资源列表
caodianwei
- 运用有限差分法和超松弛迭代法求解金属槽槽内各个离散节点电位数值解-Combining the finite difference method( FDM) with over-relaxation iteration to solving the potential of each discrete nodes in the metal slots
zy6_1
- 字符串的相关操作，包括删除相同的子串，统计字符第一次出现的位置和出现的次数。-String related operations, including deleting the same substring, statistical character position of the first occurrence and number of occurrences.
shuipingji
- 图像分割就是把图像分成若干个特定的、具有独特性质的区域并提出感兴趣目标的技术和过程-Image segmentation is to divide the image into several specific area, unique quality and interested target technology and process are put forward
leapfrog
- 使用蛙跳差分格式计算流体力学中一维激波管问题-Leapfrog difference scheme using a one-dimensional computational fluid dynamics shock tube problem
Lax1d
- 使用Lax差分格式计算流体力学中一维激波管问题-Lax difference scheme using computational fluid dynamics problems in one-dimensional shock tube
compact3order
- 使用三阶紧致和人工压缩算法计算带障碍的简单槽道流问题-Using third-order compact compression algorithm and artificial obstacles with simple channel flow problem
JCTVC-D262-v2
- Context-Adaptive Binary Arithmetic Coding (CABAC) is one of two entropy engines used by the AVC video coding standard
data-classification
- 最短距离分类算法，是数据挖掘算法中的一种，功能就是实现坐标点的分类。-The shortest distance classification algorithm, a data mining algorithm, the function is to realize the coordinate points classification.
xishujuzhendexiangcheng
- 数据结构中用三元数组实现稀疏矩阵的相乘算法源代码-Data structures implemented using triplet sparse matrix multiplication algorithm source code
pinv
- //奇异值分解法求双精度浮点数矩阵的广义逆 //功能：利用奇异值分解求解一般的m×n阶实矩阵A的广义逆A+。 //方法说明：设m×n阶实矩阵A的奇异值分解式为 //其中 Σ = diag(σ0, σ1, ……,σp)(p≤min(m,n) -1 )且σ0≥σ1≥……≥σp＞0 //设U = （U1,U2）,其中U1为U中前P+1列列正交向量组构成的m×(p+1)矩阵；V = (V1，V2)，其中V1为前P+1列//列正交向量组构成的n×(p+1)矩阵。则A的广义逆为： //A
Exercise8-linear-decoder
- 斯坦福深度学习教程中关于linear decoder 的练习代码，源代码中需要补全的地方，全部把代码补完整，把手写体识别的数据库放到路径下，可以直接运行-Stanford deep learning tutorial exercises on linear decoder code, source code need to fill all places, all the full complement of the code, the handwriting recognition into
Exercise7-stacked-autoencoder
- 斯坦福深度学习教程中关于stacked autoencoder的练习代码，源代码中需要补全的地方，全部把代码补完整，把手写体识别的数据库放到路径下，可以直接运行-Stanford deep learning tutorial exercises on stacked autoencoder code, source code need to fill all places, all the full complement of the code, the handwriting recognit