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+# Logistic Regression
+
+自己编程实现Logistic Regression的多分类问题。使用的数据可以是sklearn的digital数据。
+
+![digit](images/digit.png)
+
+加载数据的方式是：
+```
+import matplotlib.pyplot as plt 
+from sklearn.datasets import load_digits
+
+# load data
+digits = load_digits()
+
+# plot the digits
+fig = plt.figure(figsize=(6, 6))  # figure size in inches
+fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
+
+# plot the digits: each image is 8x8 pixels
+for i in range(64):
+    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
+    ax.imshow(digits.images[i], cmap=plt.cm.binary)
+    
+    # label the image with the target value
+    ax.text(0, 7, str(digits.target[i]))
+```
+
+要求：
+1. 自己编程实现Logistic Regression的多分了。
+2. 对比自己实现与sklearn的方法的精度。
+3. 如何将分类错误的样本可视化出来？
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