搜索资源列表
Floyd-Matlab
- floyd算法的matlab程序 floyd-最短路问题 输入： B-邻接矩阵(bij)，指i到j之间的距离，可以是有向的。 sp- 起点标号。 ep- 终点标号。 输出： d- 最短路的距离。 path-最短路的路径。-floyd algorithm matlab program floyd-shortest path problem Input: B-adjacency matrix (bij), refers to the distan
2(2)
- 最小生成树之Prim算法 Prim算法用于求无向图的最小生成树 设图G =（V，E），其生成树的顶点集合为U。 ①、把v0放入U。 ②、在所有u∈U，v∈V-U的边(u，v)∈E中找一条最小权值的边，加入生成树。 ③、把②找到的边的v加入U集合。如果U集合已有n个元素，则结束，否则继续执行②。 其算法的时间复杂度为O(n^2) Prim算法实现： （1）集合：设置一个数组set(i=0,1,..,n-1),初始值为 0,代表对
sevaluate
- gets label matrix of a tissue in segmented and ground truth and returns similarity indices-gets label matrix of a tissue in segmented and ground truth and returns similarity indices
Dijkstra-matlab
- 求第一个城市到其它城市的最短路径.用矩阵（为顶点个数）存放各边权的邻接矩阵，行向量、、、分别用来存放标号信息、标号顶点顺序、标号顶点索引、最短通路的值-The first city to find the shortest path to other cities. With a matrix (for the number of vertices) records of the right side of the adjacency matrix, row vector, were used
Etap1
- It is the corresponding testing module of the function that is specified in the training phase. test_set is a NxD matrix where N is the number of samples in the test set and D is the dimension of the feature space. true_labels is a Nx1
kmeans
- function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers
jiuzhengbujunyunzhaoming
- 首先读取图像并生成二值图像，然后生成标注矩阵并用彩色显示，最后确定图像的统计性能-First reads the image and generates a binary image, and then generate the label matrix and displayed in color, and finally to determine the statistical properties of the image
DeepLearningDropout-master
- dropout和深度学习算法的结合使用，有详细的使用说明和数据集(Three types of layers: - C: convolutional layer (matrix map) - MP: max-pooling layer (matrix map) - F: fully connected layer (vector map) - O: output layer Convolutional Layers: - Scale: scale (size of p
ELM分类器
- ELM是基于深度学习的分类器，运算速度快。 在B_data.m里导入待分类矩阵B.mat（1-n列为特征值，n列为标签）；运行B_data.m；再打开fuzzyEn_main.m并运行即可。(ELM is based on depth learning classifier, computing speed. In B_data.m imported matrix to be classified B.mat (1-n as eigenvalues, n as a label); Run B