|
773 | 773 | "# 5. 矩阵分割" |
774 | 774 | ] |
775 | 775 | }, |
| 776 | + { |
| 777 | + "cell_type": "code", |
| 778 | + "execution_count": 12, |
| 779 | + "metadata": {}, |
| 780 | + "outputs": [ |
| 781 | + { |
| 782 | + "name": "stdout", |
| 783 | + "output_type": "stream", |
| 784 | + "text": [ |
| 785 | + "[[ 0 1 2 3]\n", |
| 786 | + " [ 4 5 6 7]\n", |
| 787 | + " [ 8 9 10 11]]\n" |
| 788 | + ] |
| 789 | + } |
| 790 | + ], |
| 791 | + "source": [ |
| 792 | + "import numpy as np\n", |
| 793 | + "a = np.arange(12).reshape((3,4))\n", |
| 794 | + "print(a)" |
| 795 | + ] |
| 796 | + }, |
| 797 | + { |
| 798 | + "cell_type": "code", |
| 799 | + "execution_count": 13, |
| 800 | + "metadata": {}, |
| 801 | + "outputs": [ |
| 802 | + { |
| 803 | + "name": "stdout", |
| 804 | + "output_type": "stream", |
| 805 | + "text": [ |
| 806 | + "[array([[0, 1],\n", |
| 807 | + " [4, 5],\n", |
| 808 | + " [8, 9]]), array([[ 2, 3],\n", |
| 809 | + " [ 6, 7],\n", |
| 810 | + " [10, 11]])]\n" |
| 811 | + ] |
| 812 | + } |
| 813 | + ], |
| 814 | + "source": [ |
| 815 | + "print(np.split(a, 2,axis=1))" |
| 816 | + ] |
| 817 | + }, |
| 818 | + { |
| 819 | + "cell_type": "code", |
| 820 | + "execution_count": 15, |
| 821 | + "metadata": {}, |
| 822 | + "outputs": [ |
| 823 | + { |
| 824 | + "name": "stdout", |
| 825 | + "output_type": "stream", |
| 826 | + "text": [ |
| 827 | + "[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8, 9, 10, 11]])]\n" |
| 828 | + ] |
| 829 | + } |
| 830 | + ], |
| 831 | + "source": [ |
| 832 | + "print(np.split(a, 3, axis=0))" |
| 833 | + ] |
| 834 | + }, |
| 835 | + { |
| 836 | + "cell_type": "markdown", |
| 837 | + "metadata": {}, |
| 838 | + "source": [ |
| 839 | + "# 6. 矩阵复制" |
| 840 | + ] |
| 841 | + }, |
| 842 | + { |
| 843 | + "cell_type": "code", |
| 844 | + "execution_count": 16, |
| 845 | + "metadata": {}, |
| 846 | + "outputs": [ |
| 847 | + { |
| 848 | + "name": "stdout", |
| 849 | + "output_type": "stream", |
| 850 | + "text": [ |
| 851 | + "[0 1 2 3]\n" |
| 852 | + ] |
| 853 | + } |
| 854 | + ], |
| 855 | + "source": [ |
| 856 | + "import numpy as np\n", |
| 857 | + "a = np.arange(4)\n", |
| 858 | + "print(a)" |
| 859 | + ] |
| 860 | + }, |
| 861 | + { |
| 862 | + "cell_type": "code", |
| 863 | + "execution_count": 17, |
| 864 | + "metadata": {}, |
| 865 | + "outputs": [ |
| 866 | + { |
| 867 | + "name": "stdout", |
| 868 | + "output_type": "stream", |
| 869 | + "text": [ |
| 870 | + "[12 1 2 3]\n" |
| 871 | + ] |
| 872 | + } |
| 873 | + ], |
| 874 | + "source": [ |
| 875 | + "#当使用=时相当于多个指针指向同一变量\n", |
| 876 | + "b = a\n", |
| 877 | + "b[0] = 12\n", |
| 878 | + "print(a)" |
| 879 | + ] |
| 880 | + }, |
| 881 | + { |
| 882 | + "cell_type": "code", |
| 883 | + "execution_count": 18, |
| 884 | + "metadata": {}, |
| 885 | + "outputs": [ |
| 886 | + { |
| 887 | + "name": "stdout", |
| 888 | + "output_type": "stream", |
| 889 | + "text": [ |
| 890 | + "[12 1 2 3]\n" |
| 891 | + ] |
| 892 | + } |
| 893 | + ], |
| 894 | + "source": [ |
| 895 | + "#使用copy函数则会开辟一块新的空间,将原数据复制一份\n", |
| 896 | + "c = a.copy()\n", |
| 897 | + "print(c)" |
| 898 | + ] |
| 899 | + }, |
| 900 | + { |
| 901 | + "cell_type": "code", |
| 902 | + "execution_count": 19, |
| 903 | + "metadata": {}, |
| 904 | + "outputs": [ |
| 905 | + { |
| 906 | + "name": "stdout", |
| 907 | + "output_type": "stream", |
| 908 | + "text": [ |
| 909 | + "[11 1 2 3]\n", |
| 910 | + "[12 1 2 3]\n" |
| 911 | + ] |
| 912 | + } |
| 913 | + ], |
| 914 | + "source": [ |
| 915 | + "c[0] = 11\n", |
| 916 | + "print(c)\n", |
| 917 | + "print(a)" |
| 918 | + ] |
| 919 | + }, |
| 920 | + { |
| 921 | + "cell_type": "markdown", |
| 922 | + "metadata": {}, |
| 923 | + "source": [ |
| 924 | + "# 7. 广播" |
| 925 | + ] |
| 926 | + }, |
| 927 | + { |
| 928 | + "cell_type": "code", |
| 929 | + "execution_count": 23, |
| 930 | + "metadata": {}, |
| 931 | + "outputs": [ |
| 932 | + { |
| 933 | + "name": "stdout", |
| 934 | + "output_type": "stream", |
| 935 | + "text": [ |
| 936 | + "a = [[ 0 1 2 3]\n", |
| 937 | + " [ 4 5 6 7]\n", |
| 938 | + " [ 8 9 10 11]]\n", |
| 939 | + "b = [1 2 3 4]\n" |
| 940 | + ] |
| 941 | + } |
| 942 | + ], |
| 943 | + "source": [ |
| 944 | + "#numpy中最为重要的就是广播机制,当两个矩阵维度不同时仍然可以进行运算操作\n", |
| 945 | + "a = np.arange(12).reshape((3,4))\n", |
| 946 | + "b = np.array([1,2,3,4])\n", |
| 947 | + "print(\"a = \", a)\n", |
| 948 | + "print(\"b = \", b)" |
| 949 | + ] |
| 950 | + }, |
| 951 | + { |
| 952 | + "cell_type": "code", |
| 953 | + "execution_count": 24, |
| 954 | + "metadata": {}, |
| 955 | + "outputs": [ |
| 956 | + { |
| 957 | + "name": "stdout", |
| 958 | + "output_type": "stream", |
| 959 | + "text": [ |
| 960 | + "[[ 1 3 5 7]\n", |
| 961 | + " [ 5 7 9 11]\n", |
| 962 | + " [ 9 11 13 15]]\n" |
| 963 | + ] |
| 964 | + } |
| 965 | + ], |
| 966 | + "source": [ |
| 967 | + "print(a+b)#此时numpy会自动将b与a中的每行分别相加" |
| 968 | + ] |
| 969 | + }, |
| 970 | + { |
| 971 | + "cell_type": "code", |
| 972 | + "execution_count": null, |
| 973 | + "metadata": {}, |
| 974 | + "outputs": [], |
| 975 | + "source": [] |
| 976 | + }, |
| 977 | + { |
| 978 | + "cell_type": "code", |
| 979 | + "execution_count": null, |
| 980 | + "metadata": {}, |
| 981 | + "outputs": [], |
| 982 | + "source": [] |
| 983 | + }, |
776 | 984 | { |
777 | 985 | "cell_type": "code", |
778 | 986 | "execution_count": null, |
|
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