Texture-Segmentation

实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))

various coding..........................

应用matlab对负责数学模型分析求解方法解析(Application the matlab responsible mathematical model analysis method for solving resolution)

一种基于压缩感知的正弦信号重构算法，已经经过仿真验证(A sinusoidal signal reconstruction algorithm based on compressed sensing has been verified through simulation)

原创：随机抽样。从一系列的总体中抽取出指定数量的不重复样本点。(random sampling. From a range of the overall extraction of a specified quantity of sample points of non-repetition.)

Matlab code for mlms

说明：  matlab写的扫雷小游戏,有兴趣的可以试玩一下。(matlab write mine game, are interested in you can try.)

Toolbox to read netcdf files on Matlab Install: javaaddpath([pwd /netcdfAll-4.2.jar ]) unzip( mexcdf.r4000.zip ) addpath ([pwd /mexcdf/mexnc ]) addpath ([pwd /mexcdf/snctools ])

用响应面法计算可靠度.已知极限状态方程，变量相互独立，服从标准正态分布。(Calculation of reliability by response surface methodology. Known limit state equation, variables are independent standard normal distribution.)

verhulst模型预测代码，MATLAB代码，，，，，(Verhulst model prediction code, MATLAB code,)