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

python-Machine-learning-master

于 2019-04-17 发布
0 166
下载积分: 1 下载次数: 1

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

  • 3107002005_2nd_A_LDL
    说明:  LDL分解。作为矩阵方程数值解法最基础的矩阵分解算法,LDL算法可以高效分解对称矩阵。(LDL decomposition. Numerical Solution of matrix equation as the most basic matrix factorization algorithm, LDL decomposition algorithm can be efficient symmetric matrix.)
    2008-10-27 10:47:26下载
    积分:1
  • LSSVM
    一个案例代码,用最小二乘支持向量机的,预测煤炭量(A case code, using the least squares support vector machine to predict the volume of coal)
    2012-06-18 13:26:33下载
    积分:1
  • finity2D
    二维有限元源代码,为位势问题基本代码,具有一定实用性(Two-dimensional finite element source code for potential problems basic code, with some practical)
    2013-12-23 09:25:45下载
    积分:1
  • FilterCal
    这个我写的一个计算LC滤波器的程序,可以显示出最后的频率截止情况,有最平法,椭圆法等计算方法(this, I wrote a calculation of the LC filter procedures can demonstrate that the frequency of the final deadline, the most-France, oval law calculation method)
    2005-08-16 15:43:42下载
    积分:1
  • Plane_finite_element_program
    简单的平面有限元程序,采用三结点三角形单元;采用等带宽存贮技术;采用高斯消元法解线性方程组。(Simple plane finite element program, using three node triangular element use of such bandwidth, storage technology using Gauss elimination method to solve linear equations.)
    2010-09-27 09:23:39下载
    积分:1
  • Finite element mesh generation
    全面介绍有限元网格生成算法的英文教材,可以作为网格生成算法的编程资料(finite element mesh generation has not been taken as a formal subject of teaching in universities,as it encompasses several disciplines including classical geometry, computational geometry and topology, finite element method, data structures and algorithms, computer programming and, to a certain extent, even computer graphics.)
    2018-01-29 10:55:18下载
    积分:1
  • fdtd-3D
    计算同轴电缆的时域有限差分算法的matlab程序(Finite-difference time-domain algorithm matlab program calculated coaxial cable)
    2012-10-18 15:06:49下载
    积分:1
  • A-Recursive-Dynamic-CGE
    动态CGE模型代码,可以直接粘贴至GAMS软件运行,代码清晰,易于修改。(Dynamic CGE model code, can be directly pasted to the GAMS software operation, the code clear, easy to modify.)
    2021-04-23 08:28:48下载
    积分:1
  • insert_and_simulate
    插值、拟合与规划问题 插值、拟合与规划问题(Interpolation, fitting with the planning of interpolation, fitting and planning issues)
    2008-08-04 16:42:39下载
    积分:1
  • paper1007
    利用样本协方差矩阵特征值分解实现双通道SAR动目标检测.pdf(Dual Channels SAR Ground Moving Target Detection with Eigen-decomposition of the Sample Covariance Matrix)
    2012-07-29 13:21:49下载
    积分:1
  • 696518资源总数
  • 106235会员总数
  • 12今日下载