资源列表
粗糙集
- 主要用于计算集合p的q正域等等粗糙集基本量。。。。。。。。。。。。。。。。。。。。。(Mainly used to calculate the set of P Q positive field)
java网络爬虫
- 是一个无须配置、便于二次开发的JAVA爬虫框架（内核），它提供精简的的API，只需少量代码即可实现一个功能强大的爬虫(Is a JAVA reptile framework (kernel) that does not need to be configured for easy development. It provides a streamlined API that requires a small amount of code to implement a powerful crawl
code
- ssmfa将高光谱数据从高维观测空间投影到低维流形空间，达到约减数据维数的目的(ssmfa hyperspectral data is projected from the high dimensional observation space into the low dimensional manifold space, so as to reduce the dimensionality of data)
MIMGA
- 基于互信息最大化的特征提取代码，希望对大家有用(Feature extraction code based on maximization of mutual information)
实验6_NMF-ICA
- 实现非负矩阵分解算法和独立成分分析，得到遥感遥感图像解混结果(The non negative matrix factorization algorithm and independent component analysis are implemented to get the unmixing results of remote sensing images)
regress
- 一个xgboost实现的回归模型预测，数据集来源于kaggle的taxi竞赛(Regression model prediction based on a xgboost implementation)
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)
kmeans
- 对数据和图像进行聚类分析，k-means聚类方法多应用于模式识别，人工智能，机器学习等方面(Clustering analysis of data and images, K-means clustering method should be used in pattern recognition, artificial intelligence, machine learning and so on)
cc
- 可以执行矩阵的相关性子矩阵挖掘，代码开始部分的备注里包含实例矩阵，大家可以实验看看，代码原创，实验可以，但是如果用在商业或者学术里，请和我联系~(Relative matrix mining of matrices can be performed)
K_Means
- K-Means是聚类算法中的一种，其中K表示类别数，Means表示均值。顾名思义K-Means是一种通过均值对数据点进行聚类的算法。K-Means算法通过预先设定的K值及每个类别的初始质心对相似的数据点进行划分。并通过划分后的均值迭代优化获得最优的聚类结果。(K-Means is one of the clustering algorithms, in which K represents the number of classes, and Means means the mean. As t
emd1d
- 用python写的EMD分解，可以实现一维与二维分解(EMD decomposition written in Python)
天气爬虫
- 爬取各个地区近8年的天气历史数据，大家可以帮忙看看还有什么可以优化的。(Climb the historical weather)