Description: 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. Platform: |
Size: 1024 |
Author:qingpg518 |
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Description: 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 image of an important information carrier put forward a new turning point detection algorithm the algorithm does not look for continuous space discrete approximation of the curvature calculation method, but from the appearance of characteristics of discrete curve derived discrete curve the inflection point at k-points between the squared Euclidean distance and the local minimum of this important property. Based on the nature of the algorithm is the first to use Freeman chain code in nature. Filter out objects clearly can not become a turning point on the border pixel, and then in the remaining boundary points by finding out the turning point of the local minimum position. The algorithm is given inflection point detection algorithm with the four well-known comparison of experiment. Platform: |
Size: 180224 |
Author:changhe.cheng |
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Description: 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 contraction coefficient (beta)
SW_BFRQ Brunt-Vaisala Frequency Squared (N^2)
SW_COPY Copyright and Licence file
SW_CP Heat Capacity (Cp) of Sea Water
SW_DENS Density of sea water
SW_DENS0 Denisty of sea water at atmospheric pressure
SW_DIST Distance between two lat, lon coordinates
SW_DPTH Depth from pressure
SW_F Coriolis factor "f"
SW_FP Freezing Point of sea water
SW_G Gravitational acceleration
SW_GPAN Geopotential anomaly
SW_GVEL Geostrophic velocity
SW_INFO Information on the SEAWATER library.
SW_PDEN Potential Density
SW_PRES Pressure from depth
SW_PTMP Potential temperature
SW_SALS Salinity of sea water
Platform: |
Size: 50176 |
Author:庄伟 |
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Description: 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 Platform: |
Size: 2048 |
Author:杨松 |
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Description: 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 and maximum of the distance squared at each time step. The average distance squared should behave like the number of time steps. Platform: |
Size: 1024 |
Author:alicethe |
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Description: 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 description and analysis.
MA can be computed on a distance transform, where each point is labelled to its distance to the background. Recent algorithms allow one to
compute Squared Euclidean Distance Transform (SEDT) in linear time in any dimension Platform: |
Size: 487424 |
Author:Niu Wenjie |
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Description: 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) Platform: |
Size: 910336 |
Author:张月秋 |
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Description: 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 distance squared difference between the smallest stops. Platform: |
Size: 2048 |
Author:史丽军 |
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Description: 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 Mahalanobis distance for data set identification. Platform: |
Size: 5120 |
Author:stl |
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Description: 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 square of the distance and minimized. Platform: |
Size: 222208 |
Author:xuwenju |
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Description: 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Complex.txt
操作矩阵的类 Matrix.txt
改进欧拉法.C
杜氏分解法.C
求解线性方程组的类 LEquations .txt
求解非线性方程组的类 NLEquations .txt
牛顿下山.C
牛顿插值多项式.c
牛顿迭代.c
秦九韶.c
简单迭代.c
系统.c
综合测评.c
计算数值积分的类 Integral .txt
进行插值的类Interpolation.txt
雅克比迭代.C
高斯消去法.c
龙格库塔方法.C
龙贝格算法.c-AE Inverse Distance IDW, kriging Krige interpolation. Txt
I display method. C
www.pudn.com.txt
Three diagonal pursuit method. C
Three spline interpolation algorithm, also includes other such hermite other algorithms, it is full. Txt
Dichotomy. C
Piecewise linear interpolation. C
Main-element element elimination. C
Using the inverse distance squared weighted interpolation algorithm for establishing regular grid of discrete points in the large amount of data in case of data,. Txt
Inverse distance weighted interpolation, seemingly bad IDWUtil.java
Aite Ken. C
Composite trapezoidal method. C
Composite Simpson. C
Chord cutting method. C
Operation plural class Complex.txt
Operation Matrix class Matrix.txt
Improved Euler method. C
Duchenne decomposition. C
Solving linear equations class LEquations. Txt
Solving nonlinear equations class NLEquations. Txt
Newton downhill. C
Newton interpolation polynomial. C
Newton iteration. C
Horner. C
Simple iteration. C
Systems. C
C Platform: |
Size: 60416 |
Author:songguanjun |
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Description: After identifying the best MNT− 1 nodes with the simplified branch metric and the accumulated branch metric at stage NT − 1, we then calculate the LLR of each coded bit utilizing the standard squared Euclidian distance metric.
The log-likelihood ratio (LLR) of a posteriori probability (APP) of each coded bit conditioned on the received signal y is normally calculated using the max-log approximation.-After identifying the best MNT− 1 nodes with the simplified branch metric and the accumulated branch metric at stage NT − 1, we then calculate the LLR of each coded bit utilizing the standard squared Euclidian distance metric.
The log-likelihood ratio (LLR) of a posteriori probability (APP) of each coded bit conditioned on the received signal y is normally calculated using the max-log approximation.
Platform: |
Size: 329728 |
Author:yuva |
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Description: 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
equation (1) between vector Xi and all the codevectors of the
codebook Platform: |
Size: 1046528 |
Author:noufal |
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Description: 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 analysis. K-means clustering algorithm is to randomly K objects as the initial cluster centers. Then calculate the distance of each object and each seed cluster centers, assigning each object to its distance the nearest cluster center. Cluster centers and the object assigned to them on behalf of a cluster. Once all the objects are assigned, the cluster centers of each cluster will be recalculated based on the existing cluster object. This process is repeated until a termination condition is satisfied. Termination condition may not be (or smallest numbers) is reassigned to a different target cluster, no (or a minimum number) and then change the cluster centers, and a local minimum squared error. Platform: |
Size: 33571840 |
Author:周思洁 |
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Description: 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 Platform: |
Size: 15360 |
Author:shintenwu |
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Search - Squared Distance