▍1. 股票预测
说明： 使用神经网络进行股票预测，使用python在anacanda上进行编写(Using neural network to forecast stock, using Python to write on anacanda)
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▍2. LSTM-Human-Activity-Recognition-master
与经典的方法相比，使用具有长时间记忆细胞的递归神经网络(RNN)不需要或几乎不需要特征工程。数据可以直接输入到神经网络中，神经网络就像一个黑匣子，可以正确地对问题进行建模。其他研究在活动识别数据集上可以使用大量的特征工程，这是一种与经典数据科学技术相结合的信号处理方法。这里的方法在数据预处理的数量方面非常简单(Compared with the classical methods, the recursive neural network (RNN) with long-term memory cells does not need or almost need feature engineering. Data can be directly input into the neural network, which acts as a black box and can correctly model the problem. Other research can use a lot of Feature Engineering on activity recognition data sets, which is a signal processing method combined with classical data science and technology. The method here is very simple in terms of the number of data preprocessing)
▍3. LSTM-Human-Activity-Recognition-master
说明： 与经典的方法相比，使用具有长时间记忆细胞的递归神经网络(RNN)不需要或几乎不需要特征工程。数据可以直接输入到神经网络中，神经网络就像一个黑匣子，可以正确地对问题进行建模。其他研究在活动识别数据集上可以使用大量的特征工程，这是一种与经典数据科学技术相结合的信号处理方法。这里的方法在数据预处理的数量方面非常简单(Compared with the classical methods, the recursive neural network (RNN) with long-term memory cells does not need or almost need feature engineering. Data can be directly input into the neural network, which acts as a black box and can correctly model the problem. Other research can use a lot of Feature Engineering on activity recognition data sets, which is a signal processing method combined with classical data science and technology. The method here is very simple in terms of the number of data preprocessing)
▍4. lena
说明： SLIC算法是simple linear iterative cluster的简称，该算法用来生成超像素（superpixel） 算法步骤： 已知一副图像大小M*N,可以从RGB空间转换为LAB空间，LAB颜色空间表现的颜色更全面 假如预定义参数K，K为预生成的超像素数量，即预计将M*N大小的图像(像素数目即为M*N)分隔为K个超像素块，每个超像素块范围大小包含[（M*N）/K]个像素 假设每个超像素区域长和宽都均匀分布的话，那么每个超像素块的长和宽均可定义为S，S=sqrt(M*N/K) 遍历操作，将每个像素块的中心点的坐标(x,y)及其lab的值保存起来，加入到事先定义好的集合中(Algorithmic steps: Given the size of an image M*N, it can be converted from RGB space to LAB space, and the color space of LAB is more comprehensive. If the predefined parameter K, K is the number of pre-generated super-pixels, i.e., the image of M*N size (the number of pixels is M*N) is expected to be divided into K super-pixel blocks, each of which contains [(M*N)/K] pixels. Assuming that the length and width of each super-pixel region are evenly distributed, the length and width of each super-pixel block can be defined as S, S = sqrt (M*N/K). The traversal operation saves the coordinates (x, y) and lab values of the central points of each pixel block and adds them to a pre-defined set.)
▍5. Car_ID
用于识别车牌的代码，采用图像识别和文字识别代码。(The python code to recognize car id number.)
▍6. Car_ID
说明： 用于识别车牌的代码，采用图像识别和文字识别代码。(The python code to recognize car id number.)
▍9. Python string
对python中数据类型字符串进行详细介绍，以及对正则表达式的申明(data type alphabetic string be in progress detailed introduce declare)
▍10. Python string
说明： 对python中数据类型字符串进行详细介绍，以及对正则表达式的申明(data type alphabetic string be in progress detailed introduce declare)
▍11. deep-clustering-master 中文
说明： 用于语音分离 监督学习 直接可以调用 使用tmit数据集 进行测试(Unsupervised learning for speech separation)
▍12. 熵权法（python）
熵权法根据各指标的变异程度，利用信息熵计算出各指标的熵权，再通过熵权对各指标的权重进行修正，从而得出较为客观的指标权重。(According to the variation degree of each index, the entropy weight of each index is calculated by using information entropy, and then the weight of each index is revised by the entropy weight, thus objectively obtaining the weight of each index.)
▍13. 熵权法（python）
说明： 熵权法根据各指标的变异程度，利用信息熵计算出各指标的熵权，再通过熵权对各指标的权重进行修正，从而得出较为客观的指标权重。(According to the variation degree of each index, the entropy weight of each index is calculated by using information entropy, and then the weight of each index is revised by the entropy weight, thus objectively obtaining the weight of each index.)
▍14. 模糊聚类分析法（python)
运用python进行模糊聚类分析步骤如下：建立数据矩阵；数据标准化；建立模糊相似矩阵；改造相似关系为等价关系；确定分类数(The steps of Python fuzzy clustering analysis are as follows: establishing data matrix; standardizing data; establishing fuzzy similarity matrix; transforming similarity relation into equivalence relation; determining classification number.)
▍15. 模糊聚类分析法（python)
说明： 运用python进行模糊聚类分析步骤如下：建立数据矩阵；数据标准化；建立模糊相似矩阵；改造相似关系为等价关系；确定分类数(The steps of Python fuzzy clustering analysis are as follows: establishing data matrix; standardizing data; establishing fuzzy similarity matrix; transforming similarity relation into equivalence relation; determining classification number.)
▍16. test1
训练一个深度学习的循环神经网络，可实现获取公开数据集并进行训练，框架为Pytorch(Training a deep learning circular neural network can achieve the acquisition and training of open data sets, and the framework is Pytorch)
▍17. test1
说明： 训练一个深度学习的循环神经网络，可实现获取公开数据集并进行训练，框架为Pytorch(Training a deep learning circular neural network can achieve the acquisition and training of open data sets, and the framework is Pytorch)
▍18. 多轴疲劳临界面提取
该程序可用于对abaqus结果数据进行处理，对每个材料点应力进行计算，并获取临界面的应力应变参数。(The program can be used to process the ABAQUS odb data, calculate the stress of each material point, and obtain the stress and strain parameters of the critical plane.)
▍19. 多轴疲劳临界面提取
说明： 该程序可用于对abaqus结果数据进行处理，对每个材料点应力进行计算，并获取临界面的应力应变参数。(The program can be used to process the ABAQUS odb data, calculate the stress of each material point, and obtain the stress and strain parameters of the critical plane.)
▍20. BugId.release.2019-03-04.15.31
bugid for automatic crashes classification
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