编码数据在均值

说明：  Python Essential Reference, Second Edition

说明：  针对序列信号分类识别Classification and recognition of sequence signals(Classification and recognition of sequence signals)

说明：  制作对象数据集，有利于卷积神经网络的实现(Making object data set is beneficial to the realization of convolutional neural network)

MIT的EECS学院本科新生入学第一门课程introduction_to_cs&programming的作业答案，也是学习python语言和计算机科学很好的资料。(Institute of MIT' s EECS undergraduate admission first course introduction_to_cs & programming operating the answer is to learn good python language and computer science information.)

Python 机器学习与实战 第三章 决策树分类问题(Python Machine Learning and practical decision tree classification Chapter III)

高德地图58租房(项目源码下载)-Python项目开发案例集锦12.zip

《Python数据挖掘入门与实践》高清中文版 本书作为数据挖掘入门读物，介绍了数据挖掘的基础知识、基本工具和实践方法，通过循序渐进地讲解算法，带你轻松踏上数据挖掘之旅。本书采用理论与实践相结合的方式，呈现了如何使用决策树和随机森林算法预测美国职业篮球联赛比赛结果，如何使用亲和性分析方法推荐电影，如何使用朴素贝叶斯算法进行社会媒体挖掘，等等。本书也涉及神经网络、深度学习、大数据处理等内容。本书面向愿意学习和尝试数据挖掘的程序员。("Python Data Mining Getting Started and Practice" HD Chinese version As a primer on data mining, this book introduces the basic knowledge, basic tools and practical methods of data mining. By step-by-step explanation of the algorithm, you can easily embark on the journey of data mining. This book uses a combination of theory and practice, showing how to use the decision tree and random forest algorithm to predict the results of the American professional basketball league game, how to use the affinity analysis method to recommend movies, how to use the naive Bayesian algorithm for social media mining ,and many more. This book also covers neural networks, deep learning, big data processing and more. This book is for programmers who are willing to learn and experiment with data mining.)

从百度上下载音乐的脚本,需要用到python2.x的库。(download music from music.baidu.com)

提取森林结构参数 #读取冠层高度模型CHM raster = gdal.Open("./chm.tif") banddataraster = raster.GetRasterBand(1) dataraster = banddataraster.ReadAsArray() #对CHM进行高斯滤波，平滑数据 dataraster_gau = gaussian_filter(dataraster,sigma=1) #寻找CHM中的局部最大值作为分水岭的标记点 local_maxi = peak_local_max(dataraster_gau, indices=False) markers = ndi.label(local_maxi)[0] #利用分水岭算法进行分割，labels变量中存储了每个分割结果 labels = watershed(-dataraster_gau, markers, mask= dataraster_gau[:]>5) #利用matplotlib出图查看初步结果

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