gaoshiping
熟练使用python和c++。 其中主要使用python的pytorch进行深度学习，特别是自然语言处理和预训练模型。 熟练使用C++实现各种基础算法
LanZhou UniversityLanZhou
Pinned Repositories
DST
This project implements two dynamic spatiotemporal interpolation (DST) methods, i.e., coarse-grained DST (CGDST) and fine-grained DST (FGDST) using both temporal and spatial interpolation results. Different from other hybrid spatiotemporal interpolation methods, they make differences in the contribution of temporal and spatial interpolation results and assign them with different weights. Both CGDST and FGDST treat each missing value differently and fill it by considering the reliability of both temporal and spatial interpolation results in terms of the lengths of its column gap and row gap. CGDST treats each missing value in a continuous missing area equally and all missing values have same lengths of column and row gaps and FGDST goes beyond CGDST and treats each missing value differently based on its temporal distance to the nearest real observed values in both forward and backward directions.
git
初步git学习
GSP
RuningTensor
编译原理课程设计，专门用于矩阵运算的语言，现阶段已经完成词法分析和语法分析
SSA
使用numpy实现的奇异谱分析的去噪方法
gaoshiping's Repositories
gaoshiping/RuningTensor
编译原理课程设计，专门用于矩阵运算的语言，现阶段已经完成词法分析和语法分析
gaoshiping/DST
This project implements two dynamic spatiotemporal interpolation (DST) methods, i.e., coarse-grained DST (CGDST) and fine-grained DST (FGDST) using both temporal and spatial interpolation results. Different from other hybrid spatiotemporal interpolation methods, they make differences in the contribution of temporal and spatial interpolation results and assign them with different weights. Both CGDST and FGDST treat each missing value differently and fill it by considering the reliability of both temporal and spatial interpolation results in terms of the lengths of its column gap and row gap. CGDST treats each missing value in a continuous missing area equally and all missing values have same lengths of column and row gaps and FGDST goes beyond CGDST and treats each missing value differently based on its temporal distance to the nearest real observed values in both forward and backward directions.
gaoshiping/git
初步git学习
gaoshiping/GSP
gaoshiping/SSA
使用numpy实现的奇异谱分析的去噪方法