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UQ-PyL_Linux.tar
- 优化计算程序包,适用于水文及生态模型的优化计算及参数校准。-Uncertainty quantification (UQ) refers to quantitative characterization and reduction of uncertainties present in computer model simulations. It is widely used in engineering and geophysics fields to assess and predict t
recommendations
- Python编写简单的推荐系统，供初学者使用-Python written in a simple recommendation system for beginners
Ap
- 数据挖掘中关联规则挖掘算法-apriori，的Python实现，代码中有测试样本-Data mining association rule mining algorithm-apriori, implementation of Python code in a test sample
gplvm
- 这是一个用于高斯过程隐变量模型的工具箱，其中包含了MATLAB/C/PYTHON三种语言版本-As of July 2005 a C++ implementation of the GPLVM exists which has most of the flexibility of this software but runs much faster. However as of this time it cannot handle very large data sets as the spar
build_a_decision_tree_base_CART.tar
- 这里是最基本的决策树实现的python方法，有详细的代码说明-Here is the basic method of decision tree python to achieve, a detailed code Descr iption
svm_python
- 在机器学习领域，支持向量机SVM(Support Vector Machine)是一个有监督的学习模型，通常用来进行模式识别、分类、以及回归分析。本程序是SVM的python实现，用的是SMO算法。只能进行分类，并且能够显示图形结果。-In the field of machine learning, support vector machines SVM (Support Vector Machine) is a supervised learning model is usually use
kNN
- 使用python编写kNN算法，包括生成数据集，简单分类器，文本转换等简单算法。-Using python write kNN algorithms, including generating a data set, a simple classification, text conversion simple algorithm.
Bayes
- 本程序是使用的Python写的一个Bayes分类器，通过这个程序可以大致掌握Bayes的原理。-This procedure is used to write a Python Bayes classifier, through this program can be broadly master the principles of Bayes.
DecisionTree
- 本程序是利用python写的一个决策树算法，通过该例子可以实现简单的决策树处理，也可以学习决策树算法的基本思想。-This procedure is to use python to write a decision tree algorithm, this example can be achieved by a simple decision tree processing, you can also learn the basic idea of the decision tree alg
K-means
- 本程序是用python写的一个K均值算法，通过该算法可以学习一python实现算法的流程，以及学习该算法的使用。-The program is written in python a K-means algorithm, the algorithm can learn a python algorithm implementation processes, and learning to use the algorithm.
KNN-implement-by-python
- 该程序是用python编写一个K近邻算法，通过该例子可以掌握K近邻算法，是学习数据挖掘的一个高效的算法。-The program is written in python a K-nearest neighbor algorithm, this example can grasp the K-nearest neighbor algorithm, a learning data mining and efficient algorithms.
baseflight-master
- 用python语言实现了常用的概率算法，非常建议本科生在学习概率论的时候跑一跑这些算法，养成贝叶斯思维-Using python language commonly used probabilistic algorithms, highly recommended undergraduate study in probability theory, when these algorithms run a race, to develop Bayesian thinking
LogisticRegression
- 本例是用Python写的简单的逻辑回归的例子，可以下载试试。-This case is an example of a simple logistic regression written in Python, you can download a try.
SVM-timeseries
- 基于SVM的时序序列预测，用python实现，内附测试数据，方便可用。-SVM prediction based on a timing sequence with python to achieve, enclosing the test data to facilitate available.
python数据分析之金融欺诈行为检测
- 用python写的一个关于金融欺诈行为检测的数据分析程序，用的是回归预测模型(This is a data anlysis program for the detection of financial fraud, based on logistic regression model.)
visualization
- 用Python实现数据可视化的一个小案例，数据来自金融(Using Python to achieve data visualization of a small case, data from the financial)
Crawler.tar
- 利用了python3.5编写了一个爬虫，爬取豆瓣上电影《声之形》的评论，并统计评论词的频率，制作了词云(Using python3.5 to write a crawler, climb the comments on the movie "sound shape", and statistics the frequency of the comment word, making the word cloud)
RNN
- just a easy programer achieving addition.
FM algorithm
- 因子分解机( FM)算法是一种基于矩阵分解的机器学习算法，是一种常用的推荐算法。(Factorization algorithm is a matrix-based machine learning algorithm, which is a commonly used recommendation algorithm.)
Python数据分析与挖掘实战
- 本书共15章，分两个部分：基础篇、实战篇。基础篇介绍了数据挖掘的基本原理，实战篇介绍了一个个真实案例，通过对案例深入浅出的剖析，使读者在不知不觉中通过案例实践获得数据挖掘项目经验，同时快速领悟看似难懂的数据挖掘理论。读者在阅读过程中，应充分利用随书配套的案例建模数据，借助相关的数据挖掘建模工具，通过上机实验，以快速理解相关知识与理论。(There are 15 chapters in this book, which are divided into two parts: the basic c