diff --git a/0_python/1_Basics.ipynb b/0_python/1_Basics.ipynb index 8a73dba..1079918 100644 --- a/0_python/1_Basics.ipynb +++ b/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" } diff --git a/0_python/2_Print_Statement.ipynb b/0_python/2_Print_Statement.ipynb index 22958b0..4511eab 100644 --- a/0_python/2_Print_Statement.ipynb +++ b/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": [ { diff --git a/0_python/3_Data_Structure_1.ipynb b/0_python/3_Data_Structure_1.ipynb index bec8bba..6cdf57e 100644 --- a/0_python/3_Data_Structure_1.ipynb +++ b/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\u001b[0m in \u001b[0;36m\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\u001b[0m in \u001b[0;36m\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" } diff --git a/0_python/4_Data_Structure_2.ipynb b/0_python/4_Data_Structure_2.ipynb index 42d4887..8486e7e 100644 --- a/0_python/4_Data_Structure_2.ipynb +++ b/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))" ] diff --git a/1_numpy_matplotlib_scipy_sympy/ipython_notebook.ipynb b/1_numpy_matplotlib_scipy_sympy/ipython_notebook.ipynb index 48ca20c..1016538 100644 --- a/1_numpy_matplotlib_scipy_sympy/ipython_notebook.ipynb +++ b/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 } diff --git a/1_numpy_matplotlib_scipy_sympy/matplotlib_ani.ipynb b/1_numpy_matplotlib_scipy_sympy/matplotlib_ani.ipynb index a4c4067..c17945e 100644 --- a/1_numpy_matplotlib_scipy_sympy/matplotlib_ani.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/matplotlib_ani.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -565,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -1351,7 +1351,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" diff --git a/2_knn/knn_classification.ipynb b/2_knn/knn_classification.ipynb index 9f859f8..f63ff75 100644 --- a/2_knn/knn_classification.ipynb +++ b/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",