搜索资源列表
basic_method
- test1.m 固定增量法求分界面 test2.m 利用误差平方和函数集群 delet.m 删除数组元素 Err.m 计算误差平方和 Distance.m 欧拉距离计算 -test1.m fixed incremental method for use interface test2.m squared error function cluster de let.m delete array elements Err.m calculation error square Di
seawater
- Matlab seawater工具包，可以通过海水的温盐等信息计算海水的密度、比容、位密、地转流速等重要参数。- SW_ADTG Adiabatic temperature gradient SW_ALPHA Thermal expansion coefficient (alpha) SW_AONB Calculate alpha/beta (a on b) SW_BETA Saline contract
disteusq
- calculates the squared euclidean distance between all pairs of rows of two matrices. -calculates the squared euclidean distance between all pairs of rows of two matrices.
findkeypoint
- 摘要：拐点是数字图像中的一个重要信息载体 提出一种新的拐点检测算法 该算法并非寻找连续空间中曲率的离散近似计算方法，而是源于离散曲线的外观特征，推导出离散曲线上拐点处k个点对间欧氏距离平方和局部最小这一重要性质。基于该性质，本算法首先利用Freeman链码的性质.过滤掉物体边界上明显不可能成为拐点的象素，然后在剩余的边界点中通过寻找该局部最小值定位出拐点。给出了本算法与四种著名拐点检测算法的对比实验。 -Abstract: The inflection point is a digital
grid
- 在大数据量离散点数据的情况下，利用反距离平方加权插值算法建立规则格网DSM-The amount of data in large case of discrete point data, using inverse distance squared weighted interpolation algorithm for establishing regular grid DSM
disteusq
- 声音处理的MATLAB程序：DISTEUSQ -calculate euclidean, squared euclidean or mahanalobis distance
New-Text-Document
- The program RANDOM_WALK_2D_PLOT plots the trajectories of one or more random walks. The program RANDOM_WALK_2D_SIMULATION plots averaged data for any number of random walks that each use the same number of steps. The data plotted is the average
EM
- 利用欧氏距离计算三维模型的中轴，希望对大家有用！-Medial Axis (MA), also known as Centres of Maximal Disks, is a useful representation of a shape for image descr iption and analysis. MA can be computed on a distance transform, where each point is labelled to its distance t
point
- 用分治算法（O(nlogn)复杂度）实现寻找n个点中最邻近点对,输出为最邻近距离的平方-Looking for n points nearest point, the output of the nearest neighbor distance squared using the divide-and-conquer algorithm (O (nlogn) complexity)
CH3Clustering
- 基于k-means的聚类编程，例如：随机选取k个中心点，经过计算每个点到k个中心距离的远近，将其归类。最后总的距离平方差最小，即停止。-Programmed based on k-means clustering, for example: randomly select k central point has been calculated for each point to the k center distances, will be classified. The final total
flower
- 对花卉的数据集分别通过“五折法”、随机产生训练样本、欧式平方距离、绝对值距离、契比雪夫距离和马氏距离进行数据集的识别。-Data sets, respectively, for flowers through the " half of Law" , randomly generated training samples, European squared distance, absolute distance, Chebyshev distance and Mahalanobi
K-means-clustering-algorithm
- K均值算法使用的聚类准则函数的误差平方和准则，通过反复迭代优化聚类结果，使所有样本到各自所属类别的中心的距离平方和达到最小。-K-means clustering algorithm uses squared error criterion function and criteria through iterative optimization clustering result, all the samples to the respective classes of the center s
cz
- AE反距离IDW、克里金Krige插值.txt I显示方法.c www.pudn.com.txt 三对角线追赶法.C 三样条插值函数算法，还包括其他的比如hermite等算法，很全.txt 二分法.c 分段线性插值.c 列主元元素消元.C 利用反距离平方加权插值算法建立规则格网在大数据量离散点数据的情况下，.txt 反距离加权插值，貌似不好用IDWUtil.java 埃特肯.c 复合梯形法.c 复合辛普森.c 弦割法.c 操作复数的类Com
DEM_MSI
- DEM 移动曲面拟合插值算法，采用反距离平方加权平均-DEM moving surface fitting interpolation algorithm, using inverse distance squared weighted average
fastdist.m
- Fast calculation of squared Euclidean distance
LGD_source_code
- For encoding, image is split in blocks and each block is then converted to the training vector Xi = (xi1, xi2, ..….., xik ). The codebook is searched for the nearest codevector Cmin by computing squared Euclidean distance as presented in equa
ClusterAnalysis_2014.11.4
- 模式识别的聚类分析。K均值聚类算法是先随机选取K个对象作为初始的聚类中心。然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心。聚类中心以及分配给它们的对象就代表一个聚类。一旦全部对象都被分配了，每个聚类的聚类中心会根据聚类中现有的对象被重新计算。这个过程将不断重复直到满足某个终止条件。终止条件可以是没有（或最小数目）对象被重新分配给不同的聚类，没有（或最小数目）聚类中心再发生变化，误差平方和局部最小。-Pattern recognition clustering
statistics_kmeans
- K-means算法是一种硬聚类算法，根据数据到聚类中心的某种距离来作为判别该数据所属类别。K-means算法以距离作为相似度测度。(kmeans uses the k-means++ algorithm for centroid initialization and squared Euclidean distance by default. It is good practice to search for lower, local minima by setting the 'Replica
noma
- NOMA corrected: farther away from Base Station (BS), more allocated power; % Assuming the Base Station (BS) has total power of 1 and will allocate % power for each User as proportional to squared distance: Pwr ~ Dst^2