graph cut

[MatLab] - 厚度 - 图 + 图源码.zip

此应用程序绘制二阶振荡器（例如质量和弹簧或钟摆）的交互图。当转储系数Beta改变时，它会开始新的图x(t)、dx/dt(t)和相位图dx/dt=f(x)。二阶方程为：d²x/dt² + beta*dx/dt + x=0。您可以安装APP oscilatorApp.mlappinstall文件，也可以按照文件中的说明准备自己的APP：howToMakeAPPs.txt。

问题描述：一摆渡人欲将一只狼、一头羊、一篮菜从河西渡过河到河东。由于船小，一次只能带一物过河，并且，狼与羊、羊与菜不能独处。 渡河方法：1. 首先，将羊带到河东。2. 返回河西。3. 带狼过河。4. 将狼放在河东后，带羊回到河西。5. 将菜带到河东。6. 返回河西。7. 最后，将羊带到河东。 这样，所有物品都安全渡过了河。

OS Watcher Graph（OSWg）是Oracle开发的一种数据解析和图形显示实用程序，与OSW v2.0.0及更高版本捆绑提供。OSWg能解析支持的所有Unix平台上的vmstat文件，以及仅支持Solaris、AIX和Linux平台上的iostat文件。通过图形化显示这些信息，用户无需手动检查每个文件。

Matlab SLAM工具包提供了SLAMTB-Graph.zip的GitHub下载链接。

The Matlab code sqrt generates initial phase values for networks, stored in the file 'initial_phases.txt'. These values are evenly distributed between -pi and pi, and can be adjusted to fall between -val and val. The file 'network_generation.py' contains Python 3 code to generate two types of networks: ER random and scale-free. The function erdos_renyi_graph() from networkx generates random graphs by using the network size (N), the connection probability (N/k), and a unique seed value for each graph. Similarly, the function Barabasi_Albert_graph() creates scale-free networks with a specified network size (N) and a number of connections per new node (k/2), utilizing a preferential attachment algorithm. Each generated graph must have a distinct seed value to ensure uniqueness. The Omega values are generated using the randn() function, which produces N values with a mean of 0 and variance of 1. Further adjustments to these values are made using the formula: value = sqrt(variance) * randn() + mean. Finally, the file 'data_gener' contains additional data generation procedures.

这段代码展示了k-means、mean-shift和normalized-cut分割方法的比较测试。其中，仅使用颜色的k-means分割，颜色加空间的k-means分割，仅使用颜色的均值偏移分割，颜色加空间的均值偏移分割以及使用颜色和空间数据的normalized-cut分割。k-means的参数是簇数“K”，mean-shift的参数是平均位移带宽“bw”，normalized-cut的参数包括颜色相似度“SI”、空间相似度“SX”、空间阈值“r”和保持分区的最小Ncut值“sNcut”等。对于normalized-cut，使用了Naotoshi Seo的经过修改的实现。

ALLSPATH - 解决图中所有节点之间的最短路径问题，快速返回沿图边缘的最短节点到节点的距离。用法：B = allspath(A) A 是节点之间的距离矩阵，B 是所有节点之间的最短路径距离矩阵。注意：(1) 对于具有 n 个节点的图，A 是一个 n×n 的距离矩阵，给出相邻节点之间的距离。由于点 i 到点 j 的距离与点 j 到点 i 的距离相同，所以 A 一定是对称矩阵。(2) 从节点到自身的距离可以输入为零或无穷大，两者都会产生正确的结果。(3) 彼此不相邻的节点之间的距离必须输入为零或无穷大，两者都会产生正确的结果。(4) 如果输入图不是“连通的”，即无论经过多少条边都无法从其他节点到达某些节点，则无法。

ESP-DNN: Graph Convolutional Deep Neural Network for Predicting Electrostatic Potential Surfaces from DFT Calculations This repository contains trained models and code designed for generating ligands and proteins, creating electrostatic potential (ESP) surfaces that closely resemble DFT-quality molecular surfaces. The PQR files generated by our model include atomic charges and dipole-like atomic features, such as lone pairs, σ-conjugation, and p-orbitals. To generate ligand PQR files, a graph convolutional deep neural network (DNN) model was trained on about 100,000 molecules with ESP surfaces derived from DFT calculations. For proteins, parameterized charges of amino acids were used, ensuring compatibility with the ligand ESP surfaces generated by the DNN model. For more detailed methods and validation information, refer to the full documentation. System Requirements The program can only run on 64-bit Linux operating systems. Installation Instructions To run ESP-DNN, you will need to:1. Clone this repository.2. Set up Python and required dependencies.3. (Optional) Install additional packages. The package has been developed and tested with Python 2.7 and the following third-party libraries:- rdkit == 2018.09.3- keras == 2.2.4- tensorflow == 1.10.0- num