Search - oi - CodeBus

Search - oi - List

[matlab] MyKmeans DL : 0: 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)
Date : 2026-01-11 Size : 1kb User : 阿兜
[matlab] k_medoids DL : 0: 聚类算法：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。这里是MAtlab源代码。-err
Date : 2026-01-11 Size : 9kb User : 烈马
[matlab] oi DL : 0: 聚类和分类技术在生物信息学中的应用，不包含源代码！-Clustering and classification technology in bioinformatics applications, does not contain the source code!
Date : 2026-01-11 Size : 2.46mb User : 呙邵明
[matlab] beamforming DL : 0: hay nen lay file nay di cac ban oi
Date : 2026-01-11 Size : 1kb User : truong
[matlab] forniseight DL : 0: ws wsb wswysuy wswysk oi wuysywxubkjwyx ywuxywkh yywxwhyuwio ul lj ijljh
Date : 2026-01-11 Size : 1kb User : nikkum
[matlab] RDPTA DL : 0: Algorithm Given are P training pairs {X1,d1,X2,d2....Xp,dp}, where Xi is (n*1) di is (n*1) No of Categories=R. i=1,2,...P Yi= Augmented input pattern( obtained by appending 1 to the input vector) i=1,2,…P In the following, k denotes the training step and p denotes the step counter within the training cycle Step 1: c>0 , Emin is chosen, Step 2: Weights are initialized at w at small values, w is (n+1)*1. Counters and error are initialized. k=1,p=1,E=0 Step 3: The training cycle begins here. Input is presented and output computed: Y=Yp, d=dp Oi=f(wtY) for i=1,2,….R Step 4: Weights are updated: wi=wi+1/2c(di-oi)Y for i=1,2,…..R. Step 5: Cycle error is computed: E=1/2(di-oi)2+E for i=1,2,…..R. Step 6: If p<P then p=p+1,k=k+1, and go to Step 3: Otherwise go to Step 7. Step 7: The training cycle is completed. For E=0,terminate the training session. Outputs weights and k. If E>0,then E=0 ,p=1, and enter the new training cycle by going to step 3.
Date : 2026-01-11 Size : 12kb User : EASHAN
[matlab] 20090322 DL : 0: 一、算法伪码： 1、初始化： 1.1每一个空间的点映射到二维窗格，每个空间的点分配唯一的二维窗格坐标。一个窗格只能有一个点。 1.2为每一只蚂蚁在二维窗格分配唯一的地址（第一步需要注意的是：空间上点的位置和平面窗格上点的位置完全是两回事，空间上两个点的位置来计算两点之间的距离；而平面上点的位置，主要是用来确定半径为S的区域内的点，计算两个点的空间距离，进而计算群体相似度，最后通过群体相似度来计算拾起或者放下的概率） 2、迭代tmax次 3、所有的蚂蚁运动一次 4、产生一个0-1之间的随机数R 5、如果当前蚂蚁处于未负载状态，而且当前蚂蚁所在处的有点Oi 5.1、计算群体相似度f(Oi)和拾起概率Pp(Oi) 5.2、如果拾起概率Pp(Oi)》R 5.2.1、当前蚂蚁拾起点Oi（注意Oi在窗格中的位置是不断变动的） 5.3 5.2结束 6、如果条件5不成立，如果当前蚂蚁处于负载状态，持有点Oi，而且当前位置没有其他点 6.1计算群体相似度f(Oi)和放下概率Pd(Oi) 6.2如果放下概率Pd(Oi)》R 6.2.1放下节点Oi（注意点Oi在窗格中的位置是不断变动的） 6.3 6.2结束 7、5结束 8、当前蚂蚁移到邻近区域内的没有被其他蚂蚁占据的节点 9、所有的蚂蚁运动一次结束 10、迭代tmax次结束 -First, the algorithm pseudocode: 1. Initialization: 1.1 point each to the two-dimensional space mapping pane, the point of each space is assigned a unique two-dimensional coordinates of the pane. A pane of only one point. 1.2 for each ant is assigned a unique address in the two-dimensional pane (the first step should be noted: the location and position of plane points pane space is completely different points, two points on the spatial position is calculated distance between two points and the position of the point on the plane is mainly used to determine the radius of the dot area S within the space of two points to calculate the distance, and then calculate the similarity group, and finally through group similarity calculating pickup probability plays or put down) 2, iterative tmax 3 times, once all the ants motion 4, R 5 a generates a random number between 0 and 1, if the current state of the ant is unloaded, and the current location at the little ant Oi 5.1 calculating the similar
Date : 2026-01-11 Size : 326kb User : chensumin