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# 4.5:实战案例
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### 手写数字数据
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手写数字数据集一共有`1797`个样本，每个样本有`64`个特征。每个特征的值为`0-255`之间的像素，我们的任务就是根据这`64`个特征值识别出该数字属于`0-9`十个类别中的哪一个。
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我们可以使用`sklearn`直接对数据进行加载，代码如下：
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```python
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from sklearn.datasets import load_digits
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#加载手写数字数据集
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digits = load_digits()
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#获取数据特征与标签
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x,y = digits .data,digits .target
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```
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当然，每一个样本就是一个数字，我们可以把它还原为`8x8`的大小进行查看：
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```python
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import matplotlib.pyplot as plt
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img = x[0].reshape(8,8)
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plt.imshow(img)
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```
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然后我们划分出训练集与测试集，训练集用来训练模型，测试集用来检测模型性能。代码如下：
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```python
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from sklearn.model_selection import train_test_split
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#划分训练集测试集，其中测试集样本数为整个数据集的20%
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train_feature,test_feature,train_label,test_label = train_test_split(x,y,test_size=0.2,random_state=666)
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```
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### 进行识别
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接下来就只需要调用之前实现的`knn_clf`方法就可以对测试集的手写数字进行识别了：
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```python
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predict = knn_clf(3,train_feature,train_label,test_feature)
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predict
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>>>array([1, 5, 0, 7, 1, 0, 6, 1, 5, 4, 9, 2, 7, 8, 4, 6, 9, 3, 7, 4, 7, 1,
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8, 6, 0, 9, 6, 1, 3, 7, 5, 9, 8, 3, 2, 8, 8, 1, 1, 0, 7, 9, 0, 0,
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8, 7, 2, 7, 4, 3, 4, 3, 4, 0, 4, 7, 0, 5, 5, 5, 2, 1, 7, 0, 5, 1,
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8, 3, 3, 4, 0, 3, 7, 4, 3, 4, 2, 9, 7, 3, 2, 5, 3, 4, 1, 5, 5, 2,
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9, 2, 2, 2, 2, 7, 0, 8, 1, 7, 4, 2, 3, 8, 2, 3, 3, 0, 2, 9, 9, 2,
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3, 2, 8, 1, 1, 9, 1, 2, 0, 4, 8, 5, 4, 4, 7, 6, 7, 6, 6, 1, 7, 5,
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6, 3, 8, 3, 7, 1, 8, 5, 3, 4, 7, 8, 5, 0, 6, 0, 6, 3, 7, 6, 5, 6,
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2, 2, 2, 3, 0, 7, 6, 5, 6, 4, 1, 0, 6, 0, 6, 4, 0, 9, 3, 8, 1, 2,
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3, 1, 9, 0, 7, 6, 2, 9, 3, 5, 3, 4, 6, 3, 3, 7, 4, 9, 2, 7, 6, 1,
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6, 8, 4, 0, 3, 1, 0, 9, 9, 9, 0, 1, 8, 6, 8, 0, 9, 5, 9, 8, 2, 3,
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5, 3, 0, 8, 7, 4, 0, 3, 3, 3, 6, 3, 3, 2, 9, 1, 6, 9, 0, 4, 2, 2,
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7, 9, 1, 6, 7, 6, 3, 9, 1, 9, 3, 4, 0, 6, 4, 8, 5, 3, 6, 3, 1, 4,
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0, 4, 4, 8, 7, 9, 1, 5, 2, 7, 0, 9, 0, 4, 4, 0, 1, 0, 6, 4, 2, 8,
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5, 0, 2, 6, 0, 1, 8, 2, 0, 9, 5, 6, 2, 0, 5, 0, 9, 1, 4, 7, 1, 7,
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0, 6, 6, 8, 0, 2, 2, 6, 9, 9, 7, 5, 1, 7, 6, 4, 6, 1, 9, 4, 7, 1,
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3, 7, 8, 1, 6, 9, 8, 3, 2, 4, 8, 7, 5, 5, 6, 9, 9, 8, 5, 0, 0, 4,
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9, 3, 0, 4, 9, 4, 2, 5])
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```
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再根据测试集标签即真实分类结果，计算出正确率：
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```python
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acc = np.mean(predict==test_label)
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acc
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>>>0.994
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```
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可以看到，使用`knn`对手写数字进行识别，正确率能达到`99%`以上。
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