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python-Machine-learning-master

于 2019-04-17 发布
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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

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    说明：  通过matlab实现抽样检测功能，具体通过蒙特塔罗算法依照概率分布计算指定变量的分布规律，是初学者的好案例(The sampling detection function is realized by matlab, and the distribution rule of specified variables is calculated by Montreal algorithm according to probability distribution.)
    2019-10-06 08:28:50下载
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    Do you need to gain confidence with handling numbers and formulae? Do you want a clear, step-by-step guide to the key concepts and principles of statistics? Nearly all aspects of our lives can be subject to statistical analysis. Statistics: An Introduction shows you how to interpret, analyze and present figures. Assuming minimal knowledge of maths and using examples from a wide variety of everyday contexts, this book makes often complex concepts and techniques easy to get to grips with. This new edition has been fully updated. Whether you want to understand the statistics that you are bombarded with every day or are a student or professional coming to statistics from a wide range of disciplines, Statistics: An Introduction covers it all.
    2020-06-22 22:20:01下载
    积分：1
  • QuartercarJJJ
    通过具有随机结构参数的四分之一车辆模型研究了具有不确定性结构参数的车辆在受到来自道路的随 机激励作用下的振动响应问题。将簧上质量、簧下质量、悬挂阻尼、悬挂刚度以及轮胎刚度均认为是随机变量。将路面的不平整引起的对车辆的激励看作高斯随机过程并通过简单指数功率谱密度来建立力学模型。(By a quarter vehicle model with random parameters studied vehicle structure uncertain structural parameters of the vibration by the random excitation the road response under question. The sprung mass, unsprung mass, suspension damping, suspension stiffness and rigidity of the tire are considered to be random variables. The excitation caused by uneven road vehicle regarded as Gaussian random process and to create a mechanical model of a simple exponential power spectral density.)
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  • MCZTFFT
    说明：  CZT和FFT的C语言实现代码，能有效滤除谐波，噪声。获得信号的频率，是一种良好的频率测量算法。(CZT and FFT of the C language code, can effectively filter out harmonics and noise. Access to the signal frequency, is a good algorithm for frequency measurement.)
    2020-11-19 19:29:38下载
    积分：1
  • digital filter
    说明：  各种滤波器，包括高通、低通、带通等滤波器，可用，直接输入数值即可，可直接使用，没有bug(bandpass filter,Band pass filter, available, direct input value, can be used directly, no bug)
    2021-04-16 21:20:00下载
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  • sw_pres
    根据海水的速度 密度 温度 盐度等计算海洋各处的压力数值(Speed ​ ​ according to the density of seawater temperature and salinity values ​ ​ calculated pressure throughout the ocean)
    2013-10-10 16:36:37下载
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    浮点复数基2时分FFT完成适当的FFT，输出改写输入缓冲器。(floating-point complex FFT-based two hours to complete the FFT output rewritten input buffer.)
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