Fix some python minor errors

0_python/1_Basics.ipynb

@@ -644,9 +644,9 @@
     }
    ],
    "source": [
-    "print int('010',8)\n",
-    "print int('0xaa',16)\n",
-    "print int('1010',2)"
+    "print(int('010',8))\n",
+    "print(int('0xaa',16))\n",
+    "print(int('1010',2))"
    ]
   },
   {
@@ -864,10 +864,7 @@
     }
    ],
    "source": [
-    "print(1<2)\n",
-    "#print(cmp(1,2))\n",
-    "#print(cmp(2,1))\n",
-    "#print(cmp(2,2))"
+    "print(1<2)"
    ]
   },
   {
@@ -892,8 +889,8 @@
     }
    ],
    "source": [
-    "print pow(3,3)\n",
-    "print pow(3,3,5)"
+    "print(pow(3,3))\n",
+    "print(pow(3,3,5))"
    ]
   },
   {
@@ -905,23 +902,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "range(0, 3)\n",
-      "range(2, 9)\n",
-      "range(2, 27, 8)\n"
+      "[0, 1, 2]\n",
+      "[2, 3, 4, 5, 6, 7, 8]\n",
+      "[2, 10, 18, 26]\n"
      ]
     }
    ],
    "source": [
-    "print(range(3))\n",
-    "print(range(2,9))\n",
-    "print(range(2,27,8))"
+    "print(list(range(3)))\n",
+    "print(list(range(2,9)))\n",
+    "print(list(range(2,27,8)))"
    ]
   },
   {
@@ -940,14 +937,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Type something here and it will be stored in variable abc \tHello world!\n"
+      "Type something here and it will be stored in variable abc \taa\n"
      ]
     }
    ],
@@ -957,7 +954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -966,7 +963,7 @@
        "str"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }

0_python/2_Print_Statement.ipynb

@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -250,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -267,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -308,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {

0_python/3_Data_Structure_1.ipynb

@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -113,7 +113,7 @@
        "'apple'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -131,16 +131,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'orange'"
+       "'peach'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -201,7 +201,7 @@
        "'orange'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -247,9 +247,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'apple'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "z1[0]"
    ]
@@ -263,9 +274,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'apple'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "z[0][0]"
    ]
@@ -295,25 +317,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 1, 2, 3]\n",
+      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
+      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
+     ]
     }
    ],
    "source": [
     "num = [0,1,2,3,4,5,6,7,8,9]\n",
-    "num[0:4]\n",
-    "num[0:]\n",
-    "num[:]"
+    "print(num[0:4])\n",
+    "print(num[0:])\n",
+    "print(num[:])"
    ]
   },
   {
@@ -344,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -353,7 +374,7 @@
        "[0, 3, 6]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -378,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -387,7 +408,7 @@
        "10"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -405,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -414,7 +435,7 @@
        "0"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -425,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +455,7 @@
        "9"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -452,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -461,7 +482,7 @@
        "[1, 2, 3, 5, 4, 7]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -479,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -495,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -504,7 +525,7 @@
        "False"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -515,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -524,7 +545,7 @@
        "True"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -535,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -544,7 +565,27 @@
        "False"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'fire' in names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -562,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -571,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -597,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -606,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -639,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -696,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -705,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -730,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -739,7 +780,7 @@
        "3"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -757,7 +798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -766,7 +807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -791,7 +832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -816,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -825,7 +866,7 @@
        "0"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -836,7 +877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -846,7 +887,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-32-344da5bcf759>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-40-344da5bcf759>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: 999 is not in list"
      ]
     }
@@ -864,7 +905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -889,7 +930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -914,7 +955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -923,7 +964,7 @@
        "[1, 1, 4, 8, 7, 'Python', 1, [5, 4, 2, 8], 5, 4]"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }

0_python/4_Data_Structure_2.ipynb

@@ -52,7 +52,7 @@
     }
    ],
    "source": [
-    "print(String0 , type(String0))\n",
+    "print(String0, type(String0))\n",
     "print(String1, type(String1))\n",
     "print(String2, type(String2))"
    ]

1_numpy_matplotlib_scipy_sympy/ipython_notebook.ipynb

@@ -314,7 +314,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 2
 }

2_knn/knn_classification.ipynb

@@ -7,8 +7,6 @@
     "# kNN Classification\n",
     "\n",
     "\n",
-    "kNN最邻近规则，主要应用领域是对未知事物的识别，即判断未知事物属于哪一类，判断思想是，基于欧几里得定理，判断未知事物的特征和哪一类已知事物的的特征最接近；\n",
-    "\n",
     "K最近邻(k-Nearest Neighbor，kNN)分类算法，是一个理论上比较成熟的方法，也是最简单的机器学习算法之一。该方法的思路是：如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别，则该样本也属于这个类别。KNN算法中，所选择的邻居都是已经正确分类的对象。该方法在定类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。 KNN方法虽然从原理上也依赖于极限定理，但在类别决策时，只与极少量的相邻样本有关。由于KNN方法主要靠周围有限的邻近的样本，而不是靠判别类域的方法来确定所属类别的，因此对于类域的交叉或重叠较多的待分样本集来说，KNN方法较其他方法更为适合。\n",
     "\n",
     "kNN算法不仅可以用于分类，还可以用于回归。通过找出一个样本的k个最近邻居，将这些邻居的属性的平均值赋给该样本，就可以得到该样本的属性。更有用的方法是将不同距离的邻居对该样本产生的影响给予不同的权值(weight)，如权值与距离成正比（组合函数）。\n",