python 导入文件绘图代码

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)

手动选择三对特征点，标注特征点，实现二维图像的仿射配准；每完成一对需要按Q键进行下一对；平台是Spyter(Radiation registration by labeling feature points)

说明：  详细编写如何让使用cnn识别自己的图片，自己建立cnn模型，可修改(Write in detail how to use CNN to identify their own pictures)

机器学习实战 第7章实例代码 Machine Learning in　Action Charpter7 instance(Machine Learning in　Action Charpter7 instance)

xgboot做预测示例的代码及数据集，仅作为参考使用(The code and data set of xgboot as a prediction example are used for reference only)

鱼眼相机标定及校正，利用opencv对鱼眼相机进行标定及校正，输出校正后图像(Calibrate fisheye lens using OpenCV)

pycharm破解脚本

说明：  该代码为北京理工大学嵩天老师的《python语言程序设计》第十课课后题源码(This code is the source code of the tenth lesson of "Python language programming" by Songtian teacher of Beijing University of technology)

说明：  Python 图像处理机器学习入门开源代码，代码仅供学习使用！(Python Image Processing Machine Learning Initial Open Source Code, Code for learning only!)

说明：  机器视觉，行人识别，hog特征提取的python语言实现(Machine vision, pedestrian recognition, hog feature extraction)