# machine-learning
通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。
# 目前已经实现可运行算法
### [KNN和KdTree的实现](https://github.com/SmallVagetable/machine_learning_python/tree/master/knn)
### [感知机的基本形式和对偶形式的实现](https://github.com/SmallVagetable/machine_learning_python/tree/master/perceptron)
### [Kmeans和Kmeans++的实现](https://github.com/SmallVagetable/machine_learning_python/tree/master/kmeans)
### [EM GMM高斯混合和GMM+LASSO的实现](https://github.com/SmallVagetable/machine_learning_python/tree/master/em)
### [实现朴素贝叶斯的基本算法和高斯混合朴素贝叶斯算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/naive_bayes)
### [实现决策树的基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/decision_tree)
### [实现adaboost基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/adaboost)
### [实现svm基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/support_vector_machine)
### [实现逻辑回归基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/logistic_regression)
通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。实现算法有KNN、Kmeans、EM、Percep.zip
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2024-02-03
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通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。实现算法有KNN、Kmeans、EM、Percep.zip （56个子文件） 
ahao111 support_vector_machine 
svm.py 5KB svm.ipynb 13KB .ipynb_checkpoints svm-checkpoint.ipynb 13KB README.md 691B logistic_regression max_entropy.py 4KB logistic_regression.ipynb 35KB .ipynb_checkpoints logistic_regression-checkpoint.ipynb 35KB README.md 199B LogisticRegressionClassifier.py 2KB perceptron perceptron_dual.py 2KB perceptron.ipynb 23KB .ipynb_checkpoints perceptron-checkpoint.ipynb 23KB perceptron_base.py 1KB README.md 202B .gitattributes 33B em gmm_penality.py 4KB main.py 2KB data amix1-est.dat 4KB golub-est.dat 290KB golub-val.dat 109KB amix1-tst.dat 13KB golub-tst.dat 123KB amix2-tst.dat 3.52MB amix1-val.dat 2KB amix2-val.dat 720KB amix2-est.dat 1.41MB main_panelity.py 3KB README.md 638B gmm.py 3KB adaboost Adaboost.ipynb 13KB AdaBoost.py 5KB .ipynb_checkpoints Adaboost-checkpoint.ipynb 13KB README.md 821B utils plot.py 2KB data_generater.py 1KB misc_utils.py 1KB word_utils.py 2KB knn knn_base.py 2KB KNN.ipynb 17KB knn_kdtree.py 5KB .ipynb_checkpoints KNN-checkpoint.ipynb 4KB README.md 187B decision_tree tree_id3.py 8KB README.md 189B kmeans kmeans.ipynb 217KB kmeans_plus.py 4KB .ipynb_checkpoints kmeans-checkpoint.ipynb 154KB README.md 443B kmeans_base.py 5KB .gitignore 29B naive_bayes naiveBayesBase.py 4KB naiveBayesGaussian.py 2KB naiveBayes.ipynb 4KB .ipynb_checkpoints naiveBayes-checkpoint.ipynb 6KB README.md 236B README.md 1KB资源评论
