Improve knn description

2_knn/knn_classification.ipynb

@@ -33,7 +33,8 @@
     "4. 选取距离最小的`k`个点；\n",
     "5. 确定前`k`个点所在类别的出现频率；\n",
     "6. 返回前`k`个点中出现频率最高的类别作为测试数据的预测分类。\n",
-    "\n"
+    "\n",
+    "上述的处理过程，难点有哪些？"
    ]
   },
   {
@@ -45,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -121,12 +122,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3. Simple Program"
+    "## 3. 最简单的程序实现"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -171,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -193,14 +194,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Accuracy: 95.734597%\n"
+      "Test Accuracy: 96.682464%\n"
      ]
     }
    ],
@@ -218,12 +219,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. Complex Program"
+    "## 4. 通过类实现kNN程序"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,15 +278,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train accuracy: 0.986\n",
-      "test accuracy: 0.967\n"
+      "train accuracy: 98.568507 %\n",
+      "test accuracy: 96.682464 %\n"
      ]
     }
    ],
@@ -296,19 +297,18 @@
     "\n",
     "# knn classifier\n",
     "clf = KNN(k=3)\n",
-    "acc = clf.fit(x_train, y_train).score()\n",
-    "\n",
-    "print('train accuracy: {:.3}'.format(clf.score()))\n",
+    "train_acc = clf.fit(x_train, y_train).score() * 100.0\n",
+    "test_acc = clf.score(y_test, y_test_pred) * 100.0\n",
     "\n",
-    "y_test_pred = clf.predict(x_test)\n",
-    "print('test accuracy: {:.3}'.format(clf.score(y_test, y_test_pred)))"
+    "print('train accuracy: %f %%' % train_acc)\n",
+    "print('test accuracy: %f %%' % test_acc)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. sklearn program"
+    "## 5. sklearn program"
    ]
   },
   {
@@ -426,13 +426,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 5. 深入思考\n",
+    "## 6. 深入思考\n",
     "\n",
     "* 如果输入的数据非常多，怎么快速进行距离计算？\n",
     "    - kd-tree\n",
     "    - Fast Library for Approximate Nearest Neighbors （FLANN）\n",
     "* 如何选择最好的`k`？\n",
-    "    - https://zhuanlan.zhihu.com/p/143092725"
+    "    - https://zhuanlan.zhihu.com/p/143092725\n",
+    "* kNN存在的问题？"
    ]
   },
   {