Graph classification

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.

Pattern Classification一书中的MATLAB代码，提供了经典的各种算法的MATLAB实现。

[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。

图像强度值由反照率分量和阴影分量确定。反照率分量描述了地球表面不同物体的物理性质，土地覆盖类别因其固有的物理材料而彼此不同。因此，我们恢复高光谱图像的内在反照率特征以利用空间语义信息。然后，我们使用支持向量机（SVM）对恢复的固有反照率高光谱图像进行分类。SVM尝试最大化最小边距以实现良好的泛化性能。实验结果表明，在视觉质量和三个定量指标方面，具有内在反照率特征方法的SVM比最先进的方法实现了更好的分类性能。如果您使用这些代码，请引用论文： @文章{ZhanJEI2017july,作者= {詹、坤和王、海波和谢、元歌和张、楚彤和敏、玉芳}， title = {用于高光谱图像分类的反照率恢复}，杂志= {电子影像杂志},年= {2017},体积= {26}，数字= {4}，页数= {043010}， doi = { http://dx.doi.org

MATLAB图像分割肿瘤代码，脑肿瘤检测与分类。此源代码根据患者的MRI扫描检测脑肿瘤区域，然后通过MATLAB进行的机器学习将其分为良性和恶性类型。 %Source Code clc %% Input [I,path]=uigetfile('.jpg;.png','select a input image'); str=strcat(path,I); s=imread(str); %% Filter num_iter = 10; delta_t = 1/7; kappa = 15; option = 2; disp('Preprocessing image please wait . . .'); inp = anisodiff(s,num_iter,delta_t,kappa,option); inp = uint8(inp); inp=imresize(inp,[256,256]); if size(inp,3)>1 inp=rgb2gray(inp);

This tutorial demonstrates how to perform 1D matrix classification for 2-class and 3-class problems using a Self-Organizing Map (SOM) neural network. It includes a matrix-based AND gate example with input samples of sizes 12 and 3. The approach uses machine learning principles to classify the data, making it suitable for tasks such as pattern recognition and clustering. The MATLAB code provided helps implement and visualize the classification process in a straightforward manner. The classification results can be interpreted using the SOM algorithm, which adjusts the map neurons based on the input data features.

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。

该项目提供了基于梯度下降的softmax回归实现，专注于MNIST数据分类。此外，还包含多个领域的Matlab仿真代码，涵盖智能优化算法、神经网络预测、信号处理等应用。

模糊C均值聚类算法可有效解决遥感信息的不确定性和混合像元的划分。文中基于MATLAB平台，采用模糊C均值聚类对遥感影像进行分类，并运用混淆矩阵对分类结果进行了精度评定。实验结果表明，基于模糊C均值聚类使得分类后的图像很好地区分了地物类别，取得了较好效果。