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主要针对行为识别，常用数据处理方法及分类，可视化 包含数据集(Mainly for behavior recognition, commonly used data processing methods and classification, visualization Contain data sets)

将数据集转换为拉普拉斯矩阵，然后利用基于图论的谱聚类进行聚类。拉普拉斯矩阵采用高斯核函数，全连接方法计算。谱聚类擅长处理高维数据或非凸数据集。(The data set is transformed into Laplacian matrix, and then clustered by spectral clustering based on graph theory. The Laplacian matrix is calculated by using the Gauss kernel function and the full connection method. Spectral clustering is good at dealing with high-dimensional or non-convex data sets.)

线性拟合和二次拟合函数 pdg正交多项式作最小二乘拟合(Least Square fitting of Linear fitting and Quadratic fitting function pdg orthogonal polynomial)

最优化方法之近端梯度下降法，适用于L1正则项的最优化。(Proximal gradient descent)

说明：  GN算法是一种分裂型的社区结构发现算法。该算法根据网络中社区内部高内聚、社区之间低内聚的特点，逐步去除社区之间的边，取得相对内聚的社区结构。(GN algorithm is a divisive community structure discovery algorithm. According to the characteristics of high cohesion within the community and low cohesion between communities, the algorithm gradually removes the edges between the communities and achieves a relatively cohesive community structure.)

克里金插值法实现二维和三位等高线的绘制功能,()

哈弗曼编码，构造哈夫曼树，构造哈夫曼树非常简单，将所有的节点放到一个队列中，用一个节点替换两个频率最低的节点，新节点的频率就是这两个节点的频率之和。这样，新节点就是两个被替换节点的父节点了。如此循环，直到队列中只剩一个节点（树根）。(Hoffman coding, Huffman tree structure, Huffman tree structure is very simple, all the nodes in a queue, replace the two lowest frequency node with a node, the new node and the frequency of frequency is the two nodes. So, the new node is two replaced node parent node. So the cycle, until the queue is only one node (Shu Gen). )

说明：  Spark 机器学习代码，有大量机器学习算法实例。(Spark machine learning code, there are a large number of machine learning algorithm examples.)

用遗传算法优化支持向量回归机C、g、p参数(Optimization of C, G, P parameters of support vector regression machine by genetic algorithm)

数组排序,按照数组的某个元素进行排序,使用请查看(Sort the array, in accordance with a certain element of the array to sort, the use of please see)