Qt_test

在有限元相关软件中，施加平面的周期性边界条件，二次开发(Applying Periodic Boundary Conditions to Planes)

说明：  扫描文件夹，通过文件大小和创建时间判断图片文件是否重复，并分类和重命名(Scanning folders to determine whether image files are duplicated, categorized and renamed by file size and creation time)

用于骨折检测数据集，包括正常数据和异常数据(Data set for fracture detection, including normal and abnormal data)

附件为Sublime按键的初始状态，上次这个文件没有备份，真是真心麻烦啊，很多按键都无效！

说明：  1,載入iris 數劇集並設置提取60%的數據做為訓練集，40%數據做為測試集執行KNN 分類算法，並執行五次的交叉驗證（CV）和顯示準確率和混淆矩陣，並找出最佳K值。 2,載入iris 數劇集並設置提取60%的數據做為訓練集，40%數據做為測試集,執行支持向量機（SVM） 算法，並執行五次的交叉驗證（CV）和顯示準確率和混淆矩陣。 3,載入iris 數據集並執行KMEANS 聚類算法家數具分成五個群體，顯示準確率 4,載入iris 數據集執行線性迴歸算法，並利用特徵萃取（Feature Extraction）中的Recursive feature elimination (RFE)對iris 數據集中的特徵欄位進行重要性排序(1. Load iris series and set 60% data as training set and 40% data as test set to implement KNN classification algorithm, and perform five times of cross validation (CV) and display accuracy and confusion matrix, and find out the best K value. 2. Load iris series and set 60% data as training set and 40% data as test set, execute support vector machine (SVM) algorithm, and perform five times of cross validation (CV) and display accuracy and confusion matrix. 3. Loading iris data set and executing kmeans clustering algorithm, the number of users is divided into five groups, showing the accuracy rate 4. Load iris data set, execute linear regression algorithm, and use recursive feature elimination (RFE) in feature extraction to sort the importance of feature fields in iris dataset)

机器学习算法，预测数值型回归，岭回归和逐步线性回归(Machine learning algorithms to predict numerical regression, ridge regression and stepwise linear regression)

说明：  吴恩达第五周作业。学习算法分两部分进行理解，第一部分是根据code对算法进行综述，第二部分是代码(The learning algorithm is divided into two parts, the first part is to summarize the algorithm according to the code, the second part is the code)

《从零开始学爬虫》的配套资料（PPT和源码）("Learning Reptiles from Zero" (PPT and Source))

说明：  神经网络学习，深度学习，人工自然神经网络(Neural network learning)

说明：  利用模糊聚类算法（FCM）进行iris数据集的处理并进行可视化(Fuzzy clustering algorithm (FCM) is used to process and visualize iris data set)