朱芳清 201711010192 wk7

使用PyQT5结合Matplotlib绘制动态的曲线(Drawing dynamic curves with PyQT5 and Matplotlib)

说明：  洛谷OJ P1000,1421,1422,1425 Python 3 代码(Luogu OJ P1000,1421,1422,1425 in Python 3)

主成分分析图像，分析图像像素组成部分，高维提取低维特征(Principal component analysis image, analysis of image pixel components, high dimensional extraction of low dimensional features)

说明：  在keras神经网络框架下，训练提前收集的车流量数据，对未来车流量预测。(Under the framework of keras neural network, train the traffic data collected in advance to predict the future traffic.)

深度学习进行调制识别的数据集，用于卷积神经网络(dataset for cnn include generate_RML2016.04c and generate_RML2016.10a)

说明：  随机森林做特征选择py程序feature selection by using random forest(feature selection by using random forest)

面向地理信息的Python编程ArcGIS环境搭建(Python programming ArcGIS environment building for geographic information)

说明：  通过各种不同的算法实现室内高精度定位，比如说KNN，随机森林，SVM，等等。(Indoor high-precision positioning is achieved by various algorithms, such as KNN, random forest, SVM, and so on.)

Python密度聚类 最近在Science上的一篇基于密度的聚类算法《Clustering by fast search and find of density peaks》引起了大家的关注(在我的博文“论文中的机器学习算法——基于密度峰值的聚类算法”中也进行了中文的描述)。于是我就想了解下基于密度的聚类算法，熟悉下基于密度的聚类算法与基于距离的聚类算法，如K-Means算法之间的区别。 基于密度的聚类算法主要的目标是寻找被低密度区域分离的高密度区域。与基于距离的聚类算法不同的是，基于距离的聚类算法的聚类结果是球状的簇，而基于密度的聚类算法可以发现任意形状的聚类，这对于带有噪音点的数据起着重要的作用。(The main goal of the density based clustering algorithm is to find high density regions separated by low density regions. Different from distance based clustering algorithm, the clustering results based on distance clustering algorithm are spherical clusters, and density based clustering algorithm can detect clustering of arbitrary shapes, which plays an important role in data with noisy points.)

说明：  一个简单的python网站开发 实现nl2sql(A simple Python website development)