From 55c546e802b470d46470c38fb88baa88c339367b Mon Sep 17 00:00:00 2001 From: MaoYZ Date: Mon, 26 Aug 2024 16:36:09 +0800 Subject: [PATCH] =?UTF-8?q?series=20=E6=93=8D=E4=BD=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 10 +- .../pandasDataFrameSeriesPanel.ipynb | 212 +++++- .../\350\202\241\347\245\250.csv" | 4 + datahandling/21-SeriesBasic/seriesBasic.ipynb | 346 +++++++++ .../22-seriesIndexAndOperation.ipynb | 669 ++++++++++++++++++ ...77\346\200\247\345\233\236\345\275\222.md" | 29 +- ...46\344\270\213\351\231\215\346\263\225.md" | 21 + machinelearning/images/ml_09.png | Bin 441315 -> 0 bytes machinelearning/images/ml_22.png | Bin 0 -> 10590 bytes machinelearning/images/ml_23.png | Bin 0 -> 11663 bytes machinelearning/images/ml_24.png | Bin 0 -> 10040 bytes 11 files changed, 1275 insertions(+), 16 deletions(-) create mode 100644 "datahandling/20-PandasDataFrameSeriesPanel/\350\202\241\347\245\250.csv" create mode 100644 datahandling/21-SeriesBasic/seriesBasic.ipynb create mode 100644 datahandling/22-SerieIndexAndOperation/22-seriesIndexAndOperation.ipynb delete mode 100644 machinelearning/images/ml_09.png create mode 100644 machinelearning/images/ml_22.png create mode 100644 machinelearning/images/ml_23.png create mode 100644 machinelearning/images/ml_24.png diff --git a/README.md b/README.md index 66204ed..fa8b2dd 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,8 @@ jupyter notebook ,numpy,pandas,matplotlib - [numpy排序找索引操作](datahandling/07-NumpyArgAndSortOperation/07-ArgAndSortOperation.ipynb) - [numpy比较和神奇索引](datahandling/08-ComparisonAndFancyIndexing/08-ComparisonAndFancyIndexing.ipynb) - [pandas中的数据结构](datahandling/20-PandasDataFrameSeriesPanel/pandasDataFrameSeriesPanel.ipynb) +- [Series创建、属性、计算](datahandling/21-SeriesBasic/seriesBasic.ipynb) +- [Series的索引与基本操作](datahandling/22-SerieIndexAndOperation/22-seriesIndexAndOperation.ipynb) ### 机器学习 @@ -48,10 +50,11 @@ jupyter notebook ,numpy,pandas,matplotlib - [正规方程法实现多元线性回归](machinelearning/linearRegression/05-OurLinearRegression/OurLinearRegression.ipynb) - [sklearn中解决线性回归](machinelearning/linearRegression/06-RegressionInScikitLlearn/RegressionInScikitlearn.ipynb) - 梯度下降法 + - [梯度下降法理论、公式](machinelearning/03梯度下降法.md) - [模拟实现梯度下降法(单变量)](machinelearning/gradientDescent/01-GradientDescentSimulations/01-GradientDescentSimulations.ipynb) - [在线性回归中实现梯度下降法](machinelearning/gradientDescent/02-ImplementGradientDescentInLinearRegression/02-ImplementGradientDescentInLinearRegression.ipynb) - [梯度下降向量化公式及性能和正规方程对比](machinelearning/gradientDescent/03-VectorizeGradientDescent/03-VectorizeGradientDescent.ipynb) - - [随机梯队下降法](machinelearning/gradientDescent/04-StochasticGradientDescent/04-StochasticGradientDescent.ipynb) + - [随机梯度下降法](machinelearning/gradientDescent/04-StochasticGradientDescent/04-StochasticGradientDescent.ipynb) @@ -59,6 +62,11 @@ jupyter notebook ,numpy,pandas,matplotlib - [基于Python的数据分析与可视化](https://juejin.cn/book/7240731597035864121) - [sklearn官网](https://scikit-learn.org/stable/index.html) - [Python3入门机器学习 经典算法与应用](https://coding.imooc.com/class/chapter/169.html) +- 书籍 + - 机器学习(公式推导与代码实现) + - 从零开始机器学习的数学原理和算法实践 + - 跟着迪哥学python数据分析与机器学习实战 + diff --git a/datahandling/20-PandasDataFrameSeriesPanel/pandasDataFrameSeriesPanel.ipynb b/datahandling/20-PandasDataFrameSeriesPanel/pandasDataFrameSeriesPanel.ipynb index ae579b5..205f824 100644 --- a/datahandling/20-PandasDataFrameSeriesPanel/pandasDataFrameSeriesPanel.ipynb +++ b/datahandling/20-PandasDataFrameSeriesPanel/pandasDataFrameSeriesPanel.ipynb @@ -21,8 +21,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-08-23T09:27:47.729919Z", - "start_time": "2024-08-23T09:27:47.372801Z" + "end_time": "2024-08-26T02:31:14.536375Z", + "start_time": "2024-08-26T02:31:14.170094Z" } }, "cell_type": "code", @@ -67,8 +67,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-08-23T09:40:17.805218Z", - "start_time": "2024-08-23T09:40:17.786146Z" + "end_time": "2024-08-26T02:31:14.544772Z", + "start_time": "2024-08-26T02:31:14.538495Z" } }, "cell_type": "code", @@ -119,8 +119,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-08-23T09:48:35.003373Z", - "start_time": "2024-08-23T09:48:34.989917Z" + "end_time": "2024-08-26T02:31:14.556940Z", + "start_time": "2024-08-26T02:31:14.546285Z" } }, "cell_type": "code", @@ -148,6 +148,206 @@ } ], "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### 可读性强", + "id": "1a5ed693b5b3072d" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T02:31:14.562885Z", + "start_time": "2024-08-26T02:31:14.558289Z" + } + }, + "cell_type": "code", + "source": [ + "import numpy as np \n", + "Score=[[60,30],[90,40],[100,90]] \n", + "Score_numpy=np.array(Score) \n", + "Score_pandas=pd.DataFrame(Score,index=['钟馗','廉颇','妲己'],columns=['语文','数学']) \n", + "print(Score_numpy) \n", + "print(Score_pandas)" + ], + "id": "28e8b9c8de5b8d28", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 60 30]\n", + " [ 90 40]\n", + " [100 90]]\n", + " 语文 数学\n", + "钟馗 60 30\n", + "廉颇 90 40\n", + "妲己 100 90\n" + ] + } + ], + "execution_count": 4 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T02:31:14.569507Z", + "start_time": "2024-08-26T02:31:14.565211Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd \n", + "xinxi=pd.DataFrame({'姓名':['张飞','貂蝉','赵云'],'性别':['男','女','男'],'工资':[2000,2300,2200]}) \n", + "print(xinxi)" + ], + "id": "1e57b549be6d9985", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 姓名 性别 工资\n", + "0 张飞 男 2000\n", + "1 貂蝉 女 2300\n", + "2 赵云 男 2200\n" + ] + } + ], + "execution_count": 5 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T02:31:14.573635Z", + "start_time": "2024-08-26T02:31:14.570477Z" + } + }, + "cell_type": "code", + "source": [ + "print(Score_pandas['语文']) \n", + "print(Score_pandas.loc['廉颇'])" + ], + "id": "3d99a84cc3cfc7e0", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "钟馗 60\n", + "廉颇 90\n", + "妲己 100\n", + "Name: 语文, dtype: int64\n", + "语文 90\n", + "数学 40\n", + "Name: 廉颇, dtype: int64\n" + ] + } + ], + "execution_count": 6 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T02:40:31.259643Z", + "start_time": "2024-08-26T02:40:31.250398Z" + } + }, + "cell_type": "code", + "source": [ + "Stock=pd.read_csv('./股票.csv') \n", + "print(Stock)" + ], + "id": "9c59f7072b2a529b", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date open close high low volume\n", + "0 2020-01-02 16.024 16.244 16.324 15.924 1530231\n", + "1 2020-01-03 16.314 16.554 16.684 16.294 1116194\n", + "2 2020-01-06 16.384 16.444 16.714 16.284 862083\n" + ] + } + ], + "execution_count": 9 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T02:41:29.778861Z", + "start_time": "2024-08-26T02:41:29.755185Z" + } + }, + "cell_type": "code", + "source": [ + "date_index=pd.to_datetime(Stock['date']) \n", + "Stock.index=date_index \n", + "print(Stock.loc['2020-01'])" + ], + "id": "aa6edc390106722c", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date open close high low volume\n", + "date \n", + "2020-01-02 2020-01-02 16.024 16.244 16.324 15.924 1530231\n", + "2020-01-03 2020-01-03 16.314 16.554 16.684 16.294 1116194\n", + "2020-01-06 2020-01-06 16.384 16.444 16.714 16.284 862083\n" + ] + } + ], + "execution_count": 10 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### series 绘图", + "id": "fdb1a2bebc9dc23a" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T02:54:23.001183Z", + "start_time": "2024-08-26T02:54:22.536113Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt \n", + "from pandas import DataFrame, Series \n", + "data=[90,96,98,80,95,100] \n", + "Score=pd.Series(data,index=range(6)) \n", + "Score.plot() \n", + "plt.show()" + ], + "id": "fd52c5bb09164685", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 11 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Pandas 与 Numpy 最大的区别就在于:能够自定义行列标签,增加了数据的可读性,这也是其方便数据分析的因素之一。与 Numpy 相比,尽管它的运行效率较低,但它提供的各种聚类、分组、透视图等函数方法,能够帮助使用者快速进行数据分类。此外,Pandas 与 Numpy 还有一个区别就是它的数据结构能够存储多种数据类型的数据,而Numpy 只能存储一种数据类型的数据。由于在生活实验中,我们的数据往往是多样化的,数据类型也多是不同的,Pandas 的这个特性完美的解决了 Numpy 的这一问题,使得 Numpy 更适合进行数据分析。\n", + "\n" + ], + "id": "e8bfb3a80423ce84" } ], "metadata": { diff --git "a/datahandling/20-PandasDataFrameSeriesPanel/\350\202\241\347\245\250.csv" "b/datahandling/20-PandasDataFrameSeriesPanel/\350\202\241\347\245\250.csv" new file mode 100644 index 0000000..9c57175 --- /dev/null +++ "b/datahandling/20-PandasDataFrameSeriesPanel/\350\202\241\347\245\250.csv" @@ -0,0 +1,4 @@ +date,open,close,high,low,volume +2020-01-02,16.024,16.244,16.324,15.924,1530231 +2020-01-03,16.314,16.554,16.684,16.294,1116194 +2020-01-06,16.384,16.444,16.714,16.284,862083 \ No newline at end of file diff --git a/datahandling/21-SeriesBasic/seriesBasic.ipynb b/datahandling/21-SeriesBasic/seriesBasic.ipynb new file mode 100644 index 0000000..9a56ca6 --- /dev/null +++ b/datahandling/21-SeriesBasic/seriesBasic.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## series 篇\n", + "\n", + "\n", + " Series 数据结构由两部分组成,分别为标签索引 index 和一维数组 Values。\n", + " \n", + "\n", + "构造方法:pandas.Series(data, index)\n", + "\n", + "Series 的数据传入类型有三种形式,分别为列表、Numpy 数组、字典。\n", + "\n", + "Series有点类型php中的一维数组\n", + "\n", + "\n", + " \n" + ], + "id": "4cf95c18716226d4" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T03:09:26.689571Z", + "start_time": "2024-08-26T03:09:26.355388Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd \n", + "data1=[93,92,79,59] \n", + "index=['张飞','关羽','赵云','貂蝉'] \n", + "Score=pd.Series(data1,index) \n", + "print(Score)" + ], + "id": "bf0142e39b6f6fce", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "张飞 93\n", + "关羽 92\n", + "赵云 79\n", + "貂蝉 59\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T03:10:13.139102Z", + "start_time": "2024-08-26T03:10:13.131617Z" + } + }, + "cell_type": "code", + "source": [ + "# name 属性 表示这些数据是什么属性的\n", + "import pandas as pd \n", + "data1=[93,92,79,59] \n", + "index=['张飞','关羽','赵云','貂蝉'] \n", + "Score=pd.Series(data1,index,name='学生成绩') \n", + "print(Score)" + ], + "id": "765d1dfc79cda2ef", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "张飞 93\n", + "关羽 92\n", + "赵云 79\n", + "貂蝉 59\n", + "Name: 学生成绩, dtype: int64\n" + ] + } + ], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "#### 用列表创建 Series\n", + "id": "ece9f19202a36779" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T03:18:56.260035Z", + "start_time": "2024-08-26T03:18:56.253975Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd \n", + "data1=[93,92,79,59] \n", + "index=['张飞','关羽','赵云','貂蝉'] \n", + "Score=pd.Series(data1,index) \n", + "print(Score)" + ], + "id": "b3817d29ce8b7f10", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "张飞 93\n", + "关羽 92\n", + "赵云 79\n", + "貂蝉 59\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "#### 利用 Numpy 数组创建 Series\n", + "\n", + "\n", + "\n" + ], + "id": "35f8095263bff462" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T03:19:30.530510Z", + "start_time": "2024-08-26T03:19:30.525235Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd \n", + "import numpy as np \n", + "data1=np.array([93,92,79,59]) \n", + "index=['张飞','关羽','赵云','貂蝉'] \n", + "Score=pd.Series(data1,index) \n", + "print(Score)" + ], + "id": "c8d99a858c169a95", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "张飞 93\n", + "关羽 92\n", + "赵云 79\n", + "貂蝉 59\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 4 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T03:22:56.069167Z", + "start_time": "2024-08-26T03:22:56.062796Z" + } + }, + "cell_type": "code", + "source": [ + "# 浅copy,修改数据会影响\n", + "Score[\"张飞\"]=80\n", + "print(data1)" + ], + "id": "96eed9c8206e854e", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[80 92 79 59]\n" + ] + } + ], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "#### 利用字典创建 Series\n", + "\n", + "利用字典创建 Series 对象,key 关键字自动转化为 Series 对象的索引,不需要额外设置索引\n", + "\n", + "in 和 not in 操作在 Series 对象中是支持的。\n", + "\n", + "用字典来创建Series,不会产生副本。 \n", + "\n" + ], + "id": "1079c2719adaa357" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T03:23:49.876990Z", + "start_time": "2024-08-26T03:23:49.870028Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd \n", + "data={'张飞':93,'关羽':92,'赵云':79,'貂蝉':59} \n", + "Score=pd.Series(data) \n", + "print(Score)" + ], + "id": "5862a61fb75b1d70", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "张飞 93\n", + "关羽 92\n", + "赵云 79\n", + "貂蝉 59\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### series的属性\n", + "\n", + "方法\t作用\n", + "- size\t获取数据个数\n", + "- index\t获取索引\n", + "- dtypes\t获取数据类型\n", + "- values\t查看数据部分" + ], + "id": "766c3cafbce88ac4" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T05:27:19.432154Z", + "start_time": "2024-08-26T05:27:19.426781Z" + } + }, + "cell_type": "code", + "source": [ + "print(Score.size)\n", + "print(Score.index)\n", + "print(Score.dtypes)\n", + "print(Score.values)" + ], + "id": "aeef7b26dc8786d4", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "Index(['张飞', '关羽', '赵云', '貂蝉'], dtype='object')\n", + "int64\n", + "[93 92 79 59]\n" + ] + } + ], + "execution_count": 8 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### 支持运算", + "id": "318239e23d4aa318" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T05:27:45.902568Z", + "start_time": "2024-08-26T05:27:45.892758Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd \n", + "data={1:93,2:92,3:79,4:59} \n", + "Score=pd.Series(data) \n", + "print(Score*1.5)" + ], + "id": "fddabc21aaf710af", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 139.5\n", + "2 138.0\n", + "3 118.5\n", + "4 88.5\n", + "dtype: float64\n" + ] + } + ], + "execution_count": 9 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "16354096f564187b" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/datahandling/22-SerieIndexAndOperation/22-seriesIndexAndOperation.ipynb b/datahandling/22-SerieIndexAndOperation/22-seriesIndexAndOperation.ipynb new file mode 100644 index 0000000..1f98b75 --- /dev/null +++ b/datahandling/22-SerieIndexAndOperation/22-seriesIndexAndOperation.ipynb @@ -0,0 +1,669 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Series 的索引\n", + "\n", + "位置下标索引、标签索引、布尔索引" + ], + "id": "805d5a4c0c7dde4c" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### 位置下标索引\n", + "id": "e07b5474bda28cb2" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:27:33.345887Z", + "start_time": "2024-08-26T08:27:32.974413Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "data= [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]\n", + "Score=pd.Series(data)\n", + "print(Score[0]) \n", + "print(Score[[1,2]])" + ], + "id": "86605764edcab963", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "1 2\n", + "2 3\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### 标签索引\n", + "\n", + "使用形式:Series[’index‘]或Series[[‘index1’,’index2’]]。\n", + "\n", + "\n" + ], + "id": "f09e49d48346ee9b" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:27:33.354662Z", + "start_time": "2024-08-26T08:27:33.347985Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "data={'张飞':93,'关羽':92,'赵云':79,'貂蝉':59}\n", + "Score=pd.Series(data)\n", + "print(Score['赵云']) \n", + "print(Score[['关羽','貂蝉']]) # Serie是一维,只有一层索引" + ], + "id": "8a58b63fbeceb44b", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "79\n", + "关羽 92\n", + "貂蝉 59\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### 布尔索引\n", + "\n", + "形式:Series[布尔列表]\n" + ], + "id": "bcb562a37256f978" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:27:33.360183Z", + "start_time": "2024-08-26T08:27:33.355803Z" + } + }, + "cell_type": "code", + "source": [ + "bool=Score>=60 \n", + "print(bool) \n", + "print(\"--------\")\n", + "print(Score[bool])" + ], + "id": "be0bda792a807ccb", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "张飞 True\n", + "关羽 True\n", + "赵云 True\n", + "貂蝉 False\n", + "dtype: bool\n", + "--------\n", + "张飞 93\n", + "关羽 92\n", + "赵云 79\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Series 的切片\n", + "\n", + "位置下标切片 、标签切片\n", + "\n", + "\n" + ], + "id": "267102f15354a2a4" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### 位置下标切片\n", + "\n", + "位置下表切片即利用 Series 的元素位置下标进行切片,该方式与我们前面学习的 Numpy 数组的索引的逻辑是相同的。\n", + "\n", + "形式:Series[start:end],start 即切片的起始位置,end 结束位置。当 start 没有设置时,默认为从第一个元素开始;当 end 没有设置时,默认为最后一个元素位置的下一个位置。\n" + ], + "id": "8c2d2b210232ca13" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:27:33.364977Z", + "start_time": "2024-08-26T08:27:33.361430Z" + } + }, + "cell_type": "code", + "source": "print(Score[1:4])", + "id": "ef4dec9b023019c6", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "关羽 92\n", + "赵云 79\n", + "貂蝉 59\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### 标签切片\n", + "\n", + "标签切片即利用元素的 index 进行切片,该方法更加直观。\n", + "\n", + "形式:Series[index_start:index_end],index_start 为起始标签,index_end 为结束标签。\n", + "\n", + "值得注意的一点,利用标签切片时,结束标签的数据也能获取到,我们可以简记为左闭右闭原则。该方法的优点就是直观,我们可以根据 index 的含义访问多个连续所需元素。在不知道元素位置的情况下,也能够进行访问所需元素" + ], + "id": "c26cd1f1ddbb981f" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:27:33.371690Z", + "start_time": "2024-08-26T08:27:33.367299Z" + } + }, + "cell_type": "code", + "source": "print(Score['关羽':'貂蝉'])\n", + "id": "d7fbeb591bbbede6", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "关羽 92\n", + "赵云 79\n", + "貂蝉 59\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### loc 和 iloc 索引和切片\n", + "id": "def280fed286446d" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Loc 是 series 中的一个方法,它跟标签索引和标签切片效果相同,都是通过寻找 index 来查找相对应的数据。\n", + "\n", + "使用方法:series.loc[index]\n", + "\n" + ], + "id": "d2b4a5287e734b5a" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:27:33.375961Z", + "start_time": "2024-08-26T08:27:33.372926Z" + } + }, + "cell_type": "code", + "source": "print(Score.loc[['赵云','貂蝉']])", + "id": "76445aceb353b061", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "赵云 79\n", + "貂蝉 59\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 6 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:27:33.381947Z", + "start_time": "2024-08-26T08:27:33.377152Z" + } + }, + "cell_type": "code", + "source": [ + "score1 = pd.Series(data=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15])\n", + "# 位置索引\n", + "print(score1.iloc[0])\n", + "print(score1.iloc[1])" + ], + "id": "a3f4e81927292a0b", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n" + ] + } + ], + "execution_count": 7 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Series 的基本操作\n", + "id": "598ac1e2da965780" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### head() 和 tail()\n", + "id": "45ab3ddbeb9439e1" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:27:33.389147Z", + "start_time": "2024-08-26T08:27:33.383915Z" + } + }, + "cell_type": "code", + "source": [ + "# 最高分2个\n", + "print(Score.head(2)) \n", + "# 最低分2个\n", + "print(Score.tail(2))" + ], + "id": "1534a11d29569ef6", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "张飞 93\n", + "关羽 92\n", + "dtype: int64\n", + "赵云 79\n", + "貂蝉 59\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 8 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### 添加数据", + "id": "4b8b3cbc39261dbd" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:27:33.394044Z", + "start_time": "2024-08-26T08:27:33.390391Z" + } + }, + "cell_type": "code", + "source": [ + "Score['刘备']=100 \n", + "print(Score)" + ], + "id": "c2c55cf54dffe8a5", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "张飞 93\n", + "关羽 92\n", + "赵云 79\n", + "貂蝉 59\n", + "刘备 100\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 9 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### series 遍历\n", + "id": "33d113871bab6595" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:28:15.886074Z", + "start_time": "2024-08-26T08:28:15.878005Z" + } + }, + "cell_type": "code", + "source": [ + "data=pd.Series({'曹操':90,'诸葛亮':88}) \n", + "for index, value in data.items():\n", + " Score[index]=value\n", + "print(Score)" + ], + "id": "bd8ace16eac7b673", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "张飞 93\n", + "关羽 92\n", + "赵云 79\n", + "貂蝉 59\n", + "刘备 100\n", + "曹操 90\n", + "诸葛亮 88\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 11 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### 删除数据", + "id": "ef1232373a1df4b4" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:29:09.059885Z", + "start_time": "2024-08-26T08:29:09.054912Z" + } + }, + "cell_type": "code", + "source": [ + "Score=Score.drop(['关羽','张飞']) \n", + "print(Score)" + ], + "id": "9cbaa4dda867d744", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "赵云 79\n", + "貂蝉 59\n", + "刘备 100\n", + "曹操 90\n", + "诸葛亮 88\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 12 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### 修改数据\n", + "id": "303b713386ab433a" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:29:30.770662Z", + "start_time": "2024-08-26T08:29:30.761561Z" + } + }, + "cell_type": "code", + "source": [ + "Score['曹操']=80 \n", + "print(Score)" + ], + "id": "6041c591ea3ede12", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "赵云 79\n", + "貂蝉 59\n", + "刘备 100\n", + "曹操 80\n", + "诸葛亮 88\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 13 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### 修改索引名", + "id": "18b40e355891f503" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:33:07.313257Z", + "start_time": "2024-08-26T08:33:07.296636Z" + } + }, + "cell_type": "code", + "source": [ + "Score.rename(index={'貂蝉':'张辽'},inplace=True) \n", + "print(Score)" + ], + "id": "fbabbffa35dc2e6b", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "赵云 79\n", + "张辽 59\n", + "刘备 100\n", + "曹操 80\n", + "诸葛亮 88\n", + "dtype: int64\n" + ] + } + ], + "execution_count": 14 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Series 的聚合函数\n", + "id": "d2bdfbe18b95e9ef" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### max()/min()\n", + "id": "b88ede84ee8a7517" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:33:41.037779Z", + "start_time": "2024-08-26T08:33:41.033144Z" + } + }, + "cell_type": "code", + "source": "print(Score.max()-Score.min())\n", + "id": "e8fcdc8fa387ea03", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "41\n" + ] + } + ], + "execution_count": 15 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### mean()\n", + "\n", + "mean() 函数的作用便是求取一组数据的均值。\n", + "\n" + ], + "id": "dccfd9f07696ef0e" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:34:06.253425Z", + "start_time": "2024-08-26T08:34:06.249304Z" + } + }, + "cell_type": "code", + "source": "print(Score.mean())\n", + "id": "634709f32f6b2295", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "81.2\n" + ] + } + ], + "execution_count": 16 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### median()\n", + " 中位数\n", + " \n", + " median 的作用是求取一组数据的中位数,中位数是一组数据中排在中间位置的数值,即将数据按照大小排序后,中间位置上的数值就是中位数。如果数据中有偶数个数值,则中位数是中间两个数的平均值。中位数代表了一组数据的中间情况,当我们想了解一组数据的中间情况时便可以通过该函数求取中位数。" + ], + "id": "425569414b3729dd" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:34:46.416570Z", + "start_time": "2024-08-26T08:34:46.393914Z" + } + }, + "cell_type": "code", + "source": "print(Score.median())\n", + "id": "e8bbd0784267d69e", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80.0\n" + ] + } + ], + "execution_count": 17 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### var() 方差\n", + "\n", + "var() 函数用于求一组数据的方差,方差用来描述一组数据的分散程度,即各数据与平均数的差的平方的平均值。它通常用来比较两个或多个样本的分散程度,如果方差越大,说明数据分散程度越大,反之则说明数据分散程度越小。" + ], + "id": "2d21f68f16e90c55" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-26T08:35:33.884572Z", + "start_time": "2024-08-26T08:35:33.881106Z" + } + }, + "cell_type": "code", + "source": "print(Score.var())\n", + "id": "218345c1a265a929", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "224.70000000000005\n" + ] + } + ], + "execution_count": 18 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "81a15e118fb3bd76" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/machinelearning/02\347\272\277\346\200\247\345\233\236\345\275\222.md" "b/machinelearning/02\347\272\277\346\200\247\345\233\236\345\275\222.md" index de9b449..940c3ff 100644 --- "a/machinelearning/02\347\272\277\346\200\247\345\233\236\345\275\222.md" +++ "b/machinelearning/02\347\272\277\346\200\247\345\233\236\345\275\222.md" @@ -43,11 +43,22 @@ y=ax+b 损失函数 -![](images/ml_02.png) +#### 最小二乘法: 误差平方和 + +![](images/ml_22.png) + + +#### 均方误差(MSE,mean-square error) + +![](images/ml_23.png) + + +#### 平均绝对误差值(mean absolute error,MAE) +![](images/ml_24.png) #### 最小二乘法 -最小二乘法: 推导出来的公司,不用管过程 +最小二乘法: 公式(过程暂时忽略) ![](images/ml_03.png) @@ -72,23 +83,23 @@ y=ax+b 以下是公式推导过程: -l2范数的平方 -![](images/ml_05.png) +设定 theta0=就是截距 ![](images/ml_06.png) ![](images/ml_07.png) -用矩阵 ,矩阵的乘法 就是 xT *x (x是长度m的向量) = x1^2+x2^2+...xm^2 -就是下图的 w 公式 -![](images/ml_08.png) - -![](images/ml_09.png) +矩阵的乘法公式: 就是 xT . x =x1^2+x2^2+...xm^2, 就是下面公式就是 l2范数的平方 (其中x是长度m的向量) +![](images/ml_05.png) ![](images/ml_10.png) + + + + 实现过程 ```python import numpy as np diff --git "a/machinelearning/03\346\242\257\345\272\246\344\270\213\351\231\215\346\263\225.md" "b/machinelearning/03\346\242\257\345\272\246\344\270\213\351\231\215\346\263\225.md" index 65e116e..79cd7ce 100644 --- "a/machinelearning/03\346\242\257\345\272\246\344\270\213\351\231\215\346\263\225.md" +++ "b/machinelearning/03\346\242\257\345\272\246\344\270\213\351\231\215\346\263\225.md" @@ -157,3 +157,24 @@ def dJ(theta, X_b, y): 经验值t0=5, t1=50 +```python +# 求随机梯队, 公式 +# X_b_i X_b中的 第i个样本 只有一行记录 +def dJ_sgd(theta, X_b_i, y_i): + return 2 * X_b_i.T.dot(X_b_i.dot(theta) - y_i) + +def sgd(X_b, y, initial_theta, n_iters): + # 经验值 + t0, t1 = 5, 50 + # 学习率 随着迭代次数(时间) ,慢慢变小。 + def learning_rate(t): + return t0 / (t + t1) + + theta = initial_theta + for cur_iter in range(n_iters): + rand_i = np.random.randint(len(X_b)) + gradient = dJ_sgd(theta, X_b[rand_i], y[rand_i]) + theta = theta - learning_rate(cur_iter) * gradient + + return theta +``` diff --git a/machinelearning/images/ml_09.png b/machinelearning/images/ml_09.png deleted file mode 100644 index 27e4638e92ca2d4fddb628cad6cd15cba04d34f4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 441315 zcmeFZ_d8qv|3BU;8l#oivxrTN*wiQuRW)kGrlj_&s=Zo8gP>YkYNTTCs=Z5zKHCtR z#wuxzD5^v>ick9fT-WRMe1HCh@Ans%>m=vo z&z+-}J9my20HCKHdEF{va_-!tbGM*+))6j07HymTZSoHHcI$7t9&C)OEvhYsYj+0B 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