PCA,数据处理基础

cr-mao · Sep 8, 2024 · a7790b3 · a7790b3
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diff --git a/.gitignore b/.gitignore
@@ -1 +1,4 @@
 .idea
+
+.vscode
+mydata
diff --git a/README.md b/README.md
@@ -14,7 +14,7 @@
 
 jupyter notebook ,numpy,pandas,matplotlib
 
-- [开发环境](datahandling/docs/开发环境.md)
+- [开发环境](datahandling/开发环境.md)
 - [数据领域中的专业术语](datahandling/数据领域中的专业术语.md)
 - [numpy数据基础](datahandling/01-NumpyArrayBasics/01-NumpyArrayBasics.ipynb)
 - [numpy数组创建](datahandling/02-NumpyCreateArray/02CreateNumpyArray.ipynb)
@@ -30,52 +30,51 @@ jupyter notebook ,numpy,pandas,matplotlib
 - [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)
 - pandas
-  - [dataframe创建、基本属性与索引切片](datahandling/23-PandasDataframeBasic/dataframeBasic.ipynb)
-  - [dataframe中的方法与索引技巧](datahandling/24-PandasDataframeMethodAndIndex/dataframeMethodAndIndex.ipynb)
-  - [dataframe统计运算和逻辑运算](datahandling/25-PandasDataframeStatAndLogic/dataframeStatAndLogic.ipynb)
-  - [dataframe数据计算](datahandling/26-PandasDataframeCompute/dataframe_compute.ipynb)
-  - [时间序列](datahandling/27-PandasTime/pandas_time.ipynb)
-  - [io的读取和存储、缺失值处理、离散化处理](datahandling/28-PandasIoAndNanAndDiscrete/pandasIoNan.ipynb)
-
-
-
+    - [dataframe创建、基本属性与索引切片](datahandling/23-PandasDataframeBasic/dataframeBasic.ipynb)
+    - [dataframe中的方法与索引技巧](datahandling/24-PandasDataframeMethodAndIndex/dataframeMethodAndIndex.ipynb)
+    - [dataframe统计运算和逻辑运算](datahandling/25-PandasDataframeStatAndLogic/dataframeStatAndLogic.ipynb)
+    - [dataframe数据计算](datahandling/26-PandasDataframeCompute/dataframe_compute.ipynb)
+    - [时间序列](datahandling/27-PandasTime/pandas_time.ipynb)
+    - [io的读取和存储、缺失值处理、离散化处理](datahandling/28-PandasIoAndNanAndDiscrete/pandasIoNan.ipynb)
+- matplot
+    - [matplot基础](datahandling/31-Matplotlib-Basics/Matplotlib-Basics.ipynb)
+    - [matplot其他](datahandling/32-Matplot/matplot.ipynb)
 
 ## 机器学习
 
 - knn
-  - [knn理论、公式](machinelearning/01knn.md)  
-  - [实现自己的knn](machinelearning/knn/01-kNNBasics/kNNBasics.ipynb)
-  - [sklearn中的knn](machinelearning/knn/02-kNNInScikitLearn/kNNinScikitlearn.ipynb)
-  - [训练数据集和测试数据集拆分](machinelearning/knn/03-TrainTestSplit/TrainTestSplit.ipynb)
-  - [结果准确度](machinelearning/knn/04-AccuracyScore/AccuracyScore.ipynb)
-  - [超参数寻找](machinelearning/knn/05-HyperParameters/HyperParameters.ipynb)
-  - [网格搜索超参数](machinelearning/knn/06-GridSearch/GridSearch.ipynb)
-  - [数据归一化和标准化](machinelearning/knn/07-FeatureScaling/FeatureScaling.ipynb)
-  - [sklearn中的标准化](machinelearning/knn/08-ScalerinScikitLearn/ScalerInScikitLearn.ipynb)
+    - [knn理论、公式](machinelearning/01knn.md)
+    - [实现自己的knn](machinelearning/knn/01-kNNBasics/kNNBasics.ipynb)
+    - [sklearn中的knn](machinelearning/knn/02-kNNInScikitLearn/kNNinScikitlearn.ipynb)
+    - [训练数据集和测试数据集拆分](machinelearning/knn/03-TrainTestSplit/TrainTestSplit.ipynb)
+    - [结果准确度](machinelearning/knn/04-AccuracyScore/AccuracyScore.ipynb)
+    - [超参数寻找](machinelearning/knn/05-HyperParameters/HyperParameters.ipynb)
+    - [网格搜索超参数](machinelearning/knn/06-GridSearch/GridSearch.ipynb)
+    - [数据归一化和标准化](machinelearning/knn/07-FeatureScaling/FeatureScaling.ipynb)
+    - [sklearn中的标准化](machinelearning/knn/08-ScalerinScikitLearn/ScalerInScikitLearn.ipynb)
 - 线性回归法
-  - [线性回归理论、公式](machinelearning/02线性回归.md)
-  - [简单线性回归实现](machinelearning/linearRegression/01-SimpleLinearRegressionImplementation/SimpleLinearRegressionImplementation.ipynb)
-  - [向量化运算效率高](machinelearning/linearRegression/02-Vectorization/Vectorization.ipynb)
-  - [衡量回归算法的标准，MSE、MAE](machinelearning/linearRegression/03-RegressionMetricsMSE-vs-MAE/RegressionMetricsMSE-vs-MAE.ipynb)
-  - [最好的衡量线性回归法的指标：R Squared ](machinelearning/linearRegression/04-R-Squared/R-Squared.ipynb)
-  - [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)
-  - [模拟欠拟合与过拟合、正则化处理](machinelearning/linearRegression/08-UnderfittingAndOverfitting/underfittingAndOverfitting.ipynb)
-
+    - [线性回归理论、公式](machinelearning/02线性回归.md)
+    - [简单线性回归实现](machinelearning/linearRegression/01-SimpleLinearRegressionImplementation/SimpleLinearRegressionImplementation.ipynb)
+    - [向量化运算效率高](machinelearning/linearRegression/02-Vectorization/Vectorization.ipynb)
+    - [衡量回归算法的标准，MSE、MAE](machinelearning/linearRegression/03-RegressionMetricsMSE-vs-MAE/RegressionMetricsMSE-vs-MAE.ipynb)
+    - [最好的衡量线性回归法的指标：R Squared ](machinelearning/linearRegression/04-R-Squared/R-Squared.ipynb)
+    - [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)
+    - [模拟欠拟合与过拟合、正则化处理](machinelearning/linearRegression/08-UnderfittingAndOverfitting/underfittingAndOverfitting.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)
-  - [随机梯度下降法](machinelearning/gradientDescent/04-StochasticGradientDescent/04-StochasticGradientDescent.ipynb)
-  - [sklearn中的随机梯度下降法](machinelearning/gradientDescent/05-SGDInScikitLearn/SGDInScikitLearn.ipynb)
+    - [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)
+    - [随机梯度下降法](machinelearning/gradientDescent/04-StochasticGradientDescent/04-StochasticGradientDescent.ipynb)
+    - [sklearn中的随机梯度下降法](machinelearning/gradientDescent/05-SGDInScikitLearn/SGDInScikitLearn.ipynb)
+- PCA
+  - [PCA理论、公式](machinelearning/PCA.md)
 - 逻辑回归
-  - [逻辑回归理论、公式](machinelearning/04逻辑回归.md)
+    - [逻辑回归理论、公式](machinelearning/04逻辑回归.md)
 
 - 朴素叶贝斯
-
-
 
 ### 案例
 
@@ -86,7 +85,6 @@ jupyter notebook ,numpy,pandas,matplotlib
 - [用户消费能力、标准化欧式距离](machinelearning/recommand/02distance/distance.ipynb)
 - [NearestNeighbors、余弦相似性找出最相似的用户](machinelearning/recommand/03NearestNeighborsAndConsineSimiarity/NearestNeighbors_and_consine_simiarity.ipynb)
 
-
 ## links
 
 - 机器学习(公式推导与代码实现)

diff --git a/datahandling/04-NumpyComputationArray/04-ComputationNumpyArray.ipynb b/datahandling/04-NumpyComputationArray/04-ComputationNumpyArray.ipynb
@@ -1723,17 +1723,18 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-08-23T05:43:57.038124Z",
-     "start_time": "2024-08-23T05:43:57.033665Z"
+     "end_time": "2024-09-07T02:34:42.994496Z",
+     "start_time": "2024-09-07T02:34:42.989930Z"
     }
    },
    "cell_type": "code",
    "source": [
+    "import numpy as np \n",
     "fruit_price=np.array([[5,4,3]])  \n",
     "jinshu=np.array([[2],[3],[1]])  \n",
     "print('水果的总价格为:\\n',fruit_price@jinshu)  \n",
     "print('水果的总价格为:\\n',np.dot(fruit_price,jinshu))\n",
-    "\n",
+    "print(np.sum(np.dot(fruit_price,jinshu)))\n",
     "#  numpy 数组提供的矩阵乘法符号 @ 或者 dot 方法实现矩阵乘法，便可以非常方便地获取总价格\n"
    ],
    "outputs": [
@@ -1744,11 +1745,12 @@
       "水果的总价格为:\n",
       " [[25]]\n",
       "水果的总价格为:\n",
-      " [[25]]\n"
+      " [[25]]\n",
+      "25\n"
      ]
     }
    ],
-   "execution_count": 8
+   "execution_count": 2
   },
   {
    "metadata": {},

diff --git a/datahandling/06-NumpyAggregationOperator/06-AggregationOperator.ipynb b/datahandling/06-NumpyAggregationOperator/06-AggregationOperator.ipynb
@@ -816,6 +816,48 @@
     }
    ],
    "execution_count": 29
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-09-06T15:40:48.482736Z",
+     "start_time": "2024-09-06T15:40:48.402018Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import numpy as np \n",
+    "x=np.random.randint(0,50,(50,2))"
+   ],
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-09-06T15:41:46.155432Z",
+     "start_time": "2024-09-06T15:41:46.152552Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "print(np.mean(x))",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22.93\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": ""
   }
  ],
  "metadata": {

diff --git a/datahandling/20-PandasDataFrameSeriesPanel/pandasDataFrameSeriesPanel.ipynb b/datahandling/20-PandasDataFrameSeriesPanel/pandasDataFrameSeriesPanel.ipynb
@@ -15,7 +15,11 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "### series",
+   "source": [
+    "### series\n",
+    "\n",
+    "单列多行的结构"
+   ],
    "id": "1cef1671ff84ea8e"
   },
   {

diff --git a/datahandling/31-Matplotlib-Basics/Matplotlib-Basics.ipynb b/datahandling/31-Matplotlib-Basics/Matplotlib-Basics.ipynb
diff --git a/datahandling/31matplot/matplot.ipynb b/datahandling/31matplot/matplot.ipynb
diff --git a/datahandling/32-Matplot/matplot.ipynb b/datahandling/32-Matplot/matplot.ipynb
-Original file line number
+Diff line change
@@ -1 +1,4 @@
     .idea
+    .vscode
+    mydata