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KNN_sklearn.html
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<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
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<title>KNN_sklearn</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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<h1 id="pkgs">pkgs<a class="anchor-link" href="#pkgs">¶</a></h1>
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<h2 id="Scipy">Scipy<a class="anchor-link" href="#Scipy">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>pip3 freeze <span class="p">|</span> grep -i numpy
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<pre>numpy==1.21.3
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># 对角矩阵</span>
<span class="n">matrix</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">matrix</span><span class="p">)</span>
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<pre>[[1. 0. 0. 0. 0. 0.]
[0. 1. 0. 0. 0. 0.]
[0. 0. 1. 0. 0. 0.]
[0. 0. 0. 1. 0. 0.]
[0. 0. 0. 0. 1. 0.]
[0. 0. 0. 0. 0. 1.]]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 转为 稀疏矩阵</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
<span class="n">sparse_matrix</span><span class="o">=</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">(</span><span class="n">matrix</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">sparse_matrix</span><span class="p">)</span>
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<pre> (0, 0) 1.0
(1, 1) 1.0
(2, 2) 1.0
(3, 3) 1.0
(4, 4) 1.0
(5, 5) 1.0
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<h2 id="pandas-%E7%A4%BA%E4%BE%8B">pandas 示例<a class="anchor-link" href="#pandas-%E7%A4%BA%E4%BE%8B">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="n">p1</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
<span class="s2">"Name"</span><span class="p">:[</span><span class="s2">"Tom"</span><span class="p">,</span><span class="s2">"Lucy"</span><span class="p">,</span><span class="s2">"Tim"</span><span class="p">],</span>
<span class="s2">"City"</span><span class="p">:[</span><span class="s2">"Bj"</span><span class="p">,</span> <span class="s2">"Sh"</span><span class="p">,</span> <span class="s2">"Sz"</span><span class="p">],</span>
<span class="s2">"Age"</span><span class="p">:[</span><span class="mi">12</span><span class="p">,</span><span class="mi">15</span><span class="p">,</span><span class="mi">18</span><span class="p">],</span>
<span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="n">p1</span><span class="p">)</span>
<span class="n">p1</span>
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<pre> Name City Age
0 Tom Bj 12
1 Lucy Sh 15
2 Tim Sz 18
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<th>0</th>
<td>Tom</td>
<td>Bj</td>
<td>12</td>
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<th>1</th>
<td>Lucy</td>
<td>Sh</td>
<td>15</td>
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<th>2</th>
<td>Tim</td>
<td>Sz</td>
<td>18</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">display</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="n">p1</span><span class="o">.</span><span class="n">City</span> <span class="o">!=</span><span class="s2">"Bj"</span><span class="p">])</span>
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<th>1</th>
<td>Lucy</td>
<td>Sh</td>
<td>15</td>
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<th>2</th>
<td>Tim</td>
<td>Sz</td>
<td>18</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p1</span><span class="p">[</span><span class="n">p1</span><span class="o">.</span><span class="n">City</span> <span class="o">!=</span><span class="s2">"Bj"</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
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<pre>(2, 3)</pre>
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<h2 id="matplotlib">matplotlib<a class="anchor-link" href="#matplotlib">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># 从-20到20取20个点</span>
<span class="n">x</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="n">y</span><span class="o">=</span><span class="n">x</span><span class="o">**</span><span class="mi">3</span> <span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span><span class="mi">6</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span><span class="mi">5</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"o"</span><span class="p">)</span>
</pre></div>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
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<pre>[<matplotlib.lines.Line2D at 0x7f076be70390>]</pre>
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<img 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<h1 id="scikit-learn">scikit-learn<a class="anchor-link" href="#scikit-learn">¶</a></h1>
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<h2 id="KNN-%E4%BA%8C%E5%88%86%E7%B1%BB">KNN 二分类<a class="anchor-link" href="#KNN-%E4%BA%8C%E5%88%86%E7%B1%BB">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 训练集：生成已知标签的数据集</span>
<span class="c1"># 导入数据生成器</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">data</span><span class="o">=</span><span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">data</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">spring</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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tE/JVBYKgw2wNZCSzPJLHYzkoSSZg35uXPnmFmZ2WCscYDlAA7sMzBRjZAS/D78d/5i/Yuebm/xlo7Wjnz06FG856nV6kRfRGPy9u1b2svsGYpQPd3XYR3zIi/VUFMDDVdjNd3s3H6IYay4+GvpX/RWeLM7utMNbvwdv3MRFrGErATLFynPyMhIo5eZK0MuBiLQ4IfpJnfjgwcP9PKq1Wru2bOHixYt+uaCpXfv3tHX05d9LPpwFEbRE56cgzncgz1sZ92OWdJliTXmixcvZgVlBWqhZQxiqIWWwdAN5XxrY4U5s+ewoqIiYxATq/cBHGAG5wxxPlySQqsGrTjYXD+A2miz0WxQtUFsnlOnTlGlUHEWZvE6rnMlVtJT4ckN6zd8U3ZERASdFE68j/t68udhHutXrm8U/VOCwjkKcxu26el8FEeZzTNbomWlWUOu1WpZumBp9rTsyXd4Rw003IzNVClVvHv3bqIbwtg8f/6c6Z3Tc7jZcN7FXR7DMZZWlGbngM5x5n/16hXbNmlLhaWClmaWrFqyqsmGYDq17sT68vqxxvwSLjEd0hncdE0VTTl79myT6PgtNBoNvV28eQqnSJCBCGQP9GBplGaerHkYFRWVIuUWyVHE4FU5HOG0t7bn/fv3OWvmLLZp1IYjho745sP8a549e8aOrTtSDjmv47qe/A7WHTh8iG6ruIiICPpl8aOn5ElzmNMGNnS1cOVvA3/7pvwqRavwb/xt8Iv0s/XjmTNnktQWX2Mns2Mwgg06NpbmlrHj99VKVuMiLNLLcwiHmM0z2zeH8uJ7i7yKq8yWLvFGMbXYvn07PRWe3IqtfIu33IM9zKTIxBV/rUi0rDRryEkyNDSUjWs0po2VDR2tHZnXJy+PHDmS6EZIKe7fv8+2TdsyvWN6+nn5ccrEKXGG1NRqtSyZvyQ7W3VmCEIYjnDOkGbQw8FD79U5tYiIiGCn1p1oL7Ont9KbjjJHVjSraHDFRmIkhw4emur6fY+XL1/SzsrOQN8gBNHdzj3Fyp0zaw6LK4rHzttooeUQiyGsWKwis6bPynqKelyIhexh1YMuNi48ceJEgmWfPn2a+ezyGdRpH/axTL4yJMnr16/r7Vn5AA9Yw6oGAxoFUK1WxzvhWbN0Ta7BGoM3iSw2WXj+/PnkNcpHnJXOfIAHemW8wAsqrBSxRlplo+IzPDPQQ2Gh4Lt37xgSEsLJEyezW/tuXLhwIT98+EBSN8HtpHDiQzzUO3chFrJuxbpG0T+l2LZtG4vkLEKllZL5s+TnurXrkiQnTRvyT7x9+5bBwcFpYgIuLo4dO8bsNtkNNmUOkAdw4oSJJtPrzZs3vHPnDg8dOkRfpa+e94caahawKcDt27ebTL/4iImJocpGxdu4rdee/+AfFstVLMXK1Wg07NS6E1UyFRvYNmB2m+z0z+HPX1r/wh6WPfR0WY/1zO+bP8H37L179+gqdzXwwFmIhWxYVTdx27VdV44yH6X3/Xu8p62ZLS3MLCizkLFVg1YGPugrV66kv9Jfz3FgFVYxR8YcRvtNdWvfje2t2sfe41po2cOyB9s2bRubp0CWAtyHfXr638EdOiudeeHCBbrbu7O1vDWnYzprKWsxW4ZssROxIwaPYFFFUV7ABcYghn/jb7rKXXns2DGj6P+jk2xDDmAJgBcArnyR5gRgH4DbH/93TIisn3FBUEJYunQpWylbGbTIHMxhx1YdTayd7o2hbuW6rKSoxL3Yy0M4xDryOixXuNwPG7T/92G/s7iiOO/iLgnyLM4ysyIzN2z49nhrYtBqtTxx4gRHjRrFadOmxRqVu3fvct26dTxx4gS1Wi193X15CZf0rq0GGjpaJ25T4ErFKrG/Zf9YY34Lt+it8ObevXtJkhX9K3I3dhvcR/7w527sZihC2cuyFwtmL6jXO9doNOzYqiM95B78Rf4LK9tWZjrHdEbrjZO6TkHxvMWZ2yY3u1p3ZX7b/PTP4c/Q0NDYPH8t+4s5FDlih4+e4AnLKspy+KDhLOdfjvMwT69evS16s0vbLrF1mPjHRGZwzkBJklgoeyHu2bPHaPr/6BjDkJcGUOArQ/4ngEEf/x4EYEJCZP1XDfnZs2eZSZlJb7KJIBspGnHmjJmmVo8kGRUVxamTp7JYzmIsnL0wx48d/0OvVtVoNBw1dBSdlc50sHZgBucMXLwg4Z4qGo2GBw4c4MKFCw28c44ePcpG1RvRy86LKnMVu6AL28ja0EnhxJ07dxrIypc5H4/iqN61/YAPtLWy5evXrxOs0/Pnz1mxaEW6yd1Y0K4gnRROnD398xxFXDsyvcZrOsAhdnxaCy3z2+aPNf5fcvnyZc6aNYvr1q1LkWur1Wp54MABzpgxg3v27DEY6tFqtZw2aRpdbV2ZXpmejnJHDuoziG/evKHcQs4oROnV7TZu09PRM85y/msYZWgFgPdXhvwmAI+Pf3sAuJkQOf9VQ06S1ctUZyNZI17HdQYhiL+Z/0ZvV+8krQT9ZISmT5/O3bt3m6zXfObMGXZo3oE1S9Xk+LHjE2W0koJWqzX4EUdHRzM0NPS7C2K+JDg4mAWyFWBe27xsq2xLL4UXa5aryYiICK5euZrpFOk4F3N5FEfZB31oD3uuwioewzG62rkaeMRMmTiF5RTlGI7wWGM6wGIA61Wql6T63blzhydOnIgdI/7E3bt36WrrypmYyZd4yX/xL0uiJLtCf0FRF1kXzpz5Y3QQ4iIqKooPHz6MfZiEh4dTYangW7zVq8d5nKePm49Jdf1RSClD/uaLv6UvP8dxbkcAgQACM2bMmFr1/uEICwvjgN4D6OnoSUeFI1s1aJUoz4ZPfPkK2826GwvaFmSBbAVSfdJ0zao1dFe4c6I0kZuxmc1lzZktQza9V2ljcevWLdatWJeW5pa0k9mxW/tuyYrv3bBaQ/a36B87nhuNaFZCJRbLX4zpHdPzDM7o3cVDMITOcOYUTGEJuxIGvd2YmBi2btiarjJXNrZpzBw2OVgoZ6EEL7R58+YNO7fpTFtrW1qaW7JOhTq8fft2nHkvXrzIWuVq0dbalu627sxhmUMv9owGGuayyZWgEAWRkZEmdYX9xJs3b5g7S25mQiaWR3kuwzJGI5r1ZPU4fPBwU6v3Q5Dihvzj59cJkfNf7pEbix6/9GA7q3Z6k0q9LHsxoFFAqukQFRVFd3t3A5/q9tbt+dugb7vCJZaXL1/S08mTf0p/8h3e8QmeMMA6gOWLlE/SK3ZYWFicvb9zOEdHONLV3NXgzr2AC8yGbHSAA/1t/eNdxHLjxg2uWrWKx44dS7BuWq2WZfzLsJ1VOz7FU57DOQZIAXR3cP+uD39YWBh9PX05wGIAgxDEO7jDAOsAlipQ6pvlX7t2jVWKV6GVuRXllnK2rN/SJN5TpK4OebPkZQurFjyCI/wbf9MPflSZq1ijbI0fengvNRFDKz8ZKhuVwcKIUIRSbilP1PBCcjh//jxz2uY0uMKHcZjFchrXa2TyxMlsKW+pV44GGvoofXjq1KlEy3v79i0VlgoD75BbuEUPeFAGGd/gjd53m7CJ5VCO5VCODgqHOP3Uw8LCePjwYf7777+MiYnh8uXLWatMLdYoVYOLFy+Od+HN8ePHmVWZlR/wgQ3RkG5wY3VUpwMcWChnIUZERHyzPkFBQWzTuA0dFY50tXVlr869vvm28vLlS6ZzTMcZ0gxGIIIv8ZI9LXuycK7CJhl7nj9vPmsoa+h5dL3BGzpaOeqtCr148SKb1GxCX3dfVipa6YdePZ0SxGfIkxuPfBuAgI9/BwDYmkx5ggQSo4mBNaz10qxgBbVW/ekhm+I4OjoiJCYE0YjWSw9CEJxUTkYt68aFGygRUUIvzQxmKC4Vx40bNxItz87ODoVyF8I8zMNQDEUmZEI6pENjNEYVVIHKQoUuZl0QjnAAwH3cxxAMQRd0wT3cw6g/RsHKykpP5vJly5HBJQMG1R6ERqUaIYNDBkzqNAnNjzRHm/+1wbKey9CkVpM4r8+NGzdQDMUwAiMAAA/xEDuwA8/wDKqbKowaOuqb9UmXLh2WrluKV2Gv8Pzdc0ybOw12dnbx5l++bDnKR5ZHD/aADDI4wQnTYqYh4mEEjhw5ktjmTDYn9p1AvTDdRiGfsIc9ylmXw+nTpwEAFy9eRIXiFVBkRxFsDd6K9qfao0ujLljx14pU1/eHIy7rHtcBYA2AZwBiADwB0B6AM4AD0Lkf7gfglBBZokeefAIaB3CgxUC9lh1rNpb1q6TuUuWKRStyiMWQ2PHZIAQxmzIb//777++eGxUVxXGjx9HPy4++7r7s270vX758GWfeqZOnsoWihV591VAzszIzT58+nSTdL1y4QDvJjrVQi5dwiXdxlz3Qg5mRmXZWdixXuByVUDI3ctMJTpyIiZwlzWL2DNkN3nrOnTtHd4U7r+BK7FDXNExjdmTnO7zjZExmBVSgm5kbR40aZdDrPXnyJLMos9AJhkvQb+Im3ezcklTH+OjWvhunY7rBr7Odoh0XLFhg1LISwsC+AznQUv9+1kLL3La5Yxf/NazW0EDn0zjNjKqMP6x7rLHBz7AgSPCZoKAg+nr6spqy2jeDEyWXs2fPsmmtpsyXKR+b12nOf//9V+/7Z8+esWS+kvRSerGsfVk6yBw4buS4BMmuV7keq8ur8wRO8CIusqNVR+bOnDvOYYRXr14xvXN6jjMbx1d4xQd4wJbWLVmpeKUkDwWcOnWKmeSG7qAVUIEVS1ckSU6cMJG21rYsZluMWW2y0i+TH2/dumUgq1v7bhxjNsbAEOVCLuZCLtZFXf6Df7gcy5nZPDP79eynd75Wq2X5IuVpBjOGIUxPzhu8ocxClqQ6xse8ufNYR1nHqA/G5HDr1i2qFCoexEESuonncebjmDtz7tiHppezV+x6gS8PF5lLovz00zLCkP+EhIWFcenSpezXqx8XLlxo1HChJHnw4EG6KFw4Q5rBQARyqjSVKoWK//vf/wzyXrp0iXv37o23R/01gYGB9FJ46Y1Ra6FlZZvK/Ouvv+I85+7du2xUvRHllnI6KZ3Yq1OvZNV5/vz5bK9ob3CHTsM0du/QPTbfu3fvuH//fp49ezbeh0bTGk25CIv4F/5iO7TjIAziH/iDDnCgBIm+8OUyLCNBhiCECigMgmi9f/+eWd2zchZm6esjTWPt8rWTXM+4eP/+PbOky8IBFgP4EA95FVfZSNaIVUpWMZl/9q5du5jJNRMzKzPTRebCsv5l+fDhw9jvS+UtxS3Yotc2T/CEDnKH784h/CwIQy5INMX8ihmE6F2BFSxbsGyyZc+bNy9OIzoVU9n9l+7fOTthhIeH88SJE7x+/Xqc3+/fv595bfMahExoJW/FqZOnxuZLyPZcM2fMpJvkxvIoz3mYx5qoSU948jiOUwstj+M4syALl2M5CbIKqrBhfcNY6RcvXqSrrSt7WvXkOqxjd+vudLNz49WrV5PREnETFBTE9s3b08XWhRmcM3DQr4MYFhZm9HISg1qt5tWrV/UM+Cc2btzILIossatnn+IpqyiqsF/3fnFIMi0RERFctmwZu7btyj/G/GG0NwZhyAWJQqvVUpIkA6+OD/hAawvrZMvfvXs3C9kWMrg72srbGmXDkGVLllFlo2JBu4JMr0jP4nmKG/jrazQaFshWgL9a/MrXeM1IRHKWNIseDh58+fIljx49ymJ+xWgumdNR4cgBvQbEeqo8f/6cI4eNZI2SNdilbRf26tmLVaQqsQ+F7MhusMrzMA4zJ3Lqxn6Rm7m8c8Wp++PHjzls4DA2qNSAvw36jU+ePEl2e8RHZGQk//77by5evNhgl6MvCQsL4/37940aFvjDhw9csmQJBw8czDVr1sQrW61Wc9u2bRw+fDhbNW9Fdzt3eio86SB3YJ8ufX4IH/gvefXqFfP55mMlZSVOx3R2lHWki41xYqYLQ57GiYqKSvVXXg8HD17FVb2rdw7nkrSzydeo1WrmypSLI8xHMAxhjEEMl2EZ3ezcvrnhcEI4deoU0ynSxU48qqHmWPOx9M/hb9CGz58/Z9NaTSm3lFNmIWPFIhV55coVXrlyhS5KF67FWsYghvdxnzXkNdi+eXs+fvyYGVUZ2dG6I7diK8eYj6Gz5Mx/8E9sO5nBzOAhGIUomsOcMzCDGZCB1UqadjvAc+fOMZ1jOpa3K8+WypZ0ljlzUJ9Bem2kVqs5uO9gOsodmUGZgSobFSf9MSnZ9+K9e/fo7erNWspaHI3RLG9Tnn6Z/Ayu/bt371g8b3EWsinE3/Ab6yjrMJ1jOu7fv99gxeuPwqBfB7GNVRu9N711WMd8WfIlu92EIU+jbN++nXkz56W5ZE43OzeOHTk21fzEx44cy1KKUrEhR4MQxKKKopw8wTi70z958oQ1y9akjZUN7a3sWTRXUYPJ1KTQoUUHTpQm6t11n3zOz507F+c5kZGResMKv7T8hWPNxurJeId3dJQ5sk3TNhxgMUDvuxIowZVYGfs5N3JzP/br5dmLvXSAA/MiL7PIs3xzN5+URq1WM7N7Zq7Dulj9XuIlsymz8Z9//onNN2roKJZWlOYTPCGh87PPrczNxQuTt/NS7fK1+YfZH7Flf4qS2Cmgk16+oQOGspl1M72NZWZLs1mmQJlklZ+S+GX041mcNbj/XGQufPz4cbJkC0P+A3HmzBmOHz+eCxcu/GZckiNHjtBd4c6d2EkNNLyGayyuKM5h/Yelip5qtZoDeg6gg8yB2W2z00HuwKH9hhr9QfL69etk98K/pE65OlyP9QZ3Xmmb0ty9e3eCZJTJV4YHcMBARhH7IsykysRLuEQttLEGZj3WMzMy8yVexvbAPOHJ3djN93jP3dhNV7jSCla0MbPh8EHDTRr06cSJE8xtm9ugfvMxn81qNyOpG3pysXXR20KP0G0CkSdTniSXHRUVRStzK37ABz2593GfrrauenlzpM9hsHI4GtG0s7Iz2SrU7+Gf1Z+HcEhP50hE0t7aPtn3eXyGPLkLggSJQKvVom3TtmhcrjGeD3uOfb33wTeDL44dOxZn/kkjJmFc+DhUQzWYwQw5kANrw9di9qzZiIiISHC5ERERmDhhIkr4lUCZfGUwZ/YcqNXq755nbm6OCdMn4OHzh9h0ahMev3iMMRPHwMzMuLeNg4MDwsLC8L///Q+vX79OtrwyNcpgpeVKEJ8X3jzGY5z5cAYqlSpBMnL758Zh88N6aa/wCjejbkLlpMKf+BNucIMlLFEKpWALW7wwewFfa180sm2EibYToXHUIEAWAGc4YzAGYw7m4D7uI6MsI3LmywlJkuIqOlWIjIyEjWRjkG4DG0SGR8bmeRf+Dr7w1cvjBz88fv44yWVLkgQzyQxq6N+DMYiBuZm5QV4ttHpp/PjPlO33LZp3bI7RitGIRGRs2iTzSShSsAhcXFxSptC4rHtKH//VHvnq1avpr/TX8xPegR3xLmjI6pGV13DNoAUzKjN+c2LqS9RqNcsVLsda8lrcj/3ciZ0sqyjLJrVMv3E1qVsqX7dSXbrIXFjMvhgdZA4cPjB5vdXg4GDaSrZsgibcgR1chEX0gQ8rS5XZuU3cW/B9ze3bt+li48LZmM1QhDIQgSylKMVenXqxUolKLIACLIdylEFGFVRUQsnKpSrzwYMHXLVqFfft28enT5/SwdrBwC98EzaxQqEKSa6fMQgPD6fKRsVzOBerVwxiWFpZmkuWLCGpm/DO5ZXLYFu7ZVjGysUqJ7pMtVrNsLAwarVaNq7RWC8UrxZatrFqw16de+mdM2LoCDaQNdALCDbVbCrLFy6fzBZIOaKjo9msTjN6KjzZRtGG/rb+zOmVM05PnMQCMbRieupVrMcVWGHQIrltc8e5HViDKg04B3P08t7GbapsVAn2Hti6dSv9bfz1xhgjEUlvhXeS9mn8FF713r173zS2arWaZ8+e5b///vvNoZhWDVqxrXVbRiKSBPkMz5hXmZfLli1LtG6fuHTpErPaZOV4jGcFVGAjNOIu7OIpnGLBLAm/986fP8+aZWvS1tqWPm4+/HPcnwwODqadtR1doDPyb/GW13CNFVCB2T2z651/8+ZNZrLJpGesnuEZD+AA82XKl+T6GYuNGzZSJVfxV8tfORmT6W/jz+plqut5gWz5ews9FB5chmW8hmuchVl0USRu+7qYmBgO7TeUzkpnWptbM0+mPFy+fDlzZcrFErYl2MeyD/PZ5GPxvMUNwjmHhYWxfJHyzG2Tm/0s+rGyTWV6u3rHGxXyR+LixYtctGgR9+zZY7SVp8KQ/wDULV+Xq7DKoEXy2eXj8ePHDfKfOXOGLgoXrsAKvsZr/g//Y15FXo4fPT7BZQ7sN5BjMMagzG7W3Th16tRE6R8YGMg8mfMwnSId3eRu9M/uH+fO8AcPHmRGVUbmtM1JXxtfZvXMyrNnzxrke/v2rW7TBbzW020HdrC4X/FE6fYJrVbLbdu2UW4h5y7s0vMcWIiFyd5t/cyZM/S09GQ/9NPT+QM+0Eay0XNxjImJYQbnDDyBEzyIg/SDH53gRAUU9PPyS/DiqZTk9u3bHD5kOLv/0p1btmyJ0+AcPHiQNUrXYFaPrGxYtWGc1/Jb9O7UmxUVFXkXd6mBhjuxk24KNx45coRbtmzhxIkT49yA4hMajYb79u3jH3/8wdWrV/9nFv/EhTDkPwDLly9nUWVRRiAitjX2YR89nTzjjYr3v//9j+ULlaeNtQ1zZsjJ+XPnJ2rYYdrUaWwtb21wFSrZVuK6dQnfAPb169d0s3PjKqyiBhqqoeYiLGJ65/R63h7BwcF0VjpzD/bE9kLXYz3d7d0N3MUePXpEN7mbwYKcK7jCrB5ZE6zbJ1avXk0PpQe9JC82l5rTG94sgRJ8i7c8gRP0VHjGxud+8OAB582bx+XLlydqU4+XL1/SSXLiNmwzaNOilkUNYpRvWL+BKpmKDnDgVmylFlq+xVt2s+zGcoXLJbqOaY03b97QXmbPF3ih11ZzMTd2D1JBwhGG/AdArVazWZ1m9FX6crA0mC3lLemsdObBgwdTrMyQkBC62LhwIzZSCy3VUHOBtIAZVBkS1bOZO3cumyiaGFzNGjY1uHLlyth8UyZPYVtZW4N8NW1qcsWKFXoyNRoNfdx9YuNrfDqGmQ/jL61+SVQ9p0yYQhdzF7ZBm9hhJA00bI7mdDB3oLeLN9euXkuS/GPUH3SSOTFAEcC6NnXppHBK1L6Phf0K81f8qqdzGMLobO3MBw8eGORv2aglB0j67opqqOml9OKFCxcSVc+0xrVr1+gp92RZlKUb3FgWZbkHe3gO55g3U15Tq5fmiM+QW6TMFKogLszNzbHq71U4duwYDh48iCLORTC16dQEe1IkBZVKhW37tqFdk3boE9oHaqrh6eWJ3Zt2QyaTJVjO06CnyBaezSA9W2Q2BAUFxX4OeR4Cr0gvg3xe0V4ICQnRSzMzM8OU+VPQrFkz9I/oj0zMhFUWq3BMeQynfz+dYN0+fPiA30f8Do1Gg7EYC7OPzlhmMMN4jMdOq5248+wOzM3Ncfr0acyeMBtXIq/AAx4AgGM4hnoN6uFB8AMolcrvlrdy80oU9iuMHNE50AIt8BRP0dOqJ7wzeWPqhKkoV6UcatasCXNznQdGxJsIFGABPRnmMEcu81x4+PAh8ubNa1DG69ev8ebNG2TMmDFWzrcIDg7Grl27YGVlhZo1a8Le3v6756QGV65cQXhEOLqiK0qgBI7jONqgDapIVZC3oGG9BUkkLuue0sd/tUduSrRaLW/cuMG7d+8m6fydO3cyn00+Pe+BaEQzqzKrXhCtPXv2MLdNbr1VjWEIYwZFhngX+wQGBrJIniJUSAoWsCrAdPJ0LFOwTIK3SDt9+jTz2+anFawMNoN4iZeUW8pj8/bq3ItjJMM5g8p2lblp06YEt0dgYCArFK5AM8mMDjIH2pnbsZtlN/6JP1nQpiCrlakWu5x//NjxDJAF6JX3Fm/pJHMyCBvw5s0bNq/TnHbWdkyvSE9vF29u3PB54dCrV6+4dOlSzp49O9Zzae6suXSQObCpsmnsG8a2bdsSXJeUpIBvAb0VrwS5HdtpL9nz0qVLplYvzQExtCJIDhqNhhWKVmAteS0ewAHuxV5Wlldm7Yq19cbsNRoNa1WoxXKKctyETVyLtSysLMw2TdrEK3vHjh30UfjwAR6Q0LnBDbYYzErFKiVIt4cPH1IlU7Ee6nEs9FdjjsRINq7RODZvl7ZdOBmTDe7KOrZ1uGbNmkS3S3h4ON3s3HgKp2JlxSCGZRVluXDhQpK6cXVvV2/2t+jPK7jCgzjI4ori7Naum4G8OhXrsIN1h9gt6I7hGN0V7jx9+jR37txJJ4UTG9g0YDt5OzrLnNmray+6yl15D/diyz+DM3SUO353i7jUwEwyDFUQiUhamFmYWrU0iTDkgmQTHh7OP8f/ySLZi7BYzmKcMmlKnG6Q0dHRnD9vPqsWr8oapWpw+fLl33RBrFuhbmyI1y97+65y1wTHV69doTabWzZnBmRgIzTidExnDdSgh4OHnozdu3czhzKH3qrCm7hJB5lDkrxIjh49Sn87f4O7fB3WsVaZWrH5njx5wq7tujKLWxYW9C3IWTNnGbTJgwcP6CJz0ZsMJ8gp0hRWLlGZ9tb2PIETsenP8Izulu5sYd7CoPz6NvWT5cJpLLK4Z+FJnNTT7QRO0NfD19SqpUmEIRcYjWvXrrFm2Zq0MLOgg9yBPTv2TFZc8FJ5SsW5HD63bW4GBgYmSMbr16/ZoGoDOlg70NXKlQ6WDmzdorXBvpVarZYdWnSgr9KXv0u/81eLX+kid+GShUuSpPvp06eZ0zangefNEixho2qNEiXr2LFjLGJfxKAd9mIvXSVXlkRJg+9GSaNYAiUM0lspWnH+/Pl89uwZx40ex19a/MK5c+YaPWb995g3ex79FH68jMskyEu4xFyKXJw/Z36q6vGzIAy5wCgEBwfT3d6d06Rp/IAPfIRHbG7dnNXLVE+yzOFDhrO9tX5s8ku4RFdb10T7DAcHB/PKlSvfDG2q1Wp5+PBh9u/dnyOGjeCNGzcMvn/69GmCeugajYZZ0mXhWqyN1f01XjOXMleCtrv7kjdv3tBB5hAboOrT0RVdWQiFWAEVDH5Nf+JPqsxVsUMxBPkYj+koc+TOnTvpauvKjrKOnIM5rKOsw6zpsyZ47sEYaLVaTp88nR4OHrSzsmM6h3ScMWVGqsaZ0Wq1PHjwIAf0HcAxv49J8KroHxFhyAVGYezvY/mL7Be9KxqDGGZQZEiyK11oaCizps/K1rLW3I7tnC5Np6fCk8uWLDOq7gnh5MmTzO+bn07WTrS1smXNsjUZFBT0zXP+/fdfpnNMxwq2FRigCKCLzIV9u/WNNVYPHjzg9OnTOX369O8u0x7922jmUuTiFmzhBVxgP/RjeqSnH/xoBzvewI3Ydn+P90yP9KxVpRZ9lD4cI43hUPOhdJe7c8qfU1jMr5jBkFUvi17s1sFwbD6lUavVfPXqVarvranRaNiyfktmU2bj7/idPa160lnuzLVr1qaqHsYiRQ05gD4ArgK4At0mzbJv5ReGPHHcuHGD9SvXp421DdM5pOOQfkOMGuA/MbSq14pLsdTgqja0bci1a5P+43j58iXHjBrDKkWrsHXD1jx27FiiZezbt4/l/MvR1daVJfOW5Pbt2xN1/pMnT+hio4tBroGGYQjjMIthzOeb77sRHyMiIrhp0yYuWLBAb0/POTPn0EnmxA6yDuwg60AnmRPnzpobrxytVstVq1axTP4y9Hb2pq+FLx/iId3gximYQhVU7I3eHIER9IEPlVBy4viJPHr0KPt278tB/QbxwoULfPXqFW0sbQz2I72O6/RWeSeqXdIymzdvZn5lfoYjPLYNLuIiHRWOqT7MZAxSzJAD8ARwH4D84+f1ANp86xxhyBPOs2fP6OHgwUnSJIYilNdxnbVltU0W9OqPMX+wvUx/GCQa0UyvSG9Sd7Jdu3bRXe7ONVjDp3jKTdjE9Ir0eq5732P0yNHsat1Vr25aaJnHNg8PHTqUaJ3u3btHJ5mTnkfJPdyjs9w5Qa/3z58/jx1qqYEanI/5vI3b/B2/cyAGcjZm0wtedJQ7GmzR9v79eyotlXyHd3r1OY3TzJE+R6LrklYJaBjAuZhrYIUq2FX4YVw0E0N8htxY8UgtAMglSbIAoADw1Ehy//MsmLMAdSLq4Ff+Cmc4IzuyY13kOhzefxi3b99OdX3ad2yPXfJdmCRNwhu8wT3cQ0tZS+Qvmh+5c+dOdX0+8Xv/3zEnYg6aoik84IH6qI9l4cswst9Ig7whISGYNnUaBvQdgL///js2pO+j24+QO0q/DhIk5GZuPHz4MNE6bd68GY21jZEJmWLTMiETGmoaYvPmzd8939XVFcNGDEMJRQlkQRb8il+xG7vRBE2QF3nxB/7ABEyAj6UPLl68qHeujY0NqlaoitEWo2PD+UYjGiPlI9GyY8tE1yWtYmllqRdO9hNRiIKVlZUJNEoZkm3ISQYBmATgEYBnAN6S3Pt1PkmSOkqSFChJUuDXK/wE8XPlzBWUiSqjlyaDDIUtC+Pq1aupro+LiwsOnTqEk5VOwsPCA0VtiyJDhwxYu32t0csKCgrCtm3bEBgY+OntL14u3LyASqikl1Ye5XHt0TVoNJrYtFOnTiFX5ly4MOQCHKc6YkLrCShfpDzCw8PhX8ofO5U79WREIhIHNQdRqFChROuv1WphTsNVmRa0gFarjeMMQ34d9CuW71qOsOZhcHR0xAqsQA3UwDIswyIsQgM0QFBMUJyrg+f8NQeHfA8hj00etFK2QmZFZijLKdFvYL9E1yWt0rRdU8xWzsZLvIxNO4ADuC3dRtmyZU2nmLGJq5uemAOAI4CDAFwAWALYAqDlt84RQysJZ9jAYexp1dNgKMND7sFr166ZWr0UQavVsl/3fnSUObK6XXVmUWZh0dxFv7kTuZ+XHw/jsF47ncM5eqm89OTm9MrJjdgYm0cDDevJ6vGPMX/ww4cPzOmdk52tOvM8zvMIjrCCogJb1GuRpHrcunWLKplKzwvlMR5TJVMlKQzr/v376aXw4i3c4qdJ5iEWQ1iuUPzBt7RaLY8cOcJly5bx4sWLSapHWkar1XJIvyF0kbuwvaw9a9vUpspGlaShsh8BpOAYeSMAi7/43BrAnG+dIwx5wnn8+DFdbV05D/MYgQg+xmM2tW7KaqWrcdWqVVy7dq2Br3RaZ9myZSygLMBXeBVrbIdYDGG1UvFvVrx08VJmV2Tnv/iXhC6CYl5FXs6cNjM2z61bt5hekd7A5/sgDrJI9iIkdUHGfu3xK7Oly8b8PvnZs3tPNq7emAWzFGS7Zu0S/fCc9Mckushd2MuiF3tZ9KKL3IWTxyd9z9PZM2bTSeHEQnaF6CH3YFn/st98wAl03L59m7Nnz+aKFSv47t07U6uTZFLSkBeBzmNFAUAC8BeAHt86RxjyxHHhwgVWLlaZ5mbmtJPZsXKZynSQObCuTV3WtK1JR4Ujt27Zamo1jUbZAmUNwsRGIIKO1o7fNFpzZ81leqf0tLWypYeDB6f8OUXPX/n+/ft0k7vpxYshyF3YxZK5SxrI27FjB90UbpyFWTyFUxxrNpYuSpdEbxB9/fp1jh0zlmPHjDXwWU8KHz584PHjx3nz5s1kyxKkLVLMkOtkYxSAG9C5H64AYP2t/MKQJw21Ws07d+7QWe7M67ge26JncZaOckeGhoaaWkWjkN8nv8Gybi20zKjMqOfaFxcajYavX7+O11+5SK4inCt99mKIRCTLKspy1sxZsXnCwsI4YtgIOpk7MTMycxiG8T3ekyBnYzZrl69tvMoKBIkgPkNuFK8VkiNIZifpR7IVyShjyBXoY25ujvXr1qO5ujmyI3tsuj/8UVmqjL///tuE2hmPynUqY6nVUr20wzgMCxsL+Pj4fPNcMzMzODg4xBv6ddnGZRjvMh4VbCugq3VXZFNkg1sFN3Tq3AmAboKyVvlauDTpEnZpdmEd1uEO7qAqqkIDDeqhHk6cOWGcigoERkLEI09jRIRHwFZta5Bup7FDRESECTQyPv0G90PJDSXROLQx6kbUxQ2LG5hnNQ/LliyDmVny+h7Zs2fHzUc38c8//+Dp06doV6wd/P39Y78/ePAgQq6GYG/kXphD9zBYhVUogiLYhV2wgx08VB7J0kEgMDbCkKcxatSqgSZTm2Bg+EDYwQ4A8AIv8Lf0NwZWH2hi7YyDSqXC6cunsXTxUmzdvxWemTxxpPsR5MiRwyjyra2t0aBBgzi/CwwMRJWIKrFGHNBtUFEN1XAAB3BUcRTdBnQzih4CgbEQhjyNUaRIEdRpWQcFVxVE+/D2iJFisFC2EL1+7fXdYYe0hL29PXr37Y3efXunarleXl44LD8MfNBPP4VTOG55HAN6D0DHzh1TVSeB4HtIuvHz1MXf35+BgYGpXu7PAkkcOXIEf6/9G+YW5mjSqgmKFCliarV+CiIjI5HdKzv6hPRBF3aBBAkLpYUYbTca52+ch7u7u6lVFPyHkSTpHEl/g3RhyAUCfW7fvo1OLTrh3MVzkCAhb668mLdqXqKGdtRqNf73v/8hPDwcpUqVgp2dXQpqLPivEJ8hF0MrAsFX+Pr64uCZgwgNDQVJuLi4xJv3w4cPsLCw0NvIOjAwEA2qN4BrlCvsYY9WMa0wdfZUBLQNSA31Bf9BjBU0SyD46VCpVPEa8cuXL6Ocfzm4OrrC2c4ZTWs1xYsXLxAVFYU6letgashUnH13Fvvf7cfxiOMY0G0Arly5kso1EPxXEIZcIEgkISEhqFSyEpqda4Y36jd4GvMUnns8UbNcTezZswe+al/UR/3Y/DmQA79E/4IVS1aYUGvBz4ww5AJBIvlr6V+oHlMdHdERVrCCPewxKWYSIh5F4PTp03Clq8E5rhpXvH351gTaCv4LCEMuECSSu9fuomBEQb00CRIKsiCcnJywX70fwQiO/U4NNVbYrECVulVSW1XBfwRhyAWCRJKvaD7sU+7TS4tBDA7zMMqVK4f+g/qjmKIYpmEalmAJyijLwL2wO2rXrm0ijQU/O8L9UCBIJB8+fECB7AVQ63ktdFV3xXu8x0jZSLAEsXX/VgDA4cOHsXLBSoR/CEeNJjXQpEkTWFgIJzFB8hB+5AKBEQkODsbIQSOxfet2yK3laNGuBQYPH6znhigQGBthyAUCgSCNE58hF2PkAoFAkMYRhlwgEAjSOMKQCwQCQRpHGHKBQCBI4whDLhAIBGkcYcgFAoEgjWMUQy5JkoMkSRslSbohSdJ1SZKKGUOuQCAQCL6PsZaaTQewm2RDSZKsACiMJFcgEAgE3yHZhlySJHsApQG0AQCS0QCikytXIBAIBAnDGEMrmQCEAFgqSdJ5SZIWSZKk/DqTJEkdJUkKlCQpMCQkxAjFCgQCgQAwjiG3AFAAwFyS+QGEARj0dSaSC0j6k/T/1tZZAoFAIEgcxjDkTwA8IXn64+eN0Bl2gUAgEKQCyTbkJIMBPJYkKdvHpAoAriVXrkAgEAgShrG8VnoAWPXRY+UegLZGkisQCASC72AUQ07yAgCD0IoCgUAgSHnEyk6BQCBI4whDLhAIBGkcYcgFAoEgjSMMuUAgEKRxhCEXCASCNI4w5AKBQJDGEYZcIBAI0jjCkAsEAkEaRxhygUAgSOMIQy4QCARpHGHIBQKBII0jDLlAIBCkcYQhFwgEgjSOMOQCgUCQxhGGXCAQCNI4wpALBAJBGkcYcoFAIEjjCEMuEAgEaRxhyAUCgSCNYzRDLkmSuSRJ5yVJ+sdYMgUCgUDwfYzZI+8F4LoR5QkEAoEgARjFkEuSlB5ADQCLjCFPIBAIBAnHWD3yaQAGANDGl0GSpI6SJAVKkhQYEhJipGIFAoFAkGxDLklSTQAvSJ77Vj6SC0j6k/R3cXFJbrECgUAg+IgxeuQlANSWJOkBgLUAykuStNIIcgUCgUCQAJJtyEkOJpmepDeApgAOkmyZbM0EAoFAkCCEH7lAIBCkcSyMKYzkYQCHjSlTIBAIBN9G9MgFAoEgjSMMuUAgEKRxhCEX/DC8ePECXbu2g6enI7y9XTB4cD+EhYWZWi2B4IdHGHLBD0FERATKlSsCS8uVOHr0DXbsCMW9e7NQt24lkDS1egLBD40w5IIfgg0bNiBDhlBMnx4DHx8gVy5g9eooPH58CcePHze1egLBD40w5IIfggsXzqBChQ96aebmQPnyGly8eNFEWgkEaQNhyAU/BFmy5MC5cwq9NBIIDLSEj4+PibQSCNIGwpALfghatGiJY8dkmDFDQmQk8O4dMHiwBaKiXFGpUiVTq/efICwsDGPGjIK/f1YULZoDU6ZMQnR0tKnVEiQAYcgFPwT29vbYv/84duwoBkdHC7i7W+LevcrYs+cYzM3NTa3eT49arUbVqqVw4cJ4TJt2G+PG3cCePcPRpEktMdmcBjDqyk6BIDlkz54de/YcR3h4OEJCQvDhwwc4OTmZWq3/BP/88w+io29j/fpImH3s3pUsGYGcOY/j1KlTKFasmGkVFHwT0SMX/FC8efMGrVs3QP782VC/fjF4ebli+fJlSZan1WoRGhqKmJgY4yn5E3L69AnUqvUh1ogDgJUVUK1aDE6fPm06xQQJQhhywQ9FmzaNoFIdxJMnUbh58z127XqLoUO74ciRI4mWtXz5MmTO7AZf3/Rwd3fAb78NgEajSQGt0z6enhlx44bCIP3GDWt4enqaQCNBoiCZ6kfBggUpEHzNo0ePqFLJGBGhf8vMnQs2blw9UbK2bt1KLy8FT53Sybh/HyxTRsGhQ/ulgOZpn5cvX9LV1Y6rVoEaDRgTA86aJdHLy5WRkZGmVk/wEQCBjMOmih654IchODgYGTJYQSbTT8+WDXj27HGiZE2ZMhJTpoSjSBHdZ29vYPnycMydOwdRUVHGUfgnwsnJCTt3HsTEiVmQLp0cHh4yrFmTB3v2HIW1tbWp1RN8BzHZKfhhyJkzJx48UOPuXeBL1/G//7ZCsWIVEiXr/v1HyJ9fPy1jRsDcXIvXr1/D3d3dCBr/XBQsWBD//nsLjx49grm5OdKnT29qlQQJRPTIBT8MSqUSw4ePQtWqSqxeDZw+Dfz6qwW2bLFD794DEiWrQIH82LNHP+3CBcDKSo749owliQcPHuDJkydJrEHaR5IkeHl5CSOexhA9csEPRe/e/eDjkw1z507A8+fBKFOmKk6eHAwPD49EyRkyZByqVz8BSQpHtWrAxYtAnz4KjBz5R5x+6WfPnsUvvzTD8+dPERND5MiRHUuWrIevr6+xqiYQpBgSTeDs7+/vz8DAwFQvV6Dj/fv3WLlyBS5dOoMsWXIhIKAtVCpVsmTeuXMHO3bsgLW1NerXrw9XV1cjaZt0zpw5gzFjBuHffy/Ay8sTffqMQMOGDQ3yhYaGIleuzJg+/T0aNwa0WmDuXAlTp7rixo1HsLKyMoH2ySckJAR37tyBj4/PD3E9BMlHkqRzJP0NvohrBjSlD+G1YjqCgoLo4+PB+vWVnDULDAiQ093dnleuXEmyzHHjRlKlkrFjR2u2bKmgo6OcGzasT5IsrVbLY8eOcfjw3zh58mQGBQUlWa+EMm3aVLZuLefXt2q5crbctGlTomSdPn2ajRpVp59fRjZqVJ2nT59OAY2/TUxMDLt3b08HBxkLF7ano6OMnTq1ZnR0dKrqodVqqdFoUrXMnx3E47WSbKMMIAOAQwCuAbgKoNf3zhGG3HS0b9+cAwaY88tLMnu2xIoViyRJXmBgID09FXz27LO88+dBBwc5X716lShZGo2GAQGN6eOj5JAhEjt0sKaTk5zbtm1Lkm4JpX//PvzjD8NbtWtXa06fPj3Bcg4dOkQXFwVnzwYvXABnzwZdXBQ8ePBgCmkeN2PGjGD58gq+eqWrx5s3YJUqcg4fPihVyn///j27dWtPW1trmpubsVq1UsnqKAg+k5KG3ANAgY9/2wK4BSDnt84Rhtx0uLnZ8cED/UsSGQlaW5szPDw80fIGDOjD334z49eXuV49Gy5fvjxRsjZt2sR8+ZQMC/ss59QpUKWySZJuCWXjxo0sVsyGGs3ncqOiwEyZlInqUZcokYfr1+u3w/r1YIkSeVJG8XjIkMGZly7p63HzJujqapcq5VepUpKtWlkzKAgMDwdnzpTo7m7P4ODgVCn/ZyY+Q55srxWSz0j++/Hv9wCuAxBLwX5QZDIrvH+vnxYeDpiZmcHCIvFz3xqNBhYWhvMslpZI9CrKzZtXoGvXMCi+WGBYpAiQPbsZjh49mmjdEkrt2rUhST5o0kSGY8eAAweAGjXkKFCgNAoXLpxgOadOXUGdOl/L1qWnFg8ePEBw8CvMnw/88w/w6RJ4ewOhoe8/db5SjPPnz+P69X+xZEkU0qUD5HKge3eidu0oLFo0P0XL/i9jVPdDSZK8AeQHIIIz/KC0aNEGI0bIoFbrPpPAyJGWaNCgFiwtLRMtr379Jli6VI7Xrz+n3boF7N2rRo0aNRIly9zcPFavL9FokOgIiK9evcKJEycQFBT03byWlpbYs+cY8uQZgF69smDo0OyoUWMM1qzZmqgyPT2dcP26ftr160D69KkT+Gvbtm3w98+J9u2BDBmA4cOBunWBmBhg/XqgVKn8kCQpRXW4desWChc2x9d9gmLFInHz5oUULfs/TVzd9KQcAGwAnANQP57vOwIIBBCYMWPGlH8HEcRJeHg4a9QoS29vBQMClMyVy4ZFivgxJCQkyTIHDOjF9OkVHDjQjN27W9LZWc4lSxYmWs727duZM6eSb99+vnUOHgTd3e0ZFRWVIBlarZYDB/amvb01ixSxp5OTjM2b103U0ExERAQXLJjPJk2qs0uXtjxz5kyCzps0aTyLFlXw8WOd7o8fg0WLKjhx4h8JLjupREZG0tXVLjYkwaNH4LFjYJEiYL16ElUqBU+cOJGsMsLCwrhx40YuX76cT58+jTPPpUuX6OmpYHS0vglo21bG8ePHJqt8QQqOketkwxLAHgB9E5JfjJF/n7CwMN6+fZthYWEpIv/s2bNcvHgxjxw5Qq1Wm2x5586d44gRw/nHH+N49+7dJMnQarXs2bMjPTzk7NbNio0aKensrOSBAwcSLGP27JksXFjBFy90t9uHD2CjRjJ269YuQeeHh4ezePG8rFxZyeXLwfHjzejhoeDixQu+e65Go+GwYf3p6Cinj48NHR3l/O23AaniuXH48GEWLmzHN2/AunVBlQr09wft7EB3dxteu3Yt2fJdXGxZubItGzWyoYODjFOnTowzb506FdmggYw3b4KhoeAff5jR09MpWZ0FgY4UM+QAJADLAUxL6DnCkMePRqPhiBGD6OgoZ6ZMOmMwfPjAH9qN6/Dhw6xWrSS9vJxZtWoJHjp0KFnyLl26xMmTJ3PRokV8/fp1os7NkycTjxzRv+WePQPt7GQJCv40e/YsVqumoFb7+fwbN0AnJwXfv3+fIB0+fPjAmzdv8sOHD3F+/+bNG06a9Cfr16/I7t078NKlSwmtXrycPHmSfn62bNoU7NABsYHHHjwAvb3NuWHDhiTLDg8Pp6urHfft+9wmjx6B6dIpePbs2TjzDxjQm+7u9lQqrdioUXXevn07yeULPpOShrwkAAK4BODCx6P6t84Rhjx+pkz5k4ULK/jw4ecfTNGiCk6aNN7UqsXJ7t276eYm519/gXfvgsuXg+7uCu7cudMk+nh42Me23adDowFtbCwT9FCoU6cc160zvG1LlrTj/v37k61fSEgIs2XLwKZN5Vy3Dvz9d3O6uiq4ZcuWZMlVq9X08nKlrS34/r2+7hs2gBUqFEqy7C1btrBCBTt+3SajR5uxd++uydJbkDjiM+TG8Fo5RlIimYdkvo/HzuTK/a8yY8ZEzJsXjowZdZ8zZADmzg3HjBmTTKtYPIwY0Rfz5kWgdWsgc2agVStg/vxwjBjR16jlREVF4fLlywgODo43D0kUK1YMa9boT+jt3AlkzpwB9vb23y3Hzs4RISFfywVCQrSws7NLku5fMnnyeJQp8xxr1kSgcWPgt9802LQpHL16dUhWrHRzc3NMmTIXMhlgY6P/XaZMwIsXL5IsOyIiAra2ht4utrZaRER8SLJcgRGJy7qn9JGWe+RarZanTp3ijh07+PLlS6PLt7AwY2SkfpNFRYFmZpJRxrKNjbm5ob7R0cbVd/HiBXR1tWX27LZ0dLRmgwZVDXrXBw4cYN68malQWFIuB/v2lbhvHzhhghldXBTcs2dPgso6cOAAvb0VfPRIVxetFpw7V6KfX6YE1ycmJoa3bt3iixcvSJLv3r3jtm3buGvXLhYo4MPjxw1/Fpkz2/D69esJbZJ4y02f3pFnzujLHjDAgt26dUiy3JCQEDo4yHj37meZERFgnjw23L59e7J0FiQOpORkZ2KPtGrI7969yzx5fJgtmw0rVrSjg4OMkyYZ1yMhrkUlGzeCxYr5GbUcY+Hj48bTp/X1PXsWzJTJ1Sjy9+/fzwwZFLx8WSc7LAzs1MmK9epVjs1z9epVurgouHWrbhjl3DkwTx4zennZsn375jx//ryezHv37nHmzJlcsGBBrLH9ksmTx9PBQcYqVeyYO7cts2fPyJs3byZI3w0b1jNDBmd6eytpb2/N4sXz0t5ezgoV7FiypB2dnMy4aZN+e0VGgk5OMqOEI1izZhXTpVNw1izwwAGwZ08reno6csCAfmzRojZHjfotXo+TbzF37iy6uck5bJg5J00C/fyUbN683g89d/MzIgx5MtFqtSxQIBsnTzaLnQh79AjMlEnBffv2Ga2cgwcP0sVFzjlzwEuXdLvjuLoqEuW5kRjCwsK4fv16Lly4kPfu3Uv0+bNmTWe+fArevKlrk1u3wPz5FZwxY6pR9KtfvzIXLdK/hcLDQUdHaz558oQk2a1be44apR92IDISdHOT886dO3ry/vxzLJ2dZWzfXsZmzXRxYTZt2mhQbmhoKLdu3cr//e9/CTZWp0+fpru7gidO6HT48EE38Vi69Ge9Bg8Gs2QBQ0J0nzUacPBgC1atWjKpTWTAsWPH2Lx5HZYunYe//NKGbm72bNLEmqVLgzY2uqNDh9aJXi178eJFDhjQh926deDOnTuFETcBwpAnkwsXLjBTJqXeMm5SZ2ibNatl1LJOnTrFBg2qMGfO9GzQoApPnjxpVPmfOHnyJN3c7Fmlii1bt1bQ2VnGIUP6JWpIRKvV8s8/x9LV1ZZubnK6uNhywoQxRhtWKV48p4EXCgl6e4Pz588nSVarVpzbthnmKVfOXm+C8t9//2W6dAoGBX3Ok9S4MHHRpk1jTpki6ekQFQW6uYF37ug+a7VgjhyWtLW1ZJUq9sycWclixfLw2bNnyS4/Lho0qMqRI3XtNWwYGBQEXrsG1q4tsWbNcilSpiDlEIY8mRw5coRFixrO3P/9N1itWnHTKpcEYmJimCGDSs8AhoaCvr7KBI8nf0lUVBSDgoISvHAnofTr15Pdu+vHcrl5E3RwAHPkyECtVsuhQwewe3crvTyvXoEODtZ6BnLQoH4cNswwLkz9+vpxYWJiYrhixQo2bFiFzZvX4fbt2xP0YKpUqRB37jS85YsXh97DqH17GceNG8d//vmH//77b4rMfbx8+ZKzZs2ilZUZAwLAxo31dYqJAb28FDx37pzRyxakHPEZcrGxRAIpWLAgbt1S4/p1IEcOXRoJ/PWXHJUrNzCtckng2LFjcHWNQq1an9OcnYFevcKwevVCVK5cOVHyrKyskC5dOiNrCfTpMxDZss2ChQXQuDFw9y4wYgQwejQwenQonjx5gm7desPffyFcXNRo2VKLJ0+AwYMVCAhopbelm0ajhlxu6H1hZUWoP8YG0Gq1aNy4Jp4/P4bOncMQEQEMGLAfR460xfDh43Du3Dk4OTkhd+7cBsvdixatgG3bLqFatc97ggYFAdeuAXny6D6/fAls2ybhxIlGyJIli9HbC9DFYa9VqyIqVlRj8GAt1q3Txb6JjETsfqgWFkCpUma4fPkyChQogJiYGGzduhVXrlxB1qxZUb9+fci+3jxV8OMSl3VP6SMt9shJcvHihfTwUHDCBDOuXg3WrKmgv3+OBC8U+ZHYvXs3S5c2fMNYuhRs0qRGqury8uVLHj582GA8+xP58vmwYUPdSsWaNcEdO3STnnZ2VrGeQ3fv3mXr1g2ZPr0j8+TJxBkzphmM4R4/fpze3gq+fv25vnfugA4OstjIfDt37mTu3EpGRX3O8/o1aG9vQQcHGUuUsKe3t5KFC+fiw4cP9eQ/f/6cXl4u7NPHgmfP6iapfXzMmSGDJefMAadOxccQvf2M3IKf0Wq1zJnTS2/CXK0GK1YEJ0/+nKbRgNmy2fD48eMMCQlhnjw+LF3alr/9BlaubMMsWdLx0aNHKaanIGlADK0Yh9OnT7Njx1Zs0KASZ8+eFe/qvR+dsLAwOjsreeHC50sTFQUWLarkqlWrUkUHrVbL4cMH0t5eZyBdXeWsWbMc37x5o5dv9uxZLFFCEbvQRasFBw60YO3a5RNdZr9+3Zkhg4KDB5uxVy9LqlRyLlgwN/b7vn17cNw4/Vv20CHQ3f3zOLdGA44bZ84iRXIZDIsEBQWxV6/OzJs3E8uX9+fq1au5efNmBgQ0ZIcOLYyyqOhb3Lhxgxky6K9MJcG9e8Hs2XVugyEhOs+fkiXzU6vVslOn1uze3VLvnJEjzdmwYbUU1VWQeIQhFxiwbt1aqlRy9uljyfHjwbx5bVivXhXGxMSkSvnLly9nnjyfN6WIigI7drRis2a19fKp1Wp26tSaKpWM9evbMls2XaCvL+NbR0VFcfToEcyaNR3Tp3dip04B8brZnTlzhsOGDeHo0b/z1q1bet/9/vtI9uihP97eogU4a5b+bazRgF5eSl6+fNmILWJIcHAwR48ewQYNqrBGjars3Lk9V61axQcPHrBNm8Z0cJBTpbJht27t+fr1a964cYPu7tYGk/I7d4Lu7ta0tDSjQmHJtm2bxE7wqlQ2Bqth374FrazMqVark6X/q1evuGbNGq5Zs8bgAf2zERoayi1btvDIkSMp5tEjDLmAly5dYseOrVi5chEOHNiHT5484Z07dzhixDD27t2VO3bsSFWXspIl83D7dkMDYmdnHedy+rt373L9+vU8efKkQU+4UaMarF5dzrNnwdu3wX79LOjj48G3b98mSqf79+/T2Vkn55NORYqAW7YY3spFi9rx8OHDSah5wrh+/Trd3e3ZuLEVVSpdMKwJE8CyZRV0cLBg9+7mfPpUF0+lfXsr5snjw/TpnejkJOm5bEZFgeXKKThr1kxGRUUZGGcXF1vev69ftzdvQGtrC6rVal65coW7du1K9MYQa9eupqOjnLVr27JWLVs6Osq5cWPSY74Yg7t37/LkyZNGf5OePHk87e1lrFrVjnnz2tLX1zPZgcriQhjy/zh79+6li4uC48aZcedOsFcvK3p4OPDOnTsMDw/nw4cPU31Pxxw5PHnxov7todXqgjE9ePAgwXIuX77MdOnkBitMGzTQGa/E8vffm6lS2bB4cTvmz29La2uwXj3oDT3cvg3a2VkazI+8evWKGzdu5I4dOxIUpOtb1KlTgZMnSyxXTrdt3Jdt1K4d2KuXfpqPDzhmjG79Qbp0YOXKYJcuYObMCjZoUC3e69u1azt26mSlV78hQyxYt25lVqhQlOnTK1ixoj0dHKzZv3/PBHnZPH78mI6OMr2dis6fBx0d5SnmavktXrx4wcqVi9PNTc6CBe3o5KTgrFnTjCL78OHDzJhRfzXwggUSc+TIaPSOkTDkX3DmzBlWq1aS9vZy5syZgbNnz/whl78bC61Wy9y5vbljh/6lGDlSYv78WenoKGe6dAq6udlx+vTJjIyMZGhoaILaJCYmhjt27ODChQsTvS9jly5tOXCghZ5Ohw+DmTO7JeoHsHz5cjZvbsOvb7X588H27ZvGec6HDx+4bNkyjho1iv/8849BLzUiIoL79+/n3Llz6eenZJ48YKNGOnfTGTN0hjJXLi+9c5YsWUgHBxmrV7dj6dJ2dHe359GjRxPeIF8hk1nw0SNQoYDe5Cup8wX38tJP69IFnDIFfP4cnDkTbNsW9POzYN++fb95LV++fMmCBbOzcGFbDhhgzjJldKtZa9Ysxx49rBgTo5MfGgoWLKjk4sWL4pSj1Wp55swZHjhwgOPGjeMvv1jz62sSECDnzJmJf7gml6pVS/LXXy1j46Tfvg16e+uHbnj79i3HjRvNMmXysWbNMty4cWOCfgNt2jTm9OmGHZIcOWyMvgZEGPKPXLx4kSqVggsX6iZ9jh8HCxZUcNSooSbTKaUJCQmhvb2VwQRYr146T5BPGyFcuwb6+FhQLregvb0Vs2ZNx82b499F/u7du/T19WSxYrZs00ZBDw85AwIaJ3hc9dGjR0yf3pldulhx507wzz/N6OaWsEiAGo2GN27c4KNHj3js2DFmz25jUL927cxYpUp5gx6gbkJQxVq1bDhkCFiwoA1Llswfp/fRxYsXmSmTkm/egBMngtWrg61a6VZo1qtXMTbf1atX6eoqj13hSoJ79oAuLraJiin/4cMH9u/fi56ejpTLwfr1dYb8wwf9up0/r1sh+qXhyJMH7N9f52PfvDnYujVoawvWqlX9u+Wq1Wpu27aN48aN48aNGxkcHEw7OyuDSIo7d4IlSuQ2OP/69evMmdOL2bLZsEQJO9raWrJqVf3FUSTYo4clJ0yYkOD2MAYPHjygi4vM4GG4eDFYv74u1ENYWBgLFMjGxo1l3LMHXLUKzJVLyaFDv+9hVL9+Ra5ebWjqypSxS9KajG8hDPlHWrVqwIkT9W+wR490r3xpwQNFrVbzn3/+4aBB/Tlt2tQ4Y4V8TXh4OG1trWOXhZM6lzRHR13P5Mu22LdPZxC0Wp23hoeHIt5eZcmS+Th58ucFNuHhYMmSCs6dOyfB9Xn27BmHDRvIypWLsH375glaoLJ//376+LjTy0tJlUrGMmUKMn/+bOzd25Jv3uiCdi1dqqtfy5bWdHW11ZNbtqw/Z878fA9oNGCTJtb87TfDXea1Wi3z5s3CefMkqtW6dgkNBXPmVHLz5s2x+QYP7sdBg/TfLkiwcmXbBMcC12q1rFixGJs2teaNG7r7sm9f3SYRv/2mf+1q1ADLlQPfvQNfvgR79bKgra2uzleufM577x7o5GRlMKn7PR48eEAPD7nBw/H8eTBHjvR6edVqNX19PblggRSb/+pV0N5efyFUaCjo7i7n1atXE6VLcjl79izz5jV0tT1wACxZUvdQmjt3DmvU0Pf2CQkBHR1l341NM2/eXFaurNCbYL55U2dT3r17Z9S6CEP+kXz5MjEw0FCtbNlsU/0GSyyRkZGsVKk48+e34e+/gwEBMqpUNvzf//733XM7dGjOli2tYzccuH8ftLKCwQ81OFhnOD59XrAAbNCgioG8hw8f0sVFFvva/enYsQMsVSrldo2/f/8+VSoFd+/W6R4drdvFJ3v2jGzSpCatrc0ok4ElS+qCZ5HgsmVg8eK6H2xoaCjt7KwMtiILDDQ0UJ9YunQpnZzMKUm6Hq6trTkHDOil99rdo0dH/vmn4X3VrJmSS5cuTVDdjh8/zqxZlVSr9WWUKAEqlWCBArrhEx8f0NFRYv78vrSyMqdMZsFWrRqwQIHcbNTIUIcePSw5duyYRLWzRqNhliwe3L9fX1bfvhbs2bOTXt5Dhw4xd24lhwwBCxfWjc2vWweOGAGqVBYcN07i2LESvb1T1oc+PsLDw6lS2fDaNf26dOxoxWHDBpIkmzevzWXLDNuuVi07btoU/1spqRuGK1WqACtUUHLZMnDcON2uUgsXzjN6XeIz5P+5lZ2+vjlw5sx9FCz4OS0kBAgOjoGnp2eq6XH58mUsWTIXL18Go3z52mjWrBmsra2/ec68eXNgbn4eZ89GQLcXcSS2bQPatWuCGzcew8ws/vDyU6fOR0DAS2TMeATZslnj8uVIODlZ4MiRMJQt+znfjh2Av//nz/nzA/Pm3TGQFxUVBZnMDF/viWxjA0RGRhrkV6vVOHfuHCRJQsGCBWFubh57E35L769ZsmQ+WrVSo0oV3WdLS2DAAC3WrXuNjh374MSJk9i8+aVeHVq0ALp3v4FXr17FpvGrBZ5ff/7E2bNnMXBgNyxapEHt2sCdO0CnTlZQq2P0VnZWq1YXAweuRs+eH/DpMj57Buzapcaff1ZMUN0uXbqEMmW0Bm3aoAEQFaVb0Xr/PtC8OeDmRhQr9hShoa+hVCphZmaG6dOn4+LF/gBi9M6XybSIidFP+x5mZmaYMWMxmjVriB49opArlwb//CPDoUN2OH58uF7ex48fIygoHCEhwKRJQHAwMGoU4OsL+PsXRXBwLkiSGVaubIESJUokSg9jIJfL8ccfk1ClSl8MGRIOHx9g/XprHDzoiFOnfgUAuLp64t49cwCfY8KTwL17hJub2zfly2Qy7Nt3AqtXr8aePZvh6OiKbdu6wP/LmzClicu6p/Rhyh75iRMn6Ooq5/btulfqO3fAChUU7N27c6rpsGrVCrq6yjlypDkXLwYrVFCyRIl8341GV7ZsfoNYHlotmDlzwv2Z79+/z8OHD/PFixfcsGE9PT0VXLVK9yo4axbo7IzY6H0kOGaMOdu1a8bTp09zxIjh/PPPP/nw4UNqtVpmy5aeW7fq69KkiYy//z5cr8wjR44wY0YVc+e2pZ+fLb28XNiyZSM6OSlpbm7GihWLJDjmR/v2zThvnuFt1bSpDVesWMEcOdIbhNV99w5UKj97mJQrV4jTpukPrTRqJOPw4UMMymvatBZnzNAfinv+XLca9JNf9Pbt21m8uB/lcjMqlbpeaY8eumBZdnbm7NmzY4Imzfbv38+8eQ3H+uvWldinj2Gdc+Wy5YULF2LPv3fvHp2dZbFzHqRueMDDQ66XLy6uXLnCNWvWMDAwUE/Xy5cvs1u39qxduwzHjv2doaGhBucOHz6MdesavtkpFOD06dO/W+/U4uDBg2zSpAbLls3P4cOH6A1LXrlyhS4u8th7R63Wzdnkzp35h3KEgBha+cyuXbuYP38WWlqaUaWy4W+/DUy1RTDh4eF0dlbqud1ptWD16nLOnDnjm+dWqOCvZzg/GaGMGZVJ3pRg7969rFKlOH183Jg/vw8zZ7bmnj063+QpUySqVEq2bNmQXl4KDhpkxs6drejkJOfatat59OhRqlRKdu5szalTwbJlbVi4cC69ccHQ0FA6Oyu5e/dnnStU0L1+37unW2m4aBHo4mKToDC6ixcvZtWqSj1j9+4d6OIi4+3btzl27O+sVu2zK6JWC/bvb6m3SvHmzZvMmNGF1arZcOBAM+bNa8MyZfzjnCPJnz+znk/5pyNHDltevnyZmzdvoqennAMG6ManAwLA7t11D8QWLXTL+wsWVOoF5fqaq1evcuzYsRw3bhzz5PFh796WfPVKN+cwdapEe3tLA6+IDx9047df+3ZPnjyebm5y9u9vziFDzOjpqeBvvw2Mt+zIyEg2aFCV6dLJ2aiRLTNlUrJ8+SKJWrxTt255bthg2EZFi5px69atCZZjajZt2kgPDwfmymXLdOkULFYsN+/fv29qtfQQhjwOIiIiUj2m8pEjR1i4sOHEy6ZNYPXq345JvWDBfJYsqYgd5yZ147958vgYpdeg1Wq5bNlSFiqUjZ6ejmzUqDrnzZvHbNmUfPv2c5kXL+pCv759+5ZBQUEcN240u3XTrTj8OvrhzJkzmSePGX19wfTpwfLldV4V4eH69e/Xz4wZMjiwRIncnDlzRrwP1vDwcObPn5UtWljz6FFw2zawcGEFu3RpQ1K3wrNRoxr09FSwRQsl/fxs6O+fg8+fPzeQs3LlSo4ZM4Z79uyJ9z5o1aoBJ03S75EHBX2eyMqXz4ebNumM+Pnzn/O8eqUbyz58GNy8GaxYsXCc8seOHUk3Nzl797Zg9+6WdHS0ZokSeSmXW9LKypzVqpXismXLqFJZ098ftLMD8+YFS5UyZ9OmcYdPvnLlCocPH8ahQwd9903nt98GsXbtzx4dajXYoYMVO3Ro8c3zvqRLl7YcO1Y/qqRGo3Pv+96bwI9GdHQ0AwMDEz05nFoIQ/6D8O+//zJLFsPX5wULvh+sSq1Ws3nzuvTyUrBbNytWrWpDT08nXrx4McX07dy5DQcNAk+e1De+1arZcv369d98gGi1WqpUNnR21vW6T54EBw7UTdzduqVf/61bdROUu3frVi42bVo7Xrlv3rzhiBFDWahQVpYtW4CLFi0yMMQXL17kX3/9xSNHjiTrIXfhwgW6uCi5dKlu1emZM2CRIopYtzQzM4mrVum8SL6+1SdM0PXODx4EixXLaSD74sWLdHeXMzj48zl37+p62vfu3YtdUHTgwAGqVFZcv173gNi3D8yQwZyzZn37DS4heHmpYndf+nS8eAEqlVYJfks9f/48XV0VPHlSd35kpG5BUYkSeX+oYYmfgRQ15ACqArgJ4A6AQd/L/1825LrFOZk5b97nXl5wsC4OeEJ3nj979iynTp3K1atXJ3qXl8Tw5MkTens709VV5zGhUunc+q5d0xkSCwuJdnYydu/eIU4f7AULFlCphMHQxLBhOn/sL9P69gUHDPhsCDJm/HFiZZ84cYIVKxahQmHJ9Okd2KxZE969e5ckmTVrOo4eHb8h79YNbNpUxjFjRvHVq1ecN28eR40axYMHD3LEiN/Yv7+5wXlt2sg5e/bs2PLLlfM38FM+exbMmFGV7DdKZ2el3kYbn9rfyso8UStTN27cQE9PR/r62tDZWcbKlYsbdQXn/fv3OXXqVE6bNs0g6uR/iRQz5ADMAdwFkBmAFYCLAHJ+65z/siEndQtSsmZNz/z5bVm7ti0dHGQcPXr4909MRbRaLYsVy83hw81jXQyvXAE9PHRDI1Om6MZpHz/W+WpXrVrKQEahQvnp6mp4C1y6pJNx7ZqulztzJujqqhuX/5Snc2dZolcAhoSE8OrVq8leGh8X58+fp6enE8uUsWXz5jZ0cpJx8OC+XLBgHrNmldPe3nBoJWNG0N9fxoIFs3P//v10dbVlkyYKDh4sMUcOG+bOnZm9extudNG0qYxVq5Znpkwu9PJS0c7OMjaw2JeHjY1lsgNRtWxZnyNH6uswfz5YoULcQ0HfIiYmhpcvX+bjx4+TpdPXzJw5lU5OMnbsKGOHDjI6Ock4f37C1yr8TKSkIS8GYM8XnwcDGPytc/7rhpzUDZMcPHiQGzZsMEnsie9x8eJFenkZbm03aZIuHOqXaWq1breZrzc5Llu2FOVynVH7Mv+mTaCrq4zp0jnQysqMWbJIBjFXypWz/a7/7ifev3/Pli3r0cHBmlmz2tDFxYZz584yUkuQ7969Y7p0Thw5ErE+3qGhYPbsSm7fvp1z586iSmVDmQxs2hTs0cOMKpUF8+f35aJFi/jhwwdmzerJzZs/1y86GixbVkZbWws+efI5/fp10M5OYvPmVrx8Wfewy5gRBpPcV6+C7u72yY5O+ODBA2bIoGKrVnIuWQJ26aJbQPWjjG3funWLKpVc7yF/545us+rExOP5WUhJQ94QwKIvPrcCMCuOfB0BBAIIzJgxYypVW5BU9u/fz1Kl7Pn1Jd+8Wbes/+v0Ro1suWbNGj0Z+/bto42NLkbJp8nSW7dAT8/PbmkvXrygi4sNN27UeZio1eCsWRK9vV2/u23cvXv3uGnTJtasWZ4tW1rz3TtdGdeu6Vwy//nnn2S3w8qVy+ngIGP+/GDu3LoJzE897wULPu/XqtVqefv2bU6bNo1//PGH3rzFpUuX6ONjOC+ybRuYK5cnVSo5O3e2Yvv2MtrYWDJXLhlPndIts/+0wMbJCTx6VNdGly6BBQooOHHiuGTXj9TFWpk4cQJbt67P0aNHfHcl46f6njlzhhs2bIgdZkoJxo8fz+7dLfn1/dahgzWnTp2aYuX+qJjckH95iB75j8/r16/p4CDT6wmRYP36FixUSH9cNzoaTJ9eEeeka6tWTWlrq/Mp9vICZTKwcmX9DSFOnjzJnDkzMn16BV1d5SxWLDdv3rwZr25qtZq//NKSzs4yVq1qQ7kcel41pC5WRrVqJZLVBp98i7+cDFy9GsyQQVfnNWvAunW/v7nF5cuXmTmz0sCQb9mi82a5c+cOJ0+ezOnTp3Pw4MFs0MCMrq7g9Ok6n/4xY3QrSp2dFbSyMqe7ux0HD+5vsonEkJAQliiRjz4+Stata0eVSsYOHVowJiaGr1+//ubQVnR0NDdu3MhBg/pz7ty53x0aGjduHHv2NAx90KmTNadMmWLMaqUJxNCKINFMnjyemTMruWiRzpukZUsZs2fPSHd3B44fb8aXL3WvuY0by1irVvwG7erVq2zbti1btGgRr1vXpx5tQl6Xp06dzNKldTsG3bih6yV/fZsFBoJ583onqd6f6N+/N4cMMZyMLFFC15suW1bJRYvijgT4JVqtltmzZ9Dbfi0yEixTRsH58/WXca9fv56urpJBnPY5c8DMmV1pb29GGxvQ2hrMmNHBYDgrpVCr1dy7dy+XLFnCSpVKsk8fy9hhtw8fwNy5rent7UwbG0va2lqzc+cAA7/8t2/fsnDhXCxRwoajR4ONGyvo4eHAS5cuxVvu9evX6eIi15uQffgQdHaWJWjdwc9GShpyCwD3AGT6YrIz17fOEYb8x0ar1fLt27eMjo7mrl272LBhVZYrVyDW8+LWrVts2LAaFQorurrasl+/HomK8Jdc8ubNxKNHdbdTVJRuBeXXY+xDh5qzc+c2ySrnl19acuZMw1u4dm0wc2ZrVqlS6rvDP69fv+bz58955swZurnZsV49Jfv1M6evr5ING1Y3cPF79eoVLSxgMDcRHKx7m9mzRze88uqVLrqhSmWV4nHkHz58yJw5venvb8tWrRR0cACbNUPsJPilS7oFUNu26fR+/hxs1kzGxo313WkHD+7HVq2s9d5MFiwAS5T4dmyeiRPH0dVVzl69LNijhyVVKhmnT5+cYvX9kUlp98PqAG599F4Z+r38wpD/uOzdu5f58vlQobCgvb2MvXp1YkREhKnV0iNTJhdev/75llq0SDds89df4OnTOh9md3f7eHtsjx494uHDh7+74826detYtKh+EKvnz3VBsxYsWPBNP+ugoCDWqlWOtrZWdHCwZuHCOXjo0CEuXryY48aN4//+9784h0bUajUdHOQGO/YcO6ZbUPVlWmSkLsLg3LlzDeQYk0qVinHs2M9vJuHhYJkyurjsJNipk27450vdwsNBlUp/QjJHjvSxgcw+HTExoL291XejeF6/fp1jx47lH3/88cMu1kkNxIIgwXf5999/6eKiiO1ZBQWB9erJGBDQyNSq6dG5cxv2768/bjpsmC5EaoECPuzWrX2cS6sjIiLYsmV9OjnJWLKkbsebrl3bcvfu3VyxYoXBpF1MTAwrVy7JsmWVXLlSF4vGx0fJ338f9k39NBoN8+Tx4bBh5gwL003grlgBurraJmi7tIEDe7NaNXmst09QEOjnp4t8+PXPyd8f7NmzZ4LaLSkEBwfT0dHaYPel/ft1W+CRYKVK4K5dhrqVLGnPI0eOxMry88vIU6cMH0Z2dlZ8+fJlitXhZ0IYcsF3adu2CSdO1Pcpfv8+YTGZUxPdQiVXtmgh58qVulgqKpXyu7vx9O3blfXqyWI3adBtPSYxd25rNm1qQ5VKt7jpy0U2UVFRXLJkCRs0qMzWrRtw796939Vv3759zJ/f1mBy85dfZBw//vueJlFRUezSpQ0dHGTMnduODg4y+vll4S+/6Mt78QKUy8H9+/d/V2ZSefDgAd3cDOOSnz6te0P46y/Qz8+c3bvrfx8aqgss9mVPe/ToEaxTRz/08Z9/mrFSpWIppv/PhjDkgu9SunQeHjxoeMkKFbLnqVOnUqzc8PBw/vbbIPr6etDb24U9e3aKM8rel7x8+ZJ//jmeTZvW4KBBfb/rAqdWq2lvL4/dV5EEixfX+cV/+vz2Lejvr+Rff/2VrPosWrSIAQFKft2OM2eCnTsHJFhOaGgoz58/z7dv3/Lhw4e0tTXn4ME698q9e8FcuUA/v0zJ0vV76FYiZ+KmTZ/rodWCrVtbsUSJQmzevBZ/+20Y3dzsOG6cGR880G0mUbSogr/+2k1PVkREBKtUKcls2ZTs2dOKZcva0sfHI0XdF382hCEXfJcePTpy6FD9IYvnz0F7e1mKvfpqtVpWrVqK9erJ+O+/OiPVubMlc+fOnOCx+YiICJ44cYJXrlyJ1yUvIiKClpZmsb3Bu3d1k6Rfb4yxaRNYqVKRZNXpwoULTJ9eoTccoYtwqeTixYuTLPfy5cssXNiPDg7mdHW1YpMmjb472WoMjh8/ThcXG3bpYs2ZM8HKlZXMl8+Xr1+/js1z8+ZNtmhRj+nSOTBPnkycOXN6nOEDtFotjxw5wsmTJ3Pjxo2pov/PhDDkgu9y9+5durracvJkiU+egP/7ny5A1MCBvVOszOPHj9PXV6lnULVasGJFXXzx77Fq1Qq6uNiyQAE7ZsyoYKFCOeLt4RUr5se1a3VlXL6sv+flp+PAAbB48VzJrlejRjVYubKcx4/rvDo6dbJirlyZUtW7x5gEBQVxzJhR7Nw5gEuXLv3hJsD/KwhDLkgQV65cYcOG1ejqaks/Py/OmjUjRUP9zpw5k126yPj1bTJxIti3b/dvnnv+/Hm6uSl44YLuHI0GnDw5/s0A/ve//1GlUnDUKHP+848uCNiBA/oPkCZNZBw79vdk1ysqKooTJoxjnjze9PX1YN++3RkSEpJsuYL/NvEZckn3Xeri7+/PwMDAVC9X8OOxc+dOjB7dFCdPvtdLb9FCgWLFxqN79x7xntujxy9wd1+CoUO1sWkk4Odng0WL9qJYsWIG51y/fh2zZk3E7dtXYGvrjiNH9iMgQA1f3xhs2qTE27deOHDgFGxtbY1XSYHASEiSdI6kwR5yCd8sUSBIAapUqYL3750xfLg5wsKA6Ghg3jwJhw5ZoWXLVt88NyTkKby8tHppkgR4eZkhJCQkznNy5MiB2bOXYO/eM9i0aRvOnLkChaIvAgOboUWLWTh69Jww4oI0x39u82XBj4W5uTn27DmGbt0C4Op6FJIEFCqUB/v2/QUHB4dvnlu2bE2sXXsYLVqE49M+yM+eASdOROGvvwx74yEhITAzM4Ozs3NsWubMmTF69HhjVkkgSHVEj1xgcjw9PbFly348f/4KQUEhOHQoELly5fruea1bB+D5cy80bCjHP/8AS5YAZcoo0a/fQLi4uMTmu379OkqXLgBf3/TInDkdKlQogjt37qRklQSCVEUYcoHJ+TRPY2NjA3t7+wSfp1AocOjQGRQvPgozZhTGrl1VMXXqOgwbNio2z/v371GpUkk0a3YBISHRCAmJRs2agahUqQQiIyONXheBwBQIQy4wCTExMRgxYjDc3e1hYWGO8uUL4fTp04mWY2Njg19/7Y+9e09jw4ZdqFGjht7369evR6FCUejShbC0BKysgD59tPD1DceWLVuMVBuBwLQIQy4wCd27t8fp09Nx9Og7hIcTAQGBqFmzAm7cuGHUch4+fIA8ecIM0vPkicTDhw+NWpZAYCqEIRekOsHBwdiwYQPWrYtA1qyAtTUQEAD06BGJGTP+NGpZ/v6FsGuXDbRfOLdoNMCePTL4+xt4cQkEaRJhyAWpzt27d5EtmzW+Hg4vUUKDGzcuGLWsGjVqwNLSBy1bWuPcOeDMGaBxYxnc3PxQvnx5o5YlEJgKYcgFqU6WLFlw40Yk3rzRT//f/8yRI0d+o5Zlbm6O3bv/Bx+f3mjVKj3ats2APHn6Ydu2A5A++SwKBGkcsbJTYBK6dm2L27fXYdq0CHh7A2vXAoMHK3Hs2L/ImjWrqdUTCH5IxMpOwQ/FjBkLUbp0P1Sp4ggnJ3OsX18MO3ceFkZcIEgCokcuEKQSkZGRWL9+PQIDjyNjRh+0bt0Grq6uplZLkIaIr0culugLBKnA69evUa5cYbi4PEP16mG4fFmO3LnHYOfOQyhYsKCp1ROkcZJlyCVJmgigFoBo6DZebkvyjRH0Egh+KsaP/x3+/o+wcGH0x7gwEVixAujatRVOn75mavUEaZzkjpHvA+BHMg+AWwAGJ18lgeDnY+vW9ejePRpfOso0bw7cvn0PwcHBplNM8FOQLENOci9J9cePpwCkT75KAsHPh7W1FcLD9dNiYgCNhrCysjKNUoKfBmN6rbQDsCu+LyVJ6ihJUqAkSYHxxYoWCH5Wmjf/BaNHyxEd/Tlt4kRzlChRBE5OTqZTTPBT8F2vFUmS9gNwj+OroSS3fswzFIA/gPpMgBuM8FoR/NeIjo5G8+Z1EBh4FBUrEpcvW+DDByfs3v0/ZMiQwdTqCdIISfZaIVnxO4LbAKgJoEJCjLhA8F/EysoKGzfuwrlz5xAYGIiGDb1QqVIlmJubm1o1wU9Acr1WqgIYAKAMyfDv5RcI/usULFhQuBsKjE5yx8hnAbAFsE+SpAuSJM0zgk4CgUAgSATJ6pGTzGIsRQQCgUCQNESsFYFAIEjjCEMuEAgEaRxhyAUCgSCNY5Loh5IkhQD4WTZMVAEINbUSRuZnrBMg6pWW+BnrBCS/Xl4kXb5ONIkh/5mQJCkwLgf9tMzPWCdA1Cst8TPWCUi5eomhFYFAIEjjCEMuEAgEaRxhyJPPAlMrkAL8jHUCRL3SEj9jnYAUqpcYIxcIBII0juiRCwQCQRpHGHKBQCBI4whDnkQkSXogSdLlj8HCfprg6pIkOUiStFGSpBuSJF2XJKmYqXVKLpIkZft4nT4d7yRJ6m1qvZKLJEl9JEm6KknSFUmS1kiSJDO1TsZAkqReH+t0NS1fJ0mSlkiS9EKSpCtfpDlJkrRPkqTbH/93NEZZwpAnj3Ik8/1k/q7TAewmmR1AXgDXTaxPsiF58+N1ygegIIBwAH+bVqvkIUmSJ4CeAPxJ+gEwB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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># 导入KNN分类器</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
<span class="c1"># 导入数据集拆分工具</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="n">clf</span><span class="o">=</span><span class="n">KNeighborsClassifier</span><span class="p">()</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="c1"># 画网格背景</span>
<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">))</span>
<span class="c1"># 对区域的每一点进行预测，作为颜色值</span>
<span class="n">Z</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
<span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># 画散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Pastel1</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span> <span class="c1"># 带颜色的网格, 第三个参数是颜色</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">spring</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span> <span class="c1">#散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: KNN"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 创建分类模型，由粉色区域和灰色区域组成。</span>
<span class="c1"># 如果有新数据，落到哪里就是哪个分类了。</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 新数据 6.75, 4.82</span>
<span class="c1"># 在分类模型上画出该点：在 plt.show() 前加入这句: plt.scatter(6.75, 4.82, marker="*", c="red", s=200)</span>
<span class="c1"># 画网格背景</span>
<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">))</span>
<span class="c1"># 对区域的每一点进行预测，作为颜色值</span>
<span class="n">Z</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
<span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># 画散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Pastel1</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span> <span class="c1"># 带颜色的网格, 第三个参数是颜色</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">spring</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span> <span class="c1">#散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: KNN"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="mf">6.75</span><span class="p">,</span> <span class="mf">4.82</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"*"</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">"red"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span> <span class="c1">#画出新的数据点</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 从图中看，新点在浅色区域中。</span>
<span class="c1"># 带入模型验证一次。</span>
<span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span> <span class="p">[</span> <span class="mf">6.75</span><span class="p">,</span> <span class="mf">4.82</span><span class="p">]</span> <span class="p">])</span> <span class="c1">#确实归为第1类</span>
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<pre>array([1])</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">10</span><span class="p">]</span> <span class="p">]))</span> <span class="c1">#上方点 0</span>
<span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.29</span><span class="p">]</span> <span class="p">]))</span> <span class="c1">#下方点 1</span>
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[1]
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<h2 id="KNN-%E5%A4%9A%E5%88%86%E7%B1%BB">KNN 多分类<a class="anchor-link" href="#KNN-%E5%A4%9A%E5%88%86%E7%B1%BB">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 导入数据生成器</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># 修改 make_blobs 的 center 参数，分类数提高到5个</span>
<span class="c1"># 修改 n_samples 参数，把样本量也增加到 500个</span>
<span class="n">data2</span><span class="o">=</span><span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="n">X2</span><span class="p">,</span><span class="n">y2</span><span class="o">=</span><span class="n">data2</span>
<span class="c1"># 画散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X2</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X2</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y2</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">spring</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1">#图中可见，5类中的2类有重叠部分。</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 使用KNN建模</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># 导入KNN分类器</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
<span class="n">clf</span><span class="o">=</span><span class="n">KNeighborsClassifier</span><span class="p">()</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X2</span><span class="p">,</span> <span class="n">y2</span><span class="p">)</span>
<span class="c1"># 画图</span>
<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">X2</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">X2</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">))</span>
<span class="n">Z</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
<span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Pastel1</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X2</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X2</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y2</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">spring</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: KNN"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 建立5个分区，大部分是正确分类的，重合区域、边界附近有少部分点是错误分类的。</span>
<span class="c1">#耗时比上一次多了很多。</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 输出在训练集中的正确率</span>
<span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X2</span><span class="p">,</span> <span class="n">y2</span><span class="p">)</span> <span class="c1">#0.956</span>
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<pre>0.956</pre>
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<h2 id="KNN-%E5%9B%9E%E5%BD%92">KNN 回归<a class="anchor-link" href="#KNN-%E5%9B%9E%E5%BD%92">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 导入 make_regression 回归数据生成器</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
<span class="c1"># 生成特征数量为1，噪音为50的数据集</span>
<span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">make_regression</span><span class="p">(</span><span class="n">n_features</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="c1"># 散点图</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">"orange"</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1">#图： x范围+-3， y范围+-250，倾斜45度角的散点</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 导入用于回归分析的KNN模型</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsRegressor</span>
<span class="n">reg</span><span class="o">=</span><span class="n">KNeighborsRegressor</span><span class="p">()</span>
<span class="c1"># 用KNN模型拟合数据</span>
<span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
<span class="c1"># 可视化</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">z</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="s2">"orange"</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">z</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"KNN Regressor"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 黑色表示KNN回归生成的模型。直观看，效果不好，大量的数据点没有被模型覆盖。</span>
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>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 给模型评分</span>
<span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
</pre></div>
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