEhK4m7bRli1b0H8a6lCam5t/X9Io1bY/jJZqbYsqWdOKIz4+PnPmzCkrKxMVFWVtoVrbth8t1dq2uYkTJwoLC9++fbtpC9Xatp1QKdiwKH2hqG7evNmiAFO2VdkCCnDr4uLi1NXV3759i07K0F3U9REXF7906dIvv/zC2kFDQ8PS0hJ1jFh3Z82a9fXr1ydPnlAz2levXhkbG9fU1OBu17+hdx0vL+/Hjx/RKcL3JY1SbfvDaKnWts2hnDtt2rSKioqmkka1tm0/Wiq3Leq4o1OH06dPN22hbNt+HyrVGjY+Pt7ExAT9RHG2KMCUbVW2gALcujt37tjY2FRXVzdNwiIrK7tu3boNGzaw7vbo0cPDw+O3335j3XVycnrw4EFSUhI1o3348KG1tXW3bt369OkzevTo3bt3U+EznLZKGqXa9ofRUrNteb6dN6DAULZC2bZpIzXbtq1oqdm26BTh6tWr69evDwwMVFNTa9pOwbZtK1RKNSxKXNra2vPnz0edXXSm26IAU7BV2QgKcOvQm2DevHkMBqNpy8CBAxcsWLBlyxbWXX5+/nPnzs2ZM4d1d/v27RcuXEhNTaVmtAUFBenp6ag8Jycnu7m5VVZWopNN7J8+tVXSKNW2P4yWmm1bV1e3fPnyy5cvBwcHa2lpNW2nZtu2FS0F2xYVLdRx7N69+4kTJ1BvrPmvqNa27YRKqYZdtmwZCsPPz4+Pj+/7Aky1VmUvKMCtu379uq2tLcoLAgICrC3y8vLoRBKdo7HuioqKHj16dOHChay7zs7Ovr6+6E1MzWiby8zMRGe7165dmzFjBpZom7RV0ijVtj+MtjmKtG12djZ6P6Slpd25c0dPT6/5ryjYtu1E2xxF2haVipKSksjISBcXlyVLlqC+Y9OvqNa27YTaHN6G/fPPPzdv3hwbGysnJ4fufl+Aqdaq7AUFuHUvXrxAeRadJLI+mUEnkuLi4jdu3JgyZQprh0GDBv3666+urq6su6zBI3fv3qVmtC307t1748aNqEJjibZJWyWNUm37w2hbwN62KSkppqamqqqqqKPQp0+fFr+lWtu2H20L2Nu2OW9v78WLF6PWExERYW2hWtu2E2oLGBsW/U9lZWU19RxQ7hIUFEQb3717x9pC2VZlCyjArauqqpKWlj58+DB646K7T58+tba2/vTpk5SUFGuH+fPno9yBKh/Pty/Vocrn6Oi4atUqakbbHOpqKCsro95GW+W5y7RV0ijVtj+MtjkqtK2WlhZqsevXr/Px8X3/W6q1bfvRNkeFtm2Odd0HdTHRyS5rC9Xatp1Qm8PbsB8+fKirq2u6O3ToUA8PDxsbGxQSawtlW5UtoAC3ad26dT4+PufOnUPv2kWLFllYWKAKN2HChCVLlqD3R0xMjI6OztatW9HtP/744969e8nJyRivTrUfLToFrq+vRwGjk00nJyd+fv6oqKims05cmpc0JpNJ2bZtP1pKtS3r6yU3btxQV1dv2oj6N7a2thRs2x9GS6m2LS0tXbly5ezZs+Xl5ePi4lB/0dzcHHXcKfi+/WGolGrY5lgfQc+aNYuCrcqR1wsFuC01NTUODg6XL19G7070Vvb09EQbBw8evGHDBvTmRrdv3ryJ3rvZ2dno/XHixInhw4dTNtrbt2+jf8Lc3Fw5OTn0zt61a1fPnj1xN/C/ShqKn7Jt2360lGrb4OBgY2PjFhsPHTp04MABCrbtD6OlVNvW1tba29sHBASgrmS/fv3mzp2Lznp5KJkTfhgqpRq2OVYBnjFjBgVblSOvFwowAAAA0PWgAAMAAAAYQAEGAAAAMIACDAAAAGAABRgAAADAAAowAAAAgAEUYAAAAAADKMAAAAAABlCAAQAAAAygAANAmvLycvR/Tcx0fQCQCgowAKQ5cuRIRETEpUuXmm8cMmTI48ePqbCgPQCABQowAKSZOnWqjY1N0xrmLFCAAaAaKMAAkOPixYu3bt16/vy5jo5O9+7dp02b1lSGoQADQDVQgAEgR2xsrLe3d0hIyNq1a9HdESNGzJ07NzMzE90uKysTExNjLbsbHx+vpKSEO1gAuB0UYABow8vLa/Xq1e/evUMd2fr6+uHDh0+aNGnfvn3N9/n111/Hjh2blpa2ceNGVHHRnoGBgehnUw+YtUVYWBiV51OnTqFeMu6XBQCXggIMAG0wmUwdHR1Ud8+fP3/s2DFPT883b96gKtu0Q0lJiYqKCqq+AwcOTExM7NWrV1sFuGfPnhMnTkSPYGBggPtlAcCloAADQCfh4eGGhoYhISE///zz6dOnJ0+e3Py3t2/fXr58OdrB19d33Lhxzs7OdnZ2rRZguBgMAHZQgAGgmUWLFt28edPU1PTOnTstflVXV1dRUYFuoH5wWFiYoqLioEGDSktL+fj4iouLu3fvjm4UFRV9+PABCjAA2EEBBoBmNmzYcPjw4enTp/v4+LS1j6ysbPsfQUMBBgA7KMAA0Mnr16+NjIxu3749ZcoUX1/fsWPHttihurr648ePo0ePdnBw0NHRWbJkCRRgAKgJCjAAtFFXVzdq1CgLC4uDBw+uXbv2/v37qJvbrVu3ph0WLVp09uxZMTGxqqqqqVOnrlq1au7cua0WYElJSdYgLH19fdwvCwAuBQUYANpwdXU9duxYampqz549i4qKlJWVly9fvnPnzqYd3r17h+qxkpKSnJxc+x9Bs76G5O3t/fPPP+N+WQBwKSjAABCo+TXgwsJC1vwbLKWlpWlpafARNADYQQEGgEDfD8Jq+hVcAwaAIqAAA0CgrKysvn37Nu/4AgCoBgowAAAAgAEUYAAAAAADKMAAAAAABlCAAQAAAAygAAMAAAAYQAEGAAAAMPg/2PseS+xka1YAAAAASUVORK5CYII=)\n\n### 绘制子图\n\n`pyplot.subplot(nrows,ncols,index)`可以在一个figure中添加多个子图像。\n\n使用`plt.subtiltle('')`给整个figure添加标题，使用plt.title('')给每个subplot添加子标题，修改x轴范围使用plt.xlim(min,max)，修改x轴刻度使用plt.xticks([numlist],[textlist])。\n\n```python\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport matplotlib\nx = np.arange(0,3,0.05)\nn = np.linspace(-np.pi/2,np.pi/2,50)\ny = np.sin(x*np.pi)\nz = np.cos(x*np.pi)\np = np.tan(n)\nq = np.tan(np.pi/2-n)\n\nplt.figure(figsize = (16,9),dpi = 80)\n\nplt.suptitle('Matplotlib 绘制多图',fontsize = 40,weight = 40)\n\nplt.subplot(2,2,1)\nplt.title(u\"正弦函数\")\nplt.xlabel(u\"x 轴:\")\nplt.ylabel(u\"y 轴:\")\nplt.plot(x,y,'-',label = 'sine')\n\nplt.subplot(2, 2, 2)\nplt.title(u\"余弦函数\")\nplt.xlabel(u\"x 轴:\")\nplt.ylabel(u\"y 轴:\")\nplt.plot(x,z,'-',label = 'cosine')\n\nplt.subplot(2, 2, 3)\nplt.xlim(n.min()*1.1,n.max()*1.1)\nplt.xticks([-np.pi/2,-np.pi/4,0,np.pi/4,np.pi/2],[r'$-\\pi/2$',r'$-\\pi/4$',r'$0$',r'$\\pi/4$',r'$\\pi/2$'])\nplt.ylim(-10,10)\nplt.title(u\"正切函数\")\nplt.xlabel(u\"x 轴:\")\nplt.ylabel(u\"y 轴:\")\nplt.plot(n,p,'-',label = 'tan')\n\nplt.subplot(2, 2, 4)\nplt.xlim(n.min()*1.1,n.max()*1.1)\nplt.xticks([-np.pi/2,-np.pi/4,0,np.pi/4,np.pi/2],[r'$-\\pi/2$',r'$-\\pi/4$',r'$0$',r'$\\pi/4$',r'$\\pi/2$'])\nplt.ylim(-10,10)\nplt.title(u\"余弦函数\")\nplt.xlabel(u\"x 轴:\")\nplt.ylabel(u\"y 轴:\")\nplt.plot(n,q,'-',label = 'cotan')\n\nplt.show()\n```\n\n![绘制子图](data:image/jpeg;base64,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)\n\n### 绘制条形图\n\n`plt,bar(x,y,width = 0.8, align = 'center',  color = 'r')`绘制垂直条形图\n\n`plt.barh(x,y,width = 0.8, align = 'center', color = 'r')绘制水平条形图`\n\nx 为x值的列表，y为y值的列表，width宽度\n\n```python\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport matplotlib\nplt.figure(figsize=(16,9),dpi = 80)\nplt.title('条形图绘制',fontsize = 40)\nxlist = [10,20,30,40,50]\nylist = [5,15,10,50,45]\nplt.bar(xlist,ylist,width = 5)\nplt.show()\n```\n\n![条形图](data:image/jpeg;base64,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)\n\n`numpy.histogram(ndarray,bins = [])`自动区间分组计数器\n\n- bins:分组区间\n\n```python\nimport matplotlib\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.figure(figsize = (16,9),dpi = 80)\nplt.title('使用Histogram自动分组计数',fontsize = 30)\nplt.xlabel('分数区间')\nplt.ylabel('人数')\n\ndata = [86,62,55,42,98,45,58,60,75,79,82,85,72,77,32]\nrank,bins = np.histogram(data,[0,10,20,30,40,50,60,70,80,90,100])\nplt.xticks(bins)\nplt.xlim(bins.min(),bins.max())\nrects = plt.bar(bins[:len(bins)-1],rank,width = 6,align = 'edge',)\ndef autolabe(rects):\n\tfor rect in rects:\n\t\ty = rect.get_height()\n\t\tx = rect.get_x()\n\t\tprint(x,'\\t',y)\n\t\tplt.text(x+3,y+0.05,'{}'.format(y),color = 'b',fontsize = 20,ha = 'center')\nautolabe(rects)\nplt.show()\n```\n\n![image-20200811021445423](data:image/jpeg;base64,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)\n\n`matplotlib.pyplot.hist()`可以轻松解决上面的复杂过程。\n\n```python\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport matplotlib\nplt.figure(figsize = (16,9),dpi = 80)\nplt.title('使用Histogram自动分组计数',fontsize = 30)\nplt.xlabel('分数区间')\nplt.ylabel('人数')\nbins = np.array([0,10,20,30,40,50,60,70,80,90,100])\nplt.xticks(bins)\nplt.xlim(bins.min(),bins.max())\ndata = np.array([86,62,55,42,98,45,58,60,75,79,82,85,72,77,32])\nplt.hist(data,bins = bins,width = 6)\nplt.show()\n```\n\n![hist(ndarray,bins)生成条形图](data:image/jpeg;base64,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)\n\n### 绘制破损条形图\n\n使用`plt.broken_barh([(star,len),(star2,len2)[,],facecolor = 'b',frameon = Ture)`，`plt.broken_bar([(star,len),(star2,len2)[,],)`来绘制破损条形图。\n\n### 绘制饼图\n\n`plot.pie(x, explode=None, labels=None, colors=None, autopct=None,\n        pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None,\n        radius=None, counterclock=True, wedgeprops=None, textprops=None,\n        center=(0, 0), frame=False, rotatelabels=False, hold=None, data=None)`用来绘制饼图\n\n- x       :(每一块)的比例，如果sum(x) > 1会使用sum(x)归一化；\n- labels  :(每一块)饼图外侧显示的说明文字；\n- explode :(每一块)离开中心距离；\n- startangle :起始绘制角度,默认图是从x轴正方向逆时针画起,如设定=90则从y轴正方向画起；\n- shadow  :在饼图下面画一个阴影。默认值：False，即不画阴影；\n- labeldistance :label标记的绘制位置,相对于半径的比例，默认值为1.1, 如<1则绘制在饼图内侧；\n- autopct :控制饼图内百分比设置,可以使用format字符串或者format function\n  '%1.1f%%'指使用百分比法且精确到小数点前后1位数(没有用空格补齐)；\n- pctdistance :类似于labeldistance,指定autopct的位置刻度,默认值为0.6；\n- radius  :控制饼图半径，默认值为1；\n- counterclock ：指定指针方向；布尔值，可选参数，默认为：True，即逆时针。将值改为False即可改为顺时针。\n- wedgeprops ：字典类型，可选参数，默认值：None。参数字典传递给wedge对象用来画一个饼图。例如：wedgeprops={'linewidth':3}设置wedge线宽为3。\n- textprops ：设置标签（labels）和比例文字的格式；字典类型，可选参数，默认值为：None。传递给text对象的字典参数。\n- center ：浮点类型的列表，可选参数，默认值：(0,0)。图标中心位置。\n- frame ：布尔类型，可选参数，默认值：False。如果是true，绘制带有表的轴框架。\n- rotatelabels ：布尔类型，可选参数，默认为：False。如果为True，旋转每个label到指定的角度。\n\n```python\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport savepath\n\nplt.figure(figsize = (16,9),dpi = 80)\n\nplt.suptitle('青年人经济支出构成')\nlabels = ['娱乐','教育','衣食','住房','交通','育儿','其他']\ndata = [.1,.12,.14,.3,.11,.22,.01]\nplt.pie(data,explode = [0,0.1,0.1,0,0,0.1,0],labels = labels,autopct = '%1.1f%%',shadow = True)\nplt.legend(frameon = True,framealpha = 0.7,loc = 'upper left')\nplt.show()\n```\n\n![pie](data:image/jpeg;base64,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)\n\n\n\n### 绘制一个完整的线型图\n\n1. 首先先汇出正弦和余弦函数各一个周期。\n\n   ```python\n   import numpy as np\n   from matplotlib import pyplot as plt\n   import matplotlib\n   plt.figure(figsize = (16,9),dpi = 80)\n   plt.title('绘制正余弦函数',fontsize = 30)\n   x = np.linspace(0,2*np.pi,256)\n   y1 = np.sin(x)\n   y2 = np.cos(x)\n   plt.ylim(y1.min()*1.1,y1.max()*1.1)\n   plt.xlim(x.min()*1.1,x.max()*1.1)\n   plt.xticks([0,np.pi/4,np.pi/2,np.pi*3/4,np.pi,np.pi*5/4,np.pi*3/2,np.pi*7/4,np.pi*2],\\\n   \t\t\t[r'$0$',r'$\\frac{1}{4}\\pi$',r'$\\frac{1}{2}\\pi$',r'$\\frac{3}{4}\\pi$',r'$\\pi$',\\\n   \t\t\tr'$\\frac{5}{4}\\pi$',r'$\\frac{3}{2}\\pi$',r'$\\frac{7}{4}\\pi$',r'$2\\pi$',])\n   plt.plot(x,y1,'-b',linewidth = 2)\n   plt.plot(x,y2,'-r',linewidth = 2)\n   \n   plt.show()\n   ```\n\n   ![绘制正余弦函数Base](data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAALQCAIAAABAH0oBAADHKElEQVR4nOzddXyVdR/G8c9ZMLqb0aNBEBFQpEEaLARBBBEkR4zuGiHNSAEFkUZRQEpCSpEGle5GaUasz/PHOc4944CA235nO9f75R/jPvfuc01iu+74fS1WqxURERERERGRhM7NdAARERERERGRuKACLCIiIiIiIi5BBVhERERERERcggqwiIiIiIiIuAQVYBEREREREXEJKsAiIiIiIiLiElSARURERERExCWoAIuIiIiIiIhLUAEWERERERERl6ACLCIiIiIiIi5BBVhERERERERcggqwiIiIiIiIuAQVYBEREREREXEJKsAiIiIiIiLiElSARURERERExCWoAIuIiIiIiIhLUAEWERERERERl6ACLCIiIiIiIi5BBVhERERERERcggqwiIiIiIiIuAQVYBEREREREXEJKsAiIiIiIiLiElSARURERERExCWoAIuIiIiIiIhLUAEWERERERERl6ACLCIiIiIiIi5BBVhERERERERcggqwiIiIiIiIuAQVYBEREREREXEJKsAiIiIiIiLiElSARURERERExCWoAIuIiIiIiIhLUAEWERERERERl6ACLCIiIiIiIi5BBVhERERERERcggqwiIiIiIiIuAQVYBEREREREXEJKsAiIiIiIiLiElSARURERERExCWoAIuIiIiIiIhLUAEWERERERERl6ACLCIiIiIiIi5BBVhERERERERcggqwiIiIiIiIuAQVYBEREREREXEJKsAiIiIiIiLiElSARURERERExCWoAIuIiIiIiIhLUAEWERERERERl6ACLCIisWXJkiUtW7bcvHlzRETE0/c8f/58yZIlS5Qo0bNnzxd+ux49ehQtWrRGjRrP9VnXrl3z8fHx8fEJCAh44beOVTt27PD39/f39x83blx4eHgcvGNgYGC3bt0++eSTOHiv2BYWFrZr1647d+6YDiIiIk7Bw3QAERFJsMaPH7979+4FCxacOnUqe/bsT9mzX79+Bw4cAOrUqfOMBw8NDd21a9frr7/u5mY/mXvx4sXDhw/fv3//uUKGhYWdPn0auHXr1nN9Ytw4ffp0vXr1bP2tatWqfn5+cfCmQ4cOHT9+PFCtWrUPPvjg2T8xPDzcarX+l7d2d3e3WCzPsuexY8cKFSoE/PTTT5UqVbJtjIiICA4OTpIkSeRuBw8eLFu2LDBx4sTOnTv/l2wiIpIA6AqwiIjEigMHDuzevRv45JNPnt5+Dxw4sHDhQtvH48aNO3HixLMcf/fu3eXLl8+SJcsvv/zy39M6pzt37jRo0CDy6uWmTZt69er17J9+8uTJ957q4MGDDj9xwIAB3t7eQIcOHa5du/bs75gzZ07P/+bzzz93eOTbt283bdrU39//6QGGDx9evHjxHTt2RG6x/SEESpcu/exfiIiIJFS6AiwiIrHCdgkxUaJEffr0efqe3bp1s1qtiRIlSpw48b1791q0aLF9+3Z3d/enf9bmzZuBv/76K3fu3M+eKiwsLCgoCEiWLNlTrjT+8MMP9erVe/bDPrtly5a99957z7Lno0eP6tWrd/jwYaBGjRonTpw4e/bsmDFjvL29O3Xq9CxHuHnz5rfffvuUHVq1auVwe8qUKadOndqgQYPbt2+3bdv2+++/f5a3i1XDhw9fuHChxWIpXrz4k35rduzYMWTIkPDw8AEDBvz000+2jbYCnDRp0lKlSsVdXBERcVYqwCIiEvPOnz+/ePFioFWrVk+//BsQEGDrKj169MibN2/Lli137tw5ZMiQoUOHPv0tbK2sWLFiWbJkefZg8+fP//jjj4GzZ8/mypXr2T8xjj148KBevXq2K5klSpT45ptvLl++/Prrr9+6datr164ZMmR4ljuT06VL9+677zo8+Lp1657+ufXr169Tp87mzZuLFSsWFhbm4fFMPzBMnjz5wYMHz7JnpNOnTwcEBNjuP3d3d8+UKZPD3YYMGbJy5cqTJ082a9Zs3759efPmjbbDtWvXGjduHB4e7u3tvWTJksjtO3fuBCpUqODp6flcwUREJEFSARYRkZg3evTosLAwLy+vyMu/QUFBiRMnjrbbH3/8YbunN0+ePP369UuSJMm33367evVqf3//kiVLvvXWW086/vHjx/fv3w+UKFHi2LFjto2BgYFAaGho5JZI7u7u+fLle/b8efPm7dat2zPufPDgwU2bNgE1a9YsUqTI03cuUKDAvx7w5s2b9erVszU3Hx+f1atXJ0+evECBAqtWrapRo8b9+/ebNm16/vz53r17P/04+fLl++abbx7ffurUqcj/G7Nnz27duvVTDmJbf+vx7Tlz5jx37ly0jW+//fbTI0V1+vRpf3//+fPnh4WFAe+8846/v7/tsd7HJUuWbOHCha+//vrdu3fff//9PXv2RH01NDT0vffeu3z5cpIkSZYvX54xY0bb9uPHj9vuqI+l6/kiIhL/WEVERGLUmTNnEiVKBPTq1StyY82aNStVqrR+/frILY8ePXrppZcAi8USuf3y5cvp06cHkidP/uuvvz7pLXr06PFc3+xSpUpl+8Q5c+bYtpw9e9a25eLFi7YtgwYNerGvd/LkybYjzJkz58WOENWxY8ciL2/6+PhcvHgx6qs7duxIkSKF7dUWLVoEBwe/wFucPHnSdoS1a9fOmjXrxX5+yJkz5wt/jadOnWrRokXkVeVq1art3r37WT5xxIgROXLk2Lp1q9VqPXr0qO3Tf/rpp7t377799tsWi+Wbb76Jtr9tn9OnT4c+QVhY2At/ISIiEu/oCrCIiMSwAQMGhISEZMqUqV+/frYtO3bssN1zmzdv3jfffBOIiIho0qTJb7/9BvTq1cu2EciaNev3339ftWrV+/fv16pVa+PGjSVLlox2/Hv37j1pqaT4btmyZa1bt7579y5QokSJ1atXZ82aNeoO5cqV+/HHH+vWrXvz5s25c+eePn16/vz5OXLkeOF3rFKlyqJFi6Ju+fHHH22nCapWrfqkh4SBZMmSPeml8PDwJz3CHe2qb5kyZUaMGFGlSpVnTNurV68OHTqkTJky2vaUKVMuX758586dr732WtTt3333ne2Dx2+ZjpQqVSoNSRIRcR0qwCIiEpP27NljW9LZ398/8lpl3759geTJk0feTNuxY0dbOSlfvvywYcOiHqFcuXJz5sxp2rTp7du3K1euvGLFisghNzaTJ0++d+8eMHfu3IoVK0Zu9/X1/eGHH7JlyxZ1EWCbyFFJTuvBgwfdu3efMWOG7Ze1atVaunRp8uTJH9+zbNmyO3furF279qlTp7Zv316kSBF/f39fX98X+xrz5MmTJ0+eyF+Gh4fbnr5OkiTJrFmznnGBsTFjxhw+fDh9+vRjx44NDg6uVatW4cKFx44dG+2m9+PHjxctWtRWfW2xI+9yP3HixLx584YOHerm5ta9e/cbN24UKVIk8jr/uHHjIhe1ihT5sHGfPn3SpUsX9aUmTZo0adLk4MGD0e6UFhER0S3QIiISY8LDw21r7ZYoUcI2EtZqta5YscL2HWfYsGG2LZGNN0eOHJcvX3Z4KNsi0oCnp+ekSZMit1+9etVWC0uXLh3tUxo1asS/3ZrrnLdAb968ObJqWiyWPn36/Ot9uTdu3KhZs2bkd/PSpUsfOnTo8d2Cg4OvPyZyMtDatWuj7T937txov1nPomrVqpH/57t27Wo7QvHixY8ePRp1N9uoZ8C2VnPk9q1bt6ZNmxZ4//33Q0JCcubMCVStWjVyh6ZNmz7Xzza238oPP/wQSJw48bZt2/b8v+LFiwO5c+fes2fPgQMHnv0rFRGR+E5XgEVEJMZ8/fXXe/fuBbJnzz548OCwsLDQ0NDly5cDOXLksC0r1bdv35EjRwLp0qVbv359tFt8I3Xt2jVFihRt27YNDQ3t3Lnz+vXrZ86cmS1btv3799vGF9kOEgdGjBgxcOBAYPny5fXr14/Zg1+9erVHjx4LFiyw/dLWfmvVqmVbAevp+vbt6+bmtmbNGmD37t0vv/zyu+++27t376g3jS9durRZs2bPGObhw4e2rzR//vw9e/aM+tKPP/4YGhpap06dfz3I4MGDL126tGzZskOHDpUqVWry5Mm2ZbejKl26dOT16nnz5rVu3TokJATIlCmTw+vYzZo1izbE6NixY5G3wXfo0MHHxyfqq2XLlr18+bJtLei0adOWL18+2gFtN5nnz59fs5FERFyO6QYuIiIJx9q1a5/07WbJkiWhoaGRXShp0qQ7d+781wN+8803Xl5etk9JlizZhAkTrFbrtWvXJk6c+PjOsXQFOPJ69Xfffedwhxe7AhwYGDh06NCoNzk/5TnVp/D29i5RokTULdWrV9+8ebPtXT777LOnfG60K8C2O9WBDRs2RN1+//59b29voHjx4pcuXXr8a4l6BdgmICDAthAa0LRp03v37lmjXAG2vW9wcHCHDh1sWzw9PWfOnGn73MevAEezZs2aqI8BL1u2bOLEiVEvKVut1k8//dT2qsViefToUdSXwsPDbSOR2rZt+++/TyIikrDoCrCIiMSYKlWqvPbaaylTpkyXLl3atGnd3d0nTZoEVKxY8f333+/Vq5etf6ZKlWrixInR1ityqE2bNrt27WrUqNHx48dDQkJeeuml/v37216K/CDS77//Dty5c+fxl2wcDsU1Yv/+/TVr1rx+/brtl2nTph0/fny2bNmqV6/+vIfKlCnTrl27Jk+ePHDgQNsgqA0bNuTIkaNy5crA5cuXAR8fn+nTp0d+ypUrV5o3bx7tOCdOnBg3bhzQvHnzatWqRX3J39//0qVLtpzZsmV7llS+vr4lS5Z85513/vrrrwULFpw+fTraNe179+7VqFHj119/BTJmzLhs2bIKFSo8y5HHjh3bq1eviIiI0qVL2+7lbteu3Y0bN3744Yf58+fbxgjv2bNn9uzZtv2tVuvZs2ejTle6evVqaGgo8IxPOIuISEKiAiwiIjEmUaJEv/zyS+QvbbfRenh4BAQEAIMGDTpy5MiuXbvWr18fuT7WvypevPi+ffs6dOhQtmzZKlWq2C42PsXdu3eHDx/u8KVoN8oaVLx4cR8fn+vXr7u5ubVo0WLkyJEZM2YMDAx0uGjTjh07bA/WDh8+PHK57EjJkiVzd3fv0qXLBx984O/vP3PmzKxZs06cONH26pUrVwAfH5+onfbq1au2m5kj5+WGh4d/9NFHwcHBmTNnnjBhQtTjnzx50vY8tpeX13Mtvl2uXLk9e/bUr1//xIkT0Y4JpEyZsnjx4r/++uurr766fPly2xXmp7tx44avr+/ixYsBX1/ftm3b2qYuV6tWbdmyZRs3bixRosSSJUveeOON9u3bR0REeHh42BbcOnLkSNQCbFt7nBe95C4iIvGaCrCIiMSK48eP2y7/+vr62ub9Jk2adOnSpVeuXMmbN29oaGjkHNenSJMmDZAsWbLIxZmiLvsczZEjR65fv+7l5VW2bFmHO2TOnPnatWvP/6U8q48//vjx512j2rlzpy2bu7v7nDlzunTpMmrUKNuCTECKFCkcPpIamTlPnjxPeWY1U6ZMkydP7t69+82bNyPvrLZdAY72oHWWLFl++OGHqFtGjhy5a9cuYPr06bb/55E6d+5se0C3f//++fLle8pX97gcOXL8/PPPBw8edPg7Mm3atKJFi7Zu3TryLvenmDt3ru1Ls1gso0eP7t69+7Fjx2wvtWnTplWrVo0aNbp27VrVqlXHjx9fqFChvXv3duvWbdGiRRcuXPj555+jXvyPXAPs1Vdffa4vR0REEgAVYBERiRXt27cPCQnJmjXrkCFDIjcmSZLEdtnN09OzYMGCL3DYLVu2POmlxo0bL1myJHPmzE/ZJ7JIG1egQIGnPDL9wnLmzGl7htbGYQGO5scffxw8eDDQqFGjyLlENitWrLCFLFy4cK9evV4gT7JkyV5//fUlS5Y8fv/5+vXrO3ToYFvS7CnOnTv38ccf235Ps2bNOnPmzMfX4qpateru3btr1ap14sSJIUOG7N279/79+4MGDTp27NiFCxeijcWyXWbPmjXrf5mfLCIi8ZQKsIiIxLyZM2du3rwZmDhx4rPf7RzfNWjQINp6VNE8y42+MchqtdquHmfJkuVJ+9y/f79x48bh4eFAcHBw8+bNg4ODg4ODQ0JCQkJCDh48CHh4eMydO9e2cNTz2rNnT8eOHXfv3j18+PDatWtHbvf39x8wYEClSpVmzZr19FvTU6ZMefbsWaB58+YTJ05MnTq17Utzc3NLliwZ4O7uDuTJk+eXX35p2LDh0KFDc+XKZVt7vFSpUitWrDhw4MCNGzfSp08PhISE2Prw66+//gJfjoiIxHcqwCIiEsNOnDhhm3hUr169hg0bmo4Td956660WLVo8486BgYG2u47/1aFDh2wf/PHHHxs3bvzX/dOkSfPKK68A169ft929fPLkSdujs9G8++67yZMnT5o06e3bt4Hvv//e4QH79OnzAncLX79+vU+fPl9++aXVagWWLVsW+QDzgwcPbJOftmzZUqxYscGDB3fv3t3WYx+XNm3ab7755vr167Vq1bJtuXr16iuvvFKwYMGlS5dGLdXp0qWznXaJVKNGjQEDBoSFhS1btqxdu3bAxo0bbTOQnnHNLRERSWBUgEVEJCbdunWrQYMG9+/fT5EixbRp02wbb9++feHChcDAwHLlyh0/fvwFDps0adKoN6wuW7Zs06ZN0faxjSC+efNm27ZtHz/CtGnTHM6YNeX48ePPu+bz8OHDn7S+V1QVK1a03TBsWwELiFwTK5rg4GCgevXqy5cvT58+fYYMGVKnTp0qVapUqVKdPHnSdpASJUoMGDDguXI+ePBg4sSJY8aMsVXNVKlSDRw40NfX9/Dhw7YdkiVLtm/fvh49ekybNi0oKKh3797Lly+fN29egQIFHB4w2pPPrVu3tq3kHHXi8ZM+0fbg96JFi2wF+NtvvwXc3NycZ0lwERGJSyrAIiISk2rVqmVbnShTpkyffvrphQsXzp8/f//+faBq1ao//PBD1PV4n11kqbP5+eefn7Qi8f379x2+NGXKFKcqwHHA9gDwkyRNmtQ2qnfOnDmR45Ftbty4YbuXO1GiRPPmzXv2m59DQkJmzJgxfPjwv/76C3Bzc2vZsuWIESMyZMjw+LtPnTq1fv36LVu2vHLlyu7du19++eWRI0d26tTp8cM6fE74xo0bDm/tbt68eeST3haL5b333psyZcr27dv379+fM2fOJUuWABUqVHj6c9EiIpJQqQCLiEhMsnUq4NSpU6dOnYrcnjJlytKlS8fse6VKleq9996L/OVPP/105syZ5MmTN2rUKHLjsWPHfv7555h93xhRtGhR2+Dif7Vt27YOHToAY8aMqVmz5r/ub3syFqhTp47t9uNo2rRpM3PmzGirPUeyWq0fffSRrTyPGjWqWLFitu179+59yhrUNu3atfvyyy9tH7/66qvTpk17+qfUqFHj0KFDzZo1W7du3aNHj7p06XLhwoWnv8Xz+vTTT6dMmQL4+/sXLVr0wYMHQJMmTWL2XUREJL5QARYRkZhUrly5a9eu5c2bN2/evLlz586bN2+uXLly585tW7sIiFbJqlatantuc+3atc/S7qLKnDnz7NmzI3/ZuHHjM2fOpEuXLurGGTNmOGcBTpw4cdGiRaNt3L59e7FixSL/X9mcO3fO9oG3t3fUT9myZUulSpVe4K3v3LnD3yOmHjd69Gjbys916tTp0qULcObMmZYtW27dunXTpk1VqlR5ypEHDRq0bNmyRIkSjRgxolWrVs9yyT19+vRr1qwZOXLkwIEDM2XK1L17d9tdylFFTswaNWrUV1995enpuWbNmmgril26dKl27dqhoaGRjd2mWLFiFSpU2LZt2/fff79hwwYgQ4YMKsAiIi5LBVhERGLSqFGjRo0a9Yw7L1682NZ+27ZtG7X9rlu3rkCBArlz546ViM7qjz/+qFWrVqpUqRxO+ommZ8+eY8aMadSo0ezZsyOn/j4jWwFOly7d4y/t3Lmzf//+QNasWefOnWu79zhdunS25aBHjhz59AKcI0eOFStWFC9ePG3atI+/euvWLdsHHh7/9+OHxWLp27dvuXLlkiRJ4vCuZtvErEOHDi1cuBDo3r17tWrVou3Tp0+f0NDQVKlStW7dOtpLw4YNq1ixotVqtd2K37t378iL5CIi4mpc62koERFxHoGBgbbFovPmzTt27NjI7R9++GGtWrXee++9oKAgc+ni2q1bt95+++0HDx5cuXLl8Uug0Vy+fNm2qvOSJUtKly79vOuK3bx5E0cF+MaNG40bNw4LC3Nzc1uwYIFtbhCQKlUq26O5GzdutA3RfYrKlSvb2u+jR4/2799/9erVhw8fhoeHnz9/fsSIEbZ9HLbcihUrPuUm+bCwsJYtW4aGhrq7uz++eNiyZctsS1gPGTIkZcqU0V6tUKFC5KdkzpzZthqWiIi4JhVgERExo3fv3leuXHF3d583b17UK3Kffvqpu7v7/v37O3bsGONv2qJFC6vVarVac+XKFeMHf2GPHj2qV6+e7ZHp6tWrP2l9r0jZsmXbvXt32bJlgaNHj7766qtPmmDkkO1KbGS/jcxQv3592yO4Q4YMiXZzdZcuXWzXmSNL7L8KDw8vVapU1qxZkyVL5uHhkStXLtvC3Xnz5n2BhdBu376dP39+Nze38PDwKlWqlC9ffuXKlbbb6Tdu3NisWTPgjTfecPhn5sSJE5G9/fbt25FjpURExAWpAIuISCyyWq3Hjx+fN29ehw4dSpUqFbmS83fffWcbkjRw4MDXX3896qdUqFBh0KBBwBdffBG5otILCw8Pt33gcCVhZxASEtKwYcNffvkFePnll7/55ptnWXU5c+bMW7ZssRW/wMDAd955Z+DAgQ6XvHrc9evXgagrM0dERHz44Yc7d+4EGjRo0K9fv2ifkjZt2vbt2wMrVqw4cuTIs7xL8uTJs2XLFm1jxowZFyxY8ALLcWfIkGHRokWHDx9u2rSpu7v7jh07GjRoULhwYX9//wYNGgQHB2fLlm3x4sWPDxO+detWvXr1bHd9A8HBwW+//fb58+efN4CIiCQMegZYRERiktVqPX369P79+/ft27d37969e/feu3cv8lVbPzl//vwnn3wCVKpUyfbEaTT9+vWzLbnUoUOHkiVL2kbyPLvw8HCLxeLm5hYSErJ+/XogQ4YMj1cjZ/Do0aN33nln3bp1QP78+detW/f4HbxP4uXlNW/evKJFi/bp0yciImLYsGG///77119//fRHgh8+fGh7FDZz5syRG7t167Z8+XJbhnnz5jk8WdCtW7fJkyc/evRo1KhR8+bNe5aEU6ZMuXbtWmhoaHh4uIeHR/bs2atVq5Y0adJn/AIfV7Bgwfnz5w8YMGDo0KGLFy8+duyYbUaxu7v7t99++3jfvnXrVrVq1U6cOAGULl06bdq069atu3btWoUKFTZv3pw3b94XTiIiIvGUCrCIiMSY77777qOPPrL1q6hSpEhRsmTJ0qVL58+f//Lly/Xq1bt9+3b69OmjXQyMiIh4+Lc+ffr8/PPPQUFBTZs23bdvX+LEiZ89xtatW6tVq+bl5RUWFhYWFgZUrVr1STtH9vPIJJMmTbp48WLkDr/++qvtg7lz5+7YsePxI9gWiAKWLFnyxx9//Gu8V1991Taoyfbc77Zt24BcuXJt2LAhY8aMj+8f+Sy0wwunPXv2zJ07d7NmzYKDg7///vty5cr98MMP2bNnf9K7RybMkSOH7YOAgICJEycCyZMn/+6771KmTBkeHv7w4cMHDx4EBgbejaJgwYIHDhxYtGjR0KFDn+Ue8gYNGvzrPi+gQIECrVq12rNnz8mTJ21bwsPDa9So0a5du86dO0cW+2vXrtWuXfvAgQNAnjx5Vq1a5eXlVbp06RMnTly4cKF8+fLffPNNtLsPREQk4bOKiIjEkMhCYrFYChUq1LJly9mzZ//+++/h4eG2HWbMmBF55226dOkKFiyYO3fuLFmypE6d2svL60nfqnx9faO9UefOnYECBQpE3WhrlTlz5gwMDIx6vbdKlSqXL19+UubIh1oDAgJsW1555ZVY+p5r07x5c6vVevToUR8fH9uWXLlynTt37kkJbQtQAT/++OOT9tm8eXPkpePcuXMHBQU9ac/mzZvbdrO94/379yPXZE6cOHG6dOme5VxDu3btoh7zKecXXljVqlUfDx8eHr5ixYrKlStH7tasWbPatWtH/tLLy2vDhg1Wq/XgwYORZwFy5cp15swZ2xHOnTuXM2dO23ZPT88JEyZE/uEUERFXoCvAIiISY3x8fEaNGlWsWLHXXnvN4ZjZu3fv2h5ABW7evGlbjvhxbm5uKVKkSJEiRWho6J9//jllypS6deu++eabkTt4enp6eXk9qTMnT5786NGjISEh7u7u2bJlS5EiReRLhw8f/uCDD7JkyZIuXbrkyZNfvHjRdo80ENu9N5pHjx6FhoYC+fPn37BhQ+T12Hnz5tkuBadMmTJRokSHDx9etGgR4Onp+ZSElStX3rp1a61atW7fvj1jxgzb/5nPP//8888/z5UrV+bMmZMnT+7m5rZjxw7bVOSyZcvaemCyZMlKlCixd+9eICgo6PGVtxMnTpwmTZp06dKlSZMmbdq0p06dOnz48Jw5cwYNGpQpU6ZY+V/zmODg4F9++eX7779ftmzZ1atXbRsLFSo0adIk2/LOW7Zs6dmz5549ewoWLFipUqUvvviic+fODx48APLly7dx48bI/705c+bcsmVLtWrVTp8+HRoa2rVr18WLFy9atMjVZm6JiLgsi/XZFswQERH57wIDA318fNKnT1+gQIE8efLYalWaNGlSpUqVOnXqVKlSpUyZMlWqVMmSJbM9hnrp0qVChQrdv38/a9asJ0+efPrjo40bN16yZEnOnDnPnTv3pH2Cg4OTJ09uuy86qtdee822DFVcun79et++fUeMGBF1PaovvviiVatWj+/s5+c3bty4px/w7NmzJ0+ejDxT8NNPPzkc25s4ceKtW7dGzhwaP378d999lz179qxZs2bOnDlTpkyZMmVKnz59+vTp06VLF21k7oYNG2zH79mz52effWbb+MMPP1y6dOlZv+x/069fv1u3blWtWnXjxo22r6JOnTqPHj2K3KFIkSJ9+vT54IMPot0TvnTpUh8fny+++MK2vhpQpUqVZcuWPT6U+ObNm2+//fb27duBvHnzHjx48HlnKYuISHxl+hK0iIjI04wePbpo0aJbtmz51z0jb4F++m5FixaN+n0wbdq0zZs3v3HjRszE/c9++eWXqPdvu7m5FSpUaPz48S9wp+6tW7eSJ08eeYczkDhx4lq1au3bt++F40VERNie/s2ePXtYWNgLH+cpbJemI2+BDg0NtV2eTZUq1UcffbR9+/anf/qRI0fSp09vsVi6d+8eGhr6pN1CQkL69Onj4eGxY8eOmEwvIiLOTVeARUTEqdmu1kZtcTHCarVGRETYFouO2SPHCKvVGhYWFhERkShRov8+wCkiIiI4ODgsLCx58uT//WhTpkwJDAzs0KHDsy9Y/R/99NNPISEhVapUeZYBUcCOHTuCg4Of5cnk8+fPRz4SLCIirkAFWERERERERFyCM572FhEREREREYlxKsAiIiIiIiLiEjQGKYHz8vKKurioiIiIiIgYdP369eDgYNMpXJcKcAKXIUOGGBxNISIiIiIi/4W3t7fpCC5Nt0CLiIiIiIiIS1ABFhEREREREZegAiwiIiIiIiIuQQVYREREREREXIIKsIiIiIiIiLgEFWARERERERFxCSrAIiIiIiIi4hJUgEVERERERMQlqACLiIiIiIiIS1ABFhEREREREZegAiwiIiIiIiIuQQVYREREREREXIIKsIiIiIiIiLgEFWARERERERFxCSrAIiIiIiIi4hJUgEVERERERMQlqACLiIiIiIiIS1ABFhEREREREZegAiwiIiIiIiIuQQVYREREREREXIIKsBPZtm1bmTJlfv3116gbDx069Morr2TPnr169epXr14FrFZr7969vb29fXx8pk6daiisiIiIiIhIPKMC7Cxq1649duzYM2fORNverFmzPn36XLx4sVSpUp07dwaWL1++adOmkydPbt26dfDgwUeOHDGRV0REREREJJ7xMB1A7ObPn582bdpcuXJF3Xjo0KFbt2699957QPv27fPly2e1WufPn9+mTZskSZJky5atfv36K1asKFy48BOPe+8e/v54euLlRcqUpElD6tSkT4+3N6lTY7HE8pclAhASwrVrXL3K9evcu2f/LySE8HDCwwESJyZxYpIkIWVK0qUjfXrSpydrVhIlMh1dREREnkVEBDducPkyN29y5w63bxMYSEgIoaGEhODlRf/+piOKgAqw80ibNu3jG8+dO5c7d27bx97e3mFhYbdu3Yq6MWfOnFeuXHnacQMDGTDA8UtJk+LtTd68FChAgQIUKkSJEqRK9V++CpHwcE6d4tAhjh7l1ClOneL0aa5ff5FDWSxkzUqOHOTNS6FCFC5M4cL4+OCmO1dERETM+vNPDh7k2DGOH+f4cc6c4coVQkKeuH/atCrA4iRUgJ1acHCwh8c/v0cWi8XNzS3qRtuWaJ81fvz48ePH2z4O9/CgQgVCQwkK4u5d+wm50FCAhw85cYITJ1i79p9PzpuXkiUpU4by5Xn5ZTw9Y/PrkwTi8mV++YVffmHnTn7/nYcPn7hn6tSkSkXKlKRIgZcX7u64uwMEB/PoEY8ecfcuN27w4AGA1crly1y+zM6d/xwhRQpeeYVSpShblvLlyZgxlr82ERERAR4+ZNcuduxg92727+cpF2ASJ7bfcpgyJV5eeHri6alLLOI8VICdmre394ULF2wfX758OWnSpGnSpIm68fz580WKFIn2WX5+fn5+fpFHYNOm/3vZauXuXS5d4tIlLlzg5En7qbtTp4iI4PRpTp9m2TKAZMl4/XXefJOaNSlSRPdLS1S3brF5Mxs2sHEjjz26Ttq0FCtGvnz4+JA3L9mzkyULmTM/6y3NQUH89RcXL3LhAufPc+IER45w9Cj37hEYyJYtbNli37NwYSpWpEYNqlYlefIY/PpERERcXlgYu3ezfj0bNrB3r/0KSiQvL/tdhPnz4+ND9ux4e5Mtm74fi5OzWK1W0xnkH7ly5Vq8eHHZsmVtvwwLC8uVK9esWbNq1arVt2/fe/fuTZkyZfr06QsXLty0adP169dLly69Z8+erFmzPumA3t7ely5deqb3fvSI339n/3727WPHDo4d+79Xs2WjTh3efZfKlXVZ2JWdOcP33/P99/z8MxER/2xPmZLXXuO11yhViuLFyZYt5k+YWK1cvMi+fezZw549/Por9+//82qiRFSsSN26vPsu2bLF8FuLiIi4kPv3WbuW5ctZt447d/7Z7ubGyy/z+uu88golS1KwoH4mfDHP8fO5xAIVYOcSWYD9/PwqV65cr169Xbt2ffrppzdv3ixZsuRXX32VJk2asLCwrl27rlq1yt3d3d/f/4MPPnjKAV/8L9j16+zYwaZNrF/PqVP/bE+dmgYNaNqUKlXsd6+KC7h0iUWLWLiQgwf/2ZgsGRUrUr06VatSpEhcP5obFsb+/WzZwqZNbN1KcPA/L5UrR8OGNGpE5sxxGklERCQeCwpi5UoWLmT9eoKC/tn+8svUqEHlyrz2GilSmMuXcKgAm6UCnMDFzF+w06dZs4bvvmPr1n+u+mXNStOmtGjBU9aglnju0SOWLWPOHLZuJfKfCm9v3nqLBg2oUMFZVml+8IDNm1m9mu+/588/7Rvd3alZkxYtqFcPLy+j+URERJyW1crPP/PVVyxbxt279o2JE1OzJm+9RY0aOp0c41SAzVIBTuBi+C/Y9eusWMHixWze/E8fqlCBtm155x2VjITkjz/4/HPmz//n1qesWfngAxo35pVXnPd58PBwduxg2TKWLeOvv+wb06WjZUvatePv1dNFREQE7t7l66+ZMYPDh+1bEiWifn0aNaJWLZIlMxouIVMBNksFOIGLrb9gly6xYAFffcXRo/YtGTLQti0dOpApU8y/ncSViAjWrGHixH+WTkucmPffp3lzKlaMT/e8h4ayfj1z57JypX3NDouFWrXo0oVq1Zy3wIuIiMSFEyeYMIF58/6Z3PD66zRvTsOGpEljNJlLUAE2SwU4gYvdv2BWKzt2MGMG33xjn/zm5cWHH9K9OwULxtabSuwIDmbuXMaN4+RJ+5aiRWnThqZN4/e3whs3mDuXadM4e9a+pUQJevakYUM8tAq+iIi4mh07GDuWlSvtt/KlSsVHH9GmDY9NFZHYowJslgpwAhdHf8GuX+fzz5kyxf78pcVCw4b070+xYrH+1vKfPXjAzJmMHWsf6WexUK8eXbpQqVLCuVIaHs66dQQE8OOP9i05c9K3Ly1aOMtjzCIiIrHIauWnnxg6lK1b7Vvy58fPjw8/1K3OcU8F2CwV4AQuTv+CBQWxcCFjx/5zX/TbbzNkiGqw0woKYvp0Ro7k+nUALy8+/hg/P/LlM50s1hw6xJgxLF5MeDhAzpz060fz5qrBIiKScG3axKBB/Pyz/ZcVKtC9O3XqxPX8BvmbCrBZ+nMvMSdxYlq25I8/WLaMl14C+O47iheneXPOnTOcTf5faCgzZ+Ljg58f16+TLBndunH2LNOnJ+T2CxQvzvz5nD5N27Z4enL+PJ9+SqFCLF78f2ONRUREEoK9e6lenWrV7O23enW2bWPrVurVU/sVl6U/+hLT3Nx47z0OHOD773npJaxW5s0jf366dOHWLdPhBKuVVavsz/devkzixPbqO3YsWbKYDhdXcuZk+nROnbLX4DNn+OADSpf+Z+kvERGR+O3MGd5/n1dfZeNGgOrV2bmTH3+kfHnTyUQMUwGW2OHmRoMGHDjA/Pnkzk1oKJMmkS8fU6YQFmY6nOs6dIhq1ahfnxMn8PCgbVtOnWLsWDJkMJ3MhBw5mD6dY8do0gRg3z6qVaNBA06fNp1MRETkhQUG0qcPhQqxbBlgP7/744+ULWs6mYhTUAGW2OTmRtOmHDtGQABp0nDrFr6+FC/O5s2mk7mc27fp0IGXX7b/v2/QgMOHmT6dbNlMJzMtTx4WLLC3X2DlSgoXpm9f7t83nUxEROS5WK189RX58jFqFCEh5MnDN9/w669UqWI6mYgTUQGW2JcoEb6+nDqFry/u7hw5QtWqNG3KtWumk7kE23fDAgWYNg2rlRIl2LyZ778nf37TyZxJyZL8+CMrV5I3LyEhjBxJoUJ8/73pWCIiIs/oyBEqVaJFC/78k+TJGTWKI0d4992EM9FBJIaoAEtcSZuWgAB++81+GnLhQgoWZNo0LT0Uq06csH83vH6d1KmZOpW9e6lc2XQsp2Sb/3T4MCNHkiwZly7x9tu89RYXL5pOJiIi8hSPHtGnD8WLs20bwEcfcfIkvXrh5WU6mYgzUgGWuFW4MBs3Mn8+mTJx9y4dOlCxIidOmI6VAIWGMnIkL71k/27YvDnHj9O+Pe7uppM5Ny8vevfm6FEaNABYsYJChZg6VSdqRETEKW3fTvHijBpFWBiFC7N1K199RebMpmOJOC8VYIlzFov9weB27QB27KB4cUaP1uJYMei33yhdmr59CQ7Gx4effmLuXDJmNB0r/siene+/57vv8PbmwQM6dqRKFS2OJSIiziQwkI4dqVCBkyfx8mLECA4coEIF07FEnJ0KsBiSOjXTprFlCz4+BAXRqxdvvMHJk6ZjxXthYYwcSalSHDyIuzu9evHbb1SqZDpW/PTWWxw5Qtu2AFu38tJLTJmC1Wo6loiIyPbtvPQSU6cClCvHoUP06UOiRKZjicQDKsBiVMWKHDpE9+64ubFrFyVK8Pnnahgv7ORJKlSgb19CQylShN27GTWKJElMx4rPUqRg+nQ2bSJXLh4+xNeXWrW4etV0LBERcVnBwfTqRcWKnDtH0qRMnsy2bRQoYDqWSLyhAiymJU3KmDFs20aePDx8SNu21K3LX3+ZjhXPWK3MncvLL7NzJxYLPXuybx8lS5qOlVBUqcLvv9OmDcD69RQrxnffmc4kIiIu6MgRypRh9GisVl57jUOH6NgRN/08L/Ic9BdGnEO5chw8SKtWAGvWULw4mzaZzhRv3L1LkyZ8/DEPHpA7N9u28dlnWvoxhiVPzowZrFpFhgzcvMk779CuHUFBpmOJiIiLsFr54gtKleLQITw88Pdn2zZ8fEzHEol/VIDFaaRIwaxZfP89adNy7RrVq9Ovn1bG+ld791KiBIsXAzRpwoEDvPGG6UwJV926/P47desCzJjBG29w9qzpTCIikuDZTnW3asWjR/j4sHMn/frh4WE6lki8pAIsTqZBAw4epHx5rFZGjKBKFf7803Qm5zV7NuXKce4cyZPz1VfMn0+qVKYzJXSZMrFyJePG4eFhv8985UrTmUREJAH74w9KlbKf6v7wQ/bvp1Qp05lE4jEVYHE+2bOzeTMDB2KxsH07JUvy66+mMzmdoCBataJ1a0JCKFqUffv46CMsFtOxXIPFgp8fW7aQLRt37tCgAb166WYFERGJBUuXUqYMp06RNClz5/L116RIYTqTSPymAixOycODIUNYs4Y0abhyhYoVmTnTdCYncv48b7zBF18AfPABv/5K/vymM7mecuU4cIDq1QFGj6ZKFa5cMZ1JREQSjLAwevSgUSMePsTHh127aN7cdCaRhEAFWJxYzZrs3ctLLxESQps2tG6tRYeAH3+kZEn27cPDg0mTWLCAZMlMZ3JVGTKwdi2DB9tvVnj5ZbZuNZ1JREQSgOvXqVGDsWMB6tRhzx6KFjWdSSSBUAEW55YnD7/8wgcfAMyeTcWKXLxoOpMxEREMH07Nmty6RebM/PQTnTrptmfD3N0ZNIj160mfnr/+olo1+5V5ERGRF7RvH6VKsXkzwKBBrFxJ6tSGI4kkICrA4vSSJWPBAsaPx92d3bt55RW2bTOdyYBHj2jcmP79sVp54w3279dqz06kenUOHOCVVwgLo1UrevQgPNx0JhERiY/mzaNcOS5cIGVKVq5k8GCN+RWJWfobJfGBxULXrmzYQIYMXL9OtWrMn286U5z66y+qVGHZMoBOndi8mSxZTGeS/+ftzbZtvPsuwNixvP02gYGmM4mISDxitdK/P82bExxM4cLs2UO9eqYziSRAKsASf1SuzL59lChBaCjNmjFsGFar6Uxx4ehRypbl119xd2f6dCZNwtPTdCZxJGlSli6lb1+AVat44w0uXDCdSURE4oXgYD78kOHDARo00PqWIrFHBVjilezZ2baNWrUABg7kk08IDTWdKXb99BOvv87Zs6RIwQ8/0Lat6UDyVG5uDB/O11+TKBG//Ubp0prhJSIi/+bWLd58k4ULATp35ttvNetIJPaoAEt8kyIFK1fSpg3AnDnUrs3du6YzxZa5c3nzTe7cwdubHTuoWdN0IHk2H37I5s1kyMCff1KpEkuXmg4kIiJO68wZXn+dbduwWJg4kYkTcXc3nUkkIVMBlnjIw4Pp0/nsM4CNG+1rRSQsVisDBvDxx4SFUbIku3bx0kumM8nzKFeO3bspUoTgYBo3ZupU04FERMQJ7dpF2bIcP06SJHz3HZ07mw4kkvCpAEv8ZLHQsydLluDlxeHDlCnD/v2mM8WYsDA+/hh/f4D69dm2jaxZTWeS55crF7/8QpUqWK107MigQS7y0LqIiDyb77+ncmWuXydjRrZsoUED04FEXIIKsMRn77/Ppk2kS8e1a1SqxNatpgPFgKAgGjbkq68AOnVi+XKSJTOdSV5UypSsWWNfGnroUDp00HgkEREB4IsvePddHj2iYEF+/ZXSpU0HEnEVKsASz5Urx86d5MlDYCA1a7JmjelA/0lgIHXq8P33AMOH6zmghMDLiyVL7A+tT59OkyYEB5vOJCIiZk2cSKtWRERQvjy//ELu3KYDibgQFWCJ//LlY/t2ChcmKIgGDeLvikM3b1KtGps3A0ydSt++WCymM0lMsM2vGjAAYOlS6tbViGAREVdltTJkCF27AtSsybp1pEljOpOIa1EBlgQha1a2buWVVwgL44MP+PJL04Ge25UrVKzI7t24uzN/Pu3bmw4kMcpiYehQAgIANm6kShWuXzedSURE4pjVSrduDB4M0LAhK1aQNKnhSCKuRwVYEor06dm8mQoViIjgk0+YONF0oOdw5gxvvMHhwyROzPff07Sp6UASO3x9WbgQDw/27qViRa5dMx1IRETiTHg4rVszYQJAy5YsWkSiRKYzibgiFWBJQFKmZO1a+7Tcrl0ZOjRerLp7+DBvvMHZs6RIwbp11K1rOpDEpg8+YNUqkiTh6FEqVeLKFdOBREQkDoSE0KQJX3wB0Lkzs2ZpkQ8RU1SAJWFJmpQVK2jYEGDQIHr0cPIO/McfVK7M1aukS8fmzVSsaDqQxL6aNVm9mqRJOX6cSpW4fNl0IBERiVVBQbzzjn2NkoEDmTABN/0ELmKM/vpJgpMoEYsW0bIlwLhxztyBDx+2PwuaKRPbtlGqlOlAElcqV2bNGpIl4+RJKlXi0iXTgUREJJYEB/Puu6xeDTBuHEOGaIlLEbNUgCUhcndn1izatQMYN44+fZywAx85Ym+/GTPy008ULmw6kMStihVZu5bkyTl1iooVuXDBdCAREYlxISE0bGif0ThtGn5+pgOJiAqwJFRubkyZQuvWAJ99Rv/+TtWBjx6lShX++svefgsVMh1ITChfnnXrSJGCM2eoVInz500HEhGRGBQayvvvs2oVwOTJ9vPyImKaCrAkXG5uzJhhvxd6xAj71AEncOwYlSvz559kyMDmzbr269LKlWP9elKk4OxZKlbk7FnTgUREJEaEhtK4MStWAEyYQMeOpgOJiJ0KsCRobm7MmkXz5gBDhzJ0qOlA0dtvkSKmA4lpr73Ghg2kTMn581SurOeBRUTiv7AwmjZl+XKAsWPp0sVwHhGJQgVYEjo3N774wj5ad9AgRowwmOXECapU4do10qdn0yaKFjWYRZxImTJs3GjvwNWq8ddfpgOJiMgLCwujWTOWLQP47DO6dTMdSET+jwqwuAB3d+bOpXFjgH79GD3aSIoLF6ha1T7xaNMmihUzkkKc1KuvsmaNfTbSm29y+7bpQCIi8gLCw/n4YxYvBhg+nJ49TQcSkehUgMU1eHjw9df2+cC9ejFjRhy//19/Ub06ly6ROjWbNvHSS3H8/hIPlCvH99+TKBGHDlGnDvfvmw4kIiLPxWqlY0fmzwcYMoS+fU0HEhEHVIDFZXh4sGAB9esDtG9vn0cfJ+7epWZNTpwgaVLWrKF48Th7Z4lnqldn8WLc3dm5k7feIijIdCAREXl2Awfaz7D36cPAgabTiIhjKsDiSjw9WbKEihWxWvnwQ378MQ7e89Ej6tXjwAE8PfnuO157LQ7eU+Kxt99m7lyATZto1IjQUMN5RETkmUyciL8/QJs2DB9uOo2IPJEKsLiYxIlZuZKSJQkN5e23+fXXWH230FAaNmT7dtzcWLiQN9+M1XeTBOLDD5k2DWDlSlq0IDzcdCAREXm6r7+ma1eA999n6lQsFtOBROSJVIDF9aRMydq15MvHw4fUrs3hw7H0PhERtGjB6tUAM2fy3nux9D6SALVrx2efASxcSIcOWK2mA4mIyJOsWsXHHwO8+SZff427u+lAIvI0KsDikjJmZMMGsmXj9m3efJNz52L8HaxWOnVi4UKAMWP45JMYfwdJ4Hr2pF8/gM8/Z9gw02lERMSh7dt5/33Cwylblm+/JVEi04FE5F+oAIurypmTH38kbVquXKF6df78M2YPP3gwU6cC9OlD9+4xe2xxFcOG8emnAIMG8eWXptOIiEg0Bw9Sty5BQRQpwurVJE9uOpCI/DsVYHFhhQuzdi3JknHqFLVqERgYUweePZuhQ0ELYch/Y7EwdSr16gF8+ilr15oOJCIikc6coWZN7t0jVy7WrydtWtOBROSZqACLaytd2j569cAB3n8/RpbcXbeOtm0B3npLC2HIf+XhweLFlClDeDjvvcfevaYDiYgIcOsWtWvz559kzMiPP5Itm+lAIvKsVIDF5VWrxpw5AOvW/fflhg4coGFDwsMpU4YFC7QQhsSApElZtcq+aludOpw5YzqQiIiLCw7mrbc4fpykSVm9mnz5TAcSkeegAuxENm/eXKhQoRw5cjRs2DDw79txDx486PO33Llzu7u7//XXX/fu3fP09LRtLF26tNnYCUGTJvY7lWfNsq+9+0IuXKBOHe7fJ29eVq0iadIYCyguLkMG1q0jY0b++ouaNbl+3XQgERGXFRHBxx/bJxwuXkypUqYDicjzUQF2FkFBQU2aNJk7d+758+c9PT2H/b3qa4kSJU79rVevXi1atMiYMePNmzd9fHxsG3fv3m02eQLRpw+tWtk/WLToBQ5w5w61a3P1KunSsWYNGTLEcEBxcXnysHo1yZJx8iT16vHwoelAIiKuqX9/+88JkybZF2kQkXhFBdhZrF+/3sfHp0yZMhaLpV27ditWrIi2w/3790ePHm0rxjdv3kyfPr2JmAmXxcK0abz5JkCLFmzf/lyfHRzMO+9w+DCJE7NyJfnzx0pGcXGlSrFsGe7u7NpF48aEh5sOJCLiambNYuRIAD8/OnY0nUZEXoQKsLM4d+5c7ty5bR/nzJnzypUr0XaYPn16nTp1smbNCty+ffvIkSN58uR544031qxZE9dZEypPT5Yt46WXCAmhQQOOH3/Gz7Na+eQTfvoJi4X583n99VhNKS6tVi1mzQJYtYpevUynERFxKevW0a4dwDvvMGaM6TQi8oJUgJ1FcHCwh4eH7WOLxeLm9n+/NaGhoQEBAX5+frZfVq9e/ebNm2fOnOnbt2/jxo3Pnj0bdefx48d7/+3+/ftxkz+BSJmS1avJmpXbt6ldm7/+epZPGjiQBQsAxo/n3XdjN6DIxx/Trx/AuHHMnm06jYiIizh0yL7KZdmyzJ+Pm36EFomv9LfXWXh7e1+4cMH28fnz53PlyhX11dWrV+fNmzfyEnGk2rVr58uX78iRI1E3+vn5Xfpbcs1kf17e3vZZ9mfOUL8+jx49ffeFC/H3B+jcmS5d4iCfCEOH8t57AO3a8dNPptOIiCR4ly/bV7nMk4cVK0iSxHQgEXlxKsDOombNmnv37j106JDVap0+fXqzZs2ivrp06dJ3o1xbvHDhQlBQELBjx45z586VLFkyruMmbCVK/POoZatWTxmMtHs3LVsC1K3LuHFxF1BcnJsbX33FK68QFsa773LypOlAIiIJ2MOHNGjA5cukTcvatWTMaDqQiPwnKsDOIm3atPPnz2/UqFH27Nk9PT19fX39/PxWrVple3X79u1lypSJ3Hnv3r358uXLmzevn5/fsmXLsmTJYih1wlWzJgEBAAsXPmkw0qVLNGhAcDBFimjkr8S1pElZscJ+t37duty+bTqQiEiCZFvnY98+PDxYvlyrXIokABbrk69uSQLg7e196dIl0ynirXbtmDEDi4UVK6KNOnj4kPLl2b+fdOnYvZs8eUxFFJe2bx/ly/PoEVWrsnYtnp6mA4mIJDAjRtjXXZgxgzZtTKeRBEI/n5ulK8AiTxYQQKVKWK00acIff0Rutlpp0YL9+/Hw4Ntv1X7FmFdeYf58gE2b6NTpKXfri4jI81uxwt5+O3RQ+xVJMFSARZ7MNhgpd27u36d+fW7csG0eNoxlywCmTaNiRZMBRd55h+HDAWbMYPJk02lERBKM33+naVOAypWZMMF0GhGJMSrAIk+VPj0rV5I8OWfP0rAhoaHffMOgQQCdOtG6tel4ItCnD7ZV87p2ZdMm02lERBKA69epX58HD8ibl2XL9ISJSEKiAizyb4oWZcECLBa2bLnepPNHHwG8+aaWfRZnYbEwaxavvUZEBI0ace6c6UAiIvFaSAjvvce5c6RIwcqVpEtnOpCIxCQVYJFnUL++7TbTDN9Mb/5oev78LFmCh4fpVCJ/8/Lim2/InJmbN3n7bR4+NB1IRCSeslrx9WXbNiwWFi6kcGHTgUQkhqkAizyTsO69N2ZqAkzGd+PAbalTmw4k8v+yZuWbb/D05OBBPv1UC2KJiLyQGTOYORNg5Ejq1jWdRkRingqwyDPp09dS78/ZeyjlQXj2bu9z5YrpRCLRlStnH1+9YAETJxoOIyIS/+zcSefOAE2b0rOn6TQiEitUgEX+3dKljB1LEEl2dPmW9On580/ee4+QENO5RKJr04ZWrQB69GDzZtNpRETiEds399BQihdn5kwsFtOBRCRWqACL/Is//qBlS4Datek8LgeLF+Pmxs6d+PmZjiYSncXClCmUKUN4OI0acf686UAiIvFCWBiNGnHlCqlT8+23JE1qOpCIxBYVYJGnuXOHd96xz0GYPx83N6halREjAKZO5euvTQcUic7Li2+/JVMmbtzgnXd49Mh0IBER59e7N1u3YrGwYAF585pOIyKxSAVY5IkiIvjoI06eJGlSli8nTZq/X+jZk7ffBmjThkOHzAUUcSxbNr75Bg8P9u/XglgiIv9m2TL7bMNBg6hd23QaEYldKsAiTzR8OKtWAcyezUsvRXnBYmHuXAoU4NEj3nmH27cNBRR5ojfeYNIkgPnzmTHDdBoREad15AgffwxQuzYDBphOIyKxTgVYxLE1axg0CKBrVz744LGXU6Zk+XKSJePMGT78kIiIOA8o8i/ateOjjwC6dGHvXtNpRESc0L17vP02Dx6QJ8/fTzqJSAKnv+ciDpw7x4cfYrVSsSKfffaEnQoXZs4cgDVrGDYsDtOJPBOLhWnTKFqUkBAaNtSdCiIi/89qpUULTpwgSZL/f9JJRBIyFWCR6EJCaNSI27fJkoUlS/D0fPKuDRvSrRvAkCGsWxdXAUWeVbJkfPMNyZNz7hwtWuhhYBGRKMaO5bvvAGbOpHhx02lEJI6oAItE17Mnu3fj5sbixWTK9G97jxpFxYpYrTRrxqVLcZFP5HkUKMDs2QArVzJ2rOk0IiJO4pdf6NMHoEMHPvzQdBoRiTsqwCL/Z/ly+9JB/v5UqPAMn+DhwaJFZMzIjRt88AFhYbEcUOS5NWpEhw4AffqwfbvpNCIixt28SaNGhIfzyiv29Z9FxGWoAIv848wZWrYEqFWLXr2e+dOyZGHBAiwWduxg4MBYSyfy4saN49VXCQ+ncWP++st0GhERgyIiaN6cS5dImZKlS/HyMh1IROKUCrCIXXAw77/P3bt4ezNv3nOuBFmtmn12wsiRehhYnJCXF0uXkiYNV67QpAnh4aYDiYiYMm4cq1cDfPklefKYTiMicU0FWMSuWzf27cPdncWLSZ/++T9/4EAqVQL0MLA4p1y5mDcPYNMmhg41nUZExIjIR399fXn3XdNpRMQAFWARgKVLmToVYORIypV7oUO4u7NwoR4GFmdWt6793v5hw9i40XQaEZE4FvXR3zFjTKcRETNUgEU4eZJWrQDq1rVPNXpBehhYnJ6/P+XL25ct18PAIuJC9OiviAAqwCJBQbz/PoGB5MjBV18956O/j9PDwOLcPDxYsIC0abl2jebNiYgwHUhEJG7o0V8RAVSARXr14uBBPDxYsoS0aWPiiJEPA3/4oR4GFieUPTtffgmwbh0TJphOIyISB/Tor4j8TQVYXNrq1QQEAIwYQdmyMXTQyIeBb96kWTOttytOqEEDOnYE6N2bPXtMpxERiVV37vDBB3r0V0RsVIDFdV29SosWANWr/7dHfx+XJQtffw2wZQujR8fooUVixpgxFC9OWBiNG3Pvnuk0IiKxxGqlTRsuXCB5cpYs0aO/IqICLC4qIoKPPuLGDdKnj4lHfx/35pv2Vj1gALt2xfTRRf6rxIlZvJikSTlzhnbtsFpNBxIRiQ1z57J0KcC0aeTNazqNiJinAiwuauxY+xiYr74iS5bYeY8RIyhZkvBwmjTRJTZxQgULMmUKwMKFfPWV6TQiIjHuxAl8fQGaNqVZM9NpRMQpqACLK9qzh379ADp3pnbtWHubRIlYuNB+ic32wKWIk2nRgg8+AOjQgWPHTKcREYlBISE0acKDB+TOzbRpptOIiLNQARaXExjIBx8QFkaJEnz2WSy/WYEC9lW2vv6aBQti+c1EnpvFwowZ5MnDw4c0bkxQkOlAIiIxpX9/9u2zr0yZMqXpNCLiLFSAxeV06MDp0yRNyqJFcbIWRsuWNGwI0K4dZ87E/vuJPJ+UKVm0CA8PDh2y3xkhIhLvbdxoX/B5yJCYG/MgIgmBCrC4lvnz7cszBwRQsGCcvKXFwuefkyMHgYE0aUJoaJy8q8hzKF0af3+A8ePZtMl0GhGR/+j6dfsTvxUr0ru36TQi4lxUgMWFnDlD+/YADRvSsmUcvnGaNMyfj5sbu3YxZEgcvrHIs+renYoVAZo359Yt02lERF6Y1UrLlly7Rpo0fP017u6mA4mIc1EBFlcRHs5HHxEYSI4czJyJxRK3b1++PP37A4wYwdatcfveIv/O3Z1580iVisuXadtWU5FEJN6aNo0ffgCYPZvs2U2nERGnowIsrmL0aH7+GYuFefNIndpEggEDeP11rFY++oi7d00kEHmaHDns66QuW2Z/UkBEJJ45dozu3QE+/ZR33jGdRkSckQqwuIT9+xk4EKLc52mAhwdff03y5Fy4QKdOhkKIPE2TJvapSB07cvas6TQiIs8lNJQPPyQoiHz5GD/edBoRcVIqwJLwPXrEhx8SFsZLLzFsmNEoefIwaRLAvHl8843RKCKOTZtG9uwEBtKsGeHhptOIiDy7YcPsc4++/ppkyUynEREnpQIsCV/v3hw9SqJEzJ8fJ3OPnu7jj6lfH6BNG65eNZ1GJLrUqZk3D4uFn3+O/UHZIiIx5ddfGT4coF8/ypQxnUZEnJcKsCRwGzYQEAAwYgTFiplOA1gszJpFhgzcusUnn2itIXFClSrRowfAoEHs3Ws6jYjIv7p/n2bNiIigVCn7kpMiIk+gAiwJ2a1btGgBULkyXbsaDvOPjBmZPRtg7Vo+/9x0GhEHhg6lRAnCwmjalAcPTKcREXm67t05dYokSZg/H09P02lExKmpAEuCZbXSrh1XrpAqFXPn4uZUf9jr16dVK4Bu3ThxwnQakei8vFiwgMSJOXGC3r1NpxEReYrVq+1nk8eMoUAB02lExNk5VScQiUkLFrB0KcDUqeTIYTrN48aPJ08eHj6kWTPCwkynEYmucGH7M8BTprBpk+k0IiIOXb/OJ58A1KhB+/am04hIPKACLAnTxYt06ADQqBFNmphO41CKFMybh5sbu3czYoTpNCIOdOxIpUoAH3+s2dUi4nysVj79lD//JE0avvwSi8V0IBGJB1SAJQGyWvnkE+7dI2tWpk1z4m+I5crZ7y4dOpQ9e0ynEYnOzY05c0ienIsXnekpehERm6++4vvvAT7/nKxZDYcRkXhCBVgSoM8/Z8MGgC++IG1a02mebtAgXn6Z8HA++oigINNpRKLLlYsJEwDmzGHVKtNpREQiXbhA584AH35Iw4am04hIvKECLAnNmTN07w7QqhU1a5pO868SJWLePBIl4tgxBg40nUbEgU8+oXZtgNatuXHDdBoREcBqpVUr+71etmmHIiLPRgVYEpSICFq25MEDcuRg3DjTaZ5R0aIMGQIwdiy//GI6jUh0ttnVadLw55/2R+tFRAybNct+r9fs2aRJYzqNiMQnKsCSoEyZwtatAHPmkDKl6TTPrnt3SpfGaqVFCx4+NJ1GJLqsWZk6FWDpUhYvNp1GRFzcuXN06wbwySfUqmU6jYjEMyrAknBEDizt0IEqVUyneS4eHsydi5cXJ0/Sv7/pNCIONG7Me+8BtG/P1aum04iIy4qI4JNPuH+f7Nnjz71eIuJEVIAlgQgPp0ULHj0iTx778NJ4plAhhg8HmDiR7dtNpxGJzmJh2jQyZuT2bVq3xmo1HUhEXNOMGWzeDPDFF6RKZTqNiMQ/KsCSQIwfz86dWCzMnUuyZKbTvJguXXj9daxWPv6YBw9MpxGJLkMGZs0CWL2aOXNMpxERF3T6ND16ALRpQ/XqptOISLykAiwJwZEjDBgA0KUL5cubTvPC3N2ZM4ckSTh92n4zt4iTqV+fjz4C8PPj8mXTaUTEpURE8PHHPHxIrlyMGWM6jYjEVyrAEu+FhdGiBcHBFChgv4k4Hsufn5EjAaZM4aefTKcRcWDiRLJk4e5d2rTRjdAiEocmT7Y/IvTll6RIYTqNiMRXKsAS740dy549uLkxdy5JkphO89/5+lKhAkDLlgQGmk4jEl2aNMyYAbB6NfPnm04jIi7ixAn69AHo2JHKlU2nEZF4TAXYiWzevLlQoUI5cuRo2LBhYJTm4+fnlylTJh8fHx8fn/nz5wNWq7V3797e3t4+Pj5TbcNJXNXRowweDNCtG2XLGg4TM9zcmDOHZMk4d46ePU2nEXGgfn2aNAHo3Jlr10ynEZEELzycli3tC12OGmU6jYjEbyrAziIoKKhJkyZz5849f/68p6fnsGHDIl+6efNmQEDAqVOnTp069eGHHwLLly/ftGnTyZMnt27dOnjw4CNHjpgLblJ4OJ98QnAw+fMzZIjpNDEociXrGTPYssVwGBFHAgLsK0K3b68boUUklk2dys8/A/YTxCIi/4EKsLNYv369j49PmTJlLBZLu3btVqxYEfnSzZs306dPH3Xn+fPnt2nTJkmSJNmyZatfv37UnV3KlCn2lZ+//DJB3PwcVbt29huhW7Xi4UPTaUSiS5eOadMAvvuOpUtNpxGRBOzsWfvNzx062L8zioj8ByrAzuLcuXO5c+e2fZwzZ84rV65EvnT//v1PPvmkYMGC7du3v3v37tN3dh2nT9u/Ifr6Uq6c6TQxzs2N2bNJnJjTpxk40HQaEQfefZeGDQE6duT6ddNpRCRBslr59FMePiRHDvsikSIi/40KsLMIDg728PCwfWyxWNzc/vmt2bJly7lz53755ZfLly9379796TsD48eP9/7b/fv34+oriFMREbRuzaNH5M7NiBGm08SSfPkYOhRgwgR27TKdRsSBKVNIn54bN+jY0XQUEUmQvvySjRsBZs3Sys8iEiNUgJ2Ft7f3hQsXbB+fP38+V65c0XZImzZtq1atDh48+K87+/n5Xfpb8uTJYzm4GbNm2YcEzZqVoJ8G6tqVUqWIiKBlS4KDTacRiS5jRiZPBli6lOXLTacRkQTmyhW6dQP4+GPefNN0GhFJIFSAnUXNmjX37t176NAhq9U6ffr0Zs2aRb50/Phx4NGjR/PmzStfvjzw9ttvz5w5MyQk5PLly2vXrm3UqJGx3CZcvEiPHgCtW1O1quk0scrDgy+/xNOTI0fi/4xjSZgaNeKttwDatePmTcNhRCThsFpp1467d8mcmXHjTKcRkYRDBdhZpE2bdv78+Y0aNcqePbunp6evr6+fn9+qVauAVq1a5cqVq2jRohkzZhw+fDjQunXrEiVK5M+fv0KFCmPHjs2aNavp+HHHaqVNGwIDyZaNMWNMp4kDxYrRrx/AyJEcOmQ6jUh0FgvTp5MmDX/9RdeuptOISIKxZAkrVwL2f2JERGKIxar5FQmat7f3pUuXTKeISfPm0bw5wA8/UKeO6TRxIySEUqX4/XdeeYVff+Xvx79FnEfkX8x166hRw3QaEYnvrl+ncGFu3KBRIxYvNp1GJIYlvJ/P4xddAZb45No1unQBaNbMZdovkCgRX3yBmxv79uk2MHFOzZrZH9Br04YEuvSeiMShzp25cYN06ezLDIiIxBwVYIlPOnXi9m0yZmTCBNNR4tirr9oXAhk0iOPHTacRic5iYcYMkibl/HkGDDCdRkTitZUrWbQIICCADBlMpxGRhEYFWOKNVatYtgxgyhTSpTOdJu4NGUK+fAQH06oVERGm04hElzs3/v4AkyZpbpeIvKh792jXDqBuXT74wHQaEUmAVIAlfggMpH17gLp1ee8902mMSJKE2bMBduzgiy9MpxFxoFMnXn0Vq5VWrQgJMZ1GROKjvn25coUUKZg+HYvFdBoRSYBUgCV+6N+fS5dInpxp01z4G2KFCrRuDdCjB1evmk4jEp27O7Nn4+HBH38werTpNCIS7+zcybRpAKNG4e1tOo2IJEwqwBIP7NplXwVj+HCyZzedxqzPPiNTJu7eta8GJuJkXnqJXr0Ahg3j6FHTaUQkHgkJ4dNPsVp57TXatjWdRkQSLBVgcXahobRujdVK6dJ06GA6jXFp0hAQALB0KT/8YDqNiAP9+1OgACEhtG6tx9VF5JmNHcsff+DhwcyZuOkHVBGJLfr3RZzd2LH8/jseHsyahbu76TTOoGFD+wyo9u01cEacUOLEzJoF8PPPfP656TQiEi+cPMnQoQC9elG0qOk0IpKQqQCLUzt5kiFDALp356WXTKdxEhYLU6eSLBkXL2rgjDin8uXtNzD26sWlS6bTiIiTs1pp04bgYPLlo39/02lEJIFTARbnZbXSti3BweTNy8CBptM4lZw57QNnAgLYs8d0GhEHRo0iWzYCA/Xkgoj8m6++4qefAD7/nMSJTacRkQROBVic11dfsXkzwOefkySJ6TTOxteXUqWIiKB1a0JDTacRiS5VKqZOBVi5ku+/NxxGRJzXX3/RrRvAxx9TubLpNCKS8KkAi5OK/IbYogVVq5pO44Tc3Zk5E3d3Dh1i4kTTaUQcaNCAt94C6NiRwEDDYUTESfn5cesW6dMzZozpKCLiElSAxUl17cqtW2TIwNixpqM4rZdfpmtXgEGDOHPGdBoRBwICSJ6cy5f1FIOIOLJ+PQsWAEycSLp0ptOIiEtQARZntG4dCxeCviH+q8GDyZWLR49o1w6r1XQakeiyZ2fYMICAAPbtM51GRJzKgwf25fLefJMmTUynERFXoQIsTufBA9q1A6hRgw8+MJ3GySVLxvTpAD/+aD9nIOJkOnakZEkiImjThvBw02lExHkMGcK5cyRJwvTpWCym04iIq1ABFqczaBDnzpE0qb4hPpuaNe0nzrt04eZN02lEovPw4PPPcXNj3z77slgiIhw4wPjxAEOGkCeP6TQi4kJUgMW57N/PhAkAQ4eSO7fpNPHFhAmkScONG/ToYTqKiAOlStGxI0C/fhoLLCIQHk7r1oSHU6KEfTELEZG4ogIsTiQsjNatiYigZEk6dzadJh7JmJFx4wDmzLFPjhJxMsOGkS0b9+/TqZPpKCJi3OTJ7NuHmxszZ+LhYTqNiLgWFWBxItOmsX8/bm7MmqVviM+pRQsqVQJo04ZHjwyHEXlMypQEBAB89x0rV5pOIyIGnT9P//4AnTrx6qum04iIy1EBFmdx5Yr9G6KvLyVLmk4T71gsfP45Xl6cOsXIkabTiDjw9tvUqwfQsSP375tOIyKm+Pry4AE5ctjXiBcRiVsqwOIsunYlMJCsWRk61HSUeCp/fvr2BfjsM06cMJ1GJDqLhcmTSZqUixcZNMh0GhExYuVKVq0CmDyZ5MlNpxERV6QCLE7hxx9ZuhRgwgRSpjSdJv7q1Yt8+QgJoUMHjQUWJ5Qzp/0M16RJHDhgOo2IxLEHD/D1Bahfn/r1TacRERelAizmBQXRoQPAm2/SsKHpNPGalxfTpgFs3MiSJabTiDjQuTPFixMerrHAIq5n2DAuXCBpUvuSACIiJqgAi3mjRnHqFF5eTJ2qwb//WbVqNG4M0LUrd++aTiMSnYcHM2disbBnD9Onm04jInHm8GH7wIKBA8mZ03QaEXFdKsBi2MmT9jWb+vbFx8d0moRh/HhSpODaNQYMMB1FxIHSpWnfHqBvX65cMZ1GROKA1Ur79oSFUbiwBv+KiFkqwGKS1UqHDoSEkC8fvXqZTpNgZMmCvz/A1Kns3286jYgDw4eTJQuBgZr4LeIavv6abdsApk0jUSLTaUTEpakAi0lLl7JhA8C0aXh5mU6TkLRvz8svExFBu3Z6zlKcUKpUTJoE8M03rF1rOo2IxKpbt+jeHeCjj6hY0XQaEXF1KsBizN279tugGjemWjXTaRIYDw9mzMBiYfduZs0ynUbEgffeo1YtAF9fgoJMpxGR2NO3L9evkyYNY8aYjiIiogIs5gwYwNWrpEzJ+PGmoyRIpUvTpg1Anz78+afpNCLR2cYCe3lx+jSffWY6jYjEkl27mDkTYORIMmY0nUZERAVYDNm/n6lTAfz9yZLFdJqEasQIMmTgzh169DAdRcSBvHnp3Rtg5EhOnzadRkRiXFgYbdtitVK6NK1bm04jIgIqwGJEeDht2xIRQcmS9sVgJVakScPYsQBff83WrabTiDjQqxd58hAcTKdOWK2m04hIzJo2jYMHcXNjxgzc9DOniDgF/WMkBsycyZ49WCzMmIG7u+k0CVuzZvYVR9q1IyTEdBqR6JIkYfJkgDVrWLHCdBoRiUFXrtC/P0DHjrz8suk0IiJ2KsAS1/78kz59ANq149VXTadJ8CwWpk3Dw4OjR/WwtTin2rV56y2Azp158MBwGBGJMX5+BAaSJQvDhpmOIiLyDxVgiWvdu3P3LpkyMXy46SguonBh+/yJoUM5d85wGBFHJk4kSRIuXNA/CyIJxYYNLFkCMGECKVOaTiMi8g8VYIlTP/3E/PkA48aROrXhMC5kwABy5uTRIzp3Nh1FxIGcORkwAGDsWI4dM51GRP6joCD7Ch/Vq/P++6bTiIj8HxVgiTshIXToAFC5Mk2amE7jUpImJSAAYOVKVq40nUbEgW7dKFCA0FA6dtRqWCLx3OjRnDpFokRMmYLFYjqNiMj/UQGWuBMQwNGjeHgwdaq+Ica5+vWpXx/A15eHD02nEYkuUSL7aLRNm1i61HQaEXlhp08zYgRA797kz286jYhIdCrAEkeuXGHIEICuXSlUyHQa1xQQYH/OcuRI01FEHKhalUaN4O+lc0QkXurSheBg8uSxj/kWEXEyKsASR7p35/59sma1P+knBuTMSd++8PfNaSLOZ9w4kifnyhUGDzYdRURewA8/8MMPAJMmkSSJ6TQiIg6oAEtc2LKFRYsAxo0jRQrTaVxZ9+7kzUtICF26mI4i4kC2bPZbRSZN4vffTacRkecSFGRfarFuXerWNZ1GRMQxFWCJdbZVbYDKle33N4oxiRPbV8NavZpVq0ynEXHA15eiRQkPp0MHrYYlEq+MGcOZM3h5MXGi6SgiIk+kAiyxbsoUDh/Gw4PJk7X2lROoXdu+Glbnzjx6ZDqNSHSenkybBrB9u31qmojEA+fO2de+6tmTvHlNpxEReSIVYIldV68yaBBAp04UKWI6jdhMmICXF2fPMnq06SgiDpQvz0cfAXTvzp07hsOIyDPx8yMoiJw5tfaViDg5FWCJXT17EhhI5sz2GixOIXJxzlGjOHvWdBoRB0aPJlUq/vpLy+aJxAfr1/PddwATJ5I0qek0IiJPowIssSjyDsaxY0mZ0nQaiapXL3LnJiiIrl1NRxFxIFMmhg8HmDaNQ4dMpxGRpwgOxtcXoGZNGjQwnUZE5F+oAEtsCQujQweAChVo0sR0GokmSRL7IiUrVrB2reEwIo60bUvx4kRE4Our1bBEnNiECZw8iacnkyZpqQ8RcX4qwBJbpk3j999xd2fKFH1DdEr16lGrFkCnTgQHm04jEp27O5MnA2zfbp+jJiJO5+JFhg0D6N6d/PlNpxER+XcqwBIrrl2zP7nXsSPFiplOIw5ZLAQEkCgRp04xbpzpNCIOlC9P06YAPXpw/77pNCLyuG7dePgQb2/69TMdRUTkmagAS6zo1Yt798iUiSFDTEeRp/DxoUcPAH9/LlwwnUbEgdGjSZ6cK1fw9zcdRUSi2bSJZcsAxo8nWTLTaUREnokKsMS8n39m3jz4ex1XcWp9+5IjB48e4ednOoqIA1mz2m8nGT+e48dNpxGRSCEh9rWvqlblvfdMpxEReVYqwBLDIte+KleOZs1Mp5F/lTQp48cDfPstGzaYTiPiQJcu5M9PaChdumg1LBGnERDA0aN4eDB5spb6EJF4RAVYYtiMGRw6hJsbU6fqG2I88c47VK8O4OtLSIjpNCLRJUpEQADAunWsWmU6jYgAV67Yn3Hq0oVChUynERF5DirAEpOuX7ffrNi+PcWLm04jz8hiYfJkPD05ftw+G0nEydSoYR8v2qULjx6ZTiMitoXpsmZl4EDTUUREno8KsMSkfv24c4cMGewzESTeKFDA/gzw0KFcumQ6jYgDEybg5cXZs4wdazqKiIvbupWFCwHGjiVFCtNpRESejwqwE9m8eXOhQoVy5MjRsGHDwMDAyO0zZswoXLhw7ty5q1ateubMGeDevXuenp4+Pj4+Pj6lS5c2F/n/7NvH7NkAI0eSOrXhMPLc+vcnWzYePKBnT9NRRBzInZtevQBGjuT8edNpRFxWWJh97auKFWnc2HQaEZHnpgLsLIKCgpo0aTJ37tzz5897enoOi3IJ9eHDh/v27Tt79uwrr7zSuXNn4ObNmz4+PqdOnTp16tTu3bvNpf6H1UqnTlitvPIKH39sOo28gOTJ7VfWFi1ixw7TaUQc6NXLvmZ5t26mo4i4rJkz+f133Ny09pWIxFMqwM5i/fr1Pj4+ZcqUsVgs7dq1W7FiReRLfn5+SZIkAd54440///wTuHnzZvr06Y1ldWThQn75BSAgADf9sYqnGjWifHkAX1/Cw02nEYku6prlmzaZTiPigm7epH9/gPbtKVbMdBoRkRehpuIszp07lzt3btvHOXPmvHLlSrQdHj58OHr06KZNmwK3b98+cuRInjx53njjjTVr1sR11sfcv2+/bfbDD3n9ddNp5IVZLPYTGAcP8sUXptOIOPDOO1SrBuDrS2io6TQirmbgQG7fJl06+xLQIiLxkAqwswgODvbw8LB9bLFY3P7/KurBgwfLlClTpUoV2y3Q1atXv3nz5pkzZ/r27du4ceOzZ89G3Xn8+PHef7t//34chB8xgitXSJaMzz6Lg3eT2FSiBK1bA/Trx+3bptOIRGc7S+PhwdGjTJ5sOo2IS/ntN2bMABg2jLRpTacREXlBKsDOwtvb+8KFC7aPz58/nytXrsiXvv3228aNG0+fPn3o0KHRPqt27dr58uU7cuRI1I1+fn6X/pY8efJYDs6pU4wbB9C/P1mzxva7Sezz9yd1am7cYPBg01FEHChUiE6dAAYP5to102lEXIRtqY+ICIoX59NPTacREXlxKsDOombNmnv37j106JDVap0+fXqzZs1s2+/evdumTZtNmza98cYbkTtfuHAhKCgI2LFjx7lz50qWLGkmNADduhESQt68dO1qMIXEnPTpsZ1qmTqVw4dNpxFxYNAgMmUiMJDevU1HEXER33zD1q0AAQG4u5tOIyLy4lSAnUXatGnnz5/fqFGj7Nmze3p6+vr6+vn5rVq16ujRo3fv3q1YsaLP327cuLF37958+fLlzZvXz89v2bJlWbJkMRV7/XpWroS/R3RKAtGuHUWKEB5Oly5YrabTiESXMiWjRwN89RU7d5pOI5LgPXxI9+4A779PhQqm04iI/CcWq366TdC8vb0vXboUSwcPCeGllzh+nJo1WbNG0xASlk2b7GsNffcdb71lOIzIYyIieOMNdu6kVCl27dLi8yKxafBghgwhSRKOHSNHDtNpROK9WP35XP6VfmSQFzdlCseP4+HBhAlqvwlO1aq88w6Anx9BQabTiETn5kZAABYLe/fy1Vem04gkYOfP25e47N1b7VdEEgAVYHlBf/5pH4LQuTMFC5pOI7Fh7Fi8vDh71r7KmYiTKVWKjz8G6NOHe/dMpxFJqHr0ICiInDnp0cN0FBGRGKACLC+ob1/u3SNjRgYMMB1FYknu3PYfd0aMQDfqiFMaPpwUKfjzT/z9TUcRSZB++ollywDGjSNJEtNpRERigAqwvIg9e5gzB2DUKFKlMp1GYk/v3nh78/AhPXuajiLiQObMDBwIMHEiJ0+aTiOSwISF0bkzQOXK9odiRETiPxVgeW4REXTqhNXKq6/SvLnpNBKrkiVjzBiARYvYscN0GhEHOnUiXz5CQ+nWzXQUkQRm5kx+/x03NyZN0lIfIpJgqADLc5s/n19/BQgI0MqrLqBRI8qXB+jUifBw02lEokuUiAkTAFatYv1602lEEoybN+nfH6B9e4oVM51GRCTGqL7I8wkMpFcvgI8+omxZ02kkDlgs9nP/Bw7w5Zem04g4ULs2NWsCdOlCaKjpNCIJw8CB3L5NunT2FS9FRBIKFWB5Pv7+XLtG8uSMGmU6isSZl1/m008B+vbl9m3TaUSis1iYMAEPD44dY+pU02lEEoDffmPGDIBhw0ib1nQaEZGYpAIsz+HkSfuthgMGkCWL6TQSl/z9SZ2aGzd0KUCcU8GC+PoCDB7M9eum04jEa1YrnToREUHx4vaznyIiCYgKsDyHrl0JDSVfPvuqkOJC0qe3V98pUzhyxHQaEQcGDiRDBu7e1Ww2kf/mm2/YuhUgIAB3d9NpRERimAqwPKs1a1i9GmDCBLy8TKeRuNeuHUWKEB5O585YrabTiESXOjXDhwPMnMnBg4bDiMRXDx/SvTvA++9ToYLpNCIiMU8FWJ5JSAhduwLUrk2dOqbTiBGenkyaBLBxIytWmE4j4kDLlrz8MlarztKIvKjRo7lwgSRJ7DPwREQSHBVgeSYBAZw4gaen/RlgcVFVq/L22wB+fgQFmU4jEp27u/0szbZtLFtmOo1IvHP+PJ99BtC7NzlymE4jIhIrVIDl3127xtChAF26kD+/6TRi1rhxeHlx9izjxpmOIuJA+fI0agTQowcPH5pOIxK/9OhBUBA5c9Kjh+koIiKxRQVY/l2/fgQGkikT/fubjiLG5c5t/8FoxAguXTKdRsSB0aNJkoQLFxg71nQUkXjkp5/sN06MG0eSJKbTiIjEFhVg+Rf79zNnDsDIkaRMaTqNOIPevfH25uFDevUyHUXEgRw57H82R43iwgXTaUTihbAwunQBqFyZd94xHEZEJDapAMvTWK106YLVSsmSNG9uOo04iWTJGD0aYOFCdu40nUbEgR49yJ6dR490lkbk2XzxBb/9hpsbkyZhsZhOIyISi1SA5Wm++Ybt2wEmTsRNf1gkUuPGvP46QJcuRESYTiMSXdKk9vufFy+2/yMmIk905479Gac2bShWzHQaEZHYpU4jTxQUZH/Y8/33KV/edBpxKhYLEycC7N7NggWGw4g40rChfYhp586Eh5tOI+LMhg3jxg1SpbKveCkikqCpAMsTjR/P+fN4edlnIoj8n1df5aOPAHr35v5902lEorNY7PdyHjhgX8hARBw4cYKAAIBBg0if3nQaEZFYpwIsjl29yogRAN27kyuX4TDipEaOJFkyrlyxPxIs4mRKlKB1a4C+fblzx3AYESfVvTthYeTPT4cOpqOIiMQFFWBxrG9fHjwgSxZ69zYdRZxW1qz06QMwZgznz5tOI+KAvz+pUnH9OsOGmY4i4oQ2bGDVKoDx40mUyHQaEZG4oAIsDuzdy9y5ACNHkjy54TDi1Pz8yJmToCAttivOKUMGBg8GCAjg2DHDYUScS1gYXbsC1KhB7dqm04iIxBEVYInONvoIKFWKZs0MhxFnlySJ/f7nJUv4+WfTaUQc6NCBggUJC6NbN9NRRJzKzJkcPoy7O+PHa/SRiLgOFWCJbulSe5HR6CN5Jg0b2lcJ79xZI5HECXl6MmECwJo1rFtnOo2Ik7h9m4EDAdq1o3Bh02lEROKO+o38n0eP6NkToFEjypUznUbiBYuFCROwWNi3j3nzTKcRcaBmTWrVAvDzIyzMdBoRZzBkCDdvkiaN/SEBERGXoQIs/2fcOC5cIHFiLesrz+OVV2jRAqBPHwIDDYcRcWTcONzdOXqUzz83HUXEuGPHmDoVYPBg0qUznUZEJE6pAMs/rlxh5EiAHj3IkcN0GolfRowgeXKuXWPUKNNRRBwoVMg+5GXgQG7dMp1GxKxu3QgLo1Ah2rUzHUVEJK6pAMs/+vTh4UOyZrXfBS3yHDJnpl8/gHHjOHvWdBoRBwYNIk0abt1i6FDTUUQMWreONWsAxo/H09N0GhGRuKYCLHZ79tif3xw1SqOP5IV06ULu3AQH6wyKOKe0aRkyBGDqVI1EElcVGoqfH0CtWtSsaTqNiIgBKsACUUYflS5N06aGw0h8lTgxY8YAfPMNW7eaTiPiQNu29pFI3bubjiJixIwZHD2Khwfjx5uOIiJihgqwACxZwi+/gEYfyX/0zjtUrAjQtSvh4abTiETn6Wn/sX/1atavN51GJI7dvMmgQfD3dGwREZekriM8fGi/ZfWDD3jtNdNpJF6zWJg4EYuFAweYO9d0GhEHatXSSCRxVUOGcPs2adPaa7CIiEtSARbGjePiRZIk4bPPTEeRBKBECT75BKBvX+7dM51GxAHbSKQjRzQSSVzJkSNMmwYwdChp0phOIyJijAqwq7t82T62pkcPsmc3nUYSBn9/UqTgr78YPtx0FBEHChWifXuAgQO5fdt0GpE4YLXi50d4OEWK0KaN6TQiIiapALs62+ijbNm0cK/EnEyZGDAAYOJETp82nUbEAY1EEteyZo39qfcJE/DwMJ1GRMQkFWCXtmsXX38NMGoUyZKZTiMJSadO5M1LSAg9epiOIuJAunQMHgwwZQrHjxsOIxK7Ikcf1a1L9eqm04iIGKYC7LoiRx+VKUOTJobDSELj5cXYsQDffcdPP5lOI+JAu3b2kUjdupmOIhKrpk7lxAk8PRk3znQUERHzVIBd16JF/PoraPSRxJIGDahSBaBLF41EEiekkUjiEm7cYMgQAF9f8uc3nUZExDz1Hhf18CG9egE0bUrZsqbTSIJksTBhAm5u/PYbX3xhOo2IA7VqUbMmaCSSJGCDBnHnDunT25dmEBFxeSrALmrMGC5dIkkSRo40HUUSsJdeonVrgP79uXvXdBoRByJHIs2caTqKSIz74w9mzAAYNozUqQ2HERFxDirArujSJfvI3169NPpIYtmwYaRKxfXrDBtmOoqIA4UL064daCSSJDxWK127EhFBsWK0amU6jYiIs1ABdkW9e/PoEd7eWqBXYl+GDAwcCBAQwMmTptOIODB4MGnScPOmRiJJwvLDD2zcCDBxokYfiYhEUgF2Ob/+yoIFAJ99RtKkptOIK+jYkXz5CA2le3fTUUQc0EgkSYBCQuzrm0euRygiIoAKsKuJiLCPPipblg8+MBxGXEWiRPbZGytX2i9HiDiZdu0oUICwMJ2lkYRi8mROnsTT0z6RTkRE/qYC7FoWLmTXLoBJk7BYTKcR11G3LtWrA3TposV2xQlFjkT64Qd+/NF0GpH/6K+/7Df0d+mCj4/pNCIizkUF2IU8eEDv3gDNmlG6tOk04lIsFsaPx82Nw4e12K44p1q1qFEDNBJJEoCBA7l3jwwZ6NfPdBQREaejAuxCRo/m8mWSJtXoIzGhaFHatgUYOJA7dwyHEXmMxWIfiaSzNBK//fYbs2YB+PuTKpXpNCIiTkcF2FVcuMDo0QC9e5Mtm+k04pqGDCF1ai22K06rSJF/ztJoJJLES1Yrfn5ERPDSS3zyiek0IiLOSAXYVfTuTVAQ2bPbV4UUMSB9egYNApg8mRMnTKcRcSDyLI0GV0u8tGoVmzYBTJyIu7vpNCIizkgF2CX88guLFgGMHq3RR2JUhw72xXZ1JkacUuRIJJ2lkfgncvTR229TubLpNCIiTkoFOOGLHH30+us0amQ4jLg6T0/7SCQttivOqn17naWR+GnKFE6dwtOTMWNMRxERcV4qwAnfggXs2QMwcaJGH4kTqF2bN98ELbYrTkpnaSReun79n9FHefOaTiMi4rxUgBM4q9U++uijj3j1VdNpRPh7JJIW2xUnprM0Ev8MHMjduxp9JCLyr1SAncjmzZsLFSqUI0eOhg0bBgYGRm4/dOjQK6+8kj179urVq1+9ehWwWq29e/f29vb28fGZOnXqU44ZGMiVKxp9JE5Gi+2Kc4t6lsY2UEbEqf3+u/18okYfiYj8GxVgZxEUFNSkSZO5c+eeP3/e09NzWJQVSJs1a9anT5+LFy+WKlWqc+fOwPLlyzdt2nTy5MmtW7cOHjz4yJEjTzqsrUf36UPWrLH/NYg8O41EEucWeZZmwAANrhbnZrXStatGH4mIPCMVYGexfv16Hx+fMmXKWCyWdu3arVixwrb90KFDt27deu+994D27duvXLnSarXOnz+/TZs2SZIkyZYtW/369SN3dihHDi3lIs4ncrHdKVM4ftxwGBFHBg/WWRqJD374wT76aMIEjT4SJxQWxnvvsXo1VqvpKCKACrDzOHfuXO7cuW0f58yZ88qVK49v9/b2DgsLu3Xr1pN2dmj0aJIkibXcIi9Mi+2Kc9PgaokHIkcfNWhAlSqm04g4MHs2335LgwacO2c6igigAuw8goODPTw8bB9bLBY3N7fHt0e+9KSdbcaPH+/9N3f38Pffj5MvQOR5eXoyfjzA6tWsX286jYgD7duTPz9hYXTvbjqKiENTp3LypEYfidO6c4cBAwDatOHvazcihqkAOwtvb+8LFy7YPj5//nyuXLke33758uWkSZOmSZPmSTvb+Pn5Xfpb5szuGn0kzqt2bWrWBC22K04qUSL7SKRVq9iwwXQakWiuX2fIEIBOnciXz3QaEQf8/blxg1Sp7H9URZyBCrCzqFmz5t69ew8dOmS1WqdPn96sWTPb9tKlSwcHB69duxaYNm2abfvbb789c+bMkJCQy5cvr127tlGjRiaji/wX48bh7s6RI3z+uekoIg7UqWMfidS1q87SiJMZNMg++sh2iU3EyZw6RUAAwKBBpE9vOo3I31SAnUXatGnnz5/fqFGj7Nmze3p6+vr6+vn5rVq1ysPD49tvv7UNPfrjjz9sq0O3bt26RIkS+fPnr1ChwtixY7NqiWeJvwoXpl070EgkcVK2kUhubhqJJE7mjz/s5w2HDdPoI3FOPXoQGkq+fHToYDqKSBQWq1ZkS9C8vb0vXbpkOoXIU928Sb583L5N585MnGg6jYgDHTowbRrp0nHyJGnSmE4jYrXy5pts3EixYuzfT5S1QkScxObNVK0KsGIF9eubTuNk9PO5WboCLCKmRY5EmjqVY8cMhxFxJHJwdZQZ7SLmrF7Nxo0AEyao/YoTCg+na1eAqlWpV890GpH/pwIsIk6gXTsKFtRIJHFaUUciaXC1GBY5+qh+ffslNhEn8+WX/PYbbm6MH49WYxVnowIsIk4gciTSmjWsW2c6jYgDHTrYB1drJJIYNm0aJ07g6cnYsaajiDhw9y79+gG0bs1LL5lOI/IYFWARcQ61alGrFmgkkjgpT0/7SKQffuDHH02nEZd144Z9noyvr0YfiXMaMYLr10mZkqFDTUcRcUQFWESchm0k0tGjzJhhOoqIA7VrU6MGaCSSGDRoEHfukC6dRh+Jczpzxr6cZf/+ZMxoOIyIQyrAIuI0ChWyj0oYNIhbt0ynEYnONhJJg6vFmD/+sJ8fHDaM1KkNhxFxpGdPQkLIk4dOnUxHEXkCFWARcSaDBpEmDbdu2e/xE3EyGlwtxlit+PkREUHRorRubTqNiANbt/LttwBjx+LlZTqNyBOoAIuIM0mb1l59p07l6FHTaUQcGDxYZ2nEhDVr2LABYPx4jT4SJxQeTpcuABUr8tZbZrOIPI0KsIg4mbZtKVSI8HCNRBLnpMHVYkBoKH5+APXqUb266TQiDsyZw8GDWCxMnKjRR+LUVIBFxMlEjkRau5a1a02nEXFAg6slrtlGH3l4aPSROKd79+yjj1q1okQJw2FEnk4FWEScT82a1K4N0K0boaGm04hEp8HVEqdu3rTfdeDrS/78hsOIOOLvz19/kTIl/v6mo4j8GxVgEXFK48bh4aGRSOK0atWiZk3Q4GqJAxp9JM7t1Cn76KMBAzT6SOIBFWARcUoFC2okkjg520gknaWR2HX4sP1P2NChpEljOo2IAz16EBpK3rz4+pqOIvIMVIBFxFkNHEjatNy+bb/3T8TJFCpE+/agszQSe2yjj8LDKVyYTz81nUbEgc2b+f57gHHjNPpI4gcVYBFxVmnTMnQowLRpGokkzkkjkSR2rV3Ljz8CTJig0UfihMLC7KOPqlalfn3DYUSekQqwiDixNm0oXJjwcPv8DxEno8HVEosiRx/VqcObb5pOI+LA7Nn8/jtubkyYoNFHEm+oAIuIE/PwsC+2u26dRiKJc9Lgaokt06dz/DgeHowbZzqKiAN37tjXZWvThmLFTKcReWYqwCLi3GrUoE4dAD8/jUQSJ6TB1RIrIkcfdexIgQKGw4g4MmwYN26QKpX9cSWR+EIFWEScnm0k0rFjTJ9uOoqIA5GDq3WWRmLM4MHcvk3atAwcaDqKiAMnThAQADBoEOnTm04j8jxUgEXE6RUoQMeOAIMHc/Om6TQiDkSepdFIJIkBR47Yz/dp9JE4q27dCAsjf377yEKReEQFWETig4EDSZdOI5HEaUUdXK2zNPJfdetmH33Upo3pKCIO/PgjP/wAMH48iRKZTiPynFSARSQ+SJPG/ozR9OkcOWI6jYgDkYOrNRJJ/pO1a1m3DmD8eI0+EicUFkbXrgA1atif/hCJX1SARSSe+PRTihTRYrvitKIOrtZZGnlBkaOPatemRg3TaUQc+PxzjhzB3Z3x4zX6SOIlFWARiSc8PJgwAWDdOtasMZ1GxIHIwdU6SyMvaMYMjh3D3V2jj8Q53bplX5etfXsKFzadRuSFqACLSPxRvTp164IW2xUnpbM08p/cusWgQQAdOlCwoOk0Ig4MGcKtW6RJY/+jKhIfqQCLSLxiW2z3+HGmTTMdRcSBN9/U4Gp5UUOGcPu2uoU4raNHmToVYMgQ0qUznUbkRakAi0i8kj8/vr6gkUjivHSWRl5E1G6RNq3pNCIO+PkRHk6hQrRtazqKyH+gAiwi8c2AAaRLx507ukgizkmDq+W5Wa107Up4OAULqluIc4pcnnzCBDw9TacR+Q9UgEUkvkmThmHDAGbM4PBh02lEHLANrtZZGnlWq1ezfj3AxInqFuKEIpcnr1NHy5NLvKcCLCLxUOvWFC1KeDh+flitptOIRBc5uFpnaeTfhYTYx6rWq6duIc5p2jSOHcPDQ8uTS0KgAiwi8ZCHB+PHA/z4oxbbFecUObhaZ2nkXwQEcOoUnp7qFuKcbtxg8GCAjh0pUMBwGJH/TgVYROKn6tWpVw+02K44qciRSDpLI09z7Zr9boEuXciXz3QaEQcGDeLOHdKls08AFonvVIBFJN4aNw5PT06csC+dKuJkop6lCQkxnUacU79+BAaSKRP9+5uOIuLAH38wYwbAsGGkSWM6jUhMUAEWkXgrXz77SKQhQ7hxw3QaEQfGjrWfpdFIJHFg717mzAEYOZKUKU2nEYnOasXPj4gIihaldWvTaURiiAqwiMRnAwaQPr0W2xWnFXVw9fXrptOIU7Fa6dwZq5VXXqF5c9NpRBz44Qc2bACYOBEPD9NpRGKICrCIxGepU/8zEumPP0ynEXHAdpbm7l3d4ir/b/FifvkFICAAN/08Jk4nJIRu3QDq16dqVdNpRGKO/sEVkXiuVSuKFiUiQovtinNKnZoRIwBmzeLAAdNpxEk8eECPHgBNmvD666bTiDgwZQonT+LpydixpqOIxCgVYBGJ5zw8mDgRYMMGVq82HEbEkZYtefllrFY6ddJZGgHgs8+4fJmkSfnsM9NRRBz46y/78uSdO2t5ckloVIBFJP6rWpX69UGL7YqTcncnIABgxw6WLDGdRow7d44xYwD69MHb23QaEQf69ePuXTJm1LMbkgCpAItIgmBbbPfkSSZPNh1FxIE33uCDDwB69ODBA9NpxKyePQkKImdO+xOWIk5m3z6++AJg5EhSpTKdRiSmqQCLSIKQLx9dugAMGcK1a4bDiDgyejRJk3Lpkm56dW1bt7JsGcDYsSRJYjqNSHRWK76+WK2UKkWLFqbTiMQCFWARSSj69ydzZgID6dvXdBQRB7y96dMHYPRozp41nUaMCA+nc2eASpV4913TaUQcWLCAnTtBy5NLwqU/1yKSUKRMab+yNmcOu3ebTiPiQLdu5MpFcDDdu5uOIkbMns2hQ7i5MXEiFovpNCLRBQbSsydAs2a89prpNCKxQwVYRBKQDz+kTBkAX18iIkynEYkuSRLGjQNYvpzNm02nkTh2+zb9+gF8+inFi5tOI+LAiBFcvUry5IwaZTqKSKxRARaRBMTNzb4I1u7dzJtnOo2IA2+/TdWqAJ07ExZmOo3EpaFDuXmT1KkZNsx0FBEHTp1i/HiA/v3JmtV0GpFYowIsIgnLq6/SsiVA797cu2c6jUh0FguTJuHuzh9/MGOG6TQSZ44eZcoUgCFDSJ/edBoRB2yTBH187GtKiiRUKsAikuCMGEHKlPz5py6ziHMqUoT27QEGDuTmTdNpJA5YrXTtSlgYhQrRrp3pNCIOrFvHqlUAEybg5WU6jUhsUgEWkQQnUyYGDQKYOJHjx02nEXFgyBDSpeP2bQYMMB1F4sDq1axfDzBhAp6eptOIRBcSYr/qW6sWdeoYDiMS21SARSQh6tiRggUJC6NLF6xW02lEokuTBn9/gM8/59Ah02kkVoWE0LUrQL161KhhOo2IA1OmcPw4np5MmKDlySXhUwEWkYQoUSImTQJYt47Vq02nEXGgdWuKFycigk6ddJYmQQsI4NQpPD3t6wuJOJk//2TIEIDOnSlQwHQakdinAiwiCdSbb9KgAUCXLgQHm04jEp27OwEBANu2sWyZ6TQSS/78k6FDAbp2xcfHdBoRB/r25d49MmXSExniKlSARSThGjeORIk4fZoJE0xHEXGgQgUaNQLo3p2HD02nkdjQpw+BgWTKZJ8ALOJk9uzhyy8BRo0iZUrTaUTihAqwiCRcefPSvTuAvz9XrphOI+LAmDEkScLFi4webTqKxLhff2XOHFC3ECdlewQDKF2ajz4ynUYkrqgAi0iC1qcP2bLx4AG9epmOIuJA9uz07g3w2WecP286jcSgiAh8fQHKllW3EOc0fz6//goQEICbOoG4DP1hdwoXLlyoVKmSt7d3mTJljh07FvWl7du3v/rqq3nz5i1WrNiPP/5o21iyZMlcuXL5+Pj4+PicPHnSRGSReCJ5csaMAZg/n59/Np1GxIEePciZk6AgevQwHUVi0JdfsncvFguTJ6tbiBMKDLSfGW7RgjJlTKcRiUP6F9kpdOjQoW7dupcuXWrZsuVH/3+e+NSpUytWrDh9+vSoUaPef//9iIgI4ObNm7t27Tp16tSpU6fy5ctnKLVIPNG4MW+8AdCpE+HhptOIRJckCWPHAixbxpYthsNIzLh9mz59AD75hFKlTKcRccDfn2vXSJGCkSNNRxGJWxarZi+Ydvv27axZs964cSNZsmTBwcFp0qQ5ffp0lixZou127969VKlSPXjwIGnSpClSpLh9+7aHh8e/Htzb2/vSpUuxE1wk/jhwgFdewWpl1ixatTKdRiQ6q5UqVdiyhZdeYt8+nuFfd3FunToxeTKpU3PiBBkymE4jEt2JExQtSmgoY8bY18qQuKSfz83SFWDzLly4kD59+mTJkgFeXl6ZM2e+4mi1nsGDB7/11ltJkyYNCQmJiIjInz9/8eLFx9quGojI0738Mp9+CtCnD3fuGA4j8hiLhUmTcHPjt9+YOdN0GvmPfv+dadMAhg1T+xXn5OdHaCj589sXwRJxKSrAca1///5Fo6hWrVpwcHDUa7kWi8Xt/x8Wun79+ltvvXXq1KmvvvoKSJQo0YMHD86cObN06dLp06cvWLAg2luMHz/e+2/379+Pgy9KJB7w9yd1am7cYPBg01FEHHjpJdq1AxgwgJs3TaeRF2a14utLeDjFitG2rek0Ig6sWcPq1QATJ5Iokek0InFOt0Cbd+XKFR8fnzt37iRKlCgkJCRt2rQXLlxImzat7dXjx4/Xq1evR48erVu3fvxzu3Xr5ubmNsa2xo8jusVC5B+TJ9OpE+7uHDpEkSKm04hEd+sW+fJx6xZt2zJ9uuk08mKWLKFxY4AtW6hY0XQakehCQihalJMnqVOHH34wncZV6edzs3QF2LysWbMWK1bsiy++AObMmVOpUqXI9gu0atVq5MiRUdvv3bt3//zzT+DKlSurVq0qX7583GcWiZfataNIEcLD6dwZnfsT55M2LSNGAHz+Ofv3m04jL+D+fbp1A2jcWO1XnNOkSZw8iacnEyaYjiJiiK4AO4WTJ082b9784sWLuXPn/vrrr3PmzLlo0aJjx44NGTIkceLEmTNnjrxHetKkSYULF37zzTfDw8MTJUrUsWPHjh07PuXIOsMk8n82b6ZqVYDly3n7bdNpRKILD6d0afbvp2xZfv5Z03Pim759GTmSpEk5fhxvb9NpRKK7epUCBQgMpGdPPvvMdBoXpp/PzVIBTuD0F0wkuvfe49tvyZWLI0dIksR0GpHofv2V114DmDOHFi0Mh5HncOoURYoQEsKIEfYZSCJOplkz5s8nc2ZOnCBFCtNpXJh+PjdL55ZFxMWMHUvixJw7x+jRpqOIOFC2LB9/DNCzp9Ysj1e6dCEkBB8f/PxMRxFxYNs25s8HGDtW7VdcmgqwiLiYXLno3Rtg1CjOnjWdRsSBUaNIlYrr1xk0yHQUeUarV/+zrq6Xl+k0ItGFhWF7Zq5CBZo0MZ1GxCgVYBFxPT17kjs3QUF06WI6iogDGTMybBjAlCn89pvpNPKvgoPt/5jUqUOdOobDiDgydSq//467O1OmYLGYTiNilAqwiLieJEkICABYudJ+0UbEybRrx0svERFBx45as9zpjR/PqVMkSsTEiaajiDhw7RoDBwJ07EixYqbTiJimAiwiLqluXerWBejUiaAg02lEovPwYMoUgO3bWbjQdBp5iosX8fcH6NYNHx/TaUQc6NWLe/fIlIkhQ0xHEXECKsAi4qomTcLLizNnGDPGdBQRB8qXp2lTgO7duXfPdBp5kh49ePiQbNno29d0FBEHfv6ZefMARo8mVSrTaUScgAqwiLiqPHnsq2GNGKHVsMQ5jR5N8uRcu8bQoaajiEObN7NkCcC4cSRPbjqNSHRhYXToAFCuHM2amU4j4hxUgOV/7d17fM31A8fx19lsw1znksvcJYRcKlLul9yN3OU6l7GIIURJRe4RJkbI/ZpLlEuoXMJPuYVcMrch9+tss53fHzuV1lQ42+dc3s9Hf8w5Z+e89+1cvu/z/Xw/HxE31r+/bTas3r1NRxFJRI4cvPcewIQJHDpkOIwkFB1tm1e3cmWaNTOdRiQRn37Kvn14eDB5sua+ErFRARYRN5YqFRMmAKxcydq1ptOIJKJnT4oU4f59evbUbFgOZvx4Dh8mRQp1C3FMv/3G4MEA3bvz3HOm04g4DBVgEXFv9evbli3RbFjikLy8mDgR4JtvWLrUdBr5w5kztoHpISEULWo6jUgiBgzgxg2yZLEtqyYi8VSARcTtxc+GdeIEY8aYjiKSiGrVaNoUICSEO3dMp5F48f8z/P155x3TUUQSsWMHM2cCjBxJhgyGw4g4FBVgEXF7BQrQvz/AsGGEhxsOI5KYsWNJnZqzZxk2zHQUAdavtx2O//hjzX0lDig21nZ+erlytGtnOo2Ig1EBFhGBAQPIm1ezYYnDypXLdi7fmDEcPWo6jZuLirJ1i5o1ee0102lEEjFtGj/+iMXC5Ml4aGdf5K/0mhARgVSpGD8eYMUKvvrKcBiRxISE8PTTxMRoNizTxozh2DG8vZk4UXNfiQO6fJlBgwCCgihd2nQaEcejAiwiAkCDBtSpA9CzJ1FRptOIJOTjwyefAKxbx7JlptO4rfBw2zD0fv0oVMh0GpFEDBzItWtkysSHH5qOIuKQVIBFRACwWJgwAW9vjh/XbFjimGrVsg257dWLW7dMp3FPvXoRGUmePLz9tukoIonYtYsZMwBGjMDPz3QaEYekAiwi8ruCBf+cDevUKdNpRBIxfjy+vpw7x3vvmY7ihtasYeVKgAkTSJ3adBqRhGJj6d4dq5UXX6RjR9NpRByVCrCIyAMGDCBPHiIj6dXLdBSRRPj7M3QowIQJ7N9vOo1biYykRw+AunVp0MB0GpFETJnCnj2a+0rkX+jFISLygNSp/5wNa80aw2FEEtOzJ8WLExtLt27ExZlO4z5GjuTkSXx8mDBBc1+JAzp/3jb3VffuPP+86TQiDkwFWETkrxo2pG5dgDfe4O5d02lEEvLyIjQUYPt2Zs0yHMZdnDjBiBEAAwdSoIDpNCKJ6NOHmzd56inNfSXyL1SARUT+ymJh4kRSpSI8XPsR4pheeYUOHQDeeosrV0yncXlWq21y+Pz5bdMEiDiYjRtZsABg3DgyZDAcRsTBqQCLiPxNvny88w7A6NEcOmQ6jUgiRo3Cz48rVxgwwHQUl7dyJWvXAkycSMqUptOIJBQVRXAwQLVqtGxpOo2Iw1MBFhFJTJ8+FCnC/ft064bVajqNSEKZM9vG5E6fzo4dptO4sLt3efNNgIAA21LhIg5m1CiOHsXbm8mTdX66yL9TARYRSYy3N59+CvDdd3z+uek0IokIDKRcOYBu3bh/33QaVzVsGKdPkyqVbXo8EQdz/DjDhgH0788zz5hOI+IMVIBFRB6iYkXatQPo25erV02nEUnIw4MpU/DwYN8+Jk0yncYlHTrE6NEAgweTJ4/pNCIJWa288Ybt/PSBA02nEXESKsAiIg83ejQZM3L5ss6zFMdUsiQ9ewK88w7nzplO42Li4ujalZgYihalb1/TaUQSsXQp69YBTJ5MqlSm04g4CRVgEZGHy5KFkSMBwsLYvt10GpFEDB1K9uzcvk1IiOkoLmbmTLZuBfj0U7y9TacRSejmTXr1AmjShFq1DIcRcSIqwCIi/ygwkJdeAp1nKQ4qXTo+/hhg8WLWrzedxmVcusRbbwF07EiFCqbTiCRiyBAiIkiTRuenizwaFWARkX/k4cGnn+Lpyf79fPKJ6TQiiWjWjBo1AIKDuXfPdBrXEH/mf+bMjBplOopIIvbutX0iffABOXOaTiPiVFSARUT+TYkStnFm777LmTOGw4j8jcXCpEl4e3P8uPqaPWzebJv7fexYMmUynUYkobg4goKIi6NkSd54w3QaEWejAiwi8h+89x7+/ty5Y2vCIg6mUCHbTG3Dh3P8uOk0Ti0qiqAggCpVaNPGdBqRRISFsXMnFgtTppAihek0Is5GBVhE5D9Ik8Y22mz5cr780nQakUQMGED+/ERF0a0bVqvpNM5rxAiOHsXbmylTsFhMpxFJ6LffbN92de5sWwlcRB6JCrCIyH8TEEDdugA9enD3ruk0IgmlSsWUKQAbNzJ3ruk0TuroUYYPBxgwgGeeMZ1GJBH9+nH9Olmy8NFHpqOIOCcVYBGR/8ZiYeJEUqUiPJwPPzSdRiQRNWvSujVASAiXL5tO43SsVrp1Izqap59m4EDTaUQSsWWL7fz00aPx8zOdRsQ5qQCLiPxn+fLxzjsAo0dz6JDpNCKJGDcOPz8uX6ZvX9NRnM68eWzaBBAaSsqUptOIJHTvHl27AlSsSNu2ptOIOC0VYBGRR9GnD0WKcP8+XboQF2c6jUhCWbMyZgzA7Nm2Nif/ydWrhIQAvP461aubTiOSiGHDbOenT52q89NFHp8KsIjIo4jf9QC2bSMszHQakUS0b0/lygBduxIZaTiM0+jfn0uXyJiRsWNNRxFJxMGDjBgBMHgwhQubTiPizFSARUQeUYUKdOkC8NZbRESYTiOSkMXC1Kn4+HD8OMOGmU7jFLZuZfp0gJEjyZrVdBqRhOLi6NKF+/cpWpT+/U2nEXFyKsAiIo9u5EiyZ+fmTXr0MB1FJBGFCjF4MMDIkRw8aDqNg4uOti38+/LLBAaaTiOSiE8/ZccOgLAwvL1NpxFxcirAIiKPLkMGJk4EWL6cFSsMhxFJzFtvUbQo9+/TtatOV/9H48bx88+kSMGnn+Kh/SJxOOfO2Rb+7daN8uVNpxFxfnqjFxF5LI0b07AhQHAwN26YTiOSkLc306YBbN9u+0ES8euvvP8+QN++FCtmOo1IIt54g1u3yJFDC/+K2IcKsIjIY7FYmDyZtGmJiNCSoeKYXn7ZtmhK//46XT0xVqttorC8eW0rnIk4mC++sA0zmjSJ9OkNhxFxDSrAIiKPK2dO26ScU6awbZvpNCKJGDGCbNm4eZM33zQdxQF9/jkbNwJMnUrq1KbTiCR04wbBwQABATRqZDqNiKtQARYReQJBQbZTsjp3JirKdBqRhDJk4JNPAJYuZfVq02kcysWL9O4N0LYtNWuaTiOSiIEDOX+etGmZNMl0FBEXogIsIvIEPDwIC8PLi8OHbUeDRRxMkybUqwcQHMytW6bTOI5evbh2jSxZGDfOdBSRRGzbxpQpACNGkDOn6TQiLkQFWETkyRQtajsHePhwDh82nUYkofjT1X19OXNGJ7r+bs0aFi4EmDCBTJlMpxFJKCqKzp0Bype3rdIlIvaiAiwi8sQGDuSZZ4iOpnNnLTgjDih3bj78EGDiRHbvNp3GuFu36NYNoE4dWrQwnUYkESNHcvgwXl5Mm6bFuUTsTC8pEZEnljIlYWEA27ZpwRlxTD16UKYMcXF07kxMjOk0Zg0axJkz+PoyZQoWi+k0IgkdOcKwYQADBvDss6bTiLgcFWAREXuoUIEuXQD69+fcOdNpRBLy9CQsDE9P9u1jzBjTaQzascM2odBHH5E7t+k0IgnFxdGlC9HRFCrE22+bTiPiilSARUTsZORIsmfn5k169DAdRSQRpUrRty/A0KEcOWI6jRHR0XTqhNVKuXJ07246jUgiZszg++8Bpk0jZUrTaURckQqwiIidZMjAxIkAX3zBF1+YTiOSiCFDKFSIqCgCA93ydPURIzh0CC8vpk/H09N0GpGEIiLo1w+gUycqVTKdRsRFqQCLiNhP48Y0bAgQHMz164bDiPxNqlRMnw6wfTuTJ5tOk8wOHdKJleLIrFa6dePGDbJlY9Qo02lEXJcKsIiI/cQvOJM2LefP277GF3EwFSoQHAwwcCDh4YbDJJ/YWDp2JDqawoUZNMh0GpFELFrEqlUAU6aQMaPpNCKuSwVYRMSucua0TTE0fTobNphOI5KI+Omf7tyhSxesVtNpksf48ezcicXCjBn4+JhOI5LQpUu26SOaNSMgwHAYEdemAuwQTp8+XblyZX9//7Jlyx7568wkq1atSpcuXcGCBQsWLNjj95l1Fi5cWKBAgbx58wYFBcW4+3IWIo6nc2eqVrX9cOuW6TQiCaVNy9SpABs2MGuW4TDJ4dgxBg8G6NWL8uVNpxFJRI8eXL5Mpky2qSREJOmoADuE4ODgevXqnT17tmPHjm3btn3wqitXrrRu3fr48ePHjx+fOHEicP78+R49eqxfv/7YsWOHDh0Ki199VEQch8XC9OmkTs2pUwwcaDqNSCJq1SL+0yYkhPPnTadJUnFxBAZy7x4FCvDhh6bTiCRixQoWLQKYOJGsWU2nEXF1KsDmXbt2bePGjd26dQPat29/8ODB8w/sjFy5ciVz5swP3n7x4sWvvvpqgQIFvLy8OnfuvHLlyuROLCL/Kl8+RowAmDyZ774znUYkER9/TNasXL9OcLBLD4QODbWtKhP/tZSIg7l2jW7dABo0oEUL02lE3IAKsHmnT5/OnDmzr68v4OPjky1btoiIiD+uvXHjxowZM/Lnzx8QEBA/Ojo8PDxfvnzx1+bJk+fBG4uIAwkO5pVXADp25O5d02lEEvLzs00E/cUXLFtmOk0SOXmSAQMAunencmXDYUQSExLChQukT8+UKVgsptOIuAEV4OQ2ePDgYg+oXr16VFRUihQp/riBxWLx8Pjz/8sHH3wQERHxyy+/lC5dun79+sCDt09w43jjxo3z/93t27eT/m8SkcR4eDBjBilTcuIE77xjOo1IIpo0oXFjgOBgLl82ncburFY6d+bOHXLnto3IEHEw69bZzsMfN44cOQyHEXETFqsrD3tyDhEREQULFrx+/bq3t3d0dLSfn9/p06f9/PwS3OzWrVvp0qW7ffv2hAkTTpw4MWPGDGDu3LlLliz5h1HQ/v7+Z8+eTdo/QET+wZgx9OuHxcL27ZQrZzqNSEIXLlC0KNeu0aIFCxaYTmNfYWF06QKwbh01a5pOI5LQzZsUK8aZM9Sowbp1OvzrRrR/bpaOAJuXI0eO4sWLxxfamTNnVq5c+cH2e/To0fgvKaZOnVqqVClfX99GjRqtWrXqzJkzMTExYWFhbdq0MRZdRP5V7968+CJWKx06cO+e6TQiCWXLZpt1duFC1xoIfeYMffoABAaq/YpjGjCAM2dIk4awMLVfkeSjAuwQ5s6dO2fOnFy5cs2bN2/y5MnAggULhgwZAsyYMSNXrlwFChT49ttvly1bBhQpUmT48OFVqlTJnz9/2bJlX3vtNcPpReQfeHry2Wd4e3PkCO+/bzqNSCJataJhQ4Bu3bh0yXQau7Ba6dqVW7fIkcO2LreIg9m0iSlTAEaOJE8e02lE3ImGQLs4DbEQcQjDhjF4MJ6e/PADzz9vOo1IQhcu8OyzXL1K06YsXmw6zZObNYsOHQBWr6ZePdNpRBK6eZMSJTh1ikqV2LSJv03nIi5O++dm6QUnIpL03nqLUqWIjaVtWw2EFgeULRuTJgEsWeL8BfjMGd58E6B1a7VfcUx9+3LqFL6+zJyp9iuS3PSaExFJel5ezJ6NtzeHD/Puu6bTiCSiRQvbjNDdu3Pxouk0j81qJTCQmzfJkcN2crOIg/n6a8LCAMaM4fd1LUUk+agAi4gki+LFGToUYMwYtm0znUYkIYuF0FAyZeLKFbp3x1lPkJo6lQ0bAKZPJ2NG02lEErp+nU6dAGrUoGtX02lE3JIKsIhIcunbl3LlsFpp3547d0ynEUnoqacIDQVYvpxFi0yneQy//krfvgCdOlG7tuk0Iol4803OnSNdOmbM0MzPImaoAIuIJJcUKZg1i5QpOX6cAQNMpxFJRLNmNGkCEBzMhQum0zySuDg6dODOHfLkYexY02lEErFqFZ9/DjB+PLlymU4j4q5UgEVEktEzzzBiBMCkSWzaZDqNSCJCQ8mShatXCQpyqoHQn3zCd98BzJxJunSm04gkdOUKXboA1KtH+/aGw4i4MxVgEZHk1aMHlSoBdOjAzZum04gklCWLbSD0ypW2o1VO4JdfGDgQoEcPqlQxnUYkEcHBXLxIxoxMm6bBzyImqQCLiCQvDw9mzsTXl9On6dPHdBqRRDRpQsuWAD16cOqU6TT/6v592rXj3j0KFuSjj0ynEUnEkiW28+onTSJ7dtNpRNybCrCISLLLl892juL06axdazqNSCImTyZnTm7don174uJMp/lno0ezcyceHsyeja+v6TQiCV24QPfuAI0a2b5aEhGDVIBFREzo0oWaNQE6deLqVdNpRBLKmJGZMwG2bGH8eMNh/smBAwwZAtC3L+XLm04jkpDVSqdOXL5M5sx8+qkGP4uYpwIsImKCxcKMGaRPz/nzdOvmVHMNibuoUYMePQDefpuffzadJlH37tG6NTExFC1qW2dbxMFMm8aaNQBhYWTNajqNiKgAi4gY4+/P5MkAixczb57pNCKJGDGCZ54hKorXXyc62nSavxs8mAMH8PJizhxSpjSdRiSho0cJCQHo2JGAAMNhRCSeCrCIiDmtWtG8OUBwsDPMNSRuJ3Vq5szB05O9ex3vCOvmzYwbB/D++5QubTqNSEL379OmDXfvkj+/Y59HIOJmVIBFRMyxWJgyhZw5uXmTdu2IjTUdSCShF17gnXcARoxg+3bTaf5w7Rpt22K1UqEC/fqZTiOSiGHD2LULDw8+/5y0aU2nEZHfqQCLiBiVMSOzZwN8+61tamgRB/P227z4InFxtG3L7dum08QLDubsWdKm5fPP8fQ0nUYkoZ07+eADgAEDePll02lE5AEqwCIiplWrRu/eAIMHs3ev4TAifxN/jm2qVJw4Qd++ptMA8+ezYAHApEnkzWs4jMjf3LlDmzbExlK6tG2SchFxHCrAIiIOYPhwihUjJobWrYmMNJ1GJKFChRg9GmDqVFavNhrl9GnbmqpNm9KmjdEoIonr25djx0iZkrlz8fY2nUZE/koFWETEAaRMybx5eHtz6BADB5pOI5KI7t2pVQugY0fOnzcUIi6Odu24cYMcObSmqjimNWv49FOAUaMoUsR0GhH5GxVgERHHUKIEw4YBTJjAhg2m04gkZLEwcyZZsnD5Mu3aERdnIsS4cWzZAjBrFn5+JhKI/JNLlwgMBKhZk+Bg02lEJDEqwCIiDiMkhCpVANq358oV02lEEsqWjZkzATZs4OOPk/3h9+1j0CCAN9+kRo1kf3iRf2G10qEDFy+SMSMzZ+KhvWwRh6SXpoiIw/DwYPZs0qcnIoJOnbBaTQcSSahuXXr0ABg4kJ9+SsYHvnuXli2JjqZoUT76KBkfWOS/mjiRNWsAZswgRw7TaUTkIVSARUQcSa5cTJ0KsGKF7TQyEQczapRtyraWLblzJ7ketXdvDh/Gx4f580mVKrkeVeS/2rfPtiJ1UBCNGplOIyIPpwIsIuJgmje3nUPWuzcHDphOI5JQypQsWICPD7/8QkhIsjzkkiVMmwYwZgzPPZcsDynyCO7coUUL2wAFLegu4uBUgEVEHM+ECTzzDFFRtGjB3bum04gkVKwYY8YATJvGF18k8YOdOkXnzgD162taIXFMvXtz5Ag+PixcSOrUptOIyD9SARYRcTy+vixcaFsVKZkOsYk8muBg6tYF6NSJs2eT7GHu36dVK9u6R599pnWPxAEtXUpYGMDYsRQvbjqNiPwbFWAREYdUsiSjRwNMncqyZabTiCQUvypStmxcvUqbNsTGJs3DDB3K9u1YLMydS+bMSfMYIo/v9GnbAIUGDeje3XQaEfkPVIBFRBxVjx7UqwfQqROnTplOI5JQlizMng2wZUvSTMy8ZYttcexBg2wrhIk4kvv3ad2a69fJkYMZMzRAQcQ5qACLiDiq+ENs2bNz/TqtW3P/vulAIgnVrEnfvgBDhvDtt3a968uXad0aq5WXXmLIELvetYh9DBvG1q1YLMyZowEKIk5DBVhExIFlzsy8eVgsbNvGBx+YTiOSiOHDKVuWuDhateLSJTvdqdVKYCAREaRPz/z5pEhhp/sVsZvvvuP99wEGDKBqVdNpROQ/UwEWEXFsVarw9tsAH37Ili2Gw4j8jZcXCxeSIQMREbRpQ1ycPe508mRWrQIICyNvXnvco4g9/fYbLVoQF0fZsgwdajqNiDwKFWAREYc3ZAgvvURcHC1bcvGi6TQiCeXNy8yZAOvWMWrUE9/d7t22yc87daJp0ye+OxE7i43l9dc5f56MGVm0CC8v04FE5FGoAIuIOLz4Q2x+fly4QKtWSTbfrsjjCwjgzTcBBg9m69YnuKNr12jWjJgYihdnwgQ7pROxp+HD2bABYPZs8uQxnUZEHpEKsIiIM8idm88/B9i0SePtxDGNGsULLxAbS8uWXLnyWHdhtdK+PeHhpEnDkiWkTm3niCJPbPNm3nsPoG9f6tc3HEZEHoMKsIiIk6hbl4EDAT78kHXrTKcRScjbm0WLSJ+es2dp1+6xTgYeO9Z26u/06TzzjL0DijypCxdo2ZK4OMqXZ/hw02lE5LGoAIuIOI/336diRaxWXn+ds2dNpxFJKF8+PvsMYM0axo17xF/eto0BAwC6d6d5c7tnE3lCsbG0asXFi2TKxMKFOvVXxFmpAIuIOI8UKViwgKxZuXyZFi2IiTEdSCShxo154w2AgQPZvv0//9qlSzRvTmwsZco8enUWSQ7vv8/mzQBz5pArl+k0IvK4VIBFRJxKjhzMn29bGXjQINNpRBIxZgylS3P/Pk2b/rdpy+PieP11zp0jfXqWLMHHJ8kjijyiDRtsa7EPGEDt2qbTiMgTUAEWEXE21arZ5mAZPdp2wqSII/HxYelSMmYkIoLmzbl//99+Yfhw1q8HmD2bfPmSPqDIo4mIoHVrrFYqVLDVYBFxXirAIiJOaNAgatQAaNeOkydNpxFJKF8+5s3DYuHbb21ztz3Upk0MGQLQpw8NGyZLOpFHEB1NkyZcukSWLCxYQIoUpgOJyJNRARYRcUKensybR86cXL/Oa68RGWk6kEhCtWvbiu2YMSxb9pAbnTlDixbExfHSS3z0UTKmE/mvevdmxw48PJg/n5w5TacRkSemAiwi4pyyZGHxYry8+OknunbFajUdSCShd96xnS3Zvj1Hjvzt6nv3aNzYdmRt0SJNqisOaNYsQkMBPvqI6tVNpxERe1ABFhFxWuXLM348wJw5TJpkOIzI33h4MHcuefNy+zaNG3P79gPXWa0EB/O//+HpyZIlmlRXHNCPPxIUBNCkCf36mU4jInaiAiwi4sy6daN9e4CQEL7/3nAYkb/x82PZMnx8OHyYwMAHRipMnWpbMnjsWCpVMhdQJHGXL9O4MVFRFC3KZ59hsZgOJCJ2ogIsIuLMLBamTKFMGe7fp0kTzp41HUgkodKlmTIFYPFi25AFduygZ0+A1q1tP4g4kthYWrbk1CnSpWP5ctKmNR1IROxHBVhExMmlTMny5WTOzG+/0aQJUVGmA4kk1KEDnTsD9OvHjuXnee01YmIoWZJp03RkTRzQ4MFs3Ajw+ec884zpNCJiVyrAIiLOL3duFi3Cw4OdO+nRw3QakUR88gnPP49HbLRH86acP4+fH8uXkzq16VwiCS1fzogRAIMHa2UuERekAiwi4hKqVmXUKICwMMLCTKcRSShlSr74gk9ThZS9vy0Wj8jPFpAvn+lQIgkdPky7dgC1avHee4bDiEhSUAEWEXEVISG0aAHwxhv88IPpNCIJ+W+c1TFyMjCIYW3m1IyLMx1I5K+uXKF+fW7fJl8+5s3D09N0IBFJAirAIiKuwmJh+nSKFyc6mkaNOHPGdCCRB2zfTteuwMkyTUbSf9kyPvjAdCSRB8TE0KwZJ07g68uKFfj5mQ4kIklDBVhExIXE77hlysSFCzRowJ07pgOJAHDqFAEBREdTokS+zZ+9+aYFeO89li83HUzkd716sWkTFgvz5lGihOk0IpJkVIBFRFxL/vwsX46XF3v30rYtGmYqxt26Rf36XLpE1qysWkXatGPGUL06QJs27N9vOp4IhIYSGgowbJgmvhJxcSrAIiIup2JF27qry5fz7rum04h7i4vj9dc5cABvb774gjx5gBQpWLSIAgW4e5eGDbl82XRIcW/ffGNbjrpVKwYMMJ1GRJKYCrCIiCsKDKRPH4Bhw5g/33QacWNvv82qVQDTp1O+/B8X+/nFHwwmPJwmTYiJMRZQ3NyxYzRtSmwsL77I9Olal1rE9akAi4i4qJEjqVsXoGNHdu40nUbc0uzZjBwJMGAAbdokuLJoUebNw2Lh22/p3h2r1UBAcXPXr1O/PteukTMnK1aQKpXpQCKS9FSAHcLp06crV67s7+9ftmzZI0eOPHjVs88+W/B33t7eS5cuBUqXLp03b974C48dO2YotYg4Nk9P5s/n2WeJiqJhQ00KLclt2za6dAFo2JBhwxK9Sf36fPQRwPTptqYskmzu36dFC375hVSpWLmS7NlNBxKRZKEC7BCCg4Pr1at39uzZjh07tm3b9sGrfv755+PHjx8/fnzVqlVPP/10QEAAcOXKlZ07d8Zf/vTTT5sJLSKOL106Vq8mc2YuXtSk0JKswsNp1IjoaJ57jrlz8Xjo/sZbbxEYCDBwIIsWJV9AcXNWK717s24dwOzZlCljOpCIJBeLVUOOTLt27VqOHDkuX77s6+sbFRWVMWPGEydOZP/b95B169bt0qVLw4YNgbRp0167di1FihT/euf+/v5nz55Nktwi4iy2bqVqVWJiaNCA5cvx9DQdSFzd9etUqMDBg2TNyu7d5M79zzePiaFuXTZswMeHTZsePFNYJKmMHUvfvgBDhvDee4bDiLvR/rlZOgJs3unTpzNnzuzr6wv4+Phky5YtIiIiwW1279595syZ+PYbHR0dFxdXqFCh5557bsyYMQYSi4hzeeUVpk0DWLWKHj10qqUkragoGjXi4EF8fFix4l/bL+DlxZIlFCtmG61/4kQypBS3tmSJrf22bcuQIabTiEjyUgFOboMHDy72gOrVq0dFRT14LNdisXj8bajYqFGj+sRP6Are3t537tz59ddfFy9ePGXKlHnz5iW48bhx4/x/d/v27ST9c0TEObRvb9vLmzKFUaNMpxHXFRdHx45s2YLFwrx5vPTSf/y99OlZs4Zs2bh8mTp1uHIlSVOKW9u61TYjW7VqhIVp2mcRt6Mh0OZFREQULFjw+vXr3t7e0dHRfn5+p0+f9vPz++MGly9fLliw4Llz5+KPEj+oT58+Hh4eo0ePftida4iFiNhYrQQGMnMmwLx5tGplOpC4ooEDGTECYPx43nzzUX97zx4qVuTuXSpUsI2IFrGvX36hfHmuXqVYMbZuJX1604HELWn/3CwdATYvR44cxYsXnzFjBjBz5szKlSs/2H6B5cuXV6tW7Y/2e+PGjYsXLwIRERGrV6+uUKFC8mcWEedjsTB1Kq++CtC+PZs3mw4kLic01NZ+Q0Ieo/0CZcqwYAEWC99/T8eOGq0vdnbxIrVrc/UqOXKwdq3ar4ibUgF2CHPnzp0zZ06uXLnmzZs3efJkYMGCBUN+Pyvl+++/L1u27B83vnr16iuvvJI/f/6qVav27NmzQYMGZkKLiNOJP9WyZEliYggI4MAB04HEhaxcSY8eAM2a8fBxSf+qQQPGjweYP59Bg+wTTQS4c4f69Tl5krRpWbuWXLlMBxIRQzQE2sVpiIWIJHT+POXKcfo0/v7s2IG/v+lA4vx27qRKFSIjqVCB9etJmfIJ769XLyZMAPj4Y3r1evJ84u5iY2nUiNWrSZGCNWuoWdN0IHFv2j83S0eARUTcTPbsfP01GTJw9ix163LjhulA4uSOH6dePSIjKVyYFSuevP0C48bRvDlA797Mnfvk9yduzWqle3dWrwaYNk3tV8TdqQCLiLifIkVYuRJvb/bvJyCAe/dMBxKndf48r77K5ctky8ZXX/HXOSwem4cHn39uKyodOrB2rV3uVdzUoEG2leCGDKFDB9NpRMQ0FWAREbdUsSJz5mCxsGULzZsTE2M6kDihq1epWZNffyVNGtasIW9eO963tzfLlvHii9y/T5MmbN9ux/sWNzJ6NB99BNC9u5b8FRFQARYRcV/NmhEaCrBqFR06EBdnOpA4lVu3qF2bgwfx8WH1akqXtvsjxNfqwoWJjKRuXQ4etPsjiIubPp233gJo1YqJE7Xkr4iACrCIiFsLCrKtWzNvHj16aNkZ+a/u3aNhQ3btIkUKli6lcuUkepzMmVm3Dn9/rl/n1VcJD0+ixxEXtHQpXbsC1K3LrFl4aJ9XRAAVYBERd9e/PwMGAISGMniw6TTiDGJiaN6czZuxWPj8c+rVS9JHy52b9evx8yMigpo1+e23JH00cRHr19OqFXFxVKjA4sV4eZkOJCIOQwVYRMTtDR9OUJDth1GjTKcRxxYXR4cOrFoFEBpKy5bJ8JhFirB2Lb6+HDtGzZpcvZoMjylObMcOGjUiJoaSJVm9mtSpTQcSEUeiAiwi4vYsFiZPplUrgP79bfOlivyd1UqPHsybBzBihO17k2RRtizLl+Ptzb591KzJ9evJ9sjiZA4coE4d7t6lUCHWrSN9etOBRMTBqACLiAh4eDBrlm0sa1AQCxaYDiSOx2pl0CDbxGkDBtC/fzI/fs2aLFuGlxd79vDqq9y8mcyPL07g4EGqVeP6dfz9Wb+erFlNBxIRx6MCLCIiAHh5sXgxlStjtdKmDYsWmQ4kjsRq5d13bevJBAUxfLiRFPXqsXgxKVKwaxe1a3P7tpEU4qB+/pmqVbl0iaeeYsMG8uQxHUhEHJIKsIiI/C5VKlatonx5YmNp1YqFC00HEscQ334//BCgbVsmTza4nkxAAPPn4+HB9u3UrcudO6aCiGM5dMjWfrNmZfNmChc2HUhEHJUKsIiIPCBtWr7+mvLliYujdWuNhRasVt55x9Z+27fns8+MryfTtClz5mCx8N13NGhAZKTZOGLe4cNUrcpvv9nab5EipgOJiANTARYRkb+K78Avv0xcHK+/zvz5pgOJOVYrgwczbBhA+/ZMn46np+lMAK1aMXMmFgubNhEQwL17pgOJOUeOUKUKFy+SJQubNlG0qOlAIuLYVIBFRORv0qblq6945RXi4mjTxjbrr7ib+Fmv4k/37dDBcdpvvHbtbBOWr19PQAB375oOJCY82H43b+bZZ00HEhGHpwIsIiKJSZuWtWttHbhtW+bONR1IkpfVyttv22a9crz2G69TJ9uk1OvWUbu25oV2O7/8QtWqXLhA5sxs2qT2KyL/iQqwiIg8xIPHgdu1Y84c04EkuVitDBzIiBEAHTsyfbrx834fpls3ZsywnQ9cvTpXr5oOJMll3z4qVuT8eVv7LVbMdCARcRIO+nkmIiIOIU0avvqKChVsHXjyZNOBJOnFxhIczMiRAB07EhbmsO03XseOzJ9PihTs3k3lyly8aDqQJL1t26hUyTbr1TffULy46UAi4jwc+iNNRETMS5OGtWupWhWrlTfe4P33sVpNZ5IkEx1N69ZMmQLQtavjt994LVqwbBne3hw4QMWKnD1rOpAkpXXrqFGDGzfInZvvv6dECdOBRMSpOMGnmoiIGBbfgRs3BhgyhDffJC7OdCZJAnfu0KABixYBDBrElClO0X7jNWjAmjWkTs3Ro1SowIkTpgNJ0liyhPr1iYykcGG2bqVQIdOBRMTZOM0Hm4iImOTjw+LFBAYCTJxI27bExJjOJHZ19So1arBuHcC4cXz4IRaL6UyPpnp11q8nXTrCw6lQgUOHTAcSe5s+nRYtiImhdGm++45cuUwHEhEnpAIsIiL/jacnYWG89RbAvHk0aqSVZ1xHRASVKrFjB56ezJpF796mAz2ml19m0yYyZeL8eV55he+/Nx1I7GfMGDp3Ji6OihXZtIksWUwHEhHnpAIsIiL/mcXCyJG26ZHWrOHVV7l+3XAkeXLHj/Pyyxw8iI8Py5fTrp3pQE+kTBm+/ZacObl2jRo1WLzYdCB5YnFxDBhAv34Adevy9dekT286k4g4LRVgERF5RG+9ZVsXZ+tWKlXizBnTgeQJ7NrFK68QHk7atKxbR4MGpgPZwbPP8sMPFC9OVBTNmzNmjCZuc2KRkbRsafvarWVLvviCVKlMZxIRZ6YCLCIijy4wkCVL8PZm/35efJHdu00HkseydCmVKnHxIlmysGULlSqZDmQ3/v58/z3VqgH060fPnsTGms4kj+7SJapVsx3GDwlhzhy8vExnEhEnpwIsIiKPpXFjvvmGTJm4cIFKlVi+3HQgeRRWKx99RNOm3LtH4cL88AOlS5vOZGfp07N2LW3bAkyaRJMmOmndyRw5Qrly7NiBhwehoYwdi6en6Uwi4vxUgEVE5HG98go7d1K4MJGRvPYaI0dqpKlziI6mQwfefhugenV27CB/ftOZkoS3N7Nm8e67ACtWULUqly6ZziT/zebNvPQSv/5KmjR8+SXdupkOJCKuQgVYRESeQIECbN9O1aoAAwbQqRPR0aYzyT+6coUaNZg9G6BLF9auJUMGw5GSksXC0KFMn46nJzt38uKL7NtnOpP8m1mzqFmT69fx92fbNmrXNh1IRFyICrCIiDyZjBn5+ms6dwb47DNefZWrV01nkoc4epRy5fjuOywWxo7l00/d5JTKwEC+/JK0aQkP56WXWLTIdCB5iNhY3n6bDh24f5/Spdm5kxIlTGcSEdeiAiwiIk/My4upUxkzBouFLVsoW5YDB0xnkr/56ivKleP4cXx9WbGCkBAsFtOZkk+tWvzwA08/TWQkLVrQv7+mxXI4V65Qpw4ffQTQoAHffUeOHKYziYjLUQEWERF7sFjo04fly0mdmuPHKVuWOXNMZ5LfxcYyZAh163LtGjlz8v33rrHc0aMqWpRdu6hbF2DUKOrU0WAFB7JnD2XKsH49QP/+LF+Or6/pTCLiilSARUTEfgIC/jzK1rYt3boRFWU6k9u7fJk6dXj/faxWKlfmf/+jVCnTmYzJkIFVqxg8GGD9el54QYMVHMJnn/Hyy5w6Rdq0LF/OiBGa8FlEkooKsIiI2FXx4vzvfzRuDPDpp1SowKlTpjO5sZ07KV36z8NqGzaQLZvpTIZ5ePDBByxbhq8vv/5KuXI6JdikqCi6diUwkKgoihZl924aNTKdSURcmgqwiIjYW7p0LF3KmDF4erJ7N6VL8/XXpjO5H6uV0FAqVODMGdKl44svGDGCFClMx3IUjRvzww8UKMDdu7RoQffu3LtnOpP7OXOGChWYNg2gWTN27uSZZ0xnEhFXpwIsIiJJIP6U4E2byJaNq1epU4f33tOkQ8nn1i3atCE4mJgYSpRgzx4CAkxncjjFirF7N/XqAUyZQrly/PKL6UzuZOVKSpZk9248PRk3joULSZPGdCYRcQMqwCIikmQqVuTHH6lQAauVoUOpXJnwcNOZ3MCuXZQqxbx5AG3bsmMHBQuazuSgMmZk1SrGjiVFCvbto0wZZs7EajUdy9VFRhIcTEAAV6/y1FN88w29e7vVlOQiYpIKsIiIJKXs2fnmG/r3x2Jh61aee44FC0xncl2xsQwbRvnynDhBqlRMm8asWaRObTqWQ7NYCAlh2zby5uXOHTp2pGlTrlwxHct17d/Piy8SGgpQqxb791OpkulMIuJOVIBFRCSJeXkxYgQbN5IjBzdv0qoVrVtr/Rn7O3GCypUZPJjYWEqV4scf6dxZh9X+oxdfZO9eWrcGWLaM4sVtE4eJHcXGMmIEzz/PwYN4eTFuHGvWkDWr6Vgi4mZUgEVEJFlUrcr+/bbZoefPp1gx1qwxnclVxMUxeTIlSrB1K0DfvuzYQeHCpmM5mfTpmTuX+fNJn57z53n1Vbp25eZN07FcxfHjVKzIwIHExFC0KDt30rs3HtoPFZFkpzceERFJLpkysXQps2fbGka9enTooEPBT+rXX6lRgzfe4O5d8uZl82ZGj8bHx3QsZ9WyJfv3U6UKwLRpFC/Ohg2mMzm5+/cZM4YSJdi+HYuFvn3Zs8ed16IWEcNUgEVEJBlZLLRty8GD1KoFMGsWRYqwaJHmHXocMTGMGkWxYmzaBBAUxIEDVK5sOJXzy52bjRsJDcXXl9OnqVmTDh24fNl0LOe0dy/lytGvH5GR5M/Pt98yejQpU5qOJSJuTAVYRESSnb8/a9cyfToZMvDbb7RoQd26miD60ezezQsv0L8/kZHky8f69UyZomVk7MXDg27dOHiQqlUBZs2icGFmzdIXNY/g9m3eeovnn2fPHjw8CAlh/34qVDAdS0TcngqwiIiYYLEQGMjhwzRvDvDVVxQpwvvvExlpOpnDu3yZLl0oW5Z9+/D0pF8/DhygRg3TsVxQ3rxs3MjMmWTKxJUrdOhA5crs3286lsOzWlmyhMKFGT2a2FiKF+eHHxg7Fl9f08lERFSARUTEpGzZWLiQL78kTx7u3WPIEJ59llWrdKAtcffvM2UKhQoRFobVygsvsHs3o0apWCQdi4X27TlyhPbtAb77jlKlCA7WOkkPdeAANWvSrBnnzuHry6hR/O9/vPCC6VgiIr9TARYREdPq1uXwYYYMwceHkydp2JBq1fjxR9OxHInVypo1PPcc3btz7RqZMzN9Oj/8oKmEkkfmzMycybffUqIEcXGEhlKoEBMmEBVlOpkjuXiRrl0pWZKNGwGaNuXIEfr1w9vbdDIRkQeoAIuIiANIlYr33uPQIRo2BNi8mTJlaNOGU6dMJ3MAP/5I9erUq8ehQ3h6EhzM0aMEBmoNmWRWsSJ79hAaip8fV6/SqxdFirBgAXFxppOZdusWH3zA008zbRpxcbapsxcvxt/fdDIRkb/RZ6eIiDiM/PlZsYItW3j+eYC5c3n6aYKDOXfOdDJDDhygcWPKlLHN81y/PgcOMGkSGTOaTuamUqSgWzeOHaN3b7y9OXmSVq14/nlWr3bTYfv37vHxx+TPz7vvcusWTz1FWBg//UT16qaTiYg8hAqwiIg4mEqV2LmTefPIm5eYGEJDKVCAXr3cqwYfPEiLFjz3HF98AfD882zezKpVFCliOpng58e4cfzyC61bA/z0Ew0a8MILfPmlG9XgO3cYP56CBQkJ4fJl0qZlyBCOHaNTJzw9TYcTEXk4FWAREXE8Hh60asUvv/Dpp/j7ExXFhAnky0enTvzyi+lwSWzbNurXp3hx2/LIJUqwYgW7dmmBX0eTNy9z57J3LwEBAHv2UL8+pUoxbx4xMYazJanr1xk2jLx56d2bc+dImZJ+/fj1V957j7RpTYcTEfk3Fqv7fFfplvz9/c+ePWs6hYjIE7h3j7AwRo0i/t3MYqFhQ3r2pHJlLBbT4ewnJoYVK5gwgW3bbJcUL87gwTRponN9Hd9PPzF0KCtX2v6ZOze9etGhAxkymExld8eO8cknzJzJnTsAvr4EBdGnD9mzm04m4lS0f26WCrCL0wtMRFxEdDTz5zNyJEeO2C4pVow33qBVK6c/6nThAp99Rmjon2O8K1RgwABq13aphu8G9u9nzBgWLOD+fYDUqXn9dYKDKVHCdLInc/8+X3/N1KmsWWMb450hAz168OabZMpkOpyIE9L+uVkqwC5OLzARcSlxcXz5JZ98wjff2C7x9aVZMwIDKV/eyeri/ft89RUzZvDll8TGAnh60qQJPXtSvrzpcPL4zpxh/HhmzODGDdslZcvSvj3Nmzvf5GUnTzJrFp99xh+7EoUK8eabtGunxadFHp/2z81SAXZxeoGJiGs6eJBJk5g3j9u3bZcULEiLFrRowbPPGk32b+Li2LGDhQtZsoSLF20XZs1Kly4EBZEzp9FwYjd37jB/PpMns2+f7RIfHxo2pHlzatcmVSqj4f7NpUssXsz8+WzfbrvEw4M6dQgKonZtDckXeVLaPzdLBdjF6QUmIq7s9m0WL+azz/48bxYoVoyAAOrX5/nnHWhXPTqa775j9WpWrOD0aduFnp7UqUNgIHXq4OVlNJ8kCauVnTuZNYuFC/88IOzrS/36BATw6quOdZJweDgrV7JiBd9/bxuUAOTOTWAgHTtqUV8Ru9H+uVkqwC5OLzARcQtHj7JwIQsW/HmGMPDUU9SqRbVqVK1q5siq1crRo2zaxDffsH49t279edVLL9GiBU2bavogNxEZyYoVLFzI118THW270NOT8uWpXZsqVShTxsx3ILdv8+23bNjAhg0cOvTn5X5+NGtG69aUL+9A3yOJuAbtn5ulAuwojh8/PnTo0GLFivXv3//By69fv96+ffvdu3f7+flNmTLllVdeARYuXDho0KDY2NhatWpNnDjR6+GfmXqBiYgbsVrZv59ly1i9mr17/3LV00/z8suULUvZshQrloRVIzKSH39k50527mTrViIi/rzKw4NXXqF+fZo0IW/epAogju3GDVavZtkyNmywzaUcz9eXl1+mfHleeIEXXiBLlqQKYLUSHs7u3WzfzrZt7N1rm7IrXo4cBAQQEEClSnh7J1UGETen/XOzVIAdwogRIxYuXOjj49OoUaMBAwY8eFWPHj2AiRMnrlu3rmPHjqdOnbp06VKJEiV++OGH3LlzV6tWrUWLFt27d3/YPesFJiJu6vRp1qxh40Y2b+batb9c5e1NkSIUK0axYhQsSL585MtHxoyPPIeW1crFi5w8ycmTHD3KwYMcPMixY8TF/eVmOXNSrRo1a1K7Nn5+T/p3iauIiuL7721P0oMHE16bKxfPPkuRIhQtSsGC5M6Nv//jNNLISE6d4vhxjh/n2DH272f/fm7e/MttvLx4+WVq1KBmTUqX1vFekSSn/XOzVIAdwtWrV/38/Nq3b1+4cOEEBThjxoy7d+8uWLAgUKBAgblz5+7atWv37t1z584F5syZM3fu3HXr1j3snvUCExF3FxfHvn1s2WI7KhsenvjNfH3JmpWnniJLFtKmxdcXX198fGyt2GolMpI7d7hzhxs3uHSJixf57TeiohK5K4uFIkUoW5Zy5ahShYIFnWx6akl2ly7x/fd8+y27drF3L/fuJXIbi4Xs2cmcmUyZyJSJdOlIlYqUKfHxIS6O2FhiY7l3j5s3uXWL69e5cIHz5xN23T+kT89LL1G+PC+9xEsvaUpnkWSl/XOzUpgOIAB+DzkmcOPGjevXr+f9faRcnjx5IiIiwsPD8+XL9+AlyRNSRMQpeXhQqhSlStn+efEi+/fbjtb+/DO//sqlSwB37tiO5T4qi4UcOShQwHZIuVgxSpQgfXp7/gni6rJkoXFjGjcGiInh55/56ScOH+bwYQ4d4vRp7t/HaiUigsf7zE+Thqeftj1Jn3uO554jTx4d6RURN6UCnNwGDx68YsWKP/6ZLVu2jRs3PuzGUVFRHh4eHr9/RlksFg8Pj6ioqAy/zxoZf0mC3xo3bty4cePif779xwIhIiICPPUUNWpQo8afl9y+TXg4587x229cvMilS9y+bTvY++AB3lSpbIeF06a1HSvOmpVcuciTBx+f5P87xFV5eVGyJCVL/nlJbCznz3P6NGfOcOUKV65w+TK3b3PvHpGRREXh4YGnJ56e+PiQLp3tv6eeInt2smfH35+sWTUKQUTERgU4uX344Ycffvjhf7xxlixZUqRIce7cuVy5cgGnTp3Kmzevv7//iRMn4m8Qf0mC3woJCQkJCYn/2V+rFoiI/LM0aWxHbkUckqcn/v5ahUhExD40/MWhWSyWgICASZMmAevWrUuZMmWpUqUaNWq0atWqM2fOxMTEhIWFtWnTxnRMERERERERJ6AjwA5qwYIFR44cGTp06CeffNKuXbvcuXNnzpw5fuKrIkWKDB8+vEqVKlFRUS1btnzttddMhxUREREREXECmgXaxWmWORERERERx6H9c7M0BFpERERERETcggqwiIiIiIiIuAUVYBEREREREXELKsAiIiIiIiLiFlSARURERERExC2oAIuIiIiIiIhbUAEWERERERERt6ACLCIiIiIiIm5BBVhERERERETcggqwiIiIiIiIuAUVYBEREREREXELKsAiIiIiIiLiFlSARURERERExC2oAIuIiIiIiIhbUAEWERERERERt6ACLCIiIiIiIm5BBVhERERERETcggqwiIiIiIiIuAUVYBEREREREXELKsAiIiIiIiLiFixWq9V0BklCKVKkyJYtm+kULuX27dtp0qQxncJ1aHvanTap3WmT2p02qX1pe9qdNqndaZM+6NKlS1FRUaZTuK8UpgNI0sqWLdvZs2dNp3Ap/v7+2qR2pO1pd9qkdqdNanfapPal7Wl32qR2p00qjkNDoEVERERERMQtqACLiIiIiIiIW1ABdnEhISGmI7gabVL70va0O21Su9MmtTttUvvS9rQ7bVK70yYVx6FJsERERERERMQt6AiwiIiIiIiIuAUVYBEREREREXELKsAiIiIiIiLiFlSAXdaxY8fKly9fqFChF1988dChQ6bjOKVr164FBgbmypXLdBDXoU1qd/v27QsKCurUqVPHjh01p4M4oPDw8CJFigQFBb333nums7gIvertbtu2bUFBQUFBQTly5Dh27JjpOE7v3r17AQEBhQoVKlmyZK1atcLDw00nEvkLTYLlsqpWrdq2bdv27dsvXbp07NixO3bsMJ3IWVWvXn3jxo2mU7gUbdKk0LRp05kzZ6ZJk8Z0EJG/CA8Pb9asWdGiRStXrty+fXvTcVyKXvX2deHCha5du65cudJ0EKd37969TZs21a5d22KxTJo0adWqVevXrzcdSuRPOgLsmn777bcff/zx9ddfB1577bWTJ0/q6zcRV7Vhw4ZWrVplypQpderUprO4jmvXrvn7+2fOnLlkyZJFihTx8fHp2rWr6VBOKU+ePLt27Zo5c+bq1av1SWQvetUnhcmTJ3fr1s10CleQMmXKOnXqWCwWoFy5cr/++it6UxVHogLsms6cOZMjR44UKVIAFosld+7cp0+fNh1KRJJEjRo15s+f7+npuXfvXtNZXEfGjBlbtWoVEhKyd+/e8ePHv/zyy1OnTjUdyinF7wRbLJasWbPeunXLdBwXoVe93d27d++777579dVXTQdxNZ988kn9+vXRm6o4EhVglxW/zxFPA90fW1BQ0JEjR4KCgk6cOIG+v7QHbVL7WrduXc+ePbt37x4bG1u8eHG0Se1n7969pUqVAvbs2VOyZEnTcZzVli1bAgMDO3XqlDJlSj1F7UKv+qQwd+7cli1b/rHvpE1qF8OHDz927NiwYcPi/6k3VXEUVnFFFy9eTJcuXUxMjNVqjYuLe+qpp06ePGk6lIvo16/fsGHDrFbr119/XaVKFdNxXIE2qd1pk9pFlixZIiIirFZr48aNZ8+ebTqOS9FT1O60Se1Om/QJjR49ukyZMteuXfvjEr2pioPQEWDXlDVr1lKlSs2dOxdYtmxZ3rx58+bNazqUi9D3l3anTWp32qRP7ty5cx4eHtmzZwf2798ff5xN7EVPUbvTJrU7bdInMW7cuAULFmzYsCFDhgzxl+hNVRyHCrDLmjp16tSpUwsVKjRixIgZM2aYjuOULH8D7N27N/6DUJ+Ij0Gb1O60SZPITz/99MemS5cuXWhoqNE4TkxPUbvTJrU7bVL7Onv2bJ8+fa5fv16lSpWSJUuWLVsWvamKI9EySCKP4Ny5c2XKlLlw4QLw9NNPL168OP7rYXls2qR2p00qDk5PUbvTJrU7bVIRF6YjwCL/YvDgwdWrV4//Wd9fPqHNmzcHBga2bt36jTfeiL9Em9Qu9CwVB6enqB3pjTSJ6Fkq4iZ0BFjknyxZssTT0zM0NHTjxo2ms7iU+vXrz58/P23atKaDuAI9S8XB6SmaRPRGakd6loq4Dx0BFnmoY8eO7dmzp3HjxqaDuJovv/yyaNGi2mmzCz1LxcHpKZpE9EZqR3qWirgVFWCRh1q7du2NGzd69ep19OjRVatWmY7jIqZPn/6///1v5MiRpoO4CD1LxcHpKZoU9EZqX3qWirgVDYEW+XfVq1fXmCi7WLZsWa9everWrQt88MEHWbJkMZ3IdehZKg5OT1F70Rtp0tGzVMQdqACLiIiIiIiIW9AQaBEREREREXELKsAiIiIiIiLiFlSARURERERExC2oAIuIiIiIiIhbUAEWERERERERt6ACLCIiIiIiIm5BBVhERERERETcggqwiIiIiIiIuAUVYBEREREREXELKsAiIiIiIiLiFlSARURERERExC2oAIuIiIiIiIhbUAEWERERERERt6ACLCIiIiIiIm5BBVhERERERETcggqwiIiIiIiIuAUVYBEREREREXELKsAiIiIiIiLiFlSARURERERExC2oAIuIiIiIiIhbUAEWERERERERt6ACLCIiIiIiIm5BBVhERERERETcggqwiIiIiIiIuAUVYBEREREREXELKsAiIiIiIiLiFlSARURERERExC2oAIuIiIiIiIhbUAEWERERERERt6ACLCIiIiIiIm5BBVhERERERETcggqwiIiIiIiIuAUVYBEREREREXELKsAiIiIiIiLiFlSARURERERExC2oAIuIiIiIiIhbUAEWERERERERt6ACLCIiIiIiIm5BBVhERERERETcggqwiIiIiIiIuAUVYBEREREREXELKsAiIiIiIiLiFlSARURERERExC2oAIuIiIiIiIhbUAEWERERERERt6ACLCIiIiIiIm5BBVhERERERETcggqwiIiIiIiIuAUVYBEREREREXELKsAiIiIiIiLiFlSARURERERExC2oAIuIiIiIiIhbUAEWERERERERt6ACLCIiIiIiIm5BBVhERERERETcwv8BtLOK2rzMNHYAAAAASUVORK5CYII=)\n\n   ​\t图片绘制出来看起来非常平滑，非常美。虽然大体的图像已经绘制出来了，但是作为一个科学绘图库这点只是皮毛。接下来要给图像增加更多元素，使之看起来更完美。\n\n   2. 添加图例\n\n      使用`plt.legend(loc = (upper,left),frameon = True,framealpha = 0.7)`给图片添加图例。\n\n      ```python\n      import numpy as np\n      from matplotlib import pyplot as plt\n      import matplotlib\n      plt.figure(figsize = (16,9),dpi = 80)\n      plt.title('绘制正余弦函数',fontsize = 30)\n      x = np.linspace(0,2*np.pi,256)\n      y1 = np.sin(x)\n      y2 = np.cos(x)\n      plt.ylim(y1.min()*1.1,y1.max()*1.1)\n      plt.xlim(x.min()*1.1,x.max()*1.1)\n      plt.xticks([0,np.pi/4,np.pi/2,np.pi*3/4,np.pi,np.pi*5/4,np.pi*3/2,np.pi*7/4,np.pi*2],\\\n      \t\t\t[r'$0$',r'$\\frac{1}{4}\\pi$',r'$\\frac{1}{2}\\pi$',r'$\\frac{3}{4}\\pi$',r'$\\pi$',\\\n      \t\t\tr'$\\frac{5}{4}\\pi$',r'$\\frac{3}{2}\\pi$',r'$\\frac{7}{4}\\pi$',r'$2\\pi$',])\n      plt.plot(x,y1,'-b',linewidth = 2, label = 'sine')\n      plt.plot(x,y2,'-r',linewidth = 2, label = 'cosine' )\n      plt.legend(loc = 'upper left',frameon = True,framealpha = 0.7)\n       \n      plt.show()\n      ```\n\n      ![legend](data:image/jpeg;base64,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)\n\n   3.  调整坐标轴原点使其与图像中心对齐\n\n      坐标轴线和上面的记号连在一起就形成了脊柱（Spines）\n\n      在matplotlib的定义中一个图像有四条脊柱，我们为了实现目标，必须把上和右的spines置为无色，然后把左和下的spines调整到合适的位置。\n\n      ```python\n      ax = plt.gca()\n      ax.spines['top'].set_color('none')\n      ax.spines['right'].set_color('none')\n      ax.spines['bottom'].set_position(('data',0))\n      ax.spines['left'].set_position(('data',0))\n      ```\n\n      ![sin_cos_coords](data:image/jpeg;base64,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)\n\n   4. 添加锚点和虚线，添加标注\n\n      ```python\n      import numpy as np\n      from matplotlib import pyplot as plt\n      import matplotlib\n      plt.figure(figsize = (16,9),dpi = 80)\n      plt.title('绘制正余弦函数',fontsize = 40,va = 'bottom')\n      x = np.linspace(-np.pi,2*np.pi,384)\n      y1 = np.sin(x)\n      y2 = np.cos(x)\n      \n      plt.ylim(y1.min()*1.1,y1.max()*1.1)\n      plt.xlim(x.min()*1.1,x.max()*1.1)\n      plt.xticks([-np.pi,-np.pi*3/4,-np.pi/2,-np.pi/4,0,np.pi/4,np.pi/2,np.pi*3/4,np.pi,np.pi*5/4,np.pi*3/2,np.pi*7/4,np.pi*2],\\\n      \t\t\t[r'$-\\pi$',r'$-\\frac{3}{4}\\pi$',r'$-\\frac{1}{2}\\pi$',r'$-\\frac{1}{4}\\pi$',r'$0$',r'$\\frac{1}{4}\\pi$',r'$\\frac{1}{2}\\pi$',r'$\\frac{3}{4}\\pi$',r'$\\pi$',\\\n      \t\t\tr'$\\frac{5}{4}\\pi$',r'$\\frac{3}{2}\\pi$',r'$\\frac{7}{4}\\pi$',r'$2\\pi$',],fontsize = 20)\n      plt.yticks(np.linspace(-1,1,9),np.linspace(-1,1,9),fontsize = 20)\n      \n      ax = plt.gca()\n      ax.spines['top'].set_color('none')\n      ax.spines['right'].set_color('none')\n      ax.spines['bottom'].set_position(('data',0))\n      ax.spines['left'].set_position(('data',0))\n      \n      plt.plot(x,y1,'-b',linewidth = 2, label = 'sine')\n      plt.plot(x,y2,'-r',linewidth = 2, label = 'cosine' )\n      plt.legend(loc = 'upper left',frameon = True,framealpha = 0.7,fontsize = 14)\n      \n      t = np.pi*3/4\n      plt.plot([t,t],[np.sin(t),0],'--b',linewidth = 2)\n      plt.scatter(t,np.sin(t),50,color = 'b')\n      plt.annotate(r'$\\sin(\\frac{3\\pi}{4})=\\frac{\\sqrt{2}}{2}$',(t,np.sin(t)),ha = 'left',xytext = (+50,+30), xycoords = 'data',\\\n      \ttextcoords='offset points',fontsize = 16, arrowprops = dict(arrowstyle = '->',connectionstyle = 'arc3,rad = 0.2'))\n      plt.plot([t,t],[0,np.cos(t)],'--r',linewidth = 2)\n      plt.scatter(t,np.cos(t),50,color = 'r')\n      plt.annotate(r'$\\cos(\\frac{3\\pi}{4})=-\\frac{\\sqrt{2}}{2}$',(t,np.cos(t)),ha = 'left',xytext = (-130,-50),  xycoords = 'data',\\\n      \ttextcoords='offset points',fontsize = 16, arrowprops = dict(arrowstyle = '->',connectionstyle = 'arc3,rad = 0.2'))\n      \n      plt.show()\n      ```\n\n      ![sin_cos_batter](data:image/jpeg;base64,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)\n\n### 使用Axes 布局\n\n可以使用plt.axes((x,y,w,h))创建一个新的坐标系，可以适用该方法绘制出更具有制订性的多子图。相对于subplot来讲这种方法可以设计出更加复杂的布局，因此参数也更加复杂。在绘制前应该画出草稿并计算各子图的坐标及宽高。\n\n**注意：**\n\n> 该函数的参数中x,y,w,h均为0-1的浮点数。代表所占figure的百分比。\n>\n> 使用坐标轴绘图各个图之间有叠加关系，后绘的图会遮住先前绘制的图像。\n\n```python\nimport numpy as np\nimport matplotlib\nfrom matplotlib import pyplot as plt\nplt.figure(figsize = (16,9),dpi = 80)\n\nplt.axes([.05,.65,.9, .3])\nplt.xticks([])\nplt.yticks([])\nplt.text(.5,.5,'Axes1',ha = 'center',va = 'center',size = 16)\n\nplt.axes([.05,.35,.55, .25])\nplt.xticks([])\nplt.yticks([])\nplt.text(.5,.5,'Axes2',ha = 'center',va = 'center',size = 16)\n\nplt.axes([.65,.05,.3, .55])\nplt.xticks([])\nplt.yticks([])\nplt.text(.5,.5,'Axes3',ha = 'center',va = 'center',size = 16)\n\nplt.axes([.05,.05,.25, .25])\nplt.xticks([])\nplt.yticks([])\nplt.text(.5,.5,'Axes4',ha = 'center',va = 'center',size = 16)\n\nplt.axes([.35,.05,.25, .25])\nplt.xticks([])\nplt.yticks([])\nplt.text(.5,.5,'Axes5',ha = 'center',va = 'center',size = 16)\n\nplt.show()\n```\n\n![axes](data:image/jpeg;base64,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)\n\n### 记号\n\n使用Tick Locators来制定记号：\n\n下面有为不同需求设计的一些 Locators。\n\n| 类型              | 说明                                                         |\n| :---------------- | :----------------------------------------------------------- |\n| `NullLocator`     | No ticks. ![img](http://www.runoob.com/wp-content/uploads/2018/10/1540012569-1664-ticks-NullLocator.png) |\n| `IndexLocator`    | Place a tick on every multiple of some base number of points plotted.  ![img](http://www.runoob.com/wp-content/uploads/2018/10/1540012569-4591-ticks-IndexLocator.png) |\n| `FixedLocator`    | Tick locations are fixed.  ![img](http://www.runoob.com/wp-content/uploads/2018/10/1540012569-6368-ticks-FixedLocator.png) |\n| `LinearLocator`   | Determine the tick locations.  ![img](http://www.runoob.com/wp-content/uploads/2018/10/1540012570-7138-ticks-LinearLocator.png) |\n| `MultipleLocator` | Set a tick on every integer that is multiple of some base.  ![img](http://www.runoob.com/wp-content/uploads/2018/10/1540012570-8671-ticks-MultipleLocator.png) |\n| `AutoLocator`     | Select no more than n intervals at nice locations.  ![img](http://www.runoob.com/wp-content/uploads/2018/10/1540012570-3960-ticks-AutoLocator.png) |\n| `LogLocator`      | Determine the tick locations for log axes.  ![img](http://www.runoob.com/wp-content/uploads/2018/10/1540012571-8369-ticks-LogLocator.png) |\n\n这些 Locators 都是 matplotlib.ticker.Locator 的子类，你可以据此定义自己的 Locator。以日期为 ticks 特别复杂，因此 Matplotlib 提供了 matplotlib.dates 来实现这一功能。\n\n\n### 基础教程到此结束，高级教程后续更新。\n\n"
  },
  {
    "path": "NumPy 高维数组降维方法详细分析.md",
    "content": "---\ntitle: NumPy 降维方法深度解析\ndate: 2019-03-13 12:23:56\ntags:\n    - python\n    - NumPy\ntoc: true\n---\n\n\n\n## numpy的flat、flatten、ravel、reshape\n\n四个函数都是对多维数组进行降维（降至一维）\n\n<!--more-->\n\n使用方法：\n\n```python\nimport numpy as np\n\na = np.arange(64).reshape([4,4,4])\nprint(a)\n\n#对三维数组a进行降维打击\nb = a.reshape(-1)\nprint('reshape方法：\\n',b)\nc = []\nfor x in a.flat:\n    c.append(x)\nprint('flat迭代器：\\n',c)\nd = a.flatten()\nprint('flatten方法：\\n',d)\ne = a.ravel()\nprint('ravel方法：\\n',e)\na.resize(64)\nprint('resize方法：\\n',a)\n\n#\n[[[ 0  1  2  3]\n  [ 4  5  6  7]\n  [ 8  9 10 11]\n  [12 13 14 15]]\n\n [[16 17 18 19]\n  [20 21 22 23]\n  [24 25 26 27]\n  [28 29 30 31]]\n\n [[32 33 34 35]\n  [36 37 38 39]\n  [40 41 42 43]\n  [44 45 46 47]]\n\n [[48 49 50 51]\n  [52 53 54 55]\n  [56 57 58 59]\n  [60 61 62 63]]]\nreshape方法：\n [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63]\nflat迭代器：\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]\nflatten方法：\n [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63]\nravel方法：\n [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63]\nresize方法：\n [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63]\n[Finished in 0.3s]\n\n```\n\n\n\n可见这几个降维操作区别还是蛮大的,其中最明显的是flat方法其返回的是一个iterator不能直接输出，需要使用for 来遍历整个iterator。\n\n其中resize 和reshape类似，不同的是resize没有返回值。不能把结果直接赋值给另一个变量。\n\nshape是ndarray中的一个字段，可以直接赋值改变shape形状`a.shape = (3,4)`\n\nflatten 返回的是一个coper，其ndarray本身不变，修改返回值不影响原本的ndarray。\n\nreshape,类似ravel 返回的是一个原数组的一个视图,实质还是引用，虽然ndarray本身的维度不变，但是对降维后的数组进行操作，原数组仍然发生改变。\n\n`ravel()`相当于`reshape(-1)`相当于`reshape(ndarray.size())`\n\n```python\nimport numpy as np\n\na = np.arange(64).reshape([4,4,4])\nprint(a)\n\n#对三维数组a进行降维打击\nb = a.reshape(-1)\nb[0] = 111\nprint('reshape方法：\\n',b,'\\n原数组\\n',a,sep = '')\n#\n[[[ 0  1  2  3]\n  [ 4  5  6  7]\n  [ 8  9 10 11]\n  [12 13 14 15]]\n\n [[16 17 18 19]\n  [20 21 22 23]\n  [24 25 26 27]\n  [28 29 30 31]]\n\n [[32 33 34 35]\n  [36 37 38 39]\n  [40 41 42 43]\n  [44 45 46 47]]\n\n [[48 49 50 51]\n  [52 53 54 55]\n  [56 57 58 59]\n  [60 61 62 63]]]\nreshape方法：\n[111   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n  54  55  56  57  58  59  60  61  62  63]\n原数组\n[[[111   1   2   3]\n  [  4   5   6   7]\n  [  8   9  10  11]\n  [ 12  13  14  15]]\n\n [[ 16  17  18  19]\n  [ 20  21  22  23]\n  [ 24  25  26  27]\n  [ 28  29  30  31]]\n\n [[ 32  33  34  35]\n  [ 36  37  38  39]\n  [ 40  41  42  43]\n  [ 44  45  46  47]]\n\n [[ 48  49  50  51]\n  [ 52  53  54  55]\n  [ 56  57  58  59]\n  [ 60  61  62  63]]]\n[Finished in 0.3s]\n```\n\n\n\n"
  },
  {
    "path": "Python 科学计算.md",
    "content": "---\ntitle: Python科学计算\ndate: 2019-03-09 21:23:56\ntags:\n\n\t- python\n\t- science conputing\ntoc: true\n---\n# Python 科学计算\n\n---\n[TOC]\n---\n\n## NumPy(MatLab 替代品之一)\n\n>- 数组的算数和逻辑运算\n>- 傅立叶变换和用于图形操作的例程\n>-   与线性代数有关的操作。 NumPy 拥有线性代数和随机数生成的内置函数\nfrmemeta\n\n## SciPy（科学计算）\n\n>SciPy是一个开源的算法库和数学工具包。\n>\n>其包含最优化、线性代数、积分、插值、特殊函数、快速傅里叶变换、信号处理和图像处理、常微分方程求解和其他科学与工程常用的计算。\n\n## Matplotlib（科学绘图）\n\n>Matplotlib是Numpy的数值可视化包，它利用通用的图形用户界面工具包向应用程序嵌入式绘图提供接口。\n\n<!--more-->\n\n## NumPy\n\n### NumPy Ndarray\n\nNumPy 是用来处理数组的，在其内部可生成 Ndarray 对象表示数组。\n\nndarray 对象用于存放同类型元素的多维数组。\n\nndarray 中的每个元素在内存中都有相同存储大小区域。\n\nndarrary 内部构成\n\n> 一个指向数据的指针\n>\n> 数据类型 dtype(描述数据在内存中固定大小的格子)\n>\n> 一个表示数组形状 (shape) 的元组 (表示各维度大小的元组)\n>\n> 一个跨度元组 (stride) (可为负数意义大致和切片类似)\n>\n> ![ndarray内部结构](http://www.runoob.com/wp-content/uploads/2018/10/ndarray.png)\n\nnumpy 创建 array\n> `numpy.array(object, dtype = None,copy = True, order = None, subok = False， ndmin = 0)`\n> 参数说明：\n>\n> | 名称   | 描述                                          |\n> | ------ | --------------------------------------------- |\n> | object | 数组或嵌套数列                                |\n> | dtype  | 数组元素的类型                                |\n> | copy   | 深拷贝                                        |\n> | order  | 创建数组样式（C 为行，F为列，A任意(default)） |\n> | subok  | 默认返回一个与基类类型一致的数组              |\n> | ndmin  | 指定生成数组的最小维度                        |\n\neg:\n>创建一维数组\n> ```python\n> import numpy as np\n> a = np.array([1,2,3])\n> print(a)\n>```\n>创建二维数组\n>```python\n>import numpy as np\n> a = np.array([[1,2,3],[3,4,5]])\n> print(a)\n>```\n>创建二维数组\n>```python\n>import numpy as np\n> a = np.array([[1,2,3],[3,4,5]],order = F)\n> print(a)\n>```\n>创建最小维度为二维的数组\n>```python\n> import numpy as np\n> a = np.array([1,2,3],ndmin = 2)\n> print(a)\n> 【output】[[1,2,3]]\n> ```\n>创建数据类型为复数的数组\n>```python\n>import numpy as np\n> a = np.array([1,2,3],dtype = complex,dmin = 2)\n> print(a)\n> 【output】[[1.+0.j,2.+0.j,3.+0.j]]\n> ```\n### NumPy数据类型\nnumpy 支持大量的数据类型，可与C语言的数据类型做参照\n\n| 名称   | 描述                                                   | 字符代码                                               |\n| ------ | ------------------------------------------------------ | ------------------------------------------------------ |\n| bool_  | 布尔类型(True, False)                                  | b                                 |\n| int_   | 默认整数类型(类比C语言的long->int32，int64)            | int         |\n| intc  | 类比C语言的int->int32,int64                            | int                         |\n| intp   | 用于索引的正数类型类比C语言的size_t->int32.int64       | int    |\n| int8   | 8bit，1字节，类比于char(-128 to 127)                   | i1                 |\n| int16  | 16bit整数(-32768 to 32767)                             | i2                           |\n| int32  | 32bit整数(-2147483648 to 2147483647)                   | i3                 |\n| int64  | 64bit整数(-9223372036854775808 to 9223372036854775807) | i4 |\n| uint8  | 无符号8bit整数(0 to 255)                               | u1                             |\n| uint16  | 无符号16bit整数(0 to 65535)                             | u2                           |\n| uint32  | 无符号32bit整数(0 to 4294967295)                   | u3                 |\n| uint64  | 无符号64bit整数(0 to 18446744073709551615) | u4 |\n| float_  | float64类型简写 | f |\n| float16 | 半精度浮点型 ->1 个符号位，5 个指数位，10 个尾数位 | f2 |\n| float32 | 单精度浮点型 ->1 个符号位，8 个指数位，23 个尾数位 | f4 |\n| float64 | 双精度浮点型 ->1 个符号位，11 个指数位，52 个尾数位 | f8 |\n| complex_ | cpmplex128简写，128bit复数 | c16 |\n| complex64 | 64bit复数 | c8 |\n| complex128 | 128bit复数 | c16 |\n\n| 符号         | 描述                                    |\n| ------------ | --------------------------------------- |\n| `'?'`        | boolean                                 |\n| `'b'`        | (signed) byte                           |\n| `'B'`        | unsigned byte                           |\n| `'i'`        | (signed) integer                        |\n| `'u'`        | unsigned integer                        |\n| `'f'`        | floating-point                          |\n| `'c'`        | complex-floating point                  |\n| `'m'`        | timedelta                               |\n| `'M'`        | datetime                                |\n| `'O'`        | (Python) objects                        |\n| `'S'`, `'a'` | zero-terminated bytes (not recommended) |\n| `'U'`        | Unicode string                          |\n| `'V'`        | raw data (`void`)                       |\n\n| Build_In     | 等价                                       |\n| ------------------------------------------------------------ | ------------------------------------------ |\n| `int` | `int_`                                     |\n| `bool` | `bool_`                                    |\n| `float` | `float_`                                   |\n| `complex` | `cfloat`                                   |\n| `bytes` | `bytes_`                                   |\n| `str` | `bytes_` (Python2) or `unicode_` (Python3) |\n| `unicode`                                                    | `unicode_`                                 |\n| `buffer`                                                     | `void`                                     |\n| (all others)                                                 | `object_`                                  |\n\ndtype 可取类型 np.bool_,np.int32,np.float_,np.complex128\n\n*** 数据类型对象dtype ***\n\n数据类型对象是用来描述与数组对应的内存区域如何使用\n\n- 数据的类型\n- 数据的大小\n- 数据的字节顺序 (`‘<’:小端法(最小的存储在最小的地址，低位存前面，低位先存)，’>‘:大端法`)\n\n构造dtype `np.dtype(object, align, copy)`\n\nalign（True/False）如果为 true，填充字段使其类似 C 的结构体\n\ncopy（True/False）复制 dtype 对象 ，如果为 False，则是对内置数据类型对象的引用\n\neg:\n\n> ```python\n> import numpy as np\n> student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')]) \n> a = np.array([('abc', 21, 50),('xyz', 18, 75)], dtype = student) \n> print(student)\n> print(a['name'])\n> print(a['age'])\n> print(a['marks'])\n> ```\n> 输出\n> ```python\n> [('name', 'S20'), ('age', 'i1'), ('marks', '<f4')]\n> [b'abc' b'xyz']\n> [21 18]\n> [50. 75.]\n> ```\n>\n\n### NumPy数组属性\n\n数组的维度成为秩（rank), 一维数组 r=1，二维数组r=2...\n\n每一个线性数组称为一个轴（axis）,也称为维度（dimensions）。秩是轴或维度的数量\n\naxis表示轴，对于二维数组来说，axis = 0对列操作，axis = 1是对行操作。\n\nNumPy比较重要的属性有：\n\n| 属性              | 描述                                                         |\n| ----------------- | ------------------------------------------------------------ |\n| ndarray.ndim      | 秩，即轴的数量或维度的数量                                   |\n| ndarray.shape     | 数组的维度，对于矩阵（二维数组）来说即行列数（n, m）         |\n| ndarray.size      | 数组元素的总个数(n*m)                                        |\n| ndarray.dtype     | ndarray 对象的类型                                           |\n| ndarray.itemsizes | ndarray 每个元素的大小                                       |\n| ndarray.flag      | ndarray 对象的信息                                           |\n| ndarray.real      | ndarray 元素的实部                                           |\n| ndarray.imag      | ndarray 元素的虚部                                           |\n| ndarray.data      | 包含实际数组元素的缓冲区，一般通过数组的索引获得元素（可忽略） |\n\neg:\n\nnp.arange[^arange]\n\n```python\nimport numpy as np\na = np.arange\n```\n\n\n[^arange]: arange和range\n\nrange返回list，arange返回numpy.ndarray。\n\n都是以三点切片方式控制生成的数列。\n\n\n```python\narange(start, end, step)\nrange(start, end, step)\n```\n但是arange的step可以是小数，其返回值数列元素类型也为float。\n\n但是有一点类似，返回结果都不包括end。\n\nndarray.ndim\n\n```python\nimport numpy as np\na = np.arange(24)\nprint(a.ndim) #1\nb = np.array(a,shape(3,4,2))\nprint(b)\nprint(b.ndim) #3\n```\n\n\n\nndarray.shape\n\n```python\nimport numpy as np\na = n.array([[1,2,3],[4,5,6]])\nprint(a.shape) #(2, 3)\n```\n\nndarray.reshape\n\n注意:修改a.shape = shape或a.reshape()是返回一个视图，若返回的视图被修改，源数组也会发生改变。\n\n```python\nimport numpy as np\na = np.array([[1,2,3],[4,5,6]])\nb = a.reshape(3, 2)\nprint(b)\n[[1, 2],[3, 4],[5, 6]]\na.shape = (3,2)\nprint(a)\n[[1, 2],[3, 4],[5, 6]]\n```\n\nndarray.itemsize\n\n```python\nimport numpy as np\nx = np.array([1,2,3,4,5,6],dtype = np.int8)\nprint(x.itemsize) #1\ny = np.array([1,2,3,4,5],dtype = np.float64)\nprint(y.itemsize) #8\n```\n\nndarray.flags\n\n返回ndarray的对象信息\n\n| 属性             | 描述                                                         |\n| ---------------- | ------------------------------------------------------------ |\n| C_CONTIGUOUS (C) | 数据是在一个单一的C风格的连续段中                            |\n| F_CONTIGUOUS (F) | 数据是在一个单一的Fortran风格的连续段中                      |\n| OWNDATA (O)      | 数组拥有它所使用的内存或从另一个对象中借用它                 |\n| WRITEABLE (W)    | 数据区域可以被写入，将该值设置为 False，则数据为只读         |\n| ALIGNED (A)      | 数据和所有元素都适当地对齐到硬件上                           |\n| UPDATEIFCOPY (U) | 这个数组是其它数组的一个副本，当这个数组被释放时，原数组的内容将被更新 |\n\n```python\nimport numpy as np\nx = np.array([1, 2, 3, 4, 5])\nprint(x.flags)\n# \nC_CONTIGUOUS : True\nF_CONTIGUOUS : True\nOWNDATA : True\nWRITEABLE : True\nALIGNED : True\nWRITEBACKIFCOPY : False\nUPDATEIFCOPY : False\n```\n\n### NumPy创建数组\n\n1. b = np.array([...] ,dtype = ...)\n\n2. numpy.empty(shape,dtype = float, order = 'C')\n\n   | 参数  | 描述                                                         |\n   | ----- | ------------------------------------------------------------ |\n   | shape | 数组形状                                                     |\n   | dtype | 数据类型，可选                                               |\n   | order | 有\"C\"和\"F\"两个选项,分别代表，行优先和列优先，在计算机内存中的存储元素的顺序。 |\n\n   ```python\n   import numpy as np\n   x = np.empty([1,2],dtype = int)\n   print(x)\n   #未初始化所以是随机值\n   #未初始化所以是随机值\n   [[-5968 18343]\n    [32766     0]\n    [-1344 18343]]\n   [Finished in 0.3s]\n   ```\n\n3. numpy.zeros(shape, dtype = np.float, order = 'C')\n\n   ```python\n   import numpy as np\n   x = np.zeros([3,2], dtype = np.int)\n   print(x)\n   y = np.zeros([2,3], dtype = [('x','i4'),('y','i4'),('z', 'i4')])\n   print(y)\n   #\n   [[0 0]\n    [0 0]\n    [0 0]]\n   [[(0, 0) (0, 0) (0, 0)]\n    [(0, 0) (0, 0) (0, 0)]]\n   ```\n\n4. numpy.ones(shape, dtype = np.int4)\n\n   ```python\n   import numpy as np\n   x = np.ones([3,2], dtype = np.int4)\n   print(x)\n   #\n   [[1 1]\n    [1 1]\n    [1 1]]\n   ```\n\n### NumPy从已有的数组创建数组\n\n**numpy.asarray方法**\n\nnumpy.asarray 类似numpy.array，但是numpy.asarray的参数只有三个。\n\n`numpy.asarray(a, dtype = None, order = None)`\n\n| 参数  | 描述                                                         |\n| ----- | ------------------------------------------------------------ |\n| a     | 任意形式的输入参数，可以是，列表, 列表的元组, 元组, 元组的元组, 元组的列表，多维数组 |\n| dtype | 数据类型，可选                                               |\n| order | 可选，有\"C\"和\"F\"两个选项,分别代表，行优先和列优先，在计算机内存中的存储元素的顺序。 |\n\n将数列转换成 ndarray\n\n```python\nimport numpy as np\nx = [1, 2, 3]\na = np.asarray(x)\nprint(a)\n#\n[1, 2, 3]\n```\n\n将元组转化为ndarray\n\n```python\nimport numpy as np\nx = (1, 2, 3)\na = np.asarray(x)\nprint(a)\n#\n[1, 2, 3]\n```\n\n将元组列表转换成ndarray\n\n```python\nimport numpy as np\nx = [(1, 2, 3), (4, 5, 6)]\na = np.asarray(x)\nprint(a)\n#\n[[1,2,3],\n [4,5,6]]\n```\n\n**numpy.frombuffer方法**\n\nnumpy.frombuffer可以实现动态数组，其接受buffer输入参数，以流的形式读入转化成ndarray对象\n\n```python\nnumpy.frombuffer(buffer, dtype = float, count = -1, offset = 0)\n```\n\n| 参数   | 描述                                     |\n| ------ | ---------------------------------------- |\n| buffer | 可以是任意对象，会以流的形式读入。       |\n| dtype  | 返回数组的数据类型，可选                 |\n| count  | 读取的数据数量，默认为-1，读取所有数据。 |\n| offset | 读取的起始位置，默认为0。                |\n\n```python\nimport numpy as np\ns = b'Hellow,world!'\na = np.frombuffer(s,dtype = 'S1')\nprint(a)\n#\n[b'H' b'e' b'l' b'l' b'o' b'w' b',' b'w' b'o' b'r' b'l' b'd' b'!']\n```\n\n**NumPy.fromiter方法**\n\nnumpy.fromiter方法可从可迭代对象中建立ndarray 对象，返回一维数组\n\n`np.fromer(iterable, dtype, count = -1)`\n\n| 参数     | 描述                                   |\n| -------- | -------------------------------------- |\n| iterable | 可迭代对象                             |\n| dtype    | 返回数组的数据类型                     |\n| count    | 读取的数据数量，默认为-1，读取所有数据 |\n\n```python\nimport numpy as np \nl = range(9)\ni = iter(l)\nx = np,fromiter(i, dtype = np.int4)\nprint(x)\n#\n[0 1 2 3 4 5 6 7 8]\n```\n\n### NumPy从数值范围创建数组\n\n使用**numpy.arange**方法生成ndarray对象。\n\n`numpy.arange(start, stop, step, dtype)`\n\n| 参数    | 描述                                                         |\n| ------- | ------------------------------------------------------------ |\n| `start` | 起始值，默认为`0`                                            |\n| `stop`  | 终止值（不包含）                                             |\n| `step`  | 步长，默认为`1`(step 可以为浮点小数)                         |\n| `dtype` | 返回`ndarray`的数据类型，如果没有提供，则会使用输入数据的类型 |\n\neg:\n\nx = np.arange(5)\n\n#[0,1,2,3,4]\n\nx = np.arange(5, dtype = float)\n\nprint(x)\n\n#\n[0.  1.  2.  3.  4.]\n\n```python\nx =np.arange(10, 20, 2)\nprint(x)\n#\n[10 12 14 16 18]\n```\n\n**numpy.linspace**函数用于创建一个一维数组，数组是一个等差数列构成的。\n\n`np.linspace(start, stop, num = 50, endpoint = True, retstep = False, dtype = None)`\n\n| 参数     | 描述                                     |\n| -------- | ---------------------------------------- |\n| start    | 序列起始值                               |\n| stop     | 序列终止值                               |\n| num      | 要生成等步长样本的数量，默认值为50       |\n| endpoint | Ture代表包含stop,反之不包含。默认为True  |\n| retstep  | Ture代表生成的数组会显示间距，反之不显示 |\n| dtype    | ndarray的数据类型                        |\n\n```python\nimport numpy as np\na = np.linsapce(1, 10, 50,retstep = True)\nprint(a)\n#\n(array([ 1.        ,  1.59183673,  2.18367347,  2.7755102 ,  3.36734694,\n        3.95918367,  4.55102041,  5.14285714,  5.73469388,  6.32653061,\n        6.91836735,  7.51020408,  8.10204082,  8.69387755,  9.28571429,\n        9.87755102, 10.46938776, 11.06122449, 11.65306122, 12.24489796,\n       12.83673469, 13.42857143, 14.02040816, 14.6122449 , 15.20408163,\n       15.79591837, 16.3877551 , 16.97959184, 17.57142857, 18.16326531,\n       18.75510204, 19.34693878, 19.93877551, 20.53061224, 21.12244898,\n       21.71428571, 22.30612245, 22.89795918, 23.48979592, 24.08163265,\n       24.67346939, 25.26530612, 25.85714286, 26.44897959, 27.04081633,\n       27.63265306, 28.2244898 , 28.81632653, 29.40816327, 30.        ]), 0.5918367346938775)\n```\n\n**NumPy.logspace**函数用于创建一个一维数组，数组是一个等比数列构成的。\n\n`numpy.logspace(star, stop, num = 50, endpoint, base = '10.0', dtype)`\n\n| `start`    | 序列的起始值为：base ** start                                |\n| ---------- | ------------------------------------------------------------ |\n| `stop`     | 序列的终止值为：base ** stop。如果`endpoint`为`true`，该值包含于数列中 |\n| `num`      | 要生成的等步长的样本数量，默认为`50`                         |\n| `endpoint` | 该值为 `ture` 时，数列中中包含`stop`值，反之不包含，默认是True。 |\n| `base`     | 对数 log 的底数。                                            |\n| `dtype`    | `ndarray` 的数据类型，默认float                              |\n\n```python\nimport numpy as np\na = np.logspace(1,10,dtype = np.int64)\nprint(a)\n#\n[         10          15          23          35          54          82\n         126         193         294         449         686        1048\n        1599        2442        3727        5689        8685       13257\n       20235       30888       47148       71968      109854      167683\n      255954      390693      596362      910298     1389495     2120950\n     3237457     4941713     7543120    11513953    17575106    26826957\n    40949150    62505519    95409547   145634847   222299648   339322177\n   517947467   790604321  1206792640  1842069969  2811768697  4291934260\n  6551285568 10000000000]\n```\n\n```python\nimport numpy as np\na = np.logspace(1,10,num = 10,base = 2)\nprint(a)\n#\n[   2.    4.    8.   16.   32.   64.  128.  256.  512. 1024.]\n```\n\n### NumPy 切片和索引\n\nnumpy的ndarray 可以像list那样通过切片和索引操作或访问\n\n```python\nimport numpy as np\na = np.arange(8)\ns = slice(0,5,2)\nb = a[s]\nc = a[:5:2]\nprint(a,b,c,sep = '\\n')\n#\n[0 1 2 3 4 5 6 7]\n[0 2 4]\n[0 2 4]\n```\n\npython list多维数组下标访问\n\n```python\na = [\n    [[1,2,4],[4,5,6]],\n    [[7,8,9],[10,11,12]],\n    [[13,14,15],[16,17,18]]\n]\nprint(a[2][1][2])\n#\n18\n```\n\nnumpy ndarray多维数组下标访问\n\n```python\nimport numpy as np\na = np.arange(64).shape(4,4,4)\nb = a[2,2,2]\nprint(b)\n```\n\n\n\n多维数组操作\n\n```python\nimport numpy as np\na = np.array([[1,2,3],[4,5,6],[7,8,9]])\nprint(a[:2,:2])\n#\n[[1 2]\n [4 5]]\n```\n\n用`...`代替全维度`::`\n\n```python\nimport numpy as np\na = np.arange(64)\nb = a.reshape(4,4,4)\nprint(b[...,2,2])\n#\n[[[ 0  1  2  3]\n  [ 4  5  6  7]\n  [ 8  9 10 11]\n  [12 13 14 15]]\n\n [[16 17 18 19]\n  [20 21 22 23]\n  [24 25 26 27]\n  [28 29 30 31]]\n\n [[32 33 34 35]\n  [36 37 38 39]\n  [40 41 42 43]\n  [44 45 46 47]]\n\n [[48 49 50 51]\n  [52 53 54 55]\n  [56 57 58 59]\n  [60 61 62 63]]]\n\n[10 26 42 58]\n```\n\n### NumPy 高级索引\n\n- 整数数组索引\n\n  使用二维数组分别表示行列值\n\n  输出矩阵四角元素的值。\n\n  ```python\n  import numpy as np\n  x = np.arange(12).reshape(4,3)\n  print(x)\n  print(x[[0,0,3,3],[0,2,0,2]])\n  #\n  [[ 0  1  2]\n   [ 3  4  5]\n   [ 6  7  8]\n   [ 9 10 11]]\n  [0 2 9 11]\n  \n  y = x[0:-2:-1,[0:-2:-1]]\n  \n  rows = np.array([[0,0],[3,3]]) \n  cols = np.array([[0,2],[0,2]]) \n  y = x[rows,cols]  \n  print(y)\n  #\n  [[0 2]\n   [9 11]\n  ```\n\n  ```python\n  import numpy as np\n   \n  a = np.array([[1,2,3], [4,5,6],[7,8,9]])\n  b = a[1:3, 1:3]\n  c = a[1:3,[1,2]]\n  d = a[...,1:]\n  print(b)\n  print(c)\n  print(d)\n  \n  #\n  b = [5 6 8 9]\n  c = [[5 6][8 9]]\n  d = [[2 3][5 6][8 9]]\n  ```\n\n- 布尔索引\n\n  使用布尔切片过滤掉ndarray小于等于5的值\n\n  ```python\n  import numpy as np\n  x = np.array([[0,1,2],[3,4,5],[6,7,8],[9,10,11]])\n  print(x[x>5])\n  #\n  [ 6  7  8  9 10 11]\n  ```\n  使用布尔切片过滤掉ndarray等于NaN的值\n  ```python\n  import numpy as np\n  a = np.array(np.nan,1,2,np.nan,4)\n  print(a[~isnan(a)])\n  [ 1 2 4]\n  ```\n  使用布尔切片保留复数\n\n  ```python\n  import numpy as np\n  a = np.array([1, 2+6j, 5, 3.5+5j])\n  print(a[np.iscomplex(a)])\n  #\n  [2. +6.j 3.5+5.j]\n  ```\n\n- 花式索引\n\n  使用整数数组进行索引\n\n  一维数组为第一维度\n\n  ```python\n  import numpy as np\n  x = np.arange(32).reshape(8,4)\n  print(x[[4,2,1,7]])\n  #\n  [[16 17 18 19]\n   [ 8  9 10 11]\n   [ 4  5  6  7]\n   [28 29 30 31]]\n  ```\n\n  传入倒序一维数组\n\n  ```python\n  import numpy as np\n  x = np.arange(32).reshape(8,4)\n  print(x)\n  print(x[[-4,-2,-1,-7]])\n  #\n  [[16 17 18 19]\n   [24 25 26 27]\n   [28 29 30 31]\n   [ 4  5  6  7]]\n  ```\n\n  传入两个索引数组\n\n  ```python\n  import numpy as np\n  x = np.arange(32).reshape(8,4)\n  print(x)\n  print(x[np.ix_([1,5,3,2],[0,3,2])])\n  #\n  [[ 4  7  6]\n   [20 23 22]\n   [12 15 14]\n   [ 8 11 10]]\n  ```\n\n  ### 广播数组（Broadcast）\n\n  ndarray 做算术运算时要求两个ndarray同型即a.shape=b,shape\n\n  但是遇到在末尾维度相同但是前面不相同时即会自动触发广播机制。\n\n  ```python\n  import numpy as np\n  a = np.arange(12).reshape(4,3)\n  b = np.arange(12,24).reshape(4,3)\n  c = np.asarray([10,20,30])\n  print(a,b,c,sep = '\\n')\n  print(f'a,b同型直接运算：\"\\n\"{a+b}')\n  print(f'a,c不同维度，a自动出发广播然后运算：\"\\n\"{a+c}')\n  #\n  [[ 0  1  2]\n   [ 3  4  5]\n   [ 6  7  8]\n   [ 9 10 11]]\n  [[12 13 14]\n   [15 16 17]\n   [18 19 20]\n   [21 22 23]]\n  [10 20 30]\n  a,b同型直接运算：\n  [[12 14 16]\n   [18 20 22]\n   [24 26 28]\n   [30 32 34]]\n  a,c不同维度，a自动出发广播然后运算：\n  [[10 21 32]\n   [13 24 35]\n   [16 27 38]\n   [19 30 41]]\n  ```\n\n  广播原理：\n\n  ![broadcase](http://www.runoob.com/wp-content/uploads/2018/10/image0020619.gif)\n\n  **np.tile(x,y,z,...)方法**：tile即瓷砖，即将该数组进行横向纵向复制\n\n  ```python\n  import numpy as np\n  mat = np.tile(np.array([[1,2,3],[4,5,6]]),(2,3))\n  print(mat)\n  #\n  [[1 2 3 1 2 3 1 2 3]\n   [4 5 6 4 5 6 4 5 6]\n   [1 2 3 1 2 3 1 2 3]\n   [4 5 6 4 5 6 4 5 6]]\n  ```\n\n  即broadcast 执行过程：\n\n  ```python\n  import numpy as np\n  a = np.array([[0,0,0],\n                [10,10,10],\n                [20,20,20],\n                [30,30,30]\n  \t\t\t])\n  b = np.array([1,2,3])\n  c = a+b\n  d = a+np.tile(b,(4,1))\n  print(c,d,sep = '\\n')\n  #\n  [[ 1  2  3]\n   [11 12 13]\n   [21 22 23]\n   [31 32 33]]\n  [[ 1  2  3]\n   [11 12 13]\n   [21 22 23]\n   [31 32 33]]\n  ```\n\n  **广播的规则:**\n\n- 让所有输入数组都向其中形状最长的数组看齐，形状中不足的部分都通过在前面加 1 补齐。\n\n- 输出数组的形状是输入数组形状的各个维度上的最大值。\n\n- 如果输入数组的某个维度和输出数组的对应维度的长度相同或者其长度为 1 时，这个数组能够用来计算，否则出错。\n\n- 当输入数组的某个维度的长度为 1 时，沿着此维度运算时都用此维度上的第一组值。\n\n  **简单理解：**对两个数组，分别比较他们的每一个维度（若其中一个数组没有当前维度则忽略），满足：\n\n- 数组拥有相同形状。\n\n- 当前维度的值相等。\n\n- 当前维度的值有一个是 1。\n\n  若条件不满足，抛出 **\"ValueError: frames are not aligned**或者\n\n  **ValueError: operands could not be broadcast together with shapes\"** 异常。\n\n### NumPy迭代数组\n\nNumpy提供了numpt.nditer用来灵活访问数组元素\n\n\n```python\nimport numpy as np\n\na = np.arange(6).reshape(3,2)\n\nprint(a)\n\nfor x in np.nditer(a):\n\tprint(x,end = ',')\nprint('\\n')\nfor x in np.nditer(a.T):\n\tprint(x,end = ',')\nprint('\\n')\nfor x in np.nditer(a.T.copy(order = 'C')):\n\tprint(x,end = ',')\nprint('\\n')\nfor x in np.nditer(a.T,order = 'C'):\n\tprint(x,end = ',')\nprint('\\n')\nfor x in np.nditer(a.T,order = 'F'):\n\tprint(x,end = ',')\nprint('\\n')\n#\n[[0 1]\n [2 3]\n [4 5]]\n0,1,2,3,4,5,\n\n0,1,2,3,4,5,\n\n0,2,4,1,3,5,\n\n0,2,4,1,3,5,\n\n0,1,2,3,4,5, #反反得反\n```\n**ndarray.T**表示ndarray的转置\n\n```python\nimport numpy as np\n \na = np.arange(0,60,5) \na = a.reshape(3,4)  \nprint ('原始数组是：') \nprint (a) \nprint ('\\n') \nprint ('原始数组的转置是：') \nb = a.T #b是a的转置\nprint (b) \nprint ('\\n') \nprint ('以 C 风格顺序排序：') \nc = b.copy(order='C')  \nprint (c)\nfor x in np.nditer(c):  \n    print (x, end=\", \" )\nprint  ('\\n') \nprint  ('以 F 风格顺序排序：')\nc = b.copy(order='F')  \nprint (c)\nfor x in np.nditer(c):  \n    print (x, end=\", \" )\n#\n原始数组是：\n[[ 0  5 10 15]\n [20 25 30 35]\n [40 45 50 55]]\n\n\n原始数组的转置是：\n[[ 0 20 40]\n [ 5 25 45]\n [10 30 50]\n [15 35 55]]\n\n\n以 C 风格顺序排序：\n[[ 0 20 40]\n [ 5 25 45]\n [10 30 50]\n [15 35 55]]\n0, 20, 40, 5, 25, 45, 10, 30, 50, 15, 35, 55, \n\n以 F 风格顺序排序：\n[[ 0 20 40]\n [ 5 25 45]\n [10 30 50]\n [15 35 55]]\n0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,\n```\n**修改数组中元素的值**\n\nop_flags = ['readwrite']\n\n```python\nimport numpy as np\n\na = np.arange(0,60,5).reshape(3,4)\nprint('原始数据：\\n',a)\nfor x in np.nditer(a, op_flags = ['readwrite']):\n\tx[...]*=2\nprint('现在数据：\\n',a)\n#\n原始数据：\n [[ 0  5 10 15]\n [20 25 30 35]\n [40 45 50 55]]\n现在数据：\n [[  0  10  20  30]\n [ 40  50  60  70]\n [ 80  90 100 110]]\n```\n\n**使用外部循环**(不重要)\n\nnp.nditer()有很多参数其中flags参数可取下列值\n\n| 参数            | 描述                                           |\n| --------------- | ---------------------------------------------- |\n| `c_index`       | 可以跟踪 C 顺序的索引                          |\n| `f_index`       | 可以跟踪 Fortran 顺序的索引                    |\n| `multi-index`   | 每次迭代可以跟踪一种索引类型                   |\n| `external_loop` | 给出的值是具有多个值的一维数组，而不是零维数组 |\n\n```python\nimport numpy as np \na = np.arange(0,60,5) \na = a.reshape(3,4)  \nprint ('原始数组是：')\nprint (a)\nprint ('\\n')\nprint ('修改后的数组是：')\nfor x in np.nditer(a, flags =  ['external_loop'], order =  'F'):  \n   print (x, end=\", \" )\n```\n\n\n\n**使用广播循环**\n\n如果两个数组可以广播，nditer组合对象能够同时迭代它们。（广播条件，见上广播规则)\n\n```python\nimport numpy as np\na = np.arange(0,60,5).reshape(3,4)\nb = np.array([11,22,33,44],dtype = 'int')\nfor x,y in np.nditer([a,b],order = 'C'):\n    print(f'{x}:{y}',end = ',')\n#\n0:11,5:22,10:33,15:44,20:11,25:22,30:33,35:44,40:11,45:22,50:33,55:44,\n```\n\n\n\n\n### NumPy数组操作\n\n- 修改数组形状(reshape)\n\n  `ndarray.reshape`可以重定义数组行列\n\n  `ndarray.flat`是一个数组元素迭代器，可用for in 访问迭代器内的值\n\n  `ndarray.flatten`返回一份数组拷贝，对拷贝进行修改并不会改变原始数组\n\n  > ndarray.flatten(order = 'C')#按照C（即按行）展开\n  >\n  > ndarray.flatten(order = 'F')#按照F（即按列）展开\n\n  `numpy.ravel(ndarray, order = 'C')`展平数组元素，返回引用修改会引起原来的ndarray发生改变\n\n  > order：'C' -- 按行，'F' -- 按列，'A' -- 原顺序，'K' -- 元素在内存中的出现顺序\n\n- 翻转数组(transpose)\n  `numpy.transpose(ndarray, axes)`翻转数组（相当于ndarray.T）\n\n  axes：维度\n\n  `numpy.rollaxis(ndarray,axis,start)`函数向后滚动一个特定的轴到特定的位置\n\n  axis：要向后滚动的轴\n\n  start = 0：默认为0表示完整的滚动\n\n\n```python\nimport numpy as np\n\na = np.arange(27).reshape([3,3,3])\nprint(a)\n\nprint(np.rollaxis(a,2))\n\nprint(np.rollaxis(a,2,1))\nprint(np.rollaxis(a,1,0))\n#\n[[[ 0  1  2]\n  [ 3  4  5]\n  [ 6  7  8]]\n\n [[ 9 10 11]\n  [12 13 14]\n  [15 16 17]]\n\n [[18 19 20]\n  [21 22 23]\n  [24 25 26]]]\nyzx\n[[[ 0  3  6]\n  [ 9 12 15]\n  [18 21 24]]\n\n [[ 1  4  7]\n  [10 13 16]\n  [19 22 25]]\n\n [[ 2  5  8]\n  [11 14 17]\n  [20 23 26]]]\nxzy\n[[[ 0  3  6]\n  [ 1  4  7]\n  [ 2  5  8]]\n\n [[ 9 12 15]\n  [10 13 16]\n  [11 14 17]]\n\n [[18 21 24]\n  [19 22 25]\n  [20 23 26]]]\nyxz\n[[[ 0  1  2]\n  [ 9 10 11]\n  [18 19 20]]\n\n [[ 3  4  5]\n  [12 13 14]\n  [21 22 23]]\n\n [[ 6  7  8]\n  [15 16 17]\n  [24 25 26]]]\n```\n\n​\t`numpy.swapaxes(ndarray,axis1,axis2)`交换数组的两个轴\n\n```python\nimport numpy as np\n\na = np.arange(27).reshape([3,3,3])\nprint(a)\nprint(np.swapaxes(a,2,1))\nprint(np.swapaxes(a,1,0))\nprint(np.swapaxes(a,2,0))\n#\n[[[ 0  1  2]\n  [ 3  4  5]\n  [ 6  7  8]]\n\n [[ 9 10 11]\n  [12 13 14]\n  [15 16 17]]\n\n [[18 19 20]\n  [21 22 23]\n  [24 25 26]]]\nxzy\n[[[ 0  3  6]\n  [ 1  4  7]\n  [ 2  5  8]]\n\n [[ 9 12 15]\n  [10 13 16]\n  [11 14 17]]\n\n [[18 21 24]\n  [19 22 25]\n  [20 23 26]]]\nyxz\n[[[ 0  1  2]\n  [ 9 10 11]\n  [18 19 20]]\n\n [[ 3  4  5]\n  [12 13 14]\n  [21 22 23]]\n\n [[ 6  7  8]\n  [15 16 17]\n  [24 25 26]]]\n zyx\n[[[ 0  9 18]\n  [ 3 12 21]\n  [ 6 15 24]]\n\n [[ 1 10 19]\n  [ 4 13 22]\n  [ 7 16 25]]\n\n [[ 2 11 20]\n  [ 5 14 23]\n  [ 8 17 26]]]\n```\n\n- 修改数组维度(broadcast,broadcast_to,expand_dims,squeeze)\n\n  | 函数           | 描述                       |\n  | -------------- | -------------------------- |\n  | `broadcast`    | 产生模仿广播的对象         |\n  | `broadcast_to` | 将数组广播到新形状         |\n  | `expand_dims`  | 扩展数组的形状             |\n  | `squeeze`      | 从数组的形状中删除一维条目 |\n\n  `numpy.broadcast `用于模仿广播对象，它返回一个对象（numpy.broadcast object），该对象封装了将一个数组广播到另一个数组的结果。\n\n  \n\n  `numpy.broadcast_to(array, shape, subok)` 函数将数组广播到新形状。它在原始数组上返回只读视图。 它通常不连续。 如果新形状不符合 NumPy 的广播规则，该函数可能会抛出ValueError。\n\n  \n\n  ``numpy.expand_dims(arr, axis)`` 函数通过在指定位置插入新的轴来扩展数组形状。\n\n  `numpy.squeeze(arr,axis = 0)`函数从给定数组的形状中删除一维的条目\n\n  \n\n- 连接数组\n\n  | 函数          | 描述                           |\n  | ------------- | ------------------------------ |\n  | `concatenate` | 连接沿现有轴的数组序列         |\n  | `stack`       | 沿着新的轴加入一系列数组。     |\n  | `hstack`      | 水平堆叠序列中的数组（列方向） |\n  | `vstack`      | 竖直堆叠序列中的数组（行方向） |\n\n`numpy.concatenate((a1, a2, ...), axis = 0)`\n\n```python\nimport numpy as np\n \na = np.array([[1,2],[3,4]])\n \nprint ('第一个数组：')\nprint (a)\nprint ('\\n')\nb = np.array([[5,6],[7,8]])\n \nprint ('第二个数组：')\nprint (b)\nprint ('\\n')\n# 两个数组的维度相同\n \nprint ('沿轴 0 连接两个数组：')\nprint (np.concatenate((a,b)))\nprint ('\\n')\n \nprint ('沿轴 1 连接两个数组：')\nprint (np.concatenate((a,b),axis = 1))\n#\n第一个数组：\n[[1 2]\n [3 4]]\n第二个数组：\n[[5 6]\n [7 8]]\n沿轴 0 连接两个数组：\n[[1 2]\n [3 4]\n [5 6]\n [7 8]]\n沿轴 1 连接两个数组：\n[[1 2 5 6]\n [3 4 7 8]]\n[Finished in 0.3s]\n```\n\n`numpy.stack(arrays, axis)`沿新的轴连接数组，新建一个维度\n\n`numpy.hstack(ndarray1,ndarray2)`水平堆叠生成数组\n\n`numpy.vstack(ndarray1,ndarray2)`垂直堆叠生成数组\n\n- 分割数组\n\n| 函数     | 数组及操作                             |\n| -------- | -------------------------------------- |\n| `split`  | 将一个数组分割为多个子数组             |\n| `hsplit` | 将一个数组水平分割为多个子数组（按列） |\n| `vsplit` | 将一个数组垂直分割为多个子数组（按行） |\n\n`numpy.split(ary, indices_or_sections, axis)`沿特定轴将数组分割为子数组\n>`ary`：被分割的数组\n>`indices_or_sections`：果是一个整数，就用该数平均切分，如果是一个数组，为沿轴切分的位置（左开右闭）\n>`axis`：沿着哪个维度进行切向，默认为0，横向切分。为1时，纵向切分\n\n```python\nimport numpy as np\n \na = np.arange(9)\n \nprint ('第一个数组：')\nprint (a)\nprint ('\\n')\n \nprint ('将数组分为三个大小相等的子数组：')\nb = np.split(a,3)\nprint (b)\nprint ('\\n')\n#[array([0,1,2]),array([3,4,5]),array([6,7,8])]\n \nprint ('将数组在一维数组中表明的位置分割：')\nb = np.split(a,[4,7])\nprint (b)\n#[array([0,1,2,3],array([4,5,6]),array([7,8])]\n```\n\n`numpy.hsplit `函数用于水平分割数组，通过指定要返回的相同形状的数组数量来拆分原数组，用法和split类似。\n\n`numpy.vsplit` 沿着垂直轴分割，其分割方式与split用法相同\n\n- 数组元素的添加和删除\n\n| 函数     | 元素及描述                               |\n| -------- | ---------------------------------------- |\n| `resize` | 返回指定形状的新数组                     |\n| `append` | 将值添加到数组末尾                       |\n| `insert` | 沿指定轴将值插入到指定下标之前           |\n| `delete` | 删掉某个轴的子数组，并返回删除后的新数组 |\n| `unique` | 查找数组内的唯一元素                     |\n\n`numpy.resize(arr, shape)` np.resize(arr,shape)等价于arr.resize(shape)\n\n`numpy.append(arr, values, axis=None)`函数在数组的末尾添加值。 追加操作会分配整个数组，并把原来的数组复制到新数组中。 此外，输入数组的维度必须匹配否则将生成ValueError。\n\n```python\nimport numpy as np\n \na = np.array([[1,2,3],[4,5,6]])\nprint (a)\n#\n[[1 2 3]\n [4 5 6]]\n print ('向数组添加元素：')\nprint (np.append(a, [7,8,9]))\n#未传递axis数组会展开\n[1 2 3 4 5 6 7 8 9]\n \nprint ('沿轴 0 添加元素：')\nprint (np.append(a, [[7,8,9]],axis = 0))\n#\n[[1 2 3]\n [4 5 6]\n [7 8 9]] \nprint ('沿轴 1 添加元素：')\nprint (np.append(a, [[5,5,5],[7,8,9]],axis = 1))\n#\n[[1 2 3 5 5 5]\n [4 5 6 7 8 9]]\n```\n\n`numpy.insert(arr, obj, values, axis)`函数在给定索引之前，沿给定轴在输入数组中插入值。\n\n```python\nimport numpy as np\n \na = np.array([[1,2],[3,4],[5,6]])\nprint (a)\n#\n[[1 2]\n [3 4]\n [5 6]]\nprint ('未传递 Axis 参数。 在插入之前输入数组会被展开。')\nprint (np.insert(a,3,[11,12]))\n#\n[1 2 3 11 12 4 5 6]\nprint ('传递了 Axis 参数。 会广播值数组来配输入数组。')\nprint ('沿轴 0 广播：')\nprint (np.insert(a,1,[11],axis = 0))\n#自动广播\n[[1 2]\n [11 11]\n [3 4]\n [5 6]]\nprint ('沿轴 1 广播：')\nprint (np.insert(a,1,11,axis = 1))\n[[1 11 2]\n [3 11 4]\n [5 11 6]]\n```\n\n`Numpy.delete(arr, obj, axis)`函数返回从输入数组中删除指定子数组的新数组。 与 insert() 函数的情况一样，如果未提供轴参数，则输入数组将展开。\n>  `arr`：输入数组\n>   `obj`：可以被切片，整数或者整数数组，表明要从输入数组删除的子数组\n>   `axis`：沿着它删除给定子数组的轴，如果未提供，则输入数组会被展开\n\n```python\na = np.array([1,2,3,4,5,6,7,8,9,10])\nprint (np.delete(a, np.s_[::2]))\n#np.s_[：：]切片\n```\n\n`numpy.unique(arr, return_index, return_inverse, return_counts)`函数用于去除数组中的重复元素。\n\n>arr：输入数组，如果不是一维数组则会展开\n>return_index：如果为true，返回新列表元素在旧列表中的位置（下标），并以列表形式储\n>return_inverse：如果为true，返回旧列表元素在新列表中的位置（下标），并以列表形式储\n>return_counts：如果为true，返回去重数组中的元素在原数组中的出现次数\n\n### 位运算(bitwise按位)\n\n`numpy.bitwise_and(a,b)`与运算\n\n`numpy.bitwise_or(a,b)`或运算\n\n`numpy.invert()`非\n\n`left_shift(a,width = 8)`按位左移\n\n`right_shift(a,width = 8)`按位右移\n\n### 字节转换函数\n\n`ndarray.byteswap(True/False)`对ndarray中的元素进行大小端字节转换\n\n### 字符串函数\n\n| 函数           | 描述                                       |\n| -------------- | ------------------------------------------ |\n| `numpy.char.add(['s1'],['s2'])` | 对两个数组的逐个字符串元素进行连接         |\n| `numpy.char.multiply(['s'],mult)` | 返回按元素多重连接后的字符串               |\n| `numpy.char.center('str',width = Num,fillchar = '*')` | 居中字符串                                 |\n| `numpy.char.capitalize('str')` | 将字符串第一个字母转换为大写               |\n| `numpy.char.title('str')` | 将字符串的每个单词的第一个字母转换为大写   |\n| `numpy.char.lower('str')` | 数组元素转换为小写                         |\n| `numpy.char.upper('str')` | 数组元素转换为大写                         |\n| `numpy.char.split('str',sep = '*')` | 指定分隔符对字符串进行分割，并返回数组列表 |\n| `numpy.char.splitlines('str')` | 返回元素中的行列表，以换行符分割           |\n| `numpy.char.strip('str','char')` | 移除元素开头或者结尾处的特定字符           |\n| `numpy.char.join(['char1','char2'],['str1',str2])` | 通过指定分隔符来连接数组中的元素           |\n| `numpy.char.replace('str','s1',s2)` | 使用新字符串s2替换字符串中的所有子字符串s1 |\n| `numpy.char.decode()`     | 数组元素依次调用`str.decode`               |\n| `numpy.char.encode()`     | 数组元素依次调用`str.encode`               |\n\n### 数学函数\n\n **三角函数**\n\n​\tNumPy 提供了标准的三角函数：numpy.sin()、numpy.cos()、numpy.tan()\n\n```python\nimport numpy as np\n \na = np.array([0,30,45,60,90])\nprint ('不同角度的正弦值：')\n# 通过乘 pi/180 转化为弧度  \nprint (np.sin(a*np.pi/180))\nprint ('\\n')\nprint ('数组中角度的余弦值：')\nprint (np.cos(a*np.pi/180))\nprint ('\\n')\nprint ('数组中角度的正切值：')\nprint (np.tan(a*np.pi/180))\n```\n\nnumpy.arcsin，numpy.arccos，和 numpy.arctan 函数返回给定角度的 sin，cos 和 tan 的反三角函数\n\n**舍入函数**\n\n​\tnumpy.around(a,decimals)四舍五入\n\n\t>a: 数组\n\t>\n\t>decimals: 舍入的小数位数。 默认值为0。 如果为负，整数将四舍五入到小数点左侧的位置（即 numpy.around(192.586,decimals = -1） 190)\n\n​\tnumpy.floor()向下舍入\n\n​\tnumpy.ceil()向上舍入\n\n### 运算函数\n\nNumPy 算术函数包含简单的加减乘除: `numpy.add()，numpy.subtract()，numpy.multiply() 和 numpy.divide()`\n\n`numpy.reciprocal() `函数返回参数逐元素的倒数\n\n```python\nimport numpy as np \n \na = np.array([0.25,  1.33,  1,  100])  \nprint ('调用 reciprocal 函数：')\nprint (np.reciprocal(a))\n#\n调用 reciprocal 函数：\n[4.        0.7518797 1.        0.01     ]\n```\n\n`numpy.power() `函数将第一个输入数组中的元素作为底数，计算它与第二个输入数组中相应元素的幂。\n\n`numpy.mod()` 计算输入数组中相应元素的相除后的余数。 函数 numpy.remainder() 也产生相同的结果。\n\n### 统计函数\n\n`numpy.amin(arr,axis = None)`用于计算数组中的元素沿指定轴的最小值。axis 默认为None，即将arr展开。\n\n`numpy.amax(arr,axis = None) `用于计算数组中的元素沿指定轴的最大值。用法与numpy.amin相似。\n\nnumpy.ptp(arr,axis = None)函数计算数组中元素最大值与最小值的差（最大值 - 最小值）。\n\n`numpy.percentile(a,q,axis = None，keepdims = False)`百分位数是统计中使用的度量，表示小于这个值的观察值的百分比。 函数numpy.percentile()接受以下参数。\n\n> a：数组\n>\n> q：要计算的百分位数[0-100]\n>\n> axis：维度\n>\n> keepdims：保持原数组的dims\n\n`numpy.median(arr, axis = None)` 函数用于计算数组 a 中元素的中位数（中值）\n\n`numpy.mean(arr, axis = None) `函数返回数组中元素的算术平均值。 (算术平均值是沿轴的元素的总和除以元素的数量。)\n\n`numpy.average(arr, axis = None,weights = wts)` 函数根据在另一个数组中给出的各自的权重计算数组中元素的加权平均值。不指定权重默认为1，此时和mean结果相同\n\n```python\nimport numpy as np \n \na = np.array([1,2,3,4])  \nprint ('我们的数组是：')\nprint (a)\nprint ('\\n')\nprint ('调用 average() 函数：')\nprint (np.average(a))\nprint ('\\n')\n# 不指定权重时相当于 mean 函数\nwts = np.array([4,3,2,1])  \nprint ('再次调用 average() 函数：')\nprint (np.average(a,weights = wts))\nprint ('\\n')\n# 如果 returned 参数设为 true，则返回权重的和  \nprint ('权重的和：')\nprint (np.average([1,2,3,  4],weights =  [4,3,2,1], returned =  True))\n```\n\n`numpy.std(arr,axis = None)`标准差，等价于 `sqrt(numpy.mean(arr-numpy.mean(arr,axis = None)**2))`\n\n`numpy.var(arr,axis = None)`方差，等价于`numpy.mean(arr-numpy.mean(arr,axis = None)**2)`\n\n### NumPy排序\n\n`numpy.sort(ndarray,axis,kind,order) `\n\naxis 默认为None这时数组会展开；kind 可以选quicksort(defult),mergesort,heapsort\n\norder如果数组包含字段则是要排序的字段\n\n```python\nimport numpy as np\n\ndtp = [('name','S10'),('age',np.int8)]\na = np.array([(\"bob\",12),('alis',13),('john',11)],dtype = dtp)\nprint(a)\nprint('sort by name:',np.sort(a,order = 'name'))\nprint('sort by age:',np.sort(a,kind = 'quicksort',order = 'age'))\n#\n[(b'bob', 12) (b'alis', 13) (b'john', 11)]\nsort by name: [(b'alis', 13) (b'bob', 12) (b'john', 11)]\nsort by age: [(b'john', 11) (b'bob', 12) (b'alis', 13)]\n```\n\n`numpy.argsort(ndarray,axis = -1,kind = 'quicksort',order = None)`函数返回排序后的索引值。\n\n`numpy.lexsort(tuple)` 按照多个序列排序。返回排序后的索引值，可使用 for 遍历。\n\n`numpy.msort(ndarray)` = `numpy.sort(a,axis = 0)`按照第一个轴排序\n\n`sort_complex(ndarray)` 对复数数列按照先实部后虚部进行排序。\n\n`partition(ndarray, kth[, axis, kind, order])` 指定一个数对数组进行分区(即快速排序的步骤)\n\nkth接收tuple表示的区间，np.partition(arr,(1,3))即小于1的放最前面，[1,3)放中间，大于3的放最后。\n\n`argpartition(ndarray,kth[, axis, kind, order])` 返回partition结果中元素的索引\n\n`numpy.argmax()`返回给定轴最大元素的索引\n\n`numpy.argmin()`返回给定轴最小元素的索引\n\n`numpy.nonzero()`返回数组中非零元素的索引\n\n`numpy.where()`返回满足条件的元素的索引（参照布尔切片）\n\n`numpy.extract()`根据条件返回满足条件的元素\n\n```python\nimport numpy as np\nx = np.arange(9).reshape(3,3)\ncondition = np.mod(x,2) == 0#返回一个bool_numpy.ndarray\nprint(condition)\nprint(np.extract(condition,x))#print(x[x%2==0])\n#\n[[ True False  True]\n [False  True False]\n [ True False  True]]\n[0 2 4 6 8]\n```\n\n\n\n### 矩阵库\n\nNumPy 中包含一个矩阵库 numpy.matlib,操作对象和返回对象都是矩阵，而不是ndarray。\n\n`numpy.matlib.empty(shape, dtype = np.float, order)`返回一个新的矩阵\n\n`numpy.matlib.zeros(shape, dtype = np.float, order)`返回一个初始值为0的新矩阵\n\n`numpy.matlib.ones(shape, dtype = np.float, order)`返回一个初始值为1的新矩阵\n\n`numpy.matlib.eye(n, M = n, k, dtype = np.float)`返回一个主对角线为1的对角矩阵。(k为对角线偏置，为0则为主对角线，小于0向下偏，大于0向上偏)\n\n`numpy.matlib.identity()`返回给定大小的单位阵。\n\n>`np.matlib.identity(5, dtype = np.float)`等价于`numpy.matlib.eye(5,k=0,dtype = np.float)`\n\n`numpy.matlib.rand(n,M,dtype = np.float)`返回一个n维矩阵，数值随机（值小于1大于0）\n\nmatlib总是一个二维数组，其本质是ndarray。二者之间可以直接赋值或深拷贝。\n\n\n\n### 线性代数\n\nNumPy内置了线性代数库linalg。\n\n| 函数          | 描述                             |\n| ------------- | -------------------------------- |\n| `dot`         | 两个数组的点积，即元素对应相乘。 |\n| `vdot`        | 两个向量的点积，高维数组自动降维 |\n| `inner`       | 两个数组的内积                   |\n| `matmul`      | 两个数组的矩阵积                 |\n| `determinant` | 数组的行列式                     |\n| `solve`       | 求解线性矩阵方程                 |\n| `inv`         | 计算矩阵的乘法逆矩阵             |\n\n`numpy.dot(a, b, out = None)`两个向量的矢量积\n\n>a : ndarray 数组\n>\n>b : ndarray 数组\n>\n>out : ndarray, 可选，用来保存dot()的计算结果\n>\n>```python\n>import numpy as np\n>import numpy.linalg\n>import numpy.matlib\n>a = np.arange(1,7).reshape(3,2,order = 'F')\n>b = np.arange(1,7).reshape(2,3,order = 'C')\n>print(a,b,sep = '\\n')\n>n = np.dot(a,b)\n>print(n)\n>m = np.dot(b,a)\n>print(m)\n>#\n>[[1 4]\n> [2 5]\n> [3 6]]\n>[[1 2 3]\n> [4 5 6]]\n>[[17 22 27]\n> [22 29 36]\n> [27 36 45]]\n>[[14 32]\n> [32 77]]\n>```\n\n`numpy.vdot(a, b)`两个向量的数量积在计算高维数量积时，先把高维降到一维，然后进行数量积运算。\n\n`numpy.inner(ndarray)`一维ndarray内积运算，高维数组为对应维度上的内积运算。\n\n对于多维数组相当：\n\n```python\nimport numpy as np \na = np.arange(1,5).reshape(2,2)\nb = np.arange(5,9).reshape(2,2)\nprint (a,b,np.inner(a,b),sep = '\\n')\nprint(np.dot(a,b))\n#\n[[1 2]\n [3 4]]\n[[5 6]\n [7 8]]\ninner()\n[[17 23]\n [39 53]]\ndot()\n[[19 22]\n [43 50]]\ninner运算类似于\n[a11*b11+a12*b12,a11*b21+a12*b22]\n[a21*b11+a22*b12,a21*b21+a22*b22]\n```\n\n`numpy.matmul(a, b, out = None)`返回矩阵乘积，高于二维视为存在最后两个索引的矩阵的栈，若有一个为一维，则横向广播，然后再进行乘积，最后反横向广播。\n\n```python\nimport numpy as np \na = np.array([[1,2],[3,4]])\nb = np.array([[2,2],[3,3]])\nc = np.array([2,3])\nprint(np.matmul(a,c))\nprint(np.matmul(a,b))\n#\n[ 8 18]\n[[ 8  8]\n [18 18]]\n```\n\n`numpy.linalg.det(ndarray)`计算输入矩阵的行列式的值。\n\n`numpy.linalg.solve(ndarray1,ndarray2)`计算增广矩阵的解。\n\n```python\nimport numpy as np \nimport numpy.linalg\n\na = np.array([2,2,-1,1,-2,4,5,8,-1]).reshape(3,3)\nb = np.array([6,3,27])\nprint(np.linalg.solve(a,b))\n#\n[1. 3. 2.]\n```\n\n\n\n`numpy.linalg.inv(ndarray)`计算可逆矩阵的逆矩阵。\n\n```python\nimport numpy as np \nimport numpy.linalg\n\nprint(np.linalg.inv(np.array([1,2,-1,-3]).reshape(2,2)))\n#\n[[ 3.  2.]\n [-1. -1.]]\n```\n\n### 副本和视图\n\n副本即拷贝，在内存中重新划出一块区域把源数据拷贝一份，修改副本内的内容源数据不会同时修改。\n\n视图本身上还是源数据，只是改变了形状(shape)，修改视图会导致源数据发生变化。reshape,ravel,切片返回的都是视图，\n\n**视图一般发生在：**\n\n- 1、numpy 的切片操作返回原数据的视图。\n- 2、调用 ndarray 的 view() 函数产生一个视图。\n\n**副本一般发生在：**\n\n- Python 序列的切片操作，调用deepCopy()函数。\n- 调用 ndarray 的 copy() 函数产生一个副本。\n\n注意：对于numpy.ndarray对象来说，简单的赋值（a = b）不会引起产生副本。\n\n修改视图的形状不会改变原数组的形状\n"
  },
  {
    "path": "Python 进阶笔记.md",
    "content": "```php\r\ntitle: Python进阶笔记\r\ndate: 2019-03-06 21:23:56\r\ntags:\r\n    - Python\r\n    - advancement\r\n    - noteBook\r\ntoc: true\r\nform: https://github.com/LiuQixuan/PythonLearningNote\r\ninfo: Copyright©2019 AIUSoft.All Rights Reserved. \r\n```\r\n- [函数式编程](#函数式编程)\r\n- [面向对象](#面向对象)\r\n  - [变量](#变量)\r\n  - [方法(存储在内存中)](#方法存储在内存中)\r\n  - [属性（@property def func(self)）](#属性property-def-funcself)\r\n  - [创建类的两种方法](#创建类的两种方法)\r\n  - [使用枚举类(枚举类不可比较大小，但是同一枚举类的成员可以做等值is / not is判断)](#使用枚举类枚举类不可比较大小但是同一枚举类的成员可以做等值is--not-is判断)\r\n  - [定义枚举类](#定义枚举类)\r\n- [高级面向对象](#高级面向对象)\r\n  - [元类types](#元类types)\r\n  - [元类type构造对象](#元类type构造对象)\r\n  - [使用Slot限制添加属性](#使用slot限制添加属性)\r\n  - [装饰器](#装饰器)\r\n  - [python垃圾回收机制](#python垃圾回收机制)\r\n- [python 异常（Exception）、调试（Debug）、回溯（Traceback)](#python-异常exception调试debug回溯traceback)\r\n  - [异常（Exception）](#异常exception)\r\n  - [调试（Debug）](#调试debug)\r\n  - [回溯（Traceback)](#回溯traceback)\r\n- [with 用法](#with-用法)\r\n- [f_string 处理](#f_string-处理)\r\n- [数组复制的5种方法](#数组复制的5种方法)\r\n\r\n\r\nPython 进阶学习笔记 当年学了点Python2 ，那时候听说什么Python3 失去了Python的灵魂，就没管Python3 。 最近因为项目需要使用Python3，又把当年的那份热血花在了Python3的学习上。 本以为Python3只是Python2 的升级会很好上手，没想到Python3相对于Python2而言有太多的新语法、新概念。 从小养成学习记笔记的好习惯，把学习遇到的重点写下来，第一日后复习用，其次如果有其他同学看到我的笔记，并感觉有用，也是算是我劳动成果的附加收益吧\r\n\r\n## 函数式编程\r\n\r\n- 高阶函数(参数里有函数类型)\r\n>map/reduce(functools.reduce())\r\n>filter\r\n>sorted\r\n- 返回函数（返回函数类型）\r\n- 匿名函数（`lambda`）\r\n- 装饰器（`@：有参装饰器、无参装饰器,functools.wraps(function)`）\r\n- 偏函数（`functools.partor(function[,args,**kwords]`)\r\n\r\n<!--more-->\r\n\r\n## 面向对象\r\n### 变量\r\n>1.静态字段\r\n>2.普通字段(self.value)\r\n\r\n### 方法(存储在内存中)\r\n\r\n>1.普通方法def func(self[,args])\r\n>2.类方法(@classmethod def func(cls[,args]))\r\n>3.静态方法(@staticmethod def func())\r\n\r\n### 属性（@property def func(self)）\r\n\r\n>1.静态字段\r\n>2.装饰类\r\n>\r\n>>经典装饰器\r\n>>新装饰器(使用方法模拟成员变量以及对成员变量的操作)\r\n>>\r\n>>方法一\r\n>>继承object\r\n>>\r\n>>```python\r\n>>@property \t\t\tobject.funcName //user funcName to get the function return value like member value\r\n>>@funcName.setter \tobject.func = value\r\n>>@funcName.deleter \tdel object.funcName\r\n>>```\r\n>>方法二\r\n>>创建property对象的静态字段\r\n>>\r\n>>```python\r\n>>pro = property(get_value(),set_value(),del_value(),__doc__)\r\n>>obj = Object()\r\n>>ov = obj.pro\r\n>>obj.pro = value\r\n>>del obj.pro\r\n>>obj.pro.__doc__\r\n>>```\r\n\r\n>类的特殊成员\r\n>\r\n>- `__doc__` 表示类的描述信息\r\n>\r\n>- `__name__`类名\r\n>\r\n>- `__bases__`类的所有父类构成的元组:tuple,不包含该类的type (类名即type类型使用`(my_class,)`可获得该类名的元组）\r\n>\r\n>- `__mro__`类的继承顺序包含类本身<class '__main__.My_class'>, \r\n>\r\n>- `__module__` 表示当前操作的对象在那个模块 packages.module\r\n>\r\n>- `__class__` 表示当前操作的对象的类 packages.module.class\r\n>\r\n>- `__init__()` 类的构造方法\r\n>\r\n>- `__del__()` 类的析构方法\r\n>\r\n>- `__call__()` 对象的方法 obj() self()\r\n>\r\n>- `__getattr__()`当访问obj不存在的属性时会调用该方法  \r\n>\r\n>   getattr(obj, name, value)  ==> obj.name 不存在该属性返回value\r\n>\r\n>   name是函数name()需要添加@property 描述符\r\n>\r\n>- `__dict__` 类或对象中的所有成员\r\n>\r\n>- `__str__` 将类格式化成字符串 print(obj) 时调用,即toString()方法\r\n>\r\n>- `__repr__` 将类格式化成字符串 print(obj) 时调用,和 `__str__` 的区别：更适合从编程语言上理解 \r\n>> ```python\r\n>>print(obj) \r\n>>\r\n>>[output:] class_name.method(self, arg1, arg2, ...)\r\n>> ```\r\n> \r\n>- `__getitem__` 用于索引操作[num]，获取数据\r\n>\r\n>- `__setitem__` 用于索引操作[num]，设置数据\r\n>\r\n>- `__delitem__` 用于索引操作[num]，删除数据\r\n>>```python\r\n>> class mydict(object):\r\n>> \tdef __getitem__(self, key):\r\n>>  \t\treturn self.key\r\n>> \tdef __setitem__(self, key, value):\r\n>>  \t\tself.key = value\r\n>> \tdef __delitem__(self, key):\r\n>>  \t\tdel self.key\r\n>>  \t\tobj = mydict()\r\n>>  result = obj[key]\r\n>>  obj['key'] = value\r\n>>  del obj['key']\r\n>>```\r\n\r\n> **实现切片操作**\r\n>\r\n> >`__getitem__(self, n)`传入的参数n 可能是int也可能是slice\r\n> >\r\n> > ```python\r\n> > class Fib(object):\r\n> > def __getiter__(self, n):\r\n> >  if isinstance(n, int):\r\n> >        a, b = 1, 1\r\n> >        for x in range(n):\r\n> >            a, b = b, a + b\r\n> >      return a\r\n> >    if isinstanece(n, slice):\r\n> >        l = []\r\n> >        slice.start = 0 if (slice.start is None)\r\n> >        a, b = 1, 1\r\n> >        for x in range(n.stop):\r\n> >          if (x >= n.start and (x - n.start)%n.step == 0):\r\n> >              l.append(a)\r\n> >            a, b = b, a+b\r\n> >        return l\r\n> >        \r\n> >\r\n> > ```\r\n> - `__getslice__` (self, i, j) obj[-1:1]\r\n>\r\n> - `__setslice__` (self, i, j, sequence) obj[0:1] = [11,22,33,44]\r\n>\r\n> - `__delslice__` (self, i, j) del obj[0:2]\r\n>\r\n> - `__iter__` 迭代器\r\n>\r\n> - `__next__` 配合`__iter__`实现类的interable属性\r\n>\r\n> - `__new__`方法`__new__(cls, name, bases, attrs)` 类准备将自身实例化时调用，在`__init__()`调用之前调用`__new__（）`，该方法是一个类方法@classmethod\r\n>\r\n>   cls：当前准备创建的类的对象:type\r\n>\r\n>   name：类的名字:str\r\n>\r\n>   bases：类继承的父类集合:class\r\n>\r\n>   attrs：类的方法集合:dict\r\n>\r\n> - `__metaclass__` 该属性定义一个类的元类，即表示类该有哪个类（元类）来实例化\r\n### 创建类的两种方法\r\n>1.普通方法\r\n>```python\r\n>class Object(object):\r\n>\tdef func(self):\r\n>   \tprint('hello world!')\r\n>object = Object()\r\n>```\r\n>2.特殊方法（元类type构造对象）\r\n>\r\n>```python\r\n>def func(self):\r\n>   \tprint('Hello world!')\r\n>   object = type('Object', (object,), {'func':func})\r\n>#arg1:str 类名 ;arg2:tuple 基类; arg3:dict 成员;\r\n>```\r\n>***类的创建过程***\r\n>![类的创建过程](file:///C:/Users/LiuQixuan/Desktop/pypro/type_class.png)\r\n>【CODE】\r\n>\r\n>```python\r\n>class MyType(type):\r\n>    def __init__(self, what, bases=None, dict=None):\r\n>        super(MyType, self).__init__(what, bases, dict)\r\n>\r\n>    def __call__(self, *args, **kwargs):\r\n>        obj = self.__new__(self, *args, **kwargs)\r\n>        self.__init__(obj)\r\n>class Foo(object):\r\n>    __metaclass__ = MyType\r\n>    def __init__(self, name):\r\n>        self.name = name\r\n>    def __new__(cls, *args, **kwargs):\r\n>        return object.__new__(cls, *args, **kwargs)\r\n># 第一阶段：解释器从上到下执行代码创建Foo类\r\n># 第二阶段：通过Foo类创建obj对象\r\n>obj = Foo()\r\n>\r\n>```\r\n### 使用枚举类(枚举类不可比较大小，但是同一枚举类的成员可以做等值is / not is判断)\r\n>```python\r\n>from enum import Enum\r\n>month = Enum('Mouth',('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'))\r\n>for name ,member in month.__members__.items():\r\n>   print(f'{name}=>{member}=>{member.value}')\r\n>```\r\n### 定义枚举类\r\n>```python\r\n>from enum import Enum, unique\r\n>@unique#unique装饰器帮助检查Enum内部字段，保证字段不重复\r\n>class My_weekday(Enum):\r\n>   Sun = 0\r\n>   Mon = 1\r\n>   Tue = 2\r\n>   Wed = 3\r\n>   Thu = 4\r\n>   Fri = 5\r\n>   Sat = 6 \r\n>my_weekday = My_weekday()#my_weekday.Sun\r\n>print(my_weekday.Sun)#my_weekday.Sun\r\n>print(my_weekday['Sun'])#my_weekday.Sun\r\n>print(my_weekday(0))#my_weekday.Sun\r\n>print(my_weekday.Sun.value)#0\r\n>for name, member in my_weekday.__members__.item():\r\n>   print(f'{name} {member} {member.value}')\r\n>【output】\r\n>\"\"\"\r\n>Sun  My_weekday.Sun 0\r\n>Mon  My_weekday.Mon 1\r\n>Tue  My_weekday.Tue 2\r\n>Wed  My_weekday.Wed 3\r\n>Thu  My_weekday.Thu 4\r\n>Fri  My_weekday.Fri 5\r\n>Sat  My_weekday.Sat 6\r\n>\"\"\"\r\n>```\r\n\r\n\r\n## 高级面向对象\r\n\r\n### 元类types\r\n>动态地给对象添加方法(MethodType(func[, object/None], Object))\r\n>```python\r\n>from types import MethodType\r\n>\r\n>def func(self, value):\r\n>   self.age = value\r\n>class Student(object):\r\n>   def __init__(self,name):\r\n>       self.name = name\r\n>student = Student('Bob')\r\n>student.age_setter = MethodType(age_setter,func)\r\n>student.age_setter(21)\r\n>print(f'{student.name}is {student.age} years old.')\r\n>```\r\n>动态的给类添加方法\r\n>```python\r\n>def func(self, value):\r\n>   \tself.age = value\r\n>class Student(object):\r\n>   \tdef __init__(self,name):\r\n>       \tself.name = name\r\n>Student.age_setter = func\r\n>Student.age_setter = MethodType(func, None, Student)\r\n>student = Student('Bob')\r\n>student.age_setter(21)\r\n>print(f'{student.name}is {student.age} years old.')\r\n>```\r\n### 元类type构造对象\r\n>```python\r\n>def func(self):\r\n>   \tprint('Hello world!')\r\n>object = type('Object', (object,), {'func':func})\r\n>#arg1:str 类名 ;arg2:tuple 基类; arg3:dict('a' = a, 'b' = b...) 类变量/静态字段;\r\n>```\r\n### 使用Slot限制添加属性\r\n>slot是一个特殊静态字段,tuple类型.slot只对当前类起作用，对其子类并不起作用\r\n>```python\r\n>class Student(object):\r\n>       __slot__ = ('name', 'age')\r\n>       def __init__(self, name, age):\r\n>           super(Student,self).__init__()\r\n>           self.name = name\r\n>           self.age = age\r\n>       @property\r\n>       def name(self):\r\n>           return self.name\r\n>\r\n>       @name.setter\r\n>       def name(self,name):\r\n>           self.name = name\r\n>\r\n>       @property\r\n>       def age(self):\r\n>           return self.age\r\n>\r\n>       @age.setter\r\n>       def age(self,name):\r\n>           self.age = age\r\n>s1 = Student('Bill',21)\r\n>s1.score = 89                [ERROR]:不能添加score字段\r\n>```\r\n\r\n### 装饰器\r\n>一般装饰器（@decorate）\r\n\r\n>描述符装饰器\r\n>\r\n>>实现了```__get__() __set__() __del__()@property @func.setter @func.deleter```\r\n>>\r\n>>数据描述符（实现了get set）\r\n>>非数据描述符（只实现get 没有实现set)\r\n\r\n### python垃圾回收机制\r\n>- 引用计数器（当一个对象被引用机会增加其引用计数，当不被引用时减少引用计数，减少至0时在合适的时机下内存被回收）\r\n>- 循环垃圾收集器（针对循环引用）\r\n>- 手动内存回收 del obj.obj (将引用计数置零，只有最后引用该内存的对象释放后才执行析构函数__del__())\r\n\r\n\r\n\r\n\r\n## python 异常（Exception）、调试（Debug）、回溯（Traceback)\r\n### 异常（Exception）\r\n>\r\n>```python\r\n>\r\n>try:\r\n>   pass\r\n>except Exception as e:\r\n>   raise\r\n>else:\r\n>   pass\r\n>finally:\r\n>   pass\r\n>\r\n>```\r\n>\r\n>#### 常见的异常：\r\n>\r\n>>| 异常名称       |描述              |\r\n>>| ------------- | --------------- |\r\n>>| BaseException | 所有异常K的基类 |\r\n>>| SystemExit    | 解释器请求退出  |\r\n>>| KeyboardInterrupt | 用户自行中断执行^C |\r\n>>| Exception | 常规错误的基类 |\r\n>>| StopIteration | 迭代器溢出 |\r\n>>| GeneratorExit | 生成器发生异常后通知退出 |\r\n>>| StandardError | 所有标准异常类的基类 |\r\n>>| ArithmeticError | 所有数值计算错误的基类 |\r\n>>| FloattingPointError | 浮点计算错误 |\r\n>>| OverflowError | 数值运算溢出 |\r\n>>| ZeroDivisionError | 除[,取模]by0 |\r\n>>| AssertionError | 断言语句失败 |\r\n>>| AttributeError | 对象缺失该属性 |\r\n>>| EOFError | 没有内建输入，到达EOF标记 |\r\n>>| EnvironmentError | 操作系统错误的基类 |\r\n>>| IOError | 输入/输出操作失败 |\r\n>>| OSError | 操作系统错误 |\r\n>>| WindowsError | 系统调用失败 |\r\n>>| ImportError | 导入模块/对象失败 |\r\n>>| LookupError | 无效数据查询的基类 |\r\n>>| IndexError | 序列中没有此索引 |\r\n>>| KeyError | 映射中没有此键 |\r\n>>| MemoryError | 内存溢出（对于Python解释起来说非致命） |\r\n>>| NameError | 未声明/初始化对象 |\r\n>>| UnboundLocalError | 访问未初始化的本地变量 |\r\n>>| ReferenceError | 试图访问已被回收器回收的对象（弱引用） |\r\n>>| RuntimeError | 一般运行时错误 |\r\n>>| NotImplementedError | 尚未实现的方法 |\r\n>>| SyntaxError | Python语法错误 |\r\n>>| IndentationError | 缩进错误 |\r\n>>| TabError | Tab和Space混用 |\r\n>>| SystemError | 一般的解释器系统错误 |\r\n>>| TypeError | 对类型无效的操作 |\r\n>>| ValueError | 传入无效的参数 |\r\n>>| UnicodeError | Unicode相关错误 |\r\n>>| UnicodeDecodeError | Unicode解码时的错误 |\r\n>>| UnicodeEncodeError | Unicode编码时的错误 |\r\n>>| UnicodeTranslateError | Unicode转码时的错误 |\r\n>>| Warning | 警告的基类 |\r\n>>| DeprecationWarning | 关于被弃用的特性的警告 |\r\n>>| FutureWarning | 关于构造将来语义会有改变的警告 |\r\n>>| OverflowWarning | 旧的关于自动提升为长整型(long)的警告 |\r\n>>| pendingDeprecationWarning | 关于特性将会被废弃的警告 |\r\n>>| RuntimeWarning | 可疑的运行时行为的警告 |\r\n>>| SysntaxWarning | 可疑语法的警告 |\r\n>>| UserWarning | 用户代码生成的警告 |\r\n>>\r\n>\r\n### 调试（Debug）\r\n>\r\n>#### 单元测试\r\n>\r\n>>单元测试的类在unittest包中使用时直接导入包`import unittest`.\r\n>>\r\n>>```python\r\n>>import unittest\r\n>>class TestClass(unitest.TestCase):\r\n>>  def setUp(self):\r\n>>        pass\r\n>>    def tearDown(self):\r\n>>        pass\r\n>>    def test_init(self):\r\n>>        self.assertEqual()\r\n>>        self.assertTrue()\r\n>>        self.assertRaises()\r\n>>    def test_func1(self):\r\n>>        pass\r\n>>    def test_func2(self):\r\n>>        pass\r\n>>```\r\n>#### 文档测试(doctest)\r\n>>\r\n>> Python的文档测试模块可以直接提取注释中的代码并执行\r\n>>\r\n>>```python\r\n>>class Dict(dict):\r\n>>    '''\r\n>>    Simple dict but also support access as x.y style.\r\n>>\r\n>>    >>> d1 = Dict()\r\n>>    >>> d1['x'] = 100\r\n>>    >>> d1.x\r\n>>    100\r\n>>    >>> d1.y = 200\r\n>>    >>> d1['y']\r\n>>    200\r\n>>    >>> d2 = Dict(a=1, b=2, c='3')\r\n>>    >>> d2.c\r\n>>    '3'\r\n>>    >>> d2['empty']\r\n>>    Traceback (most recent call last):\r\n>>        ...\r\n>>    KeyError: 'empty'\r\n>>    >>> d2.empty\r\n>>    Traceback (most recent call last):\r\n>>        ...\r\n>>    AttributeError: 'Dict' object has no attribute 'empty'\r\n>>    '''\r\n>>    def __init__(self, **kw):\r\n>>        super(Dict, self).__init__(**kw)\r\n>>\r\n>>    def __getattr__(self, key):\r\n>>        try:\r\n>>            return self[key]\r\n>>        except KeyError:\r\n>>            raise AttributeError(r\"'Dict' object has no attribute '%s'\" % key)\r\n>>\r\n>>    def __setattr__(self, key, value):\r\n>>        self[key] = value\r\n>>\r\n>>if __name__=='__main__':\r\n>>    import doctest\r\n>>    doctest.testmod()\r\n>>```\r\n>\r\n>\r\n>\r\n### 回溯（Traceback)\r\n>代码\r\n>以下代码功能相同\r\n>```python\r\n>import traceback\r\n>\r\n>traceback.print_exc()\r\n>traceback.format_exc()\r\n>traceback.print_exception(*ss.exc_info())\r\n>\r\n>```\r\n## with 用法\r\n>事前事后用with（前后文管理器）\r\n>```python\r\n>【version 1.0】\r\n>file = open(\"/src/tmp.txt\")\r\n>data = file.read()\r\n>file.close()  \r\n>#可能会忘记关闭句柄\r\n>#可能会出现异常\r\n>【version 2.0】\r\n>file = open(\"/src/tmp.txt\")\r\n>try:\r\n>   data = file.read()\r\n>finally:\r\n>   file.close()\r\n>#总体结构安全性都有提升\r\n>【version 3.0 with】\r\n>with open(\"/src/tmp.txt\") as file:\r\n>   data = file.read()\r\n>```\r\n>\r\n>with 对处理对象需要自定义`__enter__() __exit__()`方法\r\n>\r\n>```python\r\n>class Sample:\r\n>def __enter__(self):\r\n>   print('function:enter')\r\n>   return \"SAMPLE\"\r\n>    def __exit__(self, type, value, trace):\r\n>       print('function:exit')\r\n>def get_sample():\r\n>   return Sample()\r\n>\r\n>with get_sample() as sample:\r\n>   print(f'{sample}do somethings')\r\n>\r\n>\r\n>[result:]\r\n>function:enter\r\n>SAMPLE do somethings\r\n>function:exit\r\n>\r\n>class Sample:\r\n>def __init__(self, value):\r\n>   self.num = value\r\n>def __enter__(self):\r\n>   print('function:enter')\r\n>   return self\r\n>    def __exit__(self, type, value, trace):\r\n>       print('function:exit')\r\n>       print('m_type:{type}\\tm_value:{value}\\ttrace:{trace})\r\n>    def func(self, value):\r\n>       return self.num/value\r\n>def get_sample(num):\r\n>return Sample(num)\r\n>\r\n>with get_sample() as sample:\r\n>   print(f'{sample}do somethings')\r\n>   num = input(\"input a number:\")\r\n>   sample.func(num)\r\n>\r\n>\r\n>#执行顺序：\r\n>#with 后面的函数/或type对象使用类的__init__()创建出一个对象,然后调用__enter__()方法将返回值赋给as 后的变量。在with执行完毕后调用类中的__exit__（）方法，清理或关闭句柄。\r\n>```\r\n>\r\n>- `open`(*file*, *mode='r'*, *buffering=-1*, *encoding=None*, *errors=None*, *newline=None*, *closefd=True*, *opener=None*)\r\n>\r\n## f_string 处理\r\n`__str__，__repr__`\r\n>\r\n>- `__str __（）`和`__repr __（）`方法处理对象如何呈现为字符串，因此您需要确保在类定义中包含至少一个这些方法。如果必须选择一个，请使用`__repr __（）`，因为它可以代替`__str __（）`。\r\n>- `__str __（）`返回的字符串是对象的非正式字符串表示，应该可读。`__repr __（）`返回的字符串是官方表示，应该是明确的。调用`str（）`和`repr（）`比直接使用`__str __（）`和`__repr __（）`更好。\r\n>- 默认情况下，f字符串将使用`__str __（）`，但如果包含转换标志`!r`，则可以确保它们使用`__repr __（）`。\r\n>\r\n>```python\r\n>f\"{new_comedian}\"\r\n>'This __str__'\r\n>f\"{new_comedian!r}\"\r\n>'This __repr__'\r\n>```\r\n>\r\n\r\n## 数组复制的5种方法\r\n\r\npython中简单将一个数组赋值给另一个数组,实际上是两个引用指向同一块内存,可能无法达到预期目的.因此这里整理了几种数组拷贝复制的方法.\r\n\r\n>1. 切片\r\n>``` python\r\n>newArr = oldArr[:]\r\n>```\r\n>\r\n>2. list()\r\n>``` python\r\n>newArr = list(oldArr)\r\n>```\r\n>3. Arr*1\r\n>```python\r\n>newArr = oldArr*1\r\n>```\r\n>4. copy.copy()浅拷贝方法\r\n>```python\r\n>newArr = copy.copy(oldArr)\r\n>newArr = oldArr.copy()\r\n>```\r\n>5. copy.deepcopy()深拷贝方法\r\n>```python\r\n>newArr = copy.deepcopy(oldArr)\r\n>```"
  },
  {
    "path": "README.md",
    "content": "# PythonLearningNote\n#Python 进阶学习笔记\n当年学了点Python2 ，那时候听说什么Python3 失去了Python的灵魂，就没管Python3 。\n最近因为项目需要使用Python3，又把当年的那份热血花在了Python3的学习上。 本以为Python3只是Python2 的升级会很好上手，没想到Python3相对于Python2而言有太多的新语法、新概念。 \n从小养成学习记笔记的好习惯，把学习遇到的重点写下来，第一日后复习用，其次如果有其他同学看到我的笔记，并感觉有用，也是算是我劳动成果的附加收益吧\n\n**推荐大家看<<流畅的Python>>一书,对于一些让人疑惑不解的地方讲解很透彻.**\n\n## 同步更新到[Gitpage](https://liuqixuan.github.io/) \n"
  },
  {
    "path": "SVM 原理详解.md",
    "content": "---\ntitle: SVM原理详解\ndate: 2019-3-19 17:57:14\ntags: \n\t- python\n\t- MachineLearning\ntoc:\n\tture\n---\n\n\n\n\n\n# SVM 原理详解\n\n<div style='text-align:right;'>转自：http://www.blogjava.net/zhenandaci/category/31868.html </div>\n\n**（一）SVM的简介**\n\n支持向量机(Support Vector Machine)是Cortes和Vapnik于1995年首先提出的，它在解决小样本、非线性及高维模式识别中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中[10]。 \n支持向量机方法是建立在统计学习理论的VC 维理论和结构风险最小原理基础上的，根据有限的样本信息在模型的复杂性（即对特定训练样本的学习精度，Accuracy）和学习能力（即无错误地识别任意样本的能力）之间寻求最佳折衷，以期获得最好的推广能力[14]（或称泛化能力）。\n\n以上是经常被有关SVM 的学术文献引用的介绍，我来逐一分解并解释一下。\n\n<!--more-->\n\nVapnik是统计机器学习的大牛，这想必都不用说，他出版的《Statistical Learning Theory》是一本完整阐述统计机器学习思想的名著。在该书中详细的论证了统计机器学习之所以区别于传统机器学习的本质，就在于统计机器学习能够精确的给出学习效果，能够解答需要的样本数等等一系列问题。与统计机器学习的精密思维相比，传统的机器学习基本上属于摸着石头过河，用传统的机器学习方法构造分类系统完全成了一种技巧，一个人做的结果可能很好，另一个人差不多的方法做出来却很差，缺乏指导和原则。\n\n所谓VC维是对函数类的一种度量，可以简单的理解为问题的复杂程度，VC维越高，一个问题就越复杂。正是因为SVM关注的是VC维，后面我们可以看到，SVM解决问题的时候，和样本的维数是无关的（甚至样本是上万维的都可以，这使得SVM很适合用来解决文本分类的问题，当然，有这样的能力也因为引入了核函数）。\n\n结构风险最小听上去文绉绉，其实说的也无非是下面这回事。\n\n机器学习本质上就是一种对问题真实模型的逼近（我们选择一个我们认为比较好的近似模型，这个近似模型就叫做一个假设），但毫无疑问，真实模型一定是不知道的（如果知道了，我们干吗还要机器学习？直接用真实模型解决问题不就可以了？对吧，哈哈）既然真实模型不知道，那么我们选择的假设与问题真实解之间究竟有多大差距，我们就没法得知。比如说我们认为宇宙诞生于150亿年前的一场大爆炸，这个假设能够描述很多我们观察到的现象，但它与真实的宇宙模型之间还相差多少？谁也说不清，因为我们压根就不知道真实的宇宙模型到底是什么。\n\n这个与问题真实解之间的误差，就叫做风险（更严格的说，误差的累积叫做风险）。我们选择了一个假设之后（更直观点说，我们得到了一个分类器以后），真实误差无从得知，但我们可以用某些可以掌握的量来逼近它。最直观的想法就是使用分类器在样本数据上的分类的结果与真实结果（因为样本是已经标注过的数据，是准确的数据）之间的差值来表示。这个差值叫做经验风险Remp(w)。以前的机器学习方法都把经验风险最小化作为努力的目标，但后来发现很多分类函数能够在样本集上轻易达到100%的正确率，在真实分类时却一塌糊涂（即所谓的推广能力差，或泛化能力差）。此时的情况便是选择了一个足够复杂的分类函数（它的VC维很高），能够精确的记住每一个样本，但对样本之外的数据一律分类错误。回头看看经验风险最小化原则我们就会发现，此原则适用的大前提是经验风险要确实能够逼近真实风险才行（行话叫一致），但实际上能逼近么？答案是不能，因为样本数相对于现实世界要分类的文本数来说简直九牛一毛，经验风险最小化原则只在这占很小比例的样本上做到没有误差，当然不能保证在更大比例的真实文本上也没有误差。\n\n统计学习因此而引入了泛化误差界的概念，就是指真实风险应该由两部分内容刻画，一是经验风险，代表了分类器在给定样本上的误差；二是置信风险，代表了我们在多大程度上可以信任分类器在未知文本上分类的结果。很显然，第二部分是没有办法精确计算的，因此只能给出一个估计的区间，也使得整个误差只能计算上界，而无法计算准确的值（所以叫做泛化误差界，而不叫泛化误差）。\n\n置信风险与两个量有关，一是样本数量，显然给定的样本数量越大，我们的学习结果越有可能正确，此时置信风险越小；二是分类函数的VC维，显然VC维越大，推广能力越差，置信风险会变大。\n\n泛化误差界的公式为：\n\nR(w)≤Remp(w)+Ф(n/h)\n\n公式中R(w)就是真实风险，Remp(w)就是经验风险，Ф(n/h)就是置信风险。统计学习的目标从经验风险最小化变为了寻求经验风险与置信风险的和最小，即结构风险最小。\n\nSVM正是这样一种努力最小化结构风险的算法。\n\nSVM其他的特点就比较容易理解了。\n\n小样本，并不是说样本的绝对数量少（实际上，对任何算法来说，更多的样本几乎总是能带来更好的效果），而是说与问题的复杂度比起来，SVM算法要求的样本数是相对比较少的。\n\n非线性，是指SVM擅长应付样本数据线性不可分的情况，主要通过松弛变量（也有人叫惩罚变量）和核函数技术来实现，这一部分是SVM的精髓，以后会详细讨论。多说一句，关于文本分类这个问题究竟是不是线性可分的，尚没有定论，因此不能简单的认为它是线性可分的而作简化处理，在水落石出之前，只好先当它是线性不可分的（反正线性可分也不过是线性不可分的一种特例而已，我们向来不怕方法过于通用）。\n\n高维模式识别是指样本维数很高，例如文本的向量表示，如果没有经过另一系列文章（[《文本分类入门》](https://www.baidu.com/s?wd=%E3%80%8A%E6%96%87%E6%9C%AC%E5%88%86%E7%B1%BB%E5%85%A5%E9%97%A8%E3%80%8B&tn=24004469_oem_dg&rsv_dl=gh_pl_sl_csd)）中提到过的降维处理，出现几万维的情况很正常，其他算法基本就没有能力应付了，SVM却可以，主要是因为SVM 产生的分类器很简洁，用到的样本信息很少（仅仅用到那些称之为“支持向量”的样本，此为后话），使得即使样本维数很高，也不会给存储和计算带来大麻烦（相对照而言，kNN算法在分类时就要用到所有样本，样本数巨大，每个样本维数再一高，这日子就没法过了……）。\n\n下一节开始正式讨论SVM。别嫌我说得太详细哦。\n\n**SVM入门（二）线性分类器Part 1**\n\n线性分类器(一定意义上,也可以叫做感知机) 是最简单也很有效的分类器形式.在一个线性分类器中,可以看到SVM形成的思路,并接触很多SVM的核心概念.\n\n用一个二维空间里仅有两类样本的分类问题来举个小例子。如图所示\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/hi_8CA8/clip_image002_2.gif)\n\nC1和C2是要区分的两个类别，在二维平面中它们的样本如上图所示。中间的直线就是一个分类函数，它可以将两类样本完全分开。一般的，如果一个线性函数能够将样本完全正确的分开，就称这些数据是线性可分的，否则称为非线性可分的。\n\n什么叫线性函数呢？在一维空间里就是一个点，在二维空间里就是一条直线，三维空间里就是一个平面，可以如此想象下去，如果不关注空间的维数，这种线性函数还有一个统一的名称——超平面（Hyper Plane）！\n\n实际上，一个线性函数是一个实值函数（即函数的值是连续的实数），而我们的分类问题（例如这里的二元分类问题——回答一个样本属于还是不属于一个类别的问题）需要离散的输出值，例如用1表示某个样本属于类别C1，而用0表示不属于（不属于C1也就意味着属于C2），这时候只需要简单的在实值函数的基础上附加一个阈值即可，通过分类函数执行时得到的值大于还是小于这个阈值来确定类别归属。 例如我们有一个线性函数\n\ng(x)=wx+b\n\n【看到好多人都在问g(x)=0 和 g(x)的问题，我在这里帮楼主补充一下：g(x)实际是以w为法向量的一簇超平面，在二维空间表示为一簇直线（就是一簇平行线，他们的法向量都是w），而g(x)=0只是这么多平行线中的一条。】\n\n我们可以取阈值为0，这样当有一个样本xi需要判别的时候，我们就看g(xi)的值。若g(xi)>0，就判别为类别C1，若g(xi)<0，则判别为类别C2（等于的时候我们就拒绝判断，呵呵）。此时也等价于给函数g(x)附加一个符号函数sgn()，即f(x)=sgn [g(x)]是我们真正的判别函数。\n\n关于g(x)=wx+b这个表达式要注意三点：一，式中的x不是二维坐标系中的横轴，而是样本的向量表示，例如一个样本点的坐标是(3,8)，则xT=(3,8) ，而不是x=3（一般说向量都是说列向量，因此以行向量形式来表示时，就加上转置）。二，这个形式并不局限于二维的情况，在n维空间中仍然可以使用这个表达式，只是式中的w成为了n维向量（在二维的这个例子中，w是二维向量，为了表示起来方便简洁，以下均不区别列向量和它的转置，聪明的读者一看便知）；三，g(x)不是中间那条直线的表达式，中间那条直线的表达式是g(x)=0，即wx+b=0，我们也把这个函数叫做分类面。\n\n实际上很容易看出来，中间那条分界线并不是唯一的，我们把它稍微旋转一下，只要不把两类数据分错，仍然可以达到上面说的效果，稍微平移一下，也可以。此时就牵涉到一个问题，对同一个问题存在多个分类函数的时候，哪一个函数更好呢？显然必须要先找一个指标来量化“好”的程度，通常使用的都是叫做“分类间隔”的指标。下一节我们就仔细说说分类间隔，也补一补相关的数学知识。\n\n**SVM入门（三）线性分类器Part 2**\n\n上回说到对于文本分类这样的不适定问题（有一个以上解的问题称为不适定问题），需要有一个指标来衡量解决方案（即我们通过训练建立的分类模型）的好坏，而分类间隔是一个比较好的指标。\n\n在进行文本分类的时候，我们可以让计算机这样来看待我们提供给它的训练样本，每一个样本由一个向量（就是那些文本特征所组成的向量）和一个标记（标示出这个样本属于哪个类别）组成。如下：\n\nDi=(xi,yi)\n\nxi就是文本向量（维数很高），yi就是分类标记。\n\n在二元的线性分类中，这个表示分类的标记只有两个值，1和-1（用来表示属于还是不属于这个类）。有了这种表示法，我们就可以定义一个样本点到某个超平面的间隔：\n\nδi=yi(wxi+b)\n\n这个公式乍一看没什么神秘的，也说不出什么道理，只是个定义而已，但我们做做变换，就能看出一些有意思的东西。\n\n首先注意到如果某个样本属于该类别的话，那么wxi+b>0（记得么？这是因为我们所选的g(x)=wx+b就通过大于0还是小于0来判断分类），而yi也大于0；若不属于该类别的话，那么wxi+b<0，而yi也小于0，这意味着yi(wxi+b)总是大于0的，而且它的值就等于|wxi+b|！（也就是|g(xi)|）\n\n现在把w和b进行一下归一化，即用w/||w||和b/||w||分别代替原来的w和b，那么间隔就可以写成\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMPart2_C019/clip_image002%5B28%5D.gif)\n\n【点到直线的距离，做解析几何中为：  \nD = (Ax + By + c) /sqrt(A^2+B^2)  \nsqrt(A^2+B^2)就相当于||W||, 其中向量W=[A, B];  \n(Ax + By + c)就相当于g(X), 其中向量X=[x,y]。】\n\n这个公式是不是看上去有点眼熟？没错，这不就是解析几何中点xi到直线g(x)=0的距离公式嘛！（推广一下，是到超平面g(x)=0的距离， g(x)=0就是上节中提到的分类超平面）\n\n小Tips：||w||是什么符号？||w||叫做向量w的范数，范数是对向量长度的一种度量。我们常说的向量长度其实指的是它的2-范数，范数最一般的表示形式为p-范数，可以写成如下表达式\n\n​    向量w=(w1, w2, w3,…… wn)\n\n它的p-范数为\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMPart2_C019/clip_image004%5B10%5D.gif)\n\n\n\n看看把p换成2的时候，不就是传统的向量长度么？当我们不指明p的时候，就像||w||这样使用时，就意味着我们不关心p的值，用几范数都可以；或者上文已经提到了p的值，为了叙述方便不再重复指明。\n\n当用归一化的w和b代替原值之后的间隔有一个专门的名称，叫做几何间隔，几何间隔所表示的正是点到超平面的欧氏距离，我们下面就简称几何间隔为“距离”。以上是单个点到某个超平面的距离（就是间隔，后面不再区别这两个词）定义，同样可以定义一个点的集合（就是一组样本）到某个超平面的距离为此集合中离超平面最近的点的距离。下面这张图更加直观的展示出了几何间隔的现实含义：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMRefresh_9B92/image_2.png)\n\nH是分类面，而H1和H2是平行于H，且过离H最近的两类样本的直线，H1与H，H2与H之间的距离就是几何间隔。\n\n之所以如此关心几何间隔这个东西，是因为几何间隔与样本的误分次数间存在关系：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMPart2_C019/clip_image012_2.gif)\n\n其中的δ是样本集合到分类面的间隔，R=max ||xi||  i=1,...,n，即R是所有样本中（xi是以向量表示的第i个样本）向量长度最长的值（也就是说代表样本的分布有多么广）。先不必追究误分次数的具体定义和推导过程，只要记得这个误分次数一定程度上代表分类器的误差。而从上式可以看出，误分次数的上界由几何间隔决定！（当然，是样本已知的时候）\n\n至此我们就明白为何要选择几何间隔来作为评价一个解优劣的指标了，原来几何间隔越大的解，它的误差上界越小。因此最大化几何间隔成了我们训练阶段的目标，而且，与二把刀作者所写的不同，最大化分类间隔并不是SVM的专利，而是早在线性分类时期就已有的思想。\n\n**SVM入门（四）线性分类器的求解——问题的描述Part1**\n\n上节说到我们有了一个线性分类函数，也有了判断解优劣的标准——即有了优化的目标，这个目标就是最大化几何间隔，但是看过一些关于SVM的论文的人一定记得什么优化的目标是要最小化||w||这样的说法，这是怎么回事呢？回头再看看我们对间隔和几何间隔的定义：\n\n间隔：δ=y(wx+b)=|g(x)|\n\n几何间隔：![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMPart1_EEC8/clip_image002_2.gif)\n\n可以看出δ=||w||δ几何。注意到几何间隔与||w||是成反比的，因此最大化几何间隔与最小化||w||完全是一回事。而我们常用的方法并不是固定||w||的大小而寻求最大几何间隔，而是固定间隔（例如固定为1），寻找最小的||w||。\n\n而凡是求一个函数的最小值（或最大值）的问题都可以称为寻优问题（也叫作一个规划问题），又由于找最大值的问题总可以通过加一个负号变为找最小值的问题，因此我们下面讨论的时候都针对找最小值的过程来进行。一个寻优问题最重要的部分是目标函数，顾名思义，就是指寻优的目标。例如我们想寻找最小的||w||这件事，就可以用下面的式子表示：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMPart1_EEC8/clip_image002%5B4%5D.gif)\n\n但实际上对于这个目标，我们常常使用另一个完全等价的目标函数来代替，那就是：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMPart1_EEC8/clip_image002%5B6%5D.gif)(式1)\n\n不难看出当||w||2达到最小时，||w||也达到最小，反之亦然（前提当然是||w||描述的是向量的长度，因而是非负的）。之所以采用这种形式，是因为后面的求解过程会对目标函数作一系列变换，而式（1）的形式会使变换后的形式更为简洁（正如聪明的读者所料，添加的系数二分之一和平方，皆是为求导数所需）。\n\n接下来我们自然会问的就是，这个式子是否就描述了我们的问题呢？（回想一下，我们的问题是有一堆点，可以被分成两类，我们要找出最好的分类面）\n\n如果直接来解这个求最小值问题，很容易看出当||w||=0的时候就得到了目标函数的最小值。但是你也会发现，无论你给什么样的数据，都是这个解！反映在图中，就是H1与H2两条直线间的距离无限大，这个时候，所有的样本点（无论正样本还是负样本）都跑到了H1和H2中间，而我们原本的意图是，H1右侧的被分为正类，H2 左侧的被分为负类，位于两类中间的样本则拒绝分类（拒绝分类的另一种理解是分给哪一类都有道理，因而分给哪一类也都没有道理）。这下可好，所有样本点都进入了无法分类的灰色地带。\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMPart1_EEC8/clip_image002%5B8%5D.gif)\n\n造成这种结果的原因是在描述问题的时候只考虑了目标，而没有加入约束条件，约束条件就是在求解过程中必须满足的条件，体现在我们的问题中就是样本点必须在H1或H2的某一侧（或者至少在H1和H2上），而不能跑到两者中间。我们前文提到过把间隔固定为1，这是指把所有样本点中间隔最小的那一点的间隔定为1（这也是集合的间隔的定义，有点绕嘴），也就意味着集合中的其他点间隔都不会小于1，按照间隔的定义，满足这些条件就相当于让下面的式子总是成立：\n\n​    yi[(w·xi)+b]≥1 (i=1,2,…,l) （l是总的样本数）\n\n但我们常常习惯让式子的值和0比较，因而经常用变换过的形式：\n\n​    yi[(w·xi)+b]-1≥0 (i=1,2,…,l) （l是总的样本数）\n\n因此我们的两类分类问题也被我们转化成了它的数学形式，一个带约束的最小值的问题：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMPart1_EEC8/clip_image002%5B10%5D.gif)\n\n下一节我们从最一般的意义上看看一个求最小值的问题有何特征，以及如何来解。\n\n**SVM入门（五）线性分类器的求解——问题的描述Part2**\n\n从最一般的定义上说，一个求最小值的问题就是一个优化问题（也叫寻优问题，更文绉绉的叫法是规划——Programming），它同样由两部分组成，目标函数和约束条件，可以用下面的式子表示：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMPart2_1603/clip_image002_2.gif)（式1）\n\n约束条件用函数c来表示，就是constrain的意思啦。你可以看出一共有p+q个约束条件，其中p个是不等式约束，q个等式约束。\n\n关于这个式子可以这样来理解：式中的x是自变量，但不限定它的维数必须为1（视乎你解决的问题空间维数，对我们的文本分类来说，那可是成千上万啊）。要求f(x)在哪一点上取得最小值（反倒不太关心这个最小值到底是多少，关键是哪一点），但不是在整个空间里找，而是在约束条件所划定的一个有限的空间里找，这个有限的空间就是优化理论里所说的可行域。注意可行域中的每一个点都要求满足所有p+q个条件，而不是满足其中一条或几条就可以（切记，要满足每个约束），同时可行域边界上的点有一个额外好的特性，它们可以使不等式约束取得等号！而边界内的点不行。\n\n关于可行域还有个概念不得不提，那就是凸集，凸集是指有这么一个点的集合，其中任取两个点连一条直线，这条线上的点仍然在这个集合内部，因此说“凸”是很形象的（一个反例是，二维平面上，一个月牙形的区域就不是凸集，你随便就可以找到两个点违反了刚才的规定）。\n\n回头再来看我们线性分类器问题的描述，可以看出更多的东西。\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMPart2_1603/clip_image002%5B5%5D.gif)（式2）\n\n在这个问题中，自变量就是w，而目标函数是w的二次函数，所有的约束条件都是w的线性函数（哎，千万不要把xi当成变量，它代表样本，是已知的），这种规划问题有个很有名气的称呼——二次规划（Quadratic Programming，QP），而且可以更进一步的说，由于它的可行域是一个凸集，因此它是一个凸二次规划。\n\n一下子提了这么多术语，实在不是为了让大家以后能向别人炫耀学识的渊博，这其实是我们继续下去的一个重要前提，因为在动手求一个问题的解之前（好吧，我承认，是动计算机求……），我们必须先问自己：这个问题是不是有解？如果有解，是否能找到？\n\n对于一般意义上的规划问题，两个问题的答案都是不一定，但凸二次规划让人喜欢的地方就在于，它有解（教科书里面为了严谨，常常加限定成分，说它有全局最优解，由于我们想找的本来就是全局最优的解，所以不加也罢），而且可以找到！（当然，依据你使用的算法不同，找到这个解的速度，行话叫收敛速度，会有所不同）\n\n对比（式2）和（式1）还可以发现，我们的线性分类器问题只有不等式约束，因此形式上看似乎比一般意义上的规划问题要简单，但解起来却并非如此。\n\n因为我们实际上并不知道该怎么解一个带约束的优化问题。如果你仔细回忆一下高等数学的知识，会记得我们可以轻松的解一个不带任何约束的优化问题（实际上就是当年背得烂熟的函数求极值嘛，求导再找0点呗，谁不会啊？笑），我们甚至还会解一个只带等式约束的优化问题，也是背得烂熟的，求条件极值，记得么，通过添加拉格朗日乘子，构造拉格朗日函数，来把这个问题转化为无约束的优化问题云云（如果你一时没想通，我提醒一下，构造出的拉格朗日函数就是转化之后的问题形式，它显然没有带任何条件）。\n\n读者问：如果只带等式约束的问题可以转化为无约束的问题而得以求解，那么可不可以把带不等式约束的问题向只带等式约束的问题转化一下而得以求解呢？\n\n聪明，可以，实际上我们也正是这么做的。下一节就来说说如何做这个转化，一旦转化完成，求解对任何学过高等数学的人来说，都是小菜一碟啦。\n\n**SVM入门（六）线性分类器的求解——问题的转化，直观角度**\n\n让我再一次比较完整的重复一下我们要解决的问题：我们有属于两个类别的样本点（并不限定这些点在二维空间中）若干，如图，\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_1244A/image_2.png)\n\n圆形的样本点定为正样本（连带着，我们可以把正样本所属的类叫做正类），方形的点定为负例。我们想求得这样一个线性函数（在n维空间中的线性函数）：\n\ng(x)=wx+b\n\n使得所有属于正类的点+代入以后有g(x+)≥1，而所有属于负类的点x-代入后有g(x-)≤-1（之所以总跟1比较，无论正一还是负一，都是因为我们固定了间隔为1，注意间隔和几何间隔的区别）。代入g(x)后的值如果在1和-1之间，我们就拒绝判断。\n\n求这样的g(x)的过程就是求w（一个n维向量）和b（一个实数）两个参数的过程（但实际上只需要求w，求得以后找某些样本点代入就可以求得b）。因此在求g(x)的时候，w才是变量。\n\n你肯定能看出来，一旦求出了w（也就求出了b），那么中间的直线H就知道了（因为它就是wx+b=0嘛，哈哈），那么H1和H2也就知道了（因为三者是平行的，而且相隔的距离还是||w||决定的）。那么w是谁决定的？显然是你给的样本决定的，一旦你在空间中给出了那些个样本点，三条直线的位置实际上就唯一确定了（因为我们求的是最优的那三条，当然是唯一的），我们解优化问题的过程也只不过是把这个确定了的东西算出来而已。\n\n样本确定了w，用数学的语言描述，就是w可以表示为样本的某种组合：\n\nw=α1x1+α2x2+…+αnxn\n\n式子中的αi是一个一个的数（在严格的证明过程中，这些α被称为拉格朗日乘子），而xi是样本点，因而是向量，n就是总样本点的个数。为了方便描述，以下开始严格区别数字与向量的乘积和向量间的乘积，我会用α1x1表示数字和向量的乘积，而用<x1,x2>表示向量x1,x2的内积（也叫点积，注意与向量叉积的区别）。因此g(x)的表达式严格的形式应该是：\n\ng(x)=<w,x>+b\n\n但是上面的式子还不够好，你回头看看图中正样本和负样本的位置，想像一下，我不动所有点的位置，而只是把其中一个正样本点定为负样本点（也就是把一个点的形状从圆形变为方形），结果怎么样？三条直线都必须移动（因为对这三条直线的要求是必须把方形和圆形的点正确分开）！这说明w不仅跟样本点的位置有关，还跟样本的类别有关（也就是和样本的“标签”有关）。因此用下面这个式子表示才算完整：\n\nw=α1y1x1+α2y2x2+…+αnynxn （式1）\n\n其中的yi就是第i个样本的标签，它等于1或者-1。其实以上式子的那一堆拉格朗日乘子中，只有很少的一部分不等于0（不等于0才对w起决定作用），这部分不等于0的拉格朗日乘子后面所乘的样本点，其实都落在H1和H2上，也正是这部分样本（而不需要全部样本）唯一的确定了分类函数，当然，更严格的说，这些样本的一部分就可以确定，因为例如确定一条直线，只需要两个点就可以，即便有三五个都落在上面，我们也不是全都需要。这部分我们真正需要的样本点，就叫做支持（撑）向量！（名字还挺形象吧，他们“撑”起了分界线）\n\n式子也可以用求和符号简写一下：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_1244A/clip_image002_2.gif)\n\n因此原来的g(x)表达式可以写为：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_1244A/clip_image002%5B4%5D.gif)\n\n注意式子中x才是变量，也就是你要分类哪篇文档，就把该文档的向量表示代入到 x的位置，而所有的xi统统都是已知的样本。还注意到式子中只有xi和x是向量，因此一部分可以从内积符号中拿出来，得到g(x)的式子为：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_1244A/clip_image002%5B6%5D.gif)\n\n发现了什么？w不见啦！从求w变成了求α。\n\n但肯定有人会说，这并没有把原问题简化呀。嘿嘿，其实简化了，只不过在你看不见的地方，以这样的形式描述问题以后，我们的优化问题少了很大一部分不等式约束（记得这是我们解不了极值问题的万恶之源）。但是接下来先跳过线性分类器求解的部分，来看看 SVM在线性分类器上所做的重大改进——核函数。\n\n**SVM入门（七）为何需要核函数**\n\n生存？还是毁灭？——哈姆雷特\n\n可分？还是不可分？——支持向量机\n\n之前一直在讨论的线性分类器,器如其名（汗，这是什么说法啊），只能对线性可分的样本做处理。如果提供的样本线性不可分，结果很简单，线性分类器的求解程序会无限循环，永远也解不出来。这必然使得它的适用范围大大缩小，而它的很多优点我们实在不原意放弃，怎么办呢？是否有某种方法，让线性不可分的数据变得线性可分呢？\n\n有！其思想说来也简单，来用一个二维平面中的分类问题作例子，你一看就会明白。事先声明，下面这个例子是网络早就有的，我一时找不到原作者的正确信息，在此借用，并加进了我自己的解说而已。\n\n例子是下面这张图：\n\n/![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_10595/clip_image001_2.jpg)\n\n我们把横轴上端点a和b之间红色部分里的所有点定为正类，两边的黑色部分里的点定为负类。试问能找到一个线性函数把两类正确分开么？不能，因为二维空间里的线性函数就是指直线，显然找不到符合条件的直线。\n\n但我们可以找到一条曲线，例如下面这一条：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_10595/clip_image002_2.gif)\n\n显然通过点在这条曲线的上方还是下方就可以判断点所属的类别（你在横轴上随便找一点，算算这一点的函数值，会发现负类的点函数值一定比0大，而正类的一定比0小）。这条曲线就是我们熟知的二次曲线，它的函数表达式可以写为：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_10595/clip_image002%5B5%5D.gif)\n\n问题只是它不是一个线性函数，但是，下面要注意看了，新建一个向量y和a：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_10595/clip_image002%5B7%5D.gif)\n\n这样g(x)就可以转化为f(y)=<a,y>，你可以把y和a分别回带一下，看看等不等于原来的g(x)。用内积的形式写你可能看不太清楚，实际上f(y)的形式就是：\n\ng(x)=f(y)=ay\n\n在任意维度的空间中，这种形式的函数都是一个线性函数（只不过其中的a和y都是多维向量罢了），因为自变量y的次数不大于1。\n\n看出妙在哪了么？原来在二维空间中一个线性不可分的问题，映射到四维空间后，变成了线性可分的！因此这也形成了我们最初想解决线性不可分问题的基本思路——向高维空间转化，使其变得线性可分。\n\n而转化最关键的部分就在于找到x到y的映射方法。遗憾的是，如何找到这个映射，没有系统性的方法（也就是说，纯靠猜和凑）。具体到我们的文本分类问题，文本被表示为上千维的向量，即使维数已经如此之高，也常常是线性不可分的，还要向更高的空间转化。其中的难度可想而知。\n\n小Tips:为什么说f(y)=ay是四维空间里的函数?\n\n大家可能一时没看明白。回想一下我们二维空间里的函数定义 \n  g(x)=ax+b \n变量x是一维的，为什么说它是二维空间里的函数呢？因为还有一个变量我们没写出来，它的完整形式其实是 \n  y=g(x)=ax+b \n即 \n  y=ax+b \n看看，有几个变量？两个。那是几维空间的函数？（作者五岁的弟弟答：五维的。作者：……） \n再看看 \nf(y)=ay \n里面的y是三维的变量，那f(y)是几维空间里的函数？（作者五岁的弟弟答：还是五维的。作者：……）\n\n用一个具体文本分类的例子来看看这种向高维空间映射从而分类的方法如何运作，想象一下，我们文本分类问题的原始空间是1000维的（即每个要被分类的文档被表示为一个1000维的向量），在这个维度上问题是线性不可分的。现在我们有一个2000维空间里的线性函数\n\nf(x’)=<w’,x’>+b\n\n注意向量的右上角有个 ’哦。它能够将原问题变得可分。式中的 w’和x’都是2000维的向量，只不过w’是定值，而x’是变量（好吧,严格说来这个函数是2001维的,哈哈），现在我们的输入呢，是一个1000维的向量x，分类的过程是先把x变换为2000维的向量x’，然后求这个变换后的向量x’与向量w’的内积，再把这个内积的值和b相加，就得到了结果，看结果大于阈值还是小于阈值就得到了分类结果。\n\n你发现了什么？我们其实只关心那个高维空间里内积的值，那个值算出来了，分类结果就算出来了。而从理论上说， x’是经由x变换来的，因此广义上可以把它叫做x的函数（有一个x，就确定了一个x’，对吧，确定不出第二个），而w’是常量，它是一个低维空间里的常量w经过变换得到的，所以给了一个w 和x的值，就有一个确定的f(x’)值与其对应。这让我们幻想，是否能有这样一种函数K(w,x),他接受低维空间的输入值，却能算出高维空间的内积值<w’,x’>？\n\n如果有这样的函数，那么当给了一个低维空间的输入x以后，\n\ng(x)=K(w,x)+b\n\nf(x’)=<w’,x’>+b\n\n这两个函数的计算结果就完全一样，我们也就用不着费力找那个映射关系，直接拿低维的输入往g(x)里面代就可以了（再次提醒，这回的g(x)就不是线性函数啦，因为你不能保证K(w,x)这个表达式里的x次数不高于1哦）。\n\n万幸的是，这样的K(w,x)确实存在（发现凡是我们人类能解决的问题，大都是巧得不能再巧，特殊得不能再特殊的问题，总是恰好有些能投机取巧的地方才能解决，由此感到人类的渺小），它被称作核函数（核，kernel），而且还不止一个，事实上，只要是满足了Mercer条件的函数，都可以作为核函数。核函数的基本作用就是接受两个低维空间里的向量，能够计算出经过某个变换后在高维空间里的向量内积值。几个比较常用的核函数，俄，教课书里都列过，我就不敲了（懒！）。\n\n回想我们上节说的求一个线性分类器，它的形式应该是：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_10595/clip_image002%5B9%5D.gif)\n\n现在这个就是高维空间里的线性函数（为了区别低维和高维空间里的函数和向量，我改了函数的名字，并且给w和x都加上了 ’），我们就可以用一个低维空间里的函数（再一次的，这个低维空间里的函数就不再是线性的啦）来代替，\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_10595/clip_image002%5B11%5D.gif)\n\n又发现什么了？f(x’) 和g(x)里的α，y，b全都是一样一样的！这就是说，尽管给的问题是线性不可分的，但是我们就硬当它是线性问题来求解，只不过求解过程中，凡是要求内积的时候就用你选定的核函数来算。这样求出来的α再和你选定的核函数一组合，就得到分类器啦！\n\n明白了以上这些，会自然的问接下来两个问题：\n\n1． 既然有很多的核函数，针对具体问题该怎么选择？\n\n2． 如果使用核函数向高维空间映射后，问题仍然是线性不可分的，那怎么办？\n\n第一个问题现在就可以回答你：对核函数的选择，现在还缺乏指导原则！各种实验的观察结果（不光是文本分类）的确表明，某些问题用某些核函数效果很好，用另一些就很差，但是一般来讲，径向基核函数是不会出太大偏差的一种，首选。（我做文本分类系统的时候，使用径向基核函数，没有参数调优的情况下，绝大部分类别的准确和召回都在85%以上，可见。虽然libSVM的作者林智仁认为文本分类用线性核函数效果更佳，待考证）\n\n对第二个问题的解决则引出了我们下一节的主题：松弛变量。\n\n**SVM入门（八）松弛变量**\n\n现在我们已经把一个本来线性不可分的文本分类问题，通过映射到高维空间而变成了线性可分的。就像下图这样：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_D32/image_2.png)\n\n圆形和方形的点各有成千上万个（毕竟，这就是我们训练集中文档的数量嘛，当然很大了）。现在想象我们有另一个训练集，只比原先这个训练集多了一篇文章，映射到高维空间以后（当然，也使用了相同的核函数），也就多了一个样本点，但是这个样本的位置是这样的：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_D32/image_4.png)\n\n就是图中黄色那个点，它是方形的，因而它是负类的一个样本，这单独的一个样本，使得原本线性可分的问题变成了线性不可分的。这样类似的问题（仅有少数点线性不可分）叫做“近似线性可分”的问题。\n\n以我们人类的常识来判断，说有一万个点都符合某种规律（因而线性可分），有一个点不符合，那这一个点是否就代表了分类规则中我们没有考虑到的方面呢（因而规则应该为它而做出修改）？\n\n其实我们会觉得，更有可能的是，这个样本点压根就是错误，是噪声，是提供训练集的同学人工分类时一打瞌睡错放进去的。所以我们会简单的忽略这个样本点，仍然使用原来的分类器，其效果丝毫不受影响。\n\n但这种对噪声的容错性是人的思维带来的，我们的程序可没有。由于我们原本的优化问题的表达式中，确实要考虑所有的样本点（不能忽略某一个，因为程序它怎么知道该忽略哪一个呢？），在此基础上寻找正负类之间的最大几何间隔，而几何间隔本身代表的是距离，是非负的，像上面这种有噪声的情况会使得整个问题无解。这种解法其实也叫做“硬间隔”分类法，因为他硬性的要求所有样本点都满足和分类平面间的距离必须大于某个值。\n\n因此由上面的例子中也可以看出，硬间隔的分类法其结果容易受少数点的控制，这是很危险的（尽管有句话说真理总是掌握在少数人手中，但那不过是那一小撮人聊以自慰的词句罢了，咱还是得民主）。\n\n但解决方法也很明显，就是仿照人的思路，允许一些点到分类平面的距离不满足原先的要求。由于不同的训练集各点的间距尺度不太一样，因此用间隔（而不是几何间隔）来衡量有利于我们表达形式的简洁。我们原先对样本点的要求是：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_D32/clip_image002_2.gif)\n\n意思是说离分类面最近的样本点函数间隔也要比1大。如果要引入容错性，就给1这个硬性的阈值加一个松弛变量，即允许\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_D32/clip_image002%5B5%5D.gif)\n\n因为松弛变量是非负的，因此最终的结果是要求间隔可以比1小。但是当某些点出现这种间隔比1小的情况时（这些点也叫离群点），意味着我们放弃了对这些点的精确分类，而这对我们的分类器来说是种损失。但是放弃这些点也带来了好处，那就是使分类面不必向这些点的方向移动，因而可以得到更大的几何间隔（在低维空间看来，分类边界也更平滑）。显然我们必须权衡这种损失和好处。好处很明显，我们得到的分类间隔越大，好处就越多。回顾我们原始的硬间隔分类对应的优化问题：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_D32/clip_image002%5B7%5D.gif)\n\n||w||2就是我们的目标函数（当然系数可有可无），希望它越小越好，因而损失就必然是一个能使之变大的量（能使它变小就不叫损失了，我们本来就希望目标函数值越小越好）。那如何来衡量损失，有两种常用的方式，有人喜欢用\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_D32/clip_image002%5B9%5D.gif)\n\n而有人喜欢用\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_D32/clip_image002%5B11%5D.gif)\n\n其中l都是样本的数目。两种方法没有大的区别。如果选择了第一种，得到的方法的就叫做二阶软间隔分类器，第二种就叫做一阶软间隔分类器。把损失加入到目标函数里的时候，就需要一个惩罚因子（cost，也就是libSVM的诸多参数中的C），原来的优化问题就变成了下面这样：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_D32/clip_image002%5B13%5D.gif)\n\n这个式子有这么几点要注意：\n\n一是并非所有的样本点都有一个松弛变量与其对应。实际上只有“离群点”才有，或者也可以这么看，所有没离群的点松弛变量都等于0（对负类来说，离群点就是在前面图中，跑到H2右侧的那些负样本点，对正类来说，就是跑到H1左侧的那些正样本点）。\n\n【在迭代求w的时候如何样本点非离群点，即分类正确，那么就设它的松弛变量为0了。。。】\n\n二是松弛变量的值实际上标示出了对应的点到底离群有多远，值越大，点就越远。\n\n三是惩罚因子C决定了你有多重视离群点带来的损失，显然当所有离群点的松弛变量的和一定时，你定的C越大，对目标函数的损失也越大，此时就暗示着你非常不愿意放弃这些离群点，最极端的情况是你把C定为无限大，这样只要稍有一个点离群，目标函数的值马上变成无限大，马上让问题变成无解，这就退化成了硬间隔问题。\n\n四是惩罚因子C不是一个变量，整个优化问题在解的时候，C是一个你必须事先指定的值，指定这个值以后，解一下，得到一个分类器，然后用测试数据看看结果怎么样，如果不够好，换一个C的值，再解一次优化问题，得到另一个分类器，再看看效果，如此就是一个参数寻优的过程，但这和优化问题本身决不是一回事，优化问题在解的过程中，C一直是定值，要记住。\n\n五是尽管加了松弛变量这么一说，但这个优化问题仍然是一个优化问题（汗，这不废话么），解它的过程比起原始的硬间隔问题来说，没有任何更加特殊的地方。\n\n从大的方面说优化问题解的过程，就是先试着确定一下w，也就是确定了前面图中的三条直线，这时看看间隔有多大，又有多少点离群，把目标函数的值算一算，再换一组三条直线（你可以看到，分类的直线位置如果移动了，有些原来离群的点会变得不再离群，而有的本来不离群的点会变成离群点），再把目标函数的值算一算，如此往复（迭代），直到最终找到目标函数最小时的w。\n\n啰嗦了这么多，读者一定可以马上自己总结出来，松弛变量也就是个解决线性不可分问题的方法罢了，但是回想一下，核函数的引入不也是为了解决线性不可分的问题么？为什么要为了一个问题使用两种方法呢？\n\n其实两者还有微妙的不同。一般的过程应该是这样，还以文本分类为例。在原始的低维空间中，样本相当的不可分，无论你怎么找分类平面，总会有大量的离群点，此时用核函数向高维空间映射一下，虽然结果仍然是不可分的，但比原始空间里的要更加接近线性可分的状态（就是达到了近似线性可分的状态），此时再用松弛变量处理那些少数“冥顽不化”的离群点，就简单有效得多啦。\n\n本节中的（式1）也确实是支持向量机最最常用的形式。至此一个比较完整的支持向量机框架就有了，简单说来，支持向量机就是使用了核函数的软间隔线性分类法。\n\n下一节会说说松弛变量剩下的一点点东西，顺便搞个读者调查，看看大家还想侃侃SVM的哪些方面。\n\n**SVM入门（九）松弛变量（续）**\n\n接下来要说的东西其实不是松弛变量本身，但由于是为了使用松弛变量才引入的，因此放在这里也算合适，那就是惩罚因子C。回头看一眼引入了松弛变量以后的优化问题：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_11A27/clip_image002_2.gif)\n\n注意其中C的位置，也可以回想一下C所起的作用（表征你有多么重视离群点，C越大越重视，越不想丢掉它们）。这个式子是以前做SVM的人写的，大家也就这么用，但没有任何规定说必须对所有的松弛变量都使用同一个惩罚因子，我们完全可以给每一个离群点都使用不同的C，这时就意味着你对每个样本的重视程度都不一样，有些样本丢了也就丢了，错了也就错了，这些就给一个比较小的C；而有些样本很重要，决不能分类错误（比如中央下达的文件啥的，笑），就给一个很大的C。\n\n当然实际使用的时候并没有这么极端，但一种很常用的变形可以用来解决分类问题中样本的“偏斜”问题。\n\n先来说说样本的偏斜问题，也叫数据集偏斜（unbalanced），它指的是参与分类的两个类别（也可以指多个类别）样本数量差异很大。比如说正类有10，000个样本，而负类只给了100个，这会引起的问题显而易见，可以看看下面的图：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_11A27/image_2.png)\n\n方形的点是负类。H，H1，H2是根据给的样本算出来的分类面，由于负类的样本很少很少，所以有一些本来是负类的样本点没有提供，比如图中两个灰色的方形点，如果这两个点有提供的话，那算出来的分类面应该是H’，H2’和H1，他们显然和之前的结果有出入，实际上负类给的样本点越多，就越容易出现在灰色点附近的点，我们算出的结果也就越接近于真实的分类面。但现在由于偏斜的现象存在，使得数量多的正类可以把分类面向负类的方向“推”，因而影响了结果的准确性。\n\n对付数据集偏斜问题的方法之一就是在惩罚因子上作文章，想必大家也猜到了，那就是给样本数量少的负类更大的惩罚因子，表示我们重视这部分样本（本来数量就少，再抛弃一些，那人家负类还活不活了），因此我们的目标函数中因松弛变量而损失的部分就变成了：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVM_11A27/clip_image002%5B5%5D.gif)\n\n其中i=1…p都是正样本，j=p+1…p+q都是负样本。libSVM这个算法包在解决偏斜问题的时候用的就是这种方法。\n\n那C+和C-怎么确定呢？它们的大小是试出来的（参数调优），但是他们的比例可以有些方法来确定。咱们先假定说C+是5这么大，那确定C-的一个很直观的方法就是使用两类样本数的比来算，对应到刚才举的例子，C-就可以定为500这么大（因为10，000：100=100：1嘛）。\n\n但是这样并不够好，回看刚才的图，你会发现正类之所以可以“欺负”负类，其实并不是因为负类样本少，真实的原因是负类的样本分布的不够广（没扩充到负类本应该有的区域）。说一个具体点的例子，现在想给政治类和体育类的文章做分类，政治类文章很多，而体育类只提供了几篇关于篮球的文章，这时分类会明显偏向于政治类，如果要给体育类文章增加样本，但增加的样本仍然全都是关于篮球的（也就是说，没有足球，排球，赛车，游泳等等），那结果会怎样呢？虽然体育类文章在数量上可以达到与政治类一样多，但过于集中了，结果仍会偏向于政治类！所以给C+和C-确定比例更好的方法应该是衡量他们分布的程度。比如可以算算他们在空间中占据了多大的体积，例如给负类找一个超球——就是高维空间里的球啦——它可以包含所有负类的样本，再给正类找一个，比比两个球的半径，就可以大致确定分布的情况。显然半径大的分布就比较广，就给小一点的惩罚因子。\n\n但是这样还不够好，因为有的类别样本确实很集中，这不是提供的样本数量多少的问题，这是类别本身的特征（就是某些话题涉及的面很窄，例如计算机类的文章就明显不如文化类的文章那么“天马行空”），这个时候即便超球的半径差异很大，也不应该赋予两个类别不同的惩罚因子。\n\n看到这里读者一定疯了，因为说来说去，这岂不成了一个解决不了的问题？然而事实如此，完全的方法是没有的，根据需要，选择实现简单又合用的就好（例如libSVM就直接使用样本数量的比）。\n\n**SVM入门（十）将SVM用于多类分类**\n\n从 SVM的那几张图可以看出来，SVM是一种典型的两类分类器，即它只回答属于正类还是负类的问题。而现实中要解决的问题，往往是多类的问题（少部分例外，例如垃圾邮件过滤，就只需要确定“是”还是“不是”垃圾邮件），比如文本分类，比如数字识别。如何由两类分类器得到多类分类器，就是一个值得研究的问题。\n\n还以文本分类为例，现成的方法有很多，其中一种一劳永逸的方法，就是真的一次性考虑所有样本，并求解一个多目标函数的优化问题，一次性得到多个分类面，就像下图这样：\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMSVM_CBFA/clip_image001_2.jpg)\n\n多个超平面把空间划分为多个区域，每个区域对应一个类别，给一篇文章，看它落在哪个区域就知道了它的分类。\n\n看起来很美对不对？只可惜这种算法还基本停留在纸面上，因为一次性求解的方法计算量实在太大，大到无法实用的地步。\n\n稍稍退一步，我们就会想到所谓“一类对其余”的方法，就是每次仍然解一个两类分类的问题。比如我们有5个类别，第一次就把类别1的样本定为正样本，其余2，3，4，5的样本合起来定为负样本，这样得到一个两类分类器，它能够指出一篇文章是还是不是第1类的；第二次我们把类别2 的样本定为正样本，把1，3，4，5的样本合起来定为负样本，得到一个分类器，如此下去，我们可以得到5个这样的两类分类器（总是和类别的数目一致）。到了有文章需要分类的时候，我们就拿着这篇文章挨个分类器的问：是属于你的么？是属于你的么？哪个分类器点头说是了，文章的类别就确定了。这种方法的好处是每个优化问题的规模比较小，而且分类的时候速度很快（只需要调用5个分类器就知道了结果）。但有时也会出现两种很尴尬的情况，例如拿一篇文章问了一圈，每一个分类器都说它是属于它那一类的，或者每一个分类器都说它不是它那一类的，前者叫分类重叠现象，后者叫不可分类现象。分类重叠倒还好办，随便选一个结果都不至于太离谱，或者看看这篇文章到各个超平面的距离，哪个远就判给哪个。不可分类现象就着实难办了，只能把它分给第6个类别了……更要命的是，本来各个类别的样本数目是差不多的，但“其余”的那一类样本数总是要数倍于正类（因为它是除正类以外其他类别的样本之和嘛），这就人为的造成了上一节所说的“数据集偏斜”问题。\n\n因此我们还得再退一步，还是解两类分类问题，还是每次选一个类的样本作正类样本，而负类样本则变成只选一个类（称为“一对一单挑”的方法，哦，不对，没有单挑，就是“一对一”的方法，呵呵），这就避免了偏斜。因此过程就是算出这样一些分类器，第一个只回答“是第1类还是第2类”，第二个只回答“是第1类还是第3类”，第三个只回答“是第1类还是第4类”，如此下去，你也可以马上得出，这样的分类器应该有5 X 4/2=10个（通式是，如果有k个类别，则总的两类分类器数目为k(k-1)/2）。虽然分类器的数目多了，但是在训练阶段（也就是算出这些分类器的分类平面时）所用的总时间却比“一类对其余”方法少很多，在真正用来分类的时候，把一篇文章扔给所有分类器，第一个分类器会投票说它是“1”或者“2”，第二个会说它是“1”或者“3”，让每一个都投上自己的一票，最后统计票数，如果类别“1”得票最多，就判这篇文章属于第1类。这种方法显然也会有分类重叠的现象，但不会有不可分类现象，因为总不可能所有类别的票数都是0。看起来够好么？其实不然，想想分类一篇文章，我们调用了多少个分类器？10个，这还是类别数为5的时候，类别数如果是1000，要调用的分类器数目会上升至约500,000个（类别数的平方量级）。这如何是好？\n\n看来我们必须再退一步，在分类的时候下功夫，我们还是像一对一方法那样来训练，只是在对一篇文章进行分类之前，我们先按照下面图的样子来组织分类器（如你所见，这是一个有向无环图，因此这种方法也叫做DAG SVM）\n\n![img](http://www.blogjava.net/images/blogjava_net/zhenandaci/WindowsLiveWriter/SVMSVM_CBFA/clip_image002_2.gif)\n\n这样在分类时,我们就可以先问分类器“1对5”（意思是它能够回答“是第1类还是第5类”），如果它回答5，我们就往左走，再问“2对5”这个分类器，如果它还说是“5”，我们就继续往左走，这样一直问下去，就可以得到分类结果。好处在哪？我们其实只调用了4个分类器（如果类别数是k，则只调用k-1个），分类速度飞快，且没有分类重叠和不可分类现象！缺点在哪？假如最一开始的分类器回答错误（明明是类别1的文章，它说成了5），那么后面的分类器是无论如何也无法纠正它的错误的（因为后面的分类器压根没有出现“1”这个类别标签），其实对下面每一层的分类器都存在这种错误向下累积的现象。。\n\n不过不要被DAG方法的错误累积吓倒，错误累积在一对其余和一对一方法中也都存在，DAG方法好于它们的地方就在于，累积的上限，不管是大是小，总是有定论的，有理论证明。而一对其余和一对一方法中，尽管每一个两类分类器的泛化误差限是知道的，但是合起来做多类分类的时候，误差上界是多少，没人知道，这意味着准确率低到0也是有可能的，这多让人郁闷。\n\n而且现在DAG方法根节点的选取（也就是如何选第一个参与分类的分类器），也有一些方法可以改善整体效果，我们总希望根节点少犯错误为好，因此参与第一次分类的两个类别，最好是差别特别特别大，大到以至于不太可能把他们分错；或者我们就总取在两类分类中正确率最高的那个分类器作根节点，或者我们让两类分类器在分类的时候，不光输出类别的标签，还输出一个类似“置信度”的东东，当它对自己的结果不太自信的时候，我们就不光按照它的输出走，把它旁边的那条路也走一走，等等。\n\n**大Tips：SVM的计算复杂度**\n\n使用SVM进行分类的时候，实际上是训练和分类两个完全不同的过程，因而讨论复杂度就不能[一概而论](https://www.baidu.com/s?wd=%E4%B8%80%E6%A6%82%E8%80%8C%E8%AE%BA&tn=24004469_oem_dg&rsv_dl=gh_pl_sl_csd)，我们这里所说的主要是训练阶段的复杂度，即解那个二次规划问题的复杂度。对这个问题的解，基本上要划分为两大块，解析解和数值解。\n\n解析解就是理论上的解，它的形式是表达式，因此它是精确的，一个问题只要有解（无解的问题还跟着掺和什么呀，哈哈），那它的解析解是一定存在的。当然存在是一回事，能够解出来，或者可以在可以承受的时间范围内解出来，就是另一回事了。对SVM来说，求得解析解的时间复杂度最坏可以达到O(Nsv3)，其中Nsv是支持向量的个数，而虽然没有固定的比例，但支持向量的个数多少也和训练集的大小有关。\n\n数值解就是可以使用的解，是一个一个的数，往往都是近似解。求数值解的过程非常像穷举法，从一个数开始，试一试它当解效果怎样，不满足一定条件（叫做停机条件，就是满足这个以后就认为解足够精确了，不需要继续算下去了）就试下一个，当然下一个数不是乱选的，也有一定章法可循。有的算法，每次只尝试一个数，有的就尝试多个，而且找下一个数字（或下一组数）的方法也各不相同，停机条件也各不相同，最终得到的解精度也各不相同，可见对求数值解的复杂度的讨论不能脱开具体的算法。\n\n一个具体的算法，Bunch-Kaufman训练算法，典型的时间复杂度在O(Nsv3+LNsv2+dLNsv)和O(dL2)之间，其中Nsv是支持向量的个数，L是训练集样本的个数，d是每个样本的维数（原始的维数，没有经过向高维空间映射之前的维数）。复杂度会有变化，是因为它不光跟输入问题的规模有关（不光和样本的数量，维数有关），也和问题最终的解有关（即支持向量有关），如果支持向量比较少，过程会快很多，如果支持向量很多，接近于样本的数量，就会产生O(dL2)这个十分糟糕的结果（给10，000个样本，每个样本1000维，基本就不用算了，算不出来，呵呵，而这种输入规模对文本分类来说太正常了）。\n\n这样再回头看就会明白为什么一对一方法尽管要训练的两类分类器数量多，但总时间实际上比一对其余方法要少了，因为一对其余方法每次训练都考虑了所有样本（只是每次把不同的部分划分为正类或者负类而已），自然慢上很多。"
  },
  {
    "path": "TensorFlow 安装总结.md",
    "content": "---\ntitle: TensorFlow 安装总结\ndate: 2019-3-22 12:30:24\ntags: \n\t- python\n\t- TensorFlow\n\t- MachineLearning\ntoc:\n\tture\n---\n\n\n# TensorFlow 安装总结\n\n![TensorFlow 2.0 Logo](http://poatdt87z.bkt.clouddn.com/tensorflow2.0.gif)\n\n## TensorFlow 简介：\n\nTensorFlow 是 Google 公司人工智能团队谷歌大脑（Google Brain）研发和维护的开源深度学习框架，世界上由成千上万个开发者为该项目贡献代码，框架能够随着对深度学习的研究进展及时更新。TensorFlow 核心组件包括：最底层的设备层、内核应用、执行器、分发中心。主要是对张量进行运算，有适用于高阶神经网络的Estimators接口。而且 TensorFlow 可轻松移植到网页端和移动设备上。目前最新版是TensorFlow 1.13.1。\n\nTensorFlow-GPU是TensorFlow的GPU版，能够使用支持NVIDIA CUDA 并行计算技术的显卡进行矢量运算，其训练模型的效率比CPU高几十到上百倍。\n\n\n<!--more-->\n\n## 并行计算框架安装：\n\n我们打开TensorFlow的官网可以明显的在导航栏找到 Install 页面的入口。\n\n然而相信我，安装TensorFlow并没有官方页面上写的那么简单（特别是喜欢挑战难度安装GPU版本），输入几行代码，坐到旁边喝杯茶刷刷微博，过一会就安装完成了。\n\n因为TensorFlow支持众多平台，首先我们要搞懂TensorFlow到底支持哪些操作系统。\n\n- Ubuntu 16.04 或更高版本（这里可代表Linux家族）\n- Windows 7 或更高版本\n- macOS 10.12.6 (Sierra) 或更高版本（不支持 GPU）\n- Raspbian 9.0 或更高版本\n\n可以看出来常用的操作系统都是支持的。\n\n### 安装Cuda\n\n#### Linux\n\n首先我们看一下linux版的安装步骤\n\n1. 由于GPU版本计算速度更快，所以优先考虑安装GPU版本。我们要查看我们的电脑是否支持GPU版本的硬件。\n\n   > 这里有两个问题：\n   >\n   > 首先，你的电脑需要配置有NVIDIA的显卡\n   >\n   > 其次，你的显卡需要在CUDA支持名单里 [GPUSUPPORT](https://developer.nvidia.com/cuda-gpus)\n\n2. 确认了你的GPU在NVIDIA CUDA技术支持名单里之后就可以安装驱动了。\n\n   > 我们可以直接从NVIDIA官方网站找到CUDA[下载链接](https://developer.nvidia.com/cuda-downloads)\n   >\n   > 根据自己的操作系统版本下载合适的安装包。\n   >\n   > 另外要注意CUDA并不是版本越高越好，一般情况下CUDA发布版本要低于市面上相关机器学习框架的应用版本，也就是说你下载了一个过高的版本可能无法使用，具体如何选择CUDA版本，下文会有介绍。\n\n3. \n\n安装显卡驱动和 Cuda：\n指定到对应的文件所在位置：\n`# cd /usr/tmp/tensorflow-gpu`\n（这是我的文件所在的路径）\n\ncd到驱动所在目录：\n\n```\n# sh ./NVIDIA-Linux-x86_64-375.66.run\n# sh cuda_8.0.61_375.26_linux.run\n```\n\n修改配置文件：\n\n`# vi ~/.bashrc`\n（vi 命令为在terminal终端中进入文本编辑的方法，有的是vim，有界面还可以gedit ~/.bashrc方法进入）\n按Insert键，进入插入模式：\n\n```\n#gpu driver \n\nexport CUDA_HOME=/usr/local/cuda-8.0 \n\nexportPATH=/usr/local/cuda-8.0/bin:$PATH \n\nexportLD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH \n\nexportLD_LIBRARY_PATH=\"/usr/local/cuda-8.0/lib:${LD_LIBRARY_PATH}\"\n```\n\n\n\n按 Esc 键， 并输入\n`# wq`\n表示写入并退出。\n\n修改之后需要激活所修改的配置文件：\n\n`# source ~/.bashrc`\n然后重启服务器\n`# reboot`\n接下来就可以通过查看显卡信息测试驱动是否安装成功：\n\n`# nvidia-smi`\n![mem](http://poatdt87z.bkt.clouddn.com/mem.png)\n有显示，说明显卡驱动安装成功，继续闯关！\n\n测试CUDA是否安装成功：\n`# cd /usr/local/cuda/samples/`\n如果安装成功了的话，上面这个文件夹是可以进去的\n`# make（这步运行时间比较长，不要以为存在问题）`\n`# ./bin/x86_64/linux/release/deviceQuery`\n\n\\#./bin/x86_64/linux/release/bandwidthTest\n显示 Result = PASS 则测试通过\n安装CUDNN\n指定到对应的文件所在位置：\n`# cd /usr/tmp/tensorflow-gpu`\n（这是我的文件所在的路径）\n\n```\n$ tar -zxvf cudnn-8.0-linux-x64-v6.0.tgz\n\n$ cd cuda\n\n$ cp include/* /usr/local/cuda-8.0/inlcude/\n\n$ cp lib64/lib* /usr/local/cuda-8.0/lib64/\n```\n\n\n\n注意驱动和cuda的安装顺序，先安装驱动的话，安装cuda时x-configtion选择N\n\n#### Mac OS和Raspbian\n\n因为该硬件没有NVIDIA的显卡所以压根就不支持CUDA，这里跳过\n\n#### Windows\n\nWindows安装CUDA和安装NVIDIA显卡驱动步骤一致。\n\n下载CUDA安装包，打开解压安装。选择精简版安装，安装程序会自动检测不兼容的软件，然会自己安装，自己注册环境变量，十分简单。\n\n由于国产部分杀毒防护软件敌友不分，在安装完成之后，一定要人工检查一下系统变量，看一下CUDA路径是否被添加到Windows环境变量里。\n\n### 安装cuDNN\n\n安装cuDNN也很简单，首先从 [官方网站](https://developer.nvidia.com/rdp/cudnn-download) 下载cuDNN，这里需要注册NVIDIA账户。\n\n#### Linux\n\n```\n安装cudnn:\n$ tar -xvzf cudnn-8.0-linux-x64-v6.0.tgz\n$ cp -P cuda/include/cudnn.h /usr/local/cuda-8.0/include\n$ cp -P cuda/lib64/libcudnn* /usr/local/cuda-8.0/lib64\n$ chmod a+r /usr/local/cuda-8.0/include/cudnn.h /usr/local/cuda-8.0/lib64/libcudnn*\n```\n\n#### Windows\n\n解压`cudnn-xxx-windowsxx-x64-vx.x.x.xx.zip`文件到cuda安装目录，合并到同名文件夹即可。\n\n## TensorFlow安装\n\n这里通常有两种安装方法：\n\n> 1. 从源代码构建\n> 2. 从发行版安装\n\n前者适合追求最新版本，或者需要深度制定TensorFlow的用户，当然你想体验一下一个大型软件从源码到可执行文件的编译过程也可以尝试。\n\n后者适合普通用户，可以直接使用Python 的pip工具安装。\n\n#### Linux 从源码构建\n\nLinux 和 macOS 设置\n\n安装以下构建工具以配置开发环境。\n\n安装 Python 和 TensorFlow 软件包依赖项\n\n[Ubuntu](https://tensorflow.google.cn/install/source#ubuntu)[mac OS](https://tensorflow.google.cn/install/source#mac-os)\n\n```\nsudo apt install python-dev python-pip  # or python3-dev python3-pip\n```\n\n安装 TensorFlow pip 软件包依赖项（如果使用虚拟环境，请省略 `--user` 参数）：\n\n```\npip install -U --user pip six numpy wheel mock\npip install -U --user keras_applications==1.0.5 --no-deps\npip install -U --user keras_preprocessing==1.0.3 --no-deps\n```\n\n这些依赖项列在 `REQUIRED_PACKAGES` 下的 [`setup.py`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py) 文件中。\n\n安装 Bazel\n\n[安装 Bazel](https://docs.bazel.build/versions/master/install.html)，它是用于编译 TensorFlow 的构建工具。\n\n将 Bazel 可执行文件的位置添加到 `PATH` 环境变量中。\n\n安装支持 GPU 的版本（可选，仅限 Linux）\n\n没有针对 macOS 的 GPU 支持版本。\n\n要安装在 GPU 上运行 TensorFlow 所需的驱动程序和其他软件，请参阅 [GPU 支持](https://tensorflow.google.cn/install/gpu)指南。\n\n**注意**：您可以轻松地设置 TensorFlow 的某个支持 GPU 的 [Docker 映像](https://tensorflow.google.cn/install/source#docker_linux_builds)。\n\n下载 TensorFlow 源代码\n\n使用 [Git](https://git-scm.com/) 克隆 [TensorFlow 代码库](https://github.com/tensorflow/tensorflow)：\n\n```\ngit clone https://github.com/tensorflow/tensorflow.git\ncd tensorflow\n```\n\n代码库默认为 `master` 开发分支。您也可以检出要构建的[版本分支](https://github.com/tensorflow/tensorflow/releases)：\n\n```\ngit checkout branch_name  # r1.9, r1.10, etc.\n```\n\n要测试源代码树的副本，请对 r1.12 及更早版本运行以下测试（这可能需要一段时间）：\n\n```\nbazel test -c opt -- //tensorflow/... -//tensorflow/compiler/... -//tensorflow/contrib/lite/...\n```\n\n对于 r1.12 之后的版本（如 `master`），请运行以下命令：\n\n```\nbazel test -c opt -- //tensorflow/... -//tensorflow/compiler/... -//tensorflow/lite/...\n```\n\n**要点**：如果您在使用最新的开发分支时遇到构建问题，请尝试已知可用的版本分支。\n\n配置构建\n\n通过在 TensorFlow 源代码树的根目录下运行以下命令来配置系统构建：\n\n```\n./configure\n```\n\n此脚本会提示您指定 TensorFlow 依赖项的位置，并要求指定其他构建配置选项（例如，编译器标记）。以下代码展示了 `./configure` 的示例运行会话（您的会话可能会有所不同）：\n\n查看示例配置会话\n\n配置选项\n\n对于 [GPU 支持](https://tensorflow.google.cn/install/gpu)，请指定 CUDA 和 cuDNN 的版本。如果您的系统安装了多个 CUDA 或 cuDNN 版本，请明确设置版本而不是依赖于默认版本。`./configure` 会创建指向系统 CUDA 库的符号链接，因此，如果您更新 CUDA 库路径，则必须在构建之前再次运行此配置步骤。\n\n对于编译优化标记，默认值 (`-march=native`) 会优化针对计算机的 CPU 类型生成的代码。但是，如果要针对不同类型的 CPU 构建 TensorFlow，请考虑指定一个更加具体的优化标记。要查看相关示例，请参阅 [GCC 手册](https://gcc.gnu.org/onlinedocs/gcc-4.5.3/gcc/i386-and-x86_002d64-Options.html)。\n\n您可以将一些预先配置好的构建配置添加到 `bazel build` 命令中，例如：\n\n- `--config=mkl` - 支持 [Intel® MKL-DNN](https://github.com/intel/mkl-dnn)。\n- `--config=monolithic` - 此配置适用于基本保持静态的单体构建。\n\n**注意**：从 TensorFlow 1.6 开始，二进制文件使用 AVX 指令，这些指令可能无法在旧版 CPU 上运行。\n\n构建 pip 软件包\n\nBazel 构建\n\n仅支持 CPU\n\n使用 `bazel` 构建仅支持 CPU 的 TensorFlow 软件包构建器：\n\n```\nbazel build --config=opt //tensorflow/tools/pip_package:build_pip_package\n```\n\nGPU 支持\n\n要构建支持 GPU 的 TensorFlow 软件包构建器，请运行以下命令：\n\n```\nbazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package\n```\n\nBazel 构建选项\n\n从源代码构建 TensorFlow 可能会消耗大量内存。如果系统内存有限，请使用以下命令限制 Bazel 的内存消耗量：`--local_resources 2048,.5,1.0`。\n\n[官方 TensorFlow 软件包](https://tensorflow.google.cn/install/pip)是使用 GCC 4 构建的，并使用旧版 ABI。对于 GCC 5 及更高版本，为了使您的构建与旧版 ABI 兼容，请使用 `--cxxopt=\"-D_GLIBCXX_USE_CXX11_ABI=0\"`。兼容 ABI 可确保针对官方 TensorFlow pip 软件包构建的自定义操作继续支持使用 GCC 5 构建的软件包。\n\n构建软件包\n\n`bazel build` 命令会创建一个名为 `build_pip_package` 的可执行文件，此文件是用于构建 `pip` 软件包的程序。例如，以下命令会在 `/tmp/tensorflow_pkg` 目录中构建 `.whl` 软件包：\n\n```\n./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg\n```\n\n尽管可以在同一个源代码树下构建 CUDA 和非 CUDA 配置，但建议您在同一个源代码树中的这两种配置之间切换时运行 `bazel clean`。\n\n安装软件包\n\n生成的 `.whl` 文件的文件名取决于 TensorFlow 版本和您的平台。例如，使用 `pip install` 安装软件包：\n\n```\npip install /tmp/tensorflow_pkg/tensorflow-version-tags.whl\n```\n\n**成功**：TensorFlow 现已安装完毕。\n\nDocker Linux 构建\n\n借助 TensorFlow 的 Docker 开发映像，您可以轻松设置环境，以从源代码构建 Linux 软件包。这些映像已包含构建 TensorFlow 所需的源代码和依赖项。要了解安装说明和[可用映像标记的列表](https://hub.docker.com/r/tensorflow/tensorflow/tags/)，请参阅 TensorFlow [Docker 指南](https://tensorflow.google.cn/install/docker)。\n\n#### Windows 从源码构建\n\nWindows 设置\n\n安装以下构建工具以配置 Windows 开发环境。\n\n安装 Python 和 TensorFlow 软件包依赖项\n\n安装[适用于 Windows 的 Python 3.5.x 或 Python 3.6.x 64 位版本](https://www.python.org/downloads/windows/)。选择 pip 作为可选功能，并将其添加到 `%PATH%` 环境变量中。\n\n安装 TensorFlow pip 软件包依赖项：\n\n```\npip3 install six numpy wheel\npip3 install keras_applications==1.0.5 --no-deps\npip3 install keras_preprocessing==1.0.3 --no-deps\n```\n\n这些依赖项列在 `REQUIRED_PACKAGES` 下的 [`setup.py`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py) 文件中。\n\n安装 Bazel\n\n[安装 Bazel](https://docs.bazel.build/versions/master/install-windows.html)，它是用于编译 TensorFlow 的构建工具。\n\n将 Bazel 可执行文件的位置添加到 `%PATH%` 环境变量中。\n\n安装 MSYS2\n\n为构建 TensorFlow 所需的 bin 工具[安装 MSYS2](https://www.msys2.org/)。如果 MSYS2 已安装到 `C:\\msys64` 下，请将 `C:\\msys64\\usr\\bin`添加到 `%PATH%` 环境变量中。然后，使用 `cmd.exe` 运行以下命令：\n\n```\npacman -S git patch unzip\n```\n\n安装 Visual C++ 生成工具 2015\n\n安装 Visual C++ 生成工具 2015。此软件包随附在 Visual Studio 2015 中，但可以单独安装：\n\n1. 转到 [Visual Studio 下载页面](https://visualstudio.microsoft.com/vs/older-downloads/)，\n2. 选择“可再发行组件和生成工具”，\n3. 下载并安装：\n   - *Microsoft Visual C++ 2015 Redistributable 更新 3*\n   - *Microsoft 生成工具 2015 更新 3*\n\n**注意**：TensorFlow 针对 Visual Studio 2015 更新 3 进行了测试。\n\n安装 GPU 支持（可选）\n\n要安装在 GPU 上运行 TensorFlow 所需的驱动程序和其他软件，请参阅 Windows [GPU 支持](https://tensorflow.google.cn/install/gpu)指南。\n\n下载 TensorFlow 源代码\n\n使用 [Git](https://git-scm.com/) 克隆 [TensorFlow 代码库](https://github.com/tensorflow/tensorflow)（`git` 随 MSYS2 一起安装）：\n\n```\ngit clone https://github.com/tensorflow/tensorflow.git\ncd tensorflow\n```\n\n代码库默认为 `master` 开发分支。您也可以检出要构建的[版本分支](https://github.com/tensorflow/tensorflow/releases)：\n\n```\ngit checkout branch_name  # r1.9, r1.10, etc.\n```\n\n**要点**：如果您在使用最新的开发分支时遇到构建问题，请尝试已知可用的版本分支。\n\n配置构建\n\n通过在 TensorFlow 源代码树的根目录下运行以下命令来配置系统构建：\n\n```\npython ./configure.py\n```\n\n此脚本会提示您指定 TensorFlow 依赖项的位置，并要求指定其他构建配置选项（例如，编译器标记）。以下代码展示了 `python ./configure.py` 的示例运行会话（您的会话可能会有所不同）：\n\n查看示例配置会话\n\n配置选项\n\n对于 [GPU 支持](https://tensorflow.google.cn/install/gpu)，请指定 CUDA 和 cuDNN 的版本。如果您的系统安装了多个 CUDA 或 cuDNN 版本，请明确设置版本而不是依赖于默认版本。`./configure.py` 会创建指向系统 CUDA 库的符号链接，因此，如果您更新 CUDA 库路径，则必须在构建之前再次运行此配置步骤。\n\n**注意**：从 TensorFlow 1.6 开始，二进制文件使用 AVX 指令，这些指令可能无法在旧版 CPU 上运行。\n\n构建 pip 软件包\n\nBazel 构建\n\n仅支持 CPU\n\n使用 `bazel` 构建仅支持 CPU 的 TensorFlow 软件包构建器：\n\n```\nbazel build --config=opt //tensorflow/tools/pip_package:build_pip_package\n```\n\nGPU 支持\n\n要构建支持 GPU 的 TensorFlow 软件包构建器，请运行以下命令：\n\n```\nbazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package\n```\n\nBazel 构建选项\n\n从源代码构建 TensorFlow 可能会消耗大量内存。如果系统内存有限，请使用以下命令限制 Bazel 的内存消耗量：`--local_resources 2048,.5,1.0`。\n\n如果构建支持 GPU 的 TensorFlow，请添加 `--copt=-nvcc_options=disable-warnings` 以禁止显示 nvcc 警告消息。\n\n构建软件包\n\n`bazel build` 命令会创建一个名为 `build_pip_package` 的可执行文件，此文件是用于构建 `pip` 软件包的程序。例如，以下命令会在 `C:/tmp/tensorflow_pkg` 目录中构建 `.whl` 软件包：\n\n```\nbazel-bin\\tensorflow\\tools\\pip_package\\build_pip_package C:/tmp/tensorflow_pkg\n```\n\n尽管可以在同一个源代码树下构建 CUDA 和非 CUDA 配置，但建议您在同一个源代码树中的这两种配置之间切换时运行 `bazel clean`。\n\n安装软件包\n\n生成的 `.whl` 文件的文件名取决于 TensorFlow 版本和您的平台。例如，使用 `pip3 install` 安装软件包：\n\n```\npip3 install C:/tmp/tensorflow_pkg/tensorflow-version-cp36-cp36m-win_amd64.whl\n```\n\n**成功**：TensorFlow 现已安装完毕。\n\n使用 MSYS shell 构建\n\n也可以使用 MSYS shell 构建 TensorFlow。做出下面列出的更改，然后按照之前的 Windows 原生命令行 (`cmd.exe`) 说明进行操作。\n\n停用 MSYS 路径转换\n\nMSYS 会自动将类似 Unix 路径的参数转换为 Windows 路径，此转换不适用于 `bazel`。（标签 `//foo/bar:bin` 被视为 Unix 绝对路径，因为它以斜杠开头。）\n\n```\nexport MSYS_NO_PATHCONV=1\nexport MSYS2_ARG_CONV_EXCL=\"*\"\n```\n\n设置 PATH\n\n将 Bazel 和 Python 安装目录添加到 `$PATH` 环境变量中。如果 Bazel 安装到了 `C:\\tools\\bazel.exe`，并且 Python 安装到了 `C:\\Python36\\python.exe`，请使用以下命令设置 `PATH`：\n\n```\n# Use Unix-style with ':' as separator\nexport PATH=\"/c/tools:$PATH\"\nexport PATH=\"/c/Python36:$PATH\"\n```\n\n要启用 GPU 支持，请将 CUDA 和 cuDNN bin 目录添加到 `$PATH` 中：\n\n```\nexport PATH=\"/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0/bin:$PATH\"export PATH=\"/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0/extras/CUPTI/libx64:$PATH\"export PATH=\"/c/tools/cuda/bin:$PATH\"\n```\n\n#### 使用Python自带的pip包管理工具安装\n\n执行`pip install tensorflow =='x.xx'``pip install tensorflow-gpu ==’x.xx’`\n\nlinux 执行`sudo pip install tensorflow =='x.xx'``sudo pip install tensorflow-gpu ==’x.xx’`\n\n## 重点TensorFlow-GPU 与CUDA 及cudnn 版本参照表\n\n| 版本                  | Python 版本 | cuDNN | CUDA |\n| :-------------------- | :---------- | :---- | :--- |\n| tensorflow_gpu-1.12.0 | 3.5-3.6     | 7     | 9    |\n| tensorflow_gpu-1.11.0 | 3.5-3.6     | 7     | 9    |\n| tensorflow_gpu-1.10.0 | 3.5-3.6     | 7     | 9    |\n| tensorflow_gpu-1.9.0  | 3.5-3.6     | 7     | 9    |\n| tensorflow_gpu-1.8.0  | 3.5-3.6     | 7     | 9    |\n| tensorflow_gpu-1.7.0  | 3.5-3.6     | 7     | 9    |\n| tensorflow_gpu-1.6.0  | 3.5-3.6     | 7     | 9    |\n| tensorflow_gpu-1.5.0  | 3.5-3.6     | 7     | 9    |\n| tensorflow_gpu-1.4.0  | 3.5-3.6     | 6     | 8    |\n| tensorflow_gpu-1.3.0  | 3.5-3.6     | 6     | 8    |\n| tensorflow_gpu-1.2.0  | 3.5-3.6     | 5.1   | 8    |\n| tensorflow_gpu-1.1.0  | 3.5         | 5.1   | 8    |\n| tensorflow_gpu-1.0.0  | 3.5         | 5.1   | 8    |\n\n这里经过测试\n\n| TensorFlow - GPU      | CUDA          | cuDNN                       |\n| --------------------- | ------------- | --------------------------- |\n| tensorflow_gpu-1.12.0 | CUDA 9.0.176  | cuDNNv7.4.2.24 for cuda 9.0 |\n| tensorflow_gpu-1.13.0 | CUDA 10.0.130 | cuDNNv7.4.2.24 for cuda10.0 |"
  },
  {
    "path": "数据预处理之One-Hot（独热编码）编码.md",
    "content": "---\ntitle: 数据预处理之One-Hot（独热编码）编码\ndate: 2019-03-18 17:36:48\ntags:\n\t- python\n\t- MachineLearning\ntoc:\n\ttrue\n---\n\n# 数据预处理之One-Hot（独热编码）编码\n\n## 为什么使用One-Hot编码\n\n对于机器学习任务中，特征并不总是连续值，很多是分类值。这些分类值本身没有大小的意义。为了将数据集中一个分类变量替换为一个或多个新特征，我们使用One-Hot编码对数据进行预处理。\n\n独热编码（哑变量 dummy variable）是因为大部分算法是**基于向量空间**中的度量来进行计算的，为了使非偏序关系的变量取值不具有偏序性，并且到圆点是等距的。使用one-hot编码，将离散特征的取值扩展到了欧式空间，离散特征的某个取值就对应欧式空间的某个点。将离散型特征使用one-hot编码，会让特征之间的**距离计算更加合理**。离散特征进行one-hot编码后，编码后的特征，其实每一维度的特征都可以看做是**连续的特征**。就可以跟对连续型特征的**归一化**方法一样，对每一维特征进行归一化。比如归一化到[-1,1]或归一化到均值为0,方差为1。       \n<!--more-->\n​        为什么特征向量要映射到欧式空间？\n\n​        将离散特征通过one-hot编码映射到欧式空间，是因为，在回归，分类，聚类等机器学习算法中，特征之间距离的计算或相似度的计算是非常重要的，而我们常用的距离或相似度的计算都是在欧式空间的相似度计算，计算余弦相似性，基于的就是欧式空间。\n\n## 什么是One-Hot编码\n\n独热编码即 One-Hot 编码，又称一位有效编码，其方法是使用N位状态寄存器来对N个状态进行编码，每个状态都由他独立的寄存器位，并且在任意时候，其中只有一位有效(即各种属性之间相互排斥)。\n\n简而言之，即把离散的特征的每一种取值都看成一种状态。\n\n这样做的好处主要有：\n\n1. 解决了分类器不好处理属性数据的问题\n\n2. 在一定程度上也起到了扩充特征的作用\n\neg：\n\n```java\n自然状态码为：000,001,010,011,100,101\n\n独热编码为：000001,000010,000100,001000,010000,100000\n```\n\n## One-Hot编码特征\n\n1. 经过独热编码后，就变成了m个二元特征。\n2.  这些特征互斥，每次只有一个激活。\n3. 数据会变的稀疏\n\n## One-Hot编码的优缺点\n\n- 优点：独热编码解决了分类器不好处理属性数据的问题，在一定程度上也起到了**扩充特征**的作用。它的值只有0和1，不同的类型存储在垂直的空间。\n- 缺点：当类别的数量很多时，**特征空间**会变得非常大。在这种情况下，一般可以用PCA*(principal Component Analysis，主成分分析方法)来**减少维度**。而且one hot encoding+*PCA这种组合在实际中也非常有用。\n\n## 使用One-Hot编码的场合\n\n首先要明确，独热编码用来解决**类别型数据**的离散值问题。\n\n将离散型特征进行one-hot编码的作用，是为了**让距离计算更合理**，但如果特征是离散的，并且不用one-hot编码就可以很合理的计算出距离，那么就没必要进行one-hot编码。 有些基于**树的算法**在处理变量时，并不是基于**向量空间**度量，数值只是个类别符号，即没有偏序关系，所以不用进行独热编码。  Tree Model不太需要one-hot编码： 对于决策树来说，one-hot的本质是**增加树的深度**。\n\n## One-Hot 和 哑变量（虚拟变量）\n\n首先One-Hot编码主要解决数据的向量化 表示，而虚拟变量则是一个统计学概念。\n\n对于哑变量余独热编码的相异点，最直观的理解就是哑变量将任意一个One-Hot编码状态去除。即哑变量将定性特征转化为n-1个特征，而One-hot则是转化为n个特征。意思就是哑变量在编码时会去除第一个状态，而One-hot则对所有的状态都会进行编码。\n\n这样可以使One-Hot编码减少一个冗余位，这种去除是随机的。但是对于一个没有权重的数据集而言随机取出有点过于随意。"
  }
]