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<h1 id="文本表示方法-Part1"><a href="#文本表示方法-Part1" class="headerlink" title="文本表示方法 Part1"></a>文本表示方法 Part1</h1><p>在机器学习算法的训练过程中，假设给定 $N$ 个样本，每个样本有 $M$ 个特征，这样组成了 $N × M$ 的样本矩阵，然后完成算法的训练和预测。同样的在计算机视觉中可以将图片的像素看作特征，每张图片看作 $hight×width×3$的特征图，一个三维的矩阵来进入计算机进行计算。</p>
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<h1 id="数据读取"><a href="#数据读取" class="headerlink" title="数据读取"></a>数据读取</h1><figure class="highlight py"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line">train_df = pd.read_csv(<span class="string">'./data/train_set.csv'</span>, sep=<span class="string">'\t'</span>, nrows=<span class="number">100</span>)</span><br><span class="line">train_df.head()</span><br></pre></td></tr></table></figure>
<table>
<thead>
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<th></th>
<th>label</th>
<th>text</th>
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<td>0</td>
<td>2</td>
<td>2967 6758 339 2021 1854 3731 4109 3792 4149 15…</td>
</tr>
<tr>
<td>1</td>
<td>11</td>
<td>4464 486 6352 5619 2465 4802 1452 3137 5778 54…</td>
</tr>
<tr>
<td>2</td>
<td>3</td>
<td>7346 4068 5074 3747 5681 6093 1777 2226 7354 6…</td>
</tr>
<tr>
<td>3</td>
<td>2</td>
<td>7159 948 4866 2109 5520 2490 211 3956 5520 549…</td>
</tr>
<tr>
<td>4</td>
<td>3</td>
<td>3646 3055 3055 2490 4659 6065 3370 5814 2465 5…</td>
</tr>
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<h1 id="数据载入"><a href="#数据载入" class="headerlink" title="数据载入"></a>数据载入</h1><h2 id="pd-read-csv"><a href="#pd-read-csv" class="headerlink" title="pd.read_csv"></a><code>pd.read_csv</code></h2><figure class="highlight py"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">pd.read_csv(filepath, sep, names)</span><br></pre></td></tr></table></figure>
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<li><code>names</code>：列名的列表
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