登录
首页 » Python » python-Machine-learning-master

python-Machine-learning-master

于 2019-04-17 发布
0 214
下载积分: 1 下载次数: 1
我要下载

代码说明：

说明：  一个机器学习的python文件，里面拥有各种机器学习方法，可以供大家参考(A Python file for machine learning, which has various machine learning methods, can be used for your reference.)

文件列表：

python-Machine-learning-master, 0 , 2019-03-18
python-Machine-learning-master\PCA, 0 , 2019-03-07
python-Machine-learning-master\PCA\README, 60 , 2019-03-07
__MACOSX, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\PCA, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\PCA\._README, 212 , 2019-03-07
python-Machine-learning-master\PCA\PCA.py, 1338 , 2019-03-07
__MACOSX\python-Machine-learning-master\PCA\._PCA.py, 212 , 2019-03-07
__MACOSX\python-Machine-learning-master\._PCA, 212 , 2019-03-07
python-Machine-learning-master\K-Means, 0 , 2019-03-07
python-Machine-learning-master\K-Means\city.txt, 2294 , 2019-03-07
__MACOSX\python-Machine-learning-master\K-Means, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\K-Means\._city.txt, 212 , 2019-03-07
python-Machine-learning-master\K-Means\README, 257 , 2019-03-07
__MACOSX\python-Machine-learning-master\K-Means\._README, 212 , 2019-03-07
python-Machine-learning-master\K-Means\K-Means.py, 3492 , 2019-03-07
__MACOSX\python-Machine-learning-master\K-Means\._K-Means.py, 212 , 2019-03-07
__MACOSX\python-Machine-learning-master\._K-Means, 212 , 2019-03-07
python-Machine-learning-master\KNN, 0 , 2019-03-07
python-Machine-learning-master\KNN\README, 527 , 2019-03-07
__MACOSX\python-Machine-learning-master\KNN, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\KNN\._README, 212 , 2019-03-07
python-Machine-learning-master\KNN\KNN.py, 486 , 2019-03-07
__MACOSX\python-Machine-learning-master\KNN\._KNN.py, 212 , 2019-03-07
__MACOSX\python-Machine-learning-master\._KNN, 212 , 2019-03-07
python-Machine-learning-master\.DS_Store, 6148 , 2019-03-18
__MACOSX\python-Machine-learning-master\._.DS_Store, 120 , 2019-03-18
python-Machine-learning-master\Xgboost, 0 , 2019-03-18
python-Machine-learning-master\Xgboost\.DS_Store, 6148 , 2019-03-18
__MACOSX\python-Machine-learning-master\Xgboost, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\Xgboost\._.DS_Store, 120 , 2019-03-18
python-Machine-learning-master\Xgboost\code, 0 , 2019-03-07
python-Machine-learning-master\Xgboost\code\ofoFeature.ipynb, 33515 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\code, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\Xgboost\code\._ofoFeature.ipynb, 212 , 2019-03-07
python-Machine-learning-master\Xgboost\code\Xgboost.ipynb, 13868617 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\code\._Xgboost.ipynb, 212 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\._code, 212 , 2019-03-07
python-Machine-learning-master\Xgboost\README.md, 1286 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\._README.md, 212 , 2019-03-07
python-Machine-learning-master\Xgboost\Data, 0 , 2019-03-07
python-Machine-learning-master\Xgboost\Data\data_preprocessed, 0 , 2019-03-07
python-Machine-learning-master\Xgboost\Data\data_preprocessed\ProcessDataSet3.rar, 1851524 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\Data, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\Xgboost\Data\data_preprocessed, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\Xgboost\Data\data_preprocessed\._ProcessDataSet3.rar, 212 , 2019-03-07
python-Machine-learning-master\Xgboost\Data\data_preprocessed\ProcessDataSet2.rar, 3830423 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\Data\data_preprocessed\._ProcessDataSet2.rar, 212 , 2019-03-07
python-Machine-learning-master\Xgboost\Data\data_preprocessed\ProcessDataSet1.rar, 2560997 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\Data\data_preprocessed\._ProcessDataSet1.rar, 212 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\Data\._data_preprocessed, 212 , 2019-03-07
python-Machine-learning-master\Xgboost\Data\data_origin, 0 , 2019-03-07
python-Machine-learning-master\Xgboost\Data\data_origin\sample_submission.rar, 195 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\Data\data_origin, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\Xgboost\Data\data_origin\._sample_submission.rar, 212 , 2019-03-07
python-Machine-learning-master\Xgboost\Data\data_origin\ccf_offline_stage1_test_revised.rar, 768046 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\Data\data_origin\._ccf_offline_stage1_test_revised.rar, 212 , 2019-03-07
python-Machine-learning-master\Xgboost\Data\data_origin\ccf_offline_stage1_train.rar, 10871156 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\Data\data_origin\._ccf_offline_stage1_train.rar, 212 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\Data\._data_origin, 212 , 2019-03-07
__MACOSX\python-Machine-learning-master\Xgboost\._Data, 212 , 2019-03-07
python-Machine-learning-master\Xgboost\.idea, 0 , 2019-03-18
python-Machine-learning-master\Xgboost\.idea\Xgboost.iml, 284 , 2019-03-18
python-Machine-learning-master\Xgboost\.idea\workspace.xml, 376 , 2019-03-18
python-Machine-learning-master\Xgboost\.idea\modules.xml, 266 , 2019-03-18
__MACOSX\python-Machine-learning-master\._Xgboost, 212 , 2019-03-18
python-Machine-learning-master\Decision_tree, 0 , 2019-03-07
python-Machine-learning-master\Decision_tree\tree.py, 1585 , 2019-03-07
__MACOSX\python-Machine-learning-master\Decision_tree, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\Decision_tree\._tree.py, 212 , 2019-03-07
python-Machine-learning-master\Decision_tree\source _data.txt, 132 , 2019-03-07
__MACOSX\python-Machine-learning-master\Decision_tree\._source _data.txt, 212 , 2019-03-07
python-Machine-learning-master\Decision_tree\README, 82 , 2019-03-07
__MACOSX\python-Machine-learning-master\Decision_tree\._README, 212 , 2019-03-07
python-Machine-learning-master\Decision_tree\Decision_tree.py, 1172 , 2019-03-07
__MACOSX\python-Machine-learning-master\Decision_tree\._Decision_tree.py, 212 , 2019-03-07
__MACOSX\python-Machine-learning-master\._Decision_tree, 212 , 2019-03-07
python-Machine-learning-master\RandomForest, 0 , 2019-03-07
python-Machine-learning-master\RandomForest\README, 899 , 2019-03-07
__MACOSX\python-Machine-learning-master\RandomForest, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\RandomForest\._README, 212 , 2019-03-07
python-Machine-learning-master\RandomForest\RandomForestRegressor.py, 1610 , 2019-03-07
__MACOSX\python-Machine-learning-master\RandomForest\._RandomForestRegressor.py, 212 , 2019-03-07
python-Machine-learning-master\RandomForest\RandomForestClassifier.py, 5469 , 2019-03-07
__MACOSX\python-Machine-learning-master\RandomForest\._RandomForestClassifier.py, 212 , 2019-03-07
__MACOSX\python-Machine-learning-master\._RandomForest, 212 , 2019-03-07
python-Machine-learning-master\README, 45 , 2019-03-07
__MACOSX\python-Machine-learning-master\._README, 212 , 2019-03-07
python-Machine-learning-master\SVM, 0 , 2019-03-07
python-Machine-learning-master\SVM\SVM_SVR.py, 1424 , 2019-03-07
__MACOSX\python-Machine-learning-master\SVM, 0 , 2019-04-17
__MACOSX\python-Machine-learning-master\SVM\._SVM_SVR.py, 212 , 2019-03-07
python-Machine-learning-master\SVM\README, 1204 , 2019-03-07
__MACOSX\python-Machine-learning-master\SVM\._README, 212 , 2019-03-07
python-Machine-learning-master\SVM\SVM_SVC.py, 6098 , 2019-03-07
__MACOSX\python-Machine-learning-master\SVM\._SVM_SVC.py, 212 , 2019-03-07
__MACOSX\python-Machine-learning-master\._SVM, 212 , 2019-03-07
python-Machine-learning-master\linear regression, 0 , 2019-03-07
python-Machine-learning-master\linear regression\README, 406 , 2019-03-07

下载说明：请别用迅雷下载，失败请重下，重下不扣分！

发表评论

0 个回复

  • cholesky
    用matlab编写的cholesky分解程序，希望对大家有些帮助(Matlab prepared using Cholesky decomposition procedures, in the hope that some U.S. help)
    2008-07-01 17:19:05下载
    积分：1
  • bisect
    该function文件可以利用二分法求解方程的数值解，其中有例子(The function files can be used to solve equations numerical solution of the dichotomy, of which examples)
    2010-10-10 11:35:26下载
    积分：1
  • mk
    说明：  常用于分析降水、径流、气温等要素时间序列的趋势变化，其优点在于样本不需要遵循某一特定的分布，而且很少受到异常值的的干扰，计算简便。(It is often used to analyze the trend change of time series of factors such as precipitation, runoff and air temperature. Its advantage is that the sample does not need to follow a specific distribution, and is rarely disturbed by outliers, so it is easy to calculate.)
    2018-09-07 21:26:05下载
    积分：1
  • Max-IntMax-MinMin-Min
    极小 算法（Min-Min）、极大极小算法(Max-Min)、最大时间跨度算法（Max-Int）三种典型的映射算法的算法原理以及计算程序(Minimal algorithm (Min-Min), max-min algorithm (Max-Min), maximum time-span algorithm (Max-Int) three typical mapping algorithm and the algorithm of the calculation program)
    2008-04-01 20:41:46下载
    积分：1
  • f77_gcc
    PSCAD interface with C
    2012-08-06 08:14:51下载
    积分：1
  • bi_sipls
    说明：  siPLS和biPLS是两种用于近红外光谱特征波段筛选的变量选择方法，可筛选出与兴关注指标密切相关的特征波段，并去除无关和干扰变量，提高模型稳健性(Bipls and sipls are two variable selection methods for screening the characteristic intervals of near-infrared spectra, which are closely related to performance indicators, to improve the robustness of models.)
    2019-10-04 17:01:20下载
    积分：1
  • 国内某B2C电子商务网站的数据集
    电商的数据集,可用于做数据分析 平台搭建(The data set of e-commerce can be used to build data analysis platform.)
    2021-02-01 12:40:00下载
    积分：1
  • shanchen
    多孔介质流动程序，基于Shan-Chen模型，使用格子玻尔兹曼理论(Multi-component fluid, using a LB method,based on the Shan-Chen model)
    2020-09-24 09:07:48下载
    积分：1
  • EEHMT0
    根据给定的等距结点上的函数值与一阶导数值, 用埃尔米特等距插值公式计算指定插值点处的函数值。(According to the given equidistant nodes function value with a first derivative values, with equidistant interpolation Hermite interpolation formula specified function value at the point.)
    2013-08-26 15:12:11下载
    积分：1
  • LM_sample
    Levenberg Marquardt算法的matlab代码,简洁无误,结构清晰,便于快速学习。( LevenbergMarquardt algorithm matlab code, simple and correct, clear structure, easy to learn quickly.)
    2017-06-01 19:37:34下载
    积分：1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载

热门标签

Visual C++ matlab C/C++ Java Others Unix_Linux EasyLanguage易语言 Visual Basic C# PDF C++ Delphi VHDL ASP WINDOWS
热门下载

Copyright© 2019-2026 | Powered by Imdn | 浙ICP备16007336号 | IMDN交流 | 联系客服（QQ：3324461878）

本网站资源收集自网络及网站用户上传，若有侵权请邮件至（3324461878@qq.com），核实后我们会在24小时内删除，谢谢！