# tinyml
利用numpy实现的一些周志华《机器学习》(西瓜书)一书及 斯坦福cs229课程中的算法,宜配合西瓜书和cs229课件食用。并选择性实现了一些经典算法的简易版本,
如 按照陈天奇的slides实现的XGBRegressor。
## 已经实现的算法
- **线性模型**
- [LinearRegression](/tinyml/linear_model/LinearRegression.py) [线性回归闭式解推导](notes/linear_model/linear_reg_closed_form.pdf)
- [LogisticRegression](/tinyml/linear_model/LogisticRegression.py) [逻辑回归相关推导](/notes/linear_model/logistic_regression.pdf)
- [SGDRegressor](/tinyml/linear_model/SGDRegressor.py)
- [LocallyWeightedLinearRegression](/tinyml/linear_model/LocallyWeightedLinearRegression.py)
- **判别分析**
- [LDA](/tinyml/discriminant_analysis/LDA.py)
- [GDA](/tinyml/discriminant_analysis/GDA.py)
- **决策回归树**
- [DecisionTreeClassifier](/tinyml/tree/DecisionTreeClassifier.py)
- [DecisionTreeRegressor](/tinyml/tree/DecisionTreeRegressor.py)
- **支持向量机**
- [SVC](/tinyml/svm/SVC.py)
- **贝叶斯**
- [NaiveBayesClassifier](/tinyml/bayes/NaiveBayesClassifier.py)
- **聚类算法**
- [KMeans](/tinyml/cluster/KMeans.py)
- [LVQ](/tinyml/cluster/LVQ.py)
- [GaussianMixture](/tinyml/cluster/GaussianMixture.py)
- [DBSCAN](/tinyml/cluster/DBSCAN.py)
- [AGNES](/tinyml/cluster/AGNES.py)
- **降维算法**
- [MDS](/tinyml/dimension_reduction/MDS.py)
- [PCA](/tinyml/dimension_reduction/PCA.py)
- [KernelPCA](/tinyml/dimension_reduction/KernelPCA.py)
- [LLE](/tinyml/dimension_reduction/LLE.py)
- [Isomap](/tinyml/dimension_reduction/Isomap.py)
- **集成学习**
- [AdaBoostClassifier](/tinyml/ensemble/AdaBoostClassifier.py)
- [GradientBoostingRegressor](/tinyml/ensemble/GradientBoostingRegressor.py)
- [RandomForestRegressor](/tinyml/ensemble/RandomForestRegressor.py)
- [XGBRegressor](/tinyml/ensemble/XGBRegressor.py)
- **特征选择**
- [ReliefFeatureSelection](/tinyml/feature_selection/ReliefFeatureSelection.py)
## 和sklearn实现的比较
- **回归算法结果** [代码](/tinyml/compare/compare_regresssor.py)
<table>
<tr>
<td rowspan="2">Algorithm vs. RMSE</td>
<td colspan="2">sklearn-boston</td>
</tr>
<tr>
<td>tinyml</td>
<td>sklearn</td>
</tr>
<tr>
<td>LinearRegression</td>
<td>27.196</td>
<td>27.196</td>
</tr>
<tr>
<td>SGDRegressor</td>
<td>27.246</td>
<td>27.231</td>
</tr>
<tr>
<td>DecisionTreeRegressor</td>
<td>21.887</td>
<td>21.761</td>
</tr>
<tr>
<td>RandomForestRegressor</td>
<td>21.142</td>
<td>21.142</td>
</tr>
<tr>
<td>GradientBoostRegressor</td>
<td>16.778</td>
<td>16.106</td>
</tr>
<tr>
<td>XGBRegressor</td>
<td>20.149</td>
<td>15.7</td>
</tr>
</table>
- **分类算法结果** [代码](/tinyml/compare/compare_classification.py)
<table>
<tr>
<td rowspan="2">Algorithm vs. RMSE</td>
<td colspan="2">sklearn-breast_cancer</td>
</tr>
<tr>
<td>tinyml</td>
<td>sklearn</td>
</tr>
<tr>
<td>NaiveBayes</td>
<td>90.64%</td>
<td>90.64%</td>
</tr>
<tr>
<td>LogisticRegression</td>
<td>92.98%</td>
<td>92.98%</td>
</tr>
<tr>
<td>LDA</td>
<td>94.15%</td>
<td>92.40%</td>
</tr>
<tr>
<td>GDA</td>
<td>92.40%</td>
<td>93.57%</td>
</tr>
<tr>
<td>SVC</td>
<td>86.55%</td>
<td>92.98%</td>
</tr>
<tr>
<td>AdaboostClassifier</td>
<td>92.40%</td>
<td>92.40%</td>
</tr>
</table>
- **聚类算法比较** [代码](/tinyml/compare/compare_clustering.py)
- KMeans
<div align="center">
<img src="/tinyml/compare/cluster_result/tinyml_KMeans.jpg" height="300px" alt="tinyml KMeans" >
<img src="/tinyml/compare/cluster_result/sklearn_KMeans.jpg" height="300px" alt="sklearn KMeans" >
</div>
- DBSCAN
<div align="center">
<img src="/tinyml/compare/cluster_result/tinyml_DBSCAN.jpg" height="300px" alt="tinyml DBSCAN" >
<img src="/tinyml/compare/cluster_result/sklearn_DBSCAN.jpg" height="300px" alt="sklearn DBSCAN" >
</div>
- GMM
<div align="center">
<img src="/tinyml/compare/cluster_result/tinyml_GMM.jpg" height="300px" alt="tinyml GMM" >
<img src="/tinyml/compare/cluster_result/sklearn_GMM.jpg" height="300px" alt="sklearn GMM" >
</div>
- AGNES
<div align="center">
<img src="/tinyml/compare/cluster_result/tinyml_AGNES.jpg" height="300px" alt="tinyml AGNES" >
<img src="/tinyml/compare/cluster_result/sklearn_AGNES.jpg" height="300px" alt="sklearn AGNES" >
</div>
- **降维算法比较** [代码](/tinyml/compare/compare_dimension_reduction.py)
- PCA
<div align="center">
<img src="/tinyml/compare/dimension_reduction_result/tinyml_PCA.jpg" height="300px" alt="tinyml PCA" >
<img src="/tinyml/compare/dimension_reduction_result/sklearn_PCA.jpg" height="300px" alt="sklearn PCA" >
</div>
- KernalPCA
<div align="center">
<img src="/tinyml/compare/dimension_reduction_result/tinyml_KernalPCA.jpg" height="300px" alt="tinyml KernalPCA" >
<img src="/tinyml/compare/dimension_reduction_result/sklearn_KernalPCA.jpg" height="300px" alt="sklearn KernalPCA" >
</div>
- LLE
<div align="center">
<img src="/tinyml/compare/dimension_reduction_result/tinyml_LLE.jpg" height="300px" alt="tinyml LLE" >
<img src="/tinyml/compare/dimension_reduction_result/sklearn_LLE.jpg" height="300px" alt="sklearn LLE" >
</div>
- MDS
<div align="center">
<img src="/tinyml/compare/dimension_reduction_result/tinyml_MDS.jpg" height="300px" alt="tinyml MDS" >
<img src="/tinyml/compare/dimension_reduction_result/sklearn_MDS.jpg" height="300px" alt="sklearn MDS" >
</div>
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numpy 实现的 周志华《机器学习》书中的算法及其他一些传统机器学习算法.zip
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numpy 实现的 周志华《机器学习》书中的算法及其他一些传统机器学习算法.zip (114个子文件)
tiny_ml.iml 492B
tinyml_KernalPCA.jpg 73KB
tinyml_MDS.jpg 67KB
sklearn_PCA.jpg 67KB
tinyml_PCA.jpg 67KB
sklearn_MDS.jpg 62KB
sklearn_KernalPCA.jpg 60KB
tinyml_LLE.jpg 54KB
sklearn_LLE.jpg 54KB
sklearn_AGNES.jpg 26KB
tinyml_DBSCAN.jpg 26KB
tinyml_KMeans.jpg 26KB
sklearn_KMeans.jpg 26KB
tinyml_GMM.jpg 25KB
sklearn_DBSCAN.jpg 25KB
sklearn_GMM.jpg 25KB
tinyml_AGNES.jpg 25KB
README.md 6KB
logistic_regression.pdf 58KB
linear_reg_closed_form.pdf 40KB
DecisionTreeClassifier.py 7KB
XGBRegressor.py 6KB
LogisticRegression.py 5KB
compare_regresssor.py 5KB
SVC.py 5KB
compare_classification.py 5KB
DecisionTreeRegressor.py 4KB
NaiveBayesClassifier.py 4KB
compare_clustering.py 4KB
compare_dimension_reduction.py 3KB
GaussianMixture.py 3KB
AGNES.py 3KB
AdaBoostClassifier.py 3KB
KernelPCA.py 3KB
KMeans.py 3KB
treePlotter.py 3KB
LVQ.py 3KB
SGDRegressor.py 3KB
FMClassifier.py 3KB
Isomap.py 3KB
GDA.py 2KB
DBSCAN.py 2KB
LDA.py 2KB
ReliefFeatureSelection.py 2KB
curves.py 2KB
MDS.py 2KB
LLE.py 2KB
GradientBoostingRegressor.py 2KB
RandomForestRegressor.py 2KB
LinearRegression.py 1KB
PCA.py 1KB
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XGBRegressor.cpython-37.pyc 6KB
DecisionTreeClassifier.cpython-37.pyc 6KB
LogisticRegression.cpython-37.pyc 5KB
SVC.cpython-37.pyc 4KB
AGNES.cpython-37.pyc 4KB
GaussianMixture.cpython-37.pyc 4KB
NaiveBayesClassifier.cpython-37.pyc 3KB
KernelPCA.cpython-37.pyc 3KB
DecisionTreeRegressor.cpython-37.pyc 3KB
DecisionTreeRegressor.cpython-36.pyc 3KB
AdaBoostClassifier.cpython-37.pyc 3KB
LVQ.cpython-37.pyc 3KB
Isomap.cpython-37.pyc 3KB
KMeans.cpython-37.pyc 3KB
DBSCAN.cpython-37.pyc 3KB
LLE.cpython-37.pyc 3KB
GDA.cpython-37.pyc 3KB
treePlotter.cpython-37.pyc 2KB
treePlotter.cpython-36.pyc 2KB
LDA.cpython-37.pyc 2KB
SGDRegressor.cpython-37.pyc 2KB
MDS.cpython-36.pyc 2KB
curves.cpython-37.pyc 2KB
MDS.cpython-37.pyc 2KB
GradientBoostingRegressor.cpython-37.pyc 2KB
PCA.cpython-37.pyc 2KB
RandomForestRegressor.cpython-37.pyc 2KB
LinearRegression.cpython-37.pyc 2KB
__init__.cpython-37.pyc 143B
__init__.cpython-37.pyc 141B
__init__.cpython-36.pyc 141B
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__init__.cpython-37.pyc 130B
__init__.cpython-37.pyc 129B
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