#参考1:https://blog.csdn.net/sushiqian/article/details/78614133#参考2:https://blog.csdn.net/thy_2014/article/details/51659300# coding=utf-8 import numpy as np import matplotlib.pyplot as plt import os import sys sys.path.append("/home/wit/caffe/python&qu
1.DataFrame中某一列的值衍生为新的特征 #将LBL1特征的值衍生为one-hot形式的新特征 piao=df_train_log.LBL1.value_counts().index #先构造一个临时的df df_tmp=pd.DataFrame({'USRID':df_train_log.drop_duplicates('USRID').USRID.values}) #将所有的新特征列都置为0 for i in piao: df_tmp['PIAO_'+i]=0 #进行分组便利,有这个
[占位-未完成]scikit-learn一般实例之十一:异构数据源的特征联合 Datasets can often contain components of that require different feature extraction and processing pipelines. This scenario might occur when: 1.Your dataset consists of heterogeneous data types (e.g. raster image
OpenCV特征点检测------ORB特征 ORB是是ORiented Brief的简称.ORB的描述在下面文章中: Ethan Rublee and Vincent Rabaud and Kurt Konolige and Gary Bradski, ORB: an efcient alternative to SIFT or SURF, ICCV 2011 没有加上链接是因为作者确实还没有放出论文,不过OpenCV2.3RC中已经有了实现,WillowGarage有一个talk也提到了这个