KNN、Kmeans、EM、Perceptron、决策树、逻辑回归、svm、adaboost、朴素贝叶斯

上传者: bruce__ray | 上传时间: 2022-06-02 17:10:48 | 文件大小: 2.43MB | 文件类型: GZ
实现算法有KNN、Kmeans、EM、Perceptron、决策树、逻辑回归、svm、adaboost、朴素贝叶斯

文件下载

资源详情

[{"title":"( 56 个子文件 2.43MB ) KNN、Kmeans、EM、Perceptron、决策树、逻辑回归、svm、adaboost、朴素贝叶斯","children":[{"title":"636.machine_learning_python__SmallVagetable","children":[{"title":"naive_bayes","children":[{"title":".ipynb_checkpoints","children":[{"title":"naiveBayes-checkpoint.ipynb <span style='color:#111;'> 5.71KB </span>","children":null,"spread":false}],"spread":true},{"title":"naiveBayesGaussian.py <span style='color:#111;'> 1.98KB </span>","children":null,"spread":false},{"title":"naiveBayes.ipynb <span style='color:#111;'> 4.36KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 243B </span>","children":null,"spread":false},{"title":"naiveBayesBase.py <span style='color:#111;'> 3.91KB </span>","children":null,"spread":false}],"spread":true},{"title":"support_vector_machine","children":[{"title":".ipynb_checkpoints","children":[{"title":"svm-checkpoint.ipynb <span style='color:#111;'> 13.49KB </span>","children":null,"spread":false}],"spread":true},{"title":"svm.py <span style='color:#111;'> 5.37KB </span>","children":null,"spread":false},{"title":"svm.ipynb <span style='color:#111;'> 13.49KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 702B </span>","children":null,"spread":false}],"spread":true},{"title":".gitattributes <span style='color:#111;'> 34B </span>","children":null,"spread":false},{"title":"utils","children":[{"title":"data_generater.py <span style='color:#111;'> 1.32KB </span>","children":null,"spread":false},{"title":"misc_utils.py <span style='color:#111;'> 1.30KB </span>","children":null,"spread":false},{"title":"plot.py <span style='color:#111;'> 1.62KB </span>","children":null,"spread":false},{"title":"word_utils.py <span style='color:#111;'> 2.17KB </span>","children":null,"spread":false}],"spread":true},{"title":"kmeans","children":[{"title":".ipynb_checkpoints","children":[{"title":"kmeans-checkpoint.ipynb <span style='color:#111;'> 154.21KB </span>","children":null,"spread":false}],"spread":true},{"title":"kmeans_base.py <span style='color:#111;'> 5.11KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 453B </span>","children":null,"spread":false},{"title":"kmeans.ipynb <span style='color:#111;'> 217.40KB </span>","children":null,"spread":false},{"title":"kmeans_plus.py <span style='color:#111;'> 4.08KB </span>","children":null,"spread":false}],"spread":true},{"title":"logistic_regression","children":[{"title":".ipynb_checkpoints","children":[{"title":"logistic_regression-checkpoint.ipynb <span style='color:#111;'> 35.76KB </span>","children":null,"spread":false}],"spread":true},{"title":"max_entropy.py <span style='color:#111;'> 4.47KB </span>","children":null,"spread":false},{"title":"logistic_regression.ipynb <span style='color:#111;'> 35.76KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 206B </span>","children":null,"spread":false},{"title":"LogisticRegressionClassifier.py <span style='color:#111;'> 2.10KB </span>","children":null,"spread":false}],"spread":true},{"title":"em","children":[{"title":"gmm_penality.py <span style='color:#111;'> 3.92KB </span>","children":null,"spread":false},{"title":"main.py <span style='color:#111;'> 1.72KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 651B </span>","children":null,"spread":false},{"title":"gmm.py <span style='color:#111;'> 3.15KB </span>","children":null,"spread":false},{"title":"data","children":[{"title":"amix2-tst.dat <span style='color:#111;'> 3.52MB </span>","children":null,"spread":false},{"title":"amix2-val.dat <span style='color:#111;'> 720.57KB </span>","children":null,"spread":false},{"title":"golub-est.dat <span style='color:#111;'> 289.63KB </span>","children":null,"spread":false},{"title":"amix1-val.dat <span style='color:#111;'> 2.17KB </span>","children":null,"spread":false},{"title":"amix1-est.dat <span style='color:#111;'> 4.34KB </span>","children":null,"spread":false},{"title":"golub-tst.dat <span style='color:#111;'> 123.47KB </span>","children":null,"spread":false},{"title":"amix2-est.dat <span style='color:#111;'> 1.41MB </span>","children":null,"spread":false},{"title":"golub-val.dat <span style='color:#111;'> 108.70KB </span>","children":null,"spread":false},{"title":"amix1-tst.dat <span style='color:#111;'> 14.42KB </span>","children":null,"spread":false}],"spread":true},{"title":"main_panelity.py <span style='color:#111;'> 2.97KB </span>","children":null,"spread":false}],"spread":true},{"title":"adaboost","children":[{"title":".ipynb_checkpoints","children":[{"title":"Adaboost-checkpoint.ipynb <span style='color:#111;'> 12.89KB </span>","children":null,"spread":false}],"spread":true},{"title":"AdaBoost.py <span style='color:#111;'> 5.35KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 833B </span>","children":null,"spread":false},{"title":"Adaboost.ipynb <span style='color:#111;'> 12.89KB </span>","children":null,"spread":false}],"spread":true},{"title":"knn","children":[{"title":".ipynb_checkpoints","children":[{"title":"KNN-checkpoint.ipynb <span style='color:#111;'> 3.87KB </span>","children":null,"spread":false}],"spread":true},{"title":"knn_kdtree.py <span style='color:#111;'> 5.24KB </span>","children":null,"spread":false},{"title":"knn_base.py <span style='color:#111;'> 1.81KB </span>","children":null,"spread":false},{"title":"KNN.ipynb <span style='color:#111;'> 17.23KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 194B </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 1.25KB </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 33B </span>","children":null,"spread":false},{"title":"decision_tree","children":[{"title":"tree_id3.py <span style='color:#111;'> 8.16KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 196B </span>","children":null,"spread":false}],"spread":true},{"title":"perceptron","children":[{"title":".ipynb_checkpoints","children":[{"title":"perceptron-checkpoint.ipynb <span style='color:#111;'> 22.77KB </span>","children":null,"spread":false}],"spread":false},{"title":"perceptron_dual.py <span style='color:#111;'> 2.16KB </span>","children":null,"spread":false},{"title":"perceptron_base.py <span style='color:#111;'> 1.46KB </span>","children":null,"spread":false},{"title":"perceptron.ipynb <span style='color:#111;'> 22.77KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 209B </span>","children":null,"spread":false}],"spread":true}],"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明