diff --git a/IntroductionToNumpy/01 常量.ipynb b/IntroductionToNumpy/01 常量.ipynb deleted file mode 100644 index 8708881..0000000 --- a/IntroductionToNumpy/01 常量.ipynb +++ /dev/null @@ -1,148 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 常量\n", - "\n", - "## numpy.nan\n", - "\n", - "- 表示空值。\n", - "\n", - "```\n", - "nan = NaN = NAN\n", - "```\n", - "\n", - "\n", - "【例】两个`numpy.nan`是不相等的。\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "print(np.nan == np.nan) # False\n", - "print(np.nan != np.nan) # True\n", - "```\n", - "\n", - "- `numpy.isnan(x, *args, **kwargs)` Test element-wise for NaN and return result as a boolean array.\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 1, 8, np.nan, 10])\n", - "print(x)\n", - "# [ 1. 1. 8. nan 10.]\n", - "\n", - "y = np.isnan(x)\n", - "print(y)\n", - "# [False False False True False]\n", - "\n", - "z = np.count_nonzero(y)\n", - "print(z) # 1\n", - "```\n", - "\n", - "\n", - "\n", - "## numpy.inf\n", - "- 表示正无穷大。\n", - "\n", - "```\n", - "Inf = inf = infty = Infinity = PINF\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "\n", - "```\n", - "\n", - "## numpy.pi\n", - "\n", - "- 表示圆周率\n", - "\n", - "```python\n", - "pi = 3.1415926535897932384626433...\n", - "```\n", - "\n", - "## numpy.e\n", - "\n", - "- 表示自然常数\n", - "\n", - "```python\n", - "e = 2.71828182845904523536028747135266249775724709369995...\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python35", - "language": "python", - "name": "python35" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/IntroductionToNumpy/02 数据类型.ipynb b/IntroductionToNumpy/02 数据类型.ipynb deleted file mode 100644 index 94a4686..0000000 --- a/IntroductionToNumpy/02 数据类型.ipynb +++ /dev/null @@ -1,250 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 数据类型\n", - "\n", - "## 常见数据类型\n", - "\n", - "Python 原生的数据类型相对较少， bool、int、float、str等。这在不需要关心数据在计算机中表示的所有方式的应用中是方便的。然而，对于科学计算，通常需要更多的控制。为了加以区分 numpy 在这些类型名称末尾都加了“_”。\n", - "\n", - "下表列举了常用 numpy 基本类型。\n", - "\n", - "\n", - "类型 | 备注 | 说明 \n", - "---|---|---\n", - "bool_ = bool8 | 8位 | 布尔类型\n", - "int8 = byte | 8位 | 整型\n", - "int16 = short |\t16位| 整型\n", - "int32 = intc | 32位| 整型\n", - "int_ = int64 = long = int0 = intp | 64位| 整型\n", - "uint8 = ubyte |8位 | 无符号整型\n", - "uint16 = ushort|16位| 无符号整型\n", - "uint32 = uintc|32位| 无符号整型\n", - "uint64 = uintp = uint0 = uint| 64位| 无符号整型\n", - "float16 = half|16位 | 浮点型\n", - "float32 = single| 32位| 浮点型\n", - "float_ = float64 = double| 64位| 浮点型\n", - "str_ = unicode_ = str0 = unicode| |Unicode 字符串\n", - "datetime64| |日期时间类型\n", - "timedelta64| |表示两个时间之间的间隔\n", - "\n", - "\n", - "\n", - "## 创建数据类型\n", - "\n", - "numpy 的数值类型实际上是 dtype 对象的实例。\n", - "\n", - "```python\n", - "class dtype(object):\n", - " def __init__(self, obj, align=False, copy=False):\n", - " pass\n", - "```\n", - "\n", - "\n", - "\n", - "\n", - "每个内建类型都有一个唯一定义它的字符代码，如下：\n", - "\n", - "字符 | \t对应类型|备注\n", - "---|---|---\n", - "b\t|boolean | 'b1'\n", - "i\t|signed integer | 'i1', 'i2', 'i4', 'i8'\n", - "u\t|unsigned integer | 'u1', 'u2' ,'u4' ,'u8'\n", - "f\t|floating-point | 'f2', 'f4', 'f8'\n", - "c\t|complex floating-point |\n", - "m\t|timedelta64 |表示两个时间之间的间隔\n", - "M\t|datetime64 |日期时间类型\n", - "O\t|object |\n", - "S\t|(byte-)string | S3表示长度为3的字符串\n", - "U\t|Unicode | Unicode 字符串\n", - "V\t|void\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.dtype('b1')\n", - "print(a.type) # \n", - "print(a.itemsize) # 1\n", - "\n", - "a = np.dtype('i1')\n", - "print(a.type) # \n", - "print(a.itemsize) # 1\n", - "a = np.dtype('i2')\n", - "print(a.type) # \n", - "print(a.itemsize) # 2\n", - "a = np.dtype('i4')\n", - "print(a.type) # \n", - "print(a.itemsize) # 4\n", - "a = np.dtype('i8')\n", - "print(a.type) # \n", - "print(a.itemsize) # 8\n", - "\n", - "a = np.dtype('u1')\n", - "print(a.type) # \n", - "print(a.itemsize) # 1\n", - "a = np.dtype('u2')\n", - "print(a.type) # \n", - "print(a.itemsize) # 2\n", - "a = np.dtype('u4')\n", - "print(a.type) # \n", - "print(a.itemsize) # 4\n", - "a = np.dtype('u8')\n", - "print(a.type) # \n", - "print(a.itemsize) # 8\n", - "\n", - "a = np.dtype('f2')\n", - "print(a.type) # \n", - "print(a.itemsize) # 2\n", - "a = np.dtype('f4')\n", - "print(a.type) # \n", - "print(a.itemsize) # 4\n", - "a = np.dtype('f8')\n", - "print(a.type) # \n", - "print(a.itemsize) # 8\n", - "\n", - "a = np.dtype('S')\n", - "print(a.type) # \n", - "print(a.itemsize) # 0\n", - "a = np.dtype('S3')\n", - "print(a.type) # \n", - "print(a.itemsize) # 3\n", - "\n", - "a = np.dtype('U3')\n", - "print(a.type) # \n", - "print(a.itemsize) # 12\n", - "```\n", - "\n", - "## 数据类型信息\n", - "\n", - "Python 的浮点数通常是64位浮点数，几乎等同于 `np.float64`。\n", - "\n", - "NumPy和Python整数类型的行为在整数溢出方面存在显着差异，与 NumPy 不同，Python 的`int` 是灵活的。这意味着Python整数可以扩展以容纳任何整数并且不会溢出。\n", - "\n", - "Machine limits for integer types.\n", - "```python\n", - "class iinfo(object):\n", - " def __init__(self, int_type):\n", - " pass\n", - " def min(self):\n", - " pass\n", - " def max(self):\n", - " pass\n", - "```\n", - "【例】\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "ii16 = np.iinfo(np.int16)\n", - "print(ii16.min) # -32768\n", - "print(ii16.max) # 32767\n", - "\n", - "ii32 = np.iinfo(np.int32)\n", - "print(ii32.min) # -2147483648\n", - "print(ii32.max) # 2147483647\n", - "```\n", - "\n", - "Machine limits for floating point types.\n", - "```python\n", - "class finfo(object):\n", - " def _init(self, dtype):\n", - "```\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "ff16 = np.finfo(np.float16)\n", - "print(ff16.bits) # 16\n", - "print(ff16.min) # -65500.0\n", - "print(ff16.max) # 65500.0\n", - "print(ff16.eps) # 0.000977\n", - "\n", - "ff32 = np.finfo(np.float32)\n", - "print(ff32.bits) # 32\n", - "print(ff32.min) # -3.4028235e+38\n", - "print(ff32.max) # 3.4028235e+38\n", - "print(ff32.eps) # 1.1920929e-07\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python35", - "language": "python", - "name": "python35" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "218.2px" - }, - "toc_section_display": true, - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/IntroductionToNumpy/03 时间日期和时间增量.ipynb b/IntroductionToNumpy/03 时间日期和时间增量.ipynb deleted file mode 100644 index 87f74a3..0000000 --- a/IntroductionToNumpy/03 时间日期和时间增量.ipynb +++ /dev/null @@ -1,356 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 时间日期和时间增量\n", - "\n", - "## datetime64 基础\n", - "\n", - "在 numpy 中，我们很方便的将字符串转换成时间日期类型 `datetime64`（`datetime` 已被 python 包含的日期时间库所占用）。\n", - "\n", - "`datatime64`是带单位的日期时间类型，其单位如下：\n", - "\n", - "日期单位 | 代码含义|时间单位 | 代码含义\n", - ":---:|:---:|:---:|:---:\n", - "Y | 年 |h | 小时\n", - "M | 月 |m | 分钟\n", - "W | 周 |s | 秒\n", - "D | 天 |ms | 毫秒\n", - "- | - |us | 微秒\n", - "- | - |ns | 纳秒\n", - "- | - |ps | 皮秒\n", - "- | - |fs | 飞秒\n", - "- | - |as | 阿托秒\n", - "\n", - "注意：\n", - "- 1秒 = 1000 毫秒（milliseconds）\n", - "- 1毫秒 = 1000 微秒（microseconds）\n", - "\n", - "【例】从字符串创建 datetime64 类型时，默认情况下，numpy 会根据字符串自动选择对应的单位。\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.datetime64('2020-03-01')\n", - "print(a, a.dtype) # 2020-03-01 datetime64[D]\n", - "\n", - "a = np.datetime64('2020-03')\n", - "print(a, a.dtype) # 2020-03 datetime64[M]\n", - "\n", - "a = np.datetime64('2020-03-08 20:00:05')\n", - "print(a, a.dtype) # 2020-03-08T20:00:05 datetime64[s]\n", - "\n", - "a = np.datetime64('2020-03-08 20:00')\n", - "print(a, a.dtype) # 2020-03-08T20:00 datetime64[m]\n", - "\n", - "a = np.datetime64('2020-03-08 20')\n", - "print(a, a.dtype) # 2020-03-08T20 datetime64[h]\n", - "```\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "【例】从字符串创建 datetime64 类型时，可以强制指定使用的单位。\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.datetime64('2020-03', 'D')\n", - "print(a, a.dtype) # 2020-03-01 datetime64[D]\n", - "\n", - "a = np.datetime64('2020-03', 'Y')\n", - "print(a, a.dtype) # 2020 datetime64[Y]\n", - "\n", - "print(np.datetime64('2020-03') == np.datetime64('2020-03-01')) # True\n", - "print(np.datetime64('2020-03') == np.datetime64('2020-03-02')) #False\n", - "```\n", - "\n", - "由上例可以看出，2019-03 和 2019-03-01 所表示的其实是同一个时间。\n", - "事实上，如果两个 datetime64 对象具有不同的单位，它们可能仍然代表相同的时刻。并且从较大的单位（如月份）转换为较小的单位（如天数）是安全的。\n", - "\n", - "\n", - "【例】从字符串创建 datetime64 数组时，如果单位不统一，则一律转化成其中最小的单位。\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.array(['2020-03', '2020-03-08', '2020-03-08 20:00'], dtype='datetime64')\n", - "print(a, a.dtype)\n", - "# ['2020-03-01T00:00' '2020-03-08T00:00' '2020-03-08T20:00'] datetime64[m]\n", - "```\n", - "\n", - "\n", - "【例】使用`arange()`创建 datetime64 数组，用于生成日期范围。\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.arange('2020-08-01', '2020-08-10', dtype=np.datetime64)\n", - "print(a)\n", - "# ['2020-08-01' '2020-08-02' '2020-08-03' '2020-08-04' '2020-08-05'\n", - "# '2020-08-06' '2020-08-07' '2020-08-08' '2020-08-09']\n", - "print(a.dtype) # datetime64[D]\n", - "\n", - "a = np.arange('2020-08-01 20:00', '2020-08-10', dtype=np.datetime64)\n", - "print(a)\n", - "# ['2020-08-01T20:00' '2020-08-01T20:01' '2020-08-01T20:02' ...\n", - "# '2020-08-09T23:57' '2020-08-09T23:58' '2020-08-09T23:59']\n", - "print(a.dtype) # datetime64[m]\n", - "\n", - "a = np.arange('2020-05', '2020-12', dtype=np.datetime64)\n", - "print(a)\n", - "# ['2020-05' '2020-06' '2020-07' '2020-08' '2020-09' '2020-10' '2020-11']\n", - "print(a.dtype) # datetime64[M]\n", - "```\n", - "\n", - "## datetime64 和 timedelta64 运算\n", - "\n", - "【例】timedelta64 表示两个 datetime64 之间的差。timedelta64 也是带单位的，并且和相减运算中的两个 datetime64 中的较小的单位保持一致。\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.datetime64('2020-03-08') - np.datetime64('2020-03-07')\n", - "b = np.datetime64('2020-03-08') - np.datetime64('202-03-07 08:00')\n", - "c = np.datetime64('2020-03-08') - np.datetime64('2020-03-07 23:00', 'D')\n", - "\n", - "print(a, a.dtype) # 1 days timedelta64[D]\n", - "print(b, b.dtype) # 956178240 minutes timedelta64[m]\n", - "print(c, c.dtype) # 1 days timedelta64[D]\n", - "\n", - "a = np.datetime64('2020-03') + np.timedelta64(20, 'D')\n", - "b = np.datetime64('2020-06-15 00:00') + np.timedelta64(12, 'h')\n", - "print(a, a.dtype) # 2020-03-21 datetime64[D]\n", - "print(b, b.dtype) # 2020-06-15T12:00 datetime64[m]\n", - "```\n", - "\n", - "【例】生成 timedelta64时，要注意年（'Y'）和月（'M'）这两个单位无法和其它单位进行运算（一年有几天？一个月有几个小时？这些都是不确定的）。\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.timedelta64(1, 'Y')\n", - "b = np.timedelta64(a, 'M')\n", - "print(a) # 1 years\n", - "print(b) # 12 months\n", - "\n", - "c = np.timedelta64(1, 'h')\n", - "d = np.timedelta64(c, 'm')\n", - "print(c) # 1 hours\n", - "print(d) # 60 minutes\n", - "\n", - "print(np.timedelta64(a, 'D'))\n", - "# TypeError: Cannot cast NumPy timedelta64 scalar from metadata [Y] to [D] according to the rule 'same_kind'\n", - "\n", - "print(np.timedelta64(b, 'D'))\n", - "# TypeError: Cannot cast NumPy timedelta64 scalar from metadata [M] to [D] according to the rule 'same_kind'\n", - "```\n", - "\n", - "\n", - "【例】timedelta64 的运算。\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.timedelta64(1, 'Y')\n", - "b = np.timedelta64(6, 'M')\n", - "c = np.timedelta64(1, 'W')\n", - "d = np.timedelta64(1, 'D')\n", - "e = np.timedelta64(10, 'D')\n", - "\n", - "print(a) # 1 years\n", - "print(b) # 6 months\n", - "print(a + b) # 18 months\n", - "print(a - b) # 6 months\n", - "print(2 * a) # 2 years\n", - "print(a / b) # 2.0\n", - "print(c / d) # 7.0\n", - "print(c % e) # 7 days\n", - "```\n", - "\n", - "【例】numpy.datetime64 与 datetime.datetime 相互转换\n", - "```python\n", - "import numpy as np\n", - "import datetime\n", - "\n", - "dt = datetime.datetime(year=2020, month=6, day=1, hour=20, minute=5, second=30)\n", - "dt64 = np.datetime64(dt, 's')\n", - "print(dt64, dt64.dtype)\n", - "# 2020-06-01T20:05:30 datetime64[s]\n", - "\n", - "dt2 = dt64.astype(datetime.datetime)\n", - "print(dt2, type(dt2))\n", - "# 2020-06-01 20:05:30 \n", - "```\n", - "\n", - "## datetime64 的应用\n", - "\n", - "为了允许在只有一周中某些日子有效的上下文中使用日期时间，NumPy包含一组“busday”（工作日）功能。\n", - "\n", - "- `numpy.busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None)` First adjusts the date to fall on a valid day according to the roll rule, then applies offsets to the given dates counted in valid days.\n", - "\n", - "参数`roll`：{'raise', 'nat', 'forward', 'following', 'backward', 'preceding', 'modifiedfollowing', 'modifiedpreceding'}\n", - "- 'raise' means to raise an exception for an invalid day.\n", - "- 'nat' means to return a NaT (not-a-time) for an invalid day.\n", - "- 'forward' and 'following' mean to take the first valid day later in time.\n", - "- 'backward' and 'preceding' mean to take the first valid day earlier in time.\n", - "\n", - "\n", - "【例】将指定的偏移量应用于工作日，单位天（'D'）。计算下一个工作日，如果当前日期为非工作日，默认报错。可以指定 `forward` 或 `backward` 规则来避免报错。（一个是向前取第一个有效的工作日，一个是向后取第一个有效的工作日）\n", - "```python\n", - "import numpy as np\n", - "\n", - "# 2020-07-10 星期五\n", - "a = np.busday_offset('2020-07-10', offsets=1)\n", - "print(a) # 2020-07-13\n", - "\n", - "a = np.busday_offset('2020-07-11', offsets=1)\n", - "print(a)\n", - "# ValueError: Non-business day date in busday_offset\n", - "\n", - "a = np.busday_offset('2020-07-11', offsets=0, roll='forward')\n", - "b = np.busday_offset('2020-07-11', offsets=0, roll='backward')\n", - "print(a) # 2020-07-13\n", - "print(b) # 2020-07-10\n", - "\n", - "a = np.busday_offset('2020-07-11', offsets=1, roll='forward')\n", - "b = np.busday_offset('2020-07-11', offsets=1, roll='backward')\n", - "print(a) # 2020-07-14\n", - "print(b) # 2020-07-13\n", - "```\n", - "\n", - "可以指定偏移量为 0 来获取当前日期向前或向后最近的工作日，当然，如果当前日期本身就是工作日，则直接返回当前日期。\n", - "\n", - "- `numpy.is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None)` Calculates which of the given dates are valid days, and which are not.\n", - "\n", - "【例】返回指定日期是否是工作日。\n", - "```python\n", - "import numpy as np\n", - "\n", - "# 2020-07-10 星期五\n", - "a = np.is_busday('2020-07-10')\n", - "b = np.is_busday('2020-07-11')\n", - "print(a) # True\n", - "print(b) # False\n", - "```\n", - "\n", - "【例】统计一个 `datetime64[D]` 数组中的工作日天数。\n", - "```python\n", - "import numpy as np\n", - "\n", - "# 2020-07-10 星期五\n", - "begindates = np.datetime64('2020-07-10')\n", - "enddates = np.datetime64('2020-07-20')\n", - "a = np.arange(begindates, enddates, dtype='datetime64')\n", - "b = np.count_nonzero(np.is_busday(a))\n", - "print(a)\n", - "# ['2020-07-10' '2020-07-11' '2020-07-12' '2020-07-13' '2020-07-14'\n", - "# '2020-07-15' '2020-07-16' '2020-07-17' '2020-07-18' '2020-07-19']\n", - "print(b) # 6\n", - "```\n", - "\n", - "\n", - "【例】自定义周掩码值，即指定一周中哪些星期是工作日。\n", - "```python\n", - "import numpy as np\n", - "\n", - "# 2020-07-10 星期五\n", - "a = np.is_busday('2020-07-10', weekmask=[1, 1, 1, 1, 1, 0, 0])\n", - "b = np.is_busday('2020-07-10', weekmask=[1, 1, 1, 1, 0, 0, 1])\n", - "print(a) # True\n", - "print(b) # False\n", - "```\n", - "\n", - "- `numpy.busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None)`Counts the number of valid days between `begindates` and `enddates`, not including the day of `enddates`.\n", - "\n", - "【例】返回两个日期之间的工作日数量。\n", - "```python\n", - "import numpy as np\n", - "\n", - "# 2020-07-10 星期五\n", - "begindates = np.datetime64('2020-07-10')\n", - "enddates = np.datetime64('2020-07-20')\n", - "a = np.busday_count(begindates, enddates)\n", - "b = np.busday_count(enddates, begindates)\n", - "print(a) # 6\n", - "print(b) # -6\n", - "```\n", - "\n", - "\n", - "---\n", - "参考图文\n", - "\n", - "- https://www.jianshu.com/p/336cd77d9914\n", - "- https://www.cnblogs.com/gl1573/p/10549547.html#h2datetime64\n", - "- https://www.numpy.org.cn/reference/arrays/datetime.html#%E6%97%A5%E6%9C%9F%E6%97%B6%E9%97%B4%E5%8D%95%E4%BD%8D\n", - 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"varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/IntroductionToNumpy/04 数组的创建.ipynb b/IntroductionToNumpy/04 数组的创建.ipynb deleted file mode 100644 index f44260b..0000000 --- a/IntroductionToNumpy/04 数组的创建.ipynb +++ /dev/null @@ -1,617 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "[toc]\n", - "\n", - "---\n", - "# 数组的创建\n", - "\n", - "导入 numpy。\n", - "\n", - "```\n", - "import numpy as np\n", - "```\n", - "\n", - "numpy 提供的最重要的数据结构是`ndarray`，它是 python 中`list`的扩展。\n", - "\n", - "## 1. 依据现有数据来创建 ndarray\n", - "\n", - "### **（a）通过array()函数进行创建。**\n", - "\n", - "```python\n", - "def array(p_object, dtype=None, copy=True, order='K', subok=False, ndmin=0): \n", - "``` \n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "# 创建一维数组\n", - "a = np.array([0, 1, 2, 3, 4])\n", - "b = np.array((0, 1, 2, 3, 4))\n", - "print(a, type(a))\n", - "# [0 1 2 3 4] \n", - "print(b, type(b))\n", - "# [0 1 2 3 4] \n", - "\n", - "# 创建二维数组\n", - "c = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "print(c, type(c))\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]] \n", - "\n", - "# 创建三维数组\n", - "d = np.array([[(1.5, 2, 3), (4, 5, 6)],\n", - " [(3, 2, 1), (4, 5, 6)]])\n", - "print(d, type(d))\n", - "# [[[1.5 2. 3. ]\n", - "# [4. 5. 6. ]]\n", - "#\n", - "# [[3. 2. 1. ]\n", - "# [4. 5. 6. ]]] \n", - "```\n", - "\n", - "### **（b）通过asarray()函数进行创建**\n", - "\n", - "`array()`和`asarray()`都可以将结构数据转化为 ndarray，但是`array()`和`asarray()`主要区别就是当数据源是**ndarray** 时，`array()`仍然会 copy 出一个副本，占用新的内存，但不改变 dtype 时 `asarray()`不会。\n", - "\n", - "```python\n", - "def asarray(a, dtype=None, order=None):\n", - " return array(a, dtype, copy=False, order=order)\n", - "```\n", - "\n", - "【例】`array()`和`asarray()`都可以将结构数据转化为 ndarray\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = [[1, 1, 1], [1, 1, 1], [1, 1, 1]]\n", - "y = np.array(x)\n", - "z = np.asarray(x)\n", - "x[1][2] = 2\n", - "print(x,type(x))\n", - "# [[1, 1, 1], [1, 1, 2], [1, 1, 1]] \n", - "\n", - "print(y,type(y))\n", - "# [[1 1 1]\n", - "# [1 1 1]\n", - "# [1 1 1]] \n", - "\n", - "print(z,type(z))\n", - "# [[1 1 1]\n", - "# [1 1 1]\n", - "# [1 1 1]] \n", - "```\n", - "\n", - "【例】`array()`和`asarray()`的区别。(`array()`和`asarray()`主要区别就是当数据源是**ndarray** 时，`array()`仍然会 copy 出一个副本，占用新的内存，但不改变 dtype 时 `asarray()`不会。)\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])\n", - "y = np.array(x)\n", - "z = np.asarray(x)\n", - "w = np.asarray(x, dtype=np.int)\n", - "x[1][2] = 2\n", - "print(x,type(x),x.dtype)\n", - "# [[1 1 1]\n", - "# [1 1 2]\n", - "# [1 1 1]] int32\n", - "\n", - "print(y,type(y),y.dtype)\n", - "# [[1 1 1]\n", - "# [1 1 1]\n", - "# [1 1 1]] int32\n", - "\n", - "print(z,type(z),z.dtype)\n", - "# [[1 1 1]\n", - "# [1 1 2]\n", - "# [1 1 1]] int32\n", - "\n", - "print(w,type(w),w.dtype)\n", - "# [[1 1 1]\n", - "# [1 1 2]\n", - "# [1 1 1]] int32\n", - "```\n", - "\n", - "\n", - "【例】更改为较大的dtype时，其大小必须是array的最后一个axis的总大小（以字节为单位）的除数\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])\n", - "print(x, x.dtype)\n", - "# [[1 1 1]\n", - "# [1 1 1]\n", - "# [1 1 1]] int32\n", - "x.dtype = np.float\n", - "\n", - "# ValueError: When changing to a larger dtype, its size must be a divisor of the total size in bytes of the last axis of the array.\n", - "```\n", - "\n", - "### **（c）通过fromfunction()函数进行创建**\n", - "\n", - "给函数绘图的时候可能会用到`fromfunction()`，该函数可从函数中创建数组。\n", - "\n", - "```python\n", - "def fromfunction(function, shape, **kwargs):\n", - "```\n", - "\n", - "\n", - "【例】通过在每个坐标上执行一个函数来构造数组。\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "def f(x, y):\n", - " return 10 * x + y\n", - "\n", - "x = np.fromfunction(f, (5, 4), dtype=int)\n", - "print(x)\n", - "# [[ 0 1 2 3]\n", - "# [10 11 12 13]\n", - "# [20 21 22 23]\n", - "# [30 31 32 33]\n", - "# [40 41 42 43]]\n", - "\n", - "x = np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)\n", - "print(x)\n", - "# [[ True False False]\n", - "# [False True False]\n", - "# [False False True]]\n", - "\n", - "x = np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)\n", - "print(x)\n", - "# [[0 1 2]\n", - "# [1 2 3]\n", - "# [2 3 4]]\n", - "```\n", - "\n", - "\n", - "\n", - "## 2. 依据 ones 和 zeros 填充方式\n", - "\n", - "在机器学习任务中经常做的一件事就是初始化参数，需要用常数值或者随机值来创建一个固定大小的矩阵。\n", - "\n", - "### **（a）零数组**\n", - "\n", - "- `zeros()`函数：返回给定形状和类型的零数组。\n", - "- `zeros_like()`函数：返回与给定数组形状和类型相同的零数组。\n", - "\n", - "```python\n", - "def zeros(shape, dtype=None, order='C'):\n", - "def zeros_like(a, dtype=None, order='K', subok=True, shape=None):\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.zeros(5)\n", - "print(x) # [0. 0. 0. 0. 0.]\n", - "x = np.zeros([2, 3])\n", - "print(x)\n", - "# [[0. 0. 0.]\n", - "# [0. 0. 0.]]\n", - "\n", - "x = np.array([[1, 2, 3], [4, 5, 6]])\n", - "y = np.zeros_like(x)\n", - "print(y)\n", - "# [[0 0 0]\n", - "# [0 0 0]]\n", - "```\n", - "\n", - "### **（b）1数组**\n", - "\n", - "- `ones()`函数：返回给定形状和类型的1数组。\n", - "- `ones_like()`函数：返回与给定数组形状和类型相同的1数组。\n", - "\n", - "```python\n", - "def ones(shape, dtype=None, order='C'):\n", - "def ones_like(a, dtype=None, order='K', subok=True, shape=None):\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.ones(5)\n", - "print(x) # [1. 1. 1. 1. 1.]\n", - "x = np.ones([2, 3])\n", - "print(x)\n", - "# [[1. 1. 1.]\n", - "# [1. 1. 1.]]\n", - "\n", - "x = np.array([[1, 2, 3], [4, 5, 6]])\n", - "y = np.ones_like(x)\n", - "print(y)\n", - "# [[1 1 1]\n", - "# [1 1 1]]\n", - "```\n", - "\n", - "### **（c）空数组**\n", - "\n", - "- `empty()`函数：返回一个空数组，数组元素为随机数。\n", - "- `empty_like`函数：返回与给定数组具有相同形状和类型的新数组。\n", - "\n", - "```python\n", - "def empty(shape, dtype=None, order='C'): \n", - "def empty_like(prototype, dtype=None, order='K', subok=True, shape=None):\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.empty(5)\n", - "print(x)\n", - "# [1.95821574e-306 1.60219035e-306 1.37961506e-306 \n", - "# 9.34609790e-307 1.24610383e-306]\n", - "\n", - "x = np.empty((3, 2))\n", - "print(x)\n", - "# [[1.60220393e-306 9.34587382e-307]\n", - "# [8.45599367e-307 7.56598449e-307]\n", - "# [1.33509389e-306 3.59412896e-317]]\n", - "\n", - "x = np.array([[1, 2, 3], [4, 5, 6]])\n", - "y = np.empty_like(x)\n", - "print(y)\n", - "# [[ 7209029 6422625 6619244]\n", - "# [ 100 707539280 504]]\n", - "```\n", - "\n", - "### **（d）单位数组**\n", - "\n", - "- `eye()`函数：返回一个对角线上为1，其它地方为零的单位数组。\n", - "- `identity()`函数：返回一个方的单位数组。\n", - "\n", - "```python\n", - "def eye(N, M=None, k=0, dtype=float, order='C'):\n", - "def identity(n, dtype=None):\n", - "```\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.eye(4)\n", - "print(x)\n", - "# [[1. 0. 0. 0.]\n", - "# [0. 1. 0. 0.]\n", - "# [0. 0. 1. 0.]\n", - "# [0. 0. 0. 1.]]\n", - "\n", - "x = np.eye(2, 3)\n", - "print(x)\n", - "# [[1. 0. 0.]\n", - "# [0. 1. 0.]]\n", - "\n", - "x = np.identity(4)\n", - "print(x)\n", - "# [[1. 0. 0. 0.]\n", - "# [0. 1. 0. 0.]\n", - "# [0. 0. 1. 0.]\n", - "# [0. 0. 0. 1.]]\n", - "```\n", - "\n", - "### **（e）对角数组**\n", - "\n", - "- `diag()`函数：提取对角线或构造对角数组。\n", - "\n", - "```python\n", - "def diag(v, k=0):\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.arange(9).reshape((3, 3))\n", - "print(x)\n", - "# [[0 1 2]\n", - "# [3 4 5]\n", - "# [6 7 8]]\n", - "print(np.diag(x)) # [0 4 8]\n", - "print(np.diag(x, k=1)) # [1 5]\n", - "print(np.diag(x, k=-1)) # [3 7]\n", - "\n", - "v = [1, 3, 5, 7]\n", - "x = np.diag(v)\n", - "print(x)\n", - "# [[1 0 0 0]\n", - "# [0 3 0 0]\n", - "# [0 0 5 0]\n", - "# [0 0 0 7]]\n", - "```\n", - "\n", - "### **（f）常数数组**\n", - "\n", - "- `full()`函数：返回一个常数数组。\n", - "- `full_like()`函数：返回与给定数组具有相同形状和类型的常数数组。\n", - "\n", - "```python\n", - "def full(shape, fill_value, dtype=None, order='C'):\n", - "def full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None):\n", - "```\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.full((2,), 7)\n", - "print(x)\n", - "# [7 7]\n", - "\n", - "x = np.full(2, 7)\n", - "print(x)\n", - "# [7 7]\n", - "\n", - "x = np.full((2, 7), 7)\n", - "print(x)\n", - "# [[7 7 7 7 7 7 7]\n", - "# [7 7 7 7 7 7 7]]\n", - "\n", - "x = np.array([[1, 2, 3], [4, 5, 6]])\n", - "y = np.full_like(x, 7)\n", - "print(y)\n", - "# [[7 7 7]\n", - "# [7 7 7]]\n", - "```\n", - "\n", - "\n", - "\n", - "## 3. 利用数值范围来创建ndarray\n", - " - `arange()`函数：返回给定间隔内的均匀间隔的值。\n", - " - `linspace()`函数：返回指定间隔内的等间隔数字。\n", - " - `logspace()`函数：返回数以对数刻度均匀分布。\n", - " - `numpy.random.rand()` 返回一个由[0,1)内的随机数组成的数组。\n", - "\n", - "```python\n", - "def arange([start,] stop[, step,], dtype=None): \n", - "def linspace(start, stop, num=50, endpoint=True, retstep=False, \n", - " dtype=None, axis=0):\n", - "def logspace(start, stop, num=50, endpoint=True, base=10.0, \n", - " dtype=None, axis=0):\n", - "def rand(d0, d1, ..., dn): \n", - "```\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.arange(5)\n", - "print(x) # [0 1 2 3 4]\n", - "\n", - "x = np.arange(3, 7, 2)\n", - "print(x) # [3 5]\n", - "\n", - "x = np.linspace(start=0, stop=2, num=9)\n", - "print(x) \n", - "# [0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ]\n", - "\n", - "x = np.logspace(0, 1, 5)\n", - "print(np.around(x, 2))\n", - "# [ 1. 1.78 3.16 5.62 10. ] \n", - " #np.around 返回四舍五入后的值，可指定精度。\n", - " # around(a, decimals=0, out=None)\n", - " # a 输入数组\n", - " # decimals 要舍入的小数位数。 默认值为0。 如果为负，整数将四舍五入到小数点左侧的位置\n", - "\n", - "\n", - "x = np.linspace(start=0, stop=1, num=5)\n", - "x = [10 ** i for i in x]\n", - "print(np.around(x, 2))\n", - "# [ 1. 1.78 3.16 5.62 10. ]\n", - "\n", - "x = np.random.random(5)\n", - "print(x)\n", - "# [0.41768753 0.16315577 0.80167915 0.99690199 0.11812291]\n", - "\n", - "x = np.random.random([2, 3])\n", - "print(x)\n", - "# [[0.41151858 0.93785153 0.57031309]\n", - "# [0.13482333 0.20583516 0.45429181]]\n", - "```\n", - "\n", - "\n", - "\n", - "---\n", - "## 4. 结构数组的创建\n", - "\n", - "结构数组，首先需要定义结构，然后利用`np.array()`来创建数组，其参数`dtype`为定义的结构。\n", - "\n", - "### **（a）利用字典来定义结构**\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "personType = np.dtype({\n", - " 'names': ['name', 'age', 'weight'],\n", - " 'formats': ['U30', 'i8', 'f8']})\n", - "\n", - "a = np.array([('Liming', 24, 63.9), ('Mike', 15, 67.), ('Jan', 34, 45.8)],\n", - " dtype=personType)\n", - "print(a, type(a))\n", - "# [('Liming', 24, 63.9) ('Mike', 15, 67. ) ('Jan', 34, 45.8)]\n", - "# \n", - "```\n", - "\n", - "### **（b）利用包含多个元组的列表来定义结构**\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "personType = np.dtype([('name', 'U30'), ('age', 'i8'), ('weight', 'f8')])\n", - "a = np.array([('Liming', 24, 63.9), ('Mike', 15, 67.), ('Jan', 34, 45.8)],\n", - " dtype=personType)\n", - "print(a, type(a))\n", - "# [('Liming', 24, 63.9) ('Mike', 15, 67. ) ('Jan', 34, 45.8)]\n", - "# \n", - "\n", - "# 结构数组的取值方式和一般数组差不多，可以通过下标取得元素：\n", - "print(a[0])\n", - "# ('Liming', 24, 63.9)\n", - "\n", - "print(a[-2:])\n", - "# [('Mike', 15, 67. ) ('Jan', 34, 45.8)]\n", - "\n", - "# 我们可以使用字段名作为下标获取对应的值\n", - "print(a['name'])\n", - "# ['Liming' 'Mike' 'Jan']\n", - "print(a['age'])\n", - "# [24 15 34]\n", - "print(a['weight'])\n", - "# [63.9 67. 45.8]\n", - "```\n", - "\n", - "---\n", - "# 数组的属性\n", - "\n", - "在使用 numpy 时，你会想知道数组的某些信息。很幸运，在这个包里边包含了很多便捷的方法，可以给你想要的信息。\n", - "\n", - "\n", - "\n", - " - `numpy.ndarray.ndim`用于返回数组的维数（轴的个数）也称为秩，一维数组的秩为 1，二维数组的秩为 2，以此类推。\n", - " - `numpy.ndarray.shape`表示数组的维度，返回一个元组，这个元组的长度就是维度的数目，即 `ndim` 属性(秩)。\n", - " - `numpy.ndarray.size`数组中所有元素的总量，相当于数组的`shape`中所有元素的乘积，例如矩阵的元素总量为行与列的乘积。\n", - " - `numpy.ndarray.dtype` `ndarray` 对象的元素类型。\n", - " - `numpy.ndarray.itemsize`以字节的形式返回数组中每一个元素的大小。\n", - "\n", - "```python\n", - "class ndarray(object):\n", - " shape = property(lambda self: object(), lambda self, v: None, lambda self: None)\n", - " dtype = property(lambda self: object(), lambda self, v: None, lambda self: None)\n", - " size = property(lambda self: object(), lambda self, v: None, lambda self: None)\n", - " ndim = property(lambda self: object(), lambda self, v: None, lambda self: None)\n", - " itemsize = property(lambda self: object(), lambda self, v: None, lambda self: None)\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.array([1, 2, 3, 4, 5])\n", - "print(a.shape) # (5,)\n", - "print(a.dtype) # int32\n", - "print(a.size) # 5\n", - "print(a.ndim) # 1\n", - "print(a.itemsize) # 4\n", - "\n", - "b = np.array([[1, 2, 3], [4, 5, 6.0]])\n", - "print(b.shape) # (2, 3)\n", - "print(b.dtype) # float64\n", - "print(b.size) # 6\n", - "print(b.ndim) # 2\n", - "print(b.itemsize) # 8\n", - "```\n", - "\n", - "在`ndarray`中所有元素必须是同一类型，否则会自动向下转换，`int->float->str`。\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.array([1, 2, 3, 4, 5])\n", - "print(a) # [1 2 3 4 5]\n", - "b = np.array([1, 2, 3, 4, '5'])\n", - "print(b) # ['1' '2' '3' '4' '5']\n", - "c = np.array([1, 2, 3, 4, 5.0])\n", - "print(c) # [1. 2. 3. 4. 5.]\n", - "```\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python35", - "language": "python", - "name": "python35" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "307.2px" - }, - "toc_section_display": true, - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/IntroductionToNumpy/05 索引、切片与迭代.ipynb b/IntroductionToNumpy/05 索引、切片与迭代.ipynb deleted file mode 100644 index 4c8d54e..0000000 --- a/IntroductionToNumpy/05 索引、切片与迭代.ipynb +++ /dev/null @@ -1,731 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2020-08-29T16:29:25.164182Z", - "start_time": "2020-08-29T16:29:25.155206Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[11 12 13 14 15]\n", - " [16 17 18 19 20]]\n", - "\n", - " [[26 27 28 29 30]\n", - " [31 32 33 34 35]]]\n", - "[[[11 12 13 14 15]\n", - " [11 12 13 14 15]]\n", - "\n", - " [[31 32 33 34 35]\n", - " [31 32 33 34 35]]]\n", - "[[11 15]\n", - " [31 35]]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "r = np.array([[0, 1], [3, 4]])\n", - "print(x[r])\n", - "# [[[11 12 13 14 15]\n", - "# [16 17 18 19 20]]\n", - "#\n", - "# [[26 27 28 29 30]\n", - "# [31 32 33 34 35]]]\n", - "\n", - "# 获取了 5X5 数组中的四个角的元素。\n", - "# 行索引是 [0,0] 和 [4,4]，而列索引是 [0,4] 和 [0,4]。\n", - "r = np.array([[0, 0], [4, 4]])\n", - "c = np.array([[0, 4], [0, 4]])\n", - "print(x[r])\n", - "# [[[11 12 13 14 15]\n", - "# [11 12 13 14 15]]\n", - "\n", - "# [[31 32 33 34 35]\n", - "# [31 32 33 34 35]]]\n", - "y = x[r, c]\n", - "print(y)\n", - "# [[11 15]\n", - "# [31 35]]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2020-08-29T16:28:32.299732Z", - "start_time": "2020-08-29T16:28:32.291752Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[11, 12, 13, 14, 15],\n", - " [11, 12, 13, 14, 15]],\n", - "\n", - " [[31, 32, 33, 34, 35],\n", - " [31, 32, 33, 34, 35]]])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x[r][c]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2020-08-29T16:35:08.531558Z", - "start_time": "2020-08-29T16:35:08.525575Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[11 12 13]\n", - " [13 14 15]]\n", - "\n", - " [[16 17 18]\n", - " [18 19 20]]\n", - "\n", - " [[21 22 23]\n", - " [23 24 25]]\n", - "\n", - " [[26 27 28]\n", - " [28 29 30]]\n", - "\n", - " [[31 32 33]\n", - " [33 34 35]]]\n" - ] - } - ], - "source": [ - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "r = [0, 1, 2]\n", - "c = [2, 3, 4]\n", - "y = np.take(x, [r, c],axis=1)\n", - "print(y)\n", - "# [[11 12 13]\n", - "# [13 14 15]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 副本与视图\n", - "在 Numpy 中，尤其是在做数组运算或数组操作时，返回结果不是数组的 **副本** 就是 **视图**。\n", - "\n", - "在 Numpy 中，所有赋值运算不会为数组和数组中的任何元素创建副本。\n", - "\n", - "\n", - "- `numpy.ndarray.copy()` 函数创建一个副本。 对副本数据进行修改，不会影响到原始数据，它们物理内存不在同一位置。\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n", - "y = x\n", - "y[0] = -1\n", - "print(x)\n", - "# [-1 2 3 4 5 6 7 8]\n", - "print(y)\n", - "# [-1 2 3 4 5 6 7 8]\n", - "\n", - "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n", - "y = x.copy()\n", - "y[0] = -1\n", - "print(x)\n", - "# [1 2 3 4 5 6 7 8]\n", - "print(y)\n", - "# [-1 2 3 4 5 6 7 8]\n", - "```\n", - "\n", - "\n", - "\n", - "【例】数组切片操作返回的对象只是原数组的视图。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = x\n", - "y[::2, :3:2] = -1\n", - "print(x)\n", - "# [[-1 12 -1 14 15]\n", - "# [16 17 18 19 20]\n", - "# [-1 22 -1 24 25]\n", - "# [26 27 28 29 30]\n", - "# [-1 32 -1 34 35]]\n", - "print(y)\n", - "# [[-1 12 -1 14 15]\n", - "# [16 17 18 19 20]\n", - "# [-1 22 -1 24 25]\n", - "# [26 27 28 29 30]\n", - "# [-1 32 -1 34 35]]\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = x.copy()\n", - "y[::2, :3:2] = -1\n", - "print(x)\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]]\n", - "print(y)\n", - "# [[-1 12 -1 14 15]\n", - "# [16 17 18 19 20]\n", - "# [-1 22 -1 24 25]\n", - "# [26 27 28 29 30]\n", - "# [-1 32 -1 34 35]]\n", - "```\n", - "\n", - "\n", - "# 索引与切片\n", - "\n", - "数组索引机制指的是用方括号（[]）加序号的形式引用单个数组元素，它的用处很多，比如抽取元素，选取数组的几个元素，甚至为其赋一个新值。\n", - "\n", - "## 整数索引\n", - "\n", - "【例】要获取数组的单个元素，指定元素的索引即可。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n", - "print(x[2]) # 3\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "print(x[2]) # [21 22 23 24 25]\n", - "print(x[2][1]) # 22\n", - "print(x[2, 1]) # 22\n", - "```\n", - "\n", - "## 切片索引\n", - "\n", - "切片操作是指抽取数组的一部分元素生成新数组。对 python **列表**进行切片操作得到的数组是原数组的**副本**，而对 **Numpy** 数据进行切片操作得到的数组则是指向相同缓冲区的**视图**。\n", - "\n", - "\n", - "如果想抽取（或查看）数组的一部分，必须使用切片语法，也就是，把几个用冒号（ `start:stop:step` ）隔开的数字置于方括号内。\n", - "\n", - "为了更好地理解切片语法，还应该了解不明确指明起始和结束位置的情况。如省去第一个数字，numpy 会认为第一个数字是0；如省去第二个数字，numpy 则会认为第二个数字是数组的最大索引值；如省去最后一个数字，它将会被理解为1，也就是抽取所有元素而不再考虑间隔。\n", - "\n", - "\n", - "【例】对一维数组的切片\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n", - "print(x[0:2]) # [1 2]\n", - "print(x[1:5:2]) # [2 4]\n", - "print(x[2:]) # [3 4 5 6 7 8]\n", - "print(x[:2]) # [1 2]\n", - "print(x[-2:]) # [7 8]\n", - "print(x[:-2]) # [1 2 3 4 5 6]\n", - "print(x[:]) # [1 2 3 4 5 6 7 8]\n", - "print(x[::-1]) # [8 7 6 5 4 3 2 1]\n", - "```\n", - "\n", - "【例】对二维数组切片\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "print(x[0:2])\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]]\n", - "\n", - "print(x[1:5:2])\n", - "# [[16 17 18 19 20]\n", - "# [26 27 28 29 30]]\n", - "\n", - "print(x[2:])\n", - "# [[21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]]\n", - "\n", - "print(x[:2])\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]]\n", - "\n", - "print(x[-2:])\n", - "# [[26 27 28 29 30]\n", - "# [31 32 33 34 35]]\n", - "\n", - "print(x[:-2])\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]]\n", - "\n", - "print(x[:])\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]]\n", - "\n", - "print(x[2, :]) # [21 22 23 24 25]\n", - "print(x[:, 2]) # [13 18 23 28 33]\n", - "print(x[0, 1:4]) # [12 13 14]\n", - "print(x[1:4, 0]) # [16 21 26]\n", - "print(x[1:3, 2:4])\n", - "# [[18 19]\n", - "# [23 24]]\n", - "\n", - "print(x[:, :])\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]]\n", - "\n", - "print(x[::2, ::2])\n", - "# [[11 13 15]\n", - "# [21 23 25]\n", - "# [31 33 35]]\n", - "\n", - "print(x[::-1, :])\n", - "# [[31 32 33 34 35]\n", - "# [26 27 28 29 30]\n", - "# [21 22 23 24 25]\n", - "# [16 17 18 19 20]\n", - "# [11 12 13 14 15]]\n", - "\n", - "print(x[:, ::-1])\n", - "# [[15 14 13 12 11]\n", - "# [20 19 18 17 16]\n", - "# [25 24 23 22 21]\n", - "# [30 29 28 27 26]\n", - "# [35 34 33 32 31]]\n", - "```\n", - "\n", - "\n", - "通过对每个以逗号分隔的维度执行单独的切片，你可以对多维数组进行切片。因此，对于二维数组，我们的第一片定义了行的切片，第二片定义了列的切片。\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "print(x)\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]]\n", - "\n", - "x[0::2, 1::3] = 0\n", - "print(x)\n", - "# [[11 0 13 14 0]\n", - "# [16 17 18 19 20]\n", - "# [21 0 23 24 0]\n", - "# [26 27 28 29 30]\n", - "# [31 0 33 34 0]]\n", - "```\n", - "\n", - "## dots 索引\n", - "\n", - "NumPy 允许使用`...`表示足够多的冒号来构建完整的索引列表。\n", - "\n", - "比如，如果 `x` 是 5 维数组：\n", - "\n", - "- `x[1,2,...]` 等于 `x[1,2,:,:,:]`\n", - "- `x[...,3]` 等于 `x[:,:,:,:,3]` \n", - "- `x[4,...,5,:]` 等于 `x[4,:,:,5,:]`\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.random.randint(1, 100, [2, 2, 3])\n", - "print(x)\n", - "# [[[ 5 64 75]\n", - "# [57 27 31]]\n", - "# \n", - "# [[68 85 3]\n", - "# [93 26 25]]]\n", - "\n", - "print(x[1, ...])\n", - "# [[68 85 3]\n", - "# [93 26 25]]\n", - "\n", - "print(x[..., 2])\n", - "# [[75 31]\n", - "# [ 3 25]]\n", - "```\n", - "\n", - "\n", - "\n", - "## 整数数组索引\n", - "\n", - "【例】方括号内传入多个索引值，可以同时选择多个元素。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n", - "r = [0, 1, 2]\n", - "print(x[r])\n", - "# [1 2 3]\n", - "\n", - "r = [0, 1, -1]\n", - "print(x[r])\n", - "# [1 2 8]\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "r = [0, 1, 2]\n", - "print(x[r])\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]]\n", - "\n", - "r = [0, 1, -1]\n", - "print(x[r])\n", - "\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [31 32 33 34 35]]\n", - "\n", - "r = [0, 1, 2]\n", - "c = [2, 3, 4]\n", - "y = x[r, c]\n", - "print(y)\n", - "# [13 19 25]\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n", - "r = np.array([[0, 1], [3, 4]])\n", - "print(x[r])\n", - "# [[1 2]\n", - "# [4 5]]\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "r = np.array([[0, 1], [3, 4]])\n", - "print(x[r])\n", - "# [[[11 12 13 14 15]\n", - "# [16 17 18 19 20]]\n", - "#\n", - "# [[26 27 28 29 30]\n", - "# [31 32 33 34 35]]]\n", - "\n", - "# 获取了 5X5 数组中的四个角的元素。\n", - "# 行索引是 [0,0] 和 [4,4]，而列索引是 [0,4] 和 [0,4]。\n", - "r = np.array([[0, 0], [4, 4]])\n", - "c = np.array([[0, 4], [0, 4]])\n", - "y = x[r, c]\n", - "print(y)\n", - "# [[11 15]\n", - "# [31 35]]\n", - "```\n", - "\n", - "【例】可以借助切片`:`与整数数组组合。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "y = x[0:3, [1, 2, 2]]\n", - "print(y)\n", - "# [[12 13 13]\n", - "# [17 18 18]\n", - "# [22 23 23]]\n", - "```\n", - "\n", - "- `numpy. take(a, indices, axis=None, out=None, mode='raise')` Take elements from an array along an axis.\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n", - "r = [0, 1, 2]\n", - "print(np.take(x, r))\n", - "# [1 2 3]\n", - "\n", - "r = [0, 1, -1]\n", - "print(np.take(x, r))\n", - "# [1 2 8]\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "r = [0, 1, 2]\n", - "print(np.take(x, r, axis=0))\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]]\n", - "\n", - "r = [0, 1, -1]\n", - "print(np.take(x, r, axis=0))\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [31 32 33 34 35]]\n", - "\n", - "r = [0, 1, 2]\n", - "c = [2, 3, 4]\n", - "y = np.take(x, [r, c])\n", - "print(y)\n", - "# [[11 12 13]\n", - "# [13 14 15]]\n", - "```\n", - "\n", - "\n", - "## 布尔索引\n", - "\n", - "我们可以通过一个布尔数组来索引目标数组。\n", - "\n", - "【例】\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n", - "y = x > 5\n", - "print(y)\n", - "# [False False False False False True True True]\n", - "print(x[x > 5])\n", - "# [6 7 8]\n", - "\n", - "x = np.array([np.nan, 1, 2, np.nan, 3, 4, 5])\n", - "y = np.logical_not(np.isnan(x))\n", - "print(x[y])\n", - "# [1. 2. 3. 4. 5.]\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = x > 25\n", - "print(y)\n", - "# [[False False False False False]\n", - "# [False False False False False]\n", - "# [False False False False False]\n", - "# [ True True True True True]\n", - "# [ True True True True True]]\n", - "print(x[x > 25])\n", - "# [26 27 28 29 30 31 32 33 34 35]\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "x = np.linspace(0, 2 * np.pi, 50)\n", - "y = np.sin(x)\n", - "print(len(x)) # 50\n", - "plt.plot(x, y)\n", - "\n", - "mask = y >= 0\n", - "print(len(x[mask])) # 25\n", - "print(mask)\n", - "'''\n", - "[ True True True True True True True True True True True True\n", - " True True True True True True True True True True True True\n", - " True False False False False False False False False False False False\n", - " False False False False False False False False False False False False\n", - " False False]\n", - "'''\n", - "plt.plot(x[mask], y[mask], 'bo')\n", - "\n", - "mask = np.logical_and(y >= 0, x <= np.pi / 2)\n", - "print(mask)\n", - "'''\n", - "[ True True True True True True True True True True True True\n", - " True False False False False False False False False False False False\n", - " False False False False False False False False False False False False\n", - " False False False False False False False False False False False False\n", - " False False]\n", - "'''\n", - "\n", - "plt.plot(x[mask], y[mask], 'go')\n", - "plt.show()\n", - "```\n", - "\n", - "![](https://img-blog.csdnimg.cn/20191109183704335.png)\n", - "\n", - "我们利用这些条件来选择图上的不同点。蓝色点（在图中还包括绿点，但绿点掩盖了蓝色点），显示值 大于0 的所有点。绿色点表示值 大于0 且 小于0.5π 的所有点。\n", - "\n", - "\n", - "\n", - "\n", - "---\n", - "# 数组迭代\n", - "\n", - "除了for循环，Numpy 还提供另外一种更为优雅的遍历方法。\n", - "\n", - "- `apply_along_axis(func1d, axis, arr)` Apply a function to 1-D slices along the given axis.\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "y = np.apply_along_axis(np.sum, 0, x)\n", - "print(y) # [105 110 115 120 125]\n", - "y = np.apply_along_axis(np.sum, 1, x)\n", - "print(y) # [ 65 90 115 140 165]\n", - "\n", - "y = np.apply_along_axis(np.mean, 0, x)\n", - "print(y) # [21. 22. 23. 24. 25.]\n", - "y = np.apply_along_axis(np.mean, 1, x)\n", - "print(y) # [13. 18. 23. 28. 33.]\n", - "\n", - "\n", - "def my_func(x):\n", - " return (x[0] + x[-1]) * 0.5\n", - "\n", - "\n", - "y = np.apply_along_axis(my_func, 0, x)\n", - "print(y) # [21. 22. 23. 24. 25.]\n", - "y = np.apply_along_axis(my_func, 1, x)\n", - "print(y) # [13. 18. 23. 28. 33.]\n", - "```\n", - "\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python35", - "language": "python", - "name": "python35" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "307.2px" - }, - "toc_section_display": true, - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/IntroductionToNumpy/06 数组操作.ipynb b/IntroductionToNumpy/06 数组操作.ipynb deleted file mode 100644 index 27be9bb..0000000 --- a/IntroductionToNumpy/06 数组操作.ipynb +++ /dev/null @@ -1,1052 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = x.flat\n", - "print(y)\n", - "# \n", - "for i in y:\n", - " print(i, end=' ')\n", - "# 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2020-08-30T08:26:03.534513Z", - "start_time": "2020-08-30T08:26:03.528496Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34\n", - " 35]\n", - "[11 12 13 0 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34\n", - " 35]\n", - "[[11 12 13 0 15]\n", - " [16 17 18 19 20]\n", - " [21 22 23 24 25]\n", - " [26 27 28 29 30]\n", - " [31 32 33 34 35]]\n" - ] - } - ], - "source": [ - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "y = np.ravel(x, order='A')\n", - "print(y)\n", - "# [11 16 21 26 31 12 17 22 27 32 13 18 23 28 33 14 19 24 29 34 15 20 25 30\n", - "# 35]\n", - "\n", - "y[3] = 0\n", - "print(y)\n", - "print(x)\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2020-08-30T09:05:52.576693Z", - "start_time": "2020-08-30T09:05:52.565776Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1 2 3 7 8 9]\n" - ] - }, - { - "ename": "AxisError", - "evalue": "axis 1 is out of bounds for array of dimension 1", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAxisError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;31m# [1 2 3 7 8 9]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;31m# [1 2 3 7 8 9]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3])\n", - "y = np.array([7, 8, 9])\n", - "z = np.concatenate([x, y])\n", - "print(z)\n", - "# [1 2 3 7 8 9]\n", - "\n", - "z = np.concatenate([x, y], axis=1)\n", - "print(z)\n", - "# [1 2 3 7 8 9]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 数组操作\n", - "\n", - "## 更改形状\n", - "\n", - "在对数组进行操作时，为了满足格式和计算的要求通常会改变其形状。\n", - "\n", - "- `numpy.ndarray.shape`表示数组的维度，返回一个元组，这个元组的长度就是维度的数目，即 `ndim` 属性(秩)。\n", - "\n", - "【例】通过修改 shap 属性来改变数组的形状。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 9, 4, 5, 6, 7, 8])\n", - "print(x.shape) # (8,)\n", - "x.shape = [2, 4]\n", - "print(x)\n", - "# [[1 2 9 4]\n", - "# [5 6 7 8]]\n", - "```\n", - "\n", - "- `numpy.ndarray.flat` 将数组转换为一维的迭代器，可以用for访问数组每一个元素。\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = x.flat\n", - "print(y)\n", - "# \n", - "for i in y:\n", - " print(i, end=' ')\n", - "# 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n", - "\n", - "y[3] = 0\n", - "print(end='\\n')\n", - "print(x)\n", - "# [[11 12 13 0 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]]\n", - "```\n", - "\n", - "- `numpy.ndarray.flatten([order='C'])` 将数组的副本转换为一维数组，并返回。\n", - " - order：'C' -- 按行，'F' -- 按列，'A' -- 原顺序，'k' -- 元素在内存中的出现顺序。(简记)\n", - " - order：{'C / F，'A，K}，可选使用此索引顺序读取a的元素。'C'意味着以行大的C风格顺序对元素进行索引，最后一个轴索引会更改F表示以列大的Fortran样式顺序索引元素，其中第一个索引变化最快，最后一个索引变化最快。请注意，'C'和'F'选项不考虑基础数组的内存布局，仅引用轴索引的顺序.A'表示如果a为Fortran，则以类似Fortran的索引顺序读取元素在内存中连续，否则类似C的顺序。“ K”表示按照步序在内存中的顺序读取元素，但步幅为负时反转数据除外。默认情况下，使用Cindex顺序。\n", - "\n", - "【例】`flatten()`函数返回的是拷贝。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = x.flatten()\n", - "print(y)\n", - "# [11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34\n", - "# 35]\n", - "\n", - "y[3] = 0\n", - "print(x)\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]]\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "y = x.flatten(order='F')\n", - "print(y)\n", - "# [11 16 21 26 31 12 17 22 27 32 13 18 23 28 33 14 19 24 29 34 15 20 25 30\n", - "# 35]\n", - "\n", - "y[3] = 0\n", - "print(x)\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]]\n", - "```\n", - "\n", - "\n", - "\n", - "- `numpy.ravel(a, order='C')`Return a contiguous flattened array.\n", - "\n", - "【例】`ravel()`返回的是视图。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.ravel(x)\n", - "print(y)\n", - "# [11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34\n", - "# 35]\n", - "\n", - "y[3] = 0\n", - "print(x)\n", - "# [[11 12 13 0 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]]\n", - "\n", - "【例】order=F 就是拷贝\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "y = np.ravel(x, order='F')\n", - "print(y)\n", - "# [11 16 21 26 31 12 17 22 27 32 13 18 23 28 33 14 19 24 29 34 15 20 25 30\n", - "# 35]\n", - "\n", - "y[3] = 0\n", - "print(x)\n", - "# [[11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [31 32 33 34 35]]\n", - "```\n", - "\n", - "- `numpy.reshape(a, newshape[, order='C'])`在不更改数据的情况下为数组赋予新的形状。\n", - "\n", - "【例】`reshape()`函数当参数`newshape = [rows,-1]`时，将根据行数自动确定列数。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.arange(12)\n", - "y = np.reshape(x, [3, 4])\n", - "print(y.dtype) # int32\n", - "print(y)\n", - "# [[ 0 1 2 3]\n", - "# [ 4 5 6 7]\n", - "# [ 8 9 10 11]]\n", - "\n", - "y = np.reshape(x, [3, -1])\n", - "print(y)\n", - "# [[ 0 1 2 3]\n", - "# [ 4 5 6 7]\n", - "# [ 8 9 10 11]]\n", - "\n", - "y = np.reshape(x,[-1,3])\n", - "print(y)\n", - "# [[ 0 1 2]\n", - "# [ 3 4 5]\n", - "# [ 6 7 8]\n", - "# [ 9 10 11]]\n", - "\n", - "y[0, 1] = 10\n", - "print(x)\n", - "# [ 0 10 2 3 4 5 6 7 8 9 10 11]（改变x去reshape后y中的值，x对应元素也改变）\n", - "```\n", - "\n", - "【例】`reshape()`函数当参数`newshape = -1`时，表示将数组降为一维。\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.random.randint(12, size=[2, 2, 3])\n", - "print(x)\n", - "# [[[11 9 1]\n", - "# [ 1 10 3]]\n", - "# \n", - "# [[ 0 6 1]\n", - "# [ 4 11 3]]]\n", - "y = np.reshape(x, -1)\n", - "print(y)\n", - "# [11 9 1 1 10 3 0 6 1 4 11 3]\n", - "```\n", - "\n", - "\n", - "\n", - "## 数组转置\n", - "\n", - "\n", - "- `numpy.transpose(a, axes=None)` Permute the dimensions of an array.\n", - "- `numpy.ndarray.T` Same as `self.transpose()`, except that self is returned if `self.ndim < 2`.\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.random.rand(5, 5) * 10\n", - "x = np.around(x, 2)\n", - "print(x)\n", - "# [[6.74 8.46 6.74 5.45 1.25]\n", - "# [3.54 3.49 8.62 1.94 9.92]\n", - "# [5.03 7.22 1.6 8.7 0.43]\n", - "# [7.5 7.31 5.69 9.67 7.65]\n", - "# [1.8 9.52 2.78 5.87 4.14]]\n", - "y = x.T\n", - "print(y)\n", - "# [[6.74 3.54 5.03 7.5 1.8 ]\n", - "# [8.46 3.49 7.22 7.31 9.52]\n", - "# [6.74 8.62 1.6 5.69 2.78]\n", - "# [5.45 1.94 8.7 9.67 5.87]\n", - "# [1.25 9.92 0.43 7.65 4.14]]\n", - "y = np.transpose(x)\n", - "print(y)\n", - "# [[6.74 3.54 5.03 7.5 1.8 ]\n", - "# [8.46 3.49 7.22 7.31 9.52]\n", - "# [6.74 8.62 1.6 5.69 2.78]\n", - "# [5.45 1.94 8.7 9.67 5.87]\n", - "# [1.25 9.92 0.43 7.65 4.14]]\n", - "```\n", - "\n", - "\n", - "## 更改维度\n", - "\n", - "当创建一个数组之后，还可以给它增加一个维度，这在矩阵计算中经常会用到。\n", - "\n", - "- `numpy.newaxis = None` `None`的别名，对索引数组很有用。\n", - "\n", - "【例】很多工具包在进行计算时都会先判断输入数据的维度是否满足要求，如果输入数据达不到指定的维度时，可以使用`newaxis`参数来增加一个维度。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 9, 4, 5, 6, 7, 8])\n", - "print(x.shape) # (8,)\n", - "print(x) # [1 2 9 4 5 6 7 8]\n", - "\n", - "y = x[np.newaxis, :]\n", - "print(y.shape) # (1, 8)\n", - "print(y) # [[1 2 9 4 5 6 7 8]]\n", - "\n", - "y = x[:, np.newaxis]\n", - "print(y.shape) # (8, 1)\n", - "print(y)\n", - "# [[1]\n", - "# [2]\n", - "# [9]\n", - "# [4]\n", - "# [5]\n", - "# [6]\n", - "# [7]\n", - "# [8]]\n", - "```\n", - "\n", - "- `numpy.squeeze(a, axis=None)` 从数组的形状中删除单维度条目，即把shape中为1的维度去掉。\n", - " - `a`表示输入的数组；\n", - " - `axis`用于指定需要删除的维度，但是指定的维度必须为单维度，否则将会报错；\n", - "\n", - "在机器学习和深度学习中，通常算法的结果是可以表示向量的数组（即包含两对或以上的方括号形式[[]]），如果直接利用这个数组进行画图可能显示界面为空（见后面的示例）。我们可以利用`squeeze()`函数将表示向量的数组转换为秩为1的数组，这样利用 matplotlib 库函数画图时，就可以正常的显示结果了。\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.arange(10)\n", - "print(x.shape) # (10,)\n", - "x = x[np.newaxis, :]\n", - "print(x.shape) # (1, 10)\n", - "y = np.squeeze(x)\n", - "print(y.shape) # (10,)\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[[0], [1], [2]]])\n", - "print(x.shape) # (1, 3, 1)\n", - "print(x)\n", - "# [[[0]\n", - "# [1]\n", - "# [2]]]\n", - "\n", - "y = np.squeeze(x)\n", - "print(y.shape) # (3,)\n", - "print(y) # [0 1 2]\n", - "\n", - "y = np.squeeze(x, axis=0)\n", - "print(y.shape) # (3, 1)\n", - "print(y)\n", - "# [[0]\n", - "# [1]\n", - "# [2]]\n", - "\n", - "y = np.squeeze(x, axis=2)\n", - "print(y.shape) # (1, 3)\n", - "print(y) # [[0 1 2]]\n", - "\n", - "y = np.squeeze(x, axis=1)\n", - "# ValueError: cannot select an axis to squeeze out which has size not equal to one\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "x = np.array([[1, 4, 9, 16, 25]])\n", - "print(x.shape) # (1, 5)\n", - "plt.plot(x)\n", - "plt.show()\n", - "```\n", - "![](https://img-blog.csdnimg.cn/20200528095957317.png)\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "x = np.array([[1, 4, 9, 16, 25]])\n", - "x = np.squeeze(x)\n", - "print(x.shape) # (5, )\n", - "plt.plot(x)\n", - "plt.show()\n", - "```\n", - "\n", - "![](https://img-blog.csdnimg.cn/20200528100221464.png)\n", - "\n", - "## 数组组合\n", - "\n", - "如果要将两份数据组合到一起，就需要拼接操作。\n", - "\n", - "- `numpy.concatenate((a1, a2, ...), axis=0, out=None)` Join a sequence of arrays along an existing axis.\n", - "\n", - "【例】连接沿现有轴的数组序列（原来x，y都是一维的，拼接后的结果也是一维的）。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3])\n", - "y = np.array([7, 8, 9])\n", - "z = np.concatenate([x, y])\n", - "print(z)\n", - "# [1 2 3 7 8 9]\n", - "\n", - "z = np.concatenate([x, y], axis=0)\n", - "print(z)\n", - "# [1 2 3 7 8 9]\n", - "```\n", - "\n", - "【例】原来x，y都是二维的，拼接后的结果也是二维的。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3]).reshape(1, 3)\n", - "y = np.array([7, 8, 9]).reshape(1, 3)\n", - "z = np.concatenate([x, y])\n", - "print(z)\n", - "# [[ 1 2 3]\n", - "# [ 7 8 9]]\n", - "z = np.concatenate([x, y], axis=0)\n", - "print(z)\n", - "# [[ 1 2 3]\n", - "# [ 7 8 9]]\n", - "z = np.concatenate([x, y], axis=1)\n", - "print(z)\n", - "# [[ 1 2 3 7 8 9]]\n", - "```\n", - "\n", - "【例】x，y在原来的维度上进行拼接。\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[1, 2, 3], [4, 5, 6]])\n", - "y = np.array([[7, 8, 9], [10, 11, 12]])\n", - "z = np.concatenate([x, y])\n", - "print(z)\n", - "# [[ 1 2 3]\n", - "# [ 4 5 6]\n", - "# [ 7 8 9]\n", - "# [10 11 12]]\n", - "z = np.concatenate([x, y], axis=0)\n", - "print(z)\n", - "# [[ 1 2 3]\n", - "# [ 4 5 6]\n", - "# [ 7 8 9]\n", - "# [10 11 12]]\n", - "z = np.concatenate([x, y], axis=1)\n", - "print(z)\n", - "# [[ 1 2 3 7 8 9]\n", - "# [ 4 5 6 10 11 12]]\n", - "```\n", - "\n", - "- `numpy.stack(arrays, axis=0, out=None)`Join a sequence of arrays along a new axis.\n", - "\n", - "\n", - "【例】沿着新的轴加入一系列数组（stack为增加维度的拼接）。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3])\n", - "y = np.array([7, 8, 9])\n", - "z = np.stack([x, y])\n", - "print(z.shape) # (2, 3)\n", - "print(z)\n", - "# [[1 2 3]\n", - "# [7 8 9]]\n", - "\n", - "z = np.stack([x, y], axis=1)\n", - "print(z.shape) # (3, 2)\n", - "print(z)\n", - "# [[1 7]\n", - "# [2 8]\n", - "# [3 9]]\n", - "```\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3]).reshape(1, 3)\n", - "y = np.array([7, 8, 9]).reshape(1, 3)\n", - "z = np.stack([x, y])\n", - "print(z.shape) # (2, 1, 3)\n", - "print(z)\n", - "# [[[1 2 3]]\n", - "#\n", - "# [[7 8 9]]]\n", - "\n", - "z = np.stack([x, y], axis=1)\n", - "print(z.shape) # (1, 2, 3)\n", - "print(z)\n", - "# [[[1 2 3]\n", - "# [7 8 9]]]\n", - "\n", - "z = np.stack([x, y], axis=2)\n", - "print(z.shape) # (1, 3, 2)\n", - "print(z)\n", - "# [[[1 7]\n", - "# [2 8]\n", - "# [3 9]]]\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[1, 2, 3], [4, 5, 6]])\n", - "y = np.array([[7, 8, 9], [10, 11, 12]])\n", - "z = np.stack([x, y])\n", - "print(z.shape) # (2, 2, 3)\n", - "print(z)\n", - "# [[[ 1 2 3]\n", - "# [ 4 5 6]]\n", - "# \n", - "# [[ 7 8 9]\n", - "# [10 11 12]]]\n", - "\n", - "z = np.stack([x, y], axis=1)\n", - "print(z.shape) # (2, 2, 3)\n", - "print(z)\n", - "# [[[ 1 2 3]\n", - "# [ 7 8 9]]\n", - "# \n", - "# [[ 4 5 6]\n", - "# [10 11 12]]]\n", - "\n", - "z = np.stack([x, y], axis=2)\n", - "print(z.shape) # (2, 3, 2)\n", - "print(z)\n", - "# [[[ 1 7]\n", - "# [ 2 8]\n", - "# [ 3 9]]\n", - "# \n", - "# [[ 4 10]\n", - "# [ 5 11]\n", - "# [ 6 12]]]\n", - "```\n", - "\n", - "\n", - "- `numpy.vstack(tup)`Stack arrays in sequence vertically (row wise).\n", - "- `numpy.hstack(tup)`Stack arrays in sequence horizontally (column wise). \n", - "\n", - "\n", - "【例】一维的情况。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3])\n", - "y = np.array([7, 8, 9])\n", - "z = np.vstack((x, y))\n", - "print(z.shape) # (2, 3)\n", - "print(z)\n", - "# [[1 2 3]\n", - "# [7 8 9]]\n", - "\n", - "z = np.stack([x, y])\n", - "print(z.shape) # (2, 3)\n", - "print(z)\n", - "# [[1 2 3]\n", - "# [7 8 9]]\n", - "\n", - "z = np.hstack((x, y))\n", - "print(z.shape) # (6,)\n", - "print(z)\n", - "# [1 2 3 7 8 9]\n", - "\n", - "z = np.concatenate((x, y))\n", - "print(z.shape) # (6,)\n", - "print(z) # [1 2 3 7 8 9]\n", - "```\n", - "\n", - "\n", - "\n", - "【例】二维的情况。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3]).reshape(1, 3)\n", - "y = np.array([7, 8, 9]).reshape(1, 3)\n", - "z = np.vstack((x, y))\n", - "print(z.shape) # (2, 3)\n", - "print(z)\n", - "# [[1 2 3]\n", - "# [7 8 9]]\n", - "\n", - "z = np.concatenate((x, y), axis=0)\n", - "print(z.shape) # (2, 3)\n", - "print(z)\n", - "# [[1 2 3]\n", - "# [7 8 9]]\n", - "\n", - "z = np.hstack((x, y))\n", - "print(z.shape) # (1, 6)\n", - "print(z)\n", - "# [[ 1 2 3 7 8 9]]\n", - "\n", - "z = np.concatenate((x, y), axis=1)\n", - "print(z.shape) # (1, 6)\n", - "print(z)\n", - "# [[1 2 3 7 8 9]]\n", - "```\n", - "\n", - "【例】二维的情况。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[1, 2, 3], [4, 5, 6]])\n", - "y = np.array([[7, 8, 9], [10, 11, 12]])\n", - "z = np.vstack((x, y))\n", - "print(z.shape) # (4, 3)\n", - "print(z)\n", - "# [[ 1 2 3]\n", - "# [ 4 5 6]\n", - "# [ 7 8 9]\n", - "# [10 11 12]]\n", - "\n", - "z = np.concatenate((x, y), axis=0)\n", - "print(z.shape) # (4, 3)\n", - "print(z)\n", - "# [[ 1 2 3]\n", - "# [ 4 5 6]\n", - "# [ 7 8 9]\n", - "# [10 11 12]]\n", - "\n", - "z = np.hstack((x, y))\n", - "print(z.shape) # (2, 6)\n", - "print(z)\n", - "# [[ 1 2 3 7 8 9]\n", - "# [ 4 5 6 10 11 12]]\n", - "\n", - "z = np.concatenate((x, y), axis=1)\n", - "print(z.shape) # (2, 6)\n", - "print(z)\n", - "# [[ 1 2 3 7 8 9]\n", - "# [ 4 5 6 10 11 12]]\n", - "```\n", - "\n", - "`hstack(),vstack()`分别表示水平和竖直的拼接方式。在数据维度等于1时，比较特殊。而当维度大于或等于2时，它们的作用相当于`concatenate`，用于在已有轴上进行操作。\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.hstack([np.array([1, 2, 3, 4]), 5])\n", - "print(a) # [1 2 3 4 5]\n", - "\n", - "a = np.concatenate([np.array([1, 2, 3, 4]), 5])\n", - "print(a)\n", - "# all the input arrays must have same number of dimensions, but the array at index 0 has 1 dimension(s) and the array at index 1 has 0 dimension(s)\n", - "```\n", - "\n", - "\n", - "## 数组拆分\n", - "\n", - "- `numpy.split(ary, indices_or_sections, axis=0)` Split an array into multiple sub-arrays as views into ary.\n", - "\n", - "【例】拆分数组。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14],\n", - " [16, 17, 18, 19],\n", - " [21, 22, 23, 24]])\n", - "y = np.split(x, [1, 3])\n", - "print(y)\n", - "# [array([[11, 12, 13, 14]]), array([[16, 17, 18, 19],\n", - "# [21, 22, 23, 24]]), array([], shape=(0, 4), dtype=int32)]\n", - "\n", - "y = np.split(x, [1, 3], axis=1)\n", - "print(y)\n", - "# [array([[11],\n", - "# [16],\n", - "# [21]]), array([[12, 13],\n", - "# [17, 18],\n", - "# [22, 23]]), array([[14],\n", - "# [19],\n", - "# [24]])]\n", - "```\n", - "\n", - "\n", - "\n", - "- `numpy.vsplit(ary, indices_or_sections)` Split an array into multiple sub-arrays vertically (row-wise).\n", - "\n", - "【例】垂直切分是把数组按照高度切分\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14],\n", - " [16, 17, 18, 19],\n", - " [21, 22, 23, 24]])\n", - "y = np.vsplit(x, 3)\n", - "print(y)\n", - "# [array([[11, 12, 13, 14]]), array([[16, 17, 18, 19]]), array([[21, 22, 23, 24]])]\n", - "\n", - "y = np.split(x, 3)\n", - "print(y)\n", - "# [array([[11, 12, 13, 14]]), array([[16, 17, 18, 19]]), array([[21, 22, 23, 24]])]\n", - "\n", - "\n", - "y = np.vsplit(x, [1])\n", - "print(y)\n", - "# [array([[11, 12, 13, 14]]), array([[16, 17, 18, 19],\n", - "# [21, 22, 23, 24]])]\n", - "\n", - "y = np.split(x, [1])\n", - "print(y)\n", - "# [array([[11, 12, 13, 14]]), array([[16, 17, 18, 19],\n", - "# [21, 22, 23, 24]])]\n", - "\n", - "\n", - "y = np.vsplit(x, [1, 3])\n", - "print(y)\n", - "# [array([[11, 12, 13, 14]]), array([[16, 17, 18, 19],\n", - "# [21, 22, 23, 24]]), array([], shape=(0, 4), dtype=int32)]\n", - "y = np.split(x, [1, 3], axis=0)\n", - "print(y)\n", - "# [array([[11, 12, 13, 14]]), array([[16, 17, 18, 19],\n", - "# [21, 22, 23, 24]]), array([], shape=(0, 4), dtype=int32)]\n", - "```\n", - "\n", - "- `numpy.hsplit(ary, indices_or_sections)` Split an array into multiple sub-arrays horizontally (column-wise).\n", - "\n", - "\n", - "【例】水平切分是把数组按照宽度切分。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14],\n", - " [16, 17, 18, 19],\n", - " [21, 22, 23, 24]])\n", - "y = np.hsplit(x, 2)\n", - "print(y)\n", - "# [array([[11, 12],\n", - "# [16, 17],\n", - "# [21, 22]]), array([[13, 14],\n", - "# [18, 19],\n", - "# [23, 24]])]\n", - "\n", - "y = np.split(x, 2, axis=1)\n", - "print(y)\n", - "# [array([[11, 12],\n", - "# [16, 17],\n", - "# [21, 22]]), array([[13, 14],\n", - "# [18, 19],\n", - "# [23, 24]])]\n", - "\n", - "y = np.hsplit(x, [3])\n", - "print(y)\n", - "# [array([[11, 12, 13],\n", - "# [16, 17, 18],\n", - "# [21, 22, 23]]), array([[14],\n", - "# [19],\n", - "# [24]])]\n", - "\n", - "y = np.split(x, [3], axis=1)\n", - "print(y)\n", - "# [array([[11, 12, 13],\n", - "# [16, 17, 18],\n", - "# [21, 22, 23]]), array([[14],\n", - "# [19],\n", - "# [24]])]\n", - "\n", - "y = np.hsplit(x, [1, 3])\n", - "print(y)\n", - "# [array([[11],\n", - "# [16],\n", - "# [21]]), array([[12, 13],\n", - "# [17, 18],\n", - "# [22, 23]]), array([[14],\n", - "# [19],\n", - "# [24]])]\n", - "\n", - "y = np.split(x, [1, 3], axis=1)\n", - "print(y)\n", - "# [array([[11],\n", - "# [16],\n", - "# [21]]), array([[12, 13],\n", - "# [17, 18],\n", - "# [22, 23]]), array([[14],\n", - "# [19],\n", - "# [24]])]\n", - "```\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "## 数组平铺\n", - "\n", - "- `numpy.tile(A, reps)` Construct an array by repeating A the number of times given by reps.\n", - "\n", - "`tile`是瓷砖的意思，顾名思义，这个函数就是把数组像瓷砖一样铺展开来。\n", - "\n", - "【例】将原矩阵横向、纵向地复制。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[1, 2], [3, 4]])\n", - "print(x)\n", - "# [[1 2]\n", - "# [3 4]]\n", - "\n", - "y = np.tile(x, (1, 3))\n", - "print(y)\n", - "# [[1 2 1 2 1 2]\n", - "# [3 4 3 4 3 4]]\n", - "\n", - "y = np.tile(x, (3, 1))\n", - "print(y)\n", - "# [[1 2]\n", - "# [3 4]\n", - "# [1 2]\n", - "# [3 4]\n", - "# [1 2]\n", - "# [3 4]]\n", - "\n", - "y = np.tile(x, (3, 3))\n", - "print(y)\n", - "# [[1 2 1 2 1 2]\n", - "# [3 4 3 4 3 4]\n", - "# [1 2 1 2 1 2]\n", - "# [3 4 3 4 3 4]\n", - "# [1 2 1 2 1 2]\n", - "# [3 4 3 4 3 4]]\n", - "```\n", - "\n", - "- `numpy.repeat(a, repeats, axis=None)` Repeat elements of an array.\n", - " - `axis=0`，沿着y轴复制，实际上增加了行数。\n", - " - `axis=1`，沿着x轴复制，实际上增加了列数。\n", - " - `repeats`，可以为一个数，也可以为一个矩阵。\n", - " - `axis=None`时就会flatten当前矩阵，实际上就是变成了一个行向量。\n", - "\n", - "【例】重复数组的元素。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.repeat(3, 4)\n", - "print(x) # [3 3 3 3]\n", - "\n", - "x = np.array([[1, 2], [3, 4]])\n", - "y = np.repeat(x, 2)\n", - "print(y)\n", - "# [1 1 2 2 3 3 4 4]\n", - "\n", - "y = np.repeat(x, 2, axis=0)\n", - "print(y)\n", - "# [[1 2]\n", - "# [1 2]\n", - "# [3 4]\n", - "# [3 4]]\n", - "\n", - "y = np.repeat(x, 2, axis=1)\n", - "print(y)\n", - "# [[1 1 2 2]\n", - "# [3 3 4 4]]\n", - "\n", - "y = np.repeat(x, [2, 3], axis=0)\n", - "print(y)\n", - "# [[1 2]\n", - "# [1 2]\n", - "# [3 4]\n", - "# [3 4]\n", - "# [3 4]]\n", - "\n", - "y = np.repeat(x, [2, 3], axis=1)\n", - "print(y)\n", - "# [[1 1 2 2 2]\n", - "# [3 3 4 4 4]]\n", - "```\n", - "\n", - "---\n", - "## 添加和删除元素\n", - "\n", - "- `numpy.unique(ar, return_index=False, return_inverse=False,return_counts=False, axis=None)` Find the unique elements of an array.\n", - " - return_index：the indices of the input array that give the unique values\n", - " - return_inverse：the indices of the unique array that reconstruct the input array\n", - " - return_counts：the number of times each unique value comes up in the input array\n", - "\n", - "\n", - "\n", - "\n", - "【例】查找数组的唯一元素。\n", - "```python\n", - "a=np.array([1,1,2,3,3,4,4])\n", - "b=np.unique(a,return_counts=True)\n", - "print(b[0][list(b[1]).index(1)])\n", - "#2\n", - "```\n", - "\n", - "---\n", - "**参考文献**\n", - "- https://blog.csdn.net/csdn15698845876/article/details/73380803\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "ExecuteTime": { - "end_time": "2020-08-30T13:01:49.899843Z", - "start_time": "2020-08-30T13:01:49.895853Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n" - ] - } - ], - "source": [ - "a=np.array([1,1,2,3,3,4,4])\n", - "b=np.unique(a,return_counts=True)\n", - "print(b[0][list(b[1]).index(1)])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python35", - "language": "python", - "name": "python35" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "307.2px" - }, - "toc_section_display": true, - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/IntroductionToNumpy/07 数学函数.ipynb b/IntroductionToNumpy/07 数学函数.ipynb deleted file mode 100644 index ad1fea6..0000000 --- a/IntroductionToNumpy/07 数学函数.ipynb +++ /dev/null @@ -1,892 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2020-08-30T13:35:08.895856Z", - "start_time": "2020-08-30T13:35:08.889872Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1. 2. 3.]\n", - " [11. 12. 13.]\n", - " [21. 22. 23.]\n", - " [31. 32. 33.]]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "x = np.array([0.0, 10.0, 20.0, 30.0])\n", - "y = np.array([1.0, 2.0, 3.0])\n", - "z = x[:, np.newaxis] + y\n", - "print(z)\n", - "# [[ 1. 2. 3.]\n", - "# [11. 12. 13.]\n", - "# [21. 22. 23.]\n", - "# [31. 32. 33.]]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2020-08-30T13:39:26.777758Z", - "start_time": "2020-08-30T13:39:26.771774Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(4,)\n" - ] - }, - { - "data": { - "text/plain": [ - "(4, 1)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = np.array([0.0, 10.0, 20.0, 30.0])\n", - "print(x.shape)\n", - "x[:, np.newaxis].shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 向量化和广播\n", - "\n", - "向量化和广播这两个概念是 numpy 内部实现的基础。有了向量化，编写代码时无需使用显式循环。这些循环实际上不能省略，只不过是在内部实现，被代码中的其他结构代替。向量化的应用使得代码更简洁，可读性更强，也可以说使用了向量化方法的代码看上去更“Pythonic”。\n", - "\n", - "广播（Broadcasting）机制描述了 numpy 如何在算术运算期间处理具有不同形状的数组，让较小的数组在较大的数组上“广播”，以便它们具有兼容的形状。并不是所有的维度都要彼此兼容才符合广播机制的要求，但它们必须满足一定的条件。\n", - "\n", - "若两个数组的各维度兼容，也就是两个数组的每一维等长，或其中一个数组为 一维，那么广播机制就适用。如果这两个条件不满足，numpy就会抛出异常，说两个数组不兼容。\n", - "\n", - "总结来说，广播的规则有三个：\n", - "- 如果两个数组的维度数dim不相同，那么小维度数组的形状将会在左边补1。\n", - "- 如果shape维度不匹配，但是有维度是1，那么可以扩展维度是1的维度匹配另一个数组；\n", - "- 如果shape维度不匹配，但是没有任何一个维度是1，则匹配引发错误；\n", - "\n", - "【例】二维数组加一维数组\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.arange(4)\n", - "y = np.ones((3, 4))\n", - "print(x.shape) # (4,)\n", - "print(y.shape) # (3, 4)\n", - "\n", - "print((x + y).shape) # (3, 4)\n", - "print(x + y)\n", - "# [[1. 2. 3. 4.]\n", - "# [1. 2. 3. 4.]\n", - "# [1. 2. 3. 4.]]\n", - "```\n", - "\n", - "\n", - "【例】两个数组均需要广播\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.arange(4).reshape(4, 1)\n", - "y = np.ones(5)\n", - "\n", - "print(x.shape) # (4, 1)\n", - "print(y.shape) # (5,)\n", - "\n", - "print((x + y).shape) # (4, 5)\n", - "print(x + y)\n", - "# [[1. 1. 1. 1. 1.]\n", - "# [2. 2. 2. 2. 2.]\n", - "# [3. 3. 3. 3. 3.]\n", - "# [4. 4. 4. 4. 4.]]\n", - "\n", - "x = np.array([0.0, 10.0, 20.0, 30.0])\n", - "y = np.array([1.0, 2.0, 3.0])\n", - "z = x[:, np.newaxis] + y\n", - "print(z)\n", - "# [[ 1. 2. 3.]\n", - "# [11. 12. 13.]\n", - "# [21. 22. 23.]\n", - "# [31. 32. 33.]]\n", - "```\n", - "\n", - "【例】不匹配报错的例子\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.arange(4)\n", - "y = np.ones(5)\n", - "\n", - "print(x.shape) # (4,)\n", - "print(y.shape) # (5,)\n", - "\n", - "print(x + y)\n", - "# ValueError: operands could not be broadcast together with shapes (4,) (5,) \n", - "```\n", - "\n", - "\n", - "---\n", - "# 数学函数\n", - "\n", - "\n", - "## 算数运算\n", - "\n", - "- `numpy.add(x1, x2, *args, **kwargs)` Add arguments element-wise.\n", - "- `numpy.subtract(x1, x2, *args, **kwargs)` Subtract arguments element-wise.\n", - "- `numpy.multiply(x1, x2, *args, **kwargs)` Multiply arguments element-wise.\n", - "- `numpy.divide(x1, x2, *args, **kwargs)` Returns a true division of the inputs, element-wise.\n", - "- `numpy.floor_divide(x1, x2, *args, **kwargs)` Return the largest integer smaller or equal to the division of the inputs.\n", - "- `numpy.power(x1, x2, *args, **kwargs)` First array elements raised to powers from second array, element-wise.\n", - "\n", - "在 numpy 中对以上函数进行了运算符的重载，且运算符为 **元素级**。也就是说，它们只用于位置相同的元素之间，所得到的运算结果组成一个新的数组。\n", - "\n", - "【例】注意 numpy 的广播规则。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n", - "y = x + 1\n", - "print(y)\n", - "print(np.add(x, 1))\n", - "# [2 3 4 5 6 7 8 9]\n", - "\n", - "y = x - 1\n", - "print(y)\n", - "print(np.subtract(x, 1))\n", - "# [0 1 2 3 4 5 6 7]\n", - "\n", - "y = x * 2\n", - "print(y)\n", - "print(np.multiply(x, 2))\n", - "# [ 2 4 6 8 10 12 14 16]\n", - "\n", - "y = x / 2\n", - "print(y)\n", - "print(np.divide(x, 2))\n", - "# [0.5 1. 1.5 2. 2.5 3. 3.5 4. ]\n", - "\n", - "y = x // 2\n", - "print(y)\n", - "print(np.floor_divide(x, 2))\n", - "# [0 1 1 2 2 3 3 4]\n", - "\n", - "y = x ** 2\n", - "print(y)\n", - "print(np.power(x, 2))\n", - "# [ 1 4 9 16 25 36 49 64]\n", - "```\n", - "\n", - "\n", - "\n", - "\n", - "【例】注意 numpy 的广播规则。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = x + 1\n", - "print(y)\n", - "print(np.add(x, 1))\n", - "# [[12 13 14 15 16]\n", - "# [17 18 19 20 21]\n", - "# [22 23 24 25 26]\n", - "# [27 28 29 30 31]\n", - "# [32 33 34 35 36]]\n", - "\n", - "y = x - 1\n", - "print(y)\n", - "print(np.subtract(x, 1))\n", - "# [[10 11 12 13 14]\n", - "# [15 16 17 18 19]\n", - "# [20 21 22 23 24]\n", - "# [25 26 27 28 29]\n", - "# [30 31 32 33 34]]\n", - "\n", - "y = x * 2\n", - "print(y)\n", - "print(np.multiply(x, 2))\n", - "# [[22 24 26 28 30]\n", - "# [32 34 36 38 40]\n", - "# [42 44 46 48 50]\n", - "# [52 54 56 58 60]\n", - "# [62 64 66 68 70]]\n", - "\n", - "y = x / 2\n", - "print(y)\n", - "print(np.divide(x, 2))\n", - "# [[ 5.5 6. 6.5 7. 7.5]\n", - "# [ 8. 8.5 9. 9.5 10. ]\n", - "# [10.5 11. 11.5 12. 12.5]\n", - "# [13. 13.5 14. 14.5 15. ]\n", - "# [15.5 16. 16.5 17. 17.5]]\n", - "\n", - "y = x // 2\n", - "print(y)\n", - "print(np.floor_divide(x, 2))\n", - "# [[ 5 6 6 7 7]\n", - "# [ 8 8 9 9 10]\n", - "# [10 11 11 12 12]\n", - "# [13 13 14 14 15]\n", - "# [15 16 16 17 17]]\n", - "\n", - "y = x ** 2\n", - "print(y)\n", - "print(np.power(x, 2))\n", - "# [[ 121 144 169 196 225]\n", - "# [ 256 289 324 361 400]\n", - "# [ 441 484 529 576 625]\n", - "# [ 676 729 784 841 900]\n", - "# [ 961 1024 1089 1156 1225]]\n", - "```\n", - "\n", - "【例】注意 numpy 的广播规则。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "y = np.arange(1, 6)\n", - "print(y)\n", - "# [1 2 3 4 5]\n", - "\n", - "z = x + y\n", - "print(z)\n", - "print(np.add(x, y))\n", - "# [[12 14 16 18 20]\n", - "# [17 19 21 23 25]\n", - "# [22 24 26 28 30]\n", - "# [27 29 31 33 35]\n", - "# [32 34 36 38 40]]\n", - "\n", - "z = x - y\n", - "print(z)\n", - "print(np.subtract(x, y))\n", - "# [[10 10 10 10 10]\n", - "# [15 15 15 15 15]\n", - "# [20 20 20 20 20]\n", - "# [25 25 25 25 25]\n", - "# [30 30 30 30 30]]\n", - "\n", - "z = x * y\n", - "print(z)\n", - "print(np.multiply(x, y))\n", - "# [[ 11 24 39 56 75]\n", - "# [ 16 34 54 76 100]\n", - "# [ 21 44 69 96 125]\n", - "# [ 26 54 84 116 150]\n", - "# [ 31 64 99 136 175]]\n", - "\n", - "z = x / y\n", - "print(z)\n", - "print(np.divide(x, y))\n", - "# [[11. 6. 4.33333333 3.5 3. ]\n", - "# [16. 8.5 6. 4.75 4. ]\n", - "# [21. 11. 7.66666667 6. 5. ]\n", - "# [26. 13.5 9.33333333 7.25 6. ]\n", - "# [31. 16. 11. 8.5 7. ]]\n", - "\n", - "z = x // y\n", - "print(z)\n", - "print(np.floor_divide(x, y))\n", - "# [[11 6 4 3 3]\n", - "# [16 8 6 4 4]\n", - "# [21 11 7 6 5]\n", - "# [26 13 9 7 6]\n", - "# [31 16 11 8 7]]\n", - "\n", - "z = x ** np.full([1, 5], 2)\n", - "print(z)\n", - "print(np.power(x, np.full([5, 5], 2)))\n", - "# [[ 121 144 169 196 225]\n", - "# [ 256 289 324 361 400]\n", - "# [ 441 484 529 576 625]\n", - "# [ 676 729 784 841 900]\n", - "# [ 961 1024 1089 1156 1225]]\n", - "```\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "y = np.arange(1, 26).reshape([5, 5])\n", - "print(y)\n", - "# [[ 1 2 3 4 5]\n", - "# [ 6 7 8 9 10]\n", - "# [11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]]\n", - "\n", - "z = x + y\n", - "print(z)\n", - "print(np.add(x, y))\n", - "# [[12 14 16 18 20]\n", - "# [22 24 26 28 30]\n", - "# [32 34 36 38 40]\n", - "# [42 44 46 48 50]\n", - "# [52 54 56 58 60]]\n", - "\n", - "z = x - y\n", - "print(z)\n", - "print(np.subtract(x, y))\n", - "# [[10 10 10 10 10]\n", - "# [10 10 10 10 10]\n", - "# [10 10 10 10 10]\n", - "# [10 10 10 10 10]\n", - "# [10 10 10 10 10]]\n", - "\n", - "z = x * y\n", - "print(z)\n", - "print(np.multiply(x, y))\n", - "# [[ 11 24 39 56 75]\n", - "# [ 96 119 144 171 200]\n", - "# [231 264 299 336 375]\n", - "# [416 459 504 551 600]\n", - "# [651 704 759 816 875]]\n", - "\n", - "z = x / y\n", - "print(z)\n", - "print(np.divide(x, y))\n", - "# [[11. 6. 4.33333333 3.5 3. ]\n", - "# [ 2.66666667 2.42857143 2.25 2.11111111 2. ]\n", - "# [ 1.90909091 1.83333333 1.76923077 1.71428571 1.66666667]\n", - "# [ 1.625 1.58823529 1.55555556 1.52631579 1.5 ]\n", - "# [ 1.47619048 1.45454545 1.43478261 1.41666667 1.4 ]]\n", - "\n", - "z = x // y\n", - "print(z)\n", - "print(np.floor_divide(x, y))\n", - "# [[11 6 4 3 3]\n", - "# [ 2 2 2 2 2]\n", - "# [ 1 1 1 1 1]\n", - "# [ 1 1 1 1 1]\n", - "# [ 1 1 1 1 1]]\n", - "\n", - "z = x ** np.full([5, 5], 2)\n", - "print(z)\n", - "print(np.power(x, np.full([5, 5], 2)))\n", - "# [[ 121 144 169 196 225]\n", - "# [ 256 289 324 361 400]\n", - "# [ 441 484 529 576 625]\n", - "# [ 676 729 784 841 900]\n", - "# [ 961 1024 1089 1156 1225]]\n", - "```\n", - "\n", - "\n", - "\n", - "- `numpy.sqrt(x, *args, **kwargs)` Return the non-negative square-root of an array, element-wise.\n", - "- `numpy.square(x, *args, **kwargs)` Return the element-wise square of the input.\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.arange(1, 5)\n", - "print(x) # [1 2 3 4]\n", - "\n", - "y = np.sqrt(x)\n", - "print(y)\n", - "# [1. 1.41421356 1.73205081 2. ]\n", - "print(np.power(x, 0.5))\n", - "# [1. 1.41421356 1.73205081 2. ]\n", - "\n", - "y = np.square(x)\n", - "print(y)\n", - "# [ 1 4 9 16]\n", - "print(np.power(x, 2))\n", - "# [ 1 4 9 16]\n", - "```\n", - "\n", - "\n", - "---\n", - "## 三角函数\n", - "\n", - "- `numpy.sin(x, *args, **kwargs)` Trigonometric sine, element-wise.\n", - "- `numpy.cos(x, *args, **kwargs)` Cosine element-wise.\n", - "- `numpy.tan(x, *args, **kwargs)` Compute tangent element-wise.\n", - "- `numpy.arcsin(x, *args, **kwargs)` Inverse sine, element-wise.\n", - "- `numpy.arccos(x, *args, **kwargs)` Trigonometric inverse cosine, element-wise.\n", - "- `numpy.arctan(x, *args, **kwargs)` Trigonometric inverse tangent, element-wise.\n", - "\n", - "\n", - "**通用函数**（universal function）通常叫作ufunc，它对数组中的各个元素逐一进行操作。这表明，通用函数分别处理输入数组的每个元素，生成的结果组成一个新的输出数组。输出数组的大小跟输入数组相同。\n", - "\n", - "三角函数等很多数学运算符合通用函数的定义，例如，计算平方根的`sqrt()`函数、用来取对数的`log()`函数和求正弦值的`sin()`函数。\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.linspace(start=0, stop=np.pi / 2, num=10)\n", - "print(x)\n", - "# [0. 0.17453293 0.34906585 0.52359878 0.6981317 0.87266463\n", - "# 1.04719755 1.22173048 1.3962634 1.57079633]\n", - "\n", - "y = np.sin(x)\n", - "print(y)\n", - "# [0. 0.17364818 0.34202014 0.5 0.64278761 0.76604444\n", - "# 0.8660254 0.93969262 0.98480775 1. ]\n", - "\n", - "z = np.arcsin(y)\n", - "print(z)\n", - "# [0. 0.17453293 0.34906585 0.52359878 0.6981317 0.87266463\n", - "# 1.04719755 1.22173048 1.3962634 1.57079633]\n", - "\n", - "y = np.cos(x)\n", - "print(y)\n", - "# [1.00000000e+00 9.84807753e-01 9.39692621e-01 8.66025404e-01\n", - "# 7.66044443e-01 6.42787610e-01 5.00000000e-01 3.42020143e-01\n", - "# 1.73648178e-01 6.12323400e-17]\n", - "\n", - "z = np.arccos(y)\n", - "print(z)\n", - "# [0. 0.17453293 0.34906585 0.52359878 0.6981317 0.87266463\n", - "# 1.04719755 1.22173048 1.3962634 1.57079633]\n", - "\n", - "y = np.tan(x)\n", - "print(y)\n", - "# [0.00000000e+00 1.76326981e-01 3.63970234e-01 5.77350269e-01\n", - "# 8.39099631e-01 1.19175359e+00 1.73205081e+00 2.74747742e+00\n", - "# 5.67128182e+00 1.63312394e+16]\n", - "\n", - "z = np.arctan(y)\n", - "print(z)\n", - "# [0. 0.17453293 0.34906585 0.52359878 0.6981317 0.87266463\n", - "# 1.04719755 1.22173048 1.3962634 1.57079633]\n", - "```\n", - "\n", - "---\n", - "## 指数和对数\n", - "\n", - "- `numpy.exp(x, *args, **kwargs)` Calculate the exponential of all elements in the input array.\n", - "- `numpy.log(x, *args, **kwargs)` Natural logarithm, element-wise.\n", - "- `numpy.exp2(x, *args, **kwargs)` Calculate `2**p` for all `p` in the input array.\n", - "- `numpy.log2(x, *args, **kwargs)` Base-2 logarithm of `x`.\n", - "- `numpy.log10(x, *args, **kwargs)` Return the base 10 logarithm of the input array, element-wise.\n", - "\n", - "\n", - "\n", - "\n", - "【例】The natural logarithm `log` is the inverse of the exponential function, so that `log(exp(x)) = x`. The natural logarithm is logarithm in base `e`.\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.arange(1, 5)\n", - "print(x)\n", - "# [1 2 3 4]\n", - "y = np.exp(x)\n", - "print(y)\n", - "# [ 2.71828183 7.3890561 20.08553692 54.59815003]\n", - "z = np.log(y)\n", - "print(z)\n", - "# [1. 2. 3. 4.]\n", - "```\n", - "\n", - "\n", - "\n", - "---\n", - "## 加法函数、乘法函数\n", - "\n", - "- `numpy.sum(a[, axis=None, dtype=None, out=None, …])` Sum of array elements over a given axis.\n", - "\n", - "通过不同的 `axis`，numpy 会沿着不同的方向进行操作：如果不设置，那么对所有的元素操作；如果`axis=0`，则沿着纵轴进行操作；`axis=1`，则沿着横轴进行操作。但这只是简单的二位数组，如果是多维的呢？可以总结为一句话：设`axis=i`，则 numpy 沿着第`i`个下标变化的方向进行操作。\n", - "\n", - "- `numpy.cumsum(a, axis=None, dtype=None, out=None)` Return the cumulative sum of the elements along a given axis.\n", - "\n", - "**聚合函数** 是指对一组值（比如一个数组）进行操作，返回一个单一值作为结果的函数。因而，求数组所有元素之和的函数就是聚合函数。`ndarray`类实现了多个这样的函数。\n", - "\n", - "【例】返回给定轴上的数组元素的总和。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.sum(x)\n", - "print(y) # 575\n", - "\n", - "y = np.sum(x, axis=0)\n", - "print(y) # [105 110 115 120 125]\n", - "\n", - "y = np.sum(x, axis=1)\n", - "print(y) # [ 65 90 115 140 165]\n", - "```\n", - "\n", - "\n", - "【例】返回给定轴上的数组元素的累加和。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.cumsum(x)\n", - "print(y)\n", - "# [ 11 23 36 50 65 81 98 116 135 155 176 198 221 245 270 296 323 351\n", - "# 380 410 441 473 506 540 575]\n", - "\n", - "y = np.cumsum(x, axis=0)\n", - "print(y)\n", - "# [[ 11 12 13 14 15]\n", - "# [ 27 29 31 33 35]\n", - "# [ 48 51 54 57 60]\n", - "# [ 74 78 82 86 90]\n", - "# [105 110 115 120 125]]\n", - "\n", - "y = np.cumsum(x, axis=1)\n", - "print(y)\n", - "# [[ 11 23 36 50 65]\n", - "# [ 16 33 51 70 90]\n", - "# [ 21 43 66 90 115]\n", - "# [ 26 53 81 110 140]\n", - "# [ 31 63 96 130 165]]\n", - "```\n", - "\n", - "- `numpy.prod(a[, axis=None, dtype=None, out=None, …])` Return the product of array elements over a given axis.\n", - "\n", - "\n", - "- `numpy.cumprod(a, axis=None, dtype=None, out=None)` Return the cumulative product of elements along a given axis.\n", - "\n", - "【例】返回给定轴上数组元素的乘积。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.prod(x)\n", - "print(y) # 788529152\n", - "\n", - "y = np.prod(x, axis=0)\n", - "print(y)\n", - "# [2978976 3877632 4972968 6294624 7875000]\n", - "\n", - "y = np.prod(x, axis=1)\n", - "print(y)\n", - "# [ 360360 1860480 6375600 17100720 38955840]\n", - "```\n", - "\n", - "\n", - "【例】返回给定轴上数组元素的累乘。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.cumprod(x)\n", - "print(y)\n", - "# [ 11 132 1716 24024 360360 5765760\n", - "# 98017920 1764322560 -837609728 427674624 391232512 17180672\n", - "# 395155456 893796352 870072320 1147043840 905412608 -418250752\n", - "# 755630080 1194065920 -1638662144 -897581056 444596224 -2063597568\n", - "# 788529152]\n", - "\n", - "y = np.cumprod(x, axis=0)\n", - "print(y)\n", - "# [[ 11 12 13 14 15]\n", - "# [ 176 204 234 266 300]\n", - "# [ 3696 4488 5382 6384 7500]\n", - "# [ 96096 121176 150696 185136 225000]\n", - "# [2978976 3877632 4972968 6294624 7875000]]\n", - "\n", - "y = np.cumprod(x, axis=1)\n", - "print(y)\n", - "# [[ 11 132 1716 24024 360360]\n", - "# [ 16 272 4896 93024 1860480]\n", - "# [ 21 462 10626 255024 6375600]\n", - "# [ 26 702 19656 570024 17100720]\n", - "# [ 31 992 32736 1113024 38955840]]\n", - "```\n", - "\n", - "- `numpy.diff(a, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue)` Calculate the n-th discrete difference along the given axis.\n", - " - a：输入矩阵\n", - " - n：可选，代表要执行几次差值\n", - " - axis：默认是最后一个\n", - "\n", - "\n", - "The first difference is given by `out[i] = a[i+1] - a[i]` along the given axis, higher differences are calculated by using `diff` recursively.\n", - "\n", - "【例】沿着指定轴计算第N维的离散差值。\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "A = np.arange(2, 14).reshape((3, 4))\n", - "A[1, 1] = 8\n", - "print(A)\n", - "# [[ 2 3 4 5]\n", - "# [ 6 8 8 9]\n", - "# [10 11 12 13]]\n", - "print(np.diff(A))\n", - "# [[1 1 1]\n", - "# [2 0 1]\n", - "# [1 1 1]]\n", - "print(np.diff(A, axis=0))\n", - "# [[4 5 4 4]\n", - "# [4 3 4 4]]\n", - "```\n", - "\n", - "\n", - "\n", - "---\n", - "## 四舍五入\n", - "\n", - "- `numpy.around(a, decimals=0, out=None)` Evenly round to the given number of decimals.\n", - "\n", - "\n", - "【例】将数组舍入到给定的小数位数。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.random.rand(3, 3) * 10\n", - "print(x)\n", - "# [[6.59144457 3.78566113 8.15321227]\n", - "# [1.68241475 3.78753332 7.68886328]\n", - "# [2.84255822 9.58106727 7.86678037]]\n", - "\n", - "y = np.around(x)\n", - "print(y)\n", - "# [[ 7. 4. 8.]\n", - "# [ 2. 4. 8.]\n", - "# [ 3. 10. 8.]]\n", - "\n", - "y = np.around(x, decimals=2)\n", - "print(y)\n", - "# [[6.59 3.79 8.15]\n", - "# [1.68 3.79 7.69]\n", - "# [2.84 9.58 7.87]]\n", - "```\n", - "\n", - "- `numpy.ceil(x, *args, **kwargs)` Return the ceiling of the input, element-wise.\n", - "- `numpy.floor(x, *args, **kwargs)` Return the floor of the input, element-wise.\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.random.rand(3, 3) * 10\n", - "print(x)\n", - "# [[0.67847795 1.33073923 4.53920122]\n", - "# [7.55724676 5.88854047 2.65502046]\n", - "# [8.67640444 8.80110812 5.97528726]]\n", - "\n", - "y = np.ceil(x)\n", - "print(y)\n", - "# [[1. 2. 5.]\n", - "# [8. 6. 3.]\n", - "# [9. 9. 6.]]\n", - "\n", - "y = np.floor(x)\n", - "print(y)\n", - "# [[0. 1. 4.]\n", - "# [7. 5. 2.]\n", - "# [8. 8. 5.]]\n", - "```\n", - "\n", - "---\n", - "## 杂项\n", - "\n", - "- `numpy.clip(a, a_min, a_max, out=None, **kwargs):` Clip (limit) the values in an array.\n", - "\n", - "Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of `[0, 1]` is specified, values smaller than 0 become 0, and values larger than 1 become 1.\n", - "\n", - "\n", - "【例】裁剪（限制）数组中的值。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.clip(x, a_min=20, a_max=30)\n", - "print(y)\n", - "# [[20 20 20 20 20]\n", - "# [20 20 20 20 20]\n", - "# [21 22 23 24 25]\n", - "# [26 27 28 29 30]\n", - "# [30 30 30 30 30]]\n", - "```\n", - "\n", - "\n", - "\n", - "- `numpy.absolute(x, *args, **kwargs)` Calculate the absolute value element-wise. \n", - "- `numpy.abs(x, *args, **kwargs)` is a shorthand for this function.\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.arange(-5, 5)\n", - "print(x)\n", - "# [-5 -4 -3 -2 -1 0 1 2 3 4]\n", - "\n", - "y = np.abs(x)\n", - "print(y)\n", - "# [5 4 3 2 1 0 1 2 3 4]\n", - "\n", - "y = np.absolute(x)\n", - "print(y)\n", - "# [5 4 3 2 1 0 1 2 3 4]\n", - "```\n", - "\n", - "- `numpy.sign(x, *args, **kwargs)` Returns an element-wise indication of the sign of a number.\n", - "\n", - "【例】\n", - "\n", - "```python\n", - "x = np.arange(-5, 5)\n", - "print(x)\n", - "#[-5 -4 -3 -2 -1 0 1 2 3 4]\n", - "print(np.sign(x))\n", - "#[-1 -1 -1 -1 -1 0 1 1 1 1]\n", - "```\n", - "\n", - "\n", - "\n", - "\n", - "---\n", - "**参考文献**\n", - "- https://mp.weixin.qq.com/s/RWsGvvmw4ptf7d8zPIDEJw\n", - "- https://blog.csdn.net/hanshuobest/article/details/78558826?utm_medium=distribute.pc_relevant.none-task-blog-baidujs-1\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2020-08-30T17:00:33.199734Z", - "start_time": "2020-08-30T17:00:33.194782Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-5 -4 -3 -2 -1 0 1 2 3 4]\n", - "[-1 -1 -1 -1 -1 0 1 1 1 1]\n" - ] - } - ], - "source": [ - "x = np.arange(-5, 5)\n", - "print(x)\n", - "print(np.sign(x))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python35", - "language": "python", - "name": "python35" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "307.2px" - }, - "toc_section_display": true, - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/IntroductionToNumpy/08 逻辑函数.ipynb b/IntroductionToNumpy/08 逻辑函数.ipynb deleted file mode 100644 index 6d97640..0000000 --- a/IntroductionToNumpy/08 逻辑函数.ipynb +++ /dev/null @@ -1,500 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 逻辑函数\n", - "\n", - "## 真值测试\n", - "\n", - "- `numpy.all(a, axis=None, out=None, keepdims=np._NoValue)` Test whether all array elements along a given axis evaluate to True.\n", - "- `numpy.any(a, axis=None, out=None, keepdims=np._NoValue)` Test whether any array element along a given axis evaluates to True.\n", - "\n", - "\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.array([0, 4, 5])\n", - "b = np.copy(a)\n", - "print(np.all(a == b)) # True\n", - "print(np.any(a == b)) # True\n", - "\n", - "b[0] = 1\n", - "print(np.all(a == b)) # False\n", - "print(np.any(a == b)) # True\n", - "\n", - "print(np.all([1.0, np.nan])) # True\n", - "print(np.any([1.0, np.nan])) # True\n", - "\n", - "a = np.eye(3)\n", - "print(np.all(a, axis=0)) # [False False False]\n", - "print(np.any(a, axis=0)) # [ True True True]\n", - "```\n", - "\n", - "\n", - "---\n", - "## 数组内容\n", - "\n", - "- `numpy.isnan(x, *args, **kwargs)` Test element-wise for NaN and return result as a boolean array.\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "a=np.array([1,2,np.nan])\n", - "print(np.isnan(a))\n", - "#[False False True]\n", - "\n", - "```\n", - "\n", - "\n", - "\n", - "\n", - "---\n", - "## 逻辑运算\n", - "\n", - "- `numpy.logical_not(x, *args, **kwargs)`Compute the truth value of NOT x element-wise.\n", - "- `numpy.logical_and(x1, x2, *args, **kwargs)` Compute the truth value of x1 AND x2 element-wise.\n", - "- `numpy.logical_or(x1, x2, *args, **kwargs)`Compute the truth value of x1 OR x2 element-wise.\n", - "- `numpy.logical_xor(x1, x2, *args, **kwargs)`Compute the truth value of x1 XOR x2, element-wise.\n", - "\n", - "\n", - "\n", - "\n", - "```python\n", - "【例】计算非x元素的真值。\n", - "\n", - "import numpy as np\n", - "\n", - "print(np.logical_not(3)) \n", - "# False\n", - "print(np.logical_not([True, False, 0, 1]))\n", - "# [False True True False]\n", - "\n", - "x = np.arange(5)\n", - "print(np.logical_not(x < 3))\n", - "# [False False False True True]\n", - "\n", - "【例】计算x1 AND x2元素的真值。\n", - "\n", - "print(np.logical_and(True, False)) \n", - "# False\n", - "print(np.logical_and([True, False], [True, False]))\n", - "# [ True False]\n", - "print(np.logical_and(x > 1, x < 4))\n", - "# [False False True True False]\n", - "\n", - "【例】逐元素计算x1 OR x2的真值。\n", - "\n", - "\n", - "print(np.logical_or(True, False))\n", - "# True\n", - "print(np.logical_or([True, False], [False, False]))\n", - "# [ True False]\n", - "print(np.logical_or(x < 1, x > 3))\n", - "# [ True False False False True]\n", - "\n", - "【例】计算x1 XOR x2的真值，按元素计算。\n", - "\n", - "print(np.logical_xor(True, False))\n", - "# True\n", - "print(np.logical_xor([True, True, False, False], [True, False, True, False]))\n", - "# [False True True False]\n", - "print(np.logical_xor(x < 1, x > 3))\n", - "# [ True False False False True]\n", - "print(np.logical_xor(0, np.eye(2)))\n", - "# [[ True False]\n", - "# [False True]]\n", - "```\n", - "\n", - "\n", - "## 对照\n", - "\n", - "- `numpy.greater(x1, x2, *args, **kwargs)` Return the truth value of (x1 > x2) element-wise.\n", - "- `numpy.greater_equal(x1, x2, *args, **kwargs)` Return the truth value of (x1 >= x2) element-wise.\n", - "- `numpy.equal(x1, x2, *args, **kwargs)` Return (x1 == x2) element-wise.\n", - "- `numpy.not_equal(x1, x2, *args, **kwargs)` Return (x1 != x2) element-wise.\n", - "- `numpy.less(x1, x2, *args, **kwargs)` Return the truth value of (x1 < x2) element-wise.\n", - "- `numpy.less_equal(x1, x2, *args, **kwargs)` Return the truth value of (x1 =< x2) element-wise.\n", - "\n", - "\n", - "\n", - "【例】numpy对以上对照函数进行了运算符的重载。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n", - "\n", - "y = x > 2\n", - "print(y)\n", - "print(np.greater(x, 2))\n", - "# [False False True True True True True True]\n", - "\n", - "y = x >= 2\n", - "print(y)\n", - "print(np.greater_equal(x, 2))\n", - "# [False True True True True True True True]\n", - "\n", - "y = x == 2\n", - "print(y)\n", - "print(np.equal(x, 2))\n", - "# [False True False False False False False False]\n", - "\n", - "y = x != 2\n", - "print(y)\n", - "print(np.not_equal(x, 2))\n", - "# [ True False True True True True True True]\n", - "\n", - "y = x < 2\n", - "print(y)\n", - "print(np.less(x, 2))\n", - "# [ True False False False False False False False]\n", - "\n", - "y = x <= 2\n", - "print(y)\n", - "print(np.less_equal(x, 2))\n", - "# [ True True False False False False False False]\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = x > 20\n", - "print(y)\n", - "print(np.greater(x, 20))\n", - "# [[False False False False False]\n", - "# [False False False False False]\n", - "# [ True True True True True]\n", - "# [ True True True True True]\n", - "# [ True True True True True]]\n", - "\n", - "y = x >= 20\n", - "print(y)\n", - "print(np.greater_equal(x, 20))\n", - "# [[False False False False False]\n", - "# [False False False False True]\n", - "# [ True True True True True]\n", - "# [ True True True True True]\n", - "# [ True True True True True]]\n", - "\n", - "y = x == 20\n", - "print(y)\n", - "print(np.equal(x, 20))\n", - "# [[False False False False False]\n", - "# [False False False False True]\n", - "# [False False False False False]\n", - "# [False False False False False]\n", - "# [False False False False False]]\n", - "\n", - "y = x != 20\n", - "print(y)\n", - "print(np.not_equal(x, 20))\n", - "# [[ True True True True True]\n", - "# [ True True True True False]\n", - "# [ True True True True True]\n", - "# [ True True True True True]\n", - "# [ True True True True True]]\n", - "\n", - "\n", - "y = x < 20\n", - "print(y)\n", - "print(np.less(x, 20))\n", - "# [[ True True True True True]\n", - "# [ True True True True False]\n", - "# [False False False False False]\n", - "# [False False False False False]\n", - "# [False False False False False]]\n", - "\n", - "y = x <= 20\n", - "print(y)\n", - "print(np.less_equal(x, 20))\n", - "# [[ True True True True True]\n", - "# [ True True True True True]\n", - "# [False False False False False]\n", - "# [False False False False False]\n", - "# [False False False False False]]\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200611)\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "y = np.random.randint(10, 40, [5, 5])\n", - "print(y)\n", - "# [[32 28 31 33 37]\n", - "# [23 37 37 30 29]\n", - "# [32 24 10 33 15]\n", - "# [27 17 10 36 16]\n", - "# [25 32 23 39 34]]\n", - "\n", - "z = x > y\n", - "print(z)\n", - "print(np.greater(x, y))\n", - "# [[False False False False False]\n", - "# [False False False False False]\n", - "# [False False True False True]\n", - "# [False True True False True]\n", - "# [ True False True False True]]\n", - "\n", - "z = x >= y\n", - "print(z)\n", - "print(np.greater_equal(x, y))\n", - "# [[False False False False False]\n", - "# [False False False False False]\n", - "# [False False True False True]\n", - "# [False True True False True]\n", - "# [ True True True False True]]\n", - "\n", - "z = x == y\n", - "print(z)\n", - "print(np.equal(x, y))\n", - "# [[False False False False False]\n", - "# [False False False False False]\n", - "# [False False False False False]\n", - "# [False False False False False]\n", - "# [False True False False False]]\n", - "\n", - "z = x != y\n", - "print(z)\n", - "print(np.not_equal(x, y))\n", - "# [[ True True True True True]\n", - "# [ True True True True True]\n", - "# [ True True True True True]\n", - "# [ True True True True True]\n", - "# [ True False True True True]]\n", - "\n", - "z = x < y\n", - "print(z)\n", - "print(np.less(x, y))\n", - "# [[ True True True True True]\n", - "# [ True True True True True]\n", - "# [ True True False True False]\n", - "# [ True False False True False]\n", - "# [False False False True False]]\n", - "\n", - "z = x <= y\n", - "print(z)\n", - "print(np.less_equal(x, y))\n", - "# [[ True True True True True]\n", - "# [ True True True True True]\n", - "# [ True True False True False]\n", - "# [ True False False True False]\n", - "# [False True False True False]]\n", - "```\n", - "\n", - "【例】注意 numpy 的广播规则。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "\n", - "np.random.seed(20200611)\n", - "y = np.random.randint(10, 50, 5)\n", - "\n", - "print(y)\n", - "# [32 37 30 24 10]\n", - "\n", - "z = x > y\n", - "print(z)\n", - "print(np.greater(x, y))\n", - "# [[False False False False True]\n", - "# [False False False False True]\n", - "# [False False False False True]\n", - "# [False False False True True]\n", - "# [False False True True True]]\n", - "\n", - "z = x >= y\n", - "print(z)\n", - "print(np.greater_equal(x, y))\n", - "# [[False False False False True]\n", - "# [False False False False True]\n", - "# [False False False True True]\n", - "# [False False False True True]\n", - "# [False False True True True]]\n", - "\n", - "z = x == y\n", - "print(z)\n", - "print(np.equal(x, y))\n", - "# [[False False False False False]\n", - "# [False False False False False]\n", - "# [False False False True False]\n", - "# [False False False False False]\n", - "# [False False False False False]]\n", - "\n", - "z = x != y\n", - "print(z)\n", - "print(np.not_equal(x, y))\n", - "# [[ True True True True True]\n", - "# [ True True True True True]\n", - "# [ True True True False True]\n", - "# [ True True True True True]\n", - "# [ True True True True True]]\n", - "\n", - "z = x < y\n", - "print(z)\n", - "print(np.less(x, y))\n", - "# [[ True True True True False]\n", - "# [ True True True True False]\n", - "# [ True True True False False]\n", - "# [ True True True False False]\n", - "# [ True True False False False]]\n", - "\n", - "z = x <= y\n", - "print(z)\n", - "print(np.less_equal(x, y))\n", - "# [[ True True True True False]\n", - "# [ True True True True False]\n", - "# [ True True True True False]\n", - "# [ True True True False False]\n", - "# [ True True False False False]]\n", - "```\n", - "\n", - "\n", - "- `numpy.isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False)` Returns a boolean array where two arrays are element-wise equal within a tolerance.\n", - "- `numpy.allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False)` Returns True if two arrays are element-wise equal within a tolerance. \n", - "\n", - "`numpy.allclose()` 等价于 `numpy.all(isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan))`。\n", - "\n", - "The tolerance values are positive, typically very small numbers. The relative difference (`rtol * abs(b)`) and the absolute difference `atol` are added together to compare against the absolute difference between `a` and `b`.\n", - "\n", - "判断是否为True的计算依据：\n", - "\n", - "```python\n", - "np.absolute(a - b) <= (atol + rtol * absolute(b))\n", - "\n", - "- atol：float，绝对公差。\n", - "- rtol：float，相对公差。\n", - "```\n", - "\n", - "NaNs are treated as equal if they are in the same place and if `equal_nan=True`. Infs are treated as equal if they are in the same place and of the same sign in both arrays.\n", - "\n", - "【例】比较两个数组是否可以认为相等。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.isclose([1e10, 1e-7], [1.00001e10, 1e-8])\n", - "print(x) # [ True False]\n", - "\n", - "x = np.allclose([1e10, 1e-7], [1.00001e10, 1e-8])\n", - "print(x) # False\n", - "\n", - "x = np.isclose([1e10, 1e-8], [1.00001e10, 1e-9])\n", - "print(x) # [ True True]\n", - "\n", - "x = np.allclose([1e10, 1e-8], [1.00001e10, 1e-9])\n", - "print(x) # True\n", - "\n", - "x = np.isclose([1e10, 1e-8], [1.0001e10, 1e-9])\n", - "print(x) # [False True]\n", - "\n", - "x = np.allclose([1e10, 1e-8], [1.0001e10, 1e-9])\n", - "print(x) # False\n", - "\n", - "x = np.isclose([1.0, np.nan], [1.0, np.nan])\n", - "print(x) # [ True False]\n", - "\n", - "x = np.allclose([1.0, np.nan], [1.0, np.nan])\n", - "print(x) # False\n", - "\n", - "x = np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)\n", - "print(x) # [ True True]\n", - "\n", - "x = np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)\n", - "print(x) # True\n", - "```\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python35", - "language": "python", - "name": "python35" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/IntroductionToNumpy/09 排序，搜索和计数.ipynb b/IntroductionToNumpy/09 排序，搜索和计数.ipynb deleted file mode 100644 index 03c5587..0000000 --- a/IntroductionToNumpy/09 排序，搜索和计数.ipynb +++ /dev/null @@ -1,828 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 排序，搜索和计数\n", - "## 排序\n", - "\n", - "- `numpy.sort(a[, axis=-1, kind='quicksort', order=None])` Return a sorted **copy** of an array.\n", - " - axis：排序沿数组的（轴）方向，0表示按行，1表示按列，None表示展开来排序，默认为-1，表示沿最后的轴排序。\n", - " - kind：排序的算法，提供了快排'quicksort'、混排'mergesort'、堆排'heapsort'， 默认为‘quicksort'。\n", - " - order：排序的字段名，可指定字段排序，默认为None。\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200612)\n", - "x = np.random.rand(5, 5) * 10\n", - "x = np.around(x, 2)\n", - "print(x)\n", - "# [[2.32 7.54 9.78 1.73 6.22]\n", - "# [6.93 5.17 9.28 9.76 8.25]\n", - "# [0.01 4.23 0.19 1.73 9.27]\n", - "# [7.99 4.97 0.88 7.32 4.29]\n", - "# [9.05 0.07 8.95 7.9 6.99]]\n", - "\n", - "y = np.sort(x)\n", - "print(y)\n", - "# [[1.73 2.32 6.22 7.54 9.78]\n", - "# [5.17 6.93 8.25 9.28 9.76]\n", - "# [0.01 0.19 1.73 4.23 9.27]\n", - "# [0.88 4.29 4.97 7.32 7.99]\n", - "# [0.07 6.99 7.9 8.95 9.05]]\n", - "\n", - "y = np.sort(x, axis=0)\n", - "print(y)\n", - "# [[0.01 0.07 0.19 1.73 4.29]\n", - "# [2.32 4.23 0.88 1.73 6.22]\n", - "# [6.93 4.97 8.95 7.32 6.99]\n", - "# [7.99 5.17 9.28 7.9 8.25]\n", - "# [9.05 7.54 9.78 9.76 9.27]]\n", - "\n", - "y = np.sort(x, axis=1)\n", - "print(y)\n", - "# [[1.73 2.32 6.22 7.54 9.78]\n", - "# [5.17 6.93 8.25 9.28 9.76]\n", - "# [0.01 0.19 1.73 4.23 9.27]\n", - "# [0.88 4.29 4.97 7.32 7.99]\n", - "# [0.07 6.99 7.9 8.95 9.05]]\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "dt = np.dtype([('name', 'S10'), ('age', np.int)])\n", - "a = np.array([(\"Mike\", 21), (\"Nancy\", 25), (\"Bob\", 17), (\"Jane\", 27)], dtype=dt)\n", - "b = np.sort(a, order='name')\n", - "print(b)\n", - "# [(b'Bob', 17) (b'Jane', 27) (b'Mike', 21) (b'Nancy', 25)]\n", - "\n", - "b = np.sort(a, order='age')\n", - "print(b)\n", - "# [(b'Bob', 17) (b'Mike', 21) (b'Nancy', 25) (b'Jane', 27)]\n", - "```\n", - "\n", - "\n", - "如果排序后，想用元素的索引位置替代排序后的实际结果，该怎么办呢？\n", - "\n", - "- `numpy.argsort(a[, axis=-1, kind='quicksort', order=None])` Returns the indices that would sort an array.\n", - "\n", - "\n", - "\n", - "【例】对数组沿给定轴执行间接排序，并使用指定排序类型返回数据的索引数组。这个索引数组用于构造排序后的数组。\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200612)\n", - "x = np.random.randint(0, 10, 10)\n", - "print(x)\n", - "# [6 1 8 5 5 4 1 2 9 1]\n", - "\n", - "y = np.argsort(x)\n", - "print(y)\n", - "# [1 6 9 7 5 3 4 0 2 8]\n", - "\n", - "print(x[y])\n", - "# [1 1 1 2 4 5 5 6 8 9]\n", - "\n", - "y = np.argsort(-x)\n", - "print(y)\n", - "# [8 2 0 3 4 5 7 1 6 9]\n", - "\n", - "print(x[y])\n", - "# [9 8 6 5 5 4 2 1 1 1]\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200612)\n", - "x = np.random.rand(5, 5) * 10\n", - "x = np.around(x, 2)\n", - "print(x)\n", - "# [[2.32 7.54 9.78 1.73 6.22]\n", - "# [6.93 5.17 9.28 9.76 8.25]\n", - "# [0.01 4.23 0.19 1.73 9.27]\n", - "# [7.99 4.97 0.88 7.32 4.29]\n", - "# [9.05 0.07 8.95 7.9 6.99]]\n", - "\n", - "y = np.argsort(x)\n", - "print(y)\n", - "# [[3 0 4 1 2]\n", - "# [1 0 4 2 3]\n", - "# [0 2 3 1 4]\n", - "# [2 4 1 3 0]\n", - "# [1 4 3 2 0]]\n", - "\n", - "y = np.argsort(x, axis=0)\n", - "print(y)\n", - "# [[2 4 2 0 3]\n", - "# [0 2 3 2 0]\n", - "# [1 3 4 3 4]\n", - "# [3 1 1 4 1]\n", - "# [4 0 0 1 2]]\n", - "\n", - "y = np.argsort(x, axis=1)\n", - "print(y)\n", - "# [[3 0 4 1 2]\n", - "# [1 0 4 2 3]\n", - "# [0 2 3 1 4]\n", - "# [2 4 1 3 0]\n", - "# [1 4 3 2 0]]\n", - "\n", - "y = np.array([np.take(x[i], np.argsort(x[i])) for i in range(5)]) \n", - "#numpy.take(a, indices, axis=None, out=None, mode='raise')沿轴从数组中获取元素。\n", - "print(y)\n", - "# [[1.73 2.32 6.22 7.54 9.78]\n", - "# [5.17 6.93 8.25 9.28 9.76]\n", - "# [0.01 0.19 1.73 4.23 9.27]\n", - "# [0.88 4.29 4.97 7.32 7.99]\n", - "# [0.07 6.99 7.9 8.95 9.05]]\n", - "```\n", - "\n", - "如何将数据按照某一指标进行排序呢？\n", - "\n", - "- `numpy.lexsort(keys[, axis=-1])` Perform an indirect stable sort using a sequence of keys.（使用键序列执行间接稳定排序。）\n", - "\n", - "- 给定多个可以在电子表格中解释为列的排序键，lexsort返回一个整数索引数组，该数组描述了按多个列排序的顺序。序列中的最后一个键用于主排序顺序，倒数第二个键用于辅助排序顺序，依此类推。keys参数必须是可以转换为相同形状的数组的对象序列。如果为keys参数提供了2D数组，则将其行解释为排序键，并根据最后一行，倒数第二行等进行排序。\n", - "\n", - "\n", - "\n", - "【例】按照第一列的升序或者降序对整体数据进行排序。\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200612)\n", - "x = np.random.rand(5, 5) * 10\n", - "x = np.around(x, 2)\n", - "print(x)\n", - "# [[2.32 7.54 9.78 1.73 6.22]\n", - "# [6.93 5.17 9.28 9.76 8.25]\n", - "# [0.01 4.23 0.19 1.73 9.27]\n", - "# [7.99 4.97 0.88 7.32 4.29]\n", - "# [9.05 0.07 8.95 7.9 6.99]]\n", - "\n", - "index = np.lexsort([x[:, 0]])\n", - "print(index)\n", - "# [2 0 1 3 4]\n", - "\n", - "y = x[index]\n", - "print(y)\n", - "# [[0.01 4.23 0.19 1.73 9.27]\n", - "# [2.32 7.54 9.78 1.73 6.22]\n", - "# [6.93 5.17 9.28 9.76 8.25]\n", - "# [7.99 4.97 0.88 7.32 4.29]\n", - "# [9.05 0.07 8.95 7.9 6.99]]\n", - "\n", - "index = np.lexsort([-1 * x[:, 0]])\n", - "print(index)\n", - "# [4 3 1 0 2]\n", - "\n", - "y = x[index]\n", - "print(y)\n", - "# [[9.05 0.07 8.95 7.9 6.99]\n", - "# [7.99 4.97 0.88 7.32 4.29]\n", - "# [6.93 5.17 9.28 9.76 8.25]\n", - "# [2.32 7.54 9.78 1.73 6.22]\n", - "# [0.01 4.23 0.19 1.73 9.27]]\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 5, 1, 4, 3, 4, 4])\n", - "y = np.array([9, 4, 0, 4, 0, 2, 1])\n", - "a = np.lexsort([x])\n", - "b = np.lexsort([y])\n", - "print(a)\n", - "# [0 2 4 3 5 6 1]\n", - "print(x[a])\n", - "# [1 1 3 4 4 4 5]\n", - "\n", - "print(b)\n", - "# [2 4 6 5 1 3 0]\n", - "print(y[b])\n", - "# [0 0 1 2 4 4 9]\n", - "\n", - "z = np.lexsort([y, x])\n", - "print(z)\n", - "# [2 0 4 6 5 3 1]\n", - "print(x[z])\n", - "# [1 1 3 4 4 4 5]\n", - "\n", - "z = np.lexsort([x, y])\n", - "print(z)\n", - "# [2 4 6 5 3 1 0]\n", - "print(y[z])\n", - "# [0 0 1 2 4 4 9]\n", - "```\n", - "\n", - "- `numpy.partition(a, kth, axis=-1, kind='introselect', order=None)` Return a partitioned copy of an array.\n", - "\n", - "Creates a copy of the array with its elements rearranged in such a way that the value of the element in k-th position is in the position it would be in a sorted array. All elements smaller than the k-th element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined.\n", - "\n", - "\n", - "【例】以索引是 kth 的元素为基准，将元素分成两部分，即大于该元素的放在其后面，小于该元素的放在其前面，这里有点类似于快排。\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(100)\n", - "x = np.random.randint(1, 30, [8, 3])\n", - "print(x)\n", - "# [[ 9 25 4]\n", - "# [ 8 24 16]\n", - "# [17 11 21]\n", - "# [ 3 22 3]\n", - "# [ 3 15 3]\n", - "# [18 17 25]\n", - "# [16 5 12]\n", - "# [29 27 17]]\n", - "\n", - "y = np.sort(x, axis=0)\n", - "print(y)\n", - "# [[ 3 5 3]\n", - "# [ 3 11 3]\n", - "# [ 8 15 4]\n", - "# [ 9 17 12]\n", - "# [16 22 16]\n", - "# [17 24 17]\n", - "# [18 25 21]\n", - "# [29 27 25]]\n", - "\n", - "z = np.partition(x, kth=2, axis=0)\n", - "print(z)\n", - "# [[ 3 5 3]\n", - "# [ 3 11 3]\n", - "# [ 8 15 4]\n", - "# [ 9 22 21]\n", - "# [17 24 16]\n", - "# [18 17 25]\n", - "# [16 25 12]\n", - "# [29 27 17]]\n", - "```\n", - "【例】选取每一列第三小的数\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(100)\n", - "x = np.random.randint(1, 30, [8, 3])\n", - "print(x)\n", - "# [[ 9 25 4]\n", - "# [ 8 24 16]\n", - "# [17 11 21]\n", - "# [ 3 22 3]\n", - "# [ 3 15 3]\n", - "# [18 17 25]\n", - "# [16 5 12]\n", - "# [29 27 17]]\n", - "z = np.partition(x, kth=2, axis=0)\n", - "print(z[2])\n", - "# [ 8 15 4]\n", - "```\n", - "\n", - "【例】选取每一列第三大的数据\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(100)\n", - "x = np.random.randint(1, 30, [8, 3])\n", - "print(x)\n", - "# [[ 9 25 4]\n", - "# [ 8 24 16]\n", - "# [17 11 21]\n", - "# [ 3 22 3]\n", - "# [ 3 15 3]\n", - "# [18 17 25]\n", - "# [16 5 12]\n", - "# [29 27 17]]\n", - "z = np.partition(x, kth=-3, axis=0)\n", - "print(z[-3])\n", - "# [17 24 17]\n", - "```\n", - "\n", - "\n", - "- `numpy.argpartition(a, kth, axis=-1, kind='introselect', order=None)`\n", - "\n", - "Perform an indirect partition along the given axis using the algorithm specified by the `kind` keyword. It returns an array of indices of the same shape as `a` that index data along the given axis in partitioned order.\n", - "\n", - "【例】\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(100)\n", - "x = np.random.randint(1, 30, [8, 3])\n", - "print(x)\n", - "# [[ 9 25 4]\n", - "# [ 8 24 16]\n", - "# [17 11 21]\n", - "# [ 3 22 3]\n", - "# [ 3 15 3]\n", - "# [18 17 25]\n", - "# [16 5 12]\n", - "# [29 27 17]]\n", - "\n", - "y = np.argsort(x, axis=0)\n", - "print(y)\n", - "# [[3 6 3]\n", - "# [4 2 4]\n", - "# [1 4 0]\n", - "# [0 5 6]\n", - "# [6 3 1]\n", - "# [2 1 7]\n", - "# [5 0 2]\n", - "# [7 7 5]]\n", - "\n", - "z = np.argpartition(x, kth=2, axis=0)\n", - "print(z)\n", - "# [[3 6 3]\n", - "# [4 2 4]\n", - "# [1 4 0]\n", - "# [0 3 2]\n", - "# [2 1 1]\n", - "# [5 5 5]\n", - "# [6 0 6]\n", - "# [7 7 7]]\n", - "```\n", - "\n", - "【例】选取每一列第三小的数的索引\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(100)\n", - "x = np.random.randint(1, 30, [8, 3])\n", - "print(x)\n", - "# [[ 9 25 4]\n", - "# [ 8 24 16]\n", - "# [17 11 21]\n", - "# [ 3 22 3]\n", - "# [ 3 15 3]\n", - "# [18 17 25]\n", - "# [16 5 12]\n", - "# [29 27 17]]\n", - "\n", - "z = np.argpartition(x, kth=2, axis=0)\n", - "print(z[2])\n", - "# [1 4 0]\n", - "```\n", - "\n", - "【例】选取每一列第三大的数的索引\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(100)\n", - "x = np.random.randint(1, 30, [8, 3])\n", - "print(x)\n", - "# [[ 9 25 4]\n", - "# [ 8 24 16]\n", - "# [17 11 21]\n", - "# [ 3 22 3]\n", - "# [ 3 15 3]\n", - "# [18 17 25]\n", - "# [16 5 12]\n", - "# [29 27 17]]\n", - "\n", - "z = np.argpartition(x, kth=-3, axis=0)\n", - "print(z[-3])\n", - "# [2 1 7]\n", - "```\n", - "\n", - "\n", - "---\n", - "## 搜索\n", - "\n", - "- `numpy.argmax(a[, axis=None, out=None])`Returns the indices of the maximum values along an axis.\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200612)\n", - "x = np.random.rand(5, 5) * 10\n", - "x = np.around(x, 2)\n", - "print(x)\n", - "# [[2.32 7.54 9.78 1.73 6.22]\n", - "# [6.93 5.17 9.28 9.76 8.25]\n", - "# [0.01 4.23 0.19 1.73 9.27]\n", - "# [7.99 4.97 0.88 7.32 4.29]\n", - "# [9.05 0.07 8.95 7.9 6.99]]\n", - "\n", - "y = np.argmax(x)\n", - "print(y) # 2\n", - "\n", - "y = np.argmax(x, axis=0)\n", - "print(y)\n", - "# [4 0 0 1 2]\n", - "\n", - "y = np.argmax(x, axis=1)\n", - "print(y)\n", - "# [2 3 4 0 0]\n", - "```\n", - "\n", - "- `numpy.argmin(a[, axis=None, out=None])`Returns the indices of the minimum values along an axis.\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200612)\n", - "x = np.random.rand(5, 5) * 10\n", - "x = np.around(x, 2)\n", - "print(x)\n", - "# [[2.32 7.54 9.78 1.73 6.22]\n", - "# [6.93 5.17 9.28 9.76 8.25]\n", - "# [0.01 4.23 0.19 1.73 9.27]\n", - "# [7.99 4.97 0.88 7.32 4.29]\n", - "# [9.05 0.07 8.95 7.9 6.99]]\n", - "\n", - "y = np.argmin(x)\n", - "print(y) # 10\n", - "\n", - "y = np.argmin(x, axis=0)\n", - "print(y)\n", - "# [2 4 2 0 3]\n", - "\n", - "y = np.argmin(x, axis=1)\n", - "print(y)\n", - "# [3 1 0 2 1]\n", - "```\n", - "\n", - "\n", - "- `numppy.nonzero(a)` Return the indices of the elements that are non-zero.\n", - "\n", - "，其值为非零元素的下标在对应轴上的值。\n", - "\n", - "1. 只有`a`中非零元素才会有索引值，那些零值元素没有索引值。\n", - "2. 返回一个长度为`a.ndim`的元组（tuple），元组的每个元素都是一个整数数组（array）。\n", - "3. 每一个array均是从一个维度上来描述其索引值。比如，如果`a`是一个二维数组，则tuple包含两个array，第一个array从行维度来描述索引值；第二个array从列维度来描述索引值。\n", - "4. 该 `np.transpose(np.nonzero(x))` 函数能够描述出每一个非零元素在不同维度的索引值。\n", - "5. 通过`a[nonzero(a)]`得到所有`a`中的非零值。\n", - "\n", - "【例】一维数组\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([0, 2, 3])\n", - "print(x) # [0 2 3]\n", - "print(x.shape) # (3,)\n", - "print(x.ndim) # 1\n", - "\n", - "y = np.nonzero(x)\n", - "print(y) # (array([1, 2], dtype=int64),)\n", - "print(np.array(y)) # [[1 2]]\n", - "print(np.array(y).shape) # (1, 2)\n", - "print(np.array(y).ndim) # 2\n", - "print(np.transpose(y))\n", - "# [[1]\n", - "# [2]]\n", - "print(x[np.nonzero(x)])\n", - "#[2, 3]\n", - "```\n", - "\n", - "【例】二维数组\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])\n", - "print(x)\n", - "# [[3 0 0]\n", - "# [0 4 0]\n", - "# [5 6 0]]\n", - "print(x.shape) # (3, 3)\n", - "print(x.ndim) # 2\n", - "\n", - "y = np.nonzero(x)\n", - "print(y)\n", - "# (array([0, 1, 2, 2], dtype=int64), array([0, 1, 0, 1], dtype=int64))\n", - "print(np.array(y))\n", - "# [[0 1 2 2]\n", - "# [0 1 0 1]]\n", - "print(np.array(y).shape) # (2, 4)\n", - "print(np.array(y).ndim) # 2\n", - "\n", - "y = x[np.nonzero(x)]\n", - "print(y) # [3 4 5 6]\n", - "\n", - "y = np.transpose(np.nonzero(x))\n", - "print(y)\n", - "# [[0 0]\n", - "# [1 1]\n", - "# [2 0]\n", - "# [2 1]]\n", - "```\n", - "【例】三维数组\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[[0, 1], [1, 0]], [[0, 1], [1, 0]], [[0, 0], [1, 0]]])\n", - "print(x)\n", - "# [[[0 1]\n", - "# [1 0]]\n", - "#\n", - "# [[0 1]\n", - "# [1 0]]\n", - "#\n", - "# [[0 0]\n", - "# [1 0]]]\n", - "print(np.shape(x)) # (3, 2, 2)\n", - "print(x.ndim) # 3\n", - "\n", - "y = np.nonzero(x)\n", - "print(np.array(y))\n", - "# [[0 0 1 1 2]\n", - "# [0 1 0 1 1]\n", - "# [1 0 1 0 0]]\n", - "print(np.array(y).shape) # (3, 5)\n", - "print(np.array(y).ndim) # 2\n", - "print(y)\n", - "# (array([0, 0, 1, 1, 2], dtype=int64), array([0, 1, 0, 1, 1], dtype=int64), array([1, 0, 1, 0, 0], dtype=int64))\n", - "print(x[np.nonzero(x)])\n", - "#[1 1 1 1 1]\n", - "```\n", - "\n", - "【例】`nonzero()`将布尔数组转换成整数数组进行操作。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", - "print(x)\n", - "# [[1 2 3]\n", - "# [4 5 6]\n", - "# [7 8 9]]\n", - "\n", - "y = x > 3\n", - "print(y)\n", - "# [[False False False]\n", - "# [ True True True]\n", - "# [ True True True]]\n", - "\n", - "y = np.nonzero(x > 3)\n", - "print(y)\n", - "# (array([1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))\n", - "\n", - "y = x[np.nonzero(x > 3)]\n", - "print(y)\n", - "# [4 5 6 7 8 9]\n", - "\n", - "y = x[x > 3]\n", - "print(y)\n", - "# [4 5 6 7 8 9]\n", - "```\n", - "\n", - "- `numpy.where(condition, [x=None, y=None])` Return elements chosen from `x` or `y` depending on `condition`.\n", - "\n", - "【例】满足条件`condition`，输出`x`，不满足输出`y`。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.arange(10)\n", - "print(x)\n", - "# [0 1 2 3 4 5 6 7 8 9]\n", - "\n", - "y = np.where(x < 5, x, 10 * x)\n", - "print(y)\n", - "# [ 0 1 2 3 4 50 60 70 80 90]\n", - "\n", - "x = np.array([[0, 1, 2],\n", - " [0, 2, 4],\n", - " [0, 3, 6]])\n", - "y = np.where(x < 4, x, -1)\n", - "print(y)\n", - "# [[ 0 1 2]\n", - "# [ 0 2 -1]\n", - "# [ 0 3 -1]]\n", - "```\n", - "\n", - "【例】只有`condition`，没有`x`和`y`，则输出满足条件 (即非0) 元素的坐标 (等价于`numpy.nonzero`)。这里的坐标以tuple的形式给出，通常原数组有多少维，输出的tuple中就包含几个数组，分别对应符合条件元素的各维坐标。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n", - "y = np.where(x > 5)\n", - "print(y)\n", - "# (array([5, 6, 7], dtype=int64),)\n", - "print(x[y])\n", - "# [6 7 8]\n", - "\n", - "y = np.nonzero(x > 5)\n", - "print(y)\n", - "# (array([5, 6, 7], dtype=int64),)\n", - "print(x[y])\n", - "# [6 7 8]\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.where(x > 25)\n", - "print(y)\n", - "# (array([3, 3, 3, 3, 3, 4, 4, 4, 4, 4], dtype=int64), array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4], dtype=int64))\n", - "\n", - "print(x[y])\n", - "# [26 27 28 29 30 31 32 33 34 35]\n", - "\n", - "y = np.nonzero(x > 25)\n", - "print(y)\n", - "# (array([3, 3, 3, 3, 3, 4, 4, 4, 4, 4], dtype=int64), array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4], dtype=int64))\n", - "print(x[y])\n", - "# [26 27 28 29 30 31 32 33 34 35]\n", - "```\n", - "\n", - "\n", - "\n", - "\n", - "- `numpy.searchsorted(a, v[, side='left', sorter=None])` Find indices where elements should be inserted to maintain order.\n", - " - a：一维输入数组。当`sorter`参数为`None`的时候，`a`必须为升序数组；否则，`sorter`不能为空，存放`a`中元素的`index`，用于反映`a`数组的升序排列方式。\n", - " - v：插入`a`数组的值，可以为单个元素，`list`或者`ndarray`。\n", - " - side：查询方向，当为`left`时，将返回第一个符合条件的元素下标；当为`right`时，将返回最后一个符合条件的元素下标。\n", - " - sorter：一维数组存放`a`数组元素的 index，index 对应元素为升序。\n", - "\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([0, 1, 5, 9, 11, 18, 26, 33])\n", - "y = np.searchsorted(x, 15)\n", - "print(y) # 5\n", - "\n", - "y = np.searchsorted(x, 15, side='right')\n", - "print(y) # 5\n", - "\n", - "y = np.searchsorted(x, -1)\n", - "print(y) # 0\n", - "\n", - "y = np.searchsorted(x, -1, side='right')\n", - "print(y) # 0\n", - "\n", - "y = np.searchsorted(x, 35)\n", - "print(y) # 8\n", - "\n", - "y = np.searchsorted(x, 35, side='right')\n", - "print(y) # 8\n", - "\n", - "y = np.searchsorted(x, 11)\n", - "print(y) # 4\n", - "\n", - "y = np.searchsorted(x, 11, side='right')\n", - "print(y) # 5\n", - "\n", - "y = np.searchsorted(x, 0)\n", - "print(y) # 0\n", - "\n", - "y = np.searchsorted(x, 0, side='right')\n", - "print(y) # 1\n", - "\n", - "y = np.searchsorted(x, 33)\n", - "print(y) # 7\n", - "\n", - "y = np.searchsorted(x, 33, side='right')\n", - "print(y) # 8\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([0, 1, 5, 9, 11, 18, 26, 33])\n", - "y = np.searchsorted(x, [-1, 0, 11, 15, 33, 35])\n", - "print(y) # [0 0 4 5 7 8]\n", - "\n", - "y = np.searchsorted(x, [-1, 0, 11, 15, 33, 35], side='right')\n", - "print(y) # [0 1 5 5 8 8]\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([0, 1, 5, 9, 11, 18, 26, 33])\n", - "np.random.shuffle(x)\n", - "print(x) # [33 1 9 18 11 26 0 5]\n", - "\n", - "x_sort = np.argsort(x)\n", - "print(x_sort) # [6 1 7 2 4 3 5 0]\n", - "\n", - "y = np.searchsorted(x, [-1, 0, 11, 15, 33, 35], sorter=x_sort)\n", - "print(y) # [0 0 4 5 7 8]\n", - "\n", - "y = np.searchsorted(x, [-1, 0, 11, 15, 33, 35], side='right', sorter=x_sort)\n", - "print(y) # [0 1 5 5 8 8]\n", - "```\n", - "\n", - "\n", - "---\n", - "## 计数\n", - "\n", - "- `numpy.count_nonzero(a, axis=None)` Counts the number of non-zero values in the array a.\n", - "\n", - "【例】返回数组中的非0元素个数。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.count_nonzero(np.eye(4))\n", - "print(x) # 4\n", - "\n", - "x = np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]])\n", - "print(x) # 5\n", - "\n", - "x = np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]], axis=0)\n", - "print(x) # [1 1 1 1 1]\n", - "\n", - "x = np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]], axis=1)\n", - "print(x) # [2 3]\n", - "```\n", - "\n", - "---\n", - "**参考文献**\n", - "\n", - "- https://blog.csdn.net/u013698770/article/details/54632047\n", - "- https://www.cnblogs.com/massquantity/p/8908859.html\n" - ] - }, - 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"varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/IntroductionToNumpy/10 集合操作.ipynb b/IntroductionToNumpy/10 集合操作.ipynb deleted file mode 100644 index 367ee93..0000000 --- a/IntroductionToNumpy/10 集合操作.ipynb +++ /dev/null @@ -1,246 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 集合操作\n", - "\n", - "## 构造集合\n", - "\n", - "- `numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None)` Find the unique elements of an array.\n", - " - `return_index=True` 表示返回新列表元素在旧列表中的位置。\n", - " - `return_inverse=True`表示返回旧列表元素在新列表中的位置。\n", - " - `return_counts=True`表示返回新列表元素在旧列表中出现的次数。\n", - " \n", - " \n", - "【例】找出数组中的唯一值并返回已排序的结果。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.unique([1, 1, 3, 2, 3, 3])\n", - "print(x) # [1 2 3]\n", - "\n", - "x = sorted(set([1, 1, 3, 2, 3, 3]))\n", - "print(x) # [1, 2, 3]\n", - "\n", - "x = np.array([[1, 1], [2, 3]])\n", - "u = np.unique(x)\n", - "print(u) # [1 2 3]\n", - "\n", - "x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]])\n", - "y = np.unique(x, axis=0)\n", - "print(y)\n", - "# [[1 0 0]\n", - "# [2 3 4]]\n", - "\n", - "x = np.array(['a', 'b', 'b', 'c', 'a'])\n", - "u, index = np.unique(x, return_index=True)\n", - "print(u) # ['a' 'b' 'c']\n", - "print(index) # [0 1 3]\n", - "print(x[index]) # ['a' 'b' 'c']\n", - "\n", - "x = np.array([1, 2, 6, 4, 2, 3, 2])\n", - "u, index = np.unique(x, return_inverse=True)\n", - "print(u) # [1 2 3 4 6]\n", - "print(index) # [0 1 4 3 1 2 1]\n", - "print(u[index]) # [1 2 6 4 2 3 2]\n", - "\n", - "u, count = np.unique(x, return_counts=True)\n", - "print(u) # [1 2 3 4 6]\n", - "print(count) # [1 3 1 1 1]\n", - "```\n", - "\n", - "## 布尔运算\n", - "\n", - "- `numpy.in1d(ar1, ar2, assume_unique=False, invert=False)` Test whether each element of a 1-D array is also present in a second array.\n", - "\n", - "Returns a boolean array the same length as `ar1` that is True where an element of `ar1` is in `ar2` and False otherwise.\n", - "\n", - "【例】前面的数组是否包含于后面的数组，返回布尔值。返回的值是针对第一个参数的数组的，所以维数和第一个参数一致，布尔值与数组的元素位置也一一对应。\n", - "```python\n", - "import numpy as np\n", - "\n", - "test = np.array([0, 1, 2, 5, 0])\n", - "states = [0, 2]\n", - "mask = np.in1d(test, states)\n", - "print(mask) # [ True False True False True]\n", - "print(test[mask]) # [0 2 0]\n", - "\n", - "mask = np.in1d(test, states, invert=True)\n", - "print(mask) # [False True False True False]\n", - "print(test[mask]) # [1 5]\n", - "```\n", - "\n", - "\n", - "### 求两个集合的交集：\n", - "\n", - "- `numpy.intersect1d(ar1, ar2, assume_unique=False, return_indices=False)` Find the intersection of two arrays.\n", - "\n", - "Return the sorted, unique values that are in both of the input arrays.\n", - "\n", - "【例】求两个数组的唯一化+求交集+排序函数。\n", - "```python\n", - "import numpy as np\n", - "from functools import reduce\n", - "\n", - "x = np.intersect1d([1, 3, 4, 3], [3, 1, 2, 1])\n", - "print(x) # [1 3]\n", - "\n", - "x = np.array([1, 1, 2, 3, 4])\n", - "y = np.array([2, 1, 4, 6])\n", - "xy, x_ind, y_ind = np.intersect1d(x, y, return_indices=True)\n", - "print(x_ind) # [0 2 4]\n", - "print(y_ind) # [1 0 2]\n", - "print(xy) # [1 2 4]\n", - "print(x[x_ind]) # [1 2 4]\n", - "print(y[y_ind]) # [1 2 4]\n", - "\n", - "x = reduce(np.intersect1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2]))\n", - "print(x) # [3]\n", - "```\n", - "\n", - "### 求两个集合的并集：\n", - "\n", - "- `numpy.union1d(ar1, ar2)` Find the union of two arrays.\n", - "\n", - "Return the unique, sorted array of values that are in either of the two input arrays.\n", - "\n", - "【例】计算两个集合的并集，唯一化并排序。\n", - "```python\n", - "import numpy as np\n", - "from functools import reduce\n", - "\n", - "x = np.union1d([-1, 0, 1], [-2, 0, 2])\n", - "print(x) # [-2 -1 0 1 2]\n", - "x = reduce(np.union1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2]))\n", - "print(x) # [1 2 3 4 6]\n", - "'''\n", - "functools.reduce(function, iterable[, initializer])\n", - "将两个参数的 function 从左至右积累地应用到 iterable 的条目，以便将该可迭代对象缩减为单一的值。 例如，reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) 是计算 ((((1+2)+3)+4)+5) 的值。 左边的参数 x 是积累值而右边的参数 y 则是来自 iterable 的更新值。 如果存在可选项 initializer，它会被放在参与计算的可迭代对象的条目之前，并在可迭代对象为空时作为默认值。 如果没有给出 initializer 并且 iterable 仅包含一个条目，则将返回第一项。\n", - "\n", - "大致相当于：\n", - "def reduce(function, iterable, initializer=None):\n", - " it = iter(iterable)\n", - " if initializer is None:\n", - " value = next(it)\n", - " else:\n", - " value = initializer\n", - " for element in it:\n", - " value = function(value, element)\n", - " return value\n", - "'''\n", - "```\n", - "\n", - "### 求两个集合的差集：\n", - "\n", - "- `numpy.setdiff1d(ar1, ar2, assume_unique=False)` Find the set difference of two arrays.\n", - "\n", - "\n", - "Return the unique values in `ar1` that are not in `ar2`.\n", - "\n", - "【例】集合的差，即元素存在于第一个函数不存在于第二个函数中。\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.array([1, 2, 3, 2, 4, 1])\n", - "b = np.array([3, 4, 5, 6])\n", - "x = np.setdiff1d(a, b)\n", - "print(x) # [1 2]\n", - "```\n", - "\n", - "### 求两个集合的异或：\n", - "\n", - "- `setxor1d(ar1, ar2, assume_unique=False)` Find the set exclusive-or of two arrays.\n", - "\n", - "【例】集合的对称差，即两个集合的交集的补集。简言之，就是两个数组中各自独自拥有的元素的集合。\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "a = np.array([1, 2, 3, 2, 4, 1])\n", - "b = np.array([3, 4, 5, 6])\n", - "x = np.setxor1d(a, b)\n", - "print(x) # [1 2 5 6]\n", - "```\n", - "\n", - "\n", - "\n", - "---\n", - "**参考文献**\n", - "- https://www.jianshu.com/p/3bfe21aa1adb\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": 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wZCNhwSiM69QTY5IdqPGI8K1WzIlHGFlHiagWzIOUYaQ8XIozB2ei7y3X4hePDsXEnadR7qcRoiJjKI9UgiWtqDi1E0vbPIa20/Yj7kwtvBIbRq/ru+tQanioQs7OCRjd4jb84DfdMTOxBEVekjxOs3LBceSmmue4HFbUVFehqrISlV95V1RUoKqqApV1Trg8jG/wweMqR1naTqz+oh/GfD4IfT56Cx/d/0e8taQAWVU2uAJ+eDwVKEtagcG//Bm6TI9HfCHTRARgfCXItyF1qI/VCFwaARVEzmllmeSWwZeOH9UFDuBsw1Q4GIBNksOgY6Wuy8BrzvDJii0EYE2chjUmiRJiBTssceMwpl1rPPLiROyrBGx+H4KWeGwb/SHevPtFdN9YhcI6M4BZArzpnJlELmKF+NJPKkeIYWPsjkkWA0FZ2OMLlaMsYzsmXTUMu48VooIP15gv5VGZcVDq/owHCxoh+OqKcW7tm3jmztvxl66TsSizXpxThkyowH0q3eKlSX2Eia8ZGIWQ4RciKPUjTjuJlVLzGPcZyUt4s6QxIBHzyNtfcQjxM3vgpdsfQseNZ3GqmtO+1cg7PBuf/+1m3P7+Mmw5XgoH7ajEl3LdoVrwpAocyZ31MY2JQJMgUWqZqGlZpOWzQXN9WBC+/F3Y8MVruObn1+Pxz/fhUL5d+RGUlEN2hAKJWPvWn/HsVXfggWdHYUcN4AwasGWtxoxPhuDd9guRHAzBHfDKVJ6s1hNb8+2QFEZb+eCFJXcXojv+DL++7nn0mHUAaXXmihzDZ64kFEkGnqpMZK1qhbteG49pO7NhMSjPE4vGD3yPtOEJMiGg+sR6rB54P/7t6lvx0bIMJFs4IFDOl6izyJWogB+lxflIT0tCYmICkpKSkJCQiISEo0hMTMSxY4nyeeKJEpTWOeD1WGDJ2oaorm+i27DliEk+jYw9szH5zdvxwzbLsfNEDsoqLKjIy0Dmhql47d/vxdjV6TheVg+bvRalThXF9e3UcKQo6uOaLgJ0cvwI+b3weypQX2FBbWUd6urqYLVXod5WA1u9DTUOD6x+U2EPeCUQnSu1RCFR47tEFTtTpmBNzAT0iS+CIdNMZ7Dv8zfw9gsv4uEhe1DPLEmS0qAIqSuGott9d+KHXeOQVGJX2dskbUsQgQAXezR+qxcCQsfTCMHgMn7DkBQDvlAZStJ3YcpVIxGfVICKxlbATIzIpZTuTZtIBcdA0FMH49QC9Hj097ivRU98vOY0OOknY4mscjYVf/4u0ZzqJxcDsPyhujLUVFhgqbWh3u1DgIuWJGY1Yn1dykSizPATg6uyA37UJizA7E4v45e39sD8HAfKfKJTwZ6zDQcG3Yf/d20HjNuciTwfSRxjZRnjRZ+ZNr/x667p9sHGe7ImQaIowcrqCPE8VPwQBVxK5FVJ8zG3y+249rob8EFUITKrlLTLph4KuhGqicOklx/Bff/aDI88Pwv7nCG4Qi7krumM/gN6ovXsVLglD5VaEksnjZ6Kwc8a2WNjF6DszFU6vopMnFv1Nm676Zd4ss8CrDpuM3O5hGN/FIkKestQlzsPXf/wBkZP2oNjFZzJ5DHfVUwUezQxCiBQcQzpSzrhjquuwfXdVmJ5epXoT4yJ4iqXiOGjOuR3w+d1wefzwOfznX97vV54PPzMA5+fqQ1qUZq0Fkv6dMBdH0Uj5kQRKr0e2HLjsGvsm/jhT3th0uTe6NSmC1q/2BmffdYd//H/PsCk+NPIPLwCuzfPxbhUK4wGpGBovO6or/R9QEAUi6AfnvoC5CbOw7gBH2Nw33EYM34upi+ZjTmL5mDxnIWI3nIYaWerRLXlQK6GS+Uc0U8yJN45COvRaVgVNRn99pWruJy6g5jz3nNo9lBLPDEjU6iJmuquRfbWsRjx3K245v7ZWJdThRpZEaycG6rX38YwTGvJhTqBAG0SzYMfASOAoFGDyvT9mHLVEOxKKkB5I5MokhqDyp5fQrLVGMESEAy/GzCSsey9P+HJG1/BK502IZMqvp+KkDmhR1Iiij6VQ6VMUSH0eG0wsqMxdUA3DBw1H6sOFaDar0iQpJNoAIokbQw7IDK+UCVORfXGkD/fg1/fOha7qxywcooVIbgKjyB18qu47sd3o/OCwzhWaQaVS4yuUsG+ndr7PvTI/90zNgkSpWKETCZu5kzxSKCxFWc2f4qRLW7Cf/5XS4xPqkChS8yJDOAhjx32xNFo9+TtuOWelmg/8RBKA6RHuYjt9yo+6dQfw2PVwK/yqqh7GK4qVJ3eieWzZmL+pNGYMH0RJq/cjoQDszBj7jR8+dnnWBS9G8cqAgj5vHCfWIUZg3uj+6eLMP9YrepiJBnSQWgJL2TQFSmZHoa3HLacZeh6229wd/N+GByVhXo+G42CLG1lHBg7Uh089Tsw89m70WvkSkSfUoZKlkiLweKy2Yu8V7YXxdak5TSk45n0TXk8okUrwiZdWJL5qcR0TEUjHq23AiWJSzH03h/gV7f3xsioTJTIUl1zVaRYbJbAA4f1DE5sm4G5EydhztSxmDR9NsYtSkChB7JiRQU7XVTwi9q98p5p8CgRemA5l4W9G9Zj/dEClNndEvRpOCpQmrkfUyetxJYVn6Bj+3fxVIu30aHTu2je7GX0H/0lRn05A3NXb0dynRteYiah6hfHqjAwlZ+bGErYmfx1UWn0r//cCLA+TdX3ogfx1ecj79hGzJo4HbMnjcSo2euwdN1WxG9bgNGTJ+PLYeMQFX8c2bXsf+wRYRmElzMHY/YLfhy0oixlK3bNGY3xm/ZiX0IKMjJycDr7LHLP5eF0/jnkn8tDSbkF9Q7GaCq7IylMZPWeWngv7T4UQp2QqEkYtKdUBRvnr8TYvz2KWx9qhccXHFdrkmX6vxa5O8Zi5NO/wjU/64U5SSUo8asFGsqmsHxs7+qz8/3afKKgqxK1J+MRPW0AerzTEm++8z66fDoTy+Kyke8OwiunBhHyOWAvyML+xaMwqN8n6NOnP/qPmIwxy/Yiz08nVYUmBAMVqEqNw7SrRyCWJIo5+gJMJan6l4Rby24NVK/VUn5iEanNEuJD7GUaUZEgPqcYUCqBQRuyFrbD24/8AQ+8+gkW5/jhNRyylo+hIJIeR3w93tGc5pNgcz+C9mLknkhH8v447Fw5HzNGj8Ko9QlIK66F06fiq9Q6S7PgEj9FGkw67Jc8XtJI5HnCxxfg6JyO6HzHHfj1bWOxp8YOq/m07qIEJE97Htf95Gd4/fPNiDvL2FIKBVwEpZ7touaqf72CCDQREqXkTLJ6qjjiaJCkGGdwZHp3dPj9Hbj+wXHYWFCPGuYYYgfx2+EoOYpN/Z7FX556Ga/1n4eY4xZ4ODftOIR5rVqg1wcjMD/NE7Z8KsjQW4nS7MNYPn0JFq1Zh/Vrp2Jc/554++UP0GHYp5g4vidaPtMcLT78FKOOlCPotaN+zyB89Oc/4a7nPsbHm86pdbUBJp5k12QHohysbK10fDEyLnisWYhqcy8e+uOraPPpRmTalXEX7iRBoOw8LnjdqdjU4Ra07jcZ4/dXi41QZIz24yskKixVC4HjcCHmPqImx2PF0yJZkURz7MRmRxZSSJmef5tBjyEP6guOIv7jO/Dfv3wY7b7YhINV4SR7anAJGtUoOxWHzfPGoPvgcZi9YCU2bFiDxbNHYsTQjzHthAMV7gjLKCQqAKfDgfJSC+xen0wfCIkMGvC77SgtPoPC03HYunUdFq5Yi5h1UYhZtBibt25FzK6jOJRZBKeYOU4ByBMrL1SC1V2ywlAET5HhVVxCpEY9IpD1Qd8xAhz02N4uanOeMpw+EoeVsxZiScx6bIr+Av3bd1QpBUZ8igmff4Tn7nsArw5fhhUnatTqXgnLZH81CYmQGNXbnIXx2LduKibOWIrNJyyocbH/q+kjBlxLpn8JcaaCYyaoNa+jSLxoWYrsBIOoT5yCNVGTMHi3BZK76OR89H7tQfz0wTZ4fO4pZW+EyNmQu30cRjW/Dldf8zYm7y1EgStsU0gaeElVZhmcZSWtyj1nhJyozj6KpMVTMWPuTEwY+RkG93oPH7z9Ft56vS+6D1+L/Xm1qLWWI//oFqz+cgRGjZuAz6fNwqx5C7Bw4ULMWbQSM1amIrvaLkQqFCpHZdoOTL5qOGKTilBOB0tIFO0M7aFaAUfopGwMG6PycnHdfFNrIWZmvJniobS17LPm3EUQsKdNw4i3muHuR97EO2sL4DCUEiWLh8zYWZ7LmiM2imCyjqgg+eGqL8aZlL3YvnIplm7ago3RS7BuayziMopR5lIr98Ln8ZlIsVmnTIaqFpRT9VK7N/iRg31j30f7n92B39w6DnE1dtSRRIUAV9FhJMx6Ctf/5Ed4ZuhabDrtUG2DJZMVnA1Q9r8JM/1dgxFoEiRKgjCF4TO/k0dEIwYABuv2YvWAVnj0urtxw8sTsfpAMpJOZOD48TSkpxzEoV0LMazV6+g4aAGWH8xHnRGA33AgVLgeQ194FR07TcTGsyrnkUxBMbt1RRKSd81Hr2G7kV7thM2Vhi2jOuCVm+7HrR2jsG/LF+j17it4ssNwfLL7HIJ+B6yp8zC1b1d0GTwDc47kyx5+TIVO48DOyQgcEenFgNGAsKMF4XPXIGXSU2jxxz/g0Xe/wOKTDpF26SUp75T8jgHSRTj46e/xfJch6Ls2z1RumPdFbQtBw0MXWEx40AVbVSlKC/NxrqAI+QUFKIjgXVhYiKIqF2xeM6M6SYW5UlGMTDgLsUzTkXh6JIjcVZmLM8v/irtv/imadZyI2Ul14o2xPIwFsJfEY9vUzni3+Qt4dNQhpJQ64Ap6cXb/PCwf8CTe31WLfCvRieDFgtDAivGkxxcyg/BpiPg5r6OSlyryF1b2OMGg3qLuCRFU+1eFVTWqAMyqE5CtGXg95pvySM1pEhVB3fzTHCJShZJCzDJ7i3Zj+4oVGDftMIoCXhi2/Zj9ZnM8cfdreG7wEpyMHYY3//wYmg9ahDkpleYKUfoZbGt0k1SQOPtqADbkrJuIpXNmY8axarXlkKn+kLgwhYqEJcvWUmpLIk5JsQOzeV/8Vn0oAFviZERFTcbA3VUw6CRmLECHV5vhB83a4PFZ2aJlcAAPBR3I3T4eo58miWqJiTvzcc5GkmZuNUXeaKoaVLyZ2Jd0jYqHr/40jseuwdIJi7H7rBNWpxv1ObHYMbEL2t57H37+r0/io6kbELN5MRaO6Ya273TEl7tycMbqgY/2x1mCsuTNGNp+GNal56PIQ4fXgsr0HZhw1RDEJpWgXGaoFFZ8YpWeQK20VlONdNC4QloQumSLYr+k3SaxFHXQTOHAJLwq514IKNuBBR++gj/d3By/77UO+TaDafmE5HB3BZ5PkyL4CRqcQWAOLvZ/xskasjND0O+Ep/AY9iweiynTpmL6mh3YdyQVCceLUVLrhMevlCypXWJLZzbAJM/Updwis/mRjT2D26Ptv9yJm26ZgJ21DtTSlhG+osM4PPcp/PI/f4QnB0ch5qRVSJzYK074ioDAtqtfVxqBJkGimCuDHhwD7JibUvRgei+n5mDkR4/g339xE25r8S46dOyBbt17oXunTujeuS96jIzC8kP5yKv3wCtGJoSA145Q5iy0f/oDtO25EgerSD0Y6q1k09rMbUjYMB4LU1ywuX0Ilu/G0oGt8dhdz+CVBTk4m7IFO9etwtrYNGRYlIilNHylCvlZOJHkqdbQuyHBoX/CDsCF/361t5Thg6e2CIfHv4Tn7r4Ov2jeHm+vyJYdoDw0ASQF5AU0cJ56pMy7E8992B2dF6WiRliNJD5RSUPp/4i6RYTKcHr/RmyaNwcLZi/CwtkLMW/uPMydO/cb3/Pmzcey3eeQW1EPQ7QaYsJnIHDkLswK7LkQiRXwI+Rzw27JROrK13H/bT/Fvz3WFe8vSUOlWfZA0IHsmN7o+/y9uPnenpiT60JhnQP2+rNIXj0P097tifknDJ5DSMwAACAASURBVFS5IzMOLIv46KYHSozCxVP4KoVB0u9JmRV+JEdq6oCYSeQ74KfxFOapVkyZdSTnkp4xlQSPiaxoV7pf6/s1IgKWI/MRu3091mXRvvgQLNiI4a89ij+/0BF9ojJgzdqCqMVrselYAXK5AET5L5Ixn0Ok0iyoOjPnUyrW95mABZ9GI9nmhsvlgcfthMfthsdth9tTB6fbDbfTA6/HrwKx2cbYFCWVi0y4iRYTbnpcnbcqahI+iS8TJQdpC9HplQfww4fexGNz0sTesN36Q/XI3vEFRj99Pa65uh0m7MhHnp0OHK0bA6NJmEz+yLABbkgs7kUA7pRliF0/H2MOVAvJCwUVmfFWH0dGVB88f+01+M/bH8ad99+CZ999F51jciWWVNKR8FrBANyWbGTNeBAjtqfiQAmn06pQkRKPiVcNRXxSMcpN+81pQT6vIelaLpAolYCShCMyp0pxQjqrPP7C5uy0EbTDvoAHzrztmPfBy3jwmlvwm//ujugcN6pdzLtpyM4HQk5YGDEkikB5RVVkhbCQLI5y0GhjJGdp/Tmc3TMPc3q+jTe6LcKS/enILq+CzemCJ+REKOAWeyxqn5A0PjBLexb7hnyId35wB264ZRx21DpQx3sEAE9hApJmP4+fX3cNHh+yEutP1qnVnLy/xHBRNw+3iEZs/PpSl0SgSZAoBi2GfFw1wXwZ9KrYsAyU7OyP3q/ehBvvexrto84i55wFxRWVKK0oQyXf1XbUujkPTiavCEHI54I3YwFaPd0RLXutwr5q+pEkNlxdEoThtsPhqEOtRykx/uMLMe7D5/CHh1ph7FEfamxWOG31sNs9cPsY7aNWopDscCUGuzI7JFfQSTnp+XFzXtIPJr7jd/DCVnoMB6e/jlc7tsTLd92CZr97Bn/uHI2TCMIt5k5dh0Yv4HUga+G9eLFzd3RYcgylNBoyzcauqabblPdGg2TA43LAVl+P+vp6WPm28m295Nvm9EnwtlohpJQeWccmnhVNsNfMWcJnDaCu5AjiV/bDk891wEvNr8Nvb2mO13qswjG7SuUVqkvF2q6v47kbbsONj3XA4Nkr8MWoQRjUrxdGfDEfMfGFsHq5+zor9NIvc6xRpoRGT1gmoaAXrwyMOkYlMFRcTpnU8Hwq78RVxyq5HjEW/c4cSjgc+mAEPcpL5jUjK9qlC6+P+I4QMNvJP6xIblFSD5vDBit3DjAcsB0eg7ZPNcPTbUZgUboXPrcD9dZa2F1eeP2ckqLKotQM9ntpZ+LwGEBlLGb26IfO7T7ByKXzsGjpEixbvByLl6zAkiVLsXzxQqxYuAhLlq3E+h3JOHG2VhwTtck6M5wznpAER63qIjmoTZqIVTETMHifRcUFnlqFYa89ghuavYXHZmeIgyblgQPZ28dj5BP/hR//sAtmHipGkZvXCqdWcKkcbKIyc8pK5T1imy9cNx27lszCukL1bNIvqJgHXKg/dxA7+92D3//2Olxz+1/xxuj1SLTT8pmkzNzSJmAvhfdAfzw7dw+WpFUDgUpUZMRi/DWczsuHhTZY0osIMxF7KHZMsqozLYKKA1UO0aWbi1L5leqjVHnlCIva5rMhVHAAczp0RJcXWuDJh+7Fz3/3J7TbXIZTtezn/CfRSyqLvGzRI9YOblGHzOSddBYlRITfMUCf9twPn8uK+vIClGUdxsZJH6JbryHos/AoTjJHlihp5nQelW0iJVhmI3Fie3T+zR349e+/xN5auwSW0zX0FB9GyswXcd1PfoxnPt2GLaeZaNOc56RNkhmNS2Oij2h8BJoEiZINg7lURfZUM/caQh2SZr+F9x6+DXc17415mX44PBzcmadI7XlEPsN93GSeXY2ogM+FQPpcvPnUB3izxwrst4h/oabbhPAoWV28QgRQFDsQfV9/Fn96eiQ2VHthNZRKRNJCYiadkcqTJJdTGx2TPYk8TNXDjC0KBzL6gx7U5e1H3PxhePmdjzEmJhaLx7TDe80fwB+f7YvVxSFYvSRGfCueEPA4kDbvLrz00cfosDgdBeazhP0bqlDKG+P9VHyBOHtCLBrAAsKkgafQWIjDqGRpSdrHyS2JwTJgOxuPjTOH48N2/TBsdSK2TmmJVx++G83eGITZGXYlkxduxJjWf8Y9dz+DF0ZtQOLpPKQmH0PikQSknMhFfi2nBU23NIK2L+W5iDDxXDWNZ9aZKZqJd2l6k6JdES/T2ZQEiBy2ZCCk2seBRE2fqmNpKE1jTyIsS88bgGEEz6EPuZIIfBOJ4neq71JRoUoc8NbhzKrX8XKzZ/F698XYV0dnSMXLCFkX0qTaE/snnSK1jF2RK1h3Y/6AKRg7bCV2nz6F7Jxs5ObkIDsnB6f5Mzsbeaez5fNzRWWosTnFhqjAZjpkKpaS9oekgu1bxURNxOA9phJVEosprZ7GXQ+1xGNzMiU1gpqsrkL2zjEY+Zcb8aPrR2JJRglKDcaOmqqKOSUkA7qUm52CVMSG40u+xIaZ07CzjP2Ryj9DBThd6YffWYaapMnodNdNuPEnv8Of2n6KGSfqZFin2qL6VFCOQ+IIvDp3L5anV4NJeS0Z2/Hlj0ZgV3IxysWRVXjTLpoijIr9EYKl1LiwQ3SpVkILJeu2qSKLz2mSOlcVajK3YNYHb6LjJ5MwZ8kMzBnSBo9dfwtu7r0He87VmCq7ibcZVC42j7izRkTlDsd+ctpPZX+nbeHNgl4rrAWJODSrP4b16otBX0Yh5mgx6oinBLoru6LsKJ+VWBbj+KKu6POnu/Hr2yZid00d6gX/ENxFh5E6pQV+8eNfoNXEeOwtVOerPf9YHq1EXao9fFvfNwkSJUKtNEIGYpIo+AF3FjZ0fwrP3/YwHmk9DwdrQ/CI0WFMD1eBqEBOdlYaOdUpuFmlE6HsJejx7Hvo0G0R4kuUFE1TQK/SY7Wg/NxxnEg/gzKnBQdntEa7Z17EY+3WID3ggSNkoDYvHedys5Fv59QdaRQHXRUDxQBSWY3HXk3SJ14fiQ3NpBvVOfuwae5ofNxrIDpN2Y+0MhcKE2di7AeP49a7X0KrDVUosbLTKhGEndfrqUPqtDvxUpe+6LryBKrF7TQ9YFndQk9HIjOAkB1VBTnITktFemoGMlLTkZaWdul3ejoyCmpQ4+SqEIWFNEp2csYOmPEDQb8b9rP7sHnGUPTvPhB9x+9Aeq0Be9ZcjHzjQTzwcEu8uygLVpqivBUY8tajuK/52+i64axEKvilXpRRYdJTzuRFSlEuJlFiaMXQKWPDuhPohf9x0FPqlPpJTqiIksSw0OiKQedzeuB21KLKUgWrmwPXBWVLYhokGzLvrF//nAj8IxLFz0kYvHBW5iM/+wQyc3NR5jiD2IH34slH2+K9L+KQTSIRcKAqMx45xSUoc6t2RbWI9ENCmNnX5Tiu1j2F7Z9PxdJp65Fsk7kY5YpIWzUdI+E0nGbjP16HA6xa1yVkzmzLzBHEfmc/OgNRq6dgwO4SITYh+wms+/hVPNf8r3ho/FHUcw89Lt9HMU5EDcfg+2/Df/5lObadqUIdY/08DjhqLSiqtMNjiKUzF5AoRTsQcuF09EisnjUGi046Jf6Ge7ypRJMeeOpzcW7LQHzw5D34w49/jFt+/wxeH7YVeTYn3IFw4tAQvHVnUbv+XfSIPoJtZ5iyxYLy9J2YdM0wxCWXwGJO+wUMP7z1pSivtsHKkAnaLklEqtR68f0iaGzKDNKRYn8WFwghRymKj67Fss+6oE3b/pi8JQ2nSk4ha+NI9Ln3Blxz76dYePgcLH61ebNaDX2RvRB7TYsethl8Pr4VwfO5ilGYsQ8HtsRg7cYYrF8+E4tXx2FPShEqrCpNDmc0eF0SJ1k4IIody2lH4c6xGPv6E/jV74ZifUE9qg1+HoDjzC4cGnMfrvuvx9F7VQLS69XnzBUlkxtC5LUdiqBZNPohTYJEcetHtV8cZ7q5S7kHwbJt+PK5B3H3717F00N2IZ95OERVUFN+VH4YJ8DOJVsQSIM24A24ECzbhMkvvol+naZh0zkGOEhEAYIhN0pPbMfW2cMwZOhK7D9zAPM7/wUtnnwDT4/dj8qQDz5nOY6umI2YmC3YX8H4CC/cdTnIOZ6E41nnUFhHs+pWREqkXYhszsBEa0EStk3ugw8/7IGWY7bhjBXgwrSAZS/Wf9YKj9x2H378URySS5wSzCgaWciA01OGlJG349U+AzFg82lZ6SPUQ0J81POKERFJuhLnkuOxJyYK66I3YH3UesSsjcHatWu/+R2zDuuT8pFfSxWJ1zRn4MUYqLDsgNcK+7kE7J7SGR+17oYeYzfhWA0JloGQNx1b+ryGV+98DHd/tBInvEG4irZgyvtP4/kX26LnlkJmQxFZOhjwwF1TgtKzp5FlNeCmhYrgRTwUKVJKHdUlmc40V9mQcPJ7MXhiBC+oCCqIVBmmsDfJKWGvPRdZyVuwbEk0Yo5Vw2Kj2qbUPBJxmeaNoGz6kP+rCHwTiWK/dyF/71IsGz8Wo2etxKGSeIx75mbc9UJfdF2djsqADf7aLGybOgAxBxORSQeHdiZA0uJVCoEow2paKRB0Inf1RKyZMQ3zj5XCIW1JRmBxqMLKKQkKaROjGMXJk/3fmHBTKTV0Cti+qaoLiVozFf13lwjtCoWqcXxBN3T/22t4oEsU0ryAkwkiXSdweNIn+Oimh/HwuGRkVLjggxM1Z5OQELUYM5buQEapDTY/B/nwXnNUOUKoSpyKmMWfYVhULur8QXhkNioAj70YeUdWYMp7j+K1Lu+j5XOPo8WNf8Afb3sPY9cfQ1apDU5vEAGPFTXZ+7F92IuYfPA4Umv5dBWwZMRjytUDEZ9UBAudSvZ9ayFObJiNZSs242BmAWq9ql8GA07BMzJrIBENakU1yRmpqKcKZcdWY/mI9/HGK20wMLYcBbWc2nehKmMl1nS5Ddf+8DH0WpaM5BouUmKd0VCQDtN2KIfRH5ToNgnHoN3w+11w11eg9PQJZGTEYcvSiZgyejQ+Xb4bSdUuWGW3CVUeke/FeSauHK/8olaKIhUAHNkbsGpYa/zxd29h8qFqFNvogNthyViOld1vwA1PDMesI9koZc4t2uDz3rRw8f+rnaxJl6tJkCg2dPHKOGfOAEaXFfaUeWjz5N34j8fb4eXF6fBQtpbO5BUSQMNEBUsNmFRtaPhImHyA9TDmvvYE+rQbiiWZKiaKod8GipG6vA96PXALfnpnfyzePAudWjyK2554By8sTITH64A3fRFGjFmC8etSUcb4LLcTZbHd0f6J3+KRV3tjZHydpFGQJa5mDLM35IJhP459g17H0w+9jL8OXoI9VT5Z+MrQzmCoEtlrh2J4sxtx7U96YXmKBRXmag8qJYb7FGI734G2Q8ZiwqFCNXVnRreqlAPCJqT/Kpmc8/f0b+nERKrzmPH6ooubwfF09dRuFEJJHZY0HJ38HB767RN4b+h67DrHJHISBSZKX250Jwx89re4/d6uWJTvQYX7LPZ8+Ta6tW2JVkuz4GEGZ8MHw1qGU5vmYX7vdpiQWA+Lk4pAhC+ZTqHJJHDKBrKYJH0Gt0oI+GXLCSqP4nNzypUxceLJqfgEWZGjnHCUp8zE+O534d+uvxU/eHMDtp6qBVONETdZ5uxX8QwRlk4f9n8OAQ7JbF9hVShcQLYF9noHDg5viTZ33Y8/vTAAG3bPwxs3/xq/emM4+m3PkYSZ9QlT0KbnGmxILIad7chQ8TE+OCR4OUDjJJyHV3QhWLIHq2bNwtt9ViPJa8Dp96os2FTIZXtZP7wSRG4OjDKQq3KJukVnTRRQ9X190lSsjpqEgbEV8hS0bL7T0VjYpzMee6QfFp8zUEXyf2It1vRpj+ebdcGcbKDaSX6QhyPzB+H9f/sZrr36NvReexLHazgdSZtIddzckN11HAlrlmJM+4XYW+VEtccHj78eZ46swIzur+HGp6YgOrUCRVnrED3wJdx/zQ24+gdvYMiyTGSWOGA/dxRHl4/Fsy8twMZcCyqFllRJTNQXP+qLuKR8lIlVKkNZ5nIM+PdrcOPVN+BvQ5djR5GaEpSFQ6LvRUijZDaedk6toPOd3YDZ3Z5Fi6dfxrsL01BiuOALucWyByqTcWJlW1x/7TW4p180VmfWm3n4FMYynSqqjyJWLAFjobi4xFNVhMK9SzH1vRboMHwG5h/IQYErADdJjhm3RMdLcl2Zhkk0RiFlgE+aBzPGhxD05eL4xhH4+J7b0HLOcaTl18PrOYeUXePQ48mb8cSUY0gscCoCJ43KEMMcqToXbt36Z+Mh0DRIlEzFAMGqkzi+bTo+aNcS7Z+9BTdf/yP84Prf4bePtUav3rOxu8iOar+SoQ16dCJJ8G+aURIDenrMcHsG24a8gF49emLorjK115I4JDaUHF6Ahf1eQbMXPkSH93rh81HD0LdTD7R58X0MG/kpug2Yh/nbM3Cywi4ehihMB0ag28v/jRsfeQsvTTiKKhmAmcFXbcYZdBbDnjIDbzz9OtoOXYGY5FKV44jUhNMAQR+sx2Owvl9z/Oba3+H95ceRUsEAU7IwK3z1uzGj2X3oNmw1VmWHV/qZMQXnpyrFjquAVxIN5ciKhxZpc5KOKh6waD5mAjqqPtxGoRCF+xej101/QKuB0diYXCarF0laxOCEAGf2Wiwf/CIevv8RPLP8DHKr7bDnxGHLrMH48P2O6D9+JT4fPxKffj4dM5bFIiGrCJXcgoX3jOQlHr+S3sMkikOjKrLodueXAkvGdBncLsRgiKepIjlM9hWAvywFKRu/xIAO7fHEL8ZgbSL3+FJL17k4QNpQhMWL5BH0Md8FAqzAryNR3C+zHic3D8Hwbq/jz690Rc8PhmHqF8PwQauP8GGbrhg2bAy6DlmA6KNFKLByvaqaFuaCLZl6E9sUnkpniFFQcsfV5CRg9/Ip6Nr9M6w+mIf8OtlGVrk2QujJYyQaXTVg81fptxIPqWwHO1d94gysjpqCgXvKpD+LQ+CuQ3HSLsSMG4KuHwzBqC+GY3D/fhjEQX5zJqocARgMewjWozRtN9YN7YoOz9+Ldxcdxt5Cl/QZOiEyDc4+EXTBWpqFlI3zMLrTB+jWoQPebvUe2nUejDHzt+HQqXKUOf2yurDuXBISo6ZjaKtX8MJTzfBgs0fxzGsfofekGGxJq4bFQVJDpCyozNiLyVcPxq6kYpSJA+SGozILB8cPxLC3nsL7oxdgbppVCI0E0zeELcjsAtMZUPWqw7Epr6Nj+zZoP34D0qqdss+ohDnQufZaUH5iDT5pfhPubD4Cn63LQikNh0GHjLFPVKADMmvAqUwqZrbcPdg8ayyGDx2DMev34+jpXOSVVqLG7oFP4p7IjpSXKbFhMi1JGmxOS5K7y3EkiWpj9lDAC3fZCWRvnoDR/TpgwKgxGDpkBIb1H4IhM1bhUHE9rB4qUGwWDBWh2qnsuRja76L7fM/v2TRIFKVWegauKlTkJWPrlg3YGr0YK5YuwIKly7Eiegvi4lKRb/PBJY4Ejw/LFOZ4KdoFWzU9MAdOremBvv37oOXMNFSTq3DaKuiHs+oM8tLisTV2P+J3HcKJrEykHz2K/Zt2Yv+hg9h18DTyym1wkZnxakE//OXJSFg/Ej2Gfo53JyTAIX2Hq7yojjHDbz08lmRs3hSHg5nFKLdTBhEBWU2bccrRWozi9J2IWrIMsacrYHGa29A4y+E6Phtt72mN4XP3IrFe7bUlnqR6GkHn78Z5ASvc8v/um/CHX//z787jIUJVlOE2bLAVn8TehdHYn1mOchtJqQq4lYtRDXKUID8jHls2rpcluvVurt2tgSUvDYd2x2LP0SwcOXoIBxNSkZ5TjhrO0zboRWaonkf+VwLcRbaFn6oyy3FCztTxilWGyaE5VcIB0V0NW8FhHIqZi3cfno3YjBLUymoaFczZEJveoEfRB19hBMx2cP6u/JvkxwtrWTrSk/dge9wh7I89htzck0jcux/7d8ThwMGj2HU4B6U2HzwcXE1bFG5avAo/u9DupFsg6KpGbX4q4nbEYk9CEvYdPIi98XHYExeH+Lh47InfgyOpp5FvsYozxsFeBmT+ZI+j42eo6Wlb4gysiZqCQbvLpP1zUCWB89sssGQnYu+OfTicsBfx+w/hcGoO8qsd51fkMT7TU1+BgoQt2DDoQfSPycBhph8gDqrwF57Jb4fNchrHdm5D7LZt2LhhC7bvPoq0M9VwB7iy2HxKww13dQFOHdmFrTHLsHTJUqxcF4u96UUo9zB9AG0s45zKUZkRh4lXj5JkmxbTCTI8dtTlZWHPpLcwZtFSLD3hVNNytKlSsPOVdIlfaEOpGRMzD8pStmDvvv04lG1RK+xoC+QZeV0vPLYSHI+NRszGY0jKq4adnVsOMacDZWuWkGw/Zc/dh4T4OGzdshPb9x5GQl4pqv1++GXBiomdXJwXCLcLVV8sk7I3F477u8/8Lvhq8nDiyE4cSDiC/fsP4/ChJKSctcDB7XIuqhel0YWvcwk49NffCgJNhEQ1Mjb07rKWYtKng9CyxzIk2gAvN80UVccMSJeWHOF9QwZcuVFYvnYZBqw5LV2aRlGmlEiiJAiVoYqRWQh6IBJsyZgdywnkLW2PZ1tOwoLdp1FhGGaMV2TXivAJvpeHyVRowA9/9XGc3j8bbfvtRXJ+DTwyAKhpXtp9/dIINAQBGZulD3Ma2Y7y4mykHzuIA3Qk4nYjfvce7I6Px4HU08irsKkUB8xKze2hqGaYygOFVDoqtsSpWLNmEgbGl8nYTMdMVh2HN7YNk36ZIuSgroKaVXAgvUo7as8cwbper2DeobM4xR2LG/ElK4lFZeLqZLqvSkULBKlE7cT4q0cijjFR55U3H2BYsHtiNyxctwnx5WqvTSpsIsFEaCcb8RHkUuzxsgDI44Q9Zy+S03JwqswGG4lNgNG4SqFm/ejX9wcBTaK+pq7pNwTcp7F39hgMf6c3JiXaUONRy/c5BadWV3zNiV/3ETuUpx6ntk/G/OhFmHW8RrhSWKoXeZ/Tig3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" 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" 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2bd+yz8Lz66Da547+WM17LXk37J+mvo/7NXw0tJMF49BtM7enMSmvWaACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBryCMZIJ+lJHJ5gzgj60+mKrb2JbK9h6UAPooooAKKKarbiRQA6iiigAooooAKKKKVwCikNJuougHUUmaTdTAdRTGycYbaO9KqkdWzQA6iiigAooooAKKKKACiiigAooooAKKKKACq2oXiWFrJcSnEUY3OfRRViuB+O3iN/Cfwq8S6ouwC3sZWJkzjO3jvUSbS0Gtz8d/wBtz4oeJ/2tv2nLTwB4Zlk1G0tbo2Wm2aMQpdsb2bnHG0Hp2r64+C3/AASF8A6N8P4ZfiE11r3jSeEtKsN2Y7O3fnCoEUMccZJJ6cV8hf8ABNDQW+I37bEOs3Uc7pYx3epGa34VZCQqhsg/Kdze/HWv3J6Uox93U0lufzv/ABt+G/jf9jH41SyaVeyeHryKeQ6ddWFwS4jBHBOemCOvWvT/AIT/ALd/x5+KXxr8E6RP42uY4LzUra0eytYkSORCwDluCSSPev0B/a0/Yl+GP7SHiH+2NS8br4d1yINxFcxbG6Z3q2T27etfnz4X+Gfgb9mr9sDwy83j/TL3SNHvEnW7jcT/ADr2fbjHWs4tpWluNK5+6Sb47MZJd1Tv1JxX4t/8Fbfh1e6T8ZdP8YiMrYaraiEsoPySJyQfru/Q1+gfjP8A4KVfAjwZcWVs3iv+157ptgXS7d5th4xu44zn9DXjv/BVbR9K8cfsz6Z4ts2lRFuIriJmXaHVgMAjHoTVocXZNM/OjRfgb8XPhb8I7b4x+H7640bQ5TH/AKVpt2yS89N4B+vX3qH9nzxfoHxE/aO0HVfjfrt5qejs+bi8vJi7FwB5YYnPy561Npf7SHiLXPgNbfBhPliu9RhjExycoWwo69if1r7W8Gf8EdtL1f4SQy6n4hntfGM6eckiMDbcjKhlxn9e9MR9TftFeGfhp8cP2Z9d0XQL3QbuC3sGn00QyxhYXVcgrjocDFfJP/BGP4nalaeKvHPw2vLndYQ266haW+chZFkKzEfXdH+VfLnxq/Yv+OH7OPns9tqF9oJLr9t0iR3iKDGdyjoOe9er/wDBH3w3q8H7T+pak1ndJZR6DcQzTSQsq7mliIBJ7/IaEtbie1j9p6Kbuo3VoZHLfEbxpZeBPDN7rF9KsdvaRNLJuPBUda/D74+fHP4i/tu/H46F4ZuLy5tZbg2WkaXauY4hGPvO+D7AkntX6Vf8FRPF0Hgz9l/VpRM0V/qMy2NvtPXfyw/IV8tf8Ea/gra6nrnif4m30RL2JGn6eSeFLAmQ47n7oqOpq7ci7mp4B/4IwXV74Xjm8W+PJrHXJF3NBYQrJHGfQs3J/CvIPjN+wj8Yf2O7V/HPhXxTNqOnWcwbzNOd0lRV5BkUHBH/ANev2ulmSCMySuscajLOxwB9TXk/xq+KvgS3+HPiWzvvFOhb5LGZBDLeRtlthwNoPrSJUmfK/wDwT5/4KGa/+0R4qh+HvirR0fXINPkuzrFuwUSKjKp3J6/MvStP/goD+058ef2c72G/8I6ZpC+DpQCNUaDz5Y2BAKurcDORg/Wvkv8A4JOpoOm/tIeMPEer+INN0aOwsHt4Bd3McC3HnS5OzcRnHlDp6iv0k/aI8afBfx18Ntb8O+LfGvh37Ld2rjaNRjkZWAyCArE5zilcrS+p8hfs9f8ABYmxuoZLH4v6U1ncb18nUtIgyhU5yXTPHbpWH/wUI/4KAWPxC8K6B4L+EWtSXK6wT/aF1Cu12VtojiHcEknOOa+Z/wBkn9nHwL8cfiP478PanrrLZ6dA0umXCuUWfG/35wAv514SNH1fwD8ULpPDyS6lfeH7wzwyQW/ngGJ8q7LgggY7jFKLUpOKHY+9/gz/AMEetc8V+C5tX8ceLX0HVb6JZ7WxsY/MKEgnE5fr26dMmvDP2Q/Guv8A7Jv7Zdp4ZvLqeGxn1E6NqMKkhJlZsIzKeODgj619z/su/wDBVrwN8QNNstF+JMqeD/EiIsZvWUmyuWwcncB+7PAyDx83FfM3/BTiDwtoPxy+H/xU8E6tY39vrHl3U8+nzpMhmglDbyBnBKsvX0q7iP2TjYMoYdCM0+ud+HviJPFngbQdajbcl/Yw3AP+8gP9a6KrMgooooAKKKKACiiigAoqnq+qW+i6bc395KIbW2jaWWRuiqBkmvzu8b/8FkfDHh/xxc6ZpPg+91bRrWQxvqHmhC+DjIX0oKUWz9GpM7eOtNBO3JHNfCOlf8Fifg3eW6teWOuWc2BuT7NuAP1FWbr/AIK/fBWGMNFBrU7f3fs23+dAWZvft1fsDWn7TNrb674Yaz0bxlBu864lTAvEIUBHPtt/WvhLwNeftZ/sT3V9pum6Lql3o8c3mSWz25vLRwM/Mh/hznt7elfUevf8Fn/h/bsi6L4O1rU2OQVkZI/m42geuea+6vhj4yPxL+Huh+I59Jm0n+1LRLhrC7ALxbhna1AX7n89X7TXx28XftDfEJvE3jSBLDU0gW1SzWMxiJFJONpHqTRe/tOfGLxB4Ni8KSeONbuNDWE232JJmKNCQBsIA5GBX9DWofC/wfq1z9ovfC2j3U//AD0lsY2b89tWYfAPhi2wIvDmkxY6bLGIf+y0D5j+Ye60m6tiBc28tuOitMjKD9Mivs/4J/8ABUL4h/Bj4d6L4N0zRdAvdO0uEQwtNHIHK5J/h6mv1p+OPxJ+E/wD8O22s+PIdL02xuJTFBusEkaRhgkKAvbI/OvKdD/a2/ZT8XCBhrPhm3eXhFvdPER/H5OPxpBc+StJ/wCC2Gv2sJTV/h7plzJgAG0upE+uQ2a39F/4LW2UbSm6+FWM42m11RI/XOdyHPavtGP4Qfs6/FxYrpPDHgXxN/EjLb28rDd7dRnH6VfT9jD4Dx/d+EXg7/wTwn/2WmSfF15/wWn0HULSa1ufhLdT206NFLG2uxYZWGCCPK5yCa+Cbv4hfDC6+Mx8WH4eXUHh77YLsaFDrMQT72dv+r6e1fufH+x18DFBA+EXg3B4P/Ektz/7LTh+x78CwoH/AAp/wSQOgOg2x/8AZKBp2Phb/h9Zpen2cVrYfCSSFYUEcStrqMoUDAHEIxXvf7En7fGq/tdeNvEOjT+CYfD9lpdml19st777R8zOQEfgYzg4+hr07XP2Cv2fvEkcsV18LfD8KuMEWEH2Ur9DEVI/Cux+CP7M/wANf2c7K/tvh94Xt9AF+VN1Mssk0020kqGkkZmIXc2BnAyaAuj01e9KFC5wMZ5NLRQSFFJRj3qbgBxwT25r8po/+ConxG/4acTwudP0yTwl/wAJANJaNF/eFTJsyGz171+q8i7kKnoRiv5q7y4Om/tBrcNKyeT4kDlgTnIuev14o5hpXP6VY38yNWxjcAadVPSJhcaXaSK25WhRg3rlRVyqEFFFFABRRSd6AEYgdao3U6Hv0qW5LeYAenaq10o2rxT6G9NLcrK0d/MsQUnByWHYV8EfFz/gq5YfDf49P4P07wzHqPhrTb77Df6h5h80tkAmMDjg5r9AbGMGTcONvP8AOv50P2noDbftJeP1xtA164xj/rqKy3Km9bH9G2j6lDrWlWd/b7vIuolmj3DB2sARn86uVy/wuYv8OfDLE5J023/9Fiuoq1scwUUUUwCiiigAooooAKKKKACimtSUDH0UyjIoAfSZqNjQtYSlrYdiSjIHU0i0MobBIzimtST8WP2+LWXx7/wUqsvD0h3QfatFsFA5+R44nb9WNftHaRCC1hjHARFX8hX45/tGCOP/AIK5aL5hCL/bOknLHA/1MeP1xX7JVqtgCvxq/wCCkn7WnxQ8T/EjxP8ACmxs7zSPCVvcm2WOG3dZL8L1bOPmUn04r9lG/TvWXdeF9J1C9W8vdMs7y8UbVnnt0dwvoCRTA/nd+BWv/GD4H+KpPEPgbw5rEWqtAYhN/Yss5VTngZTivoWL4m/th/tWzWXgVk1+wtrhw01y2mPpsQUdTJLtGQPTPNftTFp1pBgx2sMfb5YwKmjjEYwAAPYYoKufzt/tJfDnXPA/7QB8B6v4hufF2qaa1tYLcuWdixRSIlByxIZse5r6n/4LNTRr40+FkEjhXTQ2Z4/4gplx0/A/lX1347/4J26J41/a0074yS63IkEV1Ff3GktECJJ48bCG9MgH8Kz/ANvv9gG//a18ReGvEega/baJrOl2hsJFvY2eKSLzC6Yx0ILv+YqbBzHoP7Nv7Vvwg1j4LeEY18f+HtJmtNMt7eW01TUoLWWNljCkFXYdxXjP/BT74mfDP4mfsp6pYaT4/wDDOsarZ6jbXtrZ6bq9vcTSOpZCAiOSflkbtXjGm/8ABE3W5ohJf/E6xt7lvvrFo/mp+GZBitKP/giTPn978VoNuOieH1HPb/lrRyivrc1/+CJuuao/hf4laJNFMuk293a3cDsD5fmurpIoPTIESEj3HrX6cV86/sYfsixfsjeD9a0VfEX/AAkkup3n2p7j7Ettt4wFwCSfxNfRVMG7u5+H/wC1dND8cP8AgpRFoSSm/s5NcsNHcwnzF8tGUOvGeAAc+mDX7c2VrHY2cFtEoSKFFjVR0AAwBX4kfBGzh1T/AIKqWkd3zH/wlWoSYJ/iSGZlP5gV+31MQUUUUAfit+07/wAE+PiLrH7WWqad4M8L6lf+HNcuReLrbWrmxtvNJMm+XG0EfWv1L/Zp/Zr8L/sz/Duy8O+H7OP7WUVr6/YZluZepJbrjJOB2r16igBF4/nS0UUAeD/Hr9in4WftA6fe/wBt+Hrey1qdCE1qxQR3EbcYbjhunesD9jX9iXQf2RtN177LqT67rWsSKJ9RlTYfJQsUQDtjcc+tfS9FACDpS0UUAFFFFABRUdxcRWsLyzyJDEg3NJIwVVHqSelc1oHxS8HeKtYm0rRvE+k6pqUIzJa2l4kki/gDQB1NFFFABRRXzx+13+2Z4V/ZT8MxvelNU8UXqn7Bo6PgvjGWduiryOuM9qAPe7u3uJWTyZfKAPzcdashTgZ5Nfil4m/4K3fHjXi17odtpmiWEbn5YNLFwCPR3cEDHtXpfwA/4LHeIZvEun6X8UdC0+50q5kSFtW0lGilhJOC7R5IYcjgAUAfcv7bvw5+I3xN+BeoaT8MdUfTvEazJP5cMohkuUXOY1kLAKT79cV+PHwp/ZB+OP7QHinXdOs4Eg1XSbk22qNr2seTLBJnkmPeZGX/AGgpU9jX7/abqEGrafbXtq/m21xGssb4I3KRkHn2rkLf4M+ErH4qXXxFttLWDxZd2a2FxeRyMoliByNy5wWHrilYrm0sfkvff8EfvjtZ2sjwa14PvXC5EcOqXayMfQbogufqa9n/AOCbf7Dfxb+APx21fxd4+0S20bT00mWwhYX8Fy1y8kkbZXypGK7RF/EB94Y71+nFLRYVyJQwOCvHJzn3r52/au/Yd8GftZNpl3rt9f6PrOnRtFb39iwbCE5wyNwefTHWvo6imFz8e/ih/wAEj/ir8O7621H4VeJk8RSK3GLhdNuohxzvLAH/AL67V58Pif8AtefsZ68ZPEa+ILjTIWAddXZr+zkH91ZgWHfqDX7f3VzFZwPNPKkMMYLNJIQFUDqSa/Pf9tj/AIKbeEvAtre+DPAtrYeMddLGK7mvYfNsYBj8nbk9OmKB7nffsf8A/BSfwh+0NPpvhjxDGfDnj26LKlmEZrefaCco+MA8dDzX2fuFfjt/wTX/AGRvFPxB+Klj8ZddtI9G8N2dzJd2kfl7DcyknhF7ID3r9O/j1+0R4L/Zv8JQ+IfGmotZWc84toUhjaWWVyMnai8nA6ntkUBY9Opa4b4P/Gbwl8cvB9t4l8H6oupabcDPIKSRn0dDyp9jXc0EiUjBWxkZpk0yQgFzjNOBzzQAybaUwwwKSHbs+WnsoYYNCqFGAKB9D8Dv+CoikftqeOc947Q/+S0VfsX+xa/mfsr/AAxP/UDth/44K/Hv/gp9Cb79tTxisZxvjtEH1EEa/wA6/aP9mvwjc+A/gJ4D0G8KG6sdIt4pfLbcu4IM4NAM9LooooENdxGpY9BUNtex3W7YG+X+8MVM1eX/ABs/aU+HP7PFnYz+O/EUOi/bmK28PlvJJJjqQqgnAz1oGeo5pa5P4Y/FLwv8YfCdt4l8IatHrOi3BIjuo0ZQSOoIYAgj6V1lAgppB3Zp1fH/AO2V/wAFGPC/7K+rWvh3T9KXxj4rlRnnsorxYI7McbfNbDctk8AcYoA+vo1Krg1/Pt/wUe3f8NjfEcsMf6SoHI6bEr6usP8AguBftcRrefCW3WIsA7Q66SQM9RmDB496+Ff2pvjPZ/tBfG7xN450/TZtIttSKstrcuHdcBRyRx2oA/oA/ZwV4/gF8PVkOWGhWecf9cVr0evOf2cZBN8A/h64OQdDs/8A0UteikkYwM0ALXEfEn42eBvhDZi68YeJ9P0KNvurdThWb6L1NeM/t1ftlWv7JHgfTpbfTzqviXWzJFp0BI8tCgXLyc5x8w6V+Tvwj+Hvjz/gop+0JdDW9bltjMkl3c6hLG8kFuikDy0HQE54HtQB+yfhP9tD4L+N9UtNO0bx/pN3eXT7IYhMAWY9BzXtSsHUMDkHkEd6/Db9sD/gnV4k/ZS0Cy8Y6JrzeINChlUTXccJhms5Mjaxx2J9+1fop/wTU/aEufjp8A4odXvftniDQJvsF1IzZeVQAUk9cEcc+lAH1vRRRQAUUUUAFFIzbVzjNIrblzjFADqKKKACiiigAorO8Q+ItM8J6PdarrF9Bp2nWyF5rm4cIiAepNfK7f8ABVD9nuHUryzl8S30ZtpPLM39nu0cnOMoVzkUAfXNFeT/AA9/at+EXxSW1Xw18QNDvri5IEVo92sM7N/d8t8Nn8K9X9MdKAForG1vxloHhl1TV9c07S3YZVb27jhJHsGIrlNU/aM+FeikC/8AiP4Vs92QPO1i3XOOuMvQB6JRXjGrftmfA3RQhuPip4WbfnAt9TimPHrsJxXn/iv/AIKa/s8+FYyT45j1WUZ/dabayznj3C4/WgD6nor4f8K/8FdPg34s8Z2OgQaf4itUvJ0t4tQubRFh3scDOHLAfUV9uwyrPEkiHcjgMD6g0APooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAopM0ZoAWikzS0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABTVXaSadRQAUUUUAFFJmjt6UALRUaLtZiTnNK0gXrgD3NZvcYSOFxk4zSNzXIfET4seEvhlotxqviPWrLT7a3QyETTqHbHZVzya+JNc/4LN/DXS9auLWz8J6/qllGxVbq3ES78d8Mw4qS1F2ufoMfMKtjCnHFfFX7dX7SPxP8Ag9ov2LwX4W1G7nljMj61FEXitQPUAc55/KvJr7/gtfob3JGmfDm+ltlBLPd3qRsPThQ3X619mfs0fHLT/wBqr4RWni59Dk0uG6Z4JtOusSKcHHcfMKFaTsXF8utj8yvg/wD8FeviX4T1J4PHWn2vimwO1d8KfZ5ocE7jjndnj06V+jP7P37c3wt/aEhgg0bW47LW3QNJpd4fLkU+gz1qD4ufsc/APWNJv9Y8R+DtI0tI43klvIT9mxxySRxn8K/En4oafoXhn4yz2fwrvLyW2S62Wc1vIWbdnGFI5Ip3a0JSUtT+kGORZFDKQynkEHINOrg/gXa39j8IfCUOpvLJqC6bD57Tff37BnNd1uq46ohqzHUUgNLVEhRRRQAUUhbFAYGgBaKKKACiiigBO4r5d/4KPfEm3+Hv7Lfigs6i71BVsoVYE5L9eh7CvqJuBmvz6/4K8akJfgXaWzDa39qQc98ENn+VRLYuKb1PNf8Agif4J+X4i+LpCSZDb6dEOMfLudj0/wBod+1fpZ490G+8TeFdS07T702Vxc20kKuOOWXA57V8X/8ABHPS47f9mvVb0KRNd61MXb12gKP5V957aLKQSep+Inij/gln+0RJr2p3NvZ2N9HJLJIl1/bC+Y654zz1x2xXyH8Sfh/rPws8Zap4U8SWwttZ0+Ux3Cbw204HcDmv6d9tfCn7SH/BLPQv2hPjFdeOT41vNF/tCVXv7IWay7wMAiNsjaeOpDVRNzx79lf/AIJSeBvGXgXwv428T+LtQ1VdQhivktNKCwxq2d21mYNkdAeB0NfRX/BSfwwsf7IGraRpWnyTJbGERrGpby409cfzr6X+Fvw00j4RfD/RfCGhmdtL0q3W3ha4cNIQO7EADP4V0l3Y29/ayW1zDHcW8i7XilUMrD0IPWlZjTPwK/4J4/Aq++L37THhr7Tp8z6JpDf2jdyyxHy/3eNqk+7EflX79JGsaKijaqjAA7Csrw/4P0PwrG6aNpFlpav977JAsefrgVr496VmJsiuLeK5haKaNZonGGjkUMpHoQapaV4d0vQtw07TbSw3fe+zQrHn64FaWKNtUhDaKdtpAKYH51f8Fm/EFpH8IPBmkmQNcTa0ZvLHUBI+f/Q69H/4JM+Hl0j9krTroxbX1C/ubgt/eG8qD+Sivmz/AILSatp/9ueAdNtp9+qR/aZ54M/dV1jVTj3wa+7f2H/Bv/CCfsueAdKKGNv7PWdgTnmT5z/OoWruavRG7+1D8PPE/wAT/g1rnh/whfrp2uXUe2GWRyq+4JFfj18R/wDgmz8a/h74P1jxP4gm0v8AszTYmubiY37OxA9iK/d3vXPfELwHo3xN8Hap4Y8QWxu9H1KEwXESsVYqfQjkGnbqRfofz3/su/sp+Mv2p/Emr6Z4RmsbU6XFHLdTXczIqK5YLjaMnJU19HN/wR1+NEl0Q+t+Gnjz/rGupT+myv03/Zt/ZF8A/stw6yngy0uYpNVZTcTXU7SsVUnaoJPQZP517W3Q4644qvUL22P5n/FGmeI/2f8A4mazo0WqLb6tpkj2s01ixCt2IHsa/Vj/AIJN/s4f8Ir8M9Y+IHiOz83VvFJCRpeR7itupbnn+8WOfXFexfGf/gnT8J/jl8TLTxprNne2l8JPMvbexmMUd4eMb/TGP4cE5r6a0TR7Pw/pNpplhAttZWkSwwxL0VFGAPyoslqhNn50ftj/APBKiDx5q0/iv4SNa6NqExaW80K4YrbzScYaLqEJ5yDx0r8v/it8I/Gvwb1htD8Z6FfaNdJuSJZ1YwvyCTG3Q/ge9f0yVzXjj4Z+FfiVYx2fijQbHW7eNt8a3kIfY2Qcqeo6DpU2K5jk/wBl5J4/2e/h+lwnlyro1spXOekYr1KoLKyg020htbWFLe3hQJHFGMKqgYAA9KnpokKKT0rM8TeI7Hwlod7rGpzpbafZxNNNM5wFUDNFxEmva/p3hjS7jUtWvYNPsLdS8txcOERQO5Jr40+KH/BWr4OfD/XX0vS49U8XSRMVluNKiXyVx6MzDPfp6V8GftsftseJf2rvGkHhLwet5F4Yhna3trC35a+kYgZbAyRwOM96+mP2Vf8AgkX4aj8N6dr3xea6v9WnUSnQLeXyoYlPQSMvzE+wIpjasdLH/wAFpPhs0oB8FeJvL7sFhyPw3179+zn+358Lf2ltYfR9Au7rS9aVN62OqxiJ5fUIckMR7GsfVv8AgmL+zveaTc2tn4Ej024lQql5Fe3DvEexAeQjj3FflH+1p+zlrf7EvxmsBompXosGK3el6uvyPuBBIyO44yKASuf0A3VpDfW7w3EUc8LjDRyKGVh6EHrXhnxC/YZ+CHxMvnvdZ8Bael84w1xY7rZj+CEL+leh/BXxYfHXwl8Ja+0/2h9Q0yCd5T1ZigyfzrtqAPjPU/8Agkx+z/qDhhpmtWg/u22pFR+qmqK/8Eh/2f8AkG38SMf9rVzx/wCOV9s0bRQF2fG3g/8A4JS/A7wb4t0zXrS11a7lsJhOltfXnnROwORuGBnBr7HhhS3hSKJFjjQBVVRgADoBTqWgQUUUUAeIftafsv6P+1V8O4PDWp3r6XNbXIube/ijDvEf4gM+oA/KvhzVv+CJ1xNcSHT/AIoJHC55W60ze2B0wQ4r9UqaxVVLMQABkk9qB3Pxi8cf8Enfjb8PGS88G6/Z+IvLJYfZbh7WZdvIIDEjP0NZ/wADP+Cgnxx/Z98bR+BfGcF34sVLuOxOla0c3cLFtuEl6nr0ORwK+0v23v8Agox4b+BGj3PhvwTf2mueO5t0LGNt8On8D53I4Y8nAB7V8p/sE/s5eO/2kvjtbfGb4k28+o+HbWVrmO81Rc/2hOMhNqt1VTznGOBQPm7n6+aPeS6hpdpdT27Wk00SyPAxBMZIyVJHpV2kVQihVGABgAUtBIUUUUAFFFFADWbFVZJ3WUDHFWXYDAPfpUDRFpie2OKlouNuo+GQyKc1/Nx420c6h+0tqumQ/KJvE7Qpt4xm4xX9I6RjauexzX84vj5W0f8Aas1XDEND4qLAg8j/AEgUrFddD+i7wzZjT/D2m2oYsIbaNMnqcKBWnVHRW36RZN13QRn/AMdFXqoyCikam5pgPpKbk1Wubpo8gCpuUouWiHTyB+B2rK1CQpHnNZ+sa9Jp8RcRl8ntVb+0v7YsyJoGRWHBHrUOp0sdcYcq1N3TbrzliA6hufyNfz3/ALYzhv2qviIQMD+2JOB/viv6D/D0Jjt1V4yjIOM9wa/A79v7Rf7B/bB+IMAGEkvBOv8AwLaaUXcxk7s/eT4WnPw48Mn/AKh1v/6LFdMn3jXIfBtt3wp8In10u3/9FiuyrVbGLGOu5kPpQzHzFA/GmXDOqgqM+tRopuMmQfLjGPrTC3UsrnHPWlr8nP2oP27vi14W/bAvfA/gbxHDZaNBqFvpyWsltG6MxKhySw9zX6raK1y2j2RvJFluzChldBgM2BkgfWgRdooooAKKa1Qt5uTjBHapuOxM1QSSHIwdo71GIJmYktxTms2YYLcHrWb1LVluQ3cnnRFIzuJ6n0pkMLQ5Hm7zjp6VY+wiNcRttb1qG1094yxlbcxNTaXQ0UlYIw37zNWbf7tPCbelOWp5X1Ik7jlp1Qp95qlB4rSPYzZ+Ln7bzfYf+CpmgzDp/a3h9z7f8e/+FftGrblBHQ81+K37edvJcf8ABTK2giO2aa70PYffyoq/aOyVks4Fb7wjUH8q2ET0UUUAFFFFABRRRQAUUUUAFFFFAH4ifBb9z/wVYtM8Y8T34P4wy1+3dfiJ4Ojk8O/8FXoI7hGhP/CVXGRINpG+OTB59civ27oAKKKKACiiigAooooAKKKKACiivz9/4KNftgfFv9m3xl4cj8IaVFa+GXjLy6lcW5lju5eMxE/wbRj67qAP0Ab3OKQFVxz14FfFv7Gv/BSHw7+0heW/hfxBbJ4b8YtGPLQv+5u25yI89+M4969c/bT+PJ/Z3+AGv+JrZ4/7YkQWlgjnGZn4BA74GTQM+Af+Cl37a2u+MPHEnwv+G+r3D6PahoNSfSdxkupjgNEcDOFIxx610H/BI39m3xDo/jrxH4+8ZeGNT0g29pFFo9xfIYvNZy/nEqTknATqO9R/8Ep/2XY/Hmq6l8bPGMEl1P8AamXS1m+7JKSTJMR3GSAPcGv1aVQihVAUDoAKAYtFFISFBJOAKBHlv7Snx90X9m/4Val4w1n94YyILO1X71xcMDsQccdCSenFfkF+z/8ACHxp/wAFC/2jrzxP4wF1e+Gornz9VvBKBHDHk+XbpnGehHy5x3rtP29Pjpqn7YX7Rmi/CXwb5k+i6XqP2CHyT/x8XDECWYg4+VQvGfQ+tfqd+zl8CdE/Z3+FOjeDtFTK2sYNxcMoDzynlmYj3oA2vCPwb8E+BPDceg6L4Z02y0tYxEYFtlIcAfxZHzfjX5af8Fe/gd4Q+G/iTwJ4m8MaXb6PeawbiG6t7ONY4j5RjKuFAABO8/lX6+tX5Cf8FX/EU3jz9qTwD4Ht7hZ7eyjt42hBxslnlGQc8crtoGtT9S/g1I83wn8IPJ99tKty2fXyxXZVkeEdJTQvC+k6dGMJa2sUIH+6oFa9AgooooAK89+P/jzV/hj8H/FHijQdL/tjV9Ns3mt7TkhmHcgdQOtehU2SNZEZHUOjDBVhkEehoA/nv+JX7Wn7QH7RF1Jpt7retT2945jGj6NbNFEc/wAGEGW/GrXh/wD4Jx/tC+IrCO8h8A3cMcuJFF5PHA/PchiDX766T4Y0bQWdtM0mx05nOWNpbJEWPqdoGa06B3Pwab4Dftg+C4YtBtdF+ICWdou2KHR7qYwIPYxnFVx+x3+1R8aby3sdb8NeKphbEvC/im8dYkLcEqZTgHgZx7V+9tNZQ2M0D5mfN37Bf7OE37OXwQsNK1nRoNK8X3JZtWaC5E6ysGO0hgcYwe3rX0nSUtBJHLGsmNyhsetfFP7fv7e9r+zlpp8J+FGF148ukDZ2bo7RD0JP94+lfW3xG8baf8N/A+t+J9VmWCw0u0kupGb/AGVyB9ScD8a/FH9ln4Rah+3F+1bq3iPxB5174ehu31HUJpCSAhYmOIH8OlBSNnRfCv7aPxY0k+MdOu/E8ttcZmi8u6EAdTzlUJ6V1HwH/wCCjHxS/Zv8ZXPhb43WmpanZKFjaO9TF1a4JJYf3sg+vav2F0jTLbRdNtrCyhW3tLaNYookGAqgYAFea/HH9mX4f/tBaLJZeLNBtrqfafKvlQLNGxHUMOTQFz8Nv2pvino/7RH7Umo+J/C0c0enazdW8dt9sXY5OUXLDJxyK/oJ8J2z2fhfSIJMeZHaRI2OmQgr+cvXvh8vw5/aXbwpby+dHpniKK1jYnqomAFf0f6QCuk2QPXyE/8AQRQSWicUbq8x/aWu/G1j8FfE0/w8ilm8XLbk2SQhS5bPO3JAzivxzs/2nP2rvghrk2v+IbnxhDF5wFxDr1pK1lJg8gbl2j/gNA0rn7rySLGpZjtUclicAD1NfiJ+0ZL4n/by/bg1Dwr4RnhWCxd9Psri4ZvIhgiyHlbaG6kn68V9L/Fr/gqR4e8Rfslz3+hh7Tx5rMb6W9lgD7MxXEk33s7cE7T+la//AASD+BEXhv4a6r8TdSt92seIpTDazPyfsysTnkcFmJ6dQBSKWh9W/sm/s/r+zT8GNJ8EnU/7XuLctLPdBNqtI5ywUegNeyU1adTIPPvj38XtM+BXwl8ReNNVcLDptszRRkgGWYjEaDJHJbFfh/8AsyfBfXv24v2k7yXVZTJYvdHVtbupixKxF+IweeTyAPrX2x/wWW+NVrpvgfw38NLWYvfahcjUr6MY+WJOIx1zkkt27V61/wAEu/2d1+EHwIt/EWpWqxeI/E/+lzSc7hBkmJTkDnB/WgD0Txx+wH8E/F/gSTw7H4F0vTHWErb3tnF5UySbcBiw5POOua/B/wCM3w1vvg38S/Eng7URm40u5kt1YA/MoYFWBIHBFf011+I3/BX7QrPTP2o4rm3RInvNIilmCjq/Iyfc4oA/VX9jvUf7U/Zh+G1xz82i2459kAr2SvBP2EZGk/ZL+GxYEEaVGOfYV7tdXCWlrNPIcJEhdj7AZNAH4af8FLvjNJ8cP2mToGmCVbXQnXSYIpMHMxbDuuCeCcflX6wfshfs/wCifAL4NeH9KsbKFNUktElvrxU/eSyMNxyeuMmvwx8VfEyGx/ac1bxm2nDUY4fEEt79nbkuiydPTPFfsD8Jv+Cn/wADPHug276h4hPhLUEjUSWWsRmPB6YVxlW6djQB7d+034DtviX8A/HPh+5jSQXGlzvH5g4WRELqfzWvyx/4I7eOrnwv+0N4g8I/M9trOmu7qD8qPbvgHHqd5r9BfHH7eXwHHhHV41+IelXTzWc0axwSb2YlCAMD61+eX/BJjw3N4j/a217xFZYbTbCwuCzEEcSyDbj/AL5NAH7SUtFFABRSE4r4m/4KPftWeP8A4Aad4e0rwBpd19v1MSPPqYszNGijACrj+LqT9RQB9tUxpEiXLMqD/aOK/Cuz+Pn7YfxVnmi0jUPGF6ZANyWlsYkAPTqBitLT/wBkX9sbx8XuL6PXIC3zbtS1gx7s+gDGgD9sNQ8WaJpChr7WdPslPQ3F0kYP5msTT/jF4G1bWItJsvF2jXepSnEdrDexvIx9AAea/HKb/gmH+0vrkZN41mxHOLnWpG/oa9N/ZQ/4Jp/Fv4efH7wt4n8ZQ6ba6LpM/wBqeS3v2mdmA+UYwO5oA/XCiiigDmfiR8O9F+K3gvVPC3iG3+1aTqMRimQHDY9Qexr4c8Uf8EY/hpeWcy+HvGfijRbpiTF9okhuYIyf9jYrH/vqv0JooA/HL4rf8Eg/H/wz0OTX/Bfja38VahZlrh4xYnTpo1UZDRsJX3N1446V7D/wTB/bS8U+PPEM3wn8d3E2p39ratJp+ozqTNmNiJI5GJ+bAK4PHev0a8RalbaPoV/fXzpHY29vJLO8hwFjVSWP5V+K3/BPfT4fGv7fU2o6VcXNrpkLX9/G0R4dN+Ap6YB3fpQUjt/2qf8Agnf+0j8SvjJrGuWT23jDTdQu5Hguf7WjtxbRE/KrRyupHH93d07V5L8Xv+CZ/wARPgF8GdU+IHirV9FxZ+Xv02xJuJBuYL8zsAo6/wAOa/dvJBr4A/4LKeNpdD+A/hvw/FcPF/beqnzEQ/6xIlBII+rD8qCT5N/Y1/4Jz+HP2tfhw/i3/hY9zpL2t21pdabDoqMY3ADY8zzhnII/hrnf28P2KfC37Ho8F/2b4iv9dOuSzC7+0wCMwxx7MMm1jknc3X0FfbH/AARl0P8As39nDxBe5Y/2hr0kuGHTbGifj92vAP8AgtV4knm+K3gbRHIW0t9HkuU9d7yMD+iigD2/9lP/AIJy/A3xR4M8GfEiy1HxFrNxIsV9Gs92iweapzgoI84B/wBqv0Khj8lAgPyjhRjoPSvnr/gnzAtv+yH8OgqeWGsA236kmvomgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBuKNtfKt5/wUa+Gdv4guNHTTvExurcMZGk0somB6HdzXikn/BZnwV/wmr6XH4I1g6Rv8ldQknjWTfnGTHzgfjWMa0XomdEqFSHxKx+ih+WlVs1+d3j/APbl+OXxOt7i0+EHwd1JLXGV1e7PmFvQquAMfjX1t+zB4s+IHjD4T6Zf/EvQxoPinJSe3A27gOjEdia1jJSV0KdGUFdnrtFJS0zAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBK+UP+CgX7R3j79nzwDpdz4B0Vr/U9Qmkje7FubgWwVVIJQKRzuPX0r6vIpNoPUZqWrjTsz8I779un9rDxxMlvZavrAnzjytJ0bY3PriOrdn4Z/ba+L8jqrfEKcKAxNzcPYx4b03FAelfueI1T7qhfoKdtqeVluVz+ff4ofsa/tCeGtLuNd8eaJfPZQo0r3GoamlxjAycYkPNZH7Gn7Lk/7VPxSHhyS/fS9Ito/OvbmIBnjXsFUnuRX66f8FH7qy/4Zn8SCWULPHESm0884GPxr50/4Is+EdMXwn458Q+Vu1U3kdn5h/hjCbsD8TUpvmsXb3T2jwL/AMEm/gT4NkgnvbLVvElzGVYnUrweUSP9hFHB9CTXV/tQftKeG/2Jfh3Z22i+E3LSL5Vha2MAS3UgD7zDp1FfUeOawvF/gHw94+08WPiLR7TWbRTkRXcQcA+o9KuSfQzT6M/n8/aE/bU+Jv7Q8rLretyWOjrI3k6XZsUQAgcNj73Qdad+xv8AHDwV8AfiBceJfGPg248WSJGoslgdMQPk5chj1xiv2nvP2GPgbfNOZPh1pIMxyxVGGPpzxXKXn/BNH9n28Lk+Cli3dfKuXH9aOXSxV4nz7ff8FpPCCQ7dN+HmtSSgcLNPEqn6YNfV/wCyn+1t4Y/at8KXOp6HBNp9/ZkLeWFxgtETnHI6jg1xUf8AwTE/Z8jheP8A4Q5iG/iN2+R9K9k+C37PXgT9n7RZ9M8EaLHpUE7bpn3FpJD23MetGt9Cbo9GWnU37tDMR0GRVkDqKimuY7eN5JXWKNBlndgAPqa4/VvjV4B0OTy7/wAZ6Dayf3X1CLP5bqVx2O1ory+6/ae+E9ljzviH4eTPT/T0P8jUMP7VXwhuJBHH8RfD7OegF6tLmQWZ6tRVDQ9e0/xLpsOo6VewajYzDdHcW0gdHHsRV+mncQUUUUwGt92vzS/4LLeKLex8D+FdFWBjNeXhlkkHHCL8oH/fVfpa/wB09jX5b/8ABY+BLnQPC0sl0rXMFyQEXjhhj+lZya0RtDZn0F/wSh0MaT+yPo8+STe3lxOQe3zkf0r7Ir43/wCCUWqS6h+yTo8UgULa3c8KFQckbj15r7Iqo7GT3CkpaKoQUUUUAFFFFABRRRQAUg+7RSZ4FID8V/8AgsBpd5aftCaZdTuogfTFMBHXhu9fqv8Asu6l/a37Pfw/ufMEhfRrfLAY52CvzI/4LNa9YzfFnwppUcH+lw6c0ktx/eVmwB+GK/RP9hy4kuP2U/h1JJ97+y4xwMVEXZG0tj3UfeNLSL9406tDETaN2cc0MobrS0UAFFFFABRRRQAUUUUAJ3FfBv8AwVs+OTfD34OWHg6xl8vU/E0hTKtgpCp+Yn2OcV95NX4of8FhPHkHib9o7TdHtpS/9h6UttKp52uzlzgevNQ9yo7nqn/BIH9me01WTU/izrtmlx9mk+yaOrgnY4z5kmOhzwAT6V+rQzn8K/Nf9jH/AIKGfAj4U/Azw54M1nUL/wAP3+mWwE7S2MkiSyEncQ0YOfXmvoGL/gp1+znIpI8egezafcg/rHVIJbn1O1flN/wWy8RWVxffDrRUZWvrZLm5kXPKq+wL/wCgmvpjxT/wVU+AWj6Tcz6f4ln1a9WNjDbw2Mw3sBwCSoA5r8xdSb4mf8FFv2hn1G006S6DSJEGCYhsrTdwCcemetMSP2Q/YrsJ9N/Za+HEFwCJBpEJ59CMivbawvAvheDwV4N0TQbZQkOnWcVsqr0+VQK3aACiiigQUUUUAFFFFAH5z/tYf8FUdX+BPxi1bwT4c8I6fqcelkRz3epSyKWcjnaFI4+tfIfx2/4KlfF34xeHm0Wza18G6dIT57aGZFmmQ/wGRjkD6etfrt4//ZJ+EHxQ8QS654m8AaNq2rzHM15PbgyS+m496p6f+xX8CtMwbf4VeF0YfxHT0J/UUAfz+fDTxP4c0Tx7Zax420m48T6ZHMJprETbTOc5wxIOc1+rvgX/AIK9fBzRdFstJHgzXdAtLSJYo7SzijlSJQMADBXivrfVP2TfgzrSqt/8MfC92q9BNpkTf0rh/Gn/AATv+AXjTT2tZPh7pukAncJNHj+yOD9Uxn8aAOE03/grR8Ab541n1DXNOVuDJdaYdi/UqxP6V9QfDP4p+F/jD4Vt/EfhDVY9Y0efhLiNGQZ7jDAHNfCnjb/gi38PdXuml8N+L9Y0CIn/AFMypdKPoWwf1r7g+C/wssPgv8NNC8H6dJ58GmW6wm48tUMzDq5A7mgDt6KKKACikam5oAZdTQ2sLTTyJFFGCzSSMFCj1Jrx/Wf2xPgp4f1h9Kvvib4bh1BG2NB/aEbFT6HB4r88P+Cj/wC0z4r+Mvxei+B/gG5mfT47hLW5hsSQ97cMcFGI/hX0rrPhb/wRdsLrw3DP488aXVvrEqBzZ6VCpSHPYs3U/Sgq3c/R7w38SvCnjWOKXQfEWm6vGRu3WV5HJge4Br+fr41Ko/a614ADyz4pzwOP9cK+99b/AOCNU2hyLc+Bfitf6bdAk7rmAxMOOMNE2a+BvA/w7uND/ay0Pwbq93Hqt3beJIba5uslxMwlG4nd1zjvSKR/RBofzaHp3Ug28fT/AHRVfVvGOh+H7y3tdT1ezsLm4IWGG4nVGkJOBtBOTWpbwi3gjiUYVFCjHoBivw7/AG+PiFf6x+3hNJ5zyR6HqVnaW6FiVGGXOB260rk2P3IJyARyK8e/ao/aH079mX4UXnjK+s21F43WG3s1bb5rt2z2r1PQZTPoenSN957eNj+Kivz3/wCC1Vxfw/BvwPFbswsJNWm+1KDjJEa7M/iTRzCW59CfsX/tl6T+1v4a1OePTv7E17SpAt3p2/eFRs7HB98H8q+i7lo9pDDJNflr/wAES9Nh874makWLXLLa2x9No3MD9ck1+pN0wVRxyaVr6l6JmfeRwPGi+UWYHjAz+dcZ8Wvix4T+Bfgq48R+Lb23sraJGaKBnCyTsBnagPU816AzJb2rzSsscSqWdm6BQMk1+KX7XHjzWv25P2t7XwL4OmM+l2kxsLI7j5YxxJKR+H6UcpblfRHdfED/AILLeM5PEFwfBvhnS7XRVfbD9vDPK4Hc46V8VfH3476v+0F8RrnxnrVnZ2upXCqHSzUqvy9zX7UfB/8A4J9/DT4S/C290aTQLXXddvbBoby+ukDtJJtP3M/d5Pavw5+JHhG88CfEHXdBvbKTT5LK6kjW3lHKrv4+tOxCP6I/2YdUfWf2f/Ad3Ihjd9Jt8g+yAV6hXnn7P2P+FI+BtoAH9kW/A/3BX5a/8FR/i58SfBH7T2lJa67cadpunW0d5pMMLlUB/iJA6kkd6ZO7P2KkXdiqmralBoulXmoXLbLa1heeRvRVBJ/QV5F+x38a2+PvwB8MeK7l1fU5YPJvdoxiZeG/PrXPft8fGyx+Cf7N/iW6ny+o6xA+l2EQ6tJIME/gCTTF5H5m/sy+FLP9qz/goVqer3Ku2jw6jca42043eVIPLB9s4/Kv29VQqhQMADAr81/+CNPwim0rwT4r+Id/C27VrhbCydh1iiJLH/vtiPwr9J1/XrQDHUUUUCM3xFqDaRol9fpbvdvaQSTrBG2GkKqTtH1xX4/fEH/grv8AGCTxFfQ+H/DWh6HaQzPEI7i1kmnOCR853AZ+gr9kmGevTuMVUXSrOO489LWFZv76oAamxSZ+LGkf8FLv2o/Hdy1j4fs4Lq84Ij03QjMwz7ZOK10/aS/bp1SdzHp3i2Ju62vhSMj/AMejr9mh15xmlo5Quux+M918X/27ryAhrXx4kZ5Zv+EXhjx9D5dfUP8AwTzj/aT17xLq2v8Axc1TVI/DPkNFb2Gr2yxyyyluGUAAqBg/nX3xSHpT2QhvHTPNFI3Sq8kjRyLjoTzWDlqOxYbPamwr8vzcnNRvPHHk5570lreLcSMq/wANEZahy6H41ft2fL/wVE8Ont9v0An8DFX7QV+LH7eTFf8AgppoLZwBf6Gf/Ho6/aeugkKKKKACiiigAooooAKKKKACiiigD8ZPF0KTf8Fcl8ohwPESklTkA+XyK/ZuvxP+LV1L8M/+Cpp1C5z5beI4JfnGCUmAAI9ua/a5WDqGU5DDIoAdRRRQAUUUUAFFFFABRRRQAlcz8Qfhr4a+Knhu60HxTpFvq+l3K7XhuEz+IPY/SunooA/HP9tD/gnVq37PH2z4n/DDUpIvDul7bmSFpStxZndjKkdVBIrwv4uftMePv20bz4aeBZ4WkktzHaJDCebqckK0jepwP0r9uf2jtDs/EfwH8e6ffhTay6Nclt3TKxlh+oFfjX/wS08MaVrv7XuhjUTHItjZXN1bK/I84ABQPflqC1sftX8I/ANh8L/hzoHhjTrZLW30+0jiKIoGWCjcT7k5rsKatOoICvD/ANtH4vR/BP8AZx8YeIRP5F61q1nZsOvnSAquPoMn8K9wr83v+C12tXdn8Lfh5p0MxS1vdVuDPHnh9kSlc/iTQB4F/wAEfvC9r4u/aO8QeIb+IXl3pWmNIkz84klfBfnvwRX7O5Ffzl/AX/hefw61D/hLPhhoPiiP7RGFN1pWmTzRTKCeDtQhhnNfRiftZ/txXOLeLQvFoZjtH/FIOP8Ax8w8UFWP2I8eePND+G/he+8QeIdQi07TLKJpZZpSegHQAZJPsBX48fBfw/qX7dX7el745S0dPDWm36X9xMGG2OJGIhXBOTkr26d6ms/2Wf2uf2sPE1jH8SbjWtJ0Ine11rF0ixRK33gsCvkEgdNtfqh+z7+z34U/Zz8BWfhvwxYRQbEBurvb+9uZO7Mep5oHsenRqsahR0AwKfkVDLII1/2j0p0ZLKCRg1LYrElLTVp1NEhRRRTAKKKKACiiigAooooA+Bv+Cvnxrj8DfA/T/Bdv8194muP3m1sFIY8E5+pOPwrr/wDglj8Hm+Gf7M9jqV1Ei6h4imbUHYD5vLPCKT9K+Mv+CzHnp+0F4ZE0rNaNoqvHGT8qkSEE+1fqj+zo1i3wK8CnTWRrL+yLfyynT7g/rQO56Iq7Sfem3Ewt7eSVvuxqWP0AzUlY3jLVrbQvCes6heTLb2ttaSySSOcBQENAj+fVrwfET9taOeHkaj4uBTzOOBPxn8q/odtIzDawxnqiKv5Cv53P2dox4l/bC8HvanzBJ4k89T2K+azZ/Kv6KaAEIzWN4s8H6T440C90bWrKG/0+8iaGWGZAwIYYJ571tVy3xS8aWfw7+HPiPxJfyNFa6bYy3DMoBbhTjGT1zigD+cj4r+B7bw78afEfhPSXM1tb6vJYWo9MyYX+df0R/ArwPF8Ofg/4Q8OQxxxrp+mwxMI+hbYMn86/B/8AZb0Ob41ftieF2kia7W91ttRuFcdUDF8n9K/objjWONUUYVRgD0FA7igYpaKx/GWqDRPCOtagSVFrZTTZXr8qE/0oEfir8ctUh/aw/wCCjlvormZdJXV49MVXUZEUP3s4J4JB/Ov230nS7fRNLtNPtIxFbWsSwxoowAqjAr8Uv+CY/k+Mv27bnUr6NbmZrTUb5XkGSsgdcH64Y1+3FABX4U/8FYtZfWv2tNVg37hY2MMOD2AGcfrX7qswVSScAckmv51/23fHEfj/APai+IWrQ7mg+2vAjHHRCF4wenFAH7p/sqaGnh39nP4e2CbT5ej2+SvTJQE1oftGa/N4X+A/jzVbdzHPa6PcOjDsdhA/nVX9mHVYNa/Z7+H93bNuik0e3x+CAEVz/wC23dPafsp/EuSMMW/slx8vXBIFAH5Qf8E2P2dfB37TfxK8ZWPjvTrjVLO1sY7mOSC4aIxyO7ZJI6//AFq+vfEn/BGbwLqGoNJoXjrWNDs8nFrNZxXYGfQswxXEf8ETNBt7e3+JWohle5L29s3HIUAsP51+pNAH5V/Fr/gkL4a+Hvwz8S+Jz8StRupNJsZLtYpNLijV2UZAJD9+laf/AART8GiLTfiL4qZG3XM0Ngr4wpCbm4/76rtv+Csn7SzeD/Atn8MPD1yx13XWBvlhILJbngIec5Yn9K95/wCCffwTm+B/7NPh3TL2PytU1AHULtcYIaTkA+4GKAPpEZ70tFFACHP0qveabaakqrd2sN0q8gTRh8fTIqzRQBXtbC2sci3t4bdSMYiQL/KrFFFABRRUf2iITeUZUEuM7Nw3flQBJRRRQAUUV8Y/8FAv26NJ/Z78I33hXw7eJdfEG/i8uOKPDCzRhgu57HHQUAed/wDBUj9tSD4e+F7j4U+FZFudf1mArqN1G4ItYGx8gxnLNyCMgir3/BKH9lO5+F/gm7+I/iK1e21zxFGq2VvKPmgtOoJ92PPT0r51/YL/AGC/Efxn8a2XxT+JEE8XhqKcXkEOofNPqcuchuuVQHB5zmv2Ih8i0WK2j8uIKu2OJSBgDsBQO5KBivye/wCC23jS1n1j4c+F0Ob20iuL9yD0WTCAYx/sZ/Gv1ir8gP8AgpT8CPit8Vv2rrX+zPCuoatot1awW2nXdpAXiVeN5ZhwCDng0CPuD/gm74JTwX+yX4OTyWgnvojeSq3Xcx618D/8FqIy3x68IseFGgce/wC+ev1s+FfhuXwb8N/DOiTRLDNYWENu8a9FZUANfmz/AMFrvBM003w/8VR2ryQrHPYTTImQnIdSx7daAPuj9jOyTT/2X/hzCmAo0iE/L7rXtNfmh/wTx/4KEeD7PwFoPwx8cXJ0TV9PQW9pqFww+zzpngFv4SK/SHS9c0/XLVLnT763vrdxlZbeQOpH1BoAuSSLGjO7BVUZLHoBVTS9asNatzPp97BewgkF4JA4yOvSma9Hb3Gj3tvczJBBPC8TyOwAUFSCf1r8Nv2X/wBqLXv2bP2m7zSYtSm1HwhfazJY3Vm0hdMNKVV164POfegD92aKitp1ureKZPuSIHX6EZFS0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGLqHhfTL6ZZJ9Pt5WwQWMSEkHqDkdK/Jr9rj4B+DvDf7cnhRYptP0zR9UmgnuNMtwd7Nu/u4xz9a/XxslcV+OP/BTqO78H/tgeE/EaqMFbd4xvBPyuM5Hasai093c6acm92fr9oOlWel6PZ2tjAkFpHEqxxooUBcccCtHbtrH8G6g2qeFdIu2G0z2kUmPqgNbPNapuxhK93cRadSc0tMkKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAopM0E0AB7UkjbVJwT9KTJPal59KNhnxL/AMFThHZ/s4avcImyaUqh+bqMjtXln/BFa83eC/iBb4OBfxSfmmP6V3v/AAVU8y4+BuqRIJWbKZ2rlcA/pXJ/8EW7VR8KfGd35TB5NSVTJt4OE6ZrH7VzeT0P0d/ipabz1pc1sc4tFJS0AFFFFABUcu7bhfSnkUjcc5x60AfD37aXw4/aQ+MH2nQPAs9jpPhtIsv9nnMct3njazV8QeG/+CUHx88RXS/2qNL0pWPzTXd4ZT9eBX7fvIkaF3YIgGSzHAFZk3irRLeTZLrFhE/Xa90gP86iW5o5cz2PyVj/AOCLPj+Tbv8AH2hx5+9/ocjY/wDHq2LT/giT4jZozc/FPTohn51h0l8474PmV+p7+MvD8Slm1zTVUdSbuMD+dchrn7R/wv8ADbOmo+O9CtnT7y/bUYj8iaNEtSbsl+AfwftPgR8K9E8F2d5JqEWmxbDdSKFaQ9zivQ65/wAE+PPD/wARtFTV/Deq2+sac52rc2rblJHaugqlZrQQUUUUxCN0r82v+CyPhmK4+HXhrV0ixLBfrG7L0YNnGfyr9I5H2gcE59K+Fv8AgrZpouP2cpZtjsYL+CYMBwACc59uaxnuma0+pqf8Ej2U/spwgNkrqdwCPT5q+2K/Pv8A4I2+Ko9R+BXiDRcYlsdVeU89nGa/QStI7ES3Ciq9/NNb2rvb2/2mUD5YtwXP4nivyN/a28bftW6p8ULu0uNfl8B6CbhhptraarDZq0fZmYMGc49TijmSHGLlqj9eqTd82McetfiY37RH7XP7M2k2Ov6xrMviHw9cHEVxqDJf27AdcshDD6k19qfsnf8ABTzwZ8bfs2g+L1h8I+LHIRFc4trpj3Rj0z6H86OZC5WfcFFMhlWaNXRg6MMhlOQRSMjtIDuHl45XHP51RI+lpAoWloAQ0xiQBTmphPTjPP5UmM/Gb/gsRp3nfGPw5qscUiW8mmmLcw4LK2Tj86/Tb9ja0jsf2YPh1FEMJ/ZEJ5Oeor84v+CzF7P/AMLG8H2JXbCtm8g9Mk4r9DP2GdUbV/2U/h5OzBiNNSPIHpxWK2NZnu6/eNOpgYbjzUGoXi2NnNcOzBIUaRioycAZNaJmRaor5Z+HX/BRz4QfEDxndeFm1O50HV4blrZU1SLy0lYNjhs/zr6ht7iO6hSWJ1licbldDkEeoNO6G4tEtFJzmjn1o5kSLRSUtUAUUUUANavwP/b2tYdc/bi8SW+oxS2lrdX9vC7q4Vgh2ruGRX73TAshA6npk4r8mv8AgsB+zxe2PiTR/inpNnmwmiFrqk8e4tHKD8kh54GOOKl73Lie+xf8Ei/gZr2k2FzBd+KdPaS2jZmtNTjKyMVBLHdE3X2wK47xD/wRT8CXjIdG+IfiDTeSWF3bx3OR6ZBTFd5/wS1/aaf4xfCGTwnrd8LjxL4axFmQ/PLbdEY/TpX2/k00S9z8/fDX/BGT4V2FuE1zxT4j1iUEHfbvHbA88gjD8GvsT4N/AfwR8A/DSaJ4K0ODSbXAEko+aaYju7nk/wAq72lyaYh1FcR4w+NHgvwBr+kaJr/iGz0/VtWlENnZySDzJWPYCu260ALRRRQAUUUUAFFFFABRRRQAUm0ZzjmlooAKKKKACiiigBrVzHxN8YW/gD4e+IfEd0xSDTbGW4ZgcdFOP1xXU18xf8FIPGEXg/8AZB8cs0hjl1CFLCIqcHc7DofoDQNbn57/APBMnwjP8a/2vtd8cXjG4i0jztSlNwPM3vM5CEk55HrX7RV+Yf8AwRN0eBtD+JWriKNZ2ube1LbRu2hN2M+me1fp9QOW5FNIIYnc9FUt+VfzxeFdTlvv2ztMvTK7PJ4w3b2Yk/8AHyR1+lfut+0n8TrT4P8AwT8WeKLqUQi1spFibOP3jAquPfJr8Df2Z7p9c/ak8C3Vz+9Nxr8crCQZyWctk0Dif0dr90fSvwV/aisSn/BQLWoJvnD+JrdiG5zl1NfvXX4g/wDBTSyuPhz+2s3iNbVBHMtpqMSgY8woRn8SRUtBE/bbSVC6XZgDAESAf98ivzP/AOC13jOFfDnw+8LRXC/aZLme9mgB5CbQqkj6g19c/C39sj4ceMPgnp/ji58RWGmQpag3VpPOolikVeU25znI4r8Y/wBt/wDaTf8Aaa+NF34ggg+zaXZL9isU3ZLRqfvfjU9QS1Pur/gibpoXwX8R74YGdRghx34iBr9LpIxJjPavzq/4It6zp8nwl8ZaXFEU1GHU1mmYj7ylMLzX6MN2q1sKT1PFf2zPHj/Df9mfx3rMJKzrp7wRspwQzjbn9a/OT/gjb8Nl8RfFjxb41vrVpX0iyWG0mY8edIx3/jgCvs3/AIKoXL2/7H/iJUJAlureI492rzr/AII16Kln8APEWo7R5t5rDAtjnCqBilfWw+lz2rxZ8SvjPoP7Uem6Dp3hNdV+Gl5BGJdQQYMD9WbP9K/Jj/goxqNjqf7X3jI2LKYYpUiYL/fAG79a/fDWNSi0fSry+mOIraF5nJ9FBJ/lX80vxs8WSeOfi14t8QHLf2jqU7oO+3eQP5UwR/RV8C1Efwa8FKOg0m2/9FivgH/gtV4CFx4c8A+LrezDSW9xNZXVyo+YRkBlB9sk19R/8E+/jNpHxc/Zx8NpYSf6ZolumnXcLH5lZBgH8asf8FB/hxH8S/2UvGlm2BPYQjUoWxyHiOf5E0xbM8p/4I/6t/aH7LssHOLTVJoxn86+Zf8Agq98bI/jN8WPCnwn8KFdTn0m4ZZVj73kuE2fgAKyv+Cfn7bHg/8AZ2+BfxA0DxBdTQ6r5rXmlIqZ852j27fY7uao/wDBMb4I3vx5/aP1j4m+IFkfT9BuGvszDImu5GJVf+Ag5oE9z9Pv2Qfg/N8C/wBn3wl4Su1C6hbWwkuwDnEr/Mwz9TXstFFAgooooAKZJIsYyxxXB/Gj43+D/gJ4TPiPxpqn9maaG2IVRneRvRVHU14Vpn/BUb9nXUpFQ+NZbP3udOuFH5hDU3HY+rPtiM2AcVIJAehzXzTN/wAFHP2d4irH4kWTKegW1nJ/9Aqjdf8ABTf9nGzxu+ICyZ4/c6ddP/KOp36jsfUytmlbpXyPe/8ABUj9naNFaLxzMeeQNIumJ/8AHBXb/Av9t74VftFeKrrw94L1a61DU7aD7Q6z2MsC7c44LgZ+lHSwWPfs7l45qtcMIwWPapooxGo2rsB52ioLiTZJym5e/tXPLQpGcrLPJvI+XPNXrONVuXKcLVO8WE7HWRtgPKrV7TxG37xCxU9mpU3dlP4T8ZP+ChDKv/BSfRCg48/RSx98pmv2mgYeTHz/AAj+Vfi1+3Cya5/wUm02G0dbgrdaXG3knf8AMCMjjuK/aG1Xy7WFT/Cij9K7DEsUU1adTAKKKKACiiigAooooAKKKKAPxS/bzUWf/BSCxkc7FNzpTlm4GMrzX7S2DBrG2IOQY1II+gr8ZP8AgpZAln+31o85YKskWluWJwOGAr9ldDYNomnsDkG3jII/3RQBeooooAKKKKACiiigAooooAKKKKAPKP2rPEUXhX9nP4h6pMwWODR58lunzDaP1Nfjp/wSw0241T9sfQJICf3Fpc3Eij+7tGf5iv1j/b4Xd+x78UxnH/Eob/0Na/PH/giv4fhvvjZ421WSEPLY6PGIZSPu75MNj6gCgdz9jFpaKKBCV+d3/BZ/wZqGufBvwbrltGr2Oj6rILot281Aqn81NfolXzp/wUI8Kjxd+yH8QrZYvNnt7IXcKgZO9GB/lmgDy7/gkp8RR4z/AGYxo7yB7rw7fPZOO4U/MnOPQ19rs6rkk4r8j/8AgjH8XLLQfFnirwHf3CW8mrJHeWStnM0q8Mo9wvrX60Xlu83IYgY+7QVGzepH/akQkK5y3rjAqaK5FwWUcHFZg0UsxJJIP4Vj+PPiJ4T+Evh2XVvFmtWui6dCCxkuH+Z8c4VRlmPsAaDeUYJaHXR7QgzubnGSpqTntX5PeAf2jfjp+1f+15c33wo1uSDwVotwGe2vZjFZ/ZS23c6EAsWx0AJHtX6t2bTtBH9pRUm2jfsOV3d8e31rN7mJYXPOadTVp1WtiWFFFFMQUUUUAFFFFABRRRQB+c//AAWM+CX/AAknw20P4i2S4utDk+x3eBndBIcj8mr1j/gl98YrL4kfsz6RowlH9q+G82FxFnnYOUYeoIr6U+Knw90z4rfD/W/CurwrPZalbPCysOhI4Ye4OK/Dy4s/jF/wTc+ND3UUT21nJKQg3GSzvrfcSFYjjOPxFBSVz97t1fCf/BWD9oaP4a/BVfBenT/8TrxKxikEbfNFAMEk88Z6VzGk/wDBZXwDN4JW6vvDGrReJfKObKEBoTIB/fz0/CvmH4C+B/EX/BRz9qi78V+L0m/4RixP2m5AJEccYP7uFOME560Bbueef8E2vBo8RftieErW4BQ6eJbwqwwSETP581++m6vxH+M2qSfsRf8ABQyXXbC1FvoiTR3KxxLu/wBDlUBlA9QBX6z/AAY/aT+Hvx70eG/8IeIba8eRctZSOqXKf70ec0A12PUc189/t+WtxefsmeP4rZJHlNnnbECTgMCa+g1qtqul2muabc6ff26XVncxmKWGQZV1IwQRQSfiB/wSY0O61T9rTT7uK3MlvY6XcPNIoyseQAu70Oc1+5VeP/BD9lH4c/s96zrmq+C9G/s691g/6TIzlvlyWCrnoMmvYKACuU+K+m3GsfDHxVY2pUXE+mXEce4kDJjb0rq6ZLGssbI67kYFWX1BoA/Dr/glbcNoP7atvBcjEjWOoWjAY4ckeuP7pr9x91fid+2R+xj8VfgB8Xta+JHgWxupPDU1017b6hokheWzLHLLImAy/gCPeuTj/wCCjP7SHjbw9H4N0zWjc3c6i1EtjpoN7LkYK7wuQ3vQVa598ft9f8FBdJ+BekXPg3wfLFqvja7Ro5WXDQ2cZGCWbP3ueBivyjvv2Z/ihqvwyf4q3Hhm9uPDlzcSGW6CNvGfmMhU87CT1r7O/ZH/AOCXPjDxl4tsvG3xoBtdGLC5bRrq4dr66kzkeaMYVe/3sn0r9Yv+Eb0v+wRon9n2/wDZAhFsLLyx5XlgY27emMUCZ+Qv7Kf/AAVVX4J/DjTfB/irwvPrWn6XEIre60plWTbn7rByOa+9fjn8QrX40fsL+MPFOkWN3b2ur6C88VtdIBKg4OGAJHbsa5u6/wCCXvwSuviUfFzaXdRjzhP/AGTHIFtN2c/dx09q+o7rwppk3hWbw8llDFpT2zWgtVQCNYyuNuPSgR+Yv/BE/wAR2P2j4laOWKXzfZ7sBiACnK4HOc5FfoZ8evjp4a/Z9+Hep+K/Ed2sUNtGfJtww8yeTHyoo7kmvxZ+MH7K/wAcv2V/ihf6x4Z03V7Wzku5GstV0He6OhYsqnaOwPcVofCv9m39oD9tbxtBD4v1DXLfSbciWfU/EDSLDGmcERoRgsfYUAdN+zN4F8Sft6/tdT+O/EsE0nhywuhf3UrDEaorZihB6E5AyK/bKCNIYY44xtjRQqj0AHFeffAb4F+Gf2e/h3p/hLwzarDa265mnI/eXEh+87HuSa9EoAWiikOe3WgD5G/b+/bL139lHRvDf/COaJbarqOrSyKz3u7y41UAjGOpr4sP/BYz4uXEeyHwhoPnYwNqysSfYV+vGueFdG8TrEusaTZaosRJjF5brKEJ643A4rOt/hj4PtZA8PhbR43HRlsIgf8A0GgD8iJf+Cin7WXih/tOkeHJIbduQtr4feVcfUrUf/Dcn7Z0n3dC1P8A4D4Wb/4iv2ZttPtbNdtvbQwL02xxhR+lT7R6D8qAPxZuP2y/20NSheBtG11TIMK0Phpo2H0ISrP7JvwZ/aM+Kn7Rmm694ru/FXhq0gk+2XmoakskaSKpyIgpI61+zu0eg/KloAjhV0RVZt2FAz3J9a5H4weObz4a/DTxB4lsNKk1q8021aeKxjzmVgOnHOK7GkZVkUqyhlIwQRkGgD8NvGP/AAU4/aL8cazdxaRef2LHIxRNP03TcyRZ427tpYn3NeQ2v7Mv7QHxu1i81j/hAvFGt3103nS3moRGLzCe++UqD9B0r+g+x8F+H9NunubTRNPtriQ5aWK2RWY+pIFbKqFGFAUewoA/Dq1/Y5/bSttFt7W10/xFb2UMexLVfE1ugVfQKJhge1c3Z/sZ/teadrSaha+EfE1vqsTblvotai35/wB43GMV+9VFAHyB+wTB+01psetaf8ebZl0mG2j/ALKuLq4t5rp5dx3h2ikYkbcfeFfX9FFABXNfED4c+Hfil4Xu/D3inSrfWNHuhiS3nXj6g9QfcV0tIV3daAPyV/aW/wCCQOuL4iuNW+EN3bXWjSkuNFvZvLktz6K5PzfpXz3F+y5+1n8K5G0/R9E8b2sJ/wCgFcytEf8AvhsV+99FAH4QWPwB/bH8ayf2bcWXj/y3GP8Aia3kscXPu7YrufhF/wAEpPjdb/EDRdU8Qw6Xpmn295Fd3Ej3yTO21gxGFJ5r9pqKAILG3NrZwxMQTHGqcdOBip6KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDN0XUm1bTba7MMkInQSLHKAHUHpkAnB/Gvyg/4LAaDaWvxc8A30FoYru4Co9xu4cBwQMdq/Uvx74mm8D+D9S1i20u51me1hZ47CzTLysBwB6V+LP7THx2+Jf7Y3xX8LaFd/DyfQrjTb4Ja2MVrK07ZYAl2K9MfhWU1qrHTDdvoftB8KZPN+G/hlj1OnQf+gCusrnfh9pU+h+CtD0+6UJc21nFFIo7MFGRXRVqYS3YUUUUEhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUANbHc4qve6jaabaPc3d1Da2yDLTTOERR6kk4FLqFvJdWkscb+W7KQG96/Nr9vT9kr9on41+JtKn8JzRa5oUcBjfToNRitBC2epErqGyPSs5Sadi1FNXufbkn7UnwihvJrVviR4Z+0wnEkY1OIlfr81U9Q/a6+DWlKjXPxJ8Oxhzhf9PQ5P4Gvx70j/glL+0ZqF55Nz4R0/TUP/Le81m1dP/IUjH9K77T/APgjJ8Zrry/tOveDbFWPz/6XcOyj6CEg/nT1YtFue4/8FIv2tvhx4w+D1zong7xjpviHU72RYpbeyO8on97NZH/BLH9pL4d/Cf4M6vo3i/xXpnh+5k1Bpo0vZRGWGMd+tctb/wDBEfxaZl8/4k6Kse35mjs5SQfYccVY1D/giT4kht86f8StKmn9LmwkRfzBb+VLk63KurWPv63/AG1PgZcsQvxQ8Nrgfx36L/M1aj/bC+Ccwby/if4YfaMnGpR/41+Zg/4Iw/F1JXC+LvCGzPDM0+T+Hk0+T/gjL8XPL/5G3wm59FaYf+0hT1Ija+p+qvwz+O3gP4w3OoweDfE2n+IJNPx9p+wyhxHnpkiu+r5i/YT/AGQbj9kzwLqthqmqWusa1qtws881rFtWMAYCBiMsO9fTtUr9Rytf3QooopkhSUtFAHx//wAFCvh38ZfiR4LsNM+GHmNH5jPcC0uTDNjGME55Ffn7pP8AwTe/ab8XKLu8jWynxg/2jq7qzflmv3CoqGi1Kx+J03/BK/8AaRkjAOoaWQeqnWZT/wCy03Sf+CQPxy1K6YalqOgWcZH+sa+kl/QKK/bIjNG2qtoHMeJ/sf8AwBm/Zu+C2meD7y9S/v4WaS4nj+4WJ7e1e20mMUtCViQooopiEr4z/wCCpmsDT/2XdeUY3TSxQfMm4YLdR6GvstmxivjX/gq1a3Fx+yhq7wEBI7qF5RtyWUN0z2rOXYuOh59/wRo8Ow2fwY8T6yqMJ77UtjsXBXCLgADGR+Jr9Da/Nr/gjd4u0Sz+E3imwuNVhtryPUPNe3uJ1QAMOCAa/QuXxp4fhXdJrmnIvq10gH86cdEEtWbDV+XH/BXv4P8AiDxC2meMtO0lprDTU8u5njXPynoSPrX6g2d9balAs9rPHcwt0kicMp/EVx/xo8LweMfhj4i0WeHz0vrSSHZgE5IOCPelLuEW1ofFn/BMjXfD/wAfP2Yb7wJ4n061v/7ImNvJDJEu54m5BJ/Svn7/AIKH/sQ+C/2ftDh8YeBry60yX7SJHsZ5xtUH/njwDkHtk189/AX47eIP2L/j9qBD3P8AZVvdvb6jp6kfvowxxn3r0r4wfETxf/wUu/aG03QPCyT2vh6BQILeYEx2oIw0r479s0irNbnn/gv/AIKNfHXwH4NTw3pnipXtoxiO5u4BNcIvZQ7HoPpWja/tZftT3mljxZH4t8QXmjQvve4SCM249m2p0r6S+LX/AAR/j8H/AAbn1Pw5rVzrnjKzTzpYMfupwByqLjP618Z/CP8AaM8VfAa31jwnfW/2vQ7l2ivdIul+63IPB6Ua9A0P1z/4J5ftkXP7UHgO7svEnkp4y0chbswrsWdD0kC5OPevryvwu/4Jc+Lriz/bGsYbJ2tbHVorlZLdT8pUAsq/hX7oVafciSsMmBKHHWmxR7ecnJp5OcihvlXin5gn0Pxn/wCCyPiBb743eGrEDMdvpxPTnJav0F/4J0xTw/sg+AlnyG+zMRn03HFfnR/wWBsxbfHvQpG5Mmllsf8AA6+2/wBjP9qz4U+F/wBmLwRY65420fR7+0s/JmtbiXa6sCe2KyWxrPsfZqxgMT60skaNGysAVIwQ3TFfNWrf8FG/2ftJkkR/H1rO6dRbxO+fpxXnWo/8Fa/gnJff2fZDWtRMziFJI7UKrFjjjJ96DJJ3PCv24f2ffgt8QPGV23gnxppuh/ExGLtpvnbYp2B+5nojZ9K4L9jL/goN4h/Z+8USfDz4rXEt34dgk8hbqX55bJgefm6steJftjfC1/hL+0VbanK8/wBg8QTxazbmdh5ixu4O3I5GM17t/wAFKtG+Ev8AwrPwJrugC0HjO+giZ2s2BMkewZ8wdOtM1l2PWP2sv+Csdvogt9D+CzQare3UeX1qaHzBC3ZUjIIJ+oNfPXgf/gpl+0L8PfGWl6n4/kudT8PTMPPsrrSIrZZYz/EjiMEEdeDXin7FPxa8A/Br40WXiLx7oX9saei7IXHItZCf9Zs6Nx61+s/7X3wz8M/tW/sq3Wr+G5ba4SG3/tLT7yNeflGSvHtxg0EK3U+kfhr8QdJ+KfgfR/FOiXC3Om6nbrcRMpBxkcqfcHiunr80v+CN/wAZJNS8OeKfhnemRrjS5DqFszNlVjZtrIB2+bmv0srRbEPcWiiimIRq5P4o/DfR/i34D1fwpr1utzpupQmKRWzwezDB6g11lG0UAfg38Wfgj8Xv+Cf/AMWZPEXhd7630qOU/Y9ct4PMhkj6+XKCCMY9RX1l8Cf+CyGg3unwWPxS0K40y/UBTqmlp5sMh9SnVT61+keuaFp3iPTZ9O1Wxt9RsZ1KSW91GHRx6EGvjj4r/wDBJv4OfEC8mvdC+3+CLuZizjTZDJCSf+mbnj6A0FX7nouh/wDBQ/4Aa9p63cfxD0+1U9Y7sNE4PphhXk/x4/4KufC7wV4fuoPA13J4w8QupjgFrGRBG2OGZiOR9K80tf8AgiP4dS6D3HxS1GSEn5oo9HjBI9N3m8flXvfwV/4Jg/Bn4N61BrH2K88UapEBsfWpQ8SMDncsagDP+9kUC0PmT9iH9m/4iftBfGqL46fFsXT6fDJ9p06DUBgzNn5Sq8bVXtxX6q1FbW8NrCkNvGkMMY2rHGoVVA7ADoKloBhRRRQIKKKKACiiigAooooAKKKKACiiigAooooAK8B/bk+Ct/8AHr9nPxH4X0mKObV/ku7RZADmSM5AHua9+prLuFAz8Ev2Qf2svEf7EPxG1fSdb0ieTRryVYtX0yRCkkDoceYg9ccfSv1N8B/8FKPgF47tRInjSLR5woZrfVYXgce2SMH8DXafGr9jf4S/H4SS+KPClqNSc7jqmnxrbXZbGMtKo3N9GyK+G/jB/wAEXbma+N18OfGVt9mY82fiBXDp/uyIDn8QKAbuzkf+Cmv7Z+j/ABvh0X4dfDjVxrOkGcS3s9juK3MnRIvcA818meA9C1P9m39o7wbJ49tH0d7G5tr+dZBkxwtyCR9K/S39k3/glLpHwh8RWPinx/rEPibVrM+Zb6ZBF/okUgP3m3ff+mBXeftif8E49D/ae8S2/ifTddbwv4iWNYJ5DD5sM0ajCgrkYIoGnY7Xwz/wUK+CnjTxzpPhPQvEz6nqupSCKEQW0hQMR0LYrY/ay/ZH8L/tS+DTZajFHZa/aqzWOqRoBIjY4Vmxkr7Vw/7KP/BO3wJ+zPPFrc7DxL4tVdo1G5TEcR9Y0PQ+9fWasGGaBeh+Id1/wSZ+Otlq0mm21vpNzp7S/Lem+ATb2Yrnr+Fep/Gz/gmrZ/Az9kDW9bikXWfHNm0V3e3CrmNIVb51jHtnrX61VDfWNtqVpLa3lvFd2sy7JIZ0Do6+hU8EfWpaHzH53/8ABGn4bX+hfDHxX4uuiyW2s3aw2sbKRlYxy351+i9U9J0bT9BsUstMsbbTrOP7lvaQrFGv0VQAKuU0Jnhv7Z3wLvf2iPgHrnhDTJ47fVJSlxbPIfl3ocgH61+OPw6+N3xr/YG8W6jov2WbTFuJP3unanETbzbT99Pr6iv6AKwfEvgLw34yVRrug6fq+0YBvLZJCPoSM0ra3HfSx+Knxg/4KsfE74ueAdQ8LHTdO0SG9Typ7qzDeaUPUDnjNWfgJ/wTJ8X/ABi+B+seNLppdK1i4USaFZTnb9oHUs3oD2r9aY/2Ufg/DqUeoR/Dnw8t5G25ZvsS5B9a9StbWGxt47e3iSCCNQqRxqFVQOgAHSqC5+Evw/8A2D/2nfD/AIsS10TQtS8O3cMof+0U1BYrdefvfK3zflX7OeBfAurv8G7Hwr471BfEGqS6f9k1G6xxKWXDfX616HSNyMUBc/nV/a7/AGd9S/Zt+MmoeGrtQ+n3DtdaW6tnfCzfKD7gmv2b/wCCfvwYb4K/sz+GtPucHUtSj/tK6OP4pBkD8Bivcda8D+H/ABJqVrf6po1nqF3aHMMtzArtGfVcjit1FVFCqAqgYCgYAFAh1FFFAgpDjqaWigD51/bJ/Y70/wDa+8NaFpV74km8NHSbl7lJ4bNLnzCybcEMy4H0NfJMP/BEfTlwJPivOw/2dAj/APj1fqBRSsVc/NjS/wDgij4Pt3Y3/wASNYu1I4Fvp0UGD/322a11/wCCL/w24z408Qsf9yOv0QopcqDmZ+cevf8ABFrwHc2IXSfG+s2l2DnzLiBJFP4AjFew/sW/sEwfsj+JPEmr/wDCTf8ACRyavbpbBWthF5QV92c5OTX13RRyhzEY7+lV7iFpFI4Aq2aqXiv5LEde1RKK2Y4lJbBYcFD8x61YkuV03TLi6lI2QxtKcnAwoJP8qqWlrPlZJJAqk4Ix2r5K/wCCi37Xmk/Ar4Xah4T0q8hl8aa1btbxW0bbmt4mGGdvTg8VCilsXLsj8+v2fdQn+K//AAUTfXoNOGrv/a15qMNruwH8rdt5/Kvavih4b/bP/ae8ZXlqdJ1TwZ4cjuXFtH532WJF3Fc7lO5uK6j/AII5/s+tawa38V9XgJnmVtP03zFIKjOZHXPr0r6K/wCCif7X2ofst/D7TYfD1usvibXXeK2lf7tuqjJf3Pat0Z7HyZN/wTH/AGirDTRd2fxO87U1XcIP7VuRz7EtivHPE3ib9rb9kfXLHWPEd34iFjaTYE1zKbqwkHcMRnAI7nFfe3/BL3x/8TPi18O/EXjDx9r9xrFre3mywSbGEA+9t46V9c+PvA+j/EfwjqXh3XrSO90u/iaKWGUZHI4P1FMk8B/Yu/bi8OftWaC9oyLpHjGxiD3mmuwxIOnmR+oz2HSvp+vwf0Pw/rv7FP7dWm2CI0VnHqixxBWIElrK+0D3HNfu7HIJY0cdGAI/GgB9FFFABRRRQAUUUUAfi5/wVKjb/ht7Q8KSWtNPxgdfnr9kPC+R4Z0kEYP2SHIP+4K/G3/gpZqSX37fWi2+QyRQaZHwc87zkfXpX7MaKhj0exQgqVgjBB6j5RQBZuLiK1heaeRYYkG5nkYBVHqSa+Ef2qf+Cqnhb4L6+fDvgfT7Xx3q8RIubiK9UWsBx03KG3NnqOMV9WfHv4L2nx6+Hd54SvtZ1LQre6ILXWly7JMDt7ivyE+D83gX4M/tqD4eaN4U0/x9o8+prpD3XiSATTJLu+aSMAY3dR0xQUlc0/FH/BWD4/6/m60my0fRLVTu8u202WXA/wBp3JBH0xX1F+wz/wAFNJ/jh4ysvAXj+xttO8QXi7LDULc4ju5h/wAsyvZsdK+85fCOi3GjnSH0qz/svyzGLMW6CIKRjhcYHFfiT8M/B9r8Pv8AgqNoOiWMP2eztvF2Iox0RGDED9aCT9z6KKKACiiigAooooA82/aQ+Gt58YvgX418F2EscF7rWnvawyS/dDEgjP5V4F/wTd/ZN179mbwT4kl8WRW6eIdZu1P7nqkKDaFz6E8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" 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L/CRWiyzsUEe1kPAav7YrHS3TPSoOpEQfXFHbmLRtL42jjz562mcjO8VBjkHKEM76A2FSv9jn4rjjjpsIPqDS0JnYGjTPFaTlan2DGp54gTag3iANk97nJP0gaXUS7LzVzapN26TOlbskHHQEPT5ISNhceEWNN147EEgZjl6Ueh43Yu7aXBiM59sF9cj9OdZ7DZzH5aOpccb4wZjvTkLuNyCF0o/azIc0OnfP7pFNMxtee3bYGAkpklaZz9nEP0j+SInK4drXvva0VzMpUnhtr8RFYtQPITG6ZOE1TlwNI5WT6qkz7HCnoyHdkshdp4MmPdOFO0/ZSZ8wxzkkTvzu2W52RhE2atIBaAO22ykjceOQOv07U0M77lGTJN5ScVASdI618XM+F56XZXxBqn+rMVePufISvj9xg5zPxb0iUesxuo2uB/wjmTjP86r+nQT1rQ452jDpTne6016LVbgaH8n4hZVJLn8oQUr2N0bYdf+MwN9KbFFKfZL9oDdyCNyqRvt22C0ucC2I31G9kSKJWVrkqX7+CqNupFvStQ6ENJ904zPjr24dXEP4yrCVKemdjt3/Dm2BapQw5sXZxImeXOfD1jw73anHUnHQqjg8kLkXwMvEJIjkwV8/7uqP22rUvOfKqGGOY32qG1HD40/67UIte/TnfKxjveY491yc0zsakrMi2EgHuVTU+4lDZCbSkO14zxzYltREHmsJ+0IjKxYXyCc66QAJIVmrFC3QqH9UgfjrOZhAU48sO69wrm4BNYgNmNQLQZv808GwBiHhCkOe6lktNzwzaZE2adYxzjln0pLzzbqXpGUrThVEX68NTELaO8Q9zum5dRKkbJI9/Xj9o0q9l6XhoNdB5yUHD8kwUm9r8xgfNrieuF4CQyHHDAW3EuoQp4PwwqajSB3iF4c/yHmuSRc3xk0+2w11jUt9HNU3LvcPNV7uMwsQHLncq7azAIP05qMOSUu3E5B2iR9Ig4bf/vqBvNwrJK57JCXadJ4OGvka4tNZU2WYEKx5kmbtj0yapRqpJDdCmHZ1JJkjV2oEkmeNL07qBfKlP0fSeXb8VupZEKJuTPBI8ikjz0850vozuO+SH3lyzumsXWPvTHeetGecccY0IqCDZq+tjghYXaJjV1bqbaUm9Y7VlNpDW+0EHJQEnQeTh+OBeqG8yB48yYChf17WPHQP0QN2zYvDjzy2GjoAujmkQqJxpLurUJ/UH6rfR0py9OJH0jAikAaSlguZbTfSzuD+dY7uwYflgxrbOXX3jDwX8R3FJU1loQM/oiJRjZNfS0Z9vrnXtBFrBxIvSRBJB547AcOKQ3/2ZjKXeNqh5pm2JlEzbyNZpgMLxK11iB/4mbGZ8KM2Ub8RiU9PbXWi9zD5eD7S+UchNQTLEJN5FerInln6OH9C4fgRuok8ZoG+R9+MengXfMPUMNqASsgIIvru1AvEV46FOszxSNCEs4RDjkvEQa3iqA+KNKw3Niw0FIp04uFwruuJbVbOltPDNyxD6lsNH6CPxsvC3MdfNEyo1GFXhfvIS4SI8186q6FMKKmzj8lLXO97bIPtROqizVke+FhIdSRBUpbhcBZQJJ6jSS+6Vm0krvZCSqRI8RE76Yr5GKuDUdrbCUjbBEjr7LPPntoFqblH13VQ2oN0TfWAxEz+aQ8EJJ0RRdqO9IwcLQ4Zdc/gmLgBv3fNUV6kTWRIiPCN5BrE77vSEbie/Bxtmu8Zs6n2fqoPJL0jKdp9VZfOm0tnjvC1gzK8594XdUPinrvvwURlpOhAGTovnZiVmiYwQ+Q7AQftJCHwxyEFUoQXlk6LNFGvO9ezethIgCE7IvRybDW8YCZMDNOuec1rTv9xs8S32mWmXj6C+hF56Q07b3KTm0x2rYbD7EB9rKSrfDTVCdtu1PqQdJGrZ2HIaXMebeD5eDZjXDpGJlW2yyQtmxAz/PXBi0PKRlj0tpvZ4GcpyD1WOEdGiJh06H1EPJ6hzplKw7A+EiudtVVxLBgM4XVW3hfERw/MLlncqtqYc0ENQ4akWZ2hjgBJ55r6yJN6SQeKLBFq3jVxPB8LaiwxV2/1F16R/Ko/bu5d9j26H3pkeRqB+k4JKzoKumhtUPMjgVs+r5Mw6tQ2woPqXxoOaoIG5x4IicSsrz8L58FXOOc8ZLPXZno9aGm3EinHy0y35uOyOTrjfi9R6lVfTh8cRzKgyyMxIzbpdDx54eZc8tpu1Po46hCZkpFqjFaQr+E04k2cOPeOsLSRj9Pop14HQ2EkrvMidS3hnveFWv96P+ru/aCPRX4m15Ch+9aJ2+wHMSI8HZ3JMhIivS+pV1wStQ7PhkckxpSRozLGNqp1SFwjEuXbv4IAg7Rd884hYBOD2lzbj3bFpGES7gMe8IApbYSiYCxr3Xn1G9ESSHQ8Og/nSNkIFFETYLRB4quD94y6JaMLdRzzXSoO6klC8DBIZV7i008/ffofG6x7MBla2rPAhIsHupVIuXpywy12rzawMQz04Y16QhCfVEVKJMmQHuj2DB9JUfWFg5SxVGhT5EKlxNbWUWfDkcQMZfOhuw+Ea4RgGGsYH7iWe0cY9NA6Vp0WktpJGEkj949wdE7aC2nroAgR3mkQRpIUZuJLOyAtpIrAjKqg5p1j/FDPlV3rg+RI6CR5eQv3TEimOgbkh6ipOcb3Vzyqu0jPyRNqGSPm4nK+AyMqLqMt9dMRaQNCTAQWIGHrOPLdV4xlLBE7iqDHBo3f0cML8jBBj29lFF0tvacXZh3kYzhHvUFiAPmsK/eyQnokQhom0dPx2eKRHtULOObvpScFqRMCQ+rIjHqDfm2zqO0Cubc5CE/cjeJdFsiLmsf9M+cipRlJXOlKV5oWYIRUAp0ZIvBs5iYCUz8jEhK5kZLhuPDcAyTeRveSazXeRmmEj+WMaXMcwzaDmg7Gc9hs2OXBRmVUN4d14ZcVc2Xtq/x11yrGeDkfw69I7BiC9gHoGRGVj7Q2GFITTuIdh1EI2fDLMCvqDchHlcbn1yPT79FBk6RrPiQH5SZNkPSbReKTSHQYpCO6aCoLE4eRjGo5CEvddTSGuO4FoVkEsL868pSf/HVgpBskCQnXjvJOm9b68G8WKa/Cufs3CWvC1HM1eUuvru3pVRMPSG70qSRtz2EO4hrq0lHbcIi5Wuoctw7j9Y3iwhi3umA8b+wMeGbr3u/teJ47gqA1jCGr4Z6tCqkCSF6kMMMn0pXhrUknxGoYnA8EySDdk08+ec8G5LlWnWG1IaFhNJ0oacxQ3LARiSAIS1n5DamCpN8sxJWevszsOmI0kWL/XTpYJJM8vSiuG96bGKNDy4Y6iMywDbltFskzR52ayUptqU3ViV6RLhNJ0ombpEKkdJrSJZ/NQtzqEqYs+YZI6VuZjxlJUHtUqI/RgvutqouaJ8jLfZhw1I6JW+MEtS71+hg+dz3n47X6YddrafMaFxLWbhkuWBe+HdgRBI0oEQRyRsIM4K9//etPkrGP3MduT1gqAsSFsE2o+DCoAMzwImhkrsHzIdWHQApHSkzs5E8FgZxJqYbihs300pbMkirH9JuFuKRkHYlVUCRY0nFMzegY80GTXNWDfo9+jaRrcoS0bSgfU8HNQp7JWzp5mkiiMlE2MzadhWWw7lmngKB1COqQyab9gbKqU77O0P0rQwcq3CjBHIH9IpRnPwjx1FOnSPUx3m/yDIxw1JOqxHMTfw6pR/zKMpmlI4gjDHD1vF6vYToOHelYn/jVOe0+Xm+3HFexLvyKxo4gaFIiAibFIjDDfCZmJoSQjI/cx2zCzEw/MmU/iuDYRd7oRjeahr4+Jg1eP844eZBok94Ha7YYefoAkZUdvlhQRA9a89ospLEQxoQfMlRvky2kR9I7MsrHjBBNAJFkDe2RGOlSHS/v73p0DHS67s/kDpL2W342tUYaJp/Eca+ke+UaYVwWpI05z8R9mew06hGWNtQm1BPuD3kjPZ2SDpilANS8RiBk92LfY++ICaIRc+lNOFkmrT108nHULnaNc9Tewpxz9br7YGcMY72cV3Le3/elccWiPqc8t+3EjiBoKgEfKBMs5EaqY5ZFWgpZgo/5pje96UR0hu6GuPSXdJsh7bkGF4YApaPbRVYIgiQpnB7bB08FgUCRV/Jx3N+HaIadtM5EyktgEkzeyIlEG8Q8iLkfYkOQWdyibvuDWl9lIl8SKylQu+ogjjjiiIngIikbVTCPUi8r13SGl/WlTbmOOj8ry1ixCAuEM5c69NBDV0cdddQ0atI5es71R6qOYx2ce/6ezyGHHDJ1aiTjYKN6e77u2cSi0Q1i19nzezac8zGMM5pSrg4exnJyf8L46/02lgXPxmgno7Q8yzy77cCOUXHQv1JXkHYY8DN+Ry75MJAJ8kbQzNZInj48JjYkaBNx0aPmQ4lfB4CIsmgCOegQSEWILFIfadzHWIes+wtpEA6pywfunKRIBUNCpw8Whiyoauhm1Y+kjaypa5Cle98fjPV1ziSJ1GnyzRaMVqi5Z2WBzgkpaz+LQJDlZYFyU7Zy3RcVhxFN4Ho6QuorC3hI8p6j9vLcR0jjWSRv74DJRyMBErF3Jkicilonz9h9c0ZsnnvON3LSxQXyjYO8L+BZmlMgBOhs2y3DUfE5muvwXXj+9Z3hzzO8IrEjCBp8AF50H7dlv3TFdMT5MEgyCMzHTVdM+szHgGAMX0Mw9QPiECQyYFtMOmUlYWKRNMdygpQuXsDvYXH86rUZiEv6NpFFQifhC/My2JiG6RxJ3zlSQo4ktsQxnNYB+bj3d7MXeeSYFw3BIGjSpnxJ1KTH3Jey6fy1H12/Tir57A+kidNWRkCW2+vw6nUwqvD8bC5v603PLROkUONC/WgQtNEJCZqu3odW444Y8wpq+Do/pJ0g1+JcS93iF4781ct8ijq2W4ZjUcVRpTIy8G3U5wk5XpHYMQQNXm4fKwnXdosZigPCokP2PzQTSobLCJBkjPiYsGUJaJDGN3SlEz3ssMMmEkTKzuleWXSwCEAsSLKa8uXD2yxBi4tokE6G7HHKVU+rGHUkVpBZLaYs6Uiz6mJBi9EAor8sSL3jZwnDvhpB6zQQtnD3ZERiwo50rQNTB53ZKF1sBikXiXpOOh8Ss/Dk5Yi0LclFsiZObYRDV5/7lUfS1HSc0ZR8TRLqBLIUPHFyHP3eHRK6+Q0dO8cf595zjJ+jRnNulDZOEjrWdo4T5r3U+SGCdstx3hfPxbuWZ+cY/3ZgRxE06Y5UZRKJrpa6AZAJ8iV50SEjGg0tXBqETkJF2vlQIH7DehIzEy8Tgz506hASNFWKiTTERSVhCI1kQDzkTsLeDEkjWxIi8tUJpHwvAJ0sFYuRgckyEmZUIJG6qEAsosnk2hyEe8F0JAg1L1fKqg4QjU6B6kRHhDTB6IOpoU7LKkxtpw4kVPm6X0Sjk3T/6jj3IqccQOzuSQeKEGs9gJ/kgvB0tEia/tvkcK7nWD+cnFOB0aFLZwSSSULX6/NJPgEdvPfKRKjRCcef8/hZ98z5I+HLd8wb1G0uvLF8jO/ZFY3FEXQaob7s8TM588EgKh9t9M8IA+H50SUVAakQpEFW2S2OzjONDWl8hCBPkiIClc4H5+Nj9oXwEZRJrVhUAPUJUz9DIxIUpK4g75zzIyg6VgRrgUrCwaQhqxRqFhJmTPlA3oiHqoF5HSmxImVwiAixkiBJd1GjBMqLWgi0o3JJ59ozdTbB5l7pc9WZdKGjMnIJ8ZOwqT4MEdkg67hqWRXCdXLqZgTBciVx65FTtg5V56DzINGmnUD96z0kHSsXQ1QErSPJ/SROwF/z282o7QI5z3sQpL0c17XdmFeQNPX6XNx16XczFkHQHkx9OEhm7mEhCEPe6173upPEi8R8lPTSiBsJI1vSX8AvLr0y29iUlRcG2SA/lgMkRaQj3CQdYmYBQpolaZLaERGiJTkjJlK3OCTvqqvOEUhyJEbkQWVgDw1SctWr5j9uJjhJsshOfixPTJRRgZAqLQuXj2G5dHHJhwSMRHU4yM29jRAvHxlp3k56CLF+eJWgdSic+6fnjzRKFUHil96knOdR6xRHyqa2yfOLXbUy6rMKEKv2isljOjNu/Ngd876Q8NUDsUsTEq9xgurfbUgbQm0HYXm24FocuJa2r+kqci151Xhz6RI2hjf+g20n6PHh1Ac2vkjIhArDRB47VNIr0mD5gPCoOXzweYkcER0JlxkZwhWe/IB0ilyRH8sCQ3Dp5IMcWVeQaH34iFG4D59kjvCRICJRh0qaFaRFk20sQ9gZI2gTcux7Ez97c6gDSdPLbftI92VmmVrASIA5HgmUxB1VQ/JQb6oGbaIMjv4WxjpJ5x7US57yq4i0q9PQOVDBGKVoH5AfCxP3gXR1gJnQzPU4nSZJ3TOjhjJjTu8tv7qFZaB9qSt0NKM9c55tTRO/EY120smN+uc5stitcO9xOc9xbN9cA89XR2tuxNyOb8ezqvHi9/yoDj1735FnmNEcp5zEr/7G3lgEQQd5ePF7cHlhEC2VAzUEgqKKsHEQUjOUp9aYS0unSRpDCgg++uMA4XrhSMOkZ9Kb9CEwkrPl1SRRZXghUyfkwkSN5K0u6whaOgSYXb8cObrPxEe20pMC5Q/qw66XdE2KJ8UmvWF/VA0gj9yzdDoTk4mk3Fyv9RIv9+felFuh3YwSSO86MHU1kRLIi2RMlcAqBUGLn3qkPM79e0buwb2ov/bwXHzIiRfwy4Mqhx37qDpJ/IQ5ei7aSqfkWRpZ1XiJC+P5bsL4bGDOX13gWRAqvFd09uZjvIPiyDd5+4Y8O+8FIcq76v3yrSVPcSHnNazxXyxGB10fFFfDkKXhMP2rXemQYn2Y69LF+cCpP0jCo2pAPnPSVVxF4iecH2mRrknTkQaD5JGwes7lHmqeEH8NS9yKMU1N54PQTkgxSJwaL/6NMKaDnFPDIHnSMb96ak8Ocm8cfz3napwgcRNW4wY1TFzqFZN1bLkt6KkjqYqkG8N3CzyX+gxgbGuSLum4SrzgXTfCs6DLhDaizqiJi/CiQ7by0oS2bzYSNOc5zVkBOZ97Xrsd207Q48sCeYk4fsN2vbWPj7Qa87Oahr+GSVvPSWz53VWQMmrauIoa5pg0nCFfXsIqnbs2fgwhrZxXJKzGS/qgxglyD+ImHSBN6gFtVdM41nyTvmIsgzQtjrAa17mhrolCI4hIrUHSjEiZueaoTvUc5u4/4M81R+oY8xDej6iIxKlpaplQ/bsJ2qG2KyTMt0ZSpn4y35I2TNsZUVFrGc3al7xuogXiSSuOURX1IkL2PAgx7L8JSeP3VvNo/BeLkaDBwx0fnOESCwr6W3bCdJgkQz1x4kqXD5I/zjk4sq+lJ/YBk/RiiD7Grajp566T6g3Z6WgjPcK6vBKurqkvjOXPpYeN4vHH0ROT6ql/Yl0SJM6cP/UfUctNPEfDW/dPn0y68iEmjvurSLoxr/hh7nqOwuMfHUmPKofURuVS27ZiDJuLsxuhvbSvUYdvDfGa//DtBXkuSJaKkbBDnYSg89wIKNJTXflezd/oOPM8fLO+XeRtEjcmndDPYh6LmSSMy4cIzpGNXhcJ6JERrEUkhrHjpENeoriAX1xmdojLx8yGeow7ptkIXjqTIPSeXkr5B8lzjihqPYP4Hes9JKy6Me0YX5kkIDpAE486MUjaGr+6mm/C5pBrHJ2zcuio67DVUX7xB0kXjOep34jEqy4Qn1SHNMwVkJ4TXuM1/gvtUtvH0Xvim7Kkn/UO1YWwPA9xzBcgWN+fOFQYsXoS15yE78HEoMlaBB37cPBuGnGaI7BwDEmT2CFxGntjEQTtJciLAHlYjnrdrO4y0WT45OhBI4W8aMknaStyzcfrJWSRgFwz+ZQ08dewYAzzstGpkSCySKPGcawEneO6OoLwuXzq+Ziev4bxI07qhqpygZr36FwLangwF+aDZS1RLTuC+Mc0kLDx2kZh8Qf8SMEIAVlYmYksgqSraRr/gTYZ3yOjQaoqJqnmL/JO570QV4fPpFWHbBLaIi6WU655DvwslghCVCCk6CqFg4lpoxyrRE146wign9M8FqHi8HDiYHx5Rn89H1+i8fro94LQm+rlEbawpIekH9PVsHqtYqx3UNNCzmv4eD5iDK/nc2nW5QO5tq90oz8u2Nd5UMPjr/FG/7rzMdyohWrFPt/50OdQ84DxfDdh7r6p+1jykJ7NJej4xvbybhupZMToyEaeaSPVCIGJ8MSPwK20jIlnhbwIEDoCtvds/hM+V7fdjkXpoBuNxoHHSIbmT5Czvc6RL4KGMR7CZW5JyCEt+3ONka2JaEcT+UaNzOuoOEjbc6RLqqbLtp6BFF2FrMbeaIJuNHYZKiEiUJIxcjbZnQ2sKnJu0RT1BRJm4WHFJr2/9NQWiFtc0jOzS6PUQHjyUbYJZpORJnbFc62qBBv/QRN0o7GLEKKsjkUGU1GT8c4Biea6uR5qCRI0lYQwcw9W9dI1M2HNPuKk8fxBn9qp5hPk3CIWliB1MVVjbzRBNxq7CJUsSazmYZAknTDb+VyPNGui2UQ4k00LvSzvN4HIbNWCFb+So49G4OKx8rA3i0UsSLxa9wQpIz9Apouei9dogm40dh1CkMiX/tiGV3TG9MoQqRdYA1FhIHCrcZEx6x2SMlM5UjdiRtDI2RJw2yog6EwgriNeemjSNlUHi6hWcfwvmqAbjV2EEKAj0zg7DNov3a6BrJsqxCEpI17OoiQqDuSM3JndIXDSL1NLVhwIHKFzTO5MCIbwHbnAdRt1kcQtbkHQjb3RBN1o7DIgS456gxWF7XJN6mX720Ac5Gvyz6QeZxIxC1iQdOyl6Z9dtwAs8cUNITvKL3BuwtH2uXZDROhN0P+LJuhGYxch5AwWgVnV5zdqjiTjXB/dSLAwXk+ceh4Iq3BOArfa0Da6djiMeV/jv2iCbjR2EUKUjvS+Jv6OOOKIafk2lYfwObcvgl7nRtQwVh503/79yZKDFN7YG03QjcYuBKK0PS7dsz/h+0Va/vEZEh1JNuc1vBJ3Da9Yd1159tihA7dXexP0/6IJutHYZQhRkqBZYiBoi1DolIVXawrHqCqSLhj9VaURJM4YlzOxyDIEQVt9eEUSdOpQ6wXO6/1WzI0iYDNhSRu3WTRBNxq7CJUgWGDYBZCKA0HXHRlhM2SSOOviCV9HTuykLXSJisOE47p8Lg/GcqGG1Wv8mays10c/Vwk7YfHnOBe2juhhzLMJutHYRQhBcLYHtbOjfdbpoOvvx0YSid+ud+yb/XLOohUrCFltAGJjGWI/D9cvvvjiaRk3yVx+kPw59tOnnnrq9Bs7i1YOpARdy8152gHiH+Osc7kejNeCdWH1GCQul/o0QTcauwwhAGRqm1YqBgtLkG8liPjjwMSeFYdUEmyY/QSD/bTrrDCQte2AhdMrs5+ma64mdMkPudvf/bjjjpt20zvQVhzKDPGlDvzqZkFNleDn7j+uYl/Xkk9Fwus1xznXBN1o7CLkwwe2yuyPjz766MmaA2FCriduzoGUa/WhvZ79zd4qRCQsPCRsz3V/i7cRkgUoFquMBA027LdC0Z/ubbZ0IAh6rD9/JUeOrbdOJCoeYeo7pst50oOwmlfOOXm4J04Z8jea0Bkoz4ilLnFP+oom6EZjFyFkAhaSINBsekQ1EYRoKmEkzE9h6Yw5JE1NQl0SuG4ZNxWHNHN5AFI+8cQTp6XhSD3hW4lafq2HskLCSJI/hJrORhwuYQknaVd9vetJLy8dkrYVDyEzX2Qx41+MFgPpCLW1rVtdq5BH6qnsJuhGY5chxINM/D2FBEuS9fuyEJrr8YcwAnt2IGULXag5pLXEWxpAPMiZlUiQ/CqoNfxj1D8KxwnKrUK9h7EOzqlk6NIRJfLUaXB+AkEFZFtV7SKOzkyYNvP3npCyPE24GhHoaJCvUYaFP4g5v59DzCZGLZ9H1n42UaX2sc25JuhGYxehfvwIgaRnNzn/+7QvR1DjcQkjRZokZDdNcqRrPuGEE6a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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 随机抽样\n", - "\n", - "numpy.random 模块对 Python 内置的 random 进行了补充，增加了一些用于高效生成多种概率分布的样本值的函数，如正态分布、泊松分布等。\n", - "\n", - "\n", - "\n", - "- `numpy.random.seed(seed=None)` Seed the generator.\n", - "\n", - "`seed()`用于指定随机数生成时所用算法开始的整数值，如果使用相同的`seed()`值，则每次生成的随机数都相同，如果不设置这个值，则系统根据时间来自己选择这个值，此时每次生成的随机数因时间差异而不同。\n", - "\n", - "在对数据进行预处理时，经常加入新的操作或改变处理策略，此时如果伴随着随机操作，最好还是指定唯一的随机种子，避免由于随机的差异对结果产生影响。\n", - "\n", - "---\n", - "# 离散型随机变量\n", - "## 二项分布\n", - "二项分布可以用于只有一次实验只有两种结果，各结果对应的概率相等的多次实验的概率问题。比如处理猜10次拳赢6次的概率等类似的问题。\n", - "\n", - "二项分布概率函数的代码表示：binom.pmf(k) = choose(n, k) p\\*\\*k (1-p)\\*\\*(n-k)\n", - "\n", - "二项分布概率函数的数学表示：\n", - "\n", - "![image.png](attachment:image.png)\n", - "\n", - "\n", - "- `numpy.random.binomial(n, p, size=None)` Draw samples from a binomial distribution.\n", - "\n", - "表示对一个二项分布进行采样，`size`表示采样的次数，`n`表示做了`n`重伯努利试验，`p`表示成功的概率，函数的返回值表示`n`中成功的次数。\n", - "\n", - "【例】野外正在进行9（n=9）口石油勘探井的发掘工作，每一口井能够开发出油的概率是0.1（p=0.1）。请问，最终所有的勘探井都勘探失败的概率？\n", - "```python\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy import stats\n", - "\n", - "np.random.seed(20200605)\n", - "n = 9# 做某件事情的次数\n", - "p = 0.1# 做某件事情成功的概率\n", - "size = 50000\n", - "x = np.random.binomial(n, p, size)\n", - "'''或者使用binom.rvs\n", - "#使用binom.rvs(n, p, size=1)函数模拟一个二项随机变量,可视化地表现概率\n", - "y = stats.binom.rvs(n, p, size=size)#返回一个numpy.ndarray\n", - "'''\n", - "print(np.sum(x == 0) / size) # 0.3897\n", - "\n", - "plt.hist(x)\n", - "plt.xlabel('随机变量：成功次数')\n", - "plt.ylabel('样本中出现的次数')\n", - "plt.show()\n", - "#它返回一个列表，列表中每个元素表示随机变量中对应值的概率\n", - "s = stats.binom.pmf(range(10), n, p)\n", - "print(np.around(s, 3))\n", - "# [0.387 0.387 0.172 0.045 0.007 0.001 0. 0. 0. 0. ]\n", - "```\n", - "![task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E4%BA%8C%E9%A1%B9%E5%88%86%E5%B8%83-%E9%A2%91%E6%95%B0%E5%9B%BE.png](attachment:task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E4%BA%8C%E9%A1%B9%E5%88%86%E5%B8%83-%E9%A2%91%E6%95%B0%E5%9B%BE.png)\n", - "\n", - "\n", - "【例】模拟投硬币，投2次，请问两次都为正面的概率？\n", - "```python\n", - "import numpy as np\n", - "from scipy import stats\n", - "import matplotlib.pyplot as plt\n", - "\n", - "np.random.seed(20200605)\n", - "n = 2# 做某件事情的次数,这里是投两次硬币\n", - "p = 0.5#做某件事情成功的概率，在这里即投硬币为正面的概率\n", - "size = 50000\n", - "x = np.random.binomial(n, p, size)\n", - "'''或者使用binom.rvs\n", - "#使用binom.rvs(n, p, size=1)函数模拟一个二项随机变量,可视化地表现概率\n", - "y = stats.binom.rvs(n, p, size=size)#返回一个numpy.ndarray\n", - "'''\n", - "print(np.sum(x == 0) / size) # 0.25154\n", - "print(np.sum(x == 1) / size) # 0.49874\n", - "print(np.sum(x == 2) / size) # 0.24972\n", - "\n", - "plt.hist(x, density=True)\n", - "plt.xlabel('随机变量：硬币为正面次数')\n", - "plt.ylabel('50000个样本中出现的次数')\n", - "plt.show()\n", - "#它返回一个列表，列表中每个元素表示随机变量中对应值的概率\n", - "s = stats.binom.pmf(range(n + 1), n, p)\n", - "print(np.around(s, 3))\n", - "# [0.25 0.5 0.25]\n", - "```\n", - "![task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E4%BA%8C%E9%A1%B9%E5%88%86%E5%B8%83-%E6%8A%95%E7%A1%AC%E5%B8%81.png](attachment:task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E4%BA%8C%E9%A1%B9%E5%88%86%E5%B8%83-%E6%8A%95%E7%A1%AC%E5%B8%81.png)\n", - "\n", - "```\n", - "#计算期望和方差\n", - "'''\n", - "期望：E(x) = np\n", - "方差：Var(x) = np(1-p)\n", - "利用stats.binom.stats(n, p, loc=0, moments='mv')计算期望和方差\n", - "moments参数中:m为期望，v为方差\n", - "'''\n", - "```\n", - "## 泊松分布\n", - "泊松分布主要用于估计某个时间段某事件发生的概率。\n", - "\n", - "泊松概率函数的代码表示：poisson.pmf(k) = exp(-lam) lam\\*k / k!\n", - "\n", - "泊松概率函数的数学表示：\n", - "\n", - "![image.png](attachment:image.png)\n", - "\n", - "- `numpy.random.poisson(lam=1.0, size=None)` Draw samples from a Poisson distribution.\n", - "\n", - "表示对一个泊松分布进行采样，`size`表示采样的次数，`lam`表示一个单位内发生事件的平均值，函数的返回值表示一个单位内事件发生的次数。\n", - "\n", - "【例】假定某航空公司预定票处平均每小时接到42次订票电话，那么10分钟内恰好接到6次电话的概率是多少？\n", - "\n", - "```python\n", - "import numpy as np\n", - "from scipy import stats\n", - "import matplotlib.pyplot as plt\n", - "\n", - "np.random.seed(20200605)\n", - "lam = 42 / 6# 平均值：平均每十分钟接到42/6次订票电话\n", - "size = 50000\n", - "x = np.random.poisson(lam, size)\n", - "'''或者\n", - "#模拟服从泊松分布的50000个随机变量\n", - "x = stats.poisson.rvs(lam,size=size)\n", - "'''\n", - "print(np.sum(x == 6) / size) # 0.14988\n", - "\n", - "plt.hist(x)\n", - "plt.xlabel('随机变量：每十分钟接到订票电话的次数')\n", - "plt.ylabel('50000个样本中出现的次数')\n", - "plt.show()\n", - "#用poisson.pmf(k, mu)求对应分布的概率:概率质量函数 (PMF)\n", - "x = stats.poisson.pmf(6, lam)\n", - "print(x) # 0.14900277967433773\n", - "```\n", - "\n", - "![task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E6%B3%8A%E6%9D%BE%E5%88%86%E5%B8%83-%E6%AF%8F%E5%8D%81%E5%88%86%E9%92%9F%E6%8E%A5%E5%88%B0%E8%AE%A2%E7%A5%A8%E7%94%B5%E8%AF%9D%E7%9A%84%E6%AC%A1%E6%95%B0.png](attachment:task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E6%B3%8A%E6%9D%BE%E5%88%86%E5%B8%83-%E6%AF%8F%E5%8D%81%E5%88%86%E9%92%9F%E6%8E%A5%E5%88%B0%E8%AE%A2%E7%A5%A8%E7%94%B5%E8%AF%9D%E7%9A%84%E6%AC%A1%E6%95%B0.png)\n", - "\n", - "\n", - "## 超几何分布\n", - "\n", - "在超几何分布中，各次实验不是独立的，各次实验成功的概率也不等。\n", - "超几何分布概率函数的数学表示：![task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E8%B6%85%E5%87%A0%E4%BD%95%E5%88%86%E5%B8%83-%E6%95%B0%E5%AD%A6%E5%85%AC%E5%BC%8F%E8%A1%A8%E7%A4%BA.png](attachment:task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E8%B6%85%E5%87%A0%E4%BD%95%E5%88%86%E5%B8%83-%E6%95%B0%E5%AD%A6%E5%85%AC%E5%BC%8F%E8%A1%A8%E7%A4%BA.png)\n", - "\n", - "\n", - "- `numpy.random.hypergeometric(ngood, nbad, nsample, size=None)` Draw samples from a Hypergeometric distribution.\n", - "\n", - "表示对一个超几何分布进行采样，`size`表示采样的次数，`ngood`表示总体中具有成功标志的元素个数，`nbad`表示总体中不具有成功标志的元素个数，`ngood+nbad`表示总体样本容量，`nsample`表示抽取元素的次数（小于或等于总体样本容量），函数的返回值表示抽取`nsample`个元素中具有成功标识的元素个数。\n", - "\n", - "\n", - "【例】一共20只动物里有7只是狗，抽取12只有3只狗的概率（无放回抽样）。\n", - "\n", - "```python\n", - "import numpy as np\n", - "from scipy import stats\n", - "import matplotlib.pyplot as plt\n", - "\n", - "np.random.seed(20200605)\n", - "size = 500000\n", - "x = np.random.hypergeometric(ngood=7, nbad=13, nsample=12, size=size)\n", - "'''或者\n", - "#用rvs(M, n, N, loc=0, size=1, random_state=None)模拟\n", - "x = stats.hypergeom.rvs(M=20,n=7,N=12,size=size)\n", - "'''\n", - "print(np.sum(x == 3) / size) # 0.198664\n", - "\n", - "plt.hist(x, bins=8)\n", - "plt.xlabel('狗的数量')\n", - "plt.ylabel('50000个样本中出现的次数')\n", - "plt.title('超几何分布',fontsize=20)\n", - "plt.show()\n", - "\n", - "\"\"\"\n", - "M 为总体容量\n", - "n 为总体中具有成功标志的元素的个数\n", - "N,k 表示抽取N个元素有k个是成功元素\n", - "\"\"\"\n", - "x = range(8)\n", - "#用hypergeom.pmf(k, M, n, N, loc)来计算k次成功的概率\n", - "s = stats.hypergeom.pmf(k=x, M=20, n=7, N=12)\n", - "print(np.round(s, 3))\n", - "# [0. 0.004 0.048 0.199 0.358 0.286 0.095 0.01 ]\n", - "```\n", - "\n", - "![task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E8%B6%85%E5%87%A0%E4%BD%95%E5%88%86%E5%B8%83-%E7%8B%97%E7%9A%84%E6%95%B0%E9%87%8F.png](attachment:task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E8%B6%85%E5%87%A0%E4%BD%95%E5%88%86%E5%B8%83-%E7%8B%97%E7%9A%84%E6%95%B0%E9%87%8F.png)\n", - "```\n", - "'''\n", - "超几何分布的均值与方差\n", - "\n", - "均值E(x) = N(n/M)\n", - "方差Var(x) = N(n/M)(1-n/M)((M-N)/(M-1))\n", - "注释：考虑n次实验的超几何分布，令p=n/M,当总体容量足够大时((M-N)/(M-1))近似于1，此时数学期望为Np，方差为Np(1-p).\n", - "#用stats(M, n, N, loc=0, moments='mv')计算均值和方差\n", - "stats.hypergeom.stats(20,7,12,moments='mv')\n", - "'''\n", - "```\n", - "\n", - "---\n", - "# 连续型随机变量\n", - "\n", - "## 均匀分布\n", - "\n", - "- `numpy.random.uniform(low=0.0, high=1.0, size=None)` Draw samples from a uniform distribution.\n", - "\n", - "Samples are uniformly distributed over the half-open interval `[low, high)` (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by `uniform`.\n", - "\n", - "【例】在low到high范围内，创建大小为size的均匀分布的随机数。\n", - "```python\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy import stats\n", - "\n", - "np.random.seed(20200614)\n", - "a = 0\n", - "b = 100\n", - "size = 50000\n", - "x = np.random.uniform(a, b, size=size)\n", - "print(np.all(x >= 0)) # True\n", - "print(np.all(x < 100)) # True\n", - "y = (np.sum(x < 50) - np.sum(x < 10)) / size\n", - "print(y) # 0.40144\n", - "\n", - "plt.hist(x, bins=20)\n", - "plt.show()\n", - "\n", - "a = stats.uniform.cdf(10, 0, 100)\n", - "b = stats.uniform.cdf(50, 0, 100)\n", - "print(b - a) # 0.4\n", - "```\n", - "\n", - "![](https://img-blog.csdnimg.cn/20200614175837513.png)\n", - "\n", - "作为`uniform()`的特列，可以得到`[0,1)`之间的均匀分布的随机数。\n", - "\n", - "- `numpy.random.rand(d0, d1, ..., dn)` Random values in a given shape.\n", - "\n", - "\n", - "\n", - "Create an array of the given shape and populate it with random samples from a uniform distribution over `[0, 1)`.\n", - "\n", - "\n", - "\n", - "【例】根据指定大小产生[0,1)之间均匀分布的随机数。\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200614)\n", - "print(np.random.rand())\n", - "# 0.7594819171852776\n", - "\n", - "print(np.random.rand(5))\n", - "# [0.75165827 0.16552651 0.0538581 0.46671446 0.89076925]\n", - "\n", - "print(np.random.rand(4, 3))\n", - "# [[0.10073292 0.14624784 0.40273923]\n", - "# [0.21844459 0.22226682 0.37246217]\n", - "# [0.50334257 0.01714939 0.47780388]\n", - "# [0.08755349 0.86500477 0.70566398]]\n", - "\n", - "np.random.seed(20200614)\n", - "print(np.random.uniform()) # 0.7594819171852776\n", - "print(np.random.uniform(size=5))\n", - "# [0.75165827 0.16552651 0.0538581 0.46671446 0.89076925]\n", - "\n", - "print(np.random.uniform(size=(4, 3)))\n", - "# [[0.10073292 0.14624784 0.40273923]\n", - "# [0.21844459 0.22226682 0.37246217]\n", - "# [0.50334257 0.01714939 0.47780388]\n", - "# [0.08755349 0.86500477 0.70566398]]\n", - "```\n", - "\n", - "作为`uniform`的另一特例，可以得到`[low,high)`之间均匀分布的随机整数。\n", - "\n", - "- `numpy.random.randint(low, high=None, size=None, dtype='l')` Return random integers from `low` (inclusive) to `high` (exclusive).\n", - "\n", - "Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then results are from [0, low).\n", - "\n", - "\n", - "【例】若`high`不为`None`时，取[low,high)之间随机整数，否则取值[0,low)之间随机整数。\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200614)\n", - "x = np.random.randint(2, size=10)\n", - "print(x)\n", - "# [0 0 0 1 0 1 0 0 0 0]\n", - "\n", - "x = np.random.randint(1, size=10)\n", - "print(x)\n", - "# [0 0 0 0 0 0 0 0 0 0]\n", - "\n", - "x = np.random.randint(5, size=(2, 4))\n", - "print(x)\n", - "# [[3 3 0 1]\n", - "# [1 1 0 1]]\n", - "\n", - "x = np.random.randint(1, 10, [3, 4])\n", - "print(x)\n", - "# [[2 1 7 7]\n", - "# [7 2 4 6]\n", - "# [8 7 2 8]]\n", - "```\n", - "\n", - "\n", - "## 正态分布\n", - "\n", - "标准正态分布数学表示：\n", - "![task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E5%9D%87%E5%8C%80%E5%88%86%E5%B8%83-%E6%95%B0%E5%AD%A6%E8%A1%A8%E7%A4%BA%E5%85%AC%E5%BC%8F.png](attachment:task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E5%9D%87%E5%8C%80%E5%88%86%E5%B8%83-%E6%95%B0%E5%AD%A6%E8%A1%A8%E7%A4%BA%E5%85%AC%E5%BC%8F.png)\n", - "\n", - "- `numpy.random.randn(d0, d1, ..., dn)` Return a sample (or samples) from the \"standard normal\" distribution.\n", - "\n", - "\n", - "\n", - "【例】根据指定大小产生满足标准正态分布的数组（均值为0，标准差为1）。\n", - "\n", - "```python\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy import stats\n", - "\n", - "np.random.seed(20200614)\n", - "size = 50000\n", - "x = np.random.randn(size)\n", - "y1 = (np.sum(x < 1) - np.sum(x < -1)) / size\n", - "y2 = (np.sum(x < 2) - np.sum(x < -2)) / size\n", - "y3 = (np.sum(x < 3) - np.sum(x < -3)) / size\n", - "print(y1) # 0.68596\n", - "print(y2) # 0.95456\n", - "print(y3) # 0.99744\n", - "\n", - "plt.hist(x, bins=20)\n", - "plt.show()\n", - "\n", - "y1 = stats.norm.cdf(1) - stats.norm.cdf(-1)\n", - "y2 = stats.norm.cdf(2) - stats.norm.cdf(-2)\n", - "y3 = stats.norm.cdf(3) - stats.norm.cdf(-3)\n", - "print(y1) # 0.6826894921370859\n", - "print(y2) # 0.9544997361036416\n", - "print(y3) # 0.9973002039367398\n", - "```\n", - "\n", - "![](https://img-blog.csdnimg.cn/20200614200342168.png)\n", - "\n", - "\n", - "还可以指定分布以及所需参数来进行随机，例如高斯分布中的mu和sigma。\n", - "\n", - "- `numpy.random.normal(loc=0.0, scale=1.0, size=None)` Draw random samples from a normal (Gaussian) distribution.\n", - "\n", - "`normal()`为创建均值为 loc（mu），标准差为 scale（sigma），大小为 size 的数组。\n", - "![task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83-%E5%85%AC%E5%BC%8F%E5%B0%8F.jpg](attachment:task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83-%E5%85%AC%E5%BC%8F%E5%B0%8F.jpg)\n", - "\n", - "```python\n", - "sigma * np.random.randn(...) + mu\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "np.random.seed(20200614)\n", - "x = 0.5 * np.random.randn(2, 4) + 5\n", - "'''或者\n", - "#模拟10000个随机变量\n", - "x = 0.5*stats.norm.rvs(size=(2,4))+5\n", - "'''\n", - "print(x)\n", - "# [[5.39654234 5.4088702 5.49104652 4.95817289]\n", - "# [4.31977933 4.76502391 4.70720327 4.36239023]]\n", - "\n", - "np.random.seed(20200614)\n", - "mu = 5#平均值\n", - "sigma = 0.5#标准差\n", - "x = np.random.normal(mu, sigma, (2, 4))\n", - "print(x)\n", - "# [[5.39654234 5.4088702 5.49104652 4.95817289]\n", - "# [4.31977933 4.76502391 4.70720327 4.36239023]]\n", - "\n", - "size = 50000\n", - "x = np.random.normal(mu, sigma, size)\n", - "\n", - "print(np.mean(x)) # 4.996403463175092\n", - "print(np.std(x, ddof=1)) # 0.4986846716715106（#样本标准差）\n", - "'''\n", - "ddof：int, optional\n", - "Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. By default ddof is zero.\n", - "'''\n", - "```\n", - "![task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E6%A0%B7%E6%9C%AC%E6%A0%87%E5%87%86%E5%B7%AE.png](attachment:task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E6%A0%B7%E6%9C%AC%E6%A0%87%E5%87%86%E5%B7%AE.png)\n", - "```\n", - "plt.hist(x, bins=20)\n", - "plt.show()\n", - "```\n", - "\n", - "![](https://img-blog.csdnimg.cn/20200614204430538.png)\n", - "\n", - "## 指数分布\n", - "指数分布描述时间发生的时间长度间隔。\n", - "\n", - "指数分布的数学表示：![task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E6%8C%87%E6%95%B0%E5%88%86%E5%B8%83-%E6%95%B0%E5%AD%A6%E5%85%AC%E5%BC%8F.png](attachment:task11%E9%9A%8F%E6%9C%BA%E6%8A%BD%E6%A0%B7-%E6%8C%87%E6%95%B0%E5%88%86%E5%B8%83-%E6%95%B0%E5%AD%A6%E5%85%AC%E5%BC%8F.png)\n", - "\n", - "- `numpy.random.exponential(scale=1.0, size=None)` Draw samples from an exponential distribution.\n", - "\n", - "【例】`scale = 1/lambda`\n", - "```python\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy import stats\n", - "\n", - "np.random.seed(20200614)\n", - "lam = 7\n", - "size = 50000\n", - "x = np.random.exponential(1 / lam, size)\n", - "'''或者\n", - "#rvs(loc=0, scale=1/lam, size=size, random_state=None)模拟\n", - "'''\n", - "y1 = (np.sum(x < 1 / 7)) / size\n", - "y2 = (np.sum(x < 2 / 7)) / size\n", - "y3 = (np.sum(x < 3 / 7)) / size\n", - "print(y1) # 0.63218\n", - "print(y2) # 0.86518\n", - "print(y3) # 0.95056\n", - "\n", - "plt.hist(x, bins=20)\n", - "plt.show()\n", - "\n", - "y1 = stats.expon.cdf(1 / 7, scale=1 / lam)\n", - "y2 = stats.expon.cdf(2 / 7, scale=1 / lam)\n", - "y3 = stats.expon.cdf(3 / 7, scale=1 / lam)\n", - "print(y1) # 0.6321205588285577\n", - "print(y2) # 0.8646647167633873\n", - "print(y3) # 0.950212931632136\n", - "```\n", - "\n", - "![](https://img-blog.csdnimg.cn/20200614211345205.png)\n", - "\n", - "\n", - "---\n", - "# 其它随机函数\n", - "\n", - "## 随机从序列中获取元素\n", - "- `numpy.random.choice(a, size=None, replace=True, p=None)` Generates a random sample from a given 1-D array.\n", - "\n", - "从序列中获取元素，若`a`为整数，元素取值从`np.range(a)`中随机获取；若`a`为数组，取值从`a`数组元素中随机获取。该函数还可以控制生成数组中的元素是否重复`replace`，以及选取元素的概率`p`。\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200614)\n", - "x = np.random.choice(10, 3)\n", - "print(x) # [2 0 1]\n", - "\n", - "x = np.random.choice(10, 3, p=[0.05, 0, 0.05, 0.9, 0, 0, 0, 0, 0, 0])\n", - "print(x) # [3 2 3]\n", - "\n", - "x = np.random.choice(10, 3, replace=False, p=[0.05, 0, 0.05, 0.9, 0, 0, 0, 0, 0, 0])\n", - "print(x) # [3 0 2]\n", - "\n", - "aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n", - "x = np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n", - "print(x) # ['pooh' 'rabbit' 'pooh' 'pooh' 'pooh']\n", - "\n", - "np.random.seed(20200614)\n", - "x = np.random.randint(0, 10, 3)\n", - "print(x) # [2 0 1]\n", - "```\n", - "## 对数据集进行洗牌操作\n", - "\n", - "数据一般都是按照采集顺序排列的，但是在机器学习中很多算法都要求数据之间相互独立，所以需要先对数据集进行洗牌操作。\n", - "\n", - "- `numpy.random.shuffle(x)` Modify a sequence in-place by shuffling its contents.\n", - "\n", - "This function only shuffles the array along the first axis of a multi-dimensional array. The order of sub-arrays is changed but their contents remains the same.\n", - "\n", - "对`x`进行重排序，如果`x`为多维数组，只沿第 0 轴洗牌，改变原来的数组，输出为None。\n", - "\n", - "【例】洗牌，改变自身内容，打乱顺序。\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200614)\n", - "x = np.arange(10)\n", - "np.random.shuffle(x)\n", - "print(x)\n", - "# [6 8 7 5 3 9 1 4 0 2]\n", - "\n", - "print(np.random.shuffle([1, 4, 9, 12, 15]))\n", - "# None\n", - "\n", - "x = np.arange(20).reshape((5, 4))\n", - "print(x)\n", - "# [[ 0 1 2 3]\n", - "# [ 4 5 6 7]\n", - "# [ 8 9 10 11]\n", - "# [12 13 14 15]\n", - "# [16 17 18 19]]\n", - "\n", - "np.random.shuffle(x)\n", - "print(x)\n", - "# [[ 4 5 6 7]\n", - "# [ 0 1 2 3]\n", - "# [ 8 9 10 11]\n", - "# [16 17 18 19]\n", - "# [12 13 14 15]]\n", - "```\n", - "\n", - "- `numpy.random.permutation(x)` Randomly permute a sequence, or return a permuted range.\n", - "\n", - "If `x` is a multi-dimensional array, it is only shuffled along its first index.\n", - " \n", - "`permutation()`函数的作用与`shuffle()`函数相同，可以打乱第0轴的数据，但是它不会改变原来的数组。\n", - "\n", - "【例】\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200614)\n", - "x = np.arange(10)\n", - "y = np.random.permutation(x)\n", - "print(y)\n", - "# [6 8 7 5 3 9 1 4 0 2]\n", - "\n", - "print(np.random.permutation([1, 4, 9, 12, 15]))\n", - "# [ 4 1 9 15 12]\n", - "\n", - "x = np.arange(20).reshape((5, 4))\n", - "print(x)\n", - "# [[ 0 1 2 3]\n", - "# [ 4 5 6 7]\n", - "# [ 8 9 10 11]\n", - "# [12 13 14 15]\n", - "# [16 17 18 19]]\n", - "\n", - "y = np.random.permutation(x)\n", - "print(y)\n", - "# [[ 8 9 10 11]\n", - "# [ 0 1 2 3]\n", - "# [12 13 14 15]\n", - "# [16 17 18 19]\n", - "# [ 4 5 6 7]]\n", - "```\n", - "\n", - "\n", - "\n", - "\n", - "---\n", - "**参考文献**\n", - "- https://www.jianshu.com/p/63434ad5ea64\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python35", - "language": "python", - "name": "python35" - }, - "language_info": { - 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 统计相关\n", - "\n", - "## 次序统计\n", - "\n", - "### 计算最小值\n", - "- `numpy.amin(a[, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, where=np._NoValue])`Return the minimum of an array or minimum along an axis.\n", - "\n", - " \n", - " 【例】计算最小值\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.amin(x)\n", - "print(y) # 11\n", - "\n", - "y = np.amin(x, axis=0)\n", - "print(y) # [11 12 13 14 15]\n", - "\n", - "y = np.amin(x, axis=1)\n", - "print(y) # [11 16 21 26 31]\n", - "```\n", - "### 计算最大值\n", - "- `numpy.amax(a[, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, where=np._NoValue])`Return the maximum of an array or maximum along an axis.\n", - "\n", - "\n", - "【例】计算最大值\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.amax(x)\n", - "print(y) # 35\n", - "\n", - "y = np.amax(x, axis=0)\n", - "print(y) # [31 32 33 34 35]\n", - "\n", - "y = np.amax(x, axis=1)\n", - "print(y) # [15 20 25 30 35]\n", - "```\n", - "### 计算极差\n", - "- `numpy.ptp(a, axis=None, out=None, keepdims=np._NoValue)` Range of values (maximum - minimum) along an axis. The name of the function comes from the acronym for 'peak to peak'.\n", - "\n", - "【例】计算极差\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200623)\n", - "x = np.random.randint(0, 20, size=[4, 5])\n", - "print(x)\n", - "# [[10 2 1 1 16]\n", - "# [18 11 10 14 10]\n", - "# [11 1 9 18 8]\n", - "# [16 2 0 15 16]]\n", - "\n", - "print(np.ptp(x)) # 18\n", - "print(np.ptp(x, axis=0)) # [ 8 10 10 17 8]\n", - "print(np.ptp(x, axis=1)) # [15 8 17 16]\n", - "```\n", - "### 计算分位数\n", - "\n", - "- `numpy.percentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False)` Compute the q-th percentile of the data along the specified axis. Returns the q-th percentile(s) of the array elements.\n", - " - a：array，用来算分位数的对象，可以是多维的数组。\n", - " - q：介于0-100的float，用来计算是几分位的参数，如四分之一位就是25，如要算两个位置的数就[25,75]。\n", - " - axis：坐标轴的方向，一维的就不用考虑了，多维的就用这个调整计算的维度方向，取值范围0/1。\n", - "\n", - "\n", - "【例】计算分位数\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200623)\n", - "x = np.random.randint(0, 20, size=[4, 5])\n", - "print(x)\n", - "# [[10 2 1 1 16]\n", - "# [18 11 10 14 10]\n", - "# [11 1 9 18 8]\n", - "# [16 2 0 15 16]]\n", - "\n", - "print(np.percentile(x, [25, 50])) \n", - "# [ 2. 10.]\n", - "\n", - "print(np.percentile(x, [25, 50], axis=0))\n", - "# [[10.75 1.75 0.75 10.75 9.5 ]\n", - "# [13.5 2. 5. 14.5 13. ]]\n", - "\n", - "print(np.percentile(x, [25, 50], axis=1))\n", - "# [[ 1. 10. 8. 2.]\n", - "# [ 2. 11. 9. 15.]]\n", - "```\n", - "\n", - "\n", - "---\n", - "## 均值与方差\n", - "\n", - "### 计算中位数\n", - "- `numpy.median(a, axis=None, out=None, overwrite_input=False, keepdims=False)` Compute the median along the specified axis. Returns the median of the array elements.\n", - "\n", - "【例】计算中位数\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200623)\n", - "x = np.random.randint(0, 20, size=[4, 5])\n", - "print(x)\n", - "# [[10 2 1 1 16]\n", - "# [18 11 10 14 10]\n", - "# [11 1 9 18 8]\n", - "# [16 2 0 15 16]]\n", - "print(np.percentile(x, 50))\n", - "print(np.median(x))\n", - "# 10.0\n", - "\n", - "print(np.percentile(x, 50, axis=0))\n", - "print(np.median(x, axis=0))\n", - "# [13.5 2. 5. 14.5 13. ]\n", - "\n", - "print(np.percentile(x, 50, axis=1))\n", - "print(np.median(x, axis=1))\n", - "# [ 2. 11. 9. 15.]\n", - "```\n", - "\n", - "### 计算平均值\n", - "\n", - "- `numpy.mean(a[, axis=None, dtype=None, out=None, keepdims=np._NoValue)])`Compute the arithmetic mean along the specified axis.\n", - "\n", - "【例】计算平均值（沿轴的元素的总和除以元素的数量）。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.mean(x)\n", - "print(y) # 23.0\n", - "\n", - "y = np.mean(x, axis=0)\n", - "print(y) # [21. 22. 23. 24. 25.]\n", - "\n", - "y = np.mean(x, axis=1)\n", - "print(y) # [13. 18. 23. 28. 33.]\n", - "```\n", - "### 计算加权平均值\n", - "- `numpy.average(a[, axis=None, weights=None, returned=False])`Compute the weighted average along the specified axis.\n", - "\n", - "\n", - "`mean`和`average`都是计算均值的函数，在不指定权重的时候`average`和`mean`是一样的。指定权重后，`average`可以计算加权平均值。\n", - "\n", - "【例】计算加权平均值（将各数值乘以相应的权数，然后加总求和得到总体值，再除以总的单位数。）\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.average(x)\n", - "print(y) # 23.0\n", - "\n", - "y = np.average(x, axis=0)\n", - "print(y) # [21. 22. 23. 24. 25.]\n", - "\n", - "y = np.average(x, axis=1)\n", - "print(y) # [13. 18. 23. 28. 33.]\n", - "\n", - "\n", - "y = np.arange(1, 26).reshape([5, 5])\n", - "print(y)\n", - "# [[ 1 2 3 4 5]\n", - "# [ 6 7 8 9 10]\n", - "# [11 12 13 14 15]\n", - "# [16 17 18 19 20]\n", - "# [21 22 23 24 25]]\n", - "\n", - "z = np.average(x, weights=y)\n", - "print(z) # 27.0\n", - "\n", - "z = np.average(x, axis=0, weights=y)\n", - "print(z)\n", - "# [25.54545455 26.16666667 26.84615385 27.57142857 28.33333333]\n", - "\n", - "z = np.average(x, axis=1, weights=y)\n", - "print(z)\n", - "# [13.66666667 18.25 23.15384615 28.11111111 33.08695652]\n", - "```\n", - "### 计算方差\n", - "- `numpy.var(a[, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue])`Compute the variance along the specified axis.\n", - " - ddof=0：是“Delta Degrees of Freedom”，表示自由度的个数。\n", - "\n", - "要注意方差和样本方差的无偏估计，方差公式中分母上是`n`；样本方差无偏估计公式中分母上是`n-1`（`n`为样本个数）。\n", - "\n", - "[证明的链接](https://blog.csdn.net/SoftPoeter/article/details/78273117?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.add_param_isCf&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.add_param_isCf) \n", - "\n", - "【例】计算方差\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.var(x)\n", - "print(y) # 52.0\n", - "y = np.mean((x - np.mean(x)) ** 2)\n", - "print(y) # 52.0\n", - "\n", - "y = np.var(x, ddof=1)\n", - "print(y) # 54.166666666666664\n", - "y = np.sum((x - np.mean(x)) ** 2) / (x.size - 1)\n", - "print(y) # 54.166666666666664\n", - "\n", - "y = np.var(x, axis=0)\n", - "print(y) # [50. 50. 50. 50. 50.]\n", - "\n", - "y = np.var(x, axis=1)\n", - "print(y) # [2. 2. 2. 2. 2.]\n", - "```\n", - "### 计算标准差\n", - "\n", - "- `numpy.std(a[, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue])`Compute the standard deviation along the specified axis.\n", - "\n", - "标准差是一组数据平均值分散程度的一种度量，是方差的算术平方根。\n", - "\n", - "\n", - "【例】计算标准差\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25],\n", - " [26, 27, 28, 29, 30],\n", - " [31, 32, 33, 34, 35]])\n", - "y = np.std(x)\n", - "print(y) # 7.211102550927978\n", - "y = np.sqrt(np.var(x))\n", - "print(y) # 7.211102550927978\n", - "\n", - "y = np.std(x, axis=0)\n", - "print(y)\n", - "# [7.07106781 7.07106781 7.07106781 7.07106781 7.07106781]\n", - "\n", - "y = np.std(x, axis=1)\n", - "print(y)\n", - "# [1.41421356 1.41421356 1.41421356 1.41421356 1.41421356]\n", - "```\n", - "\n", - "---\n", - "## 相关\n", - "### 计算协方差矩阵\n", - "\n", - "![task12%E7%BB%9F%E8%AE%A1%E7%9B%B8%E5%85%B3-%E5%8D%8F%E6%96%B9%E5%B7%AE%E7%9F%A9%E9%98%B5-%E6%95%B0%E5%AD%A6%E5%85%AC%E5%BC%8F.png](attachment:task12%E7%BB%9F%E8%AE%A1%E7%9B%B8%E5%85%B3-%E5%8D%8F%E6%96%B9%E5%B7%AE%E7%9F%A9%E9%98%B5-%E6%95%B0%E5%AD%A6%E5%85%AC%E5%BC%8F.png)\n", - "\n", - "- `numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,aweights=None)` Estimate a covariance matrix, given data and weights.\n", - "\n", - "【例】计算协方差矩阵\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = [1, 2, 3, 4, 6]\n", - "y = [0, 2, 5, 6, 7]\n", - "print(np.cov(x)) # 3.7 #样本方差\n", - "print(np.cov(y)) # 8.5 #样本方差\n", - "print(np.cov(x, y))\n", - "# [[3.7 5.25]\n", - "# [5.25 8.5 ]]\n", - "\n", - "print(np.var(x)) # 2.96 #方差\n", - "print(np.var(x, ddof=1)) # 3.7 #样本方差\n", - "print(np.var(y)) # 6.8 #方差\n", - "print(np.var(y, ddof=1)) # 8.5 #样本方差\n", - "\n", - "z = np.mean((x - np.mean(x)) * (y - np.mean(y))) #协方差\n", - "print(z) # 4.2\n", - "\n", - "z = np.sum((x - np.mean(x)) * (y - np.mean(y))) / (len(x) - 1) #样本协方差\n", - "print(z) # 5.25\n", - "\n", - "z = np.dot(x - np.mean(x), y - np.mean(y)) / (len(x) - 1) #样本协方差 \n", - "print(z) # 5.25\n", - "```\n", - "\n", - "### 计算相关系数\n", - "- `numpy.corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue)` Return Pearson product-moment correlation coefficients.\n", - "\n", - "理解了`np.cov()`函数之后，很容易理解`np.correlate()`，二者参数几乎一模一样。\n", - "\n", - "`np.cov()`描述的是两个向量协同变化的程度，它的取值可能非常大，也可能非常小，这就导致没法直观地衡量二者协同变化的程度。相关系数实际上是正则化的协方差，`n`个变量的相关系数形成一个`n`维方阵。\n", - "\n", - "【例】计算相关系数\n", - "```python\n", - "import numpy as np\n", - "\n", - "np.random.seed(20200623)\n", - "x, y = np.random.randint(0, 20, size=(2, 4))\n", - "\n", - "print(x) # [10 2 1 1]\n", - "print(y) # [16 18 11 10]\n", - "\n", - "z = np.corrcoef(x, y)\n", - "print(z)\n", - "# [[1. 0.48510096]\n", - "# [0.48510096 1. ]]\n", - "\n", - "a = np.dot(x - np.mean(x), y - np.mean(y))\n", - "b = np.sqrt(np.dot(x - np.mean(x), x - np.mean(x)))\n", - "c = np.sqrt(np.dot(y - np.mean(y), y - np.mean(y)))\n", - "print(a / (b * c)) # 0.4851009629263671\n", - "```\n", - "\n", - "---\n", - "## 直方图\n", - "\n", - "- `numpy.digitize(x, bins, right=False)`Return the indices of the bins to which each value in input array belongs.\n", - " - x：numpy数组\n", - " - bins：一维单调数组，必须是升序或者降序\n", - " - right：间隔是否包含最右\n", - " - 返回值：x在bins中的位置。\n", - "\n", - "【例】\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([0.2, 6.4, 3.0, 1.6])\n", - "bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])\n", - "inds = np.digitize(x, bins)\n", - "print(inds) # [1 4 3 2]\n", - "for n in range(x.size):\n", - " print(bins[inds[n] - 1], \"<=\", x[n], \"<\", bins[inds[n]])\n", - "\n", - "# 0.0 <= 0.2 < 1.0\n", - "# 4.0 <= 6.4 < 10.0\n", - "# 2.5 <= 3.0 < 4.0\n", - "# 1.0 <= 1.6 < 2.5\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1.2, 10.0, 12.4, 15.5, 20.])\n", - "bins = np.array([0, 5, 10, 15, 20])\n", - "inds = np.digitize(x, bins, right=True)\n", - "print(inds) # [1 2 3 4 4]\n", - "\n", - "inds = np.digitize(x, bins, right=False)\n", - "print(inds) # [1 3 3 4 5]\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python35", - "language": "python", - "name": "python35" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 线性代数\n", - "\n", - "Numpy 定义了 `matrix` 类型，使用该 `matrix` 类型创建的是矩阵对象，它们的加减乘除运算缺省采用矩阵方式计算，因此用法和Matlab十分类似。但是由于 NumPy 中同时存在 `ndarray` 和 `matrix` 对象，因此用户很容易将两者弄混。这有违 Python 的“显式优于隐式”的原则，因此官方并不推荐在程序中使用 `matrix`。在这里，我们仍然用 `ndarray` 来介绍。\n", - "\n", - "## 矩阵和向量积\n", - "\n", - "矩阵的定义、矩阵的加法、矩阵的数乘、矩阵的转置与二维数组完全一致，不再进行说明，但矩阵的乘法有不同的表示。\n", - "\n", - "- `numpy.dot(a, b[, out])`计算两个矩阵的乘积，如果是一维数组则是它们的内积。\n", - "\n", - "【例1】\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4, 5])\n", - "y = np.array([2, 3, 4, 5, 6])\n", - "z = np.dot(x, y)\n", - "print(z) # 70\n", - "\n", - "x = np.array([[1, 2, 3], [3, 4, 5], [6, 7, 8]])\n", - "print(x)\n", - "# [[1 2 3]\n", - "# [3 4 5]\n", - "# [6 7 8]]\n", - "\n", - "y = np.array([[5, 4, 2], [1, 7, 9], [0, 4, 5]])\n", - "print(y)\n", - "# [[5 4 2]\n", - "# [1 7 9]\n", - "# [0 4 5]]\n", - "\n", - "z = np.dot(x, y)\n", - "print(z)\n", - "# [[ 7 30 35]\n", - "# [ 19 60 67]\n", - "# [ 37 105 115]]\n", - "\n", - "z = np.dot(y, x)\n", - "print(z)\n", - "# [[ 29 40 51]\n", - "# [ 76 93 110]\n", - "# [ 42 51 60]]\n", - "```\n", - "\n", - "注意：在线性代数里面讲的维数和数组的维数不同，如线代中提到的n维行向量在 Numpy 中是一维数组，而线性代数中的n维列向量在 Numpy 中是一个shape为(n, 1)的二维数组。\n", - "\n", - "---\n", - "## 矩阵特征值与特征向量\n", - "\n", - "- `numpy.linalg.eig(a)` 计算方阵的特征值和特征向量。\n", - "- `numpy.linalg.eigvals(a)` 计算方阵的特征值。\n", - "\n", - "【例1】求方阵的特征值特征向量\n", - "```python\n", - "import numpy as np\n", - "\n", - "# 创建一个对角矩阵！\n", - "x = np.diag((1, 2, 3)) \n", - "print(x)\n", - "# [[1 0 0]\n", - "# [0 2 0]\n", - "# [0 0 3]]\n", - "\n", - "print(np.linalg.eigvals(x))\n", - "# [1. 2. 3.]\n", - "\n", - "a, b = np.linalg.eig(x) \n", - "# 特征值保存在a中，特征向量保存在b中\n", - "print(a)\n", - "# [1. 2. 3.]\n", - "print(b)\n", - "# [[1. 0. 0.]\n", - "# [0. 1. 0.]\n", - "# [0. 0. 1.]]\n", - "\n", - "# 检验特征值与特征向量是否正确\n", - "for i in range(3): \n", - " if np.allclose(a[i] * b[:, i], np.dot(x, b[:, i])):\n", - " print('Right')\n", - " else:\n", - " print('Error')\n", - "# Right\n", - "# Right\n", - "# Right\n", - "```\n", - "\n", - "【例2】判断对称阵是否为正定阵（特征值是否全部为正）。\n", - "```python\n", - "import numpy as np\n", - "\n", - "A = np.arange(16).reshape(4, 4)\n", - "print(A)\n", - "# [[ 0 1 2 3]\n", - "# [ 4 5 6 7]\n", - "# [ 8 9 10 11]\n", - "# [12 13 14 15]]\n", - "\n", - "A = A + A.T # 将方阵转换成对称阵\n", - "print(A)\n", - "# [[ 0 5 10 15]\n", - "# [ 5 10 15 20]\n", - "# [10 15 20 25]\n", - "# [15 20 25 30]]\n", - "\n", - "B = np.linalg.eigvals(A) # 求A的特征值\n", - "print(B)\n", - "# [ 6.74165739e+01 -7.41657387e+00 1.82694656e-15 -1.72637110e-15]\n", - "\n", - "# 判断是不是所有的特征值都大于0，用到了all函数，显然对称阵A不是正定的\n", - "if np.all(B > 0):\n", - " print('Yes')\n", - "else:\n", - " print('No')\n", - "# No\n", - "```\n", - "\n", - "\n", - "---\n", - "## 矩阵分解\n", - "\n", - "### **奇异值分解**\n", - "\n", - "有关奇异值分解的原理：[奇异值分解（SVD）及其应用](https://mp.weixin.qq.com/s/GNHPamltnqaUpGG9NhvWxg)\n", - "\n", - "- `u, s, v = numpy.linalg.svd(a, full_matrices=True, compute_uv=True, hermitian=False)`奇异值分解\n", - " - `a` 是一个形如(M,N)矩阵\n", - " - `full_matrices`的取值是为False或者True，默认值为True，这时`u`的大小为(M,M)，`v`的大小为(N,N)。否则`u`的大小为(M,K)，`v`的大小为(K,N) ，K=min(M,N)。\n", - " - `compute_uv`的取值是为False或者True，默认值为True，表示计算`u,s,v`。为False的时候只计算`s`。\n", - " - 总共有三个返回值`u,s,v`，`u`大小为(M,M)，`s`大小为(M,N)，`v`大小为(N,N)，`a = u*s*v`。\n", - " - 其中`s`是对矩阵`a`的奇异值分解。`s`除了对角元素不为`0`，其他元素都为`0`，并且对角元素从大到小排列。`s`中有`n`个奇异值，一般排在后面的比较接近0，所以仅保留比较大的`r`个奇异值。 \n", - "\n", - "注：Numpy中返回的`v`是通常所谓奇异值分解`a=u*s*v'`中`v`的转置。\n", - "\n", - "【例1】\n", - "```python\n", - "import numpy as np\n", - "\n", - "A = np.array([[4, 11, 14], [8, 7, -2]])\n", - "print(A)\n", - "# [[ 4 11 14]\n", - "# [ 8 7 -2]]\n", - "\n", - "u, s, vh = np.linalg.svd(A, full_matrices=False)\n", - "print(u.shape) # (2, 2)\n", - "print(u)\n", - "# [[-0.9486833 -0.31622777]\n", - "# [-0.31622777 0.9486833 ]]\n", - "\n", - "print(s.shape) # (2,)\n", - "print(np.diag(s))\n", - "# [[18.97366596 0. ]\n", - "# [ 0. 9.48683298]]\n", - "\n", - "print(vh.shape) # (2, 3)\n", - "print(vh)\n", - "# [[-0.33333333 -0.66666667 -0.66666667]\n", - "# [ 0.66666667 0.33333333 -0.66666667]]\n", - "\n", - "a = np.dot(u, np.diag(s))\n", - "a = np.dot(a, vh)\n", - "print(a)\n", - "# [[ 4. 11. 14.]\n", - "# [ 8. 7. -2.]]\n", - "```\n", - "\n", - "【例2】\n", - "```python\n", - "import numpy as np\n", - "\n", - "A = np.array([[1, 1], [1, -2], [2, 1]])\n", - "print(A)\n", - "# [[ 1 1]\n", - "# [ 1 -2]\n", - "# [ 2 1]]\n", - "\n", - "u, s, vh = np.linalg.svd(A, full_matrices=False)\n", - "print(u.shape) # (3, 2)\n", - "print(u)\n", - "# [[-5.34522484e-01 -1.11022302e-16]\n", - "# [ 2.67261242e-01 -9.48683298e-01]\n", - "# [-8.01783726e-01 -3.16227766e-01]]\n", - "\n", - "print(s.shape) # (2,)\n", - "print(np.diag(s))\n", - "# [[2.64575131 0. ]\n", - "# [0. 2.23606798]]\n", - "\n", - "print(vh.shape) # (2, 2)\n", - "print(vh)\n", - "# [[-0.70710678 -0.70710678]\n", - "# [-0.70710678 0.70710678]]\n", - "\n", - "a = np.dot(u, np.diag(s))\n", - "a = np.dot(a, vh)\n", - "print(a)\n", - "# [[ 1. 1.]\n", - "# [ 1. -2.]\n", - "# [ 2. 1.]]\n", - "```\n", - "\n", - "### **QR分解**\n", - "\n", - "- `q,r = numpy.linalg.qr(a, mode='reduced')`计算矩阵`a`的QR分解。\n", - " - `a`是一个(M, N)的待分解矩阵。\n", - " - `mode = reduced`：返回(M, N)的列向量两两正交的矩阵`q`，和(N, N)的三角阵`r`（Reduced QR分解）。\n", - " - `mode = complete`：返回(M, M)的正交矩阵`q`，和(M, N)的三角阵`r`（Full QR分解）。\n", - "\n", - "【例1】\n", - "```python\n", - "import numpy as np\n", - "\n", - "A = np.array([[2, -2, 3], [1, 1, 1], [1, 3, -1]])\n", - "print(A)\n", - "# [[ 2 -2 3]\n", - "# [ 1 1 1]\n", - "# [ 1 3 -1]]\n", - "\n", - "q, r = np.linalg.qr(A)\n", - "print(q.shape) # (3, 3)\n", - "print(q)\n", - "# [[-0.81649658 0.53452248 0.21821789]\n", - "# [-0.40824829 -0.26726124 -0.87287156]\n", - "# [-0.40824829 -0.80178373 0.43643578]]\n", - "\n", - "print(r.shape) # (3, 3)\n", - "print(r)\n", - "# [[-2.44948974 0. -2.44948974]\n", - "# [ 0. -3.74165739 2.13808994]\n", - "# [ 0. 0. -0.65465367]]\n", - "\n", - "print(np.dot(q, r))\n", - "# [[ 2. -2. 3.]\n", - "# [ 1. 1. 1.]\n", - "# [ 1. 3. -1.]]\n", - "\n", - "a = np.allclose(np.dot(q.T, q), np.eye(3))\n", - "print(a) # True\n", - "```\n", - "\n", - "【例2】\n", - "```python\n", - "import numpy as np\n", - "\n", - "A = np.array([[1, 1], [1, -2], [2, 1]])\n", - "print(A)\n", - "# [[ 1 1]\n", - "# [ 1 -2]\n", - "# [ 2 1]]\n", - "\n", - "q, r = np.linalg.qr(A, mode='complete')\n", - "print(q.shape) # (3, 3)\n", - "print(q)\n", - "# [[-0.40824829 0.34503278 -0.84515425]\n", - "# [-0.40824829 -0.89708523 -0.16903085]\n", - "# [-0.81649658 0.27602622 0.50709255]]\n", - "\n", - "print(r.shape) # (3, 2)\n", - "print(r)\n", - "# [[-2.44948974 -0.40824829]\n", - "# [ 0. 2.41522946]\n", - "# [ 0. 0. ]]\n", - "\n", - "print(np.dot(q, r))\n", - "# [[ 1. 1.]\n", - "# [ 1. -2.]\n", - "# [ 2. 1.]]\n", - "\n", - "a = np.allclose(np.dot(q, q.T), np.eye(3))\n", - "print(a) # True\n", - "```\n", - "\n", - "【例3】\n", - "```python\n", - "import numpy as np\n", - "\n", - "A = np.array([[1, 1], [1, -2], [2, 1]])\n", - "print(A)\n", - "# [[ 1 1]\n", - "# [ 1 -2]\n", - "# [ 2 1]]\n", - "\n", - "q, r = np.linalg.qr(A)\n", - "print(q.shape) # (3, 2)\n", - "print(q)\n", - "# [[-0.40824829 0.34503278]\n", - "# [-0.40824829 -0.89708523]\n", - "# [-0.81649658 0.27602622]]\n", - "\n", - "print(r.shape) # (2, 2)\n", - "print(r)\n", - "# [[-2.44948974 -0.40824829]\n", - "# [ 0. 2.41522946]]\n", - "\n", - "print(np.dot(q, r))\n", - "# [[ 1. 1.]\n", - "# [ 1. -2.]\n", - "# [ 2. 1.]]\n", - "\n", - "a = np.allclose(np.dot(q.T, q), np.eye(2))\n", - "print(a) # True （说明q为正交矩阵）\n", - "```\n", - "\n", - "### **Cholesky分解**\n", - "\n", - "- `L = numpy.linalg.cholesky(a)` 返回正定矩阵`a`的 Cholesky 分解`a = L*L.T`，其中`L`是下三角。\n", - "\n", - "【例1】\n", - "```python\n", - "import numpy as np\n", - "\n", - "A = np.array([[1, 1, 1, 1], [1, 3, 3, 3],\n", - " [1, 3, 5, 5], [1, 3, 5, 7]])\n", - "print(A)\n", - "# [[1 1 1 1]\n", - "# [1 3 3 3]\n", - "# [1 3 5 5]\n", - "# [1 3 5 7]]\n", - "\n", - "print(np.linalg.eigvals(A))\n", - "# [13.13707118 1.6199144 0.51978306 0.72323135]\n", - "\n", - "L = np.linalg.cholesky(A)\n", - "print(L)\n", - "# [[1. 0. 0. 0. ]\n", - "# [1. 1.41421356 0. 0. ]\n", - "# [1. 1.41421356 1.41421356 0. ]\n", - "# [1. 1.41421356 1.41421356 1.41421356]]\n", - "\n", - "print(np.dot(L, L.T))\n", - "# [[1. 1. 1. 1.]\n", - "# [1. 3. 3. 3.]\n", - "# [1. 3. 5. 5.]\n", - "# [1. 3. 5. 7.]]\n", - "```\n", - "\n", - "\n", - "---\n", - "## 范数和其它数字\n", - "\n", - "\n", - "### **矩阵的范数**\n", - "\n", - "- `numpy.linalg.norm(x, ord=None, axis=None, keepdims=False)` 计算向量或者矩阵的范数。\n", - "\n", - "根据`ord`参数的不同，计算不同的范数：\n", - "![task13%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-%E7%9F%A9%E9%98%B5%E7%9A%84%E8%8C%83%E6%95%B0-%E5%AF%B9%E5%BA%94%E5%85%AC%E5%BC%8F.png](attachment:task13%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-%E7%9F%A9%E9%98%B5%E7%9A%84%E8%8C%83%E6%95%B0-%E5%AF%B9%E5%BA%94%E5%85%AC%E5%BC%8F.png)\n", - "\n", - "\n", - "\n", - "\n", - "【例1】求向量的范数。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([1, 2, 3, 4])\n", - "\n", - "print(np.linalg.norm(x, ord=1)) \n", - "# 10.0\n", - "print(np.sum(np.abs(x))) \n", - "# 10\n", - "\n", - "print(np.linalg.norm(x, ord=2)) \n", - "# 5.477225575051661\n", - "print(np.sum(np.abs(x) ** 2) ** 0.5) \n", - "# 5.477225575051661\n", - "\n", - "print(np.linalg.norm(x, ord=-np.inf)) \n", - "# 1.0\n", - "print(np.min(np.abs(x))) \n", - "# 1\n", - "\n", - "print(np.linalg.norm(x, ord=np.inf)) \n", - "# 4.0\n", - "print(np.max(np.abs(x))) \n", - "# 4\n", - "```\n", - "\n", - "【例2】求矩阵的范数\n", - "```python\n", - "import numpy as np\n", - "\n", - "A = np.array([[1, 2, 3, 4], [2, 3, 5, 8],\n", - " [1, 3, 5, 7], [3, 4, 7, 11]])\n", - "\n", - "print(A)\n", - "# [[ 1 2 3 4]\n", - "# [ 2 3 5 8]\n", - "# [ 1 3 5 7]\n", - "# [ 3 4 7 11]]\n", - "\n", - "print(np.linalg.norm(A, ord=1)) # 30.0\n", - "print(np.max(np.sum(A, axis=0))) # 30\n", - "\n", - "print(np.linalg.norm(A, ord=2)) \n", - "# 20.24345358700576\n", - "print(np.max(np.linalg.svd(A, compute_uv=False))) \n", - "# 20.24345358700576\n", - "\n", - "print(np.linalg.norm(A, ord=np.inf)) # 25.0\n", - "print(np.max(np.sum(A, axis=1))) # 25\n", - "\n", - "print(np.linalg.norm(A, ord='fro')) \n", - "# 20.273134932713294\n", - "print(np.sqrt(np.trace(np.dot(A.T, A)))) \n", - "# 20.273134932713294\n", - "```\n", - "\n", - "### **方阵的行列式**\n", - "\n", - "- `numpy.linalg.det(a)` 计算行列式。\n", - "\n", - "【例】计算行列式。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[1, 2], [3, 4]])\n", - "print(x)\n", - "# [[1 2]\n", - "# [3 4]]\n", - "\n", - "print(np.linalg.det(x))\n", - "# -2.0000000000000004\n", - "```\n", - "\n", - "### **矩阵的秩**\n", - "\n", - "- `numpy.linalg.matrix_rank(M, tol=None, hermitian=False)` 返回矩阵的秩。\n", - "\n", - "【例】计算矩阵的秩。\n", - "```python\n", - "import numpy as np\n", - "\n", - "I = np.eye(3) # 先创建一个单位阵\n", - "print(I)\n", - "# [[1. 0. 0.]\n", - "# [0. 1. 0.]\n", - "# [0. 0. 1.]]\n", - "\n", - "r = np.linalg.matrix_rank(I)\n", - "print(r) # 3\n", - "\n", - "I[1, 1] = 0 # 将该元素置为0\n", - "print(I)\n", - "# [[1. 0. 0.]\n", - "# [0. 0. 0.]\n", - "# [0. 0. 1.]]\n", - "\n", - "r = np.linalg.matrix_rank(I) # 此时秩变成2\n", - "print(r) # 2\n", - "```\n", - "\n", - "### **矩阵的迹**\n", - "\n", - "- `numpy.trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None)` 方阵的迹就是主对角元素之和。\n", - "\n", - "【例】计算方阵的迹。\n", - "```python\n", - "import numpy as np\n", - "\n", - "x = np.array([[1, 2, 3], [3, 4, 5], [6, 7, 8]])\n", - "print(x)\n", - "# [[1 2 3]\n", - "# [3 4 5]\n", - "# [6 7 8]]\n", - "\n", - "y = np.array([[5, 4, 2], [1, 7, 9], [0, 4, 5]])\n", - "print(y)\n", - "# [[5 4 2]\n", - "# [1 7 9]\n", - "# [0 4 5]]\n", - "\n", - "print(np.trace(x)) # A的迹等于A.T的迹\n", - "# 13\n", - "print(np.trace(np.transpose(x)))\n", - "# 13\n", - "\n", - "print(np.trace(x + y)) # 和的迹 等于 迹的和\n", - "# 30\n", - "print(np.trace(x) + np.trace(y))\n", - "# 30\n", - "```\n", - "\n", - "\n", - "---\n", - "## 解方程和逆矩阵\n", - "\n", - "### **逆矩阵（inverse matrix）**\n", - "\n", - "设 A 是数域上的一个 n 阶矩阵，若在相同数域上存在另一个 n 阶矩阵 B，使得：`AB=BA=E`（E 为单位矩阵），则我们称 B 是 A 的逆矩阵，而 A 则被称为可逆矩阵。\n", - "\n", - "- `numpy.linalg.inv(a)` 计算矩阵`a`的逆矩阵（矩阵可逆的充要条件：`det(a) != 0`，或者`a`满秩）。\n", - "\n", - "\n", - "【例】计算矩阵的逆矩阵。\n", - "\n", - "```python\n", - "import numpy as np\n", - "\n", - "A = np.array([[1, -2, 1], [0, 2, -1], [1, 1, -2]])\n", - "print(A)\n", - "# [[ 1 -2 1]\n", - "# [ 0 2 -1]\n", - "# [ 1 1 -2]]\n", - "\n", - "# 求A的行列式，不为零则存在逆矩阵\n", - "A_det = np.linalg.det(A) \n", - "print(A_det)\n", - "# -2.9999999999999996\n", - "\n", - "A_inverse = np.linalg.inv(A) # 求A的逆矩阵\n", - "print(A_inverse)\n", - "# [[ 1.00000000e+00 1.00000000e+00 -1.11022302e-16]\n", - "# [ 3.33333333e-01 1.00000000e+00 -3.33333333e-01]\n", - "# [ 6.66666667e-01 1.00000000e+00 -6.66666667e-01]]\n", - "\n", - "x = np.allclose(np.dot(A, A_inverse), np.eye(3))\n", - "print(x) # True\n", - "x = np.allclose(np.dot(A_inverse, A), np.eye(3))\n", - "print(x) # True\n", - "\n", - "A_companion = A_inverse * A_det # 求A的伴随矩阵\n", - "print(A_companion)\n", - "# [[-3.00000000e+00 -3.00000000e+00 3.33066907e-16]\n", - "# [-1.00000000e+00 -3.00000000e+00 1.00000000e+00]\n", - "# [-2.00000000e+00 -3.00000000e+00 2.00000000e+00]]\n", - "```\n", - "\n", - "\n", - "\n", - "### **求解线性方程组**\n", - "\n", - "- `numpy.linalg.solve(a, b)` 求解线性方程组或矩阵方程。\n", - "\n", - "\n", - "【例】求解线性矩阵方程\n", - "```python\n", - "# x + 2y + z = 7\n", - "# 2x - y + 3z = 7\n", - "# 3x + y + 2z =18\n", - "\n", - "import numpy as np\n", - "\n", - "A = np.array([[1, 2, 1], [2, -1, 3], [3, 1, 2]])\n", - "b = np.array([7, 7, 18])\n", - "x = np.linalg.solve(A, b)\n", - "print(x) # [ 7. 1. -2.]\n", - "\n", - "x = np.linalg.inv(A).dot(b)\n", - "print(x) # [ 7. 1. -2.]\n", - "\n", - "y = np.allclose(np.dot(A, x), b)\n", - "print(y) # True\n", - "```\n", - "\n", - "\n", - "\n", - "\n", - "---\n", - "**参考文献**\n", - "- https://www.cnblogs.com/moon1992/p/4960700.html\n", - "- https://www.cnblogs.com/moon1992/p/4948793.html\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python35", - "language": "python", - "name": "python35" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "265.2px" - }, - "toc_section_display": true, - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/IntroductionToNumpy/14 输入和输出.ipynb b/IntroductionToNumpy/14 输入和输出.ipynb deleted file mode 100644 index 154c3a9..0000000 --- a/IntroductionToNumpy/14 输入和输出.ipynb +++ /dev/null @@ -1,372 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 输入和输出\n", - "\n", - "\n", - "\n", - "## numpy 二进制文件\n", - "\n", - "`save()`、`savez()`和`load()`函数以 numpy 专用的二进制类型（npy、npz）保存和读取数据，这三个函数会自动处理ndim、dtype、shape等信息，使用它们读写数组非常方便，但是`save()`输出的文件很难与其它语言编写的程序兼容。\n", - "\n", - "npy格式：以二进制的方式存储文件，在二进制文件第一行以文本形式保存了数据的元信息（ndim，dtype，shape等），可以用二进制工具查看内容。\n", - "\n", - "npz格式：以压缩打包的方式存储文件，可以用压缩软件解压。\n", - "\n", - "\n", - "- `numpy.save(file, arr, allow_pickle=True, fix_imports=True)` Save an array to a binary file in NumPy `.npy` format.\n", - "- `numpy.load(file, mmap_mode=None, allow_pickle=False, fix_imports=True, encoding='ASCII')` Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "outfile = r'.\\test.npy'\n", - "np.random.seed(20200619)\n", - "x = np.random.uniform(0, 1, [3, 5])\n", - "np.save(outfile, x)\n", - "y = np.load(outfile)\n", - "print(y)\n", - "# [[0.01123594 0.66790705 0.50212171 0.7230908 0.61668256]\n", - "# [0.00668332 0.1234096 0.96092409 0.67925305 0.38596837]\n", - "# [0.72342998 0.26258324 0.24318845 0.98795012 0.77370715]]\n", - "```\n", - "\n", - "- `numpy.savez(file, *args, **kwds)` Save several arrays into a single file in uncompressed `.npz` format.\n", - "\n", - "`savez()`第一个参数是文件名，其后的参数都是需要保存的数组，也可以使用关键字参数为数组起一个名字，非关键字参数传递的数组会自动起名为`arr_0, arr_1, …`。\n", - "\n", - "`savez()`输出的是一个压缩文件（扩展名为npz），其中每个文件都是一个`save()`保存的npy文件，文件名对应于数组名。`load()`自动识别npz文件，并且返回一个类似于字典的对象，可以通过数组名作为关键字获取数组的内容。\n", - "\n", - "【例】将多个数组保存到一个文件，可以使用`numpy.savez()`函数。\n", - "```python\n", - "import numpy as np\n", - "\n", - "outfile = r'.\\test.npz'\n", - "x = np.linspace(0, np.pi, 5)\n", - "y = np.sin(x)\n", - "z = np.cos(x)\n", - "np.savez(outfile, x, y, z_d=z)\n", - "data = np.load(outfile)\n", - "np.set_printoptions(suppress=True)\n", - "print(data.files) \n", - "# ['z_d', 'arr_0', 'arr_1']\n", - "\n", - "print(data['arr_0'])\n", - "# [0. 0.78539816 1.57079633 2.35619449 3.14159265]\n", - "\n", - "print(data['arr_1'])\n", - "# [0. 0.70710678 1. 0.70710678 0. ]\n", - "\n", - "print(data['z_d'])\n", - "# [ 1. 0.70710678 0. -0.70710678 -1. ]\n", - "```\n", - "\n", - "用解压软件打开 test.npz 文件，会发现其中有三个文件：`arr_0.npy,arr_1.npy,z_d.npy`，其中分别保存着数组`x,y,z`的内容。\n", - "\n", - "\n", - "---\n", - "## 文本文件\n", - "`savetxt()`，`loadtxt()`和`genfromtxt()`函数用来存储和读取文本文件（如TXT，CSV等）。`genfromtxt()`比`loadtxt()`更加强大，可对缺失数据进行处理。\n", - "\n", - "- `numpy.savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\\n', header='', footer='', comments='# ', encoding=None)` Save an array to a text file.\n", - " - fname：文件路径\n", - " - X：存入文件的数组。\n", - " - fmt：写入文件中每个元素的字符串格式，默认'%.18e'（保留18位小数的浮点数形式）。\n", - " - delimiter：分割字符串，默认以空格分隔。\n", - "\n", - "\n", - "- `numpy.loadtxt(fname, dtype=float, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding='bytes', max_rows=None)` Load data from a text file. \n", - " - fname：文件路径。\n", - " - dtype：数据类型，默认为float。\n", - " - comments: 字符串或字符串组成的列表，默认为# ， 表示注释字符集开始的标志。\n", - " - skiprows：跳过多少行，一般跳过第一行表头。\n", - " - usecols：元组（元组内数据为列的数值索引）， 用来指定要读取数据的列（第一列为0）。\n", - " - unpack：当加载多列数据时是否需要将数据列进行解耦赋值给不同的变量。\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "【例】写入和读出TXT文件。\n", - "```python\n", - "import numpy as np\n", - "\n", - "outfile = r'.\\test.txt'\n", - "x = np.arange(0, 10).reshape(2, -1)\n", - "np.savetxt(outfile, x)\n", - "y = np.loadtxt(outfile)\n", - "print(y)\n", - "# [[0. 1. 2. 3. 4.]\n", - "# [5. 6. 7. 8. 9.]]\n", - "```\n", - "\n", - "test.txt文件如下：\n", - "\n", - "```python\n", - "0.000000000000000000e+00 1.000000000000000000e+00 2.000000000000000000e+00 3.000000000000000000e+00 4.000000000000000000e+00\n", - "5.000000000000000000e+00 6.000000000000000000e+00 7.000000000000000000e+00 8.000000000000000000e+00 9.000000000000000000e+00\n", - "```\n", - "\n", - "\n", - "【例】写入和读出CSV文件。\n", - "```python\n", - "import numpy as np\n", - "\n", - "outfile = r'.\\test.csv'\n", - "x = np.arange(0, 10, 0.5).reshape(4, -1)\n", - "np.savetxt(outfile, x, fmt='%.3f', delimiter=',')\n", - "y = np.loadtxt(outfile, delimiter=',')\n", - "print(y)\n", - "# [[0. 0.5 1. 1.5 2. ]\n", - "# [2.5 3. 3.5 4. 4.5]\n", - "# [5. 5.5 6. 6.5 7. ]\n", - "# [7.5 8. 8.5 9. 9.5]]\n", - "```\n", - "\n", - "test.csv文件如下：\n", - "```python\n", - "0.000,0.500,1.000,1.500,2.000\n", - "2.500,3.000,3.500,4.000,4.500\n", - "5.000,5.500,6.000,6.500,7.000\n", - "7.500,8.000,8.500,9.000,9.500\n", - "```\n", - "\n", - "\n", - "\n", - "`genfromtxt()`是面向结构数组和缺失数据处理的。\n", - "\n", - "- `numpy.genfromtxt(fname, dtype=float, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=''.join(sorted(NameValidator.defaultdeletechars)), replace_space='_', autostrip=False, case_sensitive=True, defaultfmt=\"f%i\", unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None, encoding='bytes')` Load data from a text file, with missing values handled as specified.\n", - " - names：设置为True时，程序将把第一行作为列名称。\n", - "\n", - "\n", - "data.csv文件如下：\n", - "```python\n", - "id,value1,value2,value3\n", - "1,123,1.4,23\n", - "2,110,0.5,18\n", - "3,164,2.1,19\n", - "```\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "outfile = r'.\\data.csv'\n", - "x = np.loadtxt(outfile, delimiter=',', skiprows=1)\n", - "print(x)\n", - "# [[ 1. 123. 1.4 23. ]\n", - "# [ 2. 110. 0.5 18. ]\n", - "# [ 3. 164. 2.1 19. ]]\n", - "\n", - "x = np.loadtxt(outfile, delimiter=',', skiprows=1, usecols=(1, 2))\n", - "print(x)\n", - "# [[123. 1.4]\n", - "# [110. 0.5]\n", - "# [164. 2.1]]\n", - "\n", - "val1, val2 = np.loadtxt(outfile, delimiter=',', skiprows=1, usecols=(1, 2), unpack=True)\n", - "print(val1) # [123. 110. 164.]\n", - "print(val2) # [1.4 0.5 2.1]\n", - "```\n", - "\n", - "\n", - "【例】\n", - "```python\n", - "import numpy as np\n", - "\n", - "outfile = r'.\\data.csv'\n", - "x = np.genfromtxt(outfile, delimiter=',', names=True)\n", - "print(x)\n", - "# [(1., 123., 1.4, 23.) (2., 110., 0.5, 18.) (3., 164., 2.1, 19.)]\n", - "\n", - "print(type(x)) \n", - "# \n", - "\n", - "print(x.dtype)\n", - "# [('id', '\n", - "\n", - "print(x.dtype)\n", - "# [('id', '