├── .gitignore ├── .vscode ├── launch.json └── settings.json ├── README.md ├── TF-IDF ├── .ipynb_checkpoints │ └── tf_idf-checkpoint.ipynb ├── TF-IDF原理与实现.md ├── tf_idf.ipynb ├── tfidf.model └── tfidf.py ├── bayes └── bayes.py ├── boosting_tree ├── .ipynb_checkpoints │ ├── AdaBoost-checkpoint.ipynb │ ├── LightGBM-checkpoint.ipynb │ ├── cart-checkpoint.ipynb │ ├── gbdt-checkpoint.ipynb │ └── xgboost-checkpoint.ipynb ├── AdaBoost.ipynb ├── LightGBM.ipynb ├── adaboost_demo.py ├── cart.ipynb ├── cart_demo.py ├── dtr.pdf ├── dtr.png ├── gbdt.ipynb ├── train_model_iris.model └── xgboost.ipynb ├── cilin_similarity ├── README.md ├── cilin_similarity.py ├── data │ └── cilin.txt └── 基于同义词词林的词语相似度计算方法.pdf ├── cluster ├── README.md ├── dbscan │ ├── dbscan.py │ ├── lib │ │ ├── build │ │ │ ├── pythontest.py │ │ │ └── temp.macosx-10.7-x86_64-3.7 │ │ │ │ └── kd_tree.o │ │ ├── dbscan.py │ │ ├── dbscan_test.txt │ │ ├── kd_tree.c │ │ ├── kd_tree.cpython-37m-darwin.so │ │ ├── kd_tree.pyx │ │ ├── pythontest.py │ │ ├── setup.py │ │ ├── testc.c │ │ └── testc.so │ └── optics.py ├── gif.py ├── hierarchical │ └── hierarchical.py ├── kmeans │ ├── bi_kmeans.py │ ├── data │ │ ├── testSet2_kmeans.txt │ │ └── testSet_kmeans.txt │ ├── kmeans++.py │ └── kmeans.py ├── papers │ ├── AN OVERVIEW ON CLUSTERING METHODS .pdf │ ├── OPTICS.pdf │ └── 改进的OPTICS算法及其在文本聚类中的应用.pdf └── pic │ ├── DBSCAN.png │ ├── OPTICS.png │ ├── bi_kmeans.png │ ├── com_dbscan.png │ ├── dbscan_eps.png │ ├── dbscan_minpts.png │ ├── dbscan_process.gif │ ├── dbscan划分不均匀数据.png │ ├── hierarchical_clustering.png │ ├── kmeans.png │ ├── kmeans的局限性.png │ ├── kpp_means.png │ ├── k值不同.png │ ├── optics_process.gif │ ├── test_animation.gif │ ├── 划分错误.png │ ├── 北京朝阳外网独有.png │ ├── 局部最优.png │ └── 层次.png ├── data_set ├── .ipynb_checkpoints │ └── dataset-checkpoint.ipynb └── dataset.ipynb ├── decision_tree ├── classifier.txt └── tree.py ├── fasttext ├── data │ ├── .DS_Store │ ├── cooking.stackexchange.id │ ├── cooking.stackexchange.tar.gz │ ├── cooking.stackexchange.txt │ ├── cooking.train │ └── cooking.valid ├── demo_01.py ├── demo_02.py ├── demo_03.py ├── readme.txt └── wikifil.pl ├── hmm ├── 199801人民日报.data ├── hmm.model └── hmm.py ├── logistic ├── logistic.py ├── pic │ ├── Stochastic gradient ascent.png │ ├── Stochastic gradient iteration.png │ ├── logistic iteration.png │ ├── logistic regression.png │ └── sigmoid.png └── testSet.txt ├── ml-notes ├── .ipynb_checkpoints │ └── sklearn.metrics使用大全-checkpoint.ipynb ├── sklearn.metrics使用大全.ipynb ├── 信息熵最大值证明.md ├── 数据聚类方法.xmind ├── 机器学习-PCA降维原理与实现.md ├── 机器学习-logistic回归原理与实现.md ├── 机器学习-softmax回归原理与实现.md ├── 机器学习-信息论相关知识.md ├── 机器学习-几种回归模型和sklearn实现.md ├── 机器学习-异常检测.md ├── 机器学习-极大似然估计法.md ├── 机器学习-海量数据处理.md └── 机器学习-神经网络及反向传播算法.md ├── ner ├── crf.py ├── ltp_ner.py └── model │ └── crf.model ├── neural_network ├── gen_dataset.py ├── network.py ├── network_lib.py ├── testSet.txt ├── test_dataset.txt ├── train_dataset.txt ├── 使用sklearn产生的决策边界.png ├── 决策边界.png ├── 损失函数.png └── 数据集分布.png ├── pca ├── PCA.png ├── pca.py └── testSet.txt ├── pic ├── Trie树.jpg ├── 转移模型.jpg └── 马尔科夫模型.png ├── regression ├── Source.gv ├── Source.gv.pdf ├── abalone.txt ├── cart_regression.py ├── ex0.txt ├── ex1.txt ├── pic │ ├── SVM regression.png │ ├── SVM回归损失函数.svg │ ├── decision tree regression.png │ ├── ridge regression.png │ └── tree regression.svg ├── regression.py ├── regression_lib.py └── 博客用图.graffle ├── softmax ├── dataset.png ├── loss function.png ├── softmax.py ├── test_dataset.txt └── train_dataset.txt ├── string_similarity ├── levenshtein └── levenshtein.cpp ├── svm ├── extras │ └── plotSupportVectors.py ├── svm_mlia.py ├── testSet.txt ├── testSetRBF.txt └── testSetRBF2.txt ├── trie_tree ├── DATrie.py ├── README.md ├── segment.py ├── trie.py └── trie_list.py └── word2vec ├── .ipynb_checkpoints └── demo-checkpoint.ipynb ├── demo.ipynb ├── results └── wordVectTrainResult ├── w2v_demo01.py └── word2vec的原理与实现.md /.gitignore: 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